From e8cdb2768704d0b8fe661b4e2dec3a3992255075 Mon Sep 17 00:00:00 2001
From: Diego Freitas <diego-xbm@hotmail.com>
Date: Mon, 31 Aug 2026 09:35:05 -0300
Subject: [PATCH] =?UTF-8?q?ajustado=20core=20da=20camera=20multiespectral,?=
 =?UTF-8?q?=20criado=20ferramenta=20para=20revisar=20as=20mascaras,=20ajsu?=
 =?UTF-8?q?tado=20ferramentas=20para=20calibragem=20do=20m=C3=B3dulo=20mul?=
 =?UTF-8?q?tiespectral,=20ajustado=20scripts=20de=20separa=C3=A7=C3=A3o=20?=
 =?UTF-8?q?de=20dataset,=20normalizacao,=20treinamento,=20teste,=20exporta?=
 =?UTF-8?q?=C3=A7=C3=A3o=20e=20benchmark,=20para=20modelo=20agricola?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../labelmap.txt.COLOQUE_AQUI                 |     1 +
 .../MaskReviewer_package/MaskReviewer.py      |  1449 ++
 .../MaskReviewer.settings.json                |     4 +
 .../MaskReviewer_package/MaskReviewer.spec    |    51 +
 .../README_MASKREVIEWER.txt                   |   110 +
 .../build/MaskReviewer/Analysis-00.toc        |  4402 ++++
 .../build/MaskReviewer/COLLECT-00.toc         |  3088 +++
 .../build/MaskReviewer/EXE-00.toc             |    77 +
 .../build/MaskReviewer/MaskReviewer.exe       |   Bin 0 -> 4545809 bytes
 .../build/MaskReviewer/MaskReviewer.pkg       |   Bin 0 -> 4200209 bytes
 .../build/MaskReviewer/PKG-00.toc             |    55 +
 .../build/MaskReviewer/PYZ-00.pyz             |   Bin 0 -> 4142295 bytes
 .../build/MaskReviewer/PYZ-00.toc             |   815 +
 .../build/MaskReviewer/base_library.zip       |   Bin 0 -> 1332263 bytes
 .../build/MaskReviewer/warn-MaskReviewer.txt  |   223 +
 .../build/MaskReviewer/xref-MaskReviewer.html | 18920 ++++++++++++++++
 .../MaskReviewer_package/build_windows.bat    |    81 +
 .../MaskReviewer_package/executar_sem_exe.bat |    19 +
 .../OAK/datasets/oak-fcc-3/_10_export_onnx.py |     3 +-
 .../datasets/oak-fcc-3/_11_validate_onnx.py   |     1 +
 .../oak-fcc-3/_12_benchmark_onnx_v2.py        |  1191 +
 .../oak-fcc-3/_3_select_training_samples.py   |  1205 +-
 Python/OAK/datasets/oak-fcc-3/_7_split.py     |  3200 ++-
 .../oak-fcc-3/_8_train_multihead_v2.py        |  3038 +++
 .../_9_review_dataset_multihead_v2.py         |  1478 ++
 .../datasets/oak-fcc-3/_9_test_multihead.py   |    41 +-
 .../oak-fcc-3/_9_test_multihead_v2.py         |  2406 ++
 Python/OAK/datasets/oak-fcc-3/config.json     |   179 +-
 .../OAK/datasets/oak-fcc-3/core/benchmark.py  |   620 -
 .../oak-fcc-3/core/benchmark_preview.py       |   645 -
 .../oak-fcc-3/core/oak_fcc3_client.py         |    54 +-
 .../oak-fcc-3/core/oak_fcc3_manager.py        |   204 +-
 .../oak-fcc-3/core/raw_processor_core.py      |  2911 ++-
 .../utils/_1_focus_calibration_tool.py        |  3990 ++++
 .../utils/_2_intrinsics_calibration_tool.py   |  2088 ++
 .../utils/_3_flatfield_calibration_tool.py    |  3089 +++
 .../utils/_4_radiometric_config_tool.py       |  5357 +++++
 .../utils/_5_camera_startup_profile.py        |   761 +
 .../utils/_6_homography_calibration_tool.py   |  4904 ++++
 .../oak-fcc-3/utils/_8_build_module_params.py |  2869 +++
 .../oak-fcc-3/utils/build_module_params.py    |   654 -
 .../flatfield_calibration_tool_quality.py     |  1345 --
 .../oak-fcc-3/utils/focus_calibration_tool.py |  1673 --
 .../utils/radiometric_config_tool.py          |  1617 --
 44 files changed, 66473 insertions(+), 8345 deletions(-)
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/EXEMPLO_ESTRUTURA/labelmap.txt.COLOQUE_AQUI
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.settings.json
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.spec
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/README_MASKREVIEWER.txt
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/Analysis-00.toc
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/COLLECT-00.toc
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/EXE-00.toc
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/MaskReviewer.exe
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/MaskReviewer.pkg
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/PKG-00.toc
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/PYZ-00.pyz
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/PYZ-00.toc
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/base_library.zip
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/warn-MaskReviewer.txt
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/xref-MaskReviewer.html
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build_windows.bat
 create mode 100644 Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/executar_sem_exe.bat
 create mode 100644 Python/OAK/datasets/oak-fcc-3/_12_benchmark_onnx_v2.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/_8_train_multihead_v2.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/_9_review_dataset_multihead_v2.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/_9_test_multihead_v2.py
 delete mode 100644 Python/OAK/datasets/oak-fcc-3/core/benchmark.py
 delete mode 100644 Python/OAK/datasets/oak-fcc-3/core/benchmark_preview.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/utils/_1_focus_calibration_tool.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/utils/_2_intrinsics_calibration_tool.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/utils/_3_flatfield_calibration_tool.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/utils/_4_radiometric_config_tool.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/utils/_5_camera_startup_profile.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/utils/_6_homography_calibration_tool.py
 create mode 100644 Python/OAK/datasets/oak-fcc-3/utils/_8_build_module_params.py
 delete mode 100644 Python/OAK/datasets/oak-fcc-3/utils/build_module_params.py
 delete mode 100644 Python/OAK/datasets/oak-fcc-3/utils/flatfield_calibration_tool_quality.py
 delete mode 100644 Python/OAK/datasets/oak-fcc-3/utils/focus_calibration_tool.py
 delete mode 100644 Python/OAK/datasets/oak-fcc-3/utils/radiometric_config_tool.py

diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/EXEMPLO_ESTRUTURA/labelmap.txt.COLOQUE_AQUI b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/EXEMPLO_ESTRUTURA/labelmap.txt.COLOQUE_AQUI
new file mode 100644
index 000000000..313ee5b9d
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/EXEMPLO_ESTRUTURA/labelmap.txt.COLOQUE_AQUI
@@ -0,0 +1 @@
+Coloque seu labelmap.txt real ao lado do EXE.
diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.py b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.py
new file mode 100644
index 000000000..e376cba04
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.py
@@ -0,0 +1,1449 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+MaskReviewer.py
+===============
+
+Ferramenta standalone para revisão humana de máscaras sem inferência/modelo.
+
+Estrutura preferida ao lado do EXE:
+    MaskReviewer.exe
+    labelmap.txt
+    group/
+        chao/
+            previews/
+            masks/
+            predictions/
+            final_masks/
+            review_order.csv
+        chao_cana/
+        chao_cana_erva/
+        chao_erva/
+        ...
+
+Se "group" e/ou "labelmap.txt" não forem encontrados automaticamente,
+abre seletor do Windows para o usuário apontá-los.
+
+Decisões da sessão:
+    group/review_choices.csv
+
+Controles principais:
+    1 = manter máscara HUMANA
+    2 = escolher máscara do MODELO
+    3 = nenhuma serve, marcar REMAP
+    E = editar
+    A / D = anterior / próxima
+    S = pular
+    V = mostrar/ocultar a final_mask salva
+    X = apagar decisão atual
+    Q / ESC = sair
+
+Overlay:
+    Trackbar "Overlay %" controla a mesma opacidade nos dois lados.
+
+Editor:
+    Ao pressionar E:
+      1 = editar HUMANA como base
+      2 = editar MODELO como base
+      3 = começar de CHÃO, visualmente transparente
+
+    Mouse esquerdo = adiciona ponto
+    Mouse direito = remove último ponto aberto
+    ENTER = fecha/preenche polígono com a classe selecionada
+    C = próxima classe
+    0..9 = seleciona ID de classe diretamente
+    Z = desfaz último polígono confirmado
+    R = restaura máscara base
+    [ / ] = diminui / aumenta o pincel
+    + / - ou roda do mouse = zoom
+    Setas ou arrasto do botão do meio = pan
+    F = fit/visão completa
+    G = centraliza no último ponto sob o cursor
+    M = liga/desliga o mini mapa
+    Clique no mini mapa = centraliza a visão
+    Arrasto com o botão do meio sobre o mini mapa = move o mini mapa
+    BACKSPACE / DEL = limpa pontos abertos
+    S = salva edição em final_masks
+    ESC / Q = cancela edição
+
+O programa NUNCA altera o dataset bruto.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import shutil
+import sys
+import time
+import traceback
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import cv2
+import numpy as np
+
+
+APP_NAME = "MaskReviewer"
+SETTINGS_NAME = "MaskReviewer.settings.json"
+CHOICES_NAME = "review_choices.csv"
+
+
+# ============================================================
+# Estruturas
+# ============================================================
+
+@dataclass
+class ReviewItem:
+    group: str
+    base: str
+    preview: Path
+    human_mask: Path
+    model_mask: Path
+    final_mask: Path
+    rank: Optional[int] = None
+    suspicion_pct: Optional[float] = None
+    reasons: str = ""
+
+
+# ============================================================
+# Paths / dialogs
+# ============================================================
+
+def app_dir() -> Path:
+    if getattr(sys, "frozen", False):
+        return Path(sys.executable).resolve().parent
+    return Path(__file__).resolve().parent
+
+
+def ensure_dir(path: Path) -> None:
+    path.mkdir(parents=True, exist_ok=True)
+
+
+def load_settings(path: Path) -> dict:
+    try:
+        if path.is_file():
+            with path.open("r", encoding="utf-8") as f:
+                js = json.load(f)
+            return js if isinstance(js, dict) else {}
+    except Exception:
+        pass
+    return {}
+
+
+def save_settings(path: Path, settings: dict) -> None:
+    try:
+        with path.open("w", encoding="utf-8") as f:
+            json.dump(settings, f, ensure_ascii=False, indent=2)
+    except Exception:
+        # Preferimos não impedir a revisão por uma falha de persistência.
+        pass
+
+
+def _tk_root():
+    import tkinter as tk
+    root = tk.Tk()
+    root.withdraw()
+    root.attributes("-topmost", True)
+    return root
+
+
+def choose_group_dialog(initial: Optional[Path] = None) -> Optional[Path]:
+    from tkinter import filedialog
+    root = _tk_root()
+    try:
+        selected = filedialog.askdirectory(
+            parent=root,
+            title="Selecione a pasta GROUP da revisão",
+            initialdir=str(initial) if initial and initial.exists() else str(app_dir()),
+            mustexist=True,
+        )
+        return Path(selected).resolve() if selected else None
+    finally:
+        root.destroy()
+
+
+def choose_labelmap_dialog(initial: Optional[Path] = None) -> Optional[Path]:
+    from tkinter import filedialog
+    root = _tk_root()
+    try:
+        selected = filedialog.askopenfilename(
+            parent=root,
+            title="Selecione o labelmap.txt",
+            initialdir=str(initial) if initial and initial.exists() else str(app_dir()),
+            filetypes=[("Labelmap", "labelmap.txt"), ("Arquivos TXT", "*.txt"), ("Todos", "*.*")],
+        )
+        return Path(selected).resolve() if selected else None
+    finally:
+        root.destroy()
+
+
+def show_error_dialog(message: str) -> None:
+    try:
+        from tkinter import messagebox
+        root = _tk_root()
+        try:
+            messagebox.showerror(APP_NAME, message, parent=root)
+        finally:
+            root.destroy()
+    except Exception:
+        print(message)
+
+
+def resolve_group_root(cli_group: Optional[str], settings: dict) -> Path:
+    base = app_dir()
+
+    candidates: List[Path] = []
+    if cli_group:
+        candidates.append(Path(cli_group))
+    saved = settings.get("group_root")
+    if saved:
+        candidates.append(Path(str(saved)))
+
+    candidates += [
+        base / "group",
+        base / "dataset" / "revisao" / "group",
+        base / "revisao" / "group",
+    ]
+
+    for c in candidates:
+        try:
+            p = c.expanduser().resolve()
+            if p.is_dir():
+                return p
+        except Exception:
+            continue
+
+    selected = choose_group_dialog(base)
+    if selected is None:
+        raise RuntimeError("Nenhuma pasta group foi selecionada.")
+    return selected
+
+
+def resolve_labelmap(cli_labelmap: Optional[str], group_root: Path, settings: dict) -> Path:
+    base = app_dir()
+
+    candidates: List[Path] = []
+    if cli_labelmap:
+        candidates.append(Path(cli_labelmap))
+    saved = settings.get("labelmap")
+    if saved:
+        candidates.append(Path(str(saved)))
+
+    # Pacote portátil: EXE + labelmap.txt + group/
+    candidates.append(base / "labelmap.txt")
+
+    # Estrutura do projeto: dataset/revisao/group -> dataset/labelmap.txt
+    candidates += [
+        group_root.parent / "labelmap.txt",
+        group_root.parent.parent / "labelmap.txt",
+        base / "dataset" / "labelmap.txt",
+    ]
+
+    for c in candidates:
+        try:
+            p = c.expanduser().resolve()
+            if p.is_file():
+                return p
+        except Exception:
+            continue
+
+    selected = choose_labelmap_dialog(group_root.parent)
+    if selected is None:
+        raise RuntimeError("Nenhum labelmap.txt foi selecionado.")
+    return selected
+
+
+# ============================================================
+# CSV / coleta
+# ============================================================
+
+def read_csv_rows(path: Path) -> List[dict]:
+    if not path.is_file():
+        return []
+    with path.open("r", newline="", encoding="utf-8-sig") as f:
+        return list(csv.DictReader(f))
+
+
+def write_choices(path: Path, choices: Dict[Tuple[str, str], dict]) -> None:
+    ensure_dir(path.parent)
+    fieldnames = [
+        "group",
+        "base",
+        "choice",
+        "source_mask",
+        "edit_base",
+        "final_mask",
+        "reviewed_at",
+        "suspicion_pct",
+        "review_reasons",
+    ]
+    rows = sorted(choices.values(), key=lambda r: (str(r["group"]), str(r["base"])))
+    with path.open("w", newline="", encoding="utf-8-sig") as f:
+        w = csv.DictWriter(f, fieldnames=fieldnames, extrasaction="ignore")
+        w.writeheader()
+        w.writerows(rows)
+
+
+def load_choices(path: Path) -> Dict[Tuple[str, str], dict]:
+    out: Dict[Tuple[str, str], dict] = {}
+    for row in read_csv_rows(path):
+        key = (str(row.get("group", "")), str(row.get("base", "")))
+        if all(key):
+            out[key] = row
+    return out
+
+
+def load_order(group_dir: Path) -> Dict[str, dict]:
+    path = group_dir / "review_order.csv"
+    out: Dict[str, dict] = {}
+    for row in read_csv_rows(path):
+        base = str(row.get("base", ""))
+        if base:
+            out[base] = row
+    return out
+
+
+def collect_items(group_root: Path, groups: Optional[List[str]]) -> List[ReviewItem]:
+    if not group_root.is_dir():
+        raise FileNotFoundError(f"Pasta GROUP não encontrada: {group_root}")
+
+    allowed = set(groups or [])
+    items: List[ReviewItem] = []
+
+    for group_dir in sorted(p for p in group_root.iterdir() if p.is_dir()):
+        group = group_dir.name
+        if allowed and group not in allowed:
+            continue
+
+        previews_dir = group_dir / "previews"
+        masks_dir = group_dir / "masks"
+        preds_dir = group_dir / "predictions"
+        final_dir = group_dir / "final_masks"
+        ensure_dir(final_dir)
+
+        if not masks_dir.is_dir() or not preds_dir.is_dir() or not previews_dir.is_dir():
+            continue
+
+        order = load_order(group_dir)
+
+        # O auditor standalone normaliza tudo para PNG com mesmo basename.
+        bases = sorted(
+            p.stem
+            for p in masks_dir.glob("*.png")
+            if (preds_dir / p.name).is_file() and (previews_dir / p.name).is_file()
+        )
+
+        def sort_key(base: str):
+            row = order.get(base, {})
+            try:
+                rank = int(float(row.get("review_rank_group", "")))
+            except Exception:
+                rank = 10**9
+            return (rank, base)
+
+        bases.sort(key=sort_key)
+
+        for base in bases:
+            row = order.get(base, {})
+            try:
+                rank = int(float(row.get("review_rank_group", "")))
+            except Exception:
+                rank = None
+            try:
+                suspicion = float(row.get("suspicion_pct", ""))
+            except Exception:
+                suspicion = None
+
+            items.append(
+                ReviewItem(
+                    group=group,
+                    base=base,
+                    preview=previews_dir / f"{base}.png",
+                    human_mask=masks_dir / f"{base}.png",
+                    model_mask=preds_dir / f"{base}.png",
+                    final_mask=final_dir / f"{base}.png",
+                    rank=rank,
+                    suspicion_pct=suspicion,
+                    reasons=str(row.get("review_reasons", "")),
+                )
+            )
+
+    return items
+
+
+# ============================================================
+# Imagens
+# ============================================================
+
+def imread_required(path: Path) -> np.ndarray:
+    """
+    Leitura robusta de imagens, especialmente para Windows + PyInstaller.
+
+    1) Tenta cv2.imread normalmente.
+    2) Se falhar, lê os bytes com NumPy e usa cv2.imdecode.
+    3) Repete algumas vezes para tolerar falhas transitórias de I/O.
+
+    Não altera nem regrava a imagem.
+    """
+    path = Path(path)
+
+    if not path.is_file():
+        raise RuntimeError(f"Arquivo nao existe: {path}")
+
+    errors = []
+
+    for attempt in range(3):
+        try:
+            img = cv2.imread(str(path), cv2.IMREAD_COLOR)
+            if img is not None and img.size > 0:
+                return img
+            errors.append(f"tentativa {attempt + 1}: cv2.imread retornou None")
+        except Exception as exc:
+            errors.append(f"tentativa {attempt + 1}: cv2.imread: {exc}")
+
+        try:
+            raw = np.fromfile(str(path), dtype=np.uint8)
+            if raw.size > 0:
+                img = cv2.imdecode(raw, cv2.IMREAD_COLOR)
+                if img is not None and img.size > 0:
+                    return img
+            errors.append(f"tentativa {attempt + 1}: cv2.imdecode retornou None")
+        except Exception as exc:
+            errors.append(f"tentativa {attempt + 1}: cv2.imdecode: {exc}")
+
+        time.sleep(0.05 * (attempt + 1))
+
+    try:
+        file_size = path.stat().st_size
+    except Exception:
+        file_size = -1
+
+    raise RuntimeError(
+        "Falha ao abrir imagem apos leitura robusta:\n"
+        f"{path}\n"
+        f"Tamanho do arquivo: {file_size} bytes\n"
+        + "\n".join(errors)
+    )
+
+
+def fit_to(img: np.ndarray, wh: Tuple[int, int], interpolation: int) -> np.ndarray:
+    w, h = wh
+    if img.shape[1] == w and img.shape[0] == h:
+        return img
+    return cv2.resize(img, (w, h), interpolation=interpolation)
+
+
+def add_label(img: np.ndarray, title: str, subtitle: str = "") -> np.ndarray:
+    out = img.copy()
+    h, w = out.shape[:2]
+    bar_h = max(52, int(h * 0.085))
+    overlay = out.copy()
+    cv2.rectangle(overlay, (0, 0), (w, bar_h), (0, 0, 0), -1)
+    out = cv2.addWeighted(overlay, 0.72, out, 0.28, 0)
+    cv2.putText(out, title, (14, 24), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2, cv2.LINE_AA)
+    if subtitle:
+        cv2.putText(out, subtitle, (14, 46), cv2.FONT_HERSHEY_SIMPLEX, 0.46, (220, 220, 220), 1, cv2.LINE_AA)
+    return out
+
+
+def now_iso() -> str:
+    return datetime.now().astimezone().isoformat(timespec="seconds")
+
+
+# ============================================================
+# Labelmap
+# ============================================================
+
+FALLBACK_CLASSES_RGB = {
+    0: ("chao", (85, 85, 85)),
+    1: ("cana", (0, 190, 0)),
+    2: ("erva", (230, 55, 55)),
+}
+
+
+def load_labelmap_classes(path: Path) -> Dict[int, Tuple[str, Tuple[int, int, int]]]:
+    classes: Dict[int, Tuple[str, Tuple[int, int, int]]] = {}
+
+    if not path.is_file():
+        raise FileNotFoundError(path)
+
+    next_id = 0
+    with path.open("r", encoding="utf-8") as f:
+        for raw_line in f:
+            s = raw_line.strip()
+            if not s or s.startswith("#"):
+                continue
+
+            cid = None
+            name = None
+            color = None
+
+            if ":" in s and not s.split(":", 1)[0].strip().isdigit():
+                name_part, rest = s.split(":", 1)
+                name = name_part.strip()
+                color_txt = rest.split("::", 1)[0].strip().strip(":")
+                parts = [p.strip() for p in color_txt.split(",") if p.strip()]
+                if len(parts) >= 3:
+                    try:
+                        color = tuple(int(float(x)) for x in parts[:3])
+                    except Exception:
+                        color = None
+            else:
+                parts = s.replace(",", " ").replace(":", " ").split()
+                if len(parts) >= 2 and parts[0].isdigit():
+                    cid = int(parts[0])
+                    name = parts[1]
+                    if len(parts) >= 5:
+                        try:
+                            color = tuple(int(float(x)) for x in parts[2:5])
+                        except Exception:
+                            color = None
+                elif parts:
+                    name = parts[0]
+
+            if not name:
+                continue
+
+            lname = name.lower()
+            if lname in ("ignore", "void", "background_ignore"):
+                continue
+
+            if cid is None:
+                cid = next_id
+            next_id = max(next_id, cid + 1)
+
+            if color is None:
+                color = FALLBACK_CLASSES_RGB.get(cid, (name, (255, 255, 255)))[1]
+
+            classes[int(cid)] = (str(name), tuple(map(int, color)))
+
+    if not classes:
+        raise RuntimeError(f"Labelmap não contém classes utilizáveis: {path}")
+
+    return classes
+
+
+def class_color_bgr(classes, cid: int) -> Tuple[int, int, int]:
+    _name, rgb = classes[cid]
+    return int(rgb[2]), int(rgb[1]), int(rgb[0])
+
+
+def find_chao_id(classes) -> int:
+    for cid, (name, _rgb) in classes.items():
+        if str(name).strip().lower() in ("chao", "chão", "ground", "solo"):
+            return int(cid)
+    return min(classes.keys())
+
+
+def colored_mask_to_ids(mask_bgr: np.ndarray, classes) -> np.ndarray:
+    h, w = mask_bgr.shape[:2]
+    ids = np.full((h, w), 255, dtype=np.uint8)
+
+    for cid in classes:
+        bgr = np.array(class_color_bgr(classes, cid), dtype=np.uint8)
+        match = np.all(mask_bgr == bgr[None, None, :], axis=2)
+        ids[match] = int(cid)
+
+    unmatched = int((ids == 255).sum())
+    if unmatched:
+        raise RuntimeError(
+            f"A máscara possui {unmatched} pixels com cores que não existem no labelmap. "
+            "Confira se o labelmap.txt selecionado pertence a este dataset."
+        )
+    return ids
+
+
+def ids_to_colored_mask(ids: np.ndarray, classes) -> np.ndarray:
+    h, w = ids.shape[:2]
+    out = np.zeros((h, w, 3), dtype=np.uint8)
+    for cid in classes:
+        out[ids == cid] = class_color_bgr(classes, cid)
+    return out
+
+
+# ============================================================
+# Painel principal
+# ============================================================
+
+def compose_panel(
+    item: ReviewItem,
+    choice: Optional[str],
+    index: int,
+    total: int,
+    alpha: float,
+    max_width: int,
+) -> np.ndarray:
+    preview = imread_required(item.preview)
+    human = imread_required(item.human_mask)
+    model = imread_required(item.model_mask)
+
+    h, w = preview.shape[:2]
+    human = fit_to(human, (w, h), cv2.INTER_NEAREST)
+    model = fit_to(model, (w, h), cv2.INTER_NEAREST)
+
+    overlay_human = cv2.addWeighted(preview, 1.0 - alpha, human, alpha, 0.0)
+    overlay_model = cv2.addWeighted(preview, 1.0 - alpha, model, alpha, 0.0)
+
+    sus = f"{item.suspicion_pct:.1f}/100" if item.suspicion_pct is not None else "n/a"
+    rank = str(item.rank) if item.rank is not None else "n/a"
+    common = f"{item.group}/{item.base} | suspeita={sus} | rank={rank}"
+
+    tl = add_label(human, "1  MASCARA HUMANA", common)
+    tr = add_label(model, "2  MASCARA MODELO", common)
+    bl = add_label(overlay_human, "OVERLAY HUMANA", f"alpha={alpha:.2f}")
+    br = add_label(overlay_model, "OVERLAY MODELO", f"alpha={alpha:.2f}")
+
+    top = np.hstack([tl, tr])
+    bottom = np.hstack([bl, br])
+    canvas = np.vstack([top, bottom])
+
+    footer_h = 92
+    footer = np.zeros((footer_h, canvas.shape[1], 3), dtype=np.uint8)
+    status = choice or "NAO REVISADO"
+    final_exists = item.final_mask.is_file()
+    final_status = f"FINAL_MASK={'SIM' if final_exists else 'NAO'}"
+    line1 = f"[{index + 1}/{total}] escolha atual: {status} | {final_status}"
+    line2 = "1=humana  2=modelo  3=REMAP  E=editar  V=ver final  A/D=navegar  S=pular  X=apagar  Q=sair"
+    line3 = "Se a imagem ja foi revisada/ editada, aperte V para ver exatamente o PNG salvo em final_masks."
+    cv2.putText(footer, line1, (14, 27), cv2.FONT_HERSHEY_SIMPLEX, 0.60, (255, 255, 255), 2, cv2.LINE_AA)
+    cv2.putText(footer, line2, (14, 57), cv2.FONT_HERSHEY_SIMPLEX, 0.48, (210, 210, 210), 1, cv2.LINE_AA)
+    cv2.putText(footer, line3, (14, 84), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (170, 230, 170), 1, cv2.LINE_AA)
+    canvas = np.vstack([canvas, footer])
+
+    if max_width > 0 and canvas.shape[1] > max_width:
+        scale = max_width / float(canvas.shape[1])
+        canvas = cv2.resize(
+            canvas,
+            (max_width, max(1, int(round(canvas.shape[0] * scale)))),
+            interpolation=cv2.INTER_AREA,
+        )
+
+    return canvas
+
+
+def compose_final_panel(
+    item: ReviewItem,
+    choice: Optional[str],
+    alpha: float,
+    max_width: int,
+) -> np.ndarray:
+    preview = imread_required(item.preview)
+    h, w = preview.shape[:2]
+
+    if item.final_mask.is_file():
+        final_mask = fit_to(imread_required(item.final_mask), (w, h), cv2.INTER_NEAREST)
+        final_overlay = cv2.addWeighted(preview, 1.0 - alpha, final_mask, alpha, 0.0)
+        left = add_label(final_mask, "FINAL_MASK SALVA", f"{item.group}/{item.base}")
+        right = add_label(final_overlay, "OVERLAY FINAL", f"choice={choice or 'NAO REVISADO'} | alpha={alpha:.2f}")
+    else:
+        blank = preview.copy()
+        dark = np.zeros_like(blank)
+        blank = cv2.addWeighted(blank, 0.35, dark, 0.65, 0.0)
+        cv2.putText(blank, "SEM FINAL_MASK SALVA", (30, max(60, h // 2 - 10)), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), 2, cv2.LINE_AA)
+        cv2.putText(blank, "Escolha 1, 2 ou edite antes de visualizar.", (30, max(95, h // 2 + 28)), cv2.FONT_HERSHEY_SIMPLEX, 0.62, (210, 210, 210), 1, cv2.LINE_AA)
+        left = add_label(blank, "FINAL_MASK SALVA", f"{item.group}/{item.base}")
+        right = add_label(blank, "OVERLAY FINAL", f"choice={choice or 'NAO REVISADO'}")
+
+    canvas = np.hstack([left, right])
+    footer_h = 56
+    footer = np.zeros((footer_h, canvas.shape[1], 3), dtype=np.uint8)
+    cv2.putText(footer, "Janela auxiliar da mascara final | V = mostrar/ocultar | mostra exatamente o arquivo salvo em final_masks", (14, 34), cv2.FONT_HERSHEY_SIMPLEX, 0.48, (230, 230, 230), 1, cv2.LINE_AA)
+    canvas = np.vstack([canvas, footer])
+
+    if max_width > 0 and canvas.shape[1] > max_width:
+        scale = max_width / float(canvas.shape[1])
+        canvas = cv2.resize(
+            canvas,
+            (max_width, max(1, int(round(canvas.shape[0] * scale)))),
+            interpolation=cv2.INTER_AREA,
+        )
+
+    return canvas
+
+
+# ============================================================
+# Decisões
+# ============================================================
+
+def record_choice(
+    item: ReviewItem,
+    choice_name: str,
+    source: Optional[Path],
+    choices: Dict[Tuple[str, str], dict],
+    choices_csv: Path,
+    edit_base: str = "",
+) -> None:
+    key = (item.group, item.base)
+
+    if choice_name in {"HUMAN", "MODEL"}:
+        if source is None or not source.is_file():
+            raise FileNotFoundError(f"Máscara fonte ausente: {source}")
+        ensure_dir(item.final_mask.parent)
+        shutil.copy2(source, item.final_mask)
+        final_path = str(item.final_mask)
+        source_path = str(source)
+
+    elif choice_name == "EDITED":
+        if source is None or not source.is_file():
+            raise FileNotFoundError(f"Máscara editada ausente: {source}")
+        ensure_dir(item.final_mask.parent)
+        if source.resolve() != item.final_mask.resolve():
+            shutil.copy2(source, item.final_mask)
+        final_path = str(item.final_mask)
+        source_path = str(source)
+
+    elif choice_name == "REMAP":
+        if item.final_mask.exists():
+            item.final_mask.unlink()
+        final_path = ""
+        source_path = ""
+
+    else:
+        raise ValueError(choice_name)
+
+    choices[key] = {
+        "group": item.group,
+        "base": item.base,
+        "choice": choice_name,
+        "source_mask": source_path,
+        "edit_base": edit_base,
+        "final_mask": final_path,
+        "reviewed_at": now_iso(),
+        "suspicion_pct": "" if item.suspicion_pct is None else f"{item.suspicion_pct:.6f}",
+        "review_reasons": item.reasons,
+    }
+    write_choices(choices_csv, choices)
+
+
+def remove_choice(
+    item: ReviewItem,
+    choices: Dict[Tuple[str, str], dict],
+    choices_csv: Path,
+) -> None:
+    key = (item.group, item.base)
+    choices.pop(key, None)
+    if item.final_mask.exists():
+        item.final_mask.unlink()
+    write_choices(choices_csv, choices)
+
+
+# ============================================================
+# Editor
+# ============================================================
+
+def overlay_ids(
+    preview: np.ndarray,
+    ids: np.ndarray,
+    classes,
+    alpha: float,
+    transparent_class_id: Optional[int] = None,
+) -> np.ndarray:
+    mask = ids_to_colored_mask(ids, classes)
+
+    if transparent_class_id is None:
+        return cv2.addWeighted(preview, 1.0 - alpha, mask, alpha, 0.0)
+
+    out = preview.copy()
+    region = ids != transparent_class_id
+    if np.any(region):
+        blended = cv2.addWeighted(preview, 1.0 - alpha, mask, alpha, 0.0)
+        out[region] = blended[region]
+    return out
+
+
+def choose_edit_base(window: str, panel: np.ndarray) -> Optional[str]:
+    prompt = panel.copy()
+    h, w = prompt.shape[:2]
+    bar_h = 108
+    overlay = prompt.copy()
+    cv2.rectangle(overlay, (0, 0), (w, bar_h), (0, 0, 0), -1)
+    prompt = cv2.addWeighted(overlay, 0.82, prompt, 0.18, 0)
+
+    cv2.putText(
+        prompt, "EDITOR: escolha a base",
+        (18, 35), cv2.FONT_HERSHEY_SIMPLEX, 0.85,
+        (255, 255, 255), 2, cv2.LINE_AA,
+    )
+    cv2.putText(
+        prompt, "1=HUMANA   2=MODELO   3=CHAO/VAZIO VISUALMENTE   ESC=cancelar",
+        (18, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.62,
+        (80, 255, 120), 2, cv2.LINE_AA,
+    )
+
+    cv2.imshow(window, prompt)
+
+    while True:
+        k = cv2.waitKeyEx(0)
+        if k == ord("1"):
+            return "HUMAN"
+        if k == ord("2"):
+            return "MODEL"
+        if k == ord("3"):
+            return "GROUND"
+        if k in (27, ord("q"), ord("Q")):
+            return None
+
+
+def run_polygon_editor(
+    window: str,
+    item: ReviewItem,
+    base_choice: str,
+    classes,
+    alpha_state: dict,
+) -> Optional[Tuple[np.ndarray, str]]:
+    preview = imread_required(item.preview)
+    human_bgr = fit_to(imread_required(item.human_mask), (preview.shape[1], preview.shape[0]), cv2.INTER_NEAREST)
+    model_bgr = fit_to(imread_required(item.model_mask), (preview.shape[1], preview.shape[0]), cv2.INTER_NEAREST)
+
+    chao_id = find_chao_id(classes)
+
+    if base_choice == "HUMAN":
+        base_ids = colored_mask_to_ids(human_bgr, classes)
+        transparent_class_id = None
+    elif base_choice == "MODEL":
+        base_ids = colored_mask_to_ids(model_bgr, classes)
+        transparent_class_id = None
+    elif base_choice == "GROUND":
+        base_ids = np.full(preview.shape[:2], chao_id, dtype=np.uint8)
+        transparent_class_id = chao_id
+    else:
+        raise ValueError(base_choice)
+
+    work_ids = base_ids.copy()
+    history: List[np.ndarray] = []
+    points: List[Tuple[int, int]] = []
+
+    class_ids = sorted(classes.keys())
+    selected_pos = class_ids.index(chao_id) if chao_id in class_ids else 0
+    selected_cid = class_ids[selected_pos]
+
+    tool_mode = "POLY"
+    brush_radius = 12
+    brush_radius_min = 1
+    brush_radius_max = 128
+
+    zoom = 1.0
+    zoom_min = 1.0
+    zoom_max = 12.0
+    zoom_step = 1.25
+    view_x = 0.0
+    view_y = 0.0
+
+    img_h, img_w = preview.shape[:2]
+    header_h = 92
+    footer_h = 96
+    canvas_w = img_w
+    canvas_h = img_h
+
+    is_panning = False
+    pan_anchor = (0, 0)
+    pan_anchor_view = (0.0, 0.0)
+    is_painting = False
+    last_paint_point: Optional[Tuple[int, int]] = None
+    last_mouse_img = (img_w // 2, img_h // 2)
+
+    minimap_visible = True
+    minimap_rect = None
+    is_dragging_minimap = False
+    minimap_drag_offset = (0, 0)
+
+    def minimap_size() -> Tuple[int, int]:
+        mm_max_w = 220
+        mm_max_h = 150
+        scale = min(mm_max_w / img_w, mm_max_h / img_h)
+        mm_w = max(1, int(round(img_w * scale)))
+        mm_h = max(1, int(round(img_h * scale)))
+        return mm_w, mm_h
+
+    mm_w, mm_h = minimap_size()
+    minimap_x = max(10, canvas_w - mm_w - 10)
+    minimap_y = 10
+
+    def clamp_minimap() -> None:
+        nonlocal minimap_x, minimap_y
+        minimap_x = int(min(max(0, minimap_x), max(0, canvas_w - mm_w)))
+        minimap_y = int(min(max(0, minimap_y), max(0, canvas_h - mm_h)))
+
+    def inside_minimap_display(x: int, y: int) -> bool:
+        if not minimap_visible or minimap_rect is None:
+            return False
+        x0, y0, x1, y1 = minimap_rect
+        return x0 <= x < x1 and y0 <= y < y1
+
+    def max_offsets(scale: float) -> Tuple[float, float]:
+        vw = min(img_w, img_w / scale)
+        vh = min(img_h, img_h / scale)
+        return max(0.0, img_w - vw), max(0.0, img_h - vh)
+
+    def clamp_view() -> None:
+        nonlocal view_x, view_y
+        mx, my = max_offsets(zoom)
+        view_x = min(max(0.0, view_x), mx)
+        view_y = min(max(0.0, view_y), my)
+
+    def screen_to_image(x: int, y: int) -> Optional[Tuple[int, int]]:
+        yy = float(y - header_h)
+        xx = float(x)
+        if xx < 0 or yy < 0 or xx >= canvas_w or yy >= canvas_h:
+            return None
+        ix = int(np.floor(view_x + xx / zoom))
+        iy = int(np.floor(view_y + yy / zoom))
+        ix = min(max(ix, 0), img_w - 1)
+        iy = min(max(iy, 0), img_h - 1)
+        return ix, iy
+
+    def set_zoom(new_zoom: float, anchor_screen: Optional[Tuple[int, int]] = None) -> None:
+        nonlocal zoom, view_x, view_y
+        old_zoom = zoom
+        new_zoom = min(max(float(new_zoom), zoom_min), zoom_max)
+        if abs(new_zoom - old_zoom) < 1e-9:
+            return
+        if anchor_screen is None:
+            ax = canvas_w / 2.0
+            ay = canvas_h / 2.0
+        else:
+            ax = float(anchor_screen[0])
+            ay = float(anchor_screen[1] - header_h)
+            ax = min(max(ax, 0.0), float(canvas_w - 1))
+            ay = min(max(ay, 0.0), float(canvas_h - 1))
+        anchor_ix = view_x + ax / old_zoom
+        anchor_iy = view_y + ay / old_zoom
+        zoom = new_zoom
+        view_x = anchor_ix - ax / zoom
+        view_y = anchor_iy - ay / zoom
+        clamp_view()
+
+    def fit_view() -> None:
+        nonlocal zoom, view_x, view_y
+        zoom = 1.0
+        view_x = 0.0
+        view_y = 0.0
+
+    def center_on(ix: int, iy: int) -> None:
+        nonlocal view_x, view_y
+        view_x = float(ix) - (canvas_w / zoom) / 2.0
+        view_y = float(iy) - (canvas_h / zoom) / 2.0
+        clamp_view()
+
+    def center_on_from_minimap(display_x: int, display_y: int) -> None:
+        if mm_w <= 1 or mm_h <= 1:
+            return
+        rel_x = (display_x - minimap_x) / float(mm_w)
+        rel_y = (display_y - minimap_y) / float(mm_h)
+        rel_x = min(max(rel_x, 0.0), 1.0)
+        rel_y = min(max(rel_y, 0.0), 1.0)
+        ix = int(round(rel_x * (img_w - 1)))
+        iy = int(round(rel_y * (img_h - 1)))
+        center_on(ix, iy)
+
+    def pan(dx_px: float, dy_px: float) -> None:
+        nonlocal view_x, view_y
+        view_x += dx_px / zoom
+        view_y += dy_px / zoom
+        clamp_view()
+
+    def push_history() -> None:
+        history.append(work_ids.copy())
+        if len(history) > 100:
+            del history[0]
+
+    def apply_circle(mask: np.ndarray, ix: int, iy: int, radius: int, value: int) -> None:
+        cv2.circle(mask, (int(ix), int(iy)), int(radius), int(value), thickness=-1)
+
+    def apply_from_base(mask: np.ndarray, ix: int, iy: int, radius: int) -> None:
+        yy, xx = np.ogrid[:img_h, :img_w]
+        circle = (xx - int(ix)) ** 2 + (yy - int(iy)) ** 2 <= int(radius) ** 2
+        mask[circle] = base_ids[circle]
+
+    def paint_line(mask: np.ndarray, p0: Tuple[int, int], p1: Tuple[int, int], radius: int, value: Optional[int], restore_from_base: bool) -> None:
+        x0, y0 = p0
+        x1, y1 = p1
+        dist = max(abs(x1 - x0), abs(y1 - y0), 1)
+        for t in range(dist + 1):
+            a = t / dist
+            x = int(round(x0 + (x1 - x0) * a))
+            y = int(round(y0 + (y1 - y0) * a))
+            if restore_from_base:
+                apply_from_base(mask, x, y, radius)
+            else:
+                assert value is not None
+                apply_circle(mask, x, y, radius, value)
+
+    def begin_stroke(ix: int, iy: int) -> None:
+        nonlocal is_painting, last_paint_point
+        push_history()
+        is_painting = True
+        last_paint_point = (ix, iy)
+        if tool_mode == "BRUSH":
+            apply_circle(work_ids, ix, iy, brush_radius, selected_cid)
+        elif tool_mode == "ERASER":
+            apply_from_base(work_ids, ix, iy, brush_radius)
+
+    def continue_stroke(ix: int, iy: int) -> None:
+        nonlocal last_paint_point
+        if not is_painting or last_paint_point is None:
+            return
+        if tool_mode == "BRUSH":
+            paint_line(work_ids, last_paint_point, (ix, iy), brush_radius, selected_cid, restore_from_base=False)
+        elif tool_mode == "ERASER":
+            paint_line(work_ids, last_paint_point, (ix, iy), brush_radius, None, restore_from_base=True)
+        last_paint_point = (ix, iy)
+
+    def end_stroke() -> None:
+        nonlocal is_painting, last_paint_point
+        is_painting = False
+        last_paint_point = None
+
+    def flood_component(ix: int, iy: int, new_cid: int) -> None:
+        old_cid = int(work_ids[iy, ix])
+        if old_cid == int(new_cid):
+            return
+        region = (work_ids == old_cid).astype(np.uint8)
+        flood = np.zeros((img_h + 2, img_w + 2), dtype=np.uint8)
+        flags = 4 | cv2.FLOODFILL_MASK_ONLY | (255 << 8)
+        region_copy = region.copy()
+        cv2.floodFill(region_copy, flood, (int(ix), int(iy)), 0, flags=flags)
+        selected = flood[1:-1, 1:-1] != 0
+        if not np.any(selected):
+            return
+        push_history()
+        work_ids[selected] = int(new_cid)
+
+    def draw_minimap(target: np.ndarray) -> None:
+        nonlocal minimap_rect
+        if not minimap_visible:
+            minimap_rect = None
+            return
+        thumb = cv2.resize(preview, (mm_w, mm_h), interpolation=cv2.INTER_AREA)
+        if mm_w <= 6 or mm_h <= 6:
+            minimap_rect = None
+            return
+        clamp_minimap()
+        x0 = int(minimap_x)
+        y0 = int(minimap_y)
+        x1 = x0 + mm_w
+        y1 = y0 + mm_h
+        minimap_rect = (x0, y0, x1, y1)
+        overlay = target.copy()
+        cv2.rectangle(overlay, (x0 - 3, y0 - 3), (x1 + 3, y1 + 20), (0, 0, 0), -1)
+        cv2.addWeighted(overlay, 0.55, target, 0.45, 0, target)
+        target[y0:y1, x0:x1] = thumb
+        vw = img_w / zoom
+        vh = img_h / zoom
+        rx0 = x0 + int(round((view_x / img_w) * mm_w))
+        ry0 = y0 + int(round((view_y / img_h) * mm_h))
+        rx1 = x0 + int(round(((view_x + vw) / img_w) * mm_w))
+        ry1 = y0 + int(round(((view_y + vh) / img_h) * mm_h))
+        rx1 = min(max(rx1, rx0 + 1), x1)
+        ry1 = min(max(ry1, ry0 + 1), y1)
+        cv2.rectangle(target, (x0 - 1, y0 - 1), (x1 + 1, y1 + 1), (255, 255, 255), 1)
+        cv2.rectangle(target, (rx0, ry0), (rx1, ry1), (0, 255, 255), 2)
+        cv2.putText(target, "mini mapa [M]", (x0, min(target.shape[0] - 5, y1 + 16)), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (230, 230, 230), 1, cv2.LINE_AA)
+
+    def current_view_overlay() -> np.ndarray:
+        alpha = float(alpha_state["value"]) / 100.0
+        base_overlay = overlay_ids(preview, work_ids, classes, alpha=alpha, transparent_class_id=transparent_class_id)
+        if tool_mode in {"BRUSH", "ERASER"}:
+            cx, cy = last_mouse_img
+            color = class_color_bgr(classes, selected_cid) if tool_mode == "BRUSH" else (255, 255, 255)
+            cv2.circle(base_overlay, (int(cx), int(cy)), int(brush_radius), color, 2, cv2.LINE_AA)
+        view_w = max(1, int(round(canvas_w / zoom)))
+        view_h = max(1, int(round(canvas_h / zoom)))
+        x0 = int(round(view_x))
+        y0 = int(round(view_y))
+        x1 = min(img_w, x0 + view_w)
+        y1 = min(img_h, y0 + view_h)
+        crop = base_overlay[y0:y1, x0:x1]
+        display = cv2.resize(crop, (canvas_w, canvas_h), interpolation=cv2.INTER_NEAREST)
+        return display
+
+    def on_mouse(event, x, y, flags, param):
+        nonlocal points, tool_mode, is_panning, pan_anchor, pan_anchor_view, last_mouse_img, view_x, view_y
+        nonlocal is_dragging_minimap, minimap_drag_offset, minimap_x, minimap_y
+        display_y = y - header_h
+        over_minimap = inside_minimap_display(x, display_y)
+        pos = screen_to_image(x, y)
+        if pos is not None:
+            last_mouse_img = pos
+            ix, iy = pos
+        else:
+            ix = iy = None
+        if event == cv2.EVENT_MOUSEWHEEL:
+            if over_minimap:
+                return
+            direction = 1 if flags > 0 else -1
+            if direction > 0:
+                set_zoom(zoom * zoom_step, (x, y))
+            else:
+                set_zoom(zoom / zoom_step, (x, y))
+            return
+        if event == cv2.EVENT_MBUTTONDOWN:
+            if over_minimap:
+                is_dragging_minimap = True
+                minimap_drag_offset = (x - minimap_x, display_y - minimap_y)
+            else:
+                is_panning = True
+                pan_anchor = (x, y)
+                pan_anchor_view = (view_x, view_y)
+            return
+        if event == cv2.EVENT_MBUTTONUP:
+            is_panning = False
+            is_dragging_minimap = False
+            return
+        if event == cv2.EVENT_MOUSEMOVE and is_dragging_minimap:
+            minimap_x = x - minimap_drag_offset[0]
+            minimap_y = display_y - minimap_drag_offset[1]
+            clamp_minimap()
+            return
+        if event == cv2.EVENT_MOUSEMOVE and is_panning:
+            dx = pan_anchor[0] - x
+            dy = pan_anchor[1] - y
+            view_x = pan_anchor_view[0] + dx / zoom
+            view_y = pan_anchor_view[1] + dy / zoom
+            clamp_view()
+            return
+        if over_minimap:
+            if event == cv2.EVENT_LBUTTONDOWN:
+                center_on_from_minimap(x, display_y)
+            if event == cv2.EVENT_LBUTTONUP:
+                end_stroke()
+            return
+        if pos is None:
+            if event == cv2.EVENT_LBUTTONUP:
+                end_stroke()
+            return
+        if tool_mode == "POLY":
+            if event == cv2.EVENT_LBUTTONDOWN:
+                points.append((ix, iy))
+            elif event == cv2.EVENT_RBUTTONDOWN and points:
+                points.pop()
+        elif tool_mode == "BUCKET":
+            if event == cv2.EVENT_LBUTTONDOWN:
+                points = []
+                flood_component(ix, iy, selected_cid)
+        elif tool_mode in {"BRUSH", "ERASER"}:
+            if event == cv2.EVENT_LBUTTONDOWN:
+                points = []
+                begin_stroke(ix, iy)
+            elif event == cv2.EVENT_MOUSEMOVE and (flags & cv2.EVENT_FLAG_LBUTTON):
+                continue_stroke(ix, iy)
+            elif event == cv2.EVENT_LBUTTONUP:
+                continue_stroke(ix, iy)
+                end_stroke()
+
+    cv2.setMouseCallback(window, on_mouse)
+
+    def render() -> np.ndarray:
+        alpha = float(alpha_state["value"]) / 100.0
+        display = current_view_overlay()
+        if points:
+            draw_color = class_color_bgr(classes, selected_cid)
+            visible_points = []
+            for px, py in points:
+                sx = int(round((float(px) - view_x) * zoom))
+                sy = int(round((float(py) - view_y) * zoom))
+                if -50 <= sx <= canvas_w + 50 and -50 <= sy <= canvas_h + 50:
+                    visible_points.append((sx, sy))
+            for sx, sy in visible_points:
+                cv2.circle(display, (sx, sy), 4, draw_color, -1, cv2.LINE_AA)
+                cv2.circle(display, (sx, sy), 6, (255, 255, 255), 1, cv2.LINE_AA)
+            if len(visible_points) >= 2:
+                pts = np.array(visible_points, dtype=np.int32)
+                cv2.polylines(display, [pts], False, draw_color, 2, cv2.LINE_AA)
+        draw_minimap(display)
+        header = np.zeros((header_h, canvas_w, 3), dtype=np.uint8)
+        footer = np.zeros((footer_h, canvas_w, 3), dtype=np.uint8)
+        cname, _rgb = classes[selected_cid]
+        bgr = class_color_bgr(classes, selected_cid)
+        minimap_txt = "ON" if minimap_visible else "OFF"
+        cv2.putText(header, f"EDITANDO {item.group}/{item.base} | base={base_choice} | tool={tool_mode}", (12, 28), cv2.FONT_HERSHEY_SIMPLEX, 0.62, (245, 245, 245), 2, cv2.LINE_AA)
+        cv2.rectangle(header, (12, 48), (42, 78), bgr, -1)
+        cv2.rectangle(header, (12, 48), (42, 78), (255, 255, 255), 1)
+        cv2.putText(header, f"classe {selected_cid}: {cname} | brush={brush_radius}px | alpha={alpha:.2f} | zoom={zoom:.2f}x | minimapa={minimap_txt}", (54, 72), cv2.FONT_HERSHEY_SIMPLEX, 0.48, (220, 220, 220), 1, cv2.LINE_AA)
+        cv2.putText(footer, "P=poly  B=balde  N=pincel  E=borracha | Esq=usar  Dir=remove ponto | ENTER=preenche poligono", (12, 28), cv2.FONT_HERSHEY_SIMPLEX, 0.44, (230, 230, 230), 1, cv2.LINE_AA)
+        cv2.putText(footer, "0..9=id  C=proxima classe  [ / ] pincel  + / - ou roda=zoom  setas=pan  meio=pan | meio no minimapa=arrasta", (12, 58), cv2.FONT_HERSHEY_SIMPLEX, 0.44, (230, 230, 230), 1, cv2.LINE_AA)
+        cv2.putText(footer, "F=fit  G=cursor  M=liga/desliga minimapa | clique no minimapa=centraliza | Z=undo  R=reset  S=SALVA  ESC/Q=cancela", (12, 86), cv2.FONT_HERSHEY_SIMPLEX, 0.44, (80, 255, 120), 1, cv2.LINE_AA)
+        return np.vstack([header, display, footer])
+
+    try:
+        while True:
+            cv2.imshow(window, render())
+            k = cv2.waitKeyEx(30)
+            if k == -1:
+                continue
+            if k in (27, ord("q"), ord("Q")):
+                return None
+            if k in (ord("s"), ord("S")):
+                return work_ids.copy(), base_choice
+            if k in (ord("c"), ord("C")):
+                selected_pos = (selected_pos + 1) % len(class_ids)
+                selected_cid = class_ids[selected_pos]
+                continue
+            if ord("0") <= k <= ord("9"):
+                cid = int(chr(k))
+                if cid in classes:
+                    selected_cid = cid
+                    selected_pos = class_ids.index(cid)
+                continue
+            if k in (ord("p"), ord("P")):
+                tool_mode = "POLY"
+                continue
+            if k in (ord("b"), ord("B")):
+                tool_mode = "BUCKET"
+                points = []
+                end_stroke()
+                continue
+            if k in (ord("n"), ord("N")):
+                tool_mode = "BRUSH"
+                points = []
+                end_stroke()
+                continue
+            if k in (ord("e"), ord("E")):
+                tool_mode = "ERASER"
+                points = []
+                end_stroke()
+                continue
+            if k in (10, 13):
+                if tool_mode == "POLY" and len(points) >= 3:
+                    push_history()
+                    poly = np.array(points, dtype=np.int32)
+                    cv2.fillPoly(work_ids, [poly], int(selected_cid))
+                    points = []
+                continue
+            if k in (ord("z"), ord("Z")):
+                if history:
+                    work_ids = history.pop()
+                    points = []
+                    end_stroke()
+                continue
+            if k in (ord("r"), ord("R")):
+                work_ids = base_ids.copy()
+                history = []
+                points = []
+                end_stroke()
+                fit_view()
+                tool_mode = "POLY"
+                continue
+            if k in (8, 127, 3014656):
+                points = []
+                end_stroke()
+                continue
+            if k in (ord("f"), ord("F")):
+                fit_view()
+                continue
+            if k in (ord("g"), ord("G")):
+                center_on(*last_mouse_img)
+                continue
+            if k in (ord("m"), ord("M")):
+                minimap_visible = not minimap_visible
+                continue
+            if k == ord("["):
+                brush_radius = max(brush_radius_min, brush_radius - 1)
+                continue
+            if k == ord("]"):
+                brush_radius = min(brush_radius_max, brush_radius + 1)
+                continue
+            if k in (ord("+"), ord("="), 171):
+                set_zoom(zoom * zoom_step)
+                continue
+            if k in (ord("-"), ord("_"), 173):
+                set_zoom(zoom / zoom_step)
+                continue
+            if k in (2424832, 81):
+                pan(-80, 0)
+                continue
+            if k in (2555904, 83):
+                pan(80, 0)
+                continue
+            if k == 2490368:
+                pan(0, -80)
+                continue
+            if k == 2621440:
+                pan(0, 80)
+                continue
+    finally:
+        cv2.setMouseCallback(window, lambda *args: None)
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def run_main() -> None:
+    parser = argparse.ArgumentParser(description="MaskReviewer standalone")
+    parser.add_argument("--group", default=None, help="Pasta group. Se ausente, procura ao lado do EXE ou abre seletor.")
+    parser.add_argument("--labelmap", default=None, help="labelmap.txt. Se ausente, procura automaticamente ou abre seletor.")
+    parser.add_argument("--groups", nargs="*", default=None, help="Opcional: limita grupos")
+    parser.add_argument("--alpha", type=float, default=0.45, help="Overlay inicial 0..1")
+    parser.add_argument("--max_width", type=int, default=1800)
+    parser.add_argument("--start_idx", type=int, default=None)
+    args = parser.parse_args()
+
+    if not 0.0 <= args.alpha <= 1.0:
+        raise RuntimeError("--alpha deve ficar entre 0 e 1")
+
+    base = app_dir()
+    settings_path = base / SETTINGS_NAME
+    settings = load_settings(settings_path)
+
+    group_root = resolve_group_root(args.group, settings)
+    labelmap_path = resolve_labelmap(args.labelmap, group_root, settings)
+
+    settings["group_root"] = str(group_root)
+    settings["labelmap"] = str(labelmap_path)
+    save_settings(settings_path, settings)
+
+    classes = load_labelmap_classes(labelmap_path)
+    items = collect_items(group_root, args.groups)
+    if not items:
+        raise RuntimeError(
+            "Nenhum trio preview/mask/prediction foi encontrado.\n\n"
+            f"GROUP selecionado:\n{group_root}\n\n"
+            "Estrutura esperada por grupo:\n"
+            "previews/<base>.png\nmasks/<base>.png\npredictions/<base>.png"
+        )
+
+    choices_csv = group_root / CHOICES_NAME
+    choices = load_choices(choices_csv)
+
+    if args.start_idx is not None:
+        idx = max(0, min(len(items) - 1, int(args.start_idx)))
+    else:
+        idx = 0
+        for i, item in enumerate(items):
+            if (item.group, item.base) not in choices:
+                idx = i
+                break
+        else:
+            idx = 0
+
+    alpha_state = {"value": int(round(args.alpha * 100.0))}
+    window = APP_NAME
+    final_window = f"{APP_NAME} - FINAL"
+    show_final_window = False
+
+    def on_alpha(v):
+        alpha_state["value"] = max(0, min(100, int(v)))
+
+    cv2.namedWindow(window, cv2.WINDOW_NORMAL)
+    cv2.createTrackbar("Overlay %", window, alpha_state["value"], 100, on_alpha)
+
+    while True:
+        item = items[idx]
+        current = choices.get((item.group, item.base), {}).get("choice")
+
+        alpha = float(alpha_state["value"]) / 100.0
+        canvas = compose_panel(
+            item=item,
+            choice=current,
+            index=idx,
+            total=len(items),
+            alpha=alpha,
+            max_width=args.max_width,
+        )
+        cv2.imshow(window, canvas)
+
+        if show_final_window:
+            final_canvas = compose_final_panel(item=item, choice=current, alpha=alpha, max_width=args.max_width)
+            cv2.namedWindow(final_window, cv2.WINDOW_NORMAL)
+            cv2.imshow(final_window, final_canvas)
+        else:
+            try:
+                cv2.destroyWindow(final_window)
+            except Exception:
+                pass
+
+        k = cv2.waitKeyEx(30)
+        if k == -1:
+            continue
+
+        if k in (ord("q"), ord("Q"), 27):
+            break
+
+        if k in (ord("v"), ord("V")):
+            show_final_window = not show_final_window
+            continue
+
+        if k == ord("1"):
+            record_choice(item, "HUMAN", item.human_mask, choices, choices_csv)
+            idx = (idx + 1) % len(items)
+            continue
+
+        if k == ord("2"):
+            record_choice(item, "MODEL", item.model_mask, choices, choices_csv)
+            idx = (idx + 1) % len(items)
+            continue
+
+        if k == ord("3"):
+            record_choice(item, "REMAP", None, choices, choices_csv)
+            idx = (idx + 1) % len(items)
+            continue
+
+        if k in (ord("e"), ord("E")):
+            base_choice = choose_edit_base(window, canvas)
+            if base_choice is None:
+                continue
+            edited = run_polygon_editor(
+                window=window,
+                item=item,
+                base_choice=base_choice,
+                classes=classes,
+                alpha_state=alpha_state,
+            )
+            if edited is not None:
+                edited_ids, edit_base = edited
+                edited_bgr = ids_to_colored_mask(edited_ids, classes)
+                ensure_dir(item.final_mask.parent)
+                if not cv2.imwrite(str(item.final_mask), edited_bgr):
+                    raise RuntimeError(f"Falha ao salvar máscara editada: {item.final_mask}")
+                record_choice(item, "EDITED", item.final_mask, choices, choices_csv, edit_base=edit_base)
+                idx = (idx + 1) % len(items)
+            continue
+
+        if k in (ord("x"), ord("X")):
+            remove_choice(item, choices, choices_csv)
+            continue
+
+        if k in (ord("s"), ord("S")):
+            idx = (idx + 1) % len(items)
+            continue
+
+        if k in (ord("d"), ord("D"), 83, 2555904):
+            idx = (idx + 1) % len(items)
+            continue
+
+        if k in (ord("a"), ord("A"), 81, 2424832):
+            idx = (idx - 1 + len(items)) % len(items)
+            continue
+
+    try:
+        cv2.destroyWindow(final_window)
+    except Exception:
+        pass
+    cv2.destroyAllWindows()
+
+
+def main() -> None:
+    try:
+        run_main()
+    except Exception as exc:
+        # Em build --windowed não existe console. Gravamos log ao lado do EXE.
+        log_path = app_dir() / "MaskReviewer_error.log"
+        try:
+            log_path.write_text(traceback.format_exc(), encoding="utf-8")
+        except Exception:
+            pass
+
+        show_error_dialog(
+            f"O MaskReviewer encontrou um erro:\n\n{exc}\n\n"
+            f"Detalhes foram gravados em:\n{log_path}"
+        )
+        raise
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.settings.json b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.settings.json
new file mode 100644
index 000000000..785f2553a
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.settings.json
@@ -0,0 +1,4 @@
+{
+  "group_root": "C:\\ZendionInc\\agrobot_base\\Python\\OAK\\datasets\\oak-fcc-3\\MaskReviewer_package\\dist\\MaskReviewer\\group",
+  "labelmap": "C:\\ZendionInc\\agrobot_base\\Python\\OAK\\datasets\\oak-fcc-3\\MaskReviewer_package\\dist\\MaskReviewer\\labelmap.txt"
+}
\ No newline at end of file
diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.spec b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.spec
new file mode 100644
index 000000000..b3e3eb0f2
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.spec
@@ -0,0 +1,51 @@
+# -*- mode: python ; coding: utf-8 -*-
+from PyInstaller.utils.hooks import collect_all
+
+datas = []
+binaries = []
+hiddenimports = []
+tmp_ret = collect_all('cv2')
+datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
+
+
+a = Analysis(
+    ['MaskReviewer.py'],
+    pathex=[],
+    binaries=binaries,
+    datas=datas,
+    hiddenimports=hiddenimports,
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    noarchive=False,
+    optimize=0,
+)
+pyz = PYZ(a.pure)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    [],
+    exclude_binaries=True,
+    name='MaskReviewer',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    console=False,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)
+coll = COLLECT(
+    exe,
+    a.binaries,
+    a.datas,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    name='MaskReviewer',
+)
diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/README_MASKREVIEWER.txt b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/README_MASKREVIEWER.txt
new file mode 100644
index 000000000..529818576
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/README_MASKREVIEWER.txt
@@ -0,0 +1,110 @@
+MASKREVIEWER - PACOTE WINDOWS
+================================
+
+OBJETIVO
+--------
+Revisar, comparar e eventualmente editar máscaras já separadas pelo auditor.
+Não carrega modelo, PyTorch, CUDA ou checkpoint.
+
+PACOTE PARA O ANOTADOR
+----------------------
+A estrutura recomendada é:
+
+MaskReviewer\
+    MaskReviewer.exe
+    _internal\
+    labelmap.txt
+    group\
+        chao\
+            previews\
+            masks\
+            predictions\
+            final_masks\
+            review_order.csv
+        chao_cana\
+        chao_cana_erva\
+        chao_erva\
+        ...
+
+Ao abrir MaskReviewer.exe:
+
+1. Se "group" estiver ao lado do EXE, ele usa automaticamente.
+2. Se "labelmap.txt" estiver ao lado do EXE, ele usa automaticamente.
+3. Se algum deles não existir, abre o seletor do Windows.
+4. Os caminhos escolhidos ficam lembrados em MaskReviewer.settings.json.
+5. O estado do lote fica em group\review_choices.csv.
+6. As escolhas finais ficam em cada:
+       group\<GRUPO>\final_masks\
+
+NOVO LOTE
+---------
+Para mandar uma nova revisão à mesma pessoa:
+
+1. Ela mantém:
+       MaskReviewer.exe
+       _internal\
+       labelmap.txt
+
+2. Substitui a pasta:
+       group\
+
+3. Abre o mesmo EXE.
+
+Como review_choices.csv fica DENTRO de group\, a sessão nova começa limpa junto
+com a nova pasta group.
+
+CONTROLES
+---------
+Painel 2x2:
+  1 = máscara humana
+  2 = máscara do modelo
+  3 = REMAP
+  E = editar
+  A/D = anterior/próxima
+  S = pular
+  X = apagar decisão
+  Q/ESC = sair
+
+A barra "Overlay %" controla a opacidade dos dois overlays ao mesmo tempo.
+
+Editor:
+  E e depois:
+    1 = humana como base
+    2 = modelo como base
+    3 = começar de chão
+
+  Mouse esquerdo = adiciona ponto
+  Mouse direito = remove último ponto
+  ENTER = preenche polígono
+  C = próxima classe
+  0..9 = ID de classe
+  Z = desfazer
+  R = restaurar base
+  BACKSPACE/DEL = limpar pontos abertos
+  S = salvar
+  ESC/Q = cancelar edição
+
+COMO GERAR O EXE
+----------------
+Execute com duplo clique:
+
+    build_windows.bat
+
+Recomendado:
+    Windows 10/11
+    Python 3.11 ou 3.12 instalado SOMENTE na máquina de build
+
+O script cria:
+    dist\MaskReviewer\MaskReviewer.exe
+
+Depois do build, a máquina do anotador NÃO precisa ter Python.
+
+POR QUE --ONEDIR?
+-----------------
+O build usa PyInstaller --onedir porque OpenCV/Numpy ficam mais confiáveis,
+iniciam mais rápido e são mais fáceis de diagnosticar do que um EXE --onefile.
+
+IMPORTANTE
+----------
+O MaskReviewer nunca altera o dataset bruto.
+Ele só escreve final_masks e review_choices.csv dentro do lote enviado.
diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/Analysis-00.toc b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/Analysis-00.toc
new file mode 100644
index 000000000..5192afbae
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/Analysis-00.toc
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+  ('_tcl_data\\tzdata\\America\\Punta_Arenas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Punta_Arenas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Stanley',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Stanley',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Tegucigalpa',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Tegucigalpa',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp775.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp775.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Christmas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Christmas',
+   'DATA'),
+  ('_tcl_data\\tm.tcl', 'C:\\Python312\\tcl\\tcl8.6\\tm.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Catamarca',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Catamarca',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Sao_Paulo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Sao_Paulo',
+   'DATA'),
+  ('_tcl_data\\msgs\\hr.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\hr.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Jakarta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Jakarta',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Fortaleza',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Fortaleza',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Marigot',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Marigot',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Antananarivo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Antananarivo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Abidjan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Abidjan',
+   'DATA'),
+  ('_tcl_data\\msgs\\ta_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ta_in.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-2',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-2',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Riyadh',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Riyadh',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\ComodRivadavia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\ComodRivadavia',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_mx.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_mx.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\pt_br.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\pt_br.msg',
+   'DATA'),
+  ('_tk_data\\msgs\\fi.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\fi.msg', 'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Asmera',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Asmera',
+   'DATA'),
+  ('_tk_data\\bgerror.tcl', 'C:\\Python312\\tcl\\tk8.6\\bgerror.tcl', 'DATA'),
+  ('_tcl_data\\msgs\\en_hk.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\en_hk.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\sl.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sl.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Currie',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Currie',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Pyongyang',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Pyongyang',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Universal',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Universal',
+   'DATA'),
+  ('_tk_data\\megawidget.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\megawidget.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Araguaina',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Araguaina',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Isle_of_Man',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Isle_of_Man',
+   'DATA'),
+  ('_tk_data\\iconlist.tcl', 'C:\\Python312\\tcl\\tk8.6\\iconlist.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\South',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\South',
+   'DATA'),
+  ('_tcl_data\\msgs\\af_za.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\af_za.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\sv.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sv.msg',
+   'DATA'),
+  ('_tk_data\\comdlg.tcl', 'C:\\Python312\\tcl\\tk8.6\\comdlg.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\GB', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\GB', 'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Douala',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Douala',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Managua',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Managua',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp949.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp949.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Chungking',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Chungking',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Broken_Hill',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Broken_Hill',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Grand_Turk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Grand_Turk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Macquarie',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Macquarie',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Atlantic',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Atlantic',
+   'DATA'),
+  ('_tcl_data\\msgs\\is.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\is.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Kiev',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Kiev',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Simferopol',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Simferopol',
+   'DATA'),
+  ('_tcl_data\\msgs\\tr.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\tr.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Whitehorse',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Whitehorse',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Yap',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Yap',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Santo_Domingo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Santo_Domingo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Singapore',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Singapore',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\St_Lucia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\St_Lucia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Muscat',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Muscat',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\San_Juan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\San_Juan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Guatemala',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Guatemala',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Brazil\\DeNoronha',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Brazil\\DeNoronha',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Dawson_Creek',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Dawson_Creek',
+   'DATA'),
+  ('_tk_data\\images\\pwrdLogo75.gif',
+   'C:\\Python312\\tcl\\tk8.6\\images\\pwrdLogo75.gif',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Dominica',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Dominica',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-9.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-9.enc',
+   'DATA'),
+  ('_tk_data\\msgs\\pl.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\pl.msg', 'DATA'),
+  ('_tcl_data\\msgs\\ro.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ro.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Almaty',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Almaty',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Porto-Novo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Porto-Novo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Resolute',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Resolute',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Asuncion',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Asuncion',
+   'DATA'),
+  ('_tcl_data\\word.tcl', 'C:\\Python312\\tcl\\tcl8.6\\word.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\MST7MDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\MST7MDT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Coral_Harbour',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Coral_Harbour',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+10',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+10',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Hong_Kong',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Hong_Kong',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Azores',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Azores',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Palmer',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Palmer',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Magadan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Magadan',
+   'DATA'),
+  ('_tcl_data\\msgs\\en_ie.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\en_ie.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Mexico_City',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Mexico_City',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp869.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp869.enc',
+   'DATA'),
+  ('_tcl_data\\encoding\\euc-kr.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\euc-kr.enc',
+   'DATA'),
+  ('_tk_data\\msgs\\hu.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\hu.msg', 'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT0',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT0',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Bahrain',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Bahrain',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Belgrade',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Belgrade',
+   'DATA'),
+  ('_tk_data\\ttk\\spinbox.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\spinbox.tcl',
+   'DATA'),
+  ('_tk_data\\button.tcl', 'C:\\Python312\\tcl\\tk8.6\\button.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\Tijuana',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Tijuana',
+   'DATA'),
+  ('_tcl_data\\msgs\\en_au.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\en_au.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Brisbane',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Brisbane',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+5',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+5',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Brazil\\West',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Brazil\\West',
+   'DATA'),
+  ('_tcl_data\\msgs\\zh_tw.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\zh_tw.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Ouagadougou',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Ouagadougou',
+   'DATA'),
+  ('_tcl_data\\tzdata\\W-SU',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\W-SU',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Kosrae',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Kosrae',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Chatham',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Chatham',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Fakaofo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Fakaofo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Shiprock',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Shiprock',
+   'DATA'),
+  ('_tcl_data\\msgs\\af.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\af.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Dublin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Dublin',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Budapest',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Budapest',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Mauritius',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Mauritius',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Juneau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Juneau',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-7.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-7.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Tarawa',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Tarawa',
+   'DATA'),
+  ('_tk_data\\ttk\\utils.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\utils.tcl',
+   'DATA'),
+  ('_tcl_data\\msgs\\uk.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\uk.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\GMT+0',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\GMT+0',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Mawson',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Mawson',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Hermosillo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Hermosillo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+6',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+6',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kamchatka',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kamchatka',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp857.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp857.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Jamaica',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Jamaica',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Athens',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Athens',
+   'DATA'),
+  ('_tcl_data\\msgs\\id_id.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\id_id.msg',
+   'DATA'),
+  ('_tk_data\\optMenu.tcl', 'C:\\Python312\\tcl\\tk8.6\\optMenu.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ho_Chi_Minh',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ho_Chi_Minh',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Samara',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Samara',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Lubumbashi',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Lubumbashi',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\St_Thomas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\St_Thomas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Porto_Acre',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Porto_Acre',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Winamac',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Winamac',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\UTC',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\UTC',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Freetown',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Freetown',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Bougainville',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Bougainville',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Famagusta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Famagusta',
+   'DATA'),
+  ('_tcl_data\\msgs\\es.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es.msg',
+   'DATA'),
+  ('_tk_data\\license.terms',
+   'C:\\Python312\\tcl\\tk8.6\\license.terms',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Gaza',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Gaza',
+   'DATA'),
+  ('_tk_data\\images\\logo100.gif',
+   'C:\\Python312\\tcl\\tk8.6\\images\\logo100.gif',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-7',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-7',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tashkent',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tashkent',
+   'DATA'),
+  ('_tcl_data\\msgs\\en_gb.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\en_gb.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\ta.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ta.msg',
+   'DATA'),
+  ('_tk_data\\msgs\\zh_cn.msg',
+   'C:\\Python312\\tcl\\tk8.6\\msgs\\zh_cn.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Kampala',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Kampala',
+   'DATA'),
+  ('_tk_data\\ttk\\combobox.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\combobox.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Makassar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Makassar',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Vatican',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Vatican',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_cr.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_cr.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Faroe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Faroe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Aqtau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Aqtau',
+   'DATA'),
+  ('_tcl_data\\init.tcl', 'C:\\Python312\\tcl\\tcl8.6\\init.tcl', 'DATA'),
+  ('_tcl_data\\encoding\\cns11643.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cns11643.enc',
+   'DATA'),
+  ('_tk_data\\ttk\\ttk.tcl', 'C:\\Python312\\tcl\\tk8.6\\ttk\\ttk.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Queensland',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Queensland',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\LHI',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\LHI',
+   'DATA'),
+  ('_tcl_data\\msgs\\fi.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\fi.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Troll',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Troll',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Jamaica',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Jamaica',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Comoro',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Comoro',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Reykjavik',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Reykjavik',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Qyzylorda',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Qyzylorda',
+   'DATA'),
+  ('_tcl_data\\msgs\\ar_sy.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ar_sy.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Shanghai',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Shanghai',
+   'DATA'),
+  ('_tk_data\\ttk\\cursors.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\cursors.tcl',
+   'DATA'),
+  ('_tk_data\\ttk\\notebook.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\notebook.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Yekaterinburg',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Yekaterinburg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Galapagos',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Galapagos',
+   'DATA'),
+  ('_tcl_data\\msgs\\kw.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kw.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_co.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_co.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Anguilla',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Anguilla',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Martinique',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Martinique',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Ojinaga',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Ojinaga',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Petersburg',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Petersburg',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_ve.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_ve.msg',
+   'DATA'),
+  ('_tk_data\\scrlbar.tcl', 'C:\\Python312\\tcl\\tk8.6\\scrlbar.tcl', 'DATA'),
+  ('_tk_data\\ttk\\fonts.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\fonts.tcl',
+   'DATA'),
+  ('_tk_data\\images\\pwrdLogo200.gif',
+   'C:\\Python312\\tcl\\tk8.6\\images\\pwrdLogo200.gif',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kathmandu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kathmandu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Nicosia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Nicosia',
+   'DATA'),
+  ('_tcl_data\\msgs\\mk.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\mk.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Luxembourg',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Luxembourg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Blantyre',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Blantyre',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+7',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+7',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Khandyga',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Khandyga',
+   'DATA'),
+  ('_tcl_data\\encoding\\shiftjis.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\shiftjis.enc',
+   'DATA'),
+  ('_tcl_data\\msgs\\fo_fo.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\fo_fo.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\La_Rioja',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\La_Rioja',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Lome',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Lome',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Algiers',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Algiers',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Bratislava',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Bratislava',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Monaco',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Monaco',
+   'DATA'),
+  ('_tcl_data\\tzdata\\US\\Eastern',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\US\\Eastern',
+   'DATA'),
+  ('_tcl_data\\msgs\\sq.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sq.msg',
+   'DATA'),
+  ('_tk_data\\msgs\\eo.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\eo.msg', 'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Copenhagen',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Copenhagen',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Baku',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Baku',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Antigua',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Antigua',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp936.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp936.enc',
+   'DATA'),
+  ('_tcl_data\\msgs\\be.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\be.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Chongqing',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Chongqing',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Andorra',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Andorra',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Djibouti',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Djibouti',
+   'DATA'),
+  ('_tcl_data\\msgs\\bn_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\bn_in.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\St_Helena',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\St_Helena',
+   'DATA'),
+  ('_tcl_data\\msgs\\en_nz.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\en_nz.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\eu_es.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\eu_es.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Amman',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Amman',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\PST8PDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\PST8PDT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Harbin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Harbin',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\Greenwich',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\Greenwich',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Creston',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Creston',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Inuvik',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Inuvik',
+   'DATA'),
+  ('_tcl_data\\msgs\\kok_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kok_in.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Tahiti',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Tahiti',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Maldives',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Maldives',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Malabo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Malabo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Sydney',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Sydney',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Chicago',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Chicago',
+   'DATA'),
+  ('_tcl_data\\msgs\\ar.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ar.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Phoenix',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Phoenix',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_py.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_py.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\hu.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\hu.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Detroit',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Detroit',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Belize',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Belize',
+   'DATA'),
+  ('_tk_data\\images\\tai-ku.gif',
+   'C:\\Python312\\tcl\\tk8.6\\images\\tai-ku.gif',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Palau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Palau',
+   'DATA'),
+  ('_tcl_data\\parray.tcl', 'C:\\Python312\\tcl\\tcl8.6\\parray.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Eucla',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Eucla',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kashgar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kashgar',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_pe.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_pe.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Fiji',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Fiji',
+   'DATA'),
+  ('_tcl_data\\tzdata\\GMT-0',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\GMT-0',
+   'DATA'),
+  ('_tcl_data\\encoding\\symbol.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\symbol.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Davis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Davis',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Kentucky\\Monticello',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Kentucky\\Monticello',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Ushuaia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Ushuaia',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp1253.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp1253.enc',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp862.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp862.enc',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-10.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-10.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Eastern',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Eastern',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-15.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-15.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\EST5EDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\EST5EDT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Zagreb',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Zagreb',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Cordoba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Cordoba',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Juba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Juba',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp874.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp874.enc',
+   'DATA'),
+  ('_tcl_data\\msgs\\zh_cn.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\zh_cn.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Rome',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Rome',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Niamey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Niamey',
+   'DATA'),
+  ('_tk_data\\icons.tcl', 'C:\\Python312\\tcl\\tk8.6\\icons.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\Thunder_Bay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Thunder_Bay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Johnston',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Johnston',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp932.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp932.enc',
+   'DATA'),
+  ('_tcl_data\\package.tcl', 'C:\\Python312\\tcl\\tcl8.6\\package.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\Lima',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Lima',
+   'DATA'),
+  ('_tcl_data\\encoding\\macCroatian.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\macCroatian.enc',
+   'DATA'),
+  ('_tcl_data\\msgs\\fr_ca.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\fr_ca.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Yancowinna',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Yancowinna',
+   'DATA'),
+  ('_tcl_data\\tzdata\\MET', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\MET', 'DATA'),
+  ('_tcl_data\\msgs\\bg.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\bg.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\ko.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ko.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Hebron',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Hebron',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-2.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-2.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Ponape',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Ponape',
+   'DATA'),
+  ('_tk_data\\ttk\\altTheme.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\altTheme.tcl',
+   'DATA'),
+  ('_tcl_data\\opt0.4\\optparse.tcl',
+   'C:\\Python312\\tcl\\tcl8.6\\opt0.4\\optparse.tcl',
+   'DATA'),
+  ('_tk_data\\msgs\\en_gb.msg',
+   'C:\\Python312\\tcl\\tk8.6\\msgs\\en_gb.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Lord_Howe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Lord_Howe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Vaduz',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Vaduz',
+   'DATA'),
+  ('_tcl_data\\tzdata\\US\\East-Indiana',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\US\\East-Indiana',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\South_Georgia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\South_Georgia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Puerto_Rico',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Puerto_Rico',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Khartoum',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Khartoum',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Israel',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Israel',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Jujuy',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Jujuy',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Denver',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Denver',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Porto_Velho',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Porto_Velho',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_pr.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_pr.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Truk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Truk',
+   'DATA'),
+  ('_tk_data\\mkpsenc.tcl', 'C:\\Python312\\tcl\\tk8.6\\mkpsenc.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\Bogota',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Bogota',
+   'DATA'),
+  ('_tk_data\\safetk.tcl', 'C:\\Python312\\tcl\\tk8.6\\safetk.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\Bahia_Banderas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Bahia_Banderas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Pohnpei',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Pohnpei',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\North',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\North',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Chagos',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Chagos',
+   'DATA'),
+  ('_tk_data\\clrpick.tcl', 'C:\\Python312\\tcl\\tk8.6\\clrpick.tcl', 'DATA'),
+  ('_tk_data\\ttk\\button.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\button.tcl',
+   'DATA'),
+  ('_tk_data\\ttk\\panedwindow.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\panedwindow.tcl',
+   'DATA'),
+  ('_tcl_data\\msgs\\zh.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\zh.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp865.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp865.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Reunion',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Reunion',
+   'DATA'),
+  ('_tk_data\\choosedir.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\choosedir.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Lisbon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Lisbon',
+   'DATA'),
+  ('_tk_data\\dialog.tcl', 'C:\\Python312\\tcl\\tk8.6\\dialog.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Brunei',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Brunei',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Brussels',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Brussels',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Grenada',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Grenada',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Kirov',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Kirov',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Madeira',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Madeira',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\North_Dakota\\New_Salem',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\North_Dakota\\New_Salem',
+   'DATA'),
+  ('tcl8\\8.4\\platform\\shell-1.1.4.tm',
+   'C:\\Python312\\tcl\\tcl8\\8.4\\platform\\shell-1.1.4.tm',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Kiritimati',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Kiritimati',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Marengo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Marengo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Sakhalin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Sakhalin',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\MST7',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\MST7',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Tongatapu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Tongatapu',
+   'DATA'),
+  ('_tk_data\\msgs\\ru.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\ru.msg', 'DATA'),
+  ('_tcl_data\\msgs\\mt.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\mt.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\gb1988.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\gb1988.enc',
+   'DATA'),
+  ('_tcl_data\\msgs\\te_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\te_in.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tehran',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tehran',
+   'DATA'),
+  ('_tk_data\\ttk\\defaults.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\defaults.tcl',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_gt.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_gt.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\CET', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\CET', 'DATA'),
+  ('_tcl_data\\tzdata\\GB-Eire',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\GB-Eire',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Guadalcanal',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Guadalcanal',
+   'DATA'),
+  ('_tk_data\\ttk\\sizegrip.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\sizegrip.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Qostanay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Qostanay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Jujuy',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Jujuy',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Easter',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Easter',
+   'DATA'),
+  ('_tcl_data\\msgs\\lv.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\lv.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-6.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-6.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\GMT0',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\GMT0',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp737.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp737.enc',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-16.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-16.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Cuba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Cuba',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+1',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+1',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_pa.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_pa.msg',
+   'DATA'),
+  ('_tk_data\\ttk\\entry.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\entry.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ust-Nera',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ust-Nera',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-1',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-1',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp855.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp855.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Guernsey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Guernsey',
+   'DATA'),
+  ('_tcl_data\\tclIndex', 'C:\\Python312\\tcl\\tcl8.6\\tclIndex', 'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Omsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Omsk',
+   'DATA'),
+  ('_tcl_data\\msgs\\eu.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\eu.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\sk.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sk.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Dawson',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Dawson',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Buenos_Aires',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Buenos_Aires',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Rainy_River',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Rainy_River',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Caracas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Caracas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Wallis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Wallis',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Seoul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Seoul',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Kanton',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Kanton',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Tasmania',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Tasmania',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Vancouver',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Vancouver',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Nassau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Nassau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Guadeloupe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Guadeloupe',
+   'DATA'),
+  ('_tcl_data\\msgs\\en_sg.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\en_sg.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp860.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp860.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Los_Angeles',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Los_Angeles',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Egypt',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Egypt',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Lagos',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Lagos',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso2022-kr.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso2022-kr.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\US\\Pacific',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\US\\Pacific',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Enderbury',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Enderbury',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Chuuk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Chuuk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-13',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-13',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_bo.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_bo.msg',
+   'DATA'),
+  ('_tk_data\\ttk\\menubutton.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\menubutton.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Catamarca',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Catamarca',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Dakar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Dakar',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\North_Dakota\\Center',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\North_Dakota\\Center',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Boa_Vista',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Boa_Vista',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\El_Salvador',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\El_Salvador',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Madrid',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Madrid',
+   'DATA'),
+  ('_tcl_data\\msgs\\te.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\te.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Efate',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Efate',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-0',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-0',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\New_York',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\New_York',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-3',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-3',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Phnom_Penh',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Phnom_Penh',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Mendoza',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Mendoza',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Salta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Salta',
+   'DATA'),
+  ('_tcl_data\\msgs\\en_ph.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\en_ph.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ashkhabad',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ashkhabad',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\AST4',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\AST4',
+   'DATA'),
+  ('_tcl_data\\msgs\\sw.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sw.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Mayotte',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Mayotte',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Menominee',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Menominee',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Fort_Nelson',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Fort_Nelson',
+   'DATA'),
+  ('_tcl_data\\msgs\\de_at.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\de_at.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\PST8PDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\PST8PDT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\El_Aaiun',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\El_Aaiun',
+   'DATA'),
+  ('_tcl_data\\encoding\\koi8-u.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\koi8-u.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Astrakhan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Astrakhan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Kaliningrad',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Kaliningrad',
+   'DATA'),
+  ('_tcl_data\\encoding\\ksc5601.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\ksc5601.enc',
+   'DATA'),
+  ('_tcl_data\\encoding\\macCyrillic.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\macCyrillic.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cordoba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cordoba',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_ec.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_ec.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Kwajalein',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Kwajalein',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Amsterdam',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Amsterdam',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Adak',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Adak',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Vilnius',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Vilnius',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Turkey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Turkey',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Hongkong',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Hongkong',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Montreal',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Montreal',
+   'DATA'),
+  ('_tk_data\\listbox.tcl', 'C:\\Python312\\tcl\\tk8.6\\listbox.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dhaka',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dhaka',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Yakutat',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Yakutat',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Ceuta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Ceuta',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\CST6',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\CST6',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Vladivostok',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Vladivostok',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Chisinau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Chisinau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Pacific',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Pacific',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_cl.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_cl.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\jis0212.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\jis0212.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Norfolk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Norfolk',
+   'DATA'),
+  ('_tcl_data\\msgs\\gv.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\gv.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Tirane',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Tirane',
+   'DATA'),
+  ('_tcl_data\\http1.0\\pkgIndex.tcl',
+   'C:\\Python312\\tcl\\tcl8.6\\http1.0\\pkgIndex.tcl',
+   'DATA'),
+  ('tcl8\\8.5\\msgcat-1.6.1.tm',
+   'C:\\Python312\\tcl\\tcl8\\8.5\\msgcat-1.6.1.tm',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Kyiv',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Kyiv',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Mogadishu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Mogadishu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\Universal',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\Universal',
+   'DATA'),
+  ('_tk_data\\obsolete.tcl', 'C:\\Python312\\tcl\\tk8.6\\obsolete.tcl', 'DATA'),
+  ('_tk_data\\ttk\\vistaTheme.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\vistaTheme.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Bissau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Bissau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cayman',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cayman',
+   'DATA'),
+  ('_tcl_data\\msgs\\el.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\el.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\macCentEuro.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\macCentEuro.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Vevay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Vevay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Rothera',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Rothera',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_ni.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_ni.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\South_Pole',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\South_Pole',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Buenos_Aires',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Buenos_Aires',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Cocos',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Cocos',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Miquelon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Miquelon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Libya',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Libya',
+   'DATA'),
+  ('_tcl_data\\msgs\\fa.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\fa.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Kerguelen',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Kerguelen',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ashgabat',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ashgabat',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Baghdad',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Baghdad',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Bermuda',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Bermuda',
+   'DATA'),
+  ('_tcl_data\\tzdata\\ROC', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\ROC', 'DATA'),
+  ('_tcl_data\\msgs\\he.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\he.msg',
+   'DATA'),
+  ('_tk_data\\msgs\\de.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\de.msg', 'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Karachi',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Karachi',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ulaanbaatar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ulaanbaatar',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kuwait',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kuwait',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Fort_Wayne',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Fort_Wayne',
+   'DATA'),
+  ('_tcl_data\\encoding\\dingbats.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\dingbats.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Mahe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Mahe',
+   'DATA'),
+  ('_tk_data\\panedwindow.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\panedwindow.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Mexico\\BajaNorte',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Mexico\\BajaNorte',
+   'DATA'),
+  ('_tk_data\\console.tcl', 'C:\\Python312\\tcl\\tk8.6\\console.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\Rosario',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Rosario',
+   'DATA'),
+  ('_tcl_data\\encoding\\gb12345.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\gb12345.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Jayapura',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Jayapura',
+   'DATA'),
+  ('_tcl_data\\msgs\\ga_ie.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ga_ie.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Thule',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Thule',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Thimbu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Thimbu',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_hn.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_hn.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\nb.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\nb.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Eirunepe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Eirunepe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\NZ-CHAT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\NZ-CHAT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Winnipeg',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Winnipeg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\EET', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\EET', 'DATA'),
+  ('_tcl_data\\opt0.4\\pkgIndex.tcl',
+   'C:\\Python312\\tcl\\tcl8.6\\opt0.4\\pkgIndex.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Arctic\\Longyearbyen',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Arctic\\Longyearbyen',
+   'DATA'),
+  ('_tk_data\\focus.tcl', 'C:\\Python312\\tcl\\tk8.6\\focus.tcl', 'DATA'),
+  ('_tk_data\\tclIndex', 'C:\\Python312\\tcl\\tk8.6\\tclIndex', 'DATA'),
+  ('_tcl_data\\tzdata\\Poland',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Poland',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Pago_Pago',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Pago_Pago',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tokyo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tokyo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Pangnirtung',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Pangnirtung',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cuiaba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cuiaba',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Midway',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Midway',
+   'DATA'),
+  ('_tcl_data\\msgs\\cs.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\cs.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\macThai.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\macThai.enc',
+   'DATA'),
+  ('_tk_data\\palette.tcl', 'C:\\Python312\\tcl\\tk8.6\\palette.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Maseru',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Maseru',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+8',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+8',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Marquesas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Marquesas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Qatar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Qatar',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Syowa',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Syowa',
+   'DATA'),
+  ('_tcl_data\\msgs\\sh.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sh.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Scoresbysund',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Scoresbysund',
+   'DATA'),
+  ('_tcl_data\\msgs\\pt.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\pt.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\tis-620.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\tis-620.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Beirut',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Beirut',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dili',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dili',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Thimphu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Thimphu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Calcutta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Calcutta',
+   'DATA'),
+  ('_tcl_data\\msgs\\kw_gb.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kw_gb.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Sofia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Sofia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\San_Luis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\San_Luis',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dubai',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dubai',
+   'DATA'),
+  ('_tcl_data\\msgs\\ko_kr.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ko_kr.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Metlakatla',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Metlakatla',
+   'DATA'),
+  ('_tk_data\\ttk\\classicTheme.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\classicTheme.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Taipei',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Taipei',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Vienna',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Vienna',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Nipigon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Nipigon',
+   'DATA'),
+  ('_tk_data\\ttk\\scrollbar.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\scrollbar.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cayenne',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cayenne',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Goose_Bay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Goose_Bay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+9',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+9',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Saigon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Saigon',
+   'DATA'),
+  ('_tcl_data\\msgs\\en_za.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\en_za.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Barnaul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Barnaul',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Jerusalem',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Jerusalem',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kuala_Lumpur',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kuala_Lumpur',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp1255.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp1255.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Iran',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Iran',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Virgin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Virgin',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp866.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp866.enc',
+   'DATA'),
+  ('_tcl_data\\msgs\\fo.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\fo.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp863.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp863.enc',
+   'DATA'),
+  ('_tk_data\\images\\pwrdLogo.eps',
+   'C:\\Python312\\tcl\\tk8.6\\images\\pwrdLogo.eps',
+   'DATA'),
+  ('_tk_data\\images\\pwrdLogo175.gif',
+   'C:\\Python312\\tcl\\tk8.6\\images\\pwrdLogo175.gif',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-6',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-6',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Greenwich',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Greenwich',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Rio_Branco',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Rio_Branco',
+   'DATA'),
+  ('_tcl_data\\msgs\\gl.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\gl.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Krasnoyarsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Krasnoyarsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\YST9YDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\YST9YDT',
+   'DATA'),
+  ('_tcl_data\\msgs\\id.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\id.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Mexico\\General',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Mexico\\General',
+   'DATA'),
+  ('tcl8\\8.5\\tcltest-2.5.5.tm',
+   'C:\\Python312\\tcl\\tcl8\\8.5\\tcltest-2.5.5.tm',
+   'DATA'),
+  ('_tcl_data\\encoding\\macTurkish.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\macTurkish.enc',
+   'DATA'),
+  ('_tcl_data\\msgs\\fr.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\fr.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Costa_Rica',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Costa_Rica',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Montserrat',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Montserrat',
+   'DATA'),
+  ('_tk_data\\ttk\\progress.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\progress.tcl',
+   'DATA'),
+  ('_tcl_data\\msgs\\kok.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kok.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+2',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+2',
+   'DATA'),
+  ('_tk_data\\images\\README',
+   'C:\\Python312\\tcl\\tk8.6\\images\\README',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Zulu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Zulu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Ulyanovsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Ulyanovsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Paramaribo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Paramaribo',
+   'DATA'),
+  ('_tcl_data\\msgs\\ms.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ms.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Central',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Central',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Choibalsan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Choibalsan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dacca',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dacca',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\Zulu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\Zulu',
+   'DATA'),
+  ('_tk_data\\ttk\\aquaTheme.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\aquaTheme.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tbilisi',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tbilisi',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\NSW',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\NSW',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kuching',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kuching',
+   'DATA'),
+  ('_tcl_data\\msgs\\da.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\da.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\nl.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\nl.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Macao',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Macao',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Montevideo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Montevideo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\CST6CDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\CST6CDT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Helsinki',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Helsinki',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Guayaquil',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Guayaquil',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\St_Kitts',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\St_Kitts',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Knox',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Knox',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\EST5EDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\EST5EDT',
+   'DATA'),
+  ('_tk_data\\images\\logoLarge.gif',
+   'C:\\Python312\\tcl\\tk8.6\\images\\logoLarge.gif',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Tucuman',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Tucuman',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Eire',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Eire',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Timbuktu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Timbuktu',
+   'DATA'),
+  ('_tcl_data\\msgs\\fr_be.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\fr_be.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Japan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Japan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Louisville',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Louisville',
+   'DATA'),
+  ('_tcl_data\\msgs\\vi.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\vi.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\macRoman.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\macRoman.enc',
+   'DATA'),
+  ('_tk_data\\ttk\\clamTheme.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\clamTheme.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-12',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-12',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Manaus',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Manaus',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp1252.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp1252.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\PST8',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\PST8',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Damascus',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Damascus',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp852.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp852.enc',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-13.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-13.enc',
+   'DATA'),
+  ('_tcl_data\\msgs\\pl.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\pl.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Zurich',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Zurich',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Accra',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Accra',
+   'DATA'),
+  ('_tcl_data\\tzdata\\US\\Central',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\US\\Central',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\EST5',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\EST5',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp950.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp950.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Riga',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Riga',
+   'DATA'),
+  ('_tcl_data\\msgs\\hi.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\hi.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-9',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-9',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Asmara',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Asmara',
+   'DATA'),
+  ('_tcl_data\\encoding\\macIceland.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\macIceland.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Aden',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Aden',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Santiago',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Santiago',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Noumea',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Noumea',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Bujumbura',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Bujumbura',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\UCT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\UCT',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso2022.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso2022.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Brazil\\East',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Brazil\\East',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indianapolis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indianapolis',
+   'DATA'),
+  ('_tcl_data\\auto.tcl', 'C:\\Python312\\tcl\\tcl8.6\\auto.tcl', 'DATA'),
+  ('_tcl_data\\encoding\\iso8859-4.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-4.enc',
+   'DATA'),
+  ('_tk_data\\msgs\\cs.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\cs.msg', 'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Auckland',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Auckland',
+   'DATA'),
+  ('_tk_data\\msgs\\el.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\el.msg', 'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Malta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Malta',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Cairo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Cairo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Bishkek',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Bishkek',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp1254.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp1254.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Edmonton',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Edmonton',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Lusaka',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Lusaka',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Yukon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Yukon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\YST9',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\YST9',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Srednekolymsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Srednekolymsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Nauru',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Nauru',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Nicosia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Nicosia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Bahia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Bahia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Addis_Ababa',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Addis_Ababa',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\West',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\West',
+   'DATA'),
+  ('_tcl_data\\msgs\\es_do.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_do.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Honolulu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Honolulu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Toronto',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Toronto',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Majuro',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Majuro',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Minsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Minsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Sao_Tome',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Sao_Tome',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Warsaw',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Warsaw',
+   'DATA'),
+  ('_tcl_data\\encoding\\jis0201.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\jis0201.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Anadyr',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Anadyr',
+   'DATA'),
+  ('_tcl_data\\msgs\\ar_lb.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ar_lb.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Apia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Apia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Aruba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Aruba',
+   'DATA'),
+  ('_tcl_data\\tzdata\\GMT', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\GMT', 'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Funafuti',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Funafuti',
+   'DATA'),
+  ('_tcl_data\\encoding\\big5.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\big5.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Oslo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Oslo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Curacao',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Curacao',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Recife',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Recife',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Nuuk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Nuuk',
+   'DATA'),
+  ('_tcl_data\\msgs\\bn.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\bn.msg',
+   'DATA'),
+  ('_tk_data\\tkfbox.tcl', 'C:\\Python312\\tcl\\tk8.6\\tkfbox.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\Danmarkshavn',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Danmarkshavn',
+   'DATA'),
+  ('_tcl_data\\msgs\\ru_ua.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ru_ua.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-5.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-5.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ulan_Bator',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ulan_Bator',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\McMurdo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\McMurdo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\SystemV\\AST4ADT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\SystemV\\AST4ADT',
+   'DATA'),
+  ('_tk_data\\msgs\\da.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\da.msg', 'DATA'),
+  ('_tcl_data\\msgs\\fa_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\fa_in.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-11.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-11.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Lower_Princes',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Lower_Princes',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Moncton',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Moncton',
+   'DATA'),
+  ('_tcl_data\\msgs\\de_be.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\de_be.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Atikokan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Atikokan',
+   'DATA'),
+  ('_tcl_data\\encoding\\macRomania.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\macRomania.enc',
+   'DATA'),
+  ('_tk_data\\msgs\\es.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\es.msg', 'DATA'),
+  ('_tk_data\\spinbox.tcl', 'C:\\Python312\\tcl\\tk8.6\\spinbox.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\North_Dakota\\Beulah',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\North_Dakota\\Beulah',
+   'DATA'),
+  ('_tcl_data\\clock.tcl', 'C:\\Python312\\tcl\\tcl8.6\\clock.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+4',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+4',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Pitcairn',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Pitcairn',
+   'DATA'),
+  ('_tcl_data\\msgs\\gv_gb.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\gv_gb.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp1256.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp1256.enc',
+   'DATA'),
+  ('_tk_data\\tearoff.tcl', 'C:\\Python312\\tcl\\tk8.6\\tearoff.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Casey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Casey',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Boise',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Boise',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Havana',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Havana',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Niue',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Niue',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Mendoza',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Mendoza',
+   'DATA'),
+  ('_tcl_data\\tzdata\\UTC', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\UTC', 'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Gambier',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Gambier',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Bangui',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Bangui',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-8',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-8',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp1251.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp1251.enc',
+   'DATA'),
+  ('_tk_data\\xmfbox.tcl', 'C:\\Python312\\tcl\\tk8.6\\xmfbox.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Mariehamn',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Mariehamn',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Rangoon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Rangoon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Jersey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Jersey',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT+0',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+0',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Mountain',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Mountain',
+   'DATA'),
+  ('_tk_data\\unsupported.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\unsupported.tcl',
+   'DATA'),
+  ('_tk_data\\msgs\\nl.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\nl.msg', 'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Prague',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Prague',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Atka',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Atka',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Rankin_Inlet',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Rankin_Inlet',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Conakry',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Conakry',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Tallinn',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Tallinn',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-5',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-5',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Mazatlan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Mazatlan',
+   'DATA'),
+  ('_tcl_data\\msgs\\ga.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ga.msg',
+   'DATA'),
+  ('_tk_data\\tk.tcl', 'C:\\Python312\\tcl\\tk8.6\\tk.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kabul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kabul',
+   'DATA'),
+  ('_tcl_data\\msgs\\mr_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\mr_in.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cambridge_Bay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cambridge_Bay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\US\\Michigan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\US\\Michigan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Adelaide',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Adelaide',
+   'DATA'),
+  ('_tcl_data\\encoding\\koi8-r.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\koi8-r.enc',
+   'DATA'),
+  ('_tcl_data\\msgs\\zh_sg.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\zh_sg.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\ACT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\ACT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Nome',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Nome',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Paris',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Paris',
+   'DATA'),
+  ('_tcl_data\\history.tcl', 'C:\\Python312\\tcl\\tcl8.6\\history.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Victoria',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Victoria',
+   'DATA'),
+  ('_tcl_data\\encoding\\iso8859-8.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\iso8859-8.enc',
+   'DATA'),
+  ('_tk_data\\fontchooser.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\fontchooser.tcl',
+   'DATA'),
+  ('_tcl_data\\msgs\\mr.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\mr.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\eo.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\eo.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Kwajalein',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Kwajalein',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Santarem',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Santarem',
+   'DATA'),
+  ('_tcl_data\\tzdata\\WET', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\WET', 'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Macau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Macau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Faeroe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Faeroe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Guyana',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Guyana',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Colombo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Colombo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Blanc-Sablon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Blanc-Sablon',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp437.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp437.enc',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp850.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp850.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Tiraspol',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Tiraspol',
+   'DATA'),
+  ('_tcl_data\\msgs\\hi_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\hi_in.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Indianapolis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Indianapolis',
+   'DATA'),
+  ('_tk_data\\entry.tcl', 'C:\\Python312\\tcl\\tk8.6\\entry.tcl', 'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Kigali',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Kigali',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Tortola',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Tortola',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dushanbe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dushanbe',
+   'DATA'),
+  ('_tcl_data\\msgs\\nl_be.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\nl_be.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tel_Aviv',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tel_Aviv',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ujung_Pandang',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ujung_Pandang',
+   'DATA'),
+  ('_tcl_data\\tzdata\\US\\Aleutian',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\US\\Aleutian',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Gaborone',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Gaborone',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Tunis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Tunis',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Sarajevo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Sarajevo',
+   'DATA'),
+  ('_tcl_data\\msgs\\lt.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\lt.msg',
+   'DATA'),
+  ('_tcl_data\\encoding\\cp864.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\cp864.enc',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Yellowknife',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Yellowknife',
+   'DATA'),
+  ('_tcl_data\\tzdata\\US\\Hawaii',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\US\\Hawaii',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Iqaluit',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Iqaluit',
+   'DATA'),
+  ('_tcl_data\\msgs\\en_bw.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\en_bw.msg',
+   'DATA'),
+  ('_tk_data\\ttk\\treeview.tcl',
+   'C:\\Python312\\tcl\\tk8.6\\ttk\\treeview.tcl',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Ndjamena',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Ndjamena',
+   'DATA'),
+  ('_tcl_data\\tzdata\\CST6CDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\CST6CDT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\PRC', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\PRC', 'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Mbabane',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Mbabane',
+   'DATA'),
+  ('_tcl_data\\msgs\\it.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\it.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\ar_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ar_in.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Canberra',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Canberra',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Perth',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Perth',
+   'DATA'),
+  ('_tcl_data\\msgs\\kl_gl.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kl_gl.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Campo_Grande',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Campo_Grande',
+   'DATA'),
+  ('_tcl_data\\encoding\\ascii.enc',
+   'C:\\Python312\\tcl\\tcl8.6\\encoding\\ascii.enc',
+   'DATA'),
+  ('_tk_data\\msgs\\en.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\en.msg', 'DATA'),
+  ('_tk_data\\msgs\\pt.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\pt.msg', 'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Vincennes',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Vincennes',
+   'DATA'),
+  ('_tk_data\\msgs\\fr.msg', 'C:\\Python312\\tcl\\tk8.6\\msgs\\fr.msg', 'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Banjul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Banjul',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\GMT-10',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-10',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Guam',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Guam',
+   'DATA'),
+  ('_tcl_data\\tzdata\\NZ', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\NZ', 'DATA'),
+  ('_tk_data\\text.tcl', 'C:\\Python312\\tcl\\tk8.6\\text.tcl', 'DATA'),
+  ('_tcl_data\\msgs\\es_uy.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\es_uy.msg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Pacific\\Samoa',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Pacific\\Samoa',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Berlin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Berlin',
+   'DATA'),
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diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/COLLECT-00.toc b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/COLLECT-00.toc
new file mode 100644
index 000000000..dce7bd496
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/COLLECT-00.toc
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+   'DATA'),
+  ('_tcl_data\\msgs\\it_ch.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\it_ch.msg',
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+  ('_tcl_data\\msgs\\ja.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ja.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kl.msg',
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kl_gl.msg',
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ko.msg',
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ko_kr.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kok.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\kok_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kok_in.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\kw.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kw.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\kw_gb.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\kw_gb.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\lt.msg',
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\lv.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\mk.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\mr.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\mr.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\mr_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\mr_in.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\ms.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ms.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\ms_my.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ms_my.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\mt.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\mt.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\nb.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\nb.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\nl.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\nl.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\nl_be.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\nl_be.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\nn.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\nn.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\pl.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\pl.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\pt.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\pt.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\pt_br.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\pt_br.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\ro.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ro.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\ru.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ru.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\ru_ua.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ru_ua.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\sh.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sh.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sk.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\sl.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sl.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\sq.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sq.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sr.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sv.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\sw.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ta.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\ta_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\ta_in.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\te.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\te.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\te_in.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\te_in.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\th.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\tr.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\tr.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\uk.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\uk.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\vi.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\vi.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\zh.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\zh.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\zh_cn.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\zh_cn.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\zh_hk.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\zh_hk.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\zh_sg.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\zh_sg.msg',
+   'DATA'),
+  ('_tcl_data\\msgs\\zh_tw.msg',
+   'C:\\Python312\\tcl\\tcl8.6\\msgs\\zh_tw.msg',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\opt0.4\\optparse.tcl',
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+   'C:\\Python312\\tcl\\tcl8.6\\opt0.4\\pkgIndex.tcl',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Abidjan',
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+  ('_tcl_data\\tzdata\\Africa\\Accra',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Accra',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Addis_Ababa',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Addis_Ababa',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Algiers',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Algiers',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Asmara',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Asmara',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Asmera',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Asmera',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Bamako',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Bamako',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Bangui',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Bangui',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Banjul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Banjul',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Bissau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Bissau',
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+  ('_tcl_data\\tzdata\\Africa\\Blantyre',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Blantyre',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Brazzaville',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Brazzaville',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Bujumbura',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Cairo',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Casablanca',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Ceuta',
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+  ('_tcl_data\\tzdata\\Africa\\Conakry',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Conakry',
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+  ('_tcl_data\\tzdata\\Africa\\Dakar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Dakar',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Dar_es_Salaam',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Djibouti',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Djibouti',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Douala',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Douala',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\El_Aaiun',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\El_Aaiun',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Freetown',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Freetown',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Gaborone',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Harare',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Harare',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Johannesburg',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Johannesburg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Juba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Juba',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Kampala',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Kampala',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Khartoum',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Khartoum',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Kigali',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Kigali',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Kinshasa',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Kinshasa',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Lagos',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Lagos',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Libreville',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Libreville',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Lome',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Lome',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Luanda',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Luanda',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Lubumbashi',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Lubumbashi',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Lusaka',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Lusaka',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Malabo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Malabo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Maputo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Maputo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Maseru',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Maseru',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Mbabane',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Mbabane',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Mogadishu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Mogadishu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Monrovia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Monrovia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Nairobi',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Nairobi',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Ndjamena',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Ndjamena',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Niamey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Niamey',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Nouakchott',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Nouakchott',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Ouagadougou',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Ouagadougou',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Porto-Novo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Porto-Novo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Sao_Tome',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Sao_Tome',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Timbuktu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Timbuktu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Tripoli',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Tripoli',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Tunis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Tunis',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Africa\\Windhoek',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Africa\\Windhoek',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Adak',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Adak',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Anchorage',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Anchorage',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Anguilla',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Anguilla',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Antigua',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Antigua',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Araguaina',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Araguaina',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Buenos_Aires',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Buenos_Aires',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Catamarca',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Catamarca',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\ComodRivadavia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\ComodRivadavia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Cordoba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Cordoba',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Jujuy',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Jujuy',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\La_Rioja',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\La_Rioja',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Mendoza',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Mendoza',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Rio_Gallegos',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Rio_Gallegos',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Salta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Salta',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\San_Juan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\San_Juan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\San_Luis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\San_Luis',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Tucuman',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Tucuman',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Argentina\\Ushuaia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Argentina\\Ushuaia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Aruba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Aruba',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Asuncion',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Asuncion',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Atikokan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Atikokan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Atka',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Atka',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Bahia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Bahia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Bahia_Banderas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Bahia_Banderas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Barbados',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Barbados',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Belem',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Belem',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Belize',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Belize',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Blanc-Sablon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Blanc-Sablon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Boa_Vista',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Boa_Vista',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Bogota',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Bogota',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Boise',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Boise',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Buenos_Aires',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Buenos_Aires',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cambridge_Bay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cambridge_Bay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Campo_Grande',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Campo_Grande',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cancun',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cancun',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Caracas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Caracas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Catamarca',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Catamarca',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cayenne',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cayenne',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cayman',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cayman',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Chicago',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Chicago',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Chihuahua',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Chihuahua',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Coral_Harbour',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Coral_Harbour',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cordoba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cordoba',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Costa_Rica',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Costa_Rica',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Creston',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Creston',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Cuiaba',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Cuiaba',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Curacao',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Curacao',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Danmarkshavn',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Danmarkshavn',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Dawson',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Dawson',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Dawson_Creek',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Dawson_Creek',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Denver',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Denver',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Detroit',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Detroit',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Dominica',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Dominica',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Edmonton',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Edmonton',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Eirunepe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Eirunepe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\El_Salvador',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\El_Salvador',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Ensenada',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Ensenada',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Fort_Nelson',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Fort_Nelson',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Fort_Wayne',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Fort_Wayne',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Fortaleza',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Fortaleza',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Glace_Bay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Glace_Bay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Godthab',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Godthab',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Goose_Bay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Goose_Bay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Grand_Turk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Grand_Turk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Grenada',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Grenada',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Guadeloupe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Guadeloupe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Guatemala',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Guatemala',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Guayaquil',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Guayaquil',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Guyana',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Guyana',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Halifax',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Halifax',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Havana',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Havana',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Hermosillo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Hermosillo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Indianapolis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Indianapolis',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Knox',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Knox',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Marengo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Marengo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Petersburg',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Petersburg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Tell_City',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Tell_City',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Vevay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Vevay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Vincennes',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Vincennes',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indiana\\Winamac',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indiana\\Winamac',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Indianapolis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Indianapolis',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Inuvik',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Inuvik',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Iqaluit',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Iqaluit',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Jamaica',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Jamaica',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Jujuy',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Jujuy',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Juneau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Juneau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Kentucky\\Louisville',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Kentucky\\Louisville',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Kentucky\\Monticello',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Kentucky\\Monticello',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Knox_IN',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Knox_IN',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Kralendijk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Kralendijk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\La_Paz',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\La_Paz',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Lima',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Lima',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Los_Angeles',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Los_Angeles',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Louisville',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Louisville',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Lower_Princes',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Lower_Princes',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Maceio',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Maceio',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Managua',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Managua',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Manaus',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Manaus',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Marigot',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Marigot',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Martinique',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Martinique',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Matamoros',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Matamoros',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Mazatlan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Mazatlan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Mendoza',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Mendoza',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Menominee',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Menominee',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Merida',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Merida',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Metlakatla',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Metlakatla',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Mexico_City',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Mexico_City',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Miquelon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Miquelon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Moncton',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Moncton',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Monterrey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Monterrey',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Montevideo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Montevideo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Montreal',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Montreal',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Montserrat',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Montserrat',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Nassau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Nassau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\New_York',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\New_York',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Nipigon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Nipigon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Nome',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Nome',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Noronha',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Noronha',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\North_Dakota\\Beulah',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\North_Dakota\\Beulah',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\North_Dakota\\Center',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\North_Dakota\\Center',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\North_Dakota\\New_Salem',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\North_Dakota\\New_Salem',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Nuuk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Nuuk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Ojinaga',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Ojinaga',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Panama',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Panama',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Pangnirtung',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Pangnirtung',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Paramaribo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Paramaribo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Phoenix',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Phoenix',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Port-au-Prince',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Port-au-Prince',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Port_of_Spain',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Port_of_Spain',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Porto_Acre',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Porto_Acre',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Porto_Velho',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Porto_Velho',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Puerto_Rico',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Puerto_Rico',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Punta_Arenas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Punta_Arenas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Rainy_River',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Rainy_River',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Rankin_Inlet',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Rankin_Inlet',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Recife',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Recife',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Regina',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Regina',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Resolute',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Resolute',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Rio_Branco',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Rio_Branco',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Rosario',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Rosario',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Santa_Isabel',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Santa_Isabel',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Santarem',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Santarem',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Santiago',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Santiago',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Santo_Domingo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Santo_Domingo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Sao_Paulo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Sao_Paulo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Scoresbysund',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Scoresbysund',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Shiprock',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Shiprock',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Sitka',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Sitka',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\St_Barthelemy',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\St_Barthelemy',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\St_Johns',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\St_Johns',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\St_Kitts',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\St_Kitts',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\St_Lucia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\St_Lucia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\St_Thomas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\St_Thomas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\St_Vincent',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\St_Vincent',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Swift_Current',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Swift_Current',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Tegucigalpa',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Tegucigalpa',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Thule',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Thule',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Thunder_Bay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Thunder_Bay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Tijuana',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Tijuana',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Toronto',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Toronto',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Tortola',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Tortola',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Vancouver',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Vancouver',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Virgin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Virgin',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Whitehorse',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Whitehorse',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Winnipeg',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Winnipeg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Yakutat',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Yakutat',
+   'DATA'),
+  ('_tcl_data\\tzdata\\America\\Yellowknife',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\America\\Yellowknife',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Casey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Casey',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Davis',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Davis',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\DumontDUrville',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\DumontDUrville',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Macquarie',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Macquarie',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Mawson',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Mawson',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\McMurdo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\McMurdo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Palmer',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Palmer',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Rothera',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Rothera',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\South_Pole',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\South_Pole',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Syowa',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Syowa',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Troll',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Troll',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Antarctica\\Vostok',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Antarctica\\Vostok',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Arctic\\Longyearbyen',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Arctic\\Longyearbyen',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Aden',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Aden',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Almaty',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Almaty',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Amman',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Amman',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Anadyr',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Anadyr',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Aqtau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Aqtau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Aqtobe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Aqtobe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ashgabat',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ashgabat',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ashkhabad',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ashkhabad',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Atyrau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Atyrau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Baghdad',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Baghdad',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Bahrain',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Bahrain',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Baku',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Baku',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Bangkok',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Bangkok',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Barnaul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Barnaul',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Beirut',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Beirut',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Bishkek',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Bishkek',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Brunei',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Brunei',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Calcutta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Calcutta',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Chita',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Chita',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Choibalsan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Choibalsan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Chongqing',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Chongqing',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Chungking',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Chungking',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Colombo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Colombo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dacca',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dacca',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Damascus',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Damascus',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dhaka',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dhaka',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dili',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dili',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dubai',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dubai',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Dushanbe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Dushanbe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Famagusta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Famagusta',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Gaza',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Gaza',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Harbin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Harbin',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Hebron',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Hebron',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ho_Chi_Minh',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ho_Chi_Minh',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Hong_Kong',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Hong_Kong',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Hovd',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Hovd',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Irkutsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Irkutsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Istanbul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Istanbul',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Jakarta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Jakarta',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Jayapura',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Jayapura',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Jerusalem',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Jerusalem',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kabul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kabul',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kamchatka',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kamchatka',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Karachi',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Karachi',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kashgar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kashgar',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kathmandu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kathmandu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Katmandu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Katmandu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Khandyga',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Khandyga',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kolkata',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kolkata',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Krasnoyarsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Krasnoyarsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kuala_Lumpur',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kuala_Lumpur',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kuching',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kuching',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Kuwait',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Kuwait',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Macao',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Macao',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Macau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Macau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Magadan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Magadan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Makassar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Makassar',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Manila',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Manila',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Muscat',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Muscat',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Nicosia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Nicosia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Novokuznetsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Novokuznetsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Novosibirsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Novosibirsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Omsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Omsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Oral',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Oral',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Phnom_Penh',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Phnom_Penh',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Pontianak',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Pontianak',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Pyongyang',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Pyongyang',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Qatar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Qatar',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Qostanay',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Qostanay',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Qyzylorda',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Qyzylorda',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Rangoon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Rangoon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Riyadh',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Riyadh',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Saigon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Saigon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Sakhalin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Sakhalin',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Samarkand',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Samarkand',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Seoul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Seoul',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Shanghai',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Shanghai',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Singapore',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Singapore',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Srednekolymsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Srednekolymsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Taipei',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Taipei',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tashkent',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tashkent',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tbilisi',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tbilisi',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tehran',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tehran',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tel_Aviv',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tel_Aviv',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Thimbu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Thimbu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Thimphu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Thimphu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tokyo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tokyo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Tomsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Tomsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ujung_Pandang',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ujung_Pandang',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ulaanbaatar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ulaanbaatar',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ulan_Bator',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ulan_Bator',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Urumqi',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Urumqi',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Ust-Nera',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Ust-Nera',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Vientiane',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Vientiane',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Vladivostok',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Vladivostok',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Yakutsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Yakutsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Yangon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Yangon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Yekaterinburg',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Yekaterinburg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Asia\\Yerevan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Asia\\Yerevan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Azores',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Azores',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Bermuda',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Bermuda',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Canary',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Canary',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Cape_Verde',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Cape_Verde',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Faeroe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Faeroe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Faroe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Faroe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Jan_Mayen',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Jan_Mayen',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Madeira',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Madeira',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Reykjavik',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Reykjavik',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\South_Georgia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\South_Georgia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\St_Helena',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\St_Helena',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Atlantic\\Stanley',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Atlantic\\Stanley',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\ACT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\ACT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Adelaide',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Adelaide',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Brisbane',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Brisbane',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Broken_Hill',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Broken_Hill',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Canberra',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Canberra',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Currie',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Currie',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Darwin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Darwin',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Eucla',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Eucla',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Hobart',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Hobart',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\LHI',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\LHI',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Lindeman',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Lindeman',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Lord_Howe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Lord_Howe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Melbourne',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Melbourne',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\NSW',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\NSW',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\North',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\North',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Perth',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Perth',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Queensland',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Queensland',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\South',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\South',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Sydney',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Sydney',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Tasmania',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Tasmania',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Victoria',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Victoria',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\West',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\West',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Australia\\Yancowinna',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Australia\\Yancowinna',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Brazil\\Acre',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Brazil\\Acre',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Brazil\\DeNoronha',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Brazil\\DeNoronha',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Brazil\\East',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Brazil\\East',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Brazil\\West',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Brazil\\West',
+   'DATA'),
+  ('_tcl_data\\tzdata\\CET', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\CET', 'DATA'),
+  ('_tcl_data\\tzdata\\CST6CDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\CST6CDT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Atlantic',
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+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Central',
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+  ('_tcl_data\\tzdata\\Canada\\Eastern',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Eastern',
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+  ('_tcl_data\\tzdata\\Canada\\Mountain',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Mountain',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Newfoundland',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Newfoundland',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Pacific',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Pacific',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Saskatchewan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Saskatchewan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Canada\\Yukon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Canada\\Yukon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Chile\\Continental',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Chile\\Continental',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Chile\\EasterIsland',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Chile\\EasterIsland',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Cuba',
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+  ('_tcl_data\\tzdata\\EST5EDT',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT+0',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-13',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-7',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT-9',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\GMT0',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\Greenwich',
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+  ('_tcl_data\\tzdata\\Etc\\UCT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\UCT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\UTC',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\UTC',
+   'DATA'),
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\Universal',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Etc\\Zulu',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Etc\\Zulu',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Amsterdam',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Amsterdam',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Andorra',
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+  ('_tcl_data\\tzdata\\Europe\\Astrakhan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Astrakhan',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Athens',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Belfast',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Belfast',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Belgrade',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Belgrade',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Berlin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Berlin',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Bratislava',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Bratislava',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Brussels',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Brussels',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Bucharest',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Bucharest',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Budapest',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Budapest',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Busingen',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Busingen',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Chisinau',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Chisinau',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Copenhagen',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Copenhagen',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Dublin',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Dublin',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Gibraltar',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Gibraltar',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Guernsey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Guernsey',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Helsinki',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Helsinki',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Isle_of_Man',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Isle_of_Man',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Istanbul',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Istanbul',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Jersey',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Jersey',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Kaliningrad',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Kaliningrad',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Kiev',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Kiev',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Kirov',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Kirov',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Kyiv',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Kyiv',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Lisbon',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Lisbon',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Ljubljana',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Ljubljana',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\London',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\London',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Luxembourg',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Luxembourg',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Madrid',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Madrid',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Malta',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Malta',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Mariehamn',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Mariehamn',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Minsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Minsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Monaco',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Monaco',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Moscow',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Moscow',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Nicosia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Nicosia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Oslo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Oslo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Paris',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Paris',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Podgorica',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Podgorica',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Prague',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Prague',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Riga',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Riga',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Rome',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Rome',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Samara',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Samara',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\San_Marino',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\San_Marino',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Sarajevo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Sarajevo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Saratov',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Saratov',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Simferopol',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Simferopol',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Skopje',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Skopje',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Sofia',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Sofia',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Stockholm',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Stockholm',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Tallinn',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Tallinn',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Tirane',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Tirane',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Tiraspol',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Tiraspol',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Ulyanovsk',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Ulyanovsk',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Uzhgorod',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Uzhgorod',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Vaduz',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Vaduz',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Vatican',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Vatican',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Vienna',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Vienna',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Vilnius',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Vilnius',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Volgograd',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Volgograd',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Warsaw',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Warsaw',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Zagreb',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Zagreb',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Zaporozhye',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Zaporozhye',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Europe\\Zurich',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Europe\\Zurich',
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+  ('_tcl_data\\tzdata\\GB-Eire',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\GB-Eire',
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+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\GMT0',
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+  ('_tcl_data\\tzdata\\Greenwich',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Greenwich',
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+  ('_tcl_data\\tzdata\\Hongkong',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Hongkong',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Iceland',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Iceland',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Antananarivo',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Antananarivo',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Chagos',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Chagos',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Christmas',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Christmas',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Cocos',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Cocos',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Comoro',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Comoro',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Kerguelen',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Kerguelen',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Mahe',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Mahe',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Maldives',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Maldives',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Mauritius',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Mauritius',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Mayotte',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Mayotte',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Indian\\Reunion',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Indian\\Reunion',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Iran',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Iran',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Israel',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Israel',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Jamaica',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Jamaica',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Japan',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Japan',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Kwajalein',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Kwajalein',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Libya',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Libya',
+   'DATA'),
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+  ('_tcl_data\\tzdata\\MST', 'C:\\Python312\\tcl\\tcl8.6\\tzdata\\MST', 'DATA'),
+  ('_tcl_data\\tzdata\\MST7MDT',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\MST7MDT',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Mexico\\BajaNorte',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Mexico\\BajaNorte',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Mexico\\BajaSur',
+   'C:\\Python312\\tcl\\tcl8.6\\tzdata\\Mexico\\BajaSur',
+   'DATA'),
+  ('_tcl_data\\tzdata\\Mexico\\General',
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diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/MaskReviewer.pkg b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/MaskReviewer.pkg
new file mode 100644
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new file mode 100644
index 000000000..4ae6328d5
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diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/PYZ-00.toc b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/PYZ-00.toc
new file mode 100644
index 000000000..26ac02e3a
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/PYZ-00.toc
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diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/warn-MaskReviewer.txt b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/warn-MaskReviewer.txt
new file mode 100644
index 000000000..8ed17f335
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/warn-MaskReviewer.txt
@@ -0,0 +1,223 @@
+
+This file lists modules PyInstaller was not able to find. This does not
+necessarily mean these modules are required for running your program. Both
+Python's standard library and 3rd-party Python packages often conditionally
+import optional modules, some of which may be available only on certain
+platforms.
+
+Types of import:
+* top-level: imported at the top-level - look at these first
+* conditional: imported within an if-statement
+* delayed: imported within a function
+* optional: imported within a try-except-statement
+
+IMPORTANT: Do NOT post this list to the issue-tracker. Use it as a basis for
+            tracking down the missing module yourself. Thanks!
+
+missing module named pwd - imported by posixpath (delayed, conditional, optional), shutil (delayed, optional), tarfile (optional), pathlib (delayed, optional), subprocess (delayed, conditional, optional), http.server (delayed, optional), netrc (delayed, conditional), getpass (delayed)
+missing module named grp - imported by shutil (delayed, optional), tarfile (optional), pathlib (delayed, optional), subprocess (delayed, conditional, optional)
+missing module named _posixsubprocess - imported by subprocess (conditional), multiprocessing.util (delayed)
+missing module named fcntl - imported by subprocess (optional)
+missing module named _posixshmem - imported by multiprocessing.resource_tracker (conditional), multiprocessing.shared_memory (conditional)
+missing module named _scproxy - imported by urllib.request (conditional)
+missing module named termios - imported by tty (top-level), getpass (optional)
+missing module named multiprocessing.BufferTooShort - imported by multiprocessing (top-level), multiprocessing.connection (top-level)
+missing module named multiprocessing.AuthenticationError - imported by multiprocessing (top-level), multiprocessing.connection (top-level)
+missing module named multiprocessing.get_context - imported by multiprocessing (top-level), multiprocessing.pool (top-level), multiprocessing.managers (top-level), multiprocessing.sharedctypes (top-level)
+missing module named multiprocessing.TimeoutError - imported by multiprocessing (top-level), multiprocessing.pool (top-level)
+missing module named multiprocessing.set_start_method - imported by multiprocessing (top-level), multiprocessing.spawn (top-level)
+missing module named multiprocessing.get_start_method - imported by multiprocessing (top-level), multiprocessing.spawn (top-level)
+missing module named posix - imported by os (conditional, optional), posixpath (optional), shutil (conditional), importlib._bootstrap_external (conditional)
+missing module named resource - imported by posix (top-level)
+excluded module named _frozen_importlib - imported by importlib (optional), importlib.abc (optional), zipimport (top-level)
+missing module named _frozen_importlib_external - imported by importlib._bootstrap (delayed), importlib (optional), importlib.abc (optional), zipimport (top-level)
+missing module named pyimod02_importers - imported by C:\ZendionInc\agrobot_base\Python\OAK\datasets\oak-fcc-3\MaskReviewer_package\.build_venv\Lib\site-packages\PyInstaller\hooks\rthooks\pyi_rth_pkgutil.py (delayed)
+missing module named _dummy_thread - imported by numpy._core.arrayprint (optional)
+missing module named typing_extensions - imported by numpy._typing._nested_sequence (conditional), numpy.random.bit_generator (top-level)
+missing module named charset_normalizer - imported by numpy.f2py.crackfortran (optional)
+missing module named vms_lib - imported by platform (delayed, optional)
+missing module named 'java.lang' - imported by platform (delayed, optional)
+missing module named java - imported by platform (delayed)
+missing module named _winreg - imported by platform (delayed, optional)
+missing module named psutil - imported by numpy.testing._private.utils (delayed, optional)
+missing module named readline - imported by cmd (delayed, conditional, optional), code (delayed, conditional, optional), pdb (delayed, optional)
+missing module named win32pdh - imported by numpy.testing._private.utils (delayed, conditional)
+missing module named asyncio.DefaultEventLoopPolicy - imported by asyncio (delayed, conditional), asyncio.events (delayed, conditional)
+missing module named _typeshed - imported by numpy.random._common (top-level), numpy.random.bit_generator (top-level)
+missing module named threadpoolctl - imported by numpy.lib._utils_impl (delayed, optional)
+missing module named numpy._core.zeros - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.void - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.vecmat - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.vecdot - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.ushort - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.unsignedinteger - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ulonglong - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ulong - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.uintp - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.uintc - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.uint64 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.uint32 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.uint16 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.uint - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ubyte - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.trunc - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.true_divide - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.transpose - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.lib._function_base_impl (top-level), numpy (conditional)
+missing module named numpy._core.trace - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.timedelta64 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.tensordot - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.tanh - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.tan - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.swapaxes - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.sum - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.subtract - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.str_ - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.square - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.sqrt - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional), numpy.fft._pocketfft (top-level)
+missing module named numpy._core.spacing - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.sort - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.sinh - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.single - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.signedinteger - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.signbit - imported by numpy._core (conditional), numpy (conditional), numpy.testing._private.utils (delayed)
+missing module named numpy._core.sign - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.short - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.rint - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.right_shift - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.remainder - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.reciprocal - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional), numpy.fft._pocketfft (top-level)
+missing module named numpy._core.radians - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.rad2deg - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.prod - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.power - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.positive - imported by numpy._core (conditional), numpy (conditional)
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+missing module named numpy._core.number - imported by numpy._core (conditional), numpy (conditional), numpy.testing._private.utils (delayed)
+missing module named numpy._core.not_equal - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.nextafter - imported by numpy._core (conditional), numpy (conditional)
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+missing module named numpy._core.negative - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ndarray - imported by numpy._core (top-level), numpy.lib._utils_impl (top-level), numpy (conditional), numpy.testing._private.utils (top-level)
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diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/xref-MaskReviewer.html b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/xref-MaskReviewer.html
new file mode 100644
index 000000000..b7507c528
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build/MaskReviewer/xref-MaskReviewer.html
@@ -0,0 +1,18920 @@
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta charset="UTF-8">
+    <title>modulegraph cross reference for MaskReviewer.py, pyi_rth__tkinter.py, pyi_rth_inspect.py, pyi_rth_multiprocessing.py, pyi_rth_pkgutil.py</title>
+    <style>
+      .node { padding: 0.5em 0 0.5em; border-top: thin grey dotted; }
+      .moduletype { font: smaller italic }
+      .node a { text-decoration: none; color: #006699; }
+      .node a:visited { text-decoration: none; color: #2f0099; }
+    </style>
+  </head>
+  <body>
+    <h1>modulegraph cross reference for MaskReviewer.py, pyi_rth__tkinter.py, pyi_rth_inspect.py, pyi_rth_multiprocessing.py, pyi_rth_pkgutil.py</h1>
+
+<div class="node">
+  <a name="MaskReviewer.py"></a>
+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/MaskReviewer.py" type="text/plain"><tt>MaskReviewer.py</tt></a>
+<span class="moduletype">Script</span>  <div class="import">
+imports:
+    <a href="#__future__">__future__</a>
+ &#8226;   <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#_weakrefset">_weakrefset</a>
+ &#8226;   <a href="#abc">abc</a>
+ &#8226;   <a href="#argparse">argparse</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#copyreg">copyreg</a>
+ &#8226;   <a href="#csv">csv</a>
+ &#8226;   <a href="#cv2">cv2</a>
+ &#8226;   <a href="#cv2.config">cv2.config</a>
+ &#8226;   <a href="#cv2.config-3">cv2.config-3</a>
+ &#8226;   <a href="#cv2.cv2">cv2.cv2</a>
+ &#8226;   <a href="#cv2.data">cv2.data</a>
+ &#8226;   <a href="#cv2.load_config_py2">cv2.load_config_py2</a>
+ &#8226;   <a href="#cv2.load_config_py3">cv2.load_config_py3</a>
+ &#8226;   <a href="#cv2.mat_wrapper">cv2.mat_wrapper</a>
+ &#8226;   <a href="#cv2.misc">cv2.misc</a>
+ &#8226;   <a href="#cv2.misc.version">cv2.misc.version</a>
+ &#8226;   <a href="#cv2.typing">cv2.typing</a>
+ &#8226;   <a href="#cv2.utils">cv2.utils</a>
+ &#8226;   <a href="#cv2.version">cv2.version</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#datetime">datetime</a>
+ &#8226;   <a href="#encodings">encodings</a>
+ &#8226;   <a href="#encodings.aliases">encodings.aliases</a>
+ &#8226;   <a href="#encodings.ascii">encodings.ascii</a>
+ &#8226;   <a href="#encodings.base64_codec">encodings.base64_codec</a>
+ &#8226;   <a href="#encodings.big5">encodings.big5</a>
+ &#8226;   <a href="#encodings.big5hkscs">encodings.big5hkscs</a>
+ &#8226;   <a href="#encodings.bz2_codec">encodings.bz2_codec</a>
+ &#8226;   <a href="#encodings.charmap">encodings.charmap</a>
+ &#8226;   <a href="#encodings.cp037">encodings.cp037</a>
+ &#8226;   <a href="#encodings.cp1006">encodings.cp1006</a>
+ &#8226;   <a href="#encodings.cp1026">encodings.cp1026</a>
+ &#8226;   <a href="#encodings.cp1125">encodings.cp1125</a>
+ &#8226;   <a href="#encodings.cp1140">encodings.cp1140</a>
+ &#8226;   <a href="#encodings.cp1250">encodings.cp1250</a>
+ &#8226;   <a href="#encodings.cp1251">encodings.cp1251</a>
+ &#8226;   <a href="#encodings.cp1252">encodings.cp1252</a>
+ &#8226;   <a href="#encodings.cp1253">encodings.cp1253</a>
+ &#8226;   <a href="#encodings.cp1254">encodings.cp1254</a>
+ &#8226;   <a href="#encodings.cp1255">encodings.cp1255</a>
+ &#8226;   <a href="#encodings.cp1256">encodings.cp1256</a>
+ &#8226;   <a href="#encodings.cp1257">encodings.cp1257</a>
+ &#8226;   <a href="#encodings.cp1258">encodings.cp1258</a>
+ &#8226;   <a href="#encodings.cp273">encodings.cp273</a>
+ &#8226;   <a href="#encodings.cp424">encodings.cp424</a>
+ &#8226;   <a href="#encodings.cp437">encodings.cp437</a>
+ &#8226;   <a href="#encodings.cp500">encodings.cp500</a>
+ &#8226;   <a href="#encodings.cp720">encodings.cp720</a>
+ &#8226;   <a href="#encodings.cp737">encodings.cp737</a>
+ &#8226;   <a href="#encodings.cp775">encodings.cp775</a>
+ &#8226;   <a href="#encodings.cp850">encodings.cp850</a>
+ &#8226;   <a href="#encodings.cp852">encodings.cp852</a>
+ &#8226;   <a href="#encodings.cp855">encodings.cp855</a>
+ &#8226;   <a href="#encodings.cp856">encodings.cp856</a>
+ &#8226;   <a href="#encodings.cp857">encodings.cp857</a>
+ &#8226;   <a href="#encodings.cp858">encodings.cp858</a>
+ &#8226;   <a href="#encodings.cp860">encodings.cp860</a>
+ &#8226;   <a href="#encodings.cp861">encodings.cp861</a>
+ &#8226;   <a href="#encodings.cp862">encodings.cp862</a>
+ &#8226;   <a href="#encodings.cp863">encodings.cp863</a>
+ &#8226;   <a href="#encodings.cp864">encodings.cp864</a>
+ &#8226;   <a href="#encodings.cp865">encodings.cp865</a>
+ &#8226;   <a href="#encodings.cp866">encodings.cp866</a>
+ &#8226;   <a href="#encodings.cp869">encodings.cp869</a>
+ &#8226;   <a href="#encodings.cp874">encodings.cp874</a>
+ &#8226;   <a href="#encodings.cp875">encodings.cp875</a>
+ &#8226;   <a href="#encodings.cp932">encodings.cp932</a>
+ &#8226;   <a href="#encodings.cp949">encodings.cp949</a>
+ &#8226;   <a href="#encodings.cp950">encodings.cp950</a>
+ &#8226;   <a href="#encodings.euc_jis_2004">encodings.euc_jis_2004</a>
+ &#8226;   <a href="#encodings.euc_jisx0213">encodings.euc_jisx0213</a>
+ &#8226;   <a href="#encodings.euc_jp">encodings.euc_jp</a>
+ &#8226;   <a href="#encodings.euc_kr">encodings.euc_kr</a>
+ &#8226;   <a href="#encodings.gb18030">encodings.gb18030</a>
+ &#8226;   <a href="#encodings.gb2312">encodings.gb2312</a>
+ &#8226;   <a href="#encodings.gbk">encodings.gbk</a>
+ &#8226;   <a href="#encodings.hex_codec">encodings.hex_codec</a>
+ &#8226;   <a href="#encodings.hp_roman8">encodings.hp_roman8</a>
+ &#8226;   <a href="#encodings.hz">encodings.hz</a>
+ &#8226;   <a href="#encodings.idna">encodings.idna</a>
+ &#8226;   <a href="#encodings.iso2022_jp">encodings.iso2022_jp</a>
+ &#8226;   <a href="#encodings.iso2022_jp_1">encodings.iso2022_jp_1</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2">encodings.iso2022_jp_2</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2004">encodings.iso2022_jp_2004</a>
+ &#8226;   <a href="#encodings.iso2022_jp_3">encodings.iso2022_jp_3</a>
+ &#8226;   <a href="#encodings.iso2022_jp_ext">encodings.iso2022_jp_ext</a>
+ &#8226;   <a href="#encodings.iso2022_kr">encodings.iso2022_kr</a>
+ &#8226;   <a href="#encodings.iso8859_1">encodings.iso8859_1</a>
+ &#8226;   <a href="#encodings.iso8859_10">encodings.iso8859_10</a>
+ &#8226;   <a href="#encodings.iso8859_11">encodings.iso8859_11</a>
+ &#8226;   <a href="#encodings.iso8859_13">encodings.iso8859_13</a>
+ &#8226;   <a href="#encodings.iso8859_14">encodings.iso8859_14</a>
+ &#8226;   <a href="#encodings.iso8859_15">encodings.iso8859_15</a>
+ &#8226;   <a href="#encodings.iso8859_16">encodings.iso8859_16</a>
+ &#8226;   <a href="#encodings.iso8859_2">encodings.iso8859_2</a>
+ &#8226;   <a href="#encodings.iso8859_3">encodings.iso8859_3</a>
+ &#8226;   <a href="#encodings.iso8859_4">encodings.iso8859_4</a>
+ &#8226;   <a href="#encodings.iso8859_5">encodings.iso8859_5</a>
+ &#8226;   <a href="#encodings.iso8859_6">encodings.iso8859_6</a>
+ &#8226;   <a href="#encodings.iso8859_7">encodings.iso8859_7</a>
+ &#8226;   <a href="#encodings.iso8859_8">encodings.iso8859_8</a>
+ &#8226;   <a href="#encodings.iso8859_9">encodings.iso8859_9</a>
+ &#8226;   <a href="#encodings.johab">encodings.johab</a>
+ &#8226;   <a href="#encodings.koi8_r">encodings.koi8_r</a>
+ &#8226;   <a href="#encodings.koi8_t">encodings.koi8_t</a>
+ &#8226;   <a href="#encodings.koi8_u">encodings.koi8_u</a>
+ &#8226;   <a href="#encodings.kz1048">encodings.kz1048</a>
+ &#8226;   <a href="#encodings.latin_1">encodings.latin_1</a>
+ &#8226;   <a href="#encodings.mac_arabic">encodings.mac_arabic</a>
+ &#8226;   <a href="#encodings.mac_croatian">encodings.mac_croatian</a>
+ &#8226;   <a href="#encodings.mac_cyrillic">encodings.mac_cyrillic</a>
+ &#8226;   <a href="#encodings.mac_farsi">encodings.mac_farsi</a>
+ &#8226;   <a href="#encodings.mac_greek">encodings.mac_greek</a>
+ &#8226;   <a href="#encodings.mac_iceland">encodings.mac_iceland</a>
+ &#8226;   <a href="#encodings.mac_latin2">encodings.mac_latin2</a>
+ &#8226;   <a href="#encodings.mac_roman">encodings.mac_roman</a>
+ &#8226;   <a href="#encodings.mac_romanian">encodings.mac_romanian</a>
+ &#8226;   <a href="#encodings.mac_turkish">encodings.mac_turkish</a>
+ &#8226;   <a href="#encodings.mbcs">encodings.mbcs</a>
+ &#8226;   <a href="#encodings.oem">encodings.oem</a>
+ &#8226;   <a href="#encodings.palmos">encodings.palmos</a>
+ &#8226;   <a href="#encodings.ptcp154">encodings.ptcp154</a>
+ &#8226;   <a href="#encodings.punycode">encodings.punycode</a>
+ &#8226;   <a href="#encodings.quopri_codec">encodings.quopri_codec</a>
+ &#8226;   <a href="#encodings.raw_unicode_escape">encodings.raw_unicode_escape</a>
+ &#8226;   <a href="#encodings.rot_13">encodings.rot_13</a>
+ &#8226;   <a href="#encodings.shift_jis">encodings.shift_jis</a>
+ &#8226;   <a href="#encodings.shift_jis_2004">encodings.shift_jis_2004</a>
+ &#8226;   <a href="#encodings.shift_jisx0213">encodings.shift_jisx0213</a>
+ &#8226;   <a href="#encodings.tis_620">encodings.tis_620</a>
+ &#8226;   <a href="#encodings.undefined">encodings.undefined</a>
+ &#8226;   <a href="#encodings.unicode_escape">encodings.unicode_escape</a>
+ &#8226;   <a href="#encodings.utf_16">encodings.utf_16</a>
+ &#8226;   <a href="#encodings.utf_16_be">encodings.utf_16_be</a>
+ &#8226;   <a href="#encodings.utf_16_le">encodings.utf_16_le</a>
+ &#8226;   <a href="#encodings.utf_32">encodings.utf_32</a>
+ &#8226;   <a href="#encodings.utf_32_be">encodings.utf_32_be</a>
+ &#8226;   <a href="#encodings.utf_32_le">encodings.utf_32_le</a>
+ &#8226;   <a href="#encodings.utf_7">encodings.utf_7</a>
+ &#8226;   <a href="#encodings.utf_8">encodings.utf_8</a>
+ &#8226;   <a href="#encodings.utf_8_sig">encodings.utf_8_sig</a>
+ &#8226;   <a href="#encodings.uu_codec">encodings.uu_codec</a>
+ &#8226;   <a href="#encodings.zlib_codec">encodings.zlib_codec</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#genericpath">genericpath</a>
+ &#8226;   <a href="#heapq">heapq</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#json">json</a>
+ &#8226;   <a href="#keyword">keyword</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#ntpath">ntpath</a>
+ &#8226;   <a href="#numpy">numpy</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#pyi_rth__tkinter.py">pyi_rth__tkinter.py</a>
+ &#8226;   <a href="#pyi_rth_inspect.py">pyi_rth_inspect.py</a>
+ &#8226;   <a href="#pyi_rth_multiprocessing.py">pyi_rth_multiprocessing.py</a>
+ &#8226;   <a href="#pyi_rth_pkgutil.py">pyi_rth_pkgutil.py</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#re._casefix">re._casefix</a>
+ &#8226;   <a href="#re._compiler">re._compiler</a>
+ &#8226;   <a href="#re._constants">re._constants</a>
+ &#8226;   <a href="#re._parser">re._parser</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#sre_compile">sre_compile</a>
+ &#8226;   <a href="#sre_constants">sre_constants</a>
+ &#8226;   <a href="#sre_parse">sre_parse</a>
+ &#8226;   <a href="#stat">stat</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#tkinter.filedialog">tkinter.filedialog</a>
+ &#8226;   <a href="#tkinter.messagebox">tkinter.messagebox</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="pyi_rth__tkinter.py"></a>
+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/.build_venv/Lib/site-packages/PyInstaller/hooks/rthooks/pyi_rth__tkinter.py" type="text/plain"><tt>pyi_rth__tkinter.py</tt></a>
+<span class="moduletype">Script</span>  <div class="import">
+imports:
+    <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="pyi_rth_inspect.py"></a>
+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/.build_venv/Lib/site-packages/PyInstaller/hooks/rthooks/pyi_rth_inspect.py" type="text/plain"><tt>pyi_rth_inspect.py</tt></a>
+<span class="moduletype">Script</span>  <div class="import">
+imports:
+    <a href="#inspect">inspect</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="pyi_rth_multiprocessing.py"></a>
+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/.build_venv/Lib/site-packages/PyInstaller/hooks/rthooks/pyi_rth_multiprocessing.py" type="text/plain"><tt>pyi_rth_multiprocessing.py</tt></a>
+<span class="moduletype">Script</span>  <div class="import">
+imports:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="pyi_rth_pkgutil.py"></a>
+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/.build_venv/Lib/site-packages/PyInstaller/hooks/rthooks/pyi_rth_pkgutil.py" type="text/plain"><tt>pyi_rth_pkgutil.py</tt></a>
+<span class="moduletype">Script</span>  <div class="import">
+imports:
+    <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#pyimod02_importers">pyimod02_importers</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="'java.lang'"></a>
+  <a target="code" href="" type="text/plain"><tt>'java.lang'</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#platform">platform</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="__future__"></a>
+  <a target="code" href="///C:/Python312/Lib/__future__.py" type="text/plain"><tt>__future__</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#codeop">codeop</a>
+ &#8226;   <a href="#doctest">doctest</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_abc"></a>
+  <tt>_abc</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#abc">abc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_aix_support"></a>
+  <a target="code" href="///C:/Python312/Lib/_aix_support.py" type="text/plain"><tt>_aix_support</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#sysconfig">sysconfig</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_ast"></a>
+  <tt>_ast</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#ast">ast</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_asyncio"></a>
+  <tt>_asyncio</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_asyncio.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_bisect"></a>
+  <tt>_bisect</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#bisect">bisect</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_blake2"></a>
+  <tt>_blake2</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#hashlib">hashlib</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_bz2"></a>
+  <tt>_bz2</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_bz2.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#bz2">bz2</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_codecs"></a>
+  <tt>_codecs</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#codecs">codecs</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_codecs_cn"></a>
+  <tt>_codecs_cn</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#encodings.gb18030">encodings.gb18030</a>
+ &#8226;   <a href="#encodings.gb2312">encodings.gb2312</a>
+ &#8226;   <a href="#encodings.gbk">encodings.gbk</a>
+ &#8226;   <a href="#encodings.hz">encodings.hz</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_codecs_hk"></a>
+  <tt>_codecs_hk</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#encodings.big5hkscs">encodings.big5hkscs</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_codecs_iso2022"></a>
+  <tt>_codecs_iso2022</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#encodings.iso2022_jp">encodings.iso2022_jp</a>
+ &#8226;   <a href="#encodings.iso2022_jp_1">encodings.iso2022_jp_1</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2">encodings.iso2022_jp_2</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2004">encodings.iso2022_jp_2004</a>
+ &#8226;   <a href="#encodings.iso2022_jp_3">encodings.iso2022_jp_3</a>
+ &#8226;   <a href="#encodings.iso2022_jp_ext">encodings.iso2022_jp_ext</a>
+ &#8226;   <a href="#encodings.iso2022_kr">encodings.iso2022_kr</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_codecs_jp"></a>
+  <tt>_codecs_jp</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#encodings.cp932">encodings.cp932</a>
+ &#8226;   <a href="#encodings.euc_jis_2004">encodings.euc_jis_2004</a>
+ &#8226;   <a href="#encodings.euc_jisx0213">encodings.euc_jisx0213</a>
+ &#8226;   <a href="#encodings.euc_jp">encodings.euc_jp</a>
+ &#8226;   <a href="#encodings.shift_jis">encodings.shift_jis</a>
+ &#8226;   <a href="#encodings.shift_jis_2004">encodings.shift_jis_2004</a>
+ &#8226;   <a href="#encodings.shift_jisx0213">encodings.shift_jisx0213</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_codecs_kr"></a>
+  <tt>_codecs_kr</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#encodings.cp949">encodings.cp949</a>
+ &#8226;   <a href="#encodings.euc_kr">encodings.euc_kr</a>
+ &#8226;   <a href="#encodings.johab">encodings.johab</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_codecs_tw"></a>
+  <tt>_codecs_tw</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#encodings.big5">encodings.big5</a>
+ &#8226;   <a href="#encodings.cp950">encodings.cp950</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_collections"></a>
+  <tt>_collections</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#threading">threading</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_collections_abc"></a>
+  <a target="code" href="///C:/Python312/Lib/_collections_abc.py" type="text/plain"><tt>_collections_abc</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#abc">abc</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_compat_pickle"></a>
+  <a target="code" href="///C:/Python312/Lib/_compat_pickle.py" type="text/plain"><tt>_compat_pickle</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imported by:
+    <a href="#_pickle">_pickle</a>
+ &#8226;   <a href="#pickle">pickle</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_compression"></a>
+  <a target="code" href="///C:/Python312/Lib/_compression.py" type="text/plain"><tt>_compression</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#io">io</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#bz2">bz2</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#lzma">lzma</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_contextvars"></a>
+  <tt>_contextvars</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#contextvars">contextvars</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_csv"></a>
+  <tt>_csv</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#csv">csv</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_ctypes"></a>
+  <tt>_ctypes</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_ctypes.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#ctypes">ctypes</a>
+ &#8226;   <a href="#numpy._core._dtype_ctypes">numpy._core._dtype_ctypes</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_datetime"></a>
+  <tt>_datetime</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imports:
+    <a href="#_strptime">_strptime</a>
+ &#8226;   <a href="#time">time</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#datetime">datetime</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_decimal"></a>
+  <tt>_decimal</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_decimal.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#decimal">decimal</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_dummy_thread"></a>
+  <a target="code" href="" type="text/plain"><tt>_dummy_thread</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#numpy._core.arrayprint">numpy._core.arrayprint</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_frozen_importlib"></a>
+  <a target="code" href="" type="text/plain"><tt>_frozen_importlib</tt></a>
+<span class="moduletype">ExcludedModule</span>  <div class="import">
+imported by:
+    <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib.abc">importlib.abc</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_frozen_importlib_external"></a>
+  <a target="code" href="" type="text/plain"><tt>_frozen_importlib_external</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib._bootstrap">importlib._bootstrap</a>
+ &#8226;   <a href="#importlib.abc">importlib.abc</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_functools"></a>
+  <tt>_functools</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#functools">functools</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_hashlib"></a>
+  <tt>_hashlib</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_hashlib.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#hashlib">hashlib</a>
+ &#8226;   <a href="#hmac">hmac</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_heapq"></a>
+  <tt>_heapq</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#heapq">heapq</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_imp"></a>
+  <tt>_imp</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#importlib.util">importlib.util</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_io"></a>
+  <tt>_io</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_json"></a>
+  <tt>_json</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imports:
+    <a href="#json.decoder">json.decoder</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#json.decoder">json.decoder</a>
+ &#8226;   <a href="#json.encoder">json.encoder</a>
+ &#8226;   <a href="#json.scanner">json.scanner</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_locale"></a>
+  <tt>_locale</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#locale">locale</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_lzma"></a>
+  <tt>_lzma</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_lzma.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#lzma">lzma</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_md5"></a>
+  <tt>_md5</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#hashlib">hashlib</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_multibytecodec"></a>
+  <tt>_multibytecodec</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#encodings.big5">encodings.big5</a>
+ &#8226;   <a href="#encodings.big5hkscs">encodings.big5hkscs</a>
+ &#8226;   <a href="#encodings.cp932">encodings.cp932</a>
+ &#8226;   <a href="#encodings.cp949">encodings.cp949</a>
+ &#8226;   <a href="#encodings.cp950">encodings.cp950</a>
+ &#8226;   <a href="#encodings.euc_jis_2004">encodings.euc_jis_2004</a>
+ &#8226;   <a href="#encodings.euc_jisx0213">encodings.euc_jisx0213</a>
+ &#8226;   <a href="#encodings.euc_jp">encodings.euc_jp</a>
+ &#8226;   <a href="#encodings.euc_kr">encodings.euc_kr</a>
+ &#8226;   <a href="#encodings.gb18030">encodings.gb18030</a>
+ &#8226;   <a href="#encodings.gb2312">encodings.gb2312</a>
+ &#8226;   <a href="#encodings.gbk">encodings.gbk</a>
+ &#8226;   <a href="#encodings.hz">encodings.hz</a>
+ &#8226;   <a href="#encodings.iso2022_jp">encodings.iso2022_jp</a>
+ &#8226;   <a href="#encodings.iso2022_jp_1">encodings.iso2022_jp_1</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2">encodings.iso2022_jp_2</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2004">encodings.iso2022_jp_2004</a>
+ &#8226;   <a href="#encodings.iso2022_jp_3">encodings.iso2022_jp_3</a>
+ &#8226;   <a href="#encodings.iso2022_jp_ext">encodings.iso2022_jp_ext</a>
+ &#8226;   <a href="#encodings.iso2022_kr">encodings.iso2022_kr</a>
+ &#8226;   <a href="#encodings.johab">encodings.johab</a>
+ &#8226;   <a href="#encodings.shift_jis">encodings.shift_jis</a>
+ &#8226;   <a href="#encodings.shift_jis_2004">encodings.shift_jis_2004</a>
+ &#8226;   <a href="#encodings.shift_jisx0213">encodings.shift_jisx0213</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_multiprocessing"></a>
+  <tt>_multiprocessing</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_multiprocessing.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_opcode"></a>
+  <tt>_opcode</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#opcode">opcode</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_operator"></a>
+  <tt>_operator</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#hmac">hmac</a>
+ &#8226;   <a href="#operator">operator</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_overlapped"></a>
+  <tt>_overlapped</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_overlapped.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_pickle"></a>
+  <tt>_pickle</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imports:
+    <a href="#_compat_pickle">_compat_pickle</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#copyreg">copyreg</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#pickle">pickle</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_posixshmem"></a>
+  <a target="code" href="" type="text/plain"><tt>_posixshmem</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_posixsubprocess"></a>
+  <a target="code" href="" type="text/plain"><tt>_posixsubprocess</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imports:
+    <a href="#gc">gc</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_py_abc"></a>
+  <a target="code" href="///C:/Python312/Lib/_py_abc.py" type="text/plain"><tt>_py_abc</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_weakrefset">_weakrefset</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#abc">abc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_pydatetime"></a>
+  <a target="code" href="///C:/Python312/Lib/_pydatetime.py" type="text/plain"><tt>_pydatetime</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_strptime">_strptime</a>
+ &#8226;   <a href="#math">math</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#datetime">datetime</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_pydecimal"></a>
+  <a target="code" href="///C:/Python312/Lib/_pydecimal.py" type="text/plain"><tt>_pydecimal</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#contextvars">contextvars</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#math">math</a>
+ &#8226;   <a href="#numbers">numbers</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#decimal">decimal</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_queue"></a>
+  <tt>_queue</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_queue.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#queue">queue</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_random"></a>
+  <tt>_random</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#random">random</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_scproxy"></a>
+  <a target="code" href="" type="text/plain"><tt>_scproxy</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_sha1"></a>
+  <tt>_sha1</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#hashlib">hashlib</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_sha2"></a>
+  <tt>_sha2</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#hashlib">hashlib</a>
+ &#8226;   <a href="#random">random</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_sha3"></a>
+  <tt>_sha3</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#hashlib">hashlib</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_signal"></a>
+  <tt>_signal</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#signal">signal</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_socket"></a>
+  <tt>_socket</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_socket.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#socket">socket</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_sre"></a>
+  <tt>_sre</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imports:
+    <a href="#copy">copy</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#re">re</a>
+ &#8226;   <a href="#re._compiler">re._compiler</a>
+ &#8226;   <a href="#re._constants">re._constants</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_ssl"></a>
+  <tt>_ssl</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_ssl.pyd</tt></span>  <div class="import">
+imports:
+    <a href="#socket">socket</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#ssl">ssl</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_stat"></a>
+  <tt>_stat</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#stat">stat</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_statistics"></a>
+  <tt>_statistics</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#statistics">statistics</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_string"></a>
+  <tt>_string</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#string">string</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_strptime"></a>
+  <a target="code" href="///C:/Python312/Lib/_strptime.py" type="text/plain"><tt>_strptime</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_thread">_thread</a>
+ &#8226;   <a href="#calendar">calendar</a>
+ &#8226;   <a href="#datetime">datetime</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#time">time</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_datetime">_datetime</a>
+ &#8226;   <a href="#_pydatetime">_pydatetime</a>
+ &#8226;   <a href="#time">time</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_struct"></a>
+  <tt>_struct</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#struct">struct</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_thread"></a>
+  <tt>_thread</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#_strptime">_strptime</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#numpy._core.arrayprint">numpy._core.arrayprint</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#threading">threading</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_threading_local"></a>
+  <a target="code" href="///C:/Python312/Lib/_threading_local.py" type="text/plain"><tt>_threading_local</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#threading">threading</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_tkinter"></a>
+  <tt>_tkinter</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_tkinter.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#tkinter">tkinter</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_tokenize"></a>
+  <tt>_tokenize</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#tokenize">tokenize</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_tracemalloc"></a>
+  <tt>_tracemalloc</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#tracemalloc">tracemalloc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_typeshed"></a>
+  <a target="code" href="" type="text/plain"><tt>_typeshed</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#numpy.random._common">numpy.random._common</a>
+ &#8226;   <a href="#numpy.random.bit_generator">numpy.random.bit_generator</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_typing"></a>
+  <tt>_typing</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#typing">typing</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_warnings"></a>
+  <tt>_warnings</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_weakref"></a>
+  <tt>_weakref</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#_weakrefset">_weakrefset</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#weakref">weakref</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_weakrefset"></a>
+  <a target="code" href="///C:/Python312/Lib/_weakrefset.py" type="text/plain"><tt>_weakrefset</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_weakref">_weakref</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_py_abc">_py_abc</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_winapi"></a>
+  <tt>_winapi</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
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+    <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#encodings">encodings</a>
+ &#8226;   <a href="#mimetypes">mimetypes</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#ntpath">ntpath</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_winreg"></a>
+  <a target="code" href="" type="text/plain"><tt>_winreg</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#platform">platform</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="_wmi"></a>
+  <tt>_wmi</tt> <span class="moduletype"><tt>C:\Python312\DLLs\_wmi.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#platform">platform</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="abc"></a>
+  <a target="code" href="///C:/Python312/Lib/abc.py" type="text/plain"><tt>abc</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_abc">_abc</a>
+ &#8226;   <a href="#_py_abc">_py_abc</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#email._policybase">email._policybase</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib._abc">importlib._abc</a>
+ &#8226;   <a href="#importlib.abc">importlib.abc</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#numbers">numbers</a>
+ &#8226;   <a href="#numpy.f2py._backends._backend">numpy.f2py._backends._backend</a>
+ &#8226;   <a href="#numpy.polynomial._polybase">numpy.polynomial._polybase</a>
+ &#8226;   <a href="#numpy.random.bit_generator">numpy.random.bit_generator</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#typing">typing</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="argparse"></a>
+  <a target="code" href="///C:/Python312/Lib/argparse.py" type="text/plain"><tt>argparse</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#copy">copy</a>
+ &#8226;   <a href="#gettext">gettext</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#textwrap">textwrap</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#ast">ast</a>
+ &#8226;   <a href="#calendar">calendar</a>
+ &#8226;   <a href="#code">code</a>
+ &#8226;   <a href="#dis">dis</a>
+ &#8226;   <a href="#doctest">doctest</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#numpy.f2py.f2py2e">numpy.f2py.f2py2e</a>
+ &#8226;   <a href="#py_compile">py_compile</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#unittest.main">unittest.main</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="array"></a>
+  <tt>array</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#multiprocessing.dummy">multiprocessing.dummy</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#socket">socket</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="ast"></a>
+  <a target="code" href="///C:/Python312/Lib/ast.py" type="text/plain"><tt>ast</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_ast">_ast</a>
+ &#8226;   <a href="#argparse">argparse</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#inspect">inspect</a>
+ &#8226;   <a href="#numpy._core._internal">numpy._core._internal</a>
+ &#8226;   <a href="#numpy.lib._format_impl">numpy.lib._format_impl</a>
+ &#8226;   <a href="#numpy.matrixlib.defmatrix">numpy.matrixlib.defmatrix</a>
+ &#8226;   <a href="#traceback">traceback</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/__init__.py" type="text/plain"><tt>asyncio</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.DefaultEventLoopPolicy">asyncio.DefaultEventLoopPolicy</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_futures">asyncio.base_futures</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.base_tasks">asyncio.base_tasks</a>
+ &#8226;   <a href="#asyncio.constants">asyncio.constants</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.locks">asyncio.locks</a>
+ &#8226;   <a href="#asyncio.mixins">asyncio.mixins</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.protocols">asyncio.protocols</a>
+ &#8226;   <a href="#asyncio.queues">asyncio.queues</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.staggered">asyncio.staggered</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.subprocess">asyncio.subprocess</a>
+ &#8226;   <a href="#asyncio.taskgroups">asyncio.taskgroups</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.threads">asyncio.threads</a>
+ &#8226;   <a href="#asyncio.timeouts">asyncio.timeouts</a>
+ &#8226;   <a href="#asyncio.transports">asyncio.transports</a>
+ &#8226;   <a href="#asyncio.trsock">asyncio.trsock</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_futures">asyncio.base_futures</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.base_tasks">asyncio.base_tasks</a>
+ &#8226;   <a href="#asyncio.constants">asyncio.constants</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.locks">asyncio.locks</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.mixins">asyncio.mixins</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.protocols">asyncio.protocols</a>
+ &#8226;   <a href="#asyncio.queues">asyncio.queues</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.staggered">asyncio.staggered</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.subprocess">asyncio.subprocess</a>
+ &#8226;   <a href="#asyncio.taskgroups">asyncio.taskgroups</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.threads">asyncio.threads</a>
+ &#8226;   <a href="#asyncio.timeouts">asyncio.timeouts</a>
+ &#8226;   <a href="#asyncio.transports">asyncio.transports</a>
+ &#8226;   <a href="#asyncio.trsock">asyncio.trsock</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#unittest.async_case">unittest.async_case</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.DefaultEventLoopPolicy"></a>
+  <a target="code" href="" type="text/plain"><tt>asyncio.DefaultEventLoopPolicy</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.base_events"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/base_events.py" type="text/plain"><tt>asyncio.base_events</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.constants">asyncio.constants</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.protocols">asyncio.protocols</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.staggered">asyncio.staggered</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.transports">asyncio.transports</a>
+ &#8226;   <a href="#asyncio.trsock">asyncio.trsock</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#concurrent.futures">concurrent.futures</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#heapq">heapq</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#stat">stat</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.base_futures"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/base_futures.py" type="text/plain"><tt>asyncio.base_futures</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_tasks">asyncio.base_tasks</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.base_subprocess"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/base_subprocess.py" type="text/plain"><tt>asyncio.base_subprocess</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.protocols">asyncio.protocols</a>
+ &#8226;   <a href="#asyncio.transports">asyncio.transports</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.base_tasks"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/base_tasks.py" type="text/plain"><tt>asyncio.base_tasks</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_futures">asyncio.base_futures</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+ &#8226;   <a href="#traceback">traceback</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.constants"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/constants.py" type="text/plain"><tt>asyncio.constants</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#enum">enum</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.coroutines"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/coroutines.py" type="text/plain"><tt>asyncio.coroutines</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_tasks">asyncio.base_tasks</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.events"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/events.py" type="text/plain"><tt>asyncio.events</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_asyncio">_asyncio</a>
+ &#8226;   <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.DefaultEventLoopPolicy">asyncio.DefaultEventLoopPolicy</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#contextvars">contextvars</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.mixins">asyncio.mixins</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.staggered">asyncio.staggered</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.subprocess">asyncio.subprocess</a>
+ &#8226;   <a href="#asyncio.taskgroups">asyncio.taskgroups</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.threads">asyncio.threads</a>
+ &#8226;   <a href="#asyncio.timeouts">asyncio.timeouts</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.exceptions"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/exceptions.py" type="text/plain"><tt>asyncio.exceptions</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.locks">asyncio.locks</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.staggered">asyncio.staggered</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.taskgroups">asyncio.taskgroups</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.timeouts">asyncio.timeouts</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.format_helpers"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/format_helpers.py" type="text/plain"><tt>asyncio.format_helpers</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.constants">asyncio.constants</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#traceback">traceback</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_futures">asyncio.base_futures</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.futures"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/futures.py" type="text/plain"><tt>asyncio.futures</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_asyncio">_asyncio</a>
+ &#8226;   <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_futures">asyncio.base_futures</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#concurrent.futures">concurrent.futures</a>
+ &#8226;   <a href="#contextvars">contextvars</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.locks"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/locks.py" type="text/plain"><tt>asyncio.locks</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.mixins">asyncio.mixins</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#enum">enum</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.queues">asyncio.queues</a>
+ &#8226;   <a href="#asyncio.staggered">asyncio.staggered</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.log"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/log.py" type="text/plain"><tt>asyncio.log</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#logging">logging</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.subprocess">asyncio.subprocess</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.mixins"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/mixins.py" type="text/plain"><tt>asyncio.mixins</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#threading">threading</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.locks">asyncio.locks</a>
+ &#8226;   <a href="#asyncio.queues">asyncio.queues</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.proactor_events"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/proactor_events.py" type="text/plain"><tt>asyncio.proactor_events</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.constants">asyncio.constants</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.protocols">asyncio.protocols</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.transports">asyncio.transports</a>
+ &#8226;   <a href="#asyncio.trsock">asyncio.trsock</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.protocols"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/protocols.py" type="text/plain"><tt>asyncio.protocols</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.subprocess">asyncio.subprocess</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.queues"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/queues.py" type="text/plain"><tt>asyncio.queues</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.locks">asyncio.locks</a>
+ &#8226;   <a href="#asyncio.mixins">asyncio.mixins</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#heapq">heapq</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.runners"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/runners.py" type="text/plain"><tt>asyncio.runners</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.constants">asyncio.constants</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#contextvars">contextvars</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#threading">threading</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.selector_events"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/selector_events.py" type="text/plain"><tt>asyncio.selector_events</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.constants">asyncio.constants</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.protocols">asyncio.protocols</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.transports">asyncio.transports</a>
+ &#8226;   <a href="#asyncio.trsock">asyncio.trsock</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.sslproto"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/sslproto.py" type="text/plain"><tt>asyncio.sslproto</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.constants">asyncio.constants</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.protocols">asyncio.protocols</a>
+ &#8226;   <a href="#asyncio.transports">asyncio.transports</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.staggered"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/staggered.py" type="text/plain"><tt>asyncio.staggered</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.locks">asyncio.locks</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#typing">typing</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.streams"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/streams.py" type="text/plain"><tt>asyncio.streams</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.protocols">asyncio.protocols</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.subprocess">asyncio.subprocess</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.subprocess"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/subprocess.py" type="text/plain"><tt>asyncio.subprocess</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.protocols">asyncio.protocols</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.taskgroups"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/taskgroups.py" type="text/plain"><tt>asyncio.taskgroups</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.tasks"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/tasks.py" type="text/plain"><tt>asyncio.tasks</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_asyncio">_asyncio</a>
+ &#8226;   <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_tasks">asyncio.base_tasks</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.queues">asyncio.queues</a>
+ &#8226;   <a href="#asyncio.timeouts">asyncio.timeouts</a>
+ &#8226;   <a href="#concurrent.futures">concurrent.futures</a>
+ &#8226;   <a href="#contextvars">contextvars</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.staggered">asyncio.staggered</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.subprocess">asyncio.subprocess</a>
+ &#8226;   <a href="#asyncio.taskgroups">asyncio.taskgroups</a>
+ &#8226;   <a href="#asyncio.timeouts">asyncio.timeouts</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.threads"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/threads.py" type="text/plain"><tt>asyncio.threads</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#contextvars">contextvars</a>
+ &#8226;   <a href="#functools">functools</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.timeouts"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/timeouts.py" type="text/plain"><tt>asyncio.timeouts</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#typing">typing</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.transports"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/transports.py" type="text/plain"><tt>asyncio.transports</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.trsock"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/trsock.py" type="text/plain"><tt>asyncio.trsock</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#socket">socket</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.unix_events"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/unix_events.py" type="text/plain"><tt>asyncio.unix_events</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.constants">asyncio.constants</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.transports">asyncio.transports</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#stat">stat</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.windows_events"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/windows_events.py" type="text/plain"><tt>asyncio.windows_events</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_overlapped">_overlapped</a>
+ &#8226;   <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.exceptions">asyncio.exceptions</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#math">math</a>
+ &#8226;   <a href="#msvcrt">msvcrt</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="asyncio.windows_utils"></a>
+  <a target="code" href="///C:/Python312/Lib/asyncio/windows_utils.py" type="text/plain"><tt>asyncio.windows_utils</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#msvcrt">msvcrt</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="atexit"></a>
+  <tt>atexit</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#logging">logging</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="base64"></a>
+  <a target="code" href="///C:/Python312/Lib/base64.py" type="text/plain"><tt>base64</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#binascii">binascii</a>
+ &#8226;   <a href="#getopt">getopt</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email._encoded_words">email._encoded_words</a>
+ &#8226;   <a href="#email.base64mime">email.base64mime</a>
+ &#8226;   <a href="#email.encoders">email.encoders</a>
+ &#8226;   <a href="#encodings.base64_codec">encodings.base64_codec</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#secrets">secrets</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#xmlrpc.client">xmlrpc.client</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="bdb"></a>
+  <a target="code" href="///C:/Python312/Lib/bdb.py" type="text/plain"><tt>bdb</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#fnmatch">fnmatch</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#pdb">pdb</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="binascii"></a>
+  <tt>binascii</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#base64">base64</a>
+ &#8226;   <a href="#email._encoded_words">email._encoded_words</a>
+ &#8226;   <a href="#email.base64mime">email.base64mime</a>
+ &#8226;   <a href="#email.contentmanager">email.contentmanager</a>
+ &#8226;   <a href="#email.header">email.header</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#encodings.hex_codec">encodings.hex_codec</a>
+ &#8226;   <a href="#encodings.uu_codec">encodings.uu_codec</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#quopri">quopri</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="bisect"></a>
+  <a target="code" href="///C:/Python312/Lib/bisect.py" type="text/plain"><tt>bisect</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_bisect">_bisect</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#statistics">statistics</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="builtins"></a>
+  <tt>builtins</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#bz2">bz2</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#doctest">doctest</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#gettext">gettext</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#lzma">lzma</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.numerictypes">numpy._core.numerictypes</a>
+ &#8226;   <a href="#numpy.lib._function_base_impl">numpy.lib._function_base_impl</a>
+ &#8226;   <a href="#numpy.ma.core">numpy.ma.core</a>
+ &#8226;   <a href="#numpy.ma.mrecords">numpy.ma.mrecords</a>
+ &#8226;   <a href="#numpy.random._generator">numpy.random._generator</a>
+ &#8226;   <a href="#numpy.random.mtrand">numpy.random.mtrand</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="bz2"></a>
+  <a target="code" href="///C:/Python312/Lib/bz2.py" type="text/plain"><tt>bz2</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_bz2">_bz2</a>
+ &#8226;   <a href="#_compression">_compression</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#encodings.bz2_codec">encodings.bz2_codec</a>
+ &#8226;   <a href="#fileinput">fileinput</a>
+ &#8226;   <a href="#numpy.lib._datasource">numpy.lib._datasource</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="calendar"></a>
+  <a target="code" href="///C:/Python312/Lib/calendar.py" type="text/plain"><tt>calendar</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#argparse">argparse</a>
+ &#8226;   <a href="#datetime">datetime</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_strptime">_strptime</a>
+ &#8226;   <a href="#email._parseaddr">email._parseaddr</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#ssl">ssl</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="charset_normalizer"></a>
+  <a target="code" href="" type="text/plain"><tt>charset_normalizer</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#numpy.f2py.crackfortran">numpy.f2py.crackfortran</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="cmd"></a>
+  <a target="code" href="///C:/Python312/Lib/cmd.py" type="text/plain"><tt>cmd</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#readline">readline</a>
+ &#8226;   <a href="#string">string</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#pdb">pdb</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="code"></a>
+  <a target="code" href="///C:/Python312/Lib/code.py" type="text/plain"><tt>code</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#argparse">argparse</a>
+ &#8226;   <a href="#codeop">codeop</a>
+ &#8226;   <a href="#readline">readline</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#traceback">traceback</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#pdb">pdb</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="codecs"></a>
+  <a target="code" href="///C:/Python312/Lib/codecs.py" type="text/plain"><tt>codecs</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_codecs">_codecs</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#encodings">encodings</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_pickle">_pickle</a>
+ &#8226;   <a href="#encodings">encodings</a>
+ &#8226;   <a href="#encodings.ascii">encodings.ascii</a>
+ &#8226;   <a href="#encodings.base64_codec">encodings.base64_codec</a>
+ &#8226;   <a href="#encodings.big5">encodings.big5</a>
+ &#8226;   <a href="#encodings.big5hkscs">encodings.big5hkscs</a>
+ &#8226;   <a href="#encodings.bz2_codec">encodings.bz2_codec</a>
+ &#8226;   <a href="#encodings.charmap">encodings.charmap</a>
+ &#8226;   <a href="#encodings.cp037">encodings.cp037</a>
+ &#8226;   <a href="#encodings.cp1006">encodings.cp1006</a>
+ &#8226;   <a href="#encodings.cp1026">encodings.cp1026</a>
+ &#8226;   <a href="#encodings.cp1125">encodings.cp1125</a>
+ &#8226;   <a href="#encodings.cp1140">encodings.cp1140</a>
+ &#8226;   <a href="#encodings.cp1250">encodings.cp1250</a>
+ &#8226;   <a href="#encodings.cp1251">encodings.cp1251</a>
+ &#8226;   <a href="#encodings.cp1252">encodings.cp1252</a>
+ &#8226;   <a href="#encodings.cp1253">encodings.cp1253</a>
+ &#8226;   <a href="#encodings.cp1254">encodings.cp1254</a>
+ &#8226;   <a href="#encodings.cp1255">encodings.cp1255</a>
+ &#8226;   <a href="#encodings.cp1256">encodings.cp1256</a>
+ &#8226;   <a href="#encodings.cp1257">encodings.cp1257</a>
+ &#8226;   <a href="#encodings.cp1258">encodings.cp1258</a>
+ &#8226;   <a href="#encodings.cp273">encodings.cp273</a>
+ &#8226;   <a href="#encodings.cp424">encodings.cp424</a>
+ &#8226;   <a href="#encodings.cp437">encodings.cp437</a>
+ &#8226;   <a href="#encodings.cp500">encodings.cp500</a>
+ &#8226;   <a href="#encodings.cp720">encodings.cp720</a>
+ &#8226;   <a href="#encodings.cp737">encodings.cp737</a>
+ &#8226;   <a href="#encodings.cp775">encodings.cp775</a>
+ &#8226;   <a href="#encodings.cp850">encodings.cp850</a>
+ &#8226;   <a href="#encodings.cp852">encodings.cp852</a>
+ &#8226;   <a href="#encodings.cp855">encodings.cp855</a>
+ &#8226;   <a href="#encodings.cp856">encodings.cp856</a>
+ &#8226;   <a href="#encodings.cp857">encodings.cp857</a>
+ &#8226;   <a href="#encodings.cp858">encodings.cp858</a>
+ &#8226;   <a href="#encodings.cp860">encodings.cp860</a>
+ &#8226;   <a href="#encodings.cp861">encodings.cp861</a>
+ &#8226;   <a href="#encodings.cp862">encodings.cp862</a>
+ &#8226;   <a href="#encodings.cp863">encodings.cp863</a>
+ &#8226;   <a href="#encodings.cp864">encodings.cp864</a>
+ &#8226;   <a href="#encodings.cp865">encodings.cp865</a>
+ &#8226;   <a href="#encodings.cp866">encodings.cp866</a>
+ &#8226;   <a href="#encodings.cp869">encodings.cp869</a>
+ &#8226;   <a href="#encodings.cp874">encodings.cp874</a>
+ &#8226;   <a href="#encodings.cp875">encodings.cp875</a>
+ &#8226;   <a href="#encodings.cp932">encodings.cp932</a>
+ &#8226;   <a href="#encodings.cp949">encodings.cp949</a>
+ &#8226;   <a href="#encodings.cp950">encodings.cp950</a>
+ &#8226;   <a href="#encodings.euc_jis_2004">encodings.euc_jis_2004</a>
+ &#8226;   <a href="#encodings.euc_jisx0213">encodings.euc_jisx0213</a>
+ &#8226;   <a href="#encodings.euc_jp">encodings.euc_jp</a>
+ &#8226;   <a href="#encodings.euc_kr">encodings.euc_kr</a>
+ &#8226;   <a href="#encodings.gb18030">encodings.gb18030</a>
+ &#8226;   <a href="#encodings.gb2312">encodings.gb2312</a>
+ &#8226;   <a href="#encodings.gbk">encodings.gbk</a>
+ &#8226;   <a href="#encodings.hex_codec">encodings.hex_codec</a>
+ &#8226;   <a href="#encodings.hp_roman8">encodings.hp_roman8</a>
+ &#8226;   <a href="#encodings.hz">encodings.hz</a>
+ &#8226;   <a href="#encodings.idna">encodings.idna</a>
+ &#8226;   <a href="#encodings.iso2022_jp">encodings.iso2022_jp</a>
+ &#8226;   <a href="#encodings.iso2022_jp_1">encodings.iso2022_jp_1</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2">encodings.iso2022_jp_2</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2004">encodings.iso2022_jp_2004</a>
+ &#8226;   <a href="#encodings.iso2022_jp_3">encodings.iso2022_jp_3</a>
+ &#8226;   <a href="#encodings.iso2022_jp_ext">encodings.iso2022_jp_ext</a>
+ &#8226;   <a href="#encodings.iso2022_kr">encodings.iso2022_kr</a>
+ &#8226;   <a href="#encodings.iso8859_1">encodings.iso8859_1</a>
+ &#8226;   <a href="#encodings.iso8859_10">encodings.iso8859_10</a>
+ &#8226;   <a href="#encodings.iso8859_11">encodings.iso8859_11</a>
+ &#8226;   <a href="#encodings.iso8859_13">encodings.iso8859_13</a>
+ &#8226;   <a href="#encodings.iso8859_14">encodings.iso8859_14</a>
+ &#8226;   <a href="#encodings.iso8859_15">encodings.iso8859_15</a>
+ &#8226;   <a href="#encodings.iso8859_16">encodings.iso8859_16</a>
+ &#8226;   <a href="#encodings.iso8859_2">encodings.iso8859_2</a>
+ &#8226;   <a href="#encodings.iso8859_3">encodings.iso8859_3</a>
+ &#8226;   <a href="#encodings.iso8859_4">encodings.iso8859_4</a>
+ &#8226;   <a href="#encodings.iso8859_5">encodings.iso8859_5</a>
+ &#8226;   <a href="#encodings.iso8859_6">encodings.iso8859_6</a>
+ &#8226;   <a href="#encodings.iso8859_7">encodings.iso8859_7</a>
+ &#8226;   <a href="#encodings.iso8859_8">encodings.iso8859_8</a>
+ &#8226;   <a href="#encodings.iso8859_9">encodings.iso8859_9</a>
+ &#8226;   <a href="#encodings.johab">encodings.johab</a>
+ &#8226;   <a href="#encodings.koi8_r">encodings.koi8_r</a>
+ &#8226;   <a href="#encodings.koi8_t">encodings.koi8_t</a>
+ &#8226;   <a href="#encodings.koi8_u">encodings.koi8_u</a>
+ &#8226;   <a href="#encodings.kz1048">encodings.kz1048</a>
+ &#8226;   <a href="#encodings.latin_1">encodings.latin_1</a>
+ &#8226;   <a href="#encodings.mac_arabic">encodings.mac_arabic</a>
+ &#8226;   <a href="#encodings.mac_croatian">encodings.mac_croatian</a>
+ &#8226;   <a href="#encodings.mac_cyrillic">encodings.mac_cyrillic</a>
+ &#8226;   <a href="#encodings.mac_farsi">encodings.mac_farsi</a>
+ &#8226;   <a href="#encodings.mac_greek">encodings.mac_greek</a>
+ &#8226;   <a href="#encodings.mac_iceland">encodings.mac_iceland</a>
+ &#8226;   <a href="#encodings.mac_latin2">encodings.mac_latin2</a>
+ &#8226;   <a href="#encodings.mac_roman">encodings.mac_roman</a>
+ &#8226;   <a href="#encodings.mac_romanian">encodings.mac_romanian</a>
+ &#8226;   <a href="#encodings.mac_turkish">encodings.mac_turkish</a>
+ &#8226;   <a href="#encodings.mbcs">encodings.mbcs</a>
+ &#8226;   <a href="#encodings.oem">encodings.oem</a>
+ &#8226;   <a href="#encodings.palmos">encodings.palmos</a>
+ &#8226;   <a href="#encodings.ptcp154">encodings.ptcp154</a>
+ &#8226;   <a href="#encodings.punycode">encodings.punycode</a>
+ &#8226;   <a href="#encodings.quopri_codec">encodings.quopri_codec</a>
+ &#8226;   <a href="#encodings.raw_unicode_escape">encodings.raw_unicode_escape</a>
+ &#8226;   <a href="#encodings.rot_13">encodings.rot_13</a>
+ &#8226;   <a href="#encodings.shift_jis">encodings.shift_jis</a>
+ &#8226;   <a href="#encodings.shift_jis_2004">encodings.shift_jis_2004</a>
+ &#8226;   <a href="#encodings.shift_jisx0213">encodings.shift_jisx0213</a>
+ &#8226;   <a href="#encodings.tis_620">encodings.tis_620</a>
+ &#8226;   <a href="#encodings.undefined">encodings.undefined</a>
+ &#8226;   <a href="#encodings.unicode_escape">encodings.unicode_escape</a>
+ &#8226;   <a href="#encodings.utf_16">encodings.utf_16</a>
+ &#8226;   <a href="#encodings.utf_16_be">encodings.utf_16_be</a>
+ &#8226;   <a href="#encodings.utf_16_le">encodings.utf_16_le</a>
+ &#8226;   <a href="#encodings.utf_32">encodings.utf_32</a>
+ &#8226;   <a href="#encodings.utf_32_be">encodings.utf_32_be</a>
+ &#8226;   <a href="#encodings.utf_32_le">encodings.utf_32_le</a>
+ &#8226;   <a href="#encodings.utf_7">encodings.utf_7</a>
+ &#8226;   <a href="#encodings.utf_8">encodings.utf_8</a>
+ &#8226;   <a href="#encodings.utf_8_sig">encodings.utf_8_sig</a>
+ &#8226;   <a href="#encodings.uu_codec">encodings.uu_codec</a>
+ &#8226;   <a href="#encodings.zlib_codec">encodings.zlib_codec</a>
+ &#8226;   <a href="#json">json</a>
+ &#8226;   <a href="#numpy.f2py.crackfortran">numpy.f2py.crackfortran</a>
+ &#8226;   <a href="#pickle">pickle</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="codeop"></a>
+  <a target="code" href="///C:/Python312/Lib/codeop.py" type="text/plain"><tt>codeop</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#__future__">__future__</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#code">code</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="collections"></a>
+  <a target="code" href="///C:/Python312/Lib/collections/__init__.py" type="text/plain"><tt>collections</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#_collections">_collections</a>
+ &#8226;   <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#_weakref">_weakref</a>
+ &#8226;   <a href="#copy">copy</a>
+ &#8226;   <a href="#heapq">heapq</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#keyword">keyword</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_pydecimal">_pydecimal</a>
+ &#8226;   <a href="#ast">ast</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.locks">asyncio.locks</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.queues">asyncio.queues</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#concurrent.futures._base">concurrent.futures._base</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#cv2.utils">cv2.utils</a>
+ &#8226;   <a href="#difflib">difflib</a>
+ &#8226;   <a href="#dis">dis</a>
+ &#8226;   <a href="#doctest">doctest</a>
+ &#8226;   <a href="#email.feedparser">email.feedparser</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._collections">importlib.metadata._collections</a>
+ &#8226;   <a href="#importlib.resources.readers">importlib.resources.readers</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#numpy._core.overrides">numpy._core.overrides</a>
+ &#8226;   <a href="#numpy._core.records">numpy._core.records</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#platform">platform</a>
+ &#8226;   <a href="#pprint">pprint</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#shlex">shlex</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#statistics">statistics</a>
+ &#8226;   <a href="#string">string</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#unittest._log">unittest._log</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.util">unittest.util</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="collections.abc"></a>
+  <a target="code" href="///C:/Python312/Lib/collections/abc.py" type="text/plain"><tt>collections.abc</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#collections">collections</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#http.client">http.client</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#numpy._typing._array_like">numpy._typing._array_like</a>
+ &#8226;   <a href="#numpy._typing._dtype_like">numpy._typing._dtype_like</a>
+ &#8226;   <a href="#numpy._typing._nested_sequence">numpy._typing._nested_sequence</a>
+ &#8226;   <a href="#numpy._typing._shape">numpy._typing._shape</a>
+ &#8226;   <a href="#numpy.lib._function_base_impl">numpy.lib._function_base_impl</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#numpy.polynomial._polybase">numpy.polynomial._polybase</a>
+ &#8226;   <a href="#numpy.random._common">numpy.random._common</a>
+ &#8226;   <a href="#numpy.random._generator">numpy.random._generator</a>
+ &#8226;   <a href="#numpy.random.bit_generator">numpy.random.bit_generator</a>
+ &#8226;   <a href="#numpy.random.mtrand">numpy.random.mtrand</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#tracemalloc">tracemalloc</a>
+ &#8226;   <a href="#typing">typing</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="concurrent"></a>
+  <a target="code" href="///C:/Python312/Lib/concurrent/__init__.py" type="text/plain"><tt>concurrent</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imported by:
+    <a href="#concurrent.futures">concurrent.futures</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="concurrent.futures"></a>
+  <a target="code" href="///C:/Python312/Lib/concurrent/futures/__init__.py" type="text/plain"><tt>concurrent.futures</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#concurrent">concurrent</a>
+ &#8226;   <a href="#concurrent.futures._base">concurrent.futures._base</a>
+ &#8226;   <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#concurrent.futures.thread">concurrent.futures.thread</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#concurrent.futures._base">concurrent.futures._base</a>
+ &#8226;   <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#concurrent.futures.thread">concurrent.futures.thread</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="concurrent.futures._base"></a>
+  <a target="code" href="///C:/Python312/Lib/concurrent/futures/_base.py" type="text/plain"><tt>concurrent.futures._base</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#concurrent.futures">concurrent.futures</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#concurrent.futures">concurrent.futures</a>
+ &#8226;   <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#concurrent.futures.thread">concurrent.futures.thread</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="concurrent.futures.process"></a>
+  <a target="code" href="///C:/Python312/Lib/concurrent/futures/process.py" type="text/plain"><tt>concurrent.futures.process</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#concurrent.futures">concurrent.futures</a>
+ &#8226;   <a href="#concurrent.futures._base">concurrent.futures._base</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#concurrent.futures">concurrent.futures</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="concurrent.futures.thread"></a>
+  <a target="code" href="///C:/Python312/Lib/concurrent/futures/thread.py" type="text/plain"><tt>concurrent.futures.thread</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#concurrent.futures">concurrent.futures</a>
+ &#8226;   <a href="#concurrent.futures._base">concurrent.futures._base</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#concurrent.futures">concurrent.futures</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="contextlib"></a>
+  <a target="code" href="///C:/Python312/Lib/contextlib.py" type="text/plain"><tt>contextlib</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#abc">abc</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_aix_support">_aix_support</a>
+ &#8226;   <a href="#_threading_local">_threading_local</a>
+ &#8226;   <a href="#ast">ast</a>
+ &#8226;   <a href="#asyncio.staggered">asyncio.staggered</a>
+ &#8226;   <a href="#getpass">getpass</a>
+ &#8226;   <a href="#glob">glob</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.resources._adapters">importlib.resources._adapters</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#numpy._core._methods">numpy._core._methods</a>
+ &#8226;   <a href="#numpy._core.arrayprint">numpy._core.arrayprint</a>
+ &#8226;   <a href="#numpy._core.memmap">numpy._core.memmap</a>
+ &#8226;   <a href="#numpy._core.records">numpy._core.records</a>
+ &#8226;   <a href="#numpy.lib._histograms_impl">numpy.lib._histograms_impl</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#zipfile._path">zipfile._path</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="contextvars"></a>
+  <a target="code" href="///C:/Python312/Lib/contextvars.py" type="text/plain"><tt>contextvars</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_contextvars">_contextvars</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_pydecimal">_pydecimal</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.threads">asyncio.threads</a>
+ &#8226;   <a href="#numpy._core.printoptions">numpy._core.printoptions</a>
+ &#8226;   <a href="#unittest.async_case">unittest.async_case</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="copy"></a>
+  <a target="code" href="///C:/Python312/Lib/copy.py" type="text/plain"><tt>copy</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#copyreg">copyreg</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_sre">_sre</a>
+ &#8226;   <a href="#argparse">argparse</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#cv2">cv2</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#email.generator">email.generator</a>
+ &#8226;   <a href="#gettext">gettext</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#numpy.f2py.capi_maps">numpy.f2py.capi_maps</a>
+ &#8226;   <a href="#numpy.f2py.cfuncs">numpy.f2py.cfuncs</a>
+ &#8226;   <a href="#numpy.f2py.crackfortran">numpy.f2py.crackfortran</a>
+ &#8226;   <a href="#numpy.f2py.func2subr">numpy.f2py.func2subr</a>
+ &#8226;   <a href="#numpy.f2py.rules">numpy.f2py.rules</a>
+ &#8226;   <a href="#numpy.ma.core">numpy.ma.core</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
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+imported by:
+    <a href="#email._encoded_words">email._encoded_words</a>
+ &#8226;   <a href="#email._header_value_parser">email._header_value_parser</a>
+ &#8226;   <a href="#email._parseaddr">email._parseaddr</a>
+ &#8226;   <a href="#email._policybase">email._policybase</a>
+ &#8226;   <a href="#email.base64mime">email.base64mime</a>
+ &#8226;   <a href="#email.charset">email.charset</a>
+ &#8226;   <a href="#email.contentmanager">email.contentmanager</a>
+ &#8226;   <a href="#email.encoders">email.encoders</a>
+ &#8226;   <a href="#email.errors">email.errors</a>
+ &#8226;   <a href="#email.feedparser">email.feedparser</a>
+ &#8226;   <a href="#email.generator">email.generator</a>
+ &#8226;   <a href="#email.header">email.header</a>
+ &#8226;   <a href="#email.headerregistry">email.headerregistry</a>
+ &#8226;   <a href="#email.iterators">email.iterators</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#email.parser">email.parser</a>
+ &#8226;   <a href="#email.policy">email.policy</a>
+ &#8226;   <a href="#email.quoprimime">email.quoprimime</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email._encoded_words"></a>
+  <a target="code" href="///C:/Python312/Lib/email/_encoded_words.py" type="text/plain"><tt>email._encoded_words</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#base64">base64</a>
+ &#8226;   <a href="#binascii">binascii</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#email.errors">email.errors</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#string">string</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email._header_value_parser">email._header_value_parser</a>
+ &#8226;   <a href="#email.message">email.message</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email._header_value_parser"></a>
+  <a target="code" href="///C:/Python312/Lib/email/_header_value_parser.py" type="text/plain"><tt>email._header_value_parser</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#email">email</a>
+ &#8226;   <a href="#email._encoded_words">email._encoded_words</a>
+ &#8226;   <a href="#email.errors">email.errors</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#string">string</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#urllib">urllib</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email">email</a>
+ &#8226;   <a href="#email.headerregistry">email.headerregistry</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email._parseaddr"></a>
+  <a target="code" href="///C:/Python312/Lib/email/_parseaddr.py" type="text/plain"><tt>email._parseaddr</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#calendar">calendar</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#time">time</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.utils">email.utils</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email._policybase"></a>
+  <a target="code" href="///C:/Python312/Lib/email/_policybase.py" type="text/plain"><tt>email._policybase</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#abc">abc</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#email.charset">email.charset</a>
+ &#8226;   <a href="#email.header">email.header</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.feedparser">email.feedparser</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#email.parser">email.parser</a>
+ &#8226;   <a href="#email.policy">email.policy</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.base64mime"></a>
+  <a target="code" href="///C:/Python312/Lib/email/base64mime.py" type="text/plain"><tt>email.base64mime</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#base64">base64</a>
+ &#8226;   <a href="#binascii">binascii</a>
+ &#8226;   <a href="#email">email</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.charset">email.charset</a>
+ &#8226;   <a href="#email.header">email.header</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.charset"></a>
+  <a target="code" href="///C:/Python312/Lib/email/charset.py" type="text/plain"><tt>email.charset</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#email">email</a>
+ &#8226;   <a href="#email.base64mime">email.base64mime</a>
+ &#8226;   <a href="#email.encoders">email.encoders</a>
+ &#8226;   <a href="#email.errors">email.errors</a>
+ &#8226;   <a href="#email.quoprimime">email.quoprimime</a>
+ &#8226;   <a href="#functools">functools</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email">email</a>
+ &#8226;   <a href="#email._policybase">email._policybase</a>
+ &#8226;   <a href="#email.contentmanager">email.contentmanager</a>
+ &#8226;   <a href="#email.header">email.header</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.contentmanager"></a>
+  <a target="code" href="///C:/Python312/Lib/email/contentmanager.py" type="text/plain"><tt>email.contentmanager</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#binascii">binascii</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#email.charset">email.charset</a>
+ &#8226;   <a href="#email.errors">email.errors</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#email.quoprimime">email.quoprimime</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.policy">email.policy</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.encoders"></a>
+  <a target="code" href="///C:/Python312/Lib/email/encoders.py" type="text/plain"><tt>email.encoders</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#base64">base64</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#quopri">quopri</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.charset">email.charset</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.errors"></a>
+  <a target="code" href="///C:/Python312/Lib/email/errors.py" type="text/plain"><tt>email.errors</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#email">email</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email">email</a>
+ &#8226;   <a href="#email._encoded_words">email._encoded_words</a>
+ &#8226;   <a href="#email._header_value_parser">email._header_value_parser</a>
+ &#8226;   <a href="#email.charset">email.charset</a>
+ &#8226;   <a href="#email.contentmanager">email.contentmanager</a>
+ &#8226;   <a href="#email.feedparser">email.feedparser</a>
+ &#8226;   <a href="#email.header">email.header</a>
+ &#8226;   <a href="#email.headerregistry">email.headerregistry</a>
+ &#8226;   <a href="#email.message">email.message</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.feedparser"></a>
+  <a target="code" href="///C:/Python312/Lib/email/feedparser.py" type="text/plain"><tt>email.feedparser</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#email._policybase">email._policybase</a>
+ &#8226;   <a href="#email.errors">email.errors</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.parser">email.parser</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.generator"></a>
+  <a target="code" href="///C:/Python312/Lib/email/generator.py" type="text/plain"><tt>email.generator</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#copy">copy</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.message">email.message</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.header"></a>
+  <a target="code" href="///C:/Python312/Lib/email/header.py" type="text/plain"><tt>email.header</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#binascii">binascii</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#email.base64mime">email.base64mime</a>
+ &#8226;   <a href="#email.charset">email.charset</a>
+ &#8226;   <a href="#email.errors">email.errors</a>
+ &#8226;   <a href="#email.quoprimime">email.quoprimime</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email">email</a>
+ &#8226;   <a href="#email._policybase">email._policybase</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.headerregistry"></a>
+  <a target="code" href="///C:/Python312/Lib/email/headerregistry.py" type="text/plain"><tt>email.headerregistry</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#email">email</a>
+ &#8226;   <a href="#email._header_value_parser">email._header_value_parser</a>
+ &#8226;   <a href="#email.errors">email.errors</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.policy">email.policy</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.iterators"></a>
+  <a target="code" href="///C:/Python312/Lib/email/iterators.py" type="text/plain"><tt>email.iterators</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#email">email</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.message">email.message</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.message"></a>
+  <a target="code" href="///C:/Python312/Lib/email/message.py" type="text/plain"><tt>email.message</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#binascii">binascii</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#email._encoded_words">email._encoded_words</a>
+ &#8226;   <a href="#email._policybase">email._policybase</a>
+ &#8226;   <a href="#email.charset">email.charset</a>
+ &#8226;   <a href="#email.errors">email.errors</a>
+ &#8226;   <a href="#email.generator">email.generator</a>
+ &#8226;   <a href="#email.iterators">email.iterators</a>
+ &#8226;   <a href="#email.policy">email.policy</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#quopri">quopri</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.contentmanager">email.contentmanager</a>
+ &#8226;   <a href="#email.feedparser">email.feedparser</a>
+ &#8226;   <a href="#email.policy">email.policy</a>
+ &#8226;   <a href="#http.client">http.client</a>
+ &#8226;   <a href="#importlib.metadata._adapters">importlib.metadata._adapters</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.parser"></a>
+  <a target="code" href="///C:/Python312/Lib/email/parser.py" type="text/plain"><tt>email.parser</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#email">email</a>
+ &#8226;   <a href="#email._policybase">email._policybase</a>
+ &#8226;   <a href="#email.feedparser">email.feedparser</a>
+ &#8226;   <a href="#io">io</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email">email</a>
+ &#8226;   <a href="#http.client">http.client</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.policy"></a>
+  <a target="code" href="///C:/Python312/Lib/email/policy.py" type="text/plain"><tt>email.policy</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#email">email</a>
+ &#8226;   <a href="#email._policybase">email._policybase</a>
+ &#8226;   <a href="#email.contentmanager">email.contentmanager</a>
+ &#8226;   <a href="#email.headerregistry">email.headerregistry</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.message">email.message</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.quoprimime"></a>
+  <a target="code" href="///C:/Python312/Lib/email/quoprimime.py" type="text/plain"><tt>email.quoprimime</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#email">email</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#string">string</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email.charset">email.charset</a>
+ &#8226;   <a href="#email.contentmanager">email.contentmanager</a>
+ &#8226;   <a href="#email.header">email.header</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="email.utils"></a>
+  <a target="code" href="///C:/Python312/Lib/email/utils.py" type="text/plain"><tt>email.utils</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#datetime">datetime</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#email._parseaddr">email._parseaddr</a>
+ &#8226;   <a href="#email.charset">email.charset</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#email._header_value_parser">email._header_value_parser</a>
+ &#8226;   <a href="#email._policybase">email._policybase</a>
+ &#8226;   <a href="#email.generator">email.generator</a>
+ &#8226;   <a href="#email.headerregistry">email.headerregistry</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#email.policy">email.policy</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="encodings"></a>
+  <a target="code" href="///C:/Python312/Lib/encodings/__init__.py" type="text/plain"><tt>encodings</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#encodings">encodings</a>
+ &#8226;   <a href="#encodings.aliases">encodings.aliases</a>
+ &#8226;   <a href="#encodings.ascii">encodings.ascii</a>
+ &#8226;   <a href="#encodings.base64_codec">encodings.base64_codec</a>
+ &#8226;   <a href="#encodings.big5">encodings.big5</a>
+ &#8226;   <a href="#encodings.big5hkscs">encodings.big5hkscs</a>
+ &#8226;   <a href="#encodings.bz2_codec">encodings.bz2_codec</a>
+ &#8226;   <a href="#encodings.charmap">encodings.charmap</a>
+ &#8226;   <a href="#encodings.cp037">encodings.cp037</a>
+ &#8226;   <a href="#encodings.cp1006">encodings.cp1006</a>
+ &#8226;   <a href="#encodings.cp1026">encodings.cp1026</a>
+ &#8226;   <a href="#encodings.cp1125">encodings.cp1125</a>
+ &#8226;   <a href="#encodings.cp1140">encodings.cp1140</a>
+ &#8226;   <a href="#encodings.cp1250">encodings.cp1250</a>
+ &#8226;   <a href="#encodings.cp1251">encodings.cp1251</a>
+ &#8226;   <a href="#encodings.cp1252">encodings.cp1252</a>
+ &#8226;   <a href="#encodings.cp1253">encodings.cp1253</a>
+ &#8226;   <a href="#encodings.cp1254">encodings.cp1254</a>
+ &#8226;   <a href="#encodings.cp1255">encodings.cp1255</a>
+ &#8226;   <a href="#encodings.cp1256">encodings.cp1256</a>
+ &#8226;   <a href="#encodings.cp1257">encodings.cp1257</a>
+ &#8226;   <a href="#encodings.cp1258">encodings.cp1258</a>
+ &#8226;   <a href="#encodings.cp273">encodings.cp273</a>
+ &#8226;   <a href="#encodings.cp424">encodings.cp424</a>
+ &#8226;   <a href="#encodings.cp437">encodings.cp437</a>
+ &#8226;   <a href="#encodings.cp500">encodings.cp500</a>
+ &#8226;   <a href="#encodings.cp720">encodings.cp720</a>
+ &#8226;   <a href="#encodings.cp737">encodings.cp737</a>
+ &#8226;   <a href="#encodings.cp775">encodings.cp775</a>
+ &#8226;   <a href="#encodings.cp850">encodings.cp850</a>
+ &#8226;   <a href="#encodings.cp852">encodings.cp852</a>
+ &#8226;   <a href="#encodings.cp855">encodings.cp855</a>
+ &#8226;   <a href="#encodings.cp856">encodings.cp856</a>
+ &#8226;   <a href="#encodings.cp857">encodings.cp857</a>
+ &#8226;   <a href="#encodings.cp858">encodings.cp858</a>
+ &#8226;   <a href="#encodings.cp860">encodings.cp860</a>
+ &#8226;   <a href="#encodings.cp861">encodings.cp861</a>
+ &#8226;   <a href="#encodings.cp862">encodings.cp862</a>
+ &#8226;   <a href="#encodings.cp863">encodings.cp863</a>
+ &#8226;   <a href="#encodings.cp864">encodings.cp864</a>
+ &#8226;   <a href="#encodings.cp865">encodings.cp865</a>
+ &#8226;   <a href="#encodings.cp866">encodings.cp866</a>
+ &#8226;   <a href="#encodings.cp869">encodings.cp869</a>
+ &#8226;   <a href="#encodings.cp874">encodings.cp874</a>
+ &#8226;   <a href="#encodings.cp875">encodings.cp875</a>
+ &#8226;   <a href="#encodings.cp932">encodings.cp932</a>
+ &#8226;   <a href="#encodings.cp949">encodings.cp949</a>
+ &#8226;   <a href="#encodings.cp950">encodings.cp950</a>
+ &#8226;   <a href="#encodings.euc_jis_2004">encodings.euc_jis_2004</a>
+ &#8226;   <a href="#encodings.euc_jisx0213">encodings.euc_jisx0213</a>
+ &#8226;   <a href="#encodings.euc_jp">encodings.euc_jp</a>
+ &#8226;   <a href="#encodings.euc_kr">encodings.euc_kr</a>
+ &#8226;   <a href="#encodings.gb18030">encodings.gb18030</a>
+ &#8226;   <a href="#encodings.gb2312">encodings.gb2312</a>
+ &#8226;   <a href="#encodings.gbk">encodings.gbk</a>
+ &#8226;   <a href="#encodings.hex_codec">encodings.hex_codec</a>
+ &#8226;   <a href="#encodings.hp_roman8">encodings.hp_roman8</a>
+ &#8226;   <a href="#encodings.hz">encodings.hz</a>
+ &#8226;   <a href="#encodings.idna">encodings.idna</a>
+ &#8226;   <a href="#encodings.iso2022_jp">encodings.iso2022_jp</a>
+ &#8226;   <a href="#encodings.iso2022_jp_1">encodings.iso2022_jp_1</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2">encodings.iso2022_jp_2</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2004">encodings.iso2022_jp_2004</a>
+ &#8226;   <a href="#encodings.iso2022_jp_3">encodings.iso2022_jp_3</a>
+ &#8226;   <a href="#encodings.iso2022_jp_ext">encodings.iso2022_jp_ext</a>
+ &#8226;   <a href="#encodings.iso2022_kr">encodings.iso2022_kr</a>
+ &#8226;   <a href="#encodings.iso8859_1">encodings.iso8859_1</a>
+ &#8226;   <a href="#encodings.iso8859_10">encodings.iso8859_10</a>
+ &#8226;   <a href="#encodings.iso8859_11">encodings.iso8859_11</a>
+ &#8226;   <a href="#encodings.iso8859_13">encodings.iso8859_13</a>
+ &#8226;   <a href="#encodings.iso8859_14">encodings.iso8859_14</a>
+ &#8226;   <a href="#encodings.iso8859_15">encodings.iso8859_15</a>
+ &#8226;   <a href="#encodings.iso8859_16">encodings.iso8859_16</a>
+ &#8226;   <a href="#encodings.iso8859_2">encodings.iso8859_2</a>
+ &#8226;   <a href="#encodings.iso8859_3">encodings.iso8859_3</a>
+ &#8226;   <a href="#encodings.iso8859_4">encodings.iso8859_4</a>
+ &#8226;   <a href="#encodings.iso8859_5">encodings.iso8859_5</a>
+ &#8226;   <a href="#encodings.iso8859_6">encodings.iso8859_6</a>
+ &#8226;   <a href="#encodings.iso8859_7">encodings.iso8859_7</a>
+ &#8226;   <a href="#encodings.iso8859_8">encodings.iso8859_8</a>
+ &#8226;   <a href="#encodings.iso8859_9">encodings.iso8859_9</a>
+ &#8226;   <a href="#encodings.johab">encodings.johab</a>
+ &#8226;   <a href="#encodings.koi8_r">encodings.koi8_r</a>
+ &#8226;   <a href="#encodings.koi8_t">encodings.koi8_t</a>
+ &#8226;   <a href="#encodings.koi8_u">encodings.koi8_u</a>
+ &#8226;   <a href="#encodings.kz1048">encodings.kz1048</a>
+ &#8226;   <a href="#encodings.latin_1">encodings.latin_1</a>
+ &#8226;   <a href="#encodings.mac_arabic">encodings.mac_arabic</a>
+ &#8226;   <a href="#encodings.mac_croatian">encodings.mac_croatian</a>
+ &#8226;   <a href="#encodings.mac_cyrillic">encodings.mac_cyrillic</a>
+ &#8226;   <a href="#encodings.mac_farsi">encodings.mac_farsi</a>
+ &#8226;   <a href="#encodings.mac_greek">encodings.mac_greek</a>
+ &#8226;   <a href="#encodings.mac_iceland">encodings.mac_iceland</a>
+ &#8226;   <a href="#encodings.mac_latin2">encodings.mac_latin2</a>
+ &#8226;   <a href="#encodings.mac_roman">encodings.mac_roman</a>
+ &#8226;   <a href="#encodings.mac_romanian">encodings.mac_romanian</a>
+ &#8226;   <a href="#encodings.mac_turkish">encodings.mac_turkish</a>
+ &#8226;   <a href="#encodings.mbcs">encodings.mbcs</a>
+ &#8226;   <a href="#encodings.oem">encodings.oem</a>
+ &#8226;   <a href="#encodings.palmos">encodings.palmos</a>
+ &#8226;   <a href="#encodings.ptcp154">encodings.ptcp154</a>
+ &#8226;   <a href="#encodings.punycode">encodings.punycode</a>
+ &#8226;   <a href="#encodings.quopri_codec">encodings.quopri_codec</a>
+ &#8226;   <a href="#encodings.raw_unicode_escape">encodings.raw_unicode_escape</a>
+ &#8226;   <a href="#encodings.rot_13">encodings.rot_13</a>
+ &#8226;   <a href="#encodings.shift_jis">encodings.shift_jis</a>
+ &#8226;   <a href="#encodings.shift_jis_2004">encodings.shift_jis_2004</a>
+ &#8226;   <a href="#encodings.shift_jisx0213">encodings.shift_jisx0213</a>
+ &#8226;   <a href="#encodings.tis_620">encodings.tis_620</a>
+ &#8226;   <a href="#encodings.undefined">encodings.undefined</a>
+ &#8226;   <a href="#encodings.unicode_escape">encodings.unicode_escape</a>
+ &#8226;   <a href="#encodings.utf_16">encodings.utf_16</a>
+ &#8226;   <a href="#encodings.utf_16_be">encodings.utf_16_be</a>
+ &#8226;   <a href="#encodings.utf_16_le">encodings.utf_16_le</a>
+ &#8226;   <a href="#encodings.utf_32">encodings.utf_32</a>
+ &#8226;   <a href="#encodings.utf_32_be">encodings.utf_32_be</a>
+ &#8226;   <a href="#encodings.utf_32_le">encodings.utf_32_le</a>
+ &#8226;   <a href="#encodings.utf_7">encodings.utf_7</a>
+ &#8226;   <a href="#encodings.utf_8">encodings.utf_8</a>
+ &#8226;   <a href="#encodings.utf_8_sig">encodings.utf_8_sig</a>
+ &#8226;   <a href="#encodings.uu_codec">encodings.uu_codec</a>
+ &#8226;   <a href="#encodings.zlib_codec">encodings.zlib_codec</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#encodings">encodings</a>
+ &#8226;   <a href="#encodings.aliases">encodings.aliases</a>
+ &#8226;   <a href="#encodings.ascii">encodings.ascii</a>
+ &#8226;   <a href="#encodings.base64_codec">encodings.base64_codec</a>
+ &#8226;   <a href="#encodings.big5">encodings.big5</a>
+ &#8226;   <a href="#encodings.big5hkscs">encodings.big5hkscs</a>
+ &#8226;   <a href="#encodings.bz2_codec">encodings.bz2_codec</a>
+ &#8226;   <a href="#encodings.charmap">encodings.charmap</a>
+ &#8226;   <a href="#encodings.cp037">encodings.cp037</a>
+ &#8226;   <a href="#encodings.cp1006">encodings.cp1006</a>
+ &#8226;   <a href="#encodings.cp1026">encodings.cp1026</a>
+ &#8226;   <a href="#encodings.cp1125">encodings.cp1125</a>
+ &#8226;   <a href="#encodings.cp1140">encodings.cp1140</a>
+ &#8226;   <a href="#encodings.cp1250">encodings.cp1250</a>
+ &#8226;   <a href="#encodings.cp1251">encodings.cp1251</a>
+ &#8226;   <a href="#encodings.cp1252">encodings.cp1252</a>
+ &#8226;   <a href="#encodings.cp1253">encodings.cp1253</a>
+ &#8226;   <a href="#encodings.cp1254">encodings.cp1254</a>
+ &#8226;   <a href="#encodings.cp1255">encodings.cp1255</a>
+ &#8226;   <a href="#encodings.cp1256">encodings.cp1256</a>
+ &#8226;   <a href="#encodings.cp1257">encodings.cp1257</a>
+ &#8226;   <a href="#encodings.cp1258">encodings.cp1258</a>
+ &#8226;   <a href="#encodings.cp273">encodings.cp273</a>
+ &#8226;   <a href="#encodings.cp424">encodings.cp424</a>
+ &#8226;   <a href="#encodings.cp437">encodings.cp437</a>
+ &#8226;   <a href="#encodings.cp500">encodings.cp500</a>
+ &#8226;   <a href="#encodings.cp720">encodings.cp720</a>
+ &#8226;   <a href="#encodings.cp737">encodings.cp737</a>
+ &#8226;   <a href="#encodings.cp775">encodings.cp775</a>
+ &#8226;   <a href="#encodings.cp850">encodings.cp850</a>
+ &#8226;   <a href="#encodings.cp852">encodings.cp852</a>
+ &#8226;   <a href="#encodings.cp855">encodings.cp855</a>
+ &#8226;   <a href="#encodings.cp856">encodings.cp856</a>
+ &#8226;   <a href="#encodings.cp857">encodings.cp857</a>
+ &#8226;   <a href="#encodings.cp858">encodings.cp858</a>
+ &#8226;   <a href="#encodings.cp860">encodings.cp860</a>
+ &#8226;   <a href="#encodings.cp861">encodings.cp861</a>
+ &#8226;   <a href="#encodings.cp862">encodings.cp862</a>
+ &#8226;   <a href="#encodings.cp863">encodings.cp863</a>
+ &#8226;   <a href="#encodings.cp864">encodings.cp864</a>
+ &#8226;   <a href="#encodings.cp865">encodings.cp865</a>
+ &#8226;   <a href="#encodings.cp866">encodings.cp866</a>
+ &#8226;   <a href="#encodings.cp869">encodings.cp869</a>
+ &#8226;   <a href="#encodings.cp874">encodings.cp874</a>
+ &#8226;   <a href="#encodings.cp875">encodings.cp875</a>
+ &#8226;   <a href="#encodings.cp932">encodings.cp932</a>
+ &#8226;   <a href="#encodings.cp949">encodings.cp949</a>
+ &#8226;   <a href="#encodings.cp950">encodings.cp950</a>
+ &#8226;   <a href="#encodings.euc_jis_2004">encodings.euc_jis_2004</a>
+ &#8226;   <a href="#encodings.euc_jisx0213">encodings.euc_jisx0213</a>
+ &#8226;   <a href="#encodings.euc_jp">encodings.euc_jp</a>
+ &#8226;   <a href="#encodings.euc_kr">encodings.euc_kr</a>
+ &#8226;   <a href="#encodings.gb18030">encodings.gb18030</a>
+ &#8226;   <a href="#encodings.gb2312">encodings.gb2312</a>
+ &#8226;   <a href="#encodings.gbk">encodings.gbk</a>
+ &#8226;   <a href="#encodings.hex_codec">encodings.hex_codec</a>
+ &#8226;   <a href="#encodings.hp_roman8">encodings.hp_roman8</a>
+ &#8226;   <a href="#encodings.hz">encodings.hz</a>
+ &#8226;   <a href="#encodings.idna">encodings.idna</a>
+ &#8226;   <a href="#encodings.iso2022_jp">encodings.iso2022_jp</a>
+ &#8226;   <a href="#encodings.iso2022_jp_1">encodings.iso2022_jp_1</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2">encodings.iso2022_jp_2</a>
+ &#8226;   <a href="#encodings.iso2022_jp_2004">encodings.iso2022_jp_2004</a>
+ &#8226;   <a href="#encodings.iso2022_jp_3">encodings.iso2022_jp_3</a>
+ &#8226;   <a href="#encodings.iso2022_jp_ext">encodings.iso2022_jp_ext</a>
+ &#8226;   <a href="#encodings.iso2022_kr">encodings.iso2022_kr</a>
+ &#8226;   <a href="#encodings.iso8859_1">encodings.iso8859_1</a>
+ &#8226;   <a href="#encodings.iso8859_10">encodings.iso8859_10</a>
+ &#8226;   <a href="#encodings.iso8859_11">encodings.iso8859_11</a>
+ &#8226;   <a href="#encodings.iso8859_13">encodings.iso8859_13</a>
+ &#8226;   <a href="#encodings.iso8859_14">encodings.iso8859_14</a>
+ &#8226;   <a href="#encodings.iso8859_15">encodings.iso8859_15</a>
+ &#8226;   <a href="#encodings.iso8859_16">encodings.iso8859_16</a>
+ &#8226;   <a href="#encodings.iso8859_2">encodings.iso8859_2</a>
+ &#8226;   <a href="#encodings.iso8859_3">encodings.iso8859_3</a>
+ &#8226;   <a href="#encodings.iso8859_4">encodings.iso8859_4</a>
+ &#8226;   <a href="#encodings.iso8859_5">encodings.iso8859_5</a>
+ &#8226;   <a href="#encodings.iso8859_6">encodings.iso8859_6</a>
+ &#8226;   <a href="#encodings.iso8859_7">encodings.iso8859_7</a>
+ &#8226;   <a href="#encodings.iso8859_8">encodings.iso8859_8</a>
+ &#8226;   <a href="#encodings.iso8859_9">encodings.iso8859_9</a>
+ &#8226;   <a href="#encodings.johab">encodings.johab</a>
+ &#8226;   <a href="#encodings.koi8_r">encodings.koi8_r</a>
+ &#8226;   <a href="#encodings.koi8_t">encodings.koi8_t</a>
+ &#8226;   <a href="#encodings.koi8_u">encodings.koi8_u</a>
+ &#8226;   <a href="#encodings.kz1048">encodings.kz1048</a>
+ &#8226;   <a href="#encodings.latin_1">encodings.latin_1</a>
+ &#8226;   <a href="#encodings.mac_arabic">encodings.mac_arabic</a>
+ &#8226;   <a href="#encodings.mac_croatian">encodings.mac_croatian</a>
+ &#8226;   <a href="#encodings.mac_cyrillic">encodings.mac_cyrillic</a>
+ &#8226;   <a href="#encodings.mac_farsi">encodings.mac_farsi</a>
+ &#8226;   <a href="#encodings.mac_greek">encodings.mac_greek</a>
+ &#8226;   <a href="#encodings.mac_iceland">encodings.mac_iceland</a>
+ &#8226;   <a href="#encodings.mac_latin2">encodings.mac_latin2</a>
+ &#8226;   <a href="#encodings.mac_roman">encodings.mac_roman</a>
+ &#8226;   <a href="#encodings.mac_romanian">encodings.mac_romanian</a>
+ &#8226;   <a href="#encodings.mac_turkish">encodings.mac_turkish</a>
+ &#8226;   <a href="#encodings.mbcs">encodings.mbcs</a>
+ &#8226;   <a href="#encodings.oem">encodings.oem</a>
+ &#8226;   <a href="#encodings.palmos">encodings.palmos</a>
+ &#8226;   <a href="#encodings.ptcp154">encodings.ptcp154</a>
+ &#8226;   <a href="#encodings.punycode">encodings.punycode</a>
+ &#8226;   <a href="#encodings.quopri_codec">encodings.quopri_codec</a>
+ &#8226;   <a href="#encodings.raw_unicode_escape">encodings.raw_unicode_escape</a>
+ &#8226;   <a href="#encodings.rot_13">encodings.rot_13</a>
+ &#8226;   <a href="#encodings.shift_jis">encodings.shift_jis</a>
+ &#8226;   <a href="#encodings.shift_jis_2004">encodings.shift_jis_2004</a>
+ &#8226;   <a href="#encodings.shift_jisx0213">encodings.shift_jisx0213</a>
+ &#8226;   <a href="#encodings.tis_620">encodings.tis_620</a>
+ &#8226;   <a href="#encodings.undefined">encodings.undefined</a>
+ &#8226;   <a href="#encodings.unicode_escape">encodings.unicode_escape</a>
+ &#8226;   <a href="#encodings.utf_16">encodings.utf_16</a>
+ &#8226;   <a href="#encodings.utf_16_be">encodings.utf_16_be</a>
+ &#8226;   <a href="#encodings.utf_16_le">encodings.utf_16_le</a>
+ &#8226;   <a href="#encodings.utf_32">encodings.utf_32</a>
+ &#8226;   <a href="#encodings.utf_32_be">encodings.utf_32_be</a>
+ &#8226;   <a href="#encodings.utf_32_le">encodings.utf_32_le</a>
+ &#8226;   <a href="#encodings.utf_7">encodings.utf_7</a>
+ &#8226;   <a href="#encodings.utf_8">encodings.utf_8</a>
+ &#8226;   <a href="#encodings.utf_8_sig">encodings.utf_8_sig</a>
+ &#8226;   <a href="#encodings.uu_codec">encodings.uu_codec</a>
+ &#8226;   <a href="#encodings.zlib_codec">encodings.zlib_codec</a>
+ &#8226;   <a href="#locale">locale</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="encodings.aliases"></a>
+  <a target="code" href="///C:/Python312/Lib/encodings/aliases.py" type="text/plain"><tt>encodings.aliases</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#encodings">encodings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#encodings">encodings</a>
+ &#8226;   <a href="#locale">locale</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="encodings.ascii"></a>
+  <a target="code" href="///C:/Python312/Lib/encodings/ascii.py" type="text/plain"><tt>encodings.ascii</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#codecs">codecs</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="encodings.base64_codec"></a>
+  <a target="code" href="///C:/Python312/Lib/encodings/base64_codec.py" type="text/plain"><tt>encodings.base64_codec</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#base64">base64</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="encodings.big5"></a>
+  <a target="code" href="///C:/Python312/Lib/encodings/big5.py" type="text/plain"><tt>encodings.big5</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_codecs_tw">_codecs_tw</a>
+ &#8226;   <a href="#_multibytecodec">_multibytecodec</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="encodings.big5hkscs"></a>
+  <a target="code" href="///C:/Python312/Lib/encodings/big5hkscs.py" type="text/plain"><tt>encodings.big5hkscs</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_codecs_hk">_codecs_hk</a>
+ &#8226;   <a href="#_multibytecodec">_multibytecodec</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="encodings.bz2_codec"></a>
+  <a target="code" href="///C:/Python312/Lib/encodings/bz2_codec.py" type="text/plain"><tt>encodings.bz2_codec</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#bz2">bz2</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="encodings.charmap"></a>
+  <a target="code" href="///C:/Python312/Lib/encodings/charmap.py" type="text/plain"><tt>encodings.charmap</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#codecs">codecs</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#encodings">encodings</a>
+
+  </div>
+
+</div>
+
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+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#stringprep">stringprep</a>
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+ &#8226;   <a href="#_multibytecodec">_multibytecodec</a>
+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#_multibytecodec">_multibytecodec</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#encodings">encodings</a>
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+ &#8226;   <a href="#_multibytecodec">_multibytecodec</a>
+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#_multibytecodec">_multibytecodec</a>
+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#_multibytecodec">_multibytecodec</a>
+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#_multibytecodec">_multibytecodec</a>
+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#_multibytecodec">_multibytecodec</a>
+ &#8226;   <a href="#codecs">codecs</a>
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+ &#8226;   <a href="#http">http</a>
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+ &#8226;   <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.metadata">importlib.metadata</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.machinery"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/machinery.py" type="text/plain"><tt>importlib.machinery</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib._bootstrap">importlib._bootstrap</a>
+ &#8226;   <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.abc">importlib.abc</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#py_compile">py_compile</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#runpy">runpy</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.metadata"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/metadata/__init__.py" type="text/plain"><tt>importlib.metadata</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#abc">abc</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#csv">csv</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib.abc">importlib.abc</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._adapters">importlib.metadata._adapters</a>
+ &#8226;   <a href="#importlib.metadata._collections">importlib.metadata._collections</a>
+ &#8226;   <a href="#importlib.metadata._functools">importlib.metadata._functools</a>
+ &#8226;   <a href="#importlib.metadata._itertools">importlib.metadata._itertools</a>
+ &#8226;   <a href="#importlib.metadata._meta">importlib.metadata._meta</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#textwrap">textwrap</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._adapters">importlib.metadata._adapters</a>
+ &#8226;   <a href="#importlib.metadata._collections">importlib.metadata._collections</a>
+ &#8226;   <a href="#importlib.metadata._functools">importlib.metadata._functools</a>
+ &#8226;   <a href="#importlib.metadata._itertools">importlib.metadata._itertools</a>
+ &#8226;   <a href="#importlib.metadata._meta">importlib.metadata._meta</a>
+ &#8226;   <a href="#importlib.metadata._text">importlib.metadata._text</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.metadata._adapters"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/metadata/_adapters.py" type="text/plain"><tt>importlib.metadata._adapters</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#email.message">email.message</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._text">importlib.metadata._text</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#textwrap">textwrap</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.metadata">importlib.metadata</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.metadata._collections"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/metadata/_collections.py" type="text/plain"><tt>importlib.metadata._collections</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.metadata">importlib.metadata</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.metadata._functools"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/metadata/_functools.py" type="text/plain"><tt>importlib.metadata._functools</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._text">importlib.metadata._text</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.metadata._itertools"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/metadata/_itertools.py" type="text/plain"><tt>importlib.metadata._itertools</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#itertools">itertools</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.metadata">importlib.metadata</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.metadata._meta"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/metadata/_meta.py" type="text/plain"><tt>importlib.metadata._meta</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#typing">typing</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.metadata">importlib.metadata</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.metadata._text"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/metadata/_text.py" type="text/plain"><tt>importlib.metadata._text</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._functools">importlib.metadata._functools</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.metadata._adapters">importlib.metadata._adapters</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.readers"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/readers.py" type="text/plain"><tt>importlib.readers</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib.resources.readers">importlib.resources.readers</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.resources"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/resources/__init__.py" type="text/plain"><tt>importlib.resources</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#importlib.resources._legacy">importlib.resources._legacy</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.abc">importlib.abc</a>
+ &#8226;   <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#importlib.resources._adapters">importlib.resources._adapters</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#importlib.resources._itertools">importlib.resources._itertools</a>
+ &#8226;   <a href="#importlib.resources._legacy">importlib.resources._legacy</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#importlib.resources.readers">importlib.resources.readers</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.resources._adapters"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/resources/_adapters.py" type="text/plain"><tt>importlib.resources._adapters</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#io">io</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.resources._common">importlib.resources._common</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.resources._common"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/resources/_common.py" type="text/plain"><tt>importlib.resources._common</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#importlib.resources._adapters">importlib.resources._adapters</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#importlib.resources._legacy">importlib.resources._legacy</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.resources._itertools"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/resources/_itertools.py" type="text/plain"><tt>importlib.resources._itertools</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#importlib.resources">importlib.resources</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.resources.readers">importlib.resources.readers</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.resources._legacy"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/resources/_legacy.py" type="text/plain"><tt>importlib.resources._legacy</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.resources">importlib.resources</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.resources.abc"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/resources/abc.py" type="text/plain"><tt>importlib.resources.abc</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#abc">abc</a>
+ &#8226;   <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#typing">typing</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.abc">importlib.abc</a>
+ &#8226;   <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#importlib.resources._adapters">importlib.resources._adapters</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#importlib.resources.readers">importlib.resources.readers</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.resources.readers"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/resources/readers.py" type="text/plain"><tt>importlib.resources.readers</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#importlib.resources">importlib.resources</a>
+ &#8226;   <a href="#importlib.resources._itertools">importlib.resources._itertools</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.readers">importlib.readers</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="importlib.util"></a>
+  <a target="code" href="///C:/Python312/Lib/importlib/util.py" type="text/plain"><tt>importlib.util</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_imp">_imp</a>
+ &#8226;   <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib._abc">importlib._abc</a>
+ &#8226;   <a href="#importlib._bootstrap">importlib._bootstrap</a>
+ &#8226;   <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#numpy.testing._private.extbuild">numpy.testing._private.extbuild</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#py_compile">py_compile</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#runpy">runpy</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="inspect"></a>
+  <a target="code" href="///C:/Python312/Lib/inspect.py" type="text/plain"><tt>inspect</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#abc">abc</a>
+ &#8226;   <a href="#argparse">argparse</a>
+ &#8226;   <a href="#ast">ast</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#dis">dis</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib.machinery">importlib.machinery</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#keyword">keyword</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#token">token</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#ast">ast</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#bdb">bdb</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#doctest">doctest</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#numpy._core._internal">numpy._core._internal</a>
+ &#8226;   <a href="#numpy._core.function_base">numpy._core.function_base</a>
+ &#8226;   <a href="#numpy._core.overrides">numpy._core.overrides</a>
+ &#8226;   <a href="#numpy.lib._utils_impl">numpy.lib._utils_impl</a>
+ &#8226;   <a href="#numpy.ma.core">numpy.ma.core</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#pyi_rth_inspect.py">pyi_rth_inspect.py</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#unittest.async_case">unittest.async_case</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="io"></a>
+  <a target="code" href="///C:/Python312/Lib/io.py" type="text/plain"><tt>io</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_io">_io</a>
+ &#8226;   <a href="#abc">abc</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_compression">_compression</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#bz2">bz2</a>
+ &#8226;   <a href="#csv">csv</a>
+ &#8226;   <a href="#dis">dis</a>
+ &#8226;   <a href="#doctest">doctest</a>
+ &#8226;   <a href="#email.feedparser">email.feedparser</a>
+ &#8226;   <a href="#email.generator">email.generator</a>
+ &#8226;   <a href="#email.iterators">email.iterators</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#email.parser">email.parser</a>
+ &#8226;   <a href="#encodings.quopri_codec">encodings.quopri_codec</a>
+ &#8226;   <a href="#encodings.uu_codec">encodings.uu_codec</a>
+ &#8226;   <a href="#fileinput">fileinput</a>
+ &#8226;   <a href="#getpass">getpass</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#http.client">http.client</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#importlib.resources._adapters">importlib.resources._adapters</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#lzma">lzma</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_posix">multiprocessing.popen_spawn_posix</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#numpy.lib._format_impl">numpy.lib._format_impl</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#pickle">pickle</a>
+ &#8226;   <a href="#pprint">pprint</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#quopri">quopri</a>
+ &#8226;   <a href="#runpy">runpy</a>
+ &#8226;   <a href="#shlex">shlex</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#socketserver">socketserver</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#unittest.result">unittest.result</a>
+ &#8226;   <a href="#urllib.error">urllib.error</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+ &#8226;   <a href="#xmlrpc.client">xmlrpc.client</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+ &#8226;   <a href="#zipfile._path">zipfile._path</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="ipaddress"></a>
+  <a target="code" href="///C:/Python312/Lib/ipaddress.py" type="text/plain"><tt>ipaddress</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#functools">functools</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#urllib.parse">urllib.parse</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="itertools"></a>
+  <tt>itertools</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#_pydecimal">_pydecimal</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#calendar">calendar</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#concurrent.futures.thread">concurrent.futures.thread</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#glob">glob</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._itertools">importlib.metadata._itertools</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#importlib.resources.readers">importlib.resources.readers</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#numpy._core.einsumfunc">numpy._core.einsumfunc</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.shape_base">numpy._core.shape_base</a>
+ &#8226;   <a href="#numpy.f2py._backends._meson">numpy.f2py._backends._meson</a>
+ &#8226;   <a href="#numpy.lib._index_tricks_impl">numpy.lib._index_tricks_impl</a>
+ &#8226;   <a href="#numpy.lib._iotools">numpy.lib._iotools</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#numpy.lib.recfunctions">numpy.lib.recfunctions</a>
+ &#8226;   <a href="#numpy.ma.extras">numpy.ma.extras</a>
+ &#8226;   <a href="#pickle">pickle</a>
+ &#8226;   <a href="#platform">platform</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+ &#8226;   <a href="#statistics">statistics</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#weakref">weakref</a>
+ &#8226;   <a href="#zipfile._path">zipfile._path</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="java"></a>
+  <a target="code" href="" type="text/plain"><tt>java</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#platform">platform</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="json"></a>
+  <a target="code" href="///C:/Python312/Lib/json/__init__.py" type="text/plain"><tt>json</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#codecs">codecs</a>
+ &#8226;   <a href="#json.decoder">json.decoder</a>
+ &#8226;   <a href="#json.encoder">json.encoder</a>
+ &#8226;   <a href="#json.scanner">json.scanner</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#json.decoder">json.decoder</a>
+ &#8226;   <a href="#json.encoder">json.encoder</a>
+ &#8226;   <a href="#json.scanner">json.scanner</a>
+ &#8226;   <a href="#numpy.__config__">numpy.__config__</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="json.decoder"></a>
+  <a target="code" href="///C:/Python312/Lib/json/decoder.py" type="text/plain"><tt>json.decoder</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_json">_json</a>
+ &#8226;   <a href="#json">json</a>
+ &#8226;   <a href="#json.scanner">json.scanner</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_json">_json</a>
+ &#8226;   <a href="#json">json</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="json.encoder"></a>
+  <a target="code" href="///C:/Python312/Lib/json/encoder.py" type="text/plain"><tt>json.encoder</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_json">_json</a>
+ &#8226;   <a href="#json">json</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#json">json</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="json.scanner"></a>
+  <a target="code" href="///C:/Python312/Lib/json/scanner.py" type="text/plain"><tt>json.scanner</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_json">_json</a>
+ &#8226;   <a href="#json">json</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#json">json</a>
+ &#8226;   <a href="#json.decoder">json.decoder</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="keyword"></a>
+  <a target="code" href="///C:/Python312/Lib/keyword.py" type="text/plain"><tt>keyword</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#inspect">inspect</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="linecache"></a>
+  <a target="code" href="///C:/Python312/Lib/linecache.py" type="text/plain"><tt>linecache</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#functools">functools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#asyncio.base_tasks">asyncio.base_tasks</a>
+ &#8226;   <a href="#bdb">bdb</a>
+ &#8226;   <a href="#doctest">doctest</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#tracemalloc">tracemalloc</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="locale"></a>
+  <a target="code" href="///C:/Python312/Lib/locale.py" type="text/plain"><tt>locale</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#_locale">_locale</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#encodings">encodings</a>
+ &#8226;   <a href="#encodings.aliases">encodings.aliases</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_pydecimal">_pydecimal</a>
+ &#8226;   <a href="#_strptime">_strptime</a>
+ &#8226;   <a href="#calendar">calendar</a>
+ &#8226;   <a href="#gettext">gettext</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#tkinter.filedialog">tkinter.filedialog</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="logging"></a>
+  <a target="code" href="///C:/Python312/Lib/logging/__init__.py" type="text/plain"><tt>logging</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#atexit">atexit</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pickle">pickle</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#string">string</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.log">asyncio.log</a>
+ &#8226;   <a href="#concurrent.futures._base">concurrent.futures._base</a>
+ &#8226;   <a href="#hashlib">hashlib</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#unittest._log">unittest._log</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="lzma"></a>
+  <a target="code" href="///C:/Python312/Lib/lzma.py" type="text/plain"><tt>lzma</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_compression">_compression</a>
+ &#8226;   <a href="#_lzma">_lzma</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#numpy.lib._datasource">numpy.lib._datasource</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="marshal"></a>
+  <tt>marshal</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="math"></a>
+  <tt>math</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#_pydatetime">_pydatetime</a>
+ &#8226;   <a href="#_pydecimal">_pydecimal</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#fractions">fractions</a>
+ &#8226;   <a href="#numpy._core._internal">numpy._core._internal</a>
+ &#8226;   <a href="#numpy._core.fromnumeric">numpy._core.fromnumeric</a>
+ &#8226;   <a href="#numpy._core.getlimits">numpy._core.getlimits</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy.f2py.symbolic">numpy.f2py.symbolic</a>
+ &#8226;   <a href="#numpy.lib._index_tricks_impl">numpy.lib._index_tricks_impl</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#statistics">statistics</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="mimetypes"></a>
+  <a target="code" href="///C:/Python312/Lib/mimetypes.py" type="text/plain"><tt>mimetypes</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#getopt">getopt</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+ &#8226;   <a href="#winreg">winreg</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#http.server">http.server</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="mmap"></a>
+  <tt>mmap</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+ &#8226;   <a href="#numpy._core.memmap">numpy._core.memmap</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="msvcrt"></a>
+  <tt>msvcrt</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#getpass">getpass</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/__init__.py" type="text/plain"><tt>multiprocessing</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.AuthenticationError">multiprocessing.AuthenticationError</a>
+ &#8226;   <a href="#multiprocessing.BufferTooShort">multiprocessing.BufferTooShort</a>
+ &#8226;   <a href="#multiprocessing.TimeoutError">multiprocessing.TimeoutError</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.get_context">multiprocessing.get_context</a>
+ &#8226;   <a href="#multiprocessing.get_start_method">multiprocessing.get_start_method</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.set_start_method">multiprocessing.set_start_method</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
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+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.dummy">multiprocessing.dummy</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.popen_fork">multiprocessing.popen_fork</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_posix">multiprocessing.popen_spawn_posix</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+ &#8226;   <a href="#multiprocessing.sharedctypes">multiprocessing.sharedctypes</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#pyi_rth_multiprocessing.py">pyi_rth_multiprocessing.py</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>multiprocessing.AuthenticationError</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.BufferTooShort"></a>
+  <a target="code" href="" type="text/plain"><tt>multiprocessing.BufferTooShort</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.TimeoutError"></a>
+  <a target="code" href="" type="text/plain"><tt>multiprocessing.TimeoutError</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.connection"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/connection.py" type="text/plain"><tt>multiprocessing.connection</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_multiprocessing">_multiprocessing</a>
+ &#8226;   <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#hmac">hmac</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.AuthenticationError">multiprocessing.AuthenticationError</a>
+ &#8226;   <a href="#multiprocessing.BufferTooShort">multiprocessing.BufferTooShort</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#xmlrpc.client">xmlrpc.client</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.popen_fork">multiprocessing.popen_fork</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.context"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/context.py" type="text/plain"><tt>multiprocessing.context</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.popen_fork">multiprocessing.popen_fork</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_posix">multiprocessing.popen_spawn_posix</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#multiprocessing.sharedctypes">multiprocessing.sharedctypes</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_posix">multiprocessing.popen_spawn_posix</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.sharedctypes">multiprocessing.sharedctypes</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.dummy"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/dummy/__init__.py" type="text/plain"><tt>multiprocessing.dummy</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#array">array</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.dummy.connection">multiprocessing.dummy.connection</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.dummy.connection">multiprocessing.dummy.connection</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.dummy.connection"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/dummy/connection.py" type="text/plain"><tt>multiprocessing.dummy.connection</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#multiprocessing.dummy">multiprocessing.dummy</a>
+ &#8226;   <a href="#queue">queue</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.dummy">multiprocessing.dummy</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.forkserver"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/forkserver.py" type="text/plain"><tt>multiprocessing.forkserver</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#errno">errno</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.get_context"></a>
+  <a target="code" href="" type="text/plain"><tt>multiprocessing.get_context</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.sharedctypes">multiprocessing.sharedctypes</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.get_start_method"></a>
+  <a target="code" href="" type="text/plain"><tt>multiprocessing.get_start_method</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.heap"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/heap.py" type="text/plain"><tt>multiprocessing.heap</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#bisect">bisect</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#mmap">mmap</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#threading">threading</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.sharedctypes">multiprocessing.sharedctypes</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.managers"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/managers.py" type="text/plain"><tt>multiprocessing.managers</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#array">array</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.get_context">multiprocessing.get_context</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.context">multiprocessing.context</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.pool"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/pool.py" type="text/plain"><tt>multiprocessing.pool</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.TimeoutError">multiprocessing.TimeoutError</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.dummy">multiprocessing.dummy</a>
+ &#8226;   <a href="#multiprocessing.get_context">multiprocessing.get_context</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.dummy">multiprocessing.dummy</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.popen_fork"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/popen_fork.py" type="text/plain"><tt>multiprocessing.popen_fork</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#signal">signal</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_posix">multiprocessing.popen_spawn_posix</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.popen_forkserver"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/popen_forkserver.py" type="text/plain"><tt>multiprocessing.popen_forkserver</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#io">io</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_fork">multiprocessing.popen_fork</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.context">multiprocessing.context</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.popen_spawn_posix"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/popen_spawn_posix.py" type="text/plain"><tt>multiprocessing.popen_spawn_posix</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#io">io</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.popen_fork">multiprocessing.popen_fork</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.context">multiprocessing.context</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.popen_spawn_win32"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/popen_spawn_win32.py" type="text/plain"><tt>multiprocessing.popen_spawn_win32</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#msvcrt">msvcrt</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.context">multiprocessing.context</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.process"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/process.py" type="text/plain"><tt>multiprocessing.process</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_weakrefset">_weakrefset</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#traceback">traceback</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.queues"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/queues.py" type="text/plain"><tt>multiprocessing.queues</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_multiprocessing">_multiprocessing</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.reduction"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/reduction.py" type="text/plain"><tt>multiprocessing.reduction</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#abc">abc</a>
+ &#8226;   <a href="#array">array</a>
+ &#8226;   <a href="#copyreg">copyreg</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pickle">pickle</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.resource_sharer"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/resource_sharer.py" type="text/plain"><tt>multiprocessing.resource_sharer</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.resource_tracker"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/resource_tracker.py" type="text/plain"><tt>multiprocessing.resource_tracker</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_multiprocessing">_multiprocessing</a>
+ &#8226;   <a href="#_posixshmem">_posixshmem</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_posix">multiprocessing.popen_spawn_posix</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.set_start_method"></a>
+  <a target="code" href="" type="text/plain"><tt>multiprocessing.set_start_method</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.shared_memory"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/shared_memory.py" type="text/plain"><tt>multiprocessing.shared_memory</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_posixshmem">_posixshmem</a>
+ &#8226;   <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#mmap">mmap</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#secrets">secrets</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.sharedctypes"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/sharedctypes.py" type="text/plain"><tt>multiprocessing.sharedctypes</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#ctypes">ctypes</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.get_context">multiprocessing.get_context</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.context">multiprocessing.context</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.spawn"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/spawn.py" type="text/plain"><tt>multiprocessing.spawn</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#msvcrt">msvcrt</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.get_start_method">multiprocessing.get_start_method</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.set_start_method">multiprocessing.set_start_method</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#runpy">runpy</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_posix">multiprocessing.popen_spawn_posix</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#pyi_rth_multiprocessing.py">pyi_rth_multiprocessing.py</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.synchronize"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/synchronize.py" type="text/plain"><tt>multiprocessing.synchronize</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_multiprocessing">_multiprocessing</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="multiprocessing.util"></a>
+  <a target="code" href="///C:/Python312/Lib/multiprocessing/util.py" type="text/plain"><tt>multiprocessing.util</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_posixsubprocess">_posixsubprocess</a>
+ &#8226;   <a href="#atexit">atexit</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.popen_fork">multiprocessing.popen_fork</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_posix">multiprocessing.popen_spawn_posix</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="netrc"></a>
+  <a target="code" href="///C:/Python312/Lib/netrc.py" type="text/plain"><tt>netrc</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#os">os</a>
+ &#8226;   <a href="#pwd">pwd</a>
+ &#8226;   <a href="#stat">stat</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#ftplib">ftplib</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="nt"></a>
+  <tt>nt</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#ctypes">ctypes</a>
+ &#8226;   <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#ntpath">ntpath</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#shutil">shutil</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="ntpath"></a>
+  <a target="code" href="///C:/Python312/Lib/ntpath.py" type="text/plain"><tt>ntpath</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#genericpath">genericpath</a>
+ &#8226;   <a href="#nt">nt</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#stat">stat</a>
+ &#8226;   <a href="#string">string</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#os.path">os.path</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="nturl2path"></a>
+  <a target="code" href="///C:/Python312/Lib/nturl2path.py" type="text/plain"><tt>nturl2path</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#string">string</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="numbers"></a>
+  <a target="code" href="///C:/Python312/Lib/numbers.py" type="text/plain"><tt>numbers</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#abc">abc</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_pydecimal">_pydecimal</a>
+ &#8226;   <a href="#fractions">fractions</a>
+ &#8226;   <a href="#numpy._core.arrayprint">numpy._core.arrayprint</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.numerictypes">numpy._core.numerictypes</a>
+ &#8226;   <a href="#numpy.polynomial._polybase">numpy.polynomial._polybase</a>
+ &#8226;   <a href="#statistics">statistics</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="numpy"></a>
+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/.build_venv/Lib/site-packages/numpy/__init__.py" type="text/plain"><tt>numpy</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy.__config__">numpy.__config__</a>
+ &#8226;   <a href="#numpy._array_api_info">numpy._array_api_info</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy._core._dtype_ctypes">numpy._core._dtype_ctypes</a>
+ &#8226;   <a href="#numpy._core._exceptions">numpy._core._exceptions</a>
+ &#8226;   <a href="#numpy._core._multiarray_tests">numpy._core._multiarray_tests</a>
+ &#8226;   <a href="#numpy._core.add">numpy._core.add</a>
+ &#8226;   <a href="#numpy._core.all">numpy._core.all</a>
+ &#8226;   <a href="#numpy._core.amax">numpy._core.amax</a>
+ &#8226;   <a href="#numpy._core.amin">numpy._core.amin</a>
+ &#8226;   <a href="#numpy._core.arccos">numpy._core.arccos</a>
+ &#8226;   <a href="#numpy._core.arccosh">numpy._core.arccosh</a>
+ &#8226;   <a href="#numpy._core.arcsin">numpy._core.arcsin</a>
+ &#8226;   <a href="#numpy._core.arcsinh">numpy._core.arcsinh</a>
+ &#8226;   <a href="#numpy._core.arctan">numpy._core.arctan</a>
+ &#8226;   <a href="#numpy._core.arctan2">numpy._core.arctan2</a>
+ &#8226;   <a href="#numpy._core.arctanh">numpy._core.arctanh</a>
+ &#8226;   <a href="#numpy._core.argsort">numpy._core.argsort</a>
+ &#8226;   <a href="#numpy._core.array">numpy._core.array</a>
+ &#8226;   <a href="#numpy._core.asanyarray">numpy._core.asanyarray</a>
+ &#8226;   <a href="#numpy._core.asarray">numpy._core.asarray</a>
+ &#8226;   <a href="#numpy._core.atleast_1d">numpy._core.atleast_1d</a>
+ &#8226;   <a href="#numpy._core.atleast_2d">numpy._core.atleast_2d</a>
+ &#8226;   <a href="#numpy._core.atleast_3d">numpy._core.atleast_3d</a>
+ &#8226;   <a href="#numpy._core.bitwise_and">numpy._core.bitwise_and</a>
+ &#8226;   <a href="#numpy._core.bitwise_count">numpy._core.bitwise_count</a>
+ &#8226;   <a href="#numpy._core.bitwise_or">numpy._core.bitwise_or</a>
+ &#8226;   <a href="#numpy._core.bitwise_xor">numpy._core.bitwise_xor</a>
+ &#8226;   <a href="#numpy._core.bool_">numpy._core.bool_</a>
+ &#8226;   <a href="#numpy._core.byte">numpy._core.byte</a>
+ &#8226;   <a href="#numpy._core.bytes_">numpy._core.bytes_</a>
+ &#8226;   <a href="#numpy._core.cbrt">numpy._core.cbrt</a>
+ &#8226;   <a href="#numpy._core.cdouble">numpy._core.cdouble</a>
+ &#8226;   <a href="#numpy._core.ceil">numpy._core.ceil</a>
+ &#8226;   <a href="#numpy._core.character">numpy._core.character</a>
+ &#8226;   <a href="#numpy._core.clongdouble">numpy._core.clongdouble</a>
+ &#8226;   <a href="#numpy._core.complex64">numpy._core.complex64</a>
+ &#8226;   <a href="#numpy._core.complexfloating">numpy._core.complexfloating</a>
+ &#8226;   <a href="#numpy._core.conj">numpy._core.conj</a>
+ &#8226;   <a href="#numpy._core.conjugate">numpy._core.conjugate</a>
+ &#8226;   <a href="#numpy._core.copysign">numpy._core.copysign</a>
+ &#8226;   <a href="#numpy._core.cos">numpy._core.cos</a>
+ &#8226;   <a href="#numpy._core.cosh">numpy._core.cosh</a>
+ &#8226;   <a href="#numpy._core.count_nonzero">numpy._core.count_nonzero</a>
+ &#8226;   <a href="#numpy._core.cross">numpy._core.cross</a>
+ &#8226;   <a href="#numpy._core.csingle">numpy._core.csingle</a>
+ &#8226;   <a href="#numpy._core.datetime64">numpy._core.datetime64</a>
+ &#8226;   <a href="#numpy._core.deg2rad">numpy._core.deg2rad</a>
+ &#8226;   <a href="#numpy._core.degrees">numpy._core.degrees</a>
+ &#8226;   <a href="#numpy._core.diagonal">numpy._core.diagonal</a>
+ &#8226;   <a href="#numpy._core.divide">numpy._core.divide</a>
+ &#8226;   <a href="#numpy._core.divmod">numpy._core.divmod</a>
+ &#8226;   <a href="#numpy._core.dot">numpy._core.dot</a>
+ &#8226;   <a href="#numpy._core.double">numpy._core.double</a>
+ &#8226;   <a href="#numpy._core.e">numpy._core.e</a>
+ &#8226;   <a href="#numpy._core.empty">numpy._core.empty</a>
+ &#8226;   <a href="#numpy._core.empty_like">numpy._core.empty_like</a>
+ &#8226;   <a href="#numpy._core.equal">numpy._core.equal</a>
+ &#8226;   <a href="#numpy._core.errstate">numpy._core.errstate</a>
+ &#8226;   <a href="#numpy._core.euler_gamma">numpy._core.euler_gamma</a>
+ &#8226;   <a href="#numpy._core.exp">numpy._core.exp</a>
+ &#8226;   <a href="#numpy._core.exp2">numpy._core.exp2</a>
+ &#8226;   <a href="#numpy._core.expm1">numpy._core.expm1</a>
+ &#8226;   <a href="#numpy._core.fabs">numpy._core.fabs</a>
+ &#8226;   <a href="#numpy._core.finfo">numpy._core.finfo</a>
+ &#8226;   <a href="#numpy._core.float16">numpy._core.float16</a>
+ &#8226;   <a href="#numpy._core.float32">numpy._core.float32</a>
+ &#8226;   <a href="#numpy._core.float_power">numpy._core.float_power</a>
+ &#8226;   <a href="#numpy._core.floating">numpy._core.floating</a>
+ &#8226;   <a href="#numpy._core.floor">numpy._core.floor</a>
+ &#8226;   <a href="#numpy._core.floor_divide">numpy._core.floor_divide</a>
+ &#8226;   <a href="#numpy._core.fmax">numpy._core.fmax</a>
+ &#8226;   <a href="#numpy._core.fmin">numpy._core.fmin</a>
+ &#8226;   <a href="#numpy._core.fmod">numpy._core.fmod</a>
+ &#8226;   <a href="#numpy._core.frexp">numpy._core.frexp</a>
+ &#8226;   <a href="#numpy._core.frompyfunc">numpy._core.frompyfunc</a>
+ &#8226;   <a href="#numpy._core.gcd">numpy._core.gcd</a>
+ &#8226;   <a href="#numpy._core.greater">numpy._core.greater</a>
+ &#8226;   <a href="#numpy._core.greater_equal">numpy._core.greater_equal</a>
+ &#8226;   <a href="#numpy._core.half">numpy._core.half</a>
+ &#8226;   <a href="#numpy._core.heaviside">numpy._core.heaviside</a>
+ &#8226;   <a href="#numpy._core.hstack">numpy._core.hstack</a>
+ &#8226;   <a href="#numpy._core.hypot">numpy._core.hypot</a>
+ &#8226;   <a href="#numpy._core.iinfo">numpy._core.iinfo</a>
+ &#8226;   <a href="#numpy._core.inexact">numpy._core.inexact</a>
+ &#8226;   <a href="#numpy._core.inf">numpy._core.inf</a>
+ &#8226;   <a href="#numpy._core.int16">numpy._core.int16</a>
+ &#8226;   <a href="#numpy._core.int32">numpy._core.int32</a>
+ &#8226;   <a href="#numpy._core.int64">numpy._core.int64</a>
+ &#8226;   <a href="#numpy._core.int8">numpy._core.int8</a>
+ &#8226;   <a href="#numpy._core.intc">numpy._core.intc</a>
+ &#8226;   <a href="#numpy._core.integer">numpy._core.integer</a>
+ &#8226;   <a href="#numpy._core.intp">numpy._core.intp</a>
+ &#8226;   <a href="#numpy._core.isfinite">numpy._core.isfinite</a>
+ &#8226;   <a href="#numpy._core.isnan">numpy._core.isnan</a>
+ &#8226;   <a href="#numpy._core.isscalar">numpy._core.isscalar</a>
+ &#8226;   <a href="#numpy._core.lcm">numpy._core.lcm</a>
+ &#8226;   <a href="#numpy._core.ldexp">numpy._core.ldexp</a>
+ &#8226;   <a href="#numpy._core.left_shift">numpy._core.left_shift</a>
+ &#8226;   <a href="#numpy._core.less">numpy._core.less</a>
+ &#8226;   <a href="#numpy._core.less_equal">numpy._core.less_equal</a>
+ &#8226;   <a href="#numpy._core.linspace">numpy._core.linspace</a>
+ &#8226;   <a href="#numpy._core.log">numpy._core.log</a>
+ &#8226;   <a href="#numpy._core.log10">numpy._core.log10</a>
+ &#8226;   <a href="#numpy._core.log1p">numpy._core.log1p</a>
+ &#8226;   <a href="#numpy._core.log2">numpy._core.log2</a>
+ &#8226;   <a href="#numpy._core.logaddexp">numpy._core.logaddexp</a>
+ &#8226;   <a href="#numpy._core.logaddexp2">numpy._core.logaddexp2</a>
+ &#8226;   <a href="#numpy._core.logical_and">numpy._core.logical_and</a>
+ &#8226;   <a href="#numpy._core.logical_not">numpy._core.logical_not</a>
+ &#8226;   <a href="#numpy._core.logical_or">numpy._core.logical_or</a>
+ &#8226;   <a href="#numpy._core.logical_xor">numpy._core.logical_xor</a>
+ &#8226;   <a href="#numpy._core.long">numpy._core.long</a>
+ &#8226;   <a href="#numpy._core.longdouble">numpy._core.longdouble</a>
+ &#8226;   <a href="#numpy._core.longlong">numpy._core.longlong</a>
+ &#8226;   <a href="#numpy._core.matmul">numpy._core.matmul</a>
+ &#8226;   <a href="#numpy._core.matrix_transpose">numpy._core.matrix_transpose</a>
+ &#8226;   <a href="#numpy._core.matvec">numpy._core.matvec</a>
+ &#8226;   <a href="#numpy._core.maximum">numpy._core.maximum</a>
+ &#8226;   <a href="#numpy._core.memmap">numpy._core.memmap</a>
+ &#8226;   <a href="#numpy._core.minimum">numpy._core.minimum</a>
+ &#8226;   <a href="#numpy._core.mod">numpy._core.mod</a>
+ &#8226;   <a href="#numpy._core.modf">numpy._core.modf</a>
+ &#8226;   <a href="#numpy._core.moveaxis">numpy._core.moveaxis</a>
+ &#8226;   <a href="#numpy._core.multiply">numpy._core.multiply</a>
+ &#8226;   <a href="#numpy._core.ndarray">numpy._core.ndarray</a>
+ &#8226;   <a href="#numpy._core.negative">numpy._core.negative</a>
+ &#8226;   <a href="#numpy._core.newaxis">numpy._core.newaxis</a>
+ &#8226;   <a href="#numpy._core.nextafter">numpy._core.nextafter</a>
+ &#8226;   <a href="#numpy._core.not_equal">numpy._core.not_equal</a>
+ &#8226;   <a href="#numpy._core.number">numpy._core.number</a>
+ &#8226;   <a href="#numpy._core.object_">numpy._core.object_</a>
+ &#8226;   <a href="#numpy._core.ones">numpy._core.ones</a>
+ &#8226;   <a href="#numpy._core.outer">numpy._core.outer</a>
+ &#8226;   <a href="#numpy._core.pi">numpy._core.pi</a>
+ &#8226;   <a href="#numpy._core.positive">numpy._core.positive</a>
+ &#8226;   <a href="#numpy._core.power">numpy._core.power</a>
+ &#8226;   <a href="#numpy._core.printoptions">numpy._core.printoptions</a>
+ &#8226;   <a href="#numpy._core.prod">numpy._core.prod</a>
+ &#8226;   <a href="#numpy._core.rad2deg">numpy._core.rad2deg</a>
+ &#8226;   <a href="#numpy._core.radians">numpy._core.radians</a>
+ &#8226;   <a href="#numpy._core.reciprocal">numpy._core.reciprocal</a>
+ &#8226;   <a href="#numpy._core.remainder">numpy._core.remainder</a>
+ &#8226;   <a href="#numpy._core.right_shift">numpy._core.right_shift</a>
+ &#8226;   <a href="#numpy._core.rint">numpy._core.rint</a>
+ &#8226;   <a href="#numpy._core.short">numpy._core.short</a>
+ &#8226;   <a href="#numpy._core.sign">numpy._core.sign</a>
+ &#8226;   <a href="#numpy._core.signbit">numpy._core.signbit</a>
+ &#8226;   <a href="#numpy._core.signedinteger">numpy._core.signedinteger</a>
+ &#8226;   <a href="#numpy._core.single">numpy._core.single</a>
+ &#8226;   <a href="#numpy._core.sinh">numpy._core.sinh</a>
+ &#8226;   <a href="#numpy._core.sort">numpy._core.sort</a>
+ &#8226;   <a href="#numpy._core.spacing">numpy._core.spacing</a>
+ &#8226;   <a href="#numpy._core.sqrt">numpy._core.sqrt</a>
+ &#8226;   <a href="#numpy._core.square">numpy._core.square</a>
+ &#8226;   <a href="#numpy._core.str_">numpy._core.str_</a>
+ &#8226;   <a href="#numpy._core.subtract">numpy._core.subtract</a>
+ &#8226;   <a href="#numpy._core.sum">numpy._core.sum</a>
+ &#8226;   <a href="#numpy._core.swapaxes">numpy._core.swapaxes</a>
+ &#8226;   <a href="#numpy._core.tan">numpy._core.tan</a>
+ &#8226;   <a href="#numpy._core.tanh">numpy._core.tanh</a>
+ &#8226;   <a href="#numpy._core.tensordot">numpy._core.tensordot</a>
+ &#8226;   <a href="#numpy._core.timedelta64">numpy._core.timedelta64</a>
+ &#8226;   <a href="#numpy._core.trace">numpy._core.trace</a>
+ &#8226;   <a href="#numpy._core.transpose">numpy._core.transpose</a>
+ &#8226;   <a href="#numpy._core.true_divide">numpy._core.true_divide</a>
+ &#8226;   <a href="#numpy._core.trunc">numpy._core.trunc</a>
+ &#8226;   <a href="#numpy._core.ubyte">numpy._core.ubyte</a>
+ &#8226;   <a href="#numpy._core.uint">numpy._core.uint</a>
+ &#8226;   <a href="#numpy._core.uint16">numpy._core.uint16</a>
+ &#8226;   <a href="#numpy._core.uint32">numpy._core.uint32</a>
+ &#8226;   <a href="#numpy._core.uint64">numpy._core.uint64</a>
+ &#8226;   <a href="#numpy._core.uintc">numpy._core.uintc</a>
+ &#8226;   <a href="#numpy._core.uintp">numpy._core.uintp</a>
+ &#8226;   <a href="#numpy._core.ulong">numpy._core.ulong</a>
+ &#8226;   <a href="#numpy._core.ulonglong">numpy._core.ulonglong</a>
+ &#8226;   <a href="#numpy._core.unsignedinteger">numpy._core.unsignedinteger</a>
+ &#8226;   <a href="#numpy._core.ushort">numpy._core.ushort</a>
+ &#8226;   <a href="#numpy._core.vecdot">numpy._core.vecdot</a>
+ &#8226;   <a href="#numpy._core.vecmat">numpy._core.vecmat</a>
+ &#8226;   <a href="#numpy._core.void">numpy._core.void</a>
+ &#8226;   <a href="#numpy._core.zeros">numpy._core.zeros</a>
+ &#8226;   <a href="#numpy._distributor_init">numpy._distributor_init</a>
+ &#8226;   <a href="#numpy._distributor_init_local">numpy._distributor_init_local</a>
+ &#8226;   <a href="#numpy._expired_attrs_2_0">numpy._expired_attrs_2_0</a>
+ &#8226;   <a href="#numpy._globals">numpy._globals</a>
+ &#8226;   <a href="#numpy._pytesttester">numpy._pytesttester</a>
+ &#8226;   <a href="#numpy.char">numpy.char</a>
+ &#8226;   <a href="#numpy.core">numpy.core</a>
+ &#8226;   <a href="#numpy.ctypeslib">numpy.ctypeslib</a>
+ &#8226;   <a href="#numpy.dtypes">numpy.dtypes</a>
+ &#8226;   <a href="#numpy.exceptions">numpy.exceptions</a>
+ &#8226;   <a href="#numpy.f2py">numpy.f2py</a>
+ &#8226;   <a href="#numpy.fft">numpy.fft</a>
+ &#8226;   <a href="#numpy.lib">numpy.lib</a>
+ &#8226;   <a href="#numpy.lib._arraypad_impl">numpy.lib._arraypad_impl</a>
+ &#8226;   <a href="#numpy.lib._arraysetops_impl">numpy.lib._arraysetops_impl</a>
+ &#8226;   <a href="#numpy.lib._function_base_impl">numpy.lib._function_base_impl</a>
+ &#8226;   <a href="#numpy.lib._histograms_impl">numpy.lib._histograms_impl</a>
+ &#8226;   <a href="#numpy.lib._index_tricks_impl">numpy.lib._index_tricks_impl</a>
+ &#8226;   <a href="#numpy.lib._nanfunctions_impl">numpy.lib._nanfunctions_impl</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#numpy.lib._polynomial_impl">numpy.lib._polynomial_impl</a>
+ &#8226;   <a href="#numpy.lib._shape_base_impl">numpy.lib._shape_base_impl</a>
+ &#8226;   <a href="#numpy.lib._stride_tricks_impl">numpy.lib._stride_tricks_impl</a>
+ &#8226;   <a href="#numpy.lib._twodim_base_impl">numpy.lib._twodim_base_impl</a>
+ &#8226;   <a href="#numpy.lib._type_check_impl">numpy.lib._type_check_impl</a>
+ &#8226;   <a href="#numpy.lib._ufunclike_impl">numpy.lib._ufunclike_impl</a>
+ &#8226;   <a href="#numpy.lib._utils_impl">numpy.lib._utils_impl</a>
+ &#8226;   <a href="#numpy.lib.scimath">numpy.lib.scimath</a>
+ &#8226;   <a href="#numpy.linalg">numpy.linalg</a>
+ &#8226;   <a href="#numpy.ma">numpy.ma</a>
+ &#8226;   <a href="#numpy.matlib">numpy.matlib</a>
+ &#8226;   <a href="#numpy.matrixlib">numpy.matrixlib</a>
+ &#8226;   <a href="#numpy.polynomial">numpy.polynomial</a>
+ &#8226;   <a href="#numpy.random">numpy.random</a>
+ &#8226;   <a href="#numpy.rec">numpy.rec</a>
+ &#8226;   <a href="#numpy.strings">numpy.strings</a>
+ &#8226;   <a href="#numpy.testing">numpy.testing</a>
+ &#8226;   <a href="#numpy.typing">numpy.typing</a>
+ &#8226;   <a href="#numpy.version">numpy.version</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#cv2">cv2</a>
+ &#8226;   <a href="#cv2.mat_wrapper">cv2.mat_wrapper</a>
+ &#8226;   <a href="#cv2.typing">cv2.typing</a>
+ &#8226;   <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy.__config__">numpy.__config__</a>
+ &#8226;   <a href="#numpy._array_api_info">numpy._array_api_info</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy._core._dtype">numpy._core._dtype</a>
+ &#8226;   <a href="#numpy._core._dtype_ctypes">numpy._core._dtype_ctypes</a>
+ &#8226;   <a href="#numpy._core._internal">numpy._core._internal</a>
+ &#8226;   <a href="#numpy._core._methods">numpy._core._methods</a>
+ &#8226;   <a href="#numpy._core.arrayprint">numpy._core.arrayprint</a>
+ &#8226;   <a href="#numpy._core.defchararray">numpy._core.defchararray</a>
+ &#8226;   <a href="#numpy._core.fromnumeric">numpy._core.fromnumeric</a>
+ &#8226;   <a href="#numpy._core.function_base">numpy._core.function_base</a>
+ &#8226;   <a href="#numpy._core.memmap">numpy._core.memmap</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.strings">numpy._core.strings</a>
+ &#8226;   <a href="#numpy._core.umath">numpy._core.umath</a>
+ &#8226;   <a href="#numpy._distributor_init">numpy._distributor_init</a>
+ &#8226;   <a href="#numpy._expired_attrs_2_0">numpy._expired_attrs_2_0</a>
+ &#8226;   <a href="#numpy._globals">numpy._globals</a>
+ &#8226;   <a href="#numpy._pytesttester">numpy._pytesttester</a>
+ &#8226;   <a href="#numpy._typing">numpy._typing</a>
+ &#8226;   <a href="#numpy._typing._array_like">numpy._typing._array_like</a>
+ &#8226;   <a href="#numpy._typing._dtype_like">numpy._typing._dtype_like</a>
+ &#8226;   <a href="#numpy._typing._scalars">numpy._typing._scalars</a>
+ &#8226;   <a href="#numpy._typing._ufunc">numpy._typing._ufunc</a>
+ &#8226;   <a href="#numpy._utils">numpy._utils</a>
+ &#8226;   <a href="#numpy.char">numpy.char</a>
+ &#8226;   <a href="#numpy.core">numpy.core</a>
+ &#8226;   <a href="#numpy.ctypeslib">numpy.ctypeslib</a>
+ &#8226;   <a href="#numpy.ctypeslib._ctypeslib">numpy.ctypeslib._ctypeslib</a>
+ &#8226;   <a href="#numpy.dtypes">numpy.dtypes</a>
+ &#8226;   <a href="#numpy.exceptions">numpy.exceptions</a>
+ &#8226;   <a href="#numpy.f2py">numpy.f2py</a>
+ &#8226;   <a href="#numpy.f2py.diagnose">numpy.f2py.diagnose</a>
+ &#8226;   <a href="#numpy.f2py.f90mod_rules">numpy.f2py.f90mod_rules</a>
+ &#8226;   <a href="#numpy.fft">numpy.fft</a>
+ &#8226;   <a href="#numpy.lib">numpy.lib</a>
+ &#8226;   <a href="#numpy.lib._arraypad_impl">numpy.lib._arraypad_impl</a>
+ &#8226;   <a href="#numpy.lib._arraysetops_impl">numpy.lib._arraysetops_impl</a>
+ &#8226;   <a href="#numpy.lib._format_impl">numpy.lib._format_impl</a>
+ &#8226;   <a href="#numpy.lib._function_base_impl">numpy.lib._function_base_impl</a>
+ &#8226;   <a href="#numpy.lib._histograms_impl">numpy.lib._histograms_impl</a>
+ &#8226;   <a href="#numpy.lib._index_tricks_impl">numpy.lib._index_tricks_impl</a>
+ &#8226;   <a href="#numpy.lib._iotools">numpy.lib._iotools</a>
+ &#8226;   <a href="#numpy.lib._nanfunctions_impl">numpy.lib._nanfunctions_impl</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#numpy.lib._shape_base_impl">numpy.lib._shape_base_impl</a>
+ &#8226;   <a href="#numpy.lib._stride_tricks_impl">numpy.lib._stride_tricks_impl</a>
+ &#8226;   <a href="#numpy.lib._twodim_base_impl">numpy.lib._twodim_base_impl</a>
+ &#8226;   <a href="#numpy.lib._utils_impl">numpy.lib._utils_impl</a>
+ &#8226;   <a href="#numpy.lib.recfunctions">numpy.lib.recfunctions</a>
+ &#8226;   <a href="#numpy.linalg">numpy.linalg</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+ &#8226;   <a href="#numpy.linalg._umath_linalg">numpy.linalg._umath_linalg</a>
+ &#8226;   <a href="#numpy.ma">numpy.ma</a>
+ &#8226;   <a href="#numpy.ma.core">numpy.ma.core</a>
+ &#8226;   <a href="#numpy.ma.extras">numpy.ma.extras</a>
+ &#8226;   <a href="#numpy.ma.mrecords">numpy.ma.mrecords</a>
+ &#8226;   <a href="#numpy.matlib">numpy.matlib</a>
+ &#8226;   <a href="#numpy.matrixlib">numpy.matrixlib</a>
+ &#8226;   <a href="#numpy.polynomial">numpy.polynomial</a>
+ &#8226;   <a href="#numpy.polynomial._polybase">numpy.polynomial._polybase</a>
+ &#8226;   <a href="#numpy.polynomial.chebyshev">numpy.polynomial.chebyshev</a>
+ &#8226;   <a href="#numpy.polynomial.hermite">numpy.polynomial.hermite</a>
+ &#8226;   <a href="#numpy.polynomial.hermite_e">numpy.polynomial.hermite_e</a>
+ &#8226;   <a href="#numpy.polynomial.laguerre">numpy.polynomial.laguerre</a>
+ &#8226;   <a href="#numpy.polynomial.legendre">numpy.polynomial.legendre</a>
+ &#8226;   <a href="#numpy.polynomial.polynomial">numpy.polynomial.polynomial</a>
+ &#8226;   <a href="#numpy.polynomial.polyutils">numpy.polynomial.polyutils</a>
+ &#8226;   <a href="#numpy.random">numpy.random</a>
+ &#8226;   <a href="#numpy.random._common">numpy.random._common</a>
+ &#8226;   <a href="#numpy.random._generator">numpy.random._generator</a>
+ &#8226;   <a href="#numpy.random._mt19937">numpy.random._mt19937</a>
+ &#8226;   <a href="#numpy.random._philox">numpy.random._philox</a>
+ &#8226;   <a href="#numpy.random._sfc64">numpy.random._sfc64</a>
+ &#8226;   <a href="#numpy.random.bit_generator">numpy.random.bit_generator</a>
+ &#8226;   <a href="#numpy.random.mtrand">numpy.random.mtrand</a>
+ &#8226;   <a href="#numpy.rec">numpy.rec</a>
+ &#8226;   <a href="#numpy.strings">numpy.strings</a>
+ &#8226;   <a href="#numpy.testing">numpy.testing</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#numpy.testing.overrides">numpy.testing.overrides</a>
+ &#8226;   <a href="#numpy.typing">numpy.typing</a>
+ &#8226;   <a href="#numpy.version">numpy.version</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="numpy.__config__"></a>
+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/.build_venv/Lib/site-packages/numpy/__config__.py" type="text/plain"><tt>numpy.__config__</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#enum">enum</a>
+ &#8226;   <a href="#json">json</a>
+ &#8226;   <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core._multiarray_umath">numpy._core._multiarray_umath</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#yaml">yaml</a>
+
+  </div>
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+    <a href="#numpy">numpy</a>
+
+  </div>
+
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+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy._core.complex64">numpy._core.complex64</a>
+ &#8226;   <a href="#numpy._core.float32">numpy._core.float32</a>
+ &#8226;   <a href="#numpy._core.int16">numpy._core.int16</a>
+ &#8226;   <a href="#numpy._core.int32">numpy._core.int32</a>
+ &#8226;   <a href="#numpy._core.int64">numpy._core.int64</a>
+ &#8226;   <a href="#numpy._core.int8">numpy._core.int8</a>
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+ &#8226;   <a href="#numpy._core.uint32">numpy._core.uint32</a>
+ &#8226;   <a href="#numpy._core.uint64">numpy._core.uint64</a>
+ &#8226;   <a href="#numpy._utils">numpy._utils</a>
+
+  </div>
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+    <a href="#numpy">numpy</a>
+
+  </div>
+
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+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/.build_venv/Lib/site-packages/numpy/_core/__init__.py" type="text/plain"><tt>numpy._core</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#copyreg">copyreg</a>
+ &#8226;   <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy._core._add_newdocs">numpy._core._add_newdocs</a>
+ &#8226;   <a href="#numpy._core._add_newdocs_scalars">numpy._core._add_newdocs_scalars</a>
+ &#8226;   <a href="#numpy._core._asarray">numpy._core._asarray</a>
+ &#8226;   <a href="#numpy._core._dtype">numpy._core._dtype</a>
+ &#8226;   <a href="#numpy._core._dtype_ctypes">numpy._core._dtype_ctypes</a>
+ &#8226;   <a href="#numpy._core._internal">numpy._core._internal</a>
+ &#8226;   <a href="#numpy._core._methods">numpy._core._methods</a>
+ &#8226;   <a href="#numpy._core._multiarray_umath">numpy._core._multiarray_umath</a>
+ &#8226;   <a href="#numpy._core.add">numpy._core.add</a>
+ &#8226;   <a href="#numpy._core.all">numpy._core.all</a>
+ &#8226;   <a href="#numpy._core.amax">numpy._core.amax</a>
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+ &#8226;   <a href="#numpy._core.arccos">numpy._core.arccos</a>
+ &#8226;   <a href="#numpy._core.arccosh">numpy._core.arccosh</a>
+ &#8226;   <a href="#numpy._core.arcsin">numpy._core.arcsin</a>
+ &#8226;   <a href="#numpy._core.arcsinh">numpy._core.arcsinh</a>
+ &#8226;   <a href="#numpy._core.arctan">numpy._core.arctan</a>
+ &#8226;   <a href="#numpy._core.arctan2">numpy._core.arctan2</a>
+ &#8226;   <a href="#numpy._core.arctanh">numpy._core.arctanh</a>
+ &#8226;   <a href="#numpy._core.argsort">numpy._core.argsort</a>
+ &#8226;   <a href="#numpy._core.array">numpy._core.array</a>
+ &#8226;   <a href="#numpy._core.arrayprint">numpy._core.arrayprint</a>
+ &#8226;   <a href="#numpy._core.asanyarray">numpy._core.asanyarray</a>
+ &#8226;   <a href="#numpy._core.asarray">numpy._core.asarray</a>
+ &#8226;   <a href="#numpy._core.atleast_1d">numpy._core.atleast_1d</a>
+ &#8226;   <a href="#numpy._core.atleast_2d">numpy._core.atleast_2d</a>
+ &#8226;   <a href="#numpy._core.atleast_3d">numpy._core.atleast_3d</a>
+ &#8226;   <a href="#numpy._core.bitwise_and">numpy._core.bitwise_and</a>
+ &#8226;   <a href="#numpy._core.bitwise_count">numpy._core.bitwise_count</a>
+ &#8226;   <a href="#numpy._core.bitwise_or">numpy._core.bitwise_or</a>
+ &#8226;   <a href="#numpy._core.bitwise_xor">numpy._core.bitwise_xor</a>
+ &#8226;   <a href="#numpy._core.bool_">numpy._core.bool_</a>
+ &#8226;   <a href="#numpy._core.byte">numpy._core.byte</a>
+ &#8226;   <a href="#numpy._core.bytes_">numpy._core.bytes_</a>
+ &#8226;   <a href="#numpy._core.cbrt">numpy._core.cbrt</a>
+ &#8226;   <a href="#numpy._core.cdouble">numpy._core.cdouble</a>
+ &#8226;   <a href="#numpy._core.ceil">numpy._core.ceil</a>
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+ &#8226;   <a href="#numpy._core.clongdouble">numpy._core.clongdouble</a>
+ &#8226;   <a href="#numpy._core.complex64">numpy._core.complex64</a>
+ &#8226;   <a href="#numpy._core.complexfloating">numpy._core.complexfloating</a>
+ &#8226;   <a href="#numpy._core.conj">numpy._core.conj</a>
+ &#8226;   <a href="#numpy._core.conjugate">numpy._core.conjugate</a>
+ &#8226;   <a href="#numpy._core.copysign">numpy._core.copysign</a>
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+ &#8226;   <a href="#numpy._core.cosh">numpy._core.cosh</a>
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+ &#8226;   <a href="#numpy._core.cross">numpy._core.cross</a>
+ &#8226;   <a href="#numpy._core.csingle">numpy._core.csingle</a>
+ &#8226;   <a href="#numpy._core.datetime64">numpy._core.datetime64</a>
+ &#8226;   <a href="#numpy._core.deg2rad">numpy._core.deg2rad</a>
+ &#8226;   <a href="#numpy._core.degrees">numpy._core.degrees</a>
+ &#8226;   <a href="#numpy._core.diagonal">numpy._core.diagonal</a>
+ &#8226;   <a href="#numpy._core.divide">numpy._core.divide</a>
+ &#8226;   <a href="#numpy._core.divmod">numpy._core.divmod</a>
+ &#8226;   <a href="#numpy._core.dot">numpy._core.dot</a>
+ &#8226;   <a href="#numpy._core.double">numpy._core.double</a>
+ &#8226;   <a href="#numpy._core.e">numpy._core.e</a>
+ &#8226;   <a href="#numpy._core.einsumfunc">numpy._core.einsumfunc</a>
+ &#8226;   <a href="#numpy._core.empty">numpy._core.empty</a>
+ &#8226;   <a href="#numpy._core.empty_like">numpy._core.empty_like</a>
+ &#8226;   <a href="#numpy._core.equal">numpy._core.equal</a>
+ &#8226;   <a href="#numpy._core.errstate">numpy._core.errstate</a>
+ &#8226;   <a href="#numpy._core.euler_gamma">numpy._core.euler_gamma</a>
+ &#8226;   <a href="#numpy._core.exp">numpy._core.exp</a>
+ &#8226;   <a href="#numpy._core.exp2">numpy._core.exp2</a>
+ &#8226;   <a href="#numpy._core.expm1">numpy._core.expm1</a>
+ &#8226;   <a href="#numpy._core.fabs">numpy._core.fabs</a>
+ &#8226;   <a href="#numpy._core.finfo">numpy._core.finfo</a>
+ &#8226;   <a href="#numpy._core.float16">numpy._core.float16</a>
+ &#8226;   <a href="#numpy._core.float32">numpy._core.float32</a>
+ &#8226;   <a href="#numpy._core.float_power">numpy._core.float_power</a>
+ &#8226;   <a href="#numpy._core.floating">numpy._core.floating</a>
+ &#8226;   <a href="#numpy._core.floor">numpy._core.floor</a>
+ &#8226;   <a href="#numpy._core.floor_divide">numpy._core.floor_divide</a>
+ &#8226;   <a href="#numpy._core.fmax">numpy._core.fmax</a>
+ &#8226;   <a href="#numpy._core.fmin">numpy._core.fmin</a>
+ &#8226;   <a href="#numpy._core.fmod">numpy._core.fmod</a>
+ &#8226;   <a href="#numpy._core.frexp">numpy._core.frexp</a>
+ &#8226;   <a href="#numpy._core.fromnumeric">numpy._core.fromnumeric</a>
+ &#8226;   <a href="#numpy._core.frompyfunc">numpy._core.frompyfunc</a>
+ &#8226;   <a href="#numpy._core.function_base">numpy._core.function_base</a>
+ &#8226;   <a href="#numpy._core.gcd">numpy._core.gcd</a>
+ &#8226;   <a href="#numpy._core.getlimits">numpy._core.getlimits</a>
+ &#8226;   <a href="#numpy._core.greater">numpy._core.greater</a>
+ &#8226;   <a href="#numpy._core.greater_equal">numpy._core.greater_equal</a>
+ &#8226;   <a href="#numpy._core.half">numpy._core.half</a>
+ &#8226;   <a href="#numpy._core.heaviside">numpy._core.heaviside</a>
+ &#8226;   <a href="#numpy._core.hstack">numpy._core.hstack</a>
+ &#8226;   <a href="#numpy._core.hypot">numpy._core.hypot</a>
+ &#8226;   <a href="#numpy._core.iinfo">numpy._core.iinfo</a>
+ &#8226;   <a href="#numpy._core.inexact">numpy._core.inexact</a>
+ &#8226;   <a href="#numpy._core.inf">numpy._core.inf</a>
+ &#8226;   <a href="#numpy._core.int16">numpy._core.int16</a>
+ &#8226;   <a href="#numpy._core.int32">numpy._core.int32</a>
+ &#8226;   <a href="#numpy._core.int64">numpy._core.int64</a>
+ &#8226;   <a href="#numpy._core.int8">numpy._core.int8</a>
+ &#8226;   <a href="#numpy._core.intc">numpy._core.intc</a>
+ &#8226;   <a href="#numpy._core.integer">numpy._core.integer</a>
+ &#8226;   <a href="#numpy._core.intp">numpy._core.intp</a>
+ &#8226;   <a href="#numpy._core.isfinite">numpy._core.isfinite</a>
+ &#8226;   <a href="#numpy._core.isnan">numpy._core.isnan</a>
+ &#8226;   <a href="#numpy._core.isscalar">numpy._core.isscalar</a>
+ &#8226;   <a href="#numpy._core.lcm">numpy._core.lcm</a>
+ &#8226;   <a href="#numpy._core.ldexp">numpy._core.ldexp</a>
+ &#8226;   <a href="#numpy._core.left_shift">numpy._core.left_shift</a>
+ &#8226;   <a href="#numpy._core.less">numpy._core.less</a>
+ &#8226;   <a href="#numpy._core.less_equal">numpy._core.less_equal</a>
+ &#8226;   <a href="#numpy._core.linspace">numpy._core.linspace</a>
+ &#8226;   <a href="#numpy._core.log">numpy._core.log</a>
+ &#8226;   <a href="#numpy._core.log10">numpy._core.log10</a>
+ &#8226;   <a href="#numpy._core.log1p">numpy._core.log1p</a>
+ &#8226;   <a href="#numpy._core.log2">numpy._core.log2</a>
+ &#8226;   <a href="#numpy._core.logaddexp">numpy._core.logaddexp</a>
+ &#8226;   <a href="#numpy._core.logaddexp2">numpy._core.logaddexp2</a>
+ &#8226;   <a href="#numpy._core.logical_and">numpy._core.logical_and</a>
+ &#8226;   <a href="#numpy._core.logical_not">numpy._core.logical_not</a>
+ &#8226;   <a href="#numpy._core.logical_or">numpy._core.logical_or</a>
+ &#8226;   <a href="#numpy._core.logical_xor">numpy._core.logical_xor</a>
+ &#8226;   <a href="#numpy._core.long">numpy._core.long</a>
+ &#8226;   <a href="#numpy._core.longdouble">numpy._core.longdouble</a>
+ &#8226;   <a href="#numpy._core.longlong">numpy._core.longlong</a>
+ &#8226;   <a href="#numpy._core.matmul">numpy._core.matmul</a>
+ &#8226;   <a href="#numpy._core.matrix_transpose">numpy._core.matrix_transpose</a>
+ &#8226;   <a href="#numpy._core.matvec">numpy._core.matvec</a>
+ &#8226;   <a href="#numpy._core.maximum">numpy._core.maximum</a>
+ &#8226;   <a href="#numpy._core.memmap">numpy._core.memmap</a>
+ &#8226;   <a href="#numpy._core.minimum">numpy._core.minimum</a>
+ &#8226;   <a href="#numpy._core.mod">numpy._core.mod</a>
+ &#8226;   <a href="#numpy._core.modf">numpy._core.modf</a>
+ &#8226;   <a href="#numpy._core.moveaxis">numpy._core.moveaxis</a>
+ &#8226;   <a href="#numpy._core.multiarray">numpy._core.multiarray</a>
+ &#8226;   <a href="#numpy._core.multiply">numpy._core.multiply</a>
+ &#8226;   <a href="#numpy._core.ndarray">numpy._core.ndarray</a>
+ &#8226;   <a href="#numpy._core.negative">numpy._core.negative</a>
+ &#8226;   <a href="#numpy._core.newaxis">numpy._core.newaxis</a>
+ &#8226;   <a href="#numpy._core.nextafter">numpy._core.nextafter</a>
+ &#8226;   <a href="#numpy._core.not_equal">numpy._core.not_equal</a>
+ &#8226;   <a href="#numpy._core.number">numpy._core.number</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.numerictypes">numpy._core.numerictypes</a>
+ &#8226;   <a href="#numpy._core.object_">numpy._core.object_</a>
+ &#8226;   <a href="#numpy._core.ones">numpy._core.ones</a>
+ &#8226;   <a href="#numpy._core.outer">numpy._core.outer</a>
+ &#8226;   <a href="#numpy._core.overrides">numpy._core.overrides</a>
+ &#8226;   <a href="#numpy._core.pi">numpy._core.pi</a>
+ &#8226;   <a href="#numpy._core.positive">numpy._core.positive</a>
+ &#8226;   <a href="#numpy._core.power">numpy._core.power</a>
+ &#8226;   <a href="#numpy._core.prod">numpy._core.prod</a>
+ &#8226;   <a href="#numpy._core.rad2deg">numpy._core.rad2deg</a>
+ &#8226;   <a href="#numpy._core.radians">numpy._core.radians</a>
+ &#8226;   <a href="#numpy._core.reciprocal">numpy._core.reciprocal</a>
+ &#8226;   <a href="#numpy._core.records">numpy._core.records</a>
+ &#8226;   <a href="#numpy._core.remainder">numpy._core.remainder</a>
+ &#8226;   <a href="#numpy._core.right_shift">numpy._core.right_shift</a>
+ &#8226;   <a href="#numpy._core.rint">numpy._core.rint</a>
+ &#8226;   <a href="#numpy._core.shape_base">numpy._core.shape_base</a>
+ &#8226;   <a href="#numpy._core.short">numpy._core.short</a>
+ &#8226;   <a href="#numpy._core.sign">numpy._core.sign</a>
+ &#8226;   <a href="#numpy._core.signbit">numpy._core.signbit</a>
+ &#8226;   <a href="#numpy._core.signedinteger">numpy._core.signedinteger</a>
+ &#8226;   <a href="#numpy._core.single">numpy._core.single</a>
+ &#8226;   <a href="#numpy._core.sinh">numpy._core.sinh</a>
+ &#8226;   <a href="#numpy._core.sort">numpy._core.sort</a>
+ &#8226;   <a href="#numpy._core.spacing">numpy._core.spacing</a>
+ &#8226;   <a href="#numpy._core.sqrt">numpy._core.sqrt</a>
+ &#8226;   <a href="#numpy._core.square">numpy._core.square</a>
+ &#8226;   <a href="#numpy._core.str_">numpy._core.str_</a>
+ &#8226;   <a href="#numpy._core.subtract">numpy._core.subtract</a>
+ &#8226;   <a href="#numpy._core.sum">numpy._core.sum</a>
+ &#8226;   <a href="#numpy._core.swapaxes">numpy._core.swapaxes</a>
+ &#8226;   <a href="#numpy._core.tan">numpy._core.tan</a>
+ &#8226;   <a href="#numpy._core.tanh">numpy._core.tanh</a>
+ &#8226;   <a href="#numpy._core.tensordot">numpy._core.tensordot</a>
+ &#8226;   <a href="#numpy._core.timedelta64">numpy._core.timedelta64</a>
+ &#8226;   <a href="#numpy._core.trace">numpy._core.trace</a>
+ &#8226;   <a href="#numpy._core.transpose">numpy._core.transpose</a>
+ &#8226;   <a href="#numpy._core.true_divide">numpy._core.true_divide</a>
+ &#8226;   <a href="#numpy._core.trunc">numpy._core.trunc</a>
+ &#8226;   <a href="#numpy._core.ubyte">numpy._core.ubyte</a>
+ &#8226;   <a href="#numpy._core.uint">numpy._core.uint</a>
+ &#8226;   <a href="#numpy._core.uint16">numpy._core.uint16</a>
+ &#8226;   <a href="#numpy._core.uint32">numpy._core.uint32</a>
+ &#8226;   <a href="#numpy._core.uint64">numpy._core.uint64</a>
+ &#8226;   <a href="#numpy._core.uintc">numpy._core.uintc</a>
+ &#8226;   <a href="#numpy._core.uintp">numpy._core.uintp</a>
+ &#8226;   <a href="#numpy._core.ulong">numpy._core.ulong</a>
+ &#8226;   <a href="#numpy._core.ulonglong">numpy._core.ulonglong</a>
+ &#8226;   <a href="#numpy._core.umath">numpy._core.umath</a>
+ &#8226;   <a href="#numpy._core.unsignedinteger">numpy._core.unsignedinteger</a>
+ &#8226;   <a href="#numpy._core.ushort">numpy._core.ushort</a>
+ &#8226;   <a href="#numpy._core.vecdot">numpy._core.vecdot</a>
+ &#8226;   <a href="#numpy._core.vecmat">numpy._core.vecmat</a>
+ &#8226;   <a href="#numpy._core.void">numpy._core.void</a>
+ &#8226;   <a href="#numpy._core.zeros">numpy._core.zeros</a>
+ &#8226;   <a href="#numpy._pytesttester">numpy._pytesttester</a>
+ &#8226;   <a href="#numpy.version">numpy.version</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._array_api_info">numpy._array_api_info</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy._core._add_newdocs">numpy._core._add_newdocs</a>
+ &#8226;   <a href="#numpy._core._add_newdocs_scalars">numpy._core._add_newdocs_scalars</a>
+ &#8226;   <a href="#numpy._core._asarray">numpy._core._asarray</a>
+ &#8226;   <a href="#numpy._core._dtype">numpy._core._dtype</a>
+ &#8226;   <a href="#numpy._core._dtype_ctypes">numpy._core._dtype_ctypes</a>
+ &#8226;   <a href="#numpy._core._exceptions">numpy._core._exceptions</a>
+ &#8226;   <a href="#numpy._core._internal">numpy._core._internal</a>
+ &#8226;   <a href="#numpy._core._methods">numpy._core._methods</a>
+ &#8226;   <a href="#numpy._core._multiarray_tests">numpy._core._multiarray_tests</a>
+ &#8226;   <a href="#numpy._core._multiarray_umath">numpy._core._multiarray_umath</a>
+ &#8226;   <a href="#numpy._core._string_helpers">numpy._core._string_helpers</a>
+ &#8226;   <a href="#numpy._core._type_aliases">numpy._core._type_aliases</a>
+ &#8226;   <a href="#numpy._core._ufunc_config">numpy._core._ufunc_config</a>
+ &#8226;   <a href="#numpy._core.arrayprint">numpy._core.arrayprint</a>
+ &#8226;   <a href="#numpy._core.defchararray">numpy._core.defchararray</a>
+ &#8226;   <a href="#numpy._core.einsumfunc">numpy._core.einsumfunc</a>
+ &#8226;   <a href="#numpy._core.fromnumeric">numpy._core.fromnumeric</a>
+ &#8226;   <a href="#numpy._core.function_base">numpy._core.function_base</a>
+ &#8226;   <a href="#numpy._core.getlimits">numpy._core.getlimits</a>
+ &#8226;   <a href="#numpy._core.memmap">numpy._core.memmap</a>
+ &#8226;   <a href="#numpy._core.multiarray">numpy._core.multiarray</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.numerictypes">numpy._core.numerictypes</a>
+ &#8226;   <a href="#numpy._core.overrides">numpy._core.overrides</a>
+ &#8226;   <a href="#numpy._core.printoptions">numpy._core.printoptions</a>
+ &#8226;   <a href="#numpy._core.records">numpy._core.records</a>
+ &#8226;   <a href="#numpy._core.shape_base">numpy._core.shape_base</a>
+ &#8226;   <a href="#numpy._core.strings">numpy._core.strings</a>
+ &#8226;   <a href="#numpy._core.umath">numpy._core.umath</a>
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+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#warnings">warnings</a>
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+<div class="node">
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+  </div>
+
+</div>
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+<div class="node">
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+ &#8226;   <a href="#numpy.fft._pocketfft">numpy.fft._pocketfft</a>
+ &#8226;   <a href="#numpy.lib._array_utils_impl">numpy.lib._array_utils_impl</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
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+
+</div>
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+<div class="node">
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+ &#8226;   <a href="#numpy._core">numpy._core</a>
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+  </div>
+
+</div>
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+<div class="node">
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+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
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+
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+<div class="node">
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+ &#8226;   <a href="#numpy._core">numpy._core</a>
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+ &#8226;   <a href="#numpy._core">numpy._core</a>
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+
+</div>
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+
+</div>
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+<div class="node">
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+  </div>
+
+</div>
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+  </div>
+
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+<div class="node">
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+  </div>
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+  </div>
+
+</div>
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+<div class="node">
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+
+  </div>
+
+</div>
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+</div>
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+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.ceil</tt></a>
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+  </div>
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+
+  </div>
+
+</div>
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+<div class="node">
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+  </div>
+
+</div>
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+<div class="node">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._array_api_info">numpy._array_api_info</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.complexfloating</tt></a>
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+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.conj</tt></a>
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+
+  </div>
+
+</div>
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+<div class="node">
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+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.fft._pocketfft">numpy.fft._pocketfft</a>
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+  </div>
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+</div>
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.copysign</tt></a>
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+    <a href="#numpy">numpy</a>
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+
+  </div>
+
+</div>
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+<div class="node">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
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+  </div>
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+</div>
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
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+  </div>
+
+</div>
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+<div class="node">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
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+  </div>
+
+</div>
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+<div class="node">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/.build_venv/Lib/site-packages/numpy/_core/defchararray.py" type="text/plain"><tt>numpy._core.defchararray</tt></a>
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+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy._core.multiarray">numpy._core.multiarray</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.numerictypes">numpy._core.numerictypes</a>
+ &#8226;   <a href="#numpy._core.overrides">numpy._core.overrides</a>
+ &#8226;   <a href="#numpy._core.strings">numpy._core.strings</a>
+ &#8226;   <a href="#numpy._utils">numpy._utils</a>
+ &#8226;   <a href="#numpy.strings">numpy.strings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#numpy.char">numpy.char</a>
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+  </div>
+
+</div>
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.degrees</tt></a>
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+<span class="moduletype">MissingModule</span>  <div class="import">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="numpy._core.divide"></a>
+  <a target="code" href="" type="text/plain"><tt>numpy._core.divide</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="numpy._core.divmod"></a>
+  <a target="code" href="" type="text/plain"><tt>numpy._core.divmod</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+<span class="moduletype">MissingModule</span>  <div class="import">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.lib._polynomial_impl">numpy.lib._polynomial_impl</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.double</tt></a>
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.e</tt></a>
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="///C:/ZendionInc/agrobot_base/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/.build_venv/Lib/site-packages/numpy/_core/einsumfunc.py" type="text/plain"><tt>numpy._core.einsumfunc</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+ &#8226;   <a href="#numpy._core.multiarray">numpy._core.multiarray</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.overrides">numpy._core.overrides</a>
+ &#8226;   <a href="#numpy._core.umath">numpy._core.umath</a>
+ &#8226;   <a href="#operator">operator</a>
+
+  </div>
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+  </div>
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+</div>
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.fft._helper">numpy.fft._helper</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.empty_like</tt></a>
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+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.fft._pocketfft">numpy.fft._pocketfft</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.equal</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>numpy._core.errstate</tt></a>
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+    <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="numpy._core.euler_gamma"></a>
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+ &#8226;   <a href="#textwrap">textwrap</a>
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+ &#8226;   <a href="#statistics">statistics</a>
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+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#lzma">lzma</a>
+ &#8226;   <a href="#mimetypes">mimetypes</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.popen_fork">multiprocessing.popen_fork</a>
+ &#8226;   <a href="#multiprocessing.popen_forkserver">multiprocessing.popen_forkserver</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_posix">multiprocessing.popen_spawn_posix</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#netrc">netrc</a>
+ &#8226;   <a href="#ntpath">ntpath</a>
+ &#8226;   <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy._core._add_newdocs_scalars">numpy._core._add_newdocs_scalars</a>
+ &#8226;   <a href="#numpy._core._methods">numpy._core._methods</a>
+ &#8226;   <a href="#numpy._core.records">numpy._core.records</a>
+ &#8226;   <a href="#numpy._pytesttester">numpy._pytesttester</a>
+ &#8226;   <a href="#numpy.ctypeslib._ctypeslib">numpy.ctypeslib._ctypeslib</a>
+ &#8226;   <a href="#numpy.f2py">numpy.f2py</a>
+ &#8226;   <a href="#numpy.f2py._backends._meson">numpy.f2py._backends._meson</a>
+ &#8226;   <a href="#numpy.f2py.capi_maps">numpy.f2py.capi_maps</a>
+ &#8226;   <a href="#numpy.f2py.crackfortran">numpy.f2py.crackfortran</a>
+ &#8226;   <a href="#numpy.f2py.diagnose">numpy.f2py.diagnose</a>
+ &#8226;   <a href="#numpy.f2py.f2py2e">numpy.f2py.f2py2e</a>
+ &#8226;   <a href="#numpy.f2py.rules">numpy.f2py.rules</a>
+ &#8226;   <a href="#numpy.lib._datasource">numpy.lib._datasource</a>
+ &#8226;   <a href="#numpy.lib._format_impl">numpy.lib._format_impl</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#numpy.lib._twodim_base_impl">numpy.lib._twodim_base_impl</a>
+ &#8226;   <a href="#numpy.lib._utils_impl">numpy.lib._utils_impl</a>
+ &#8226;   <a href="#numpy.polynomial._polybase">numpy.polynomial._polybase</a>
+ &#8226;   <a href="#numpy.testing._private.extbuild">numpy.testing._private.extbuild</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#os.path">os.path</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#platform">platform</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#py_compile">py_compile</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#pyi_rth__tkinter.py">pyi_rth__tkinter.py</a>
+ &#8226;   <a href="#pyi_rth_inspect.py">pyi_rth_inspect.py</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#runpy">runpy</a>
+ &#8226;   <a href="#shlex">shlex</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#socketserver">socketserver</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#tkinter.filedialog">tkinter.filedialog</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#unittest.main">unittest.main</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#webbrowser">webbrowser</a>
+ &#8226;   <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>os.path</tt></a>
+<span class="moduletype">AliasNode</span>  <div class="import">
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+    <a href="#ntpath">ntpath</a>
+ &#8226;   <a href="#os">os</a>
+
+  </div>
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+    <a href="#numpy._core.memmap">numpy._core.memmap</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#py_compile">py_compile</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+ &#8226;   <a href="#tracemalloc">tracemalloc</a>
+ &#8226;   <a href="#unittest.util">unittest.util</a>
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+  </div>
+
+</div>
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+<div class="node">
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+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#fnmatch">fnmatch</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#grp">grp</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#ntpath">ntpath</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#pwd">pwd</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#stat">stat</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+ &#8226;   <a href="#warnings">warnings</a>
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+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#importlib.resources._legacy">importlib.resources._legacy</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#importlib.resources.readers">importlib.resources.readers</a>
+ &#8226;   <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy.f2py._backends._meson">numpy.f2py._backends._meson</a>
+ &#8226;   <a href="#numpy.f2py.crackfortran">numpy.f2py.crackfortran</a>
+ &#8226;   <a href="#numpy.f2py.rules">numpy.f2py.rules</a>
+ &#8226;   <a href="#numpy.testing._private.extbuild">numpy.testing._private.extbuild</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#zipfile._path">zipfile._path</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="///C:/Python312/Lib/pdb.py" type="text/plain"><tt>pdb</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#bdb">bdb</a>
+ &#8226;   <a href="#cmd">cmd</a>
+ &#8226;   <a href="#code">code</a>
+ &#8226;   <a href="#dis">dis</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#getopt">getopt</a>
+ &#8226;   <a href="#glob">glob</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#pprint">pprint</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#readline">readline</a>
+ &#8226;   <a href="#runpy">runpy</a>
+ &#8226;   <a href="#shlex">shlex</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#token">token</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#typing">typing</a>
+
+  </div>
+  <div class="import">
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+    <a href="#doctest">doctest</a>
+ &#8226;   <a href="#pdb">pdb</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="///C:/Python312/Lib/pickle.py" type="text/plain"><tt>pickle</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_compat_pickle">_compat_pickle</a>
+ &#8226;   <a href="#_pickle">_pickle</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#copyreg">copyreg</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#pprint">pprint</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
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+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#numpy._core._methods">numpy._core._methods</a>
+ &#8226;   <a href="#numpy.lib._format_impl">numpy.lib._format_impl</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#tracemalloc">tracemalloc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="///C:/Python312/Lib/pkgutil.py" type="text/plain"><tt>pkgutil</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#collections">collections</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib.machinery">importlib.machinery</a>
+ &#8226;   <a href="#importlib.util">importlib.util</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#marshal">marshal</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#os.path">os.path</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#pyi_rth_pkgutil.py">pyi_rth_pkgutil.py</a>
+ &#8226;   <a href="#runpy">runpy</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#'java.lang'">'java.lang'</a>
+ &#8226;   <a href="#_winreg">_winreg</a>
+ &#8226;   <a href="#_wmi">_wmi</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#java">java</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#vms_lib">vms_lib</a>
+ &#8226;   <a href="#winreg">winreg</a>
+
+  </div>
+  <div class="import">
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+    <a href="#cv2">cv2</a>
+ &#8226;   <a href="#numpy.f2py.crackfortran">numpy.f2py.crackfortran</a>
+ &#8226;   <a href="#numpy.lib._utils_impl">numpy.lib._utils_impl</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+    <a href="#resource">resource</a>
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+  </div>
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+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#shutil">shutil</a>
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+</div>
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+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#posix">posix</a>
+ &#8226;   <a href="#pwd">pwd</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#stat">stat</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#fnmatch">fnmatch</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#mimetypes">mimetypes</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#zipfile._path">zipfile._path</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="pprint"></a>
+  <a target="code" href="///C:/Python312/Lib/pprint.py" type="text/plain"><tt>pprint</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
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+    <a href="#numpy.f2py.auxfuncs">numpy.f2py.auxfuncs</a>
+ &#8226;   <a href="#numpy.f2py.f2py2e">numpy.f2py.f2py2e</a>
+ &#8226;   <a href="#numpy.lib._utils_impl">numpy.lib._utils_impl</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#pickle">pickle</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+
+  </div>
+
+</div>
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+<div class="node">
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+<span class="moduletype">MissingModule</span>  <div class="import">
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+    <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+
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+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#netrc">netrc</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+
+  </div>
+
+</div>
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+  <a target="code" href="///C:/Python312/Lib/py_compile.py" type="text/plain"><tt>py_compile</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#argparse">argparse</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#importlib.machinery">importlib.machinery</a>
+ &#8226;   <a href="#importlib.util">importlib.util</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#os.path">os.path</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#traceback">traceback</a>
+
+  </div>
+  <div class="import">
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+    <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
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+<div class="node">
+  <a name="pydoc"></a>
+  <a target="code" href="///C:/Python312/Lib/pydoc.py" type="text/plain"><tt>pydoc</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#__future__">__future__</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#email.message">email.message</a>
+ &#8226;   <a href="#getopt">getopt</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#importlib._bootstrap">importlib._bootstrap</a>
+ &#8226;   <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#importlib.machinery">importlib.machinery</a>
+ &#8226;   <a href="#importlib.util">importlib.util</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#platform">platform</a>
+ &#8226;   <a href="#pydoc_data.topics">pydoc_data.topics</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#reprlib">reprlib</a>
+ &#8226;   <a href="#select">select</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#textwrap">textwrap</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#traceback">traceback</a>
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+ &#8226;   <a href="#numpy.lib._datasource">numpy.lib._datasource</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#webbrowser">webbrowser</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="signal"></a>
+  <a target="code" href="///C:/Python312/Lib/signal.py" type="text/plain"><tt>signal</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#_signal">_signal</a>
+ &#8226;   <a href="#enum">enum</a>
+
+  </div>
+  <div class="import">
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+    <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.popen_fork">multiprocessing.popen_fork</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#unittest.signals">unittest.signals</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="socket"></a>
+  <a target="code" href="///C:/Python312/Lib/socket.py" type="text/plain"><tt>socket</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_socket">_socket</a>
+ &#8226;   <a href="#array">array</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_ssl">_ssl</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.trsock">asyncio.trsock</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#ftplib">ftplib</a>
+ &#8226;   <a href="#http.client">http.client</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#platform">platform</a>
+ &#8226;   <a href="#socketserver">socketserver</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="socketserver"></a>
+  <a target="code" href="///C:/Python312/Lib/socketserver.py" type="text/plain"><tt>socketserver</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#traceback">traceback</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#http.server">http.server</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="sre_compile"></a>
+  <a target="code" href="///C:/Python312/Lib/sre_compile.py" type="text/plain"><tt>sre_compile</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#re">re</a>
+ &#8226;   <a href="#re._compiler">re._compiler</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="sre_constants"></a>
+  <a target="code" href="///C:/Python312/Lib/sre_constants.py" type="text/plain"><tt>sre_constants</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#re">re</a>
+ &#8226;   <a href="#re._constants">re._constants</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
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+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="sre_parse"></a>
+  <a target="code" href="///C:/Python312/Lib/sre_parse.py" type="text/plain"><tt>sre_parse</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#re">re</a>
+ &#8226;   <a href="#re._parser">re._parser</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
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+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="ssl"></a>
+  <a target="code" href="///C:/Python312/Lib/ssl.py" type="text/plain"><tt>ssl</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_ssl">_ssl</a>
+ &#8226;   <a href="#base64">base64</a>
+ &#8226;   <a href="#calendar">calendar</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#ftplib">ftplib</a>
+ &#8226;   <a href="#http.client">http.client</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="stat"></a>
+  <a target="code" href="///C:/Python312/Lib/stat.py" type="text/plain"><tt>stat</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_stat">_stat</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#genericpath">genericpath</a>
+ &#8226;   <a href="#glob">glob</a>
+ &#8226;   <a href="#netrc">netrc</a>
+ &#8226;   <a href="#ntpath">ntpath</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="statistics"></a>
+  <a target="code" href="///C:/Python312/Lib/statistics.py" type="text/plain"><tt>statistics</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_statistics">_statistics</a>
+ &#8226;   <a href="#bisect">bisect</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#decimal">decimal</a>
+ &#8226;   <a href="#fractions">fractions</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#math">math</a>
+ &#8226;   <a href="#numbers">numbers</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#random">random</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="string"></a>
+  <a target="code" href="///C:/Python312/Lib/string.py" type="text/plain"><tt>string</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_string">_string</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#cmd">cmd</a>
+ &#8226;   <a href="#email._encoded_words">email._encoded_words</a>
+ &#8226;   <a href="#email._header_value_parser">email._header_value_parser</a>
+ &#8226;   <a href="#email.quoprimime">email.quoprimime</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#ntpath">ntpath</a>
+ &#8226;   <a href="#nturl2path">nturl2path</a>
+ &#8226;   <a href="#numpy.f2py._backends._meson">numpy.f2py._backends._meson</a>
+ &#8226;   <a href="#numpy.f2py.crackfortran">numpy.f2py.crackfortran</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="stringprep"></a>
+  <a target="code" href="///C:/Python312/Lib/stringprep.py" type="text/plain"><tt>stringprep</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#unicodedata">unicodedata</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#encodings.idna">encodings.idna</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="struct"></a>
+  <a target="code" href="///C:/Python312/Lib/struct.py" type="text/plain"><tt>struct</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_struct">_struct</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#base64">base64</a>
+ &#8226;   <a href="#ctypes">ctypes</a>
+ &#8226;   <a href="#gettext">gettext</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#numpy.lib._format_impl">numpy.lib._format_impl</a>
+ &#8226;   <a href="#pickle">pickle</a>
+ &#8226;   <a href="#platform">platform</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="subprocess"></a>
+  <a target="code" href="///C:/Python312/Lib/subprocess.py" type="text/plain"><tt>subprocess</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_posixsubprocess">_posixsubprocess</a>
+ &#8226;   <a href="#_winapi">_winapi</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#fcntl">fcntl</a>
+ &#8226;   <a href="#grp">grp</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#msvcrt">msvcrt</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pwd">pwd</a>
+ &#8226;   <a href="#select">select</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_aix_support">_aix_support</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.subprocess">asyncio.subprocess</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#numpy.f2py">numpy.f2py</a>
+ &#8226;   <a href="#numpy.f2py._backends._meson">numpy.f2py._backends._meson</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#platform">platform</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#pyi_rth_multiprocessing.py">pyi_rth_multiprocessing.py</a>
+ &#8226;   <a href="#webbrowser">webbrowser</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="sys"></a>
+  <tt>sys</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_aix_support">_aix_support</a>
+ &#8226;   <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#_compression">_compression</a>
+ &#8226;   <a href="#_pydatetime">_pydatetime</a>
+ &#8226;   <a href="#_pydecimal">_pydecimal</a>
+ &#8226;   <a href="#argparse">argparse</a>
+ &#8226;   <a href="#ast">ast</a>
+ &#8226;   <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#base64">base64</a>
+ &#8226;   <a href="#bdb">bdb</a>
+ &#8226;   <a href="#calendar">calendar</a>
+ &#8226;   <a href="#cmd">cmd</a>
+ &#8226;   <a href="#code">code</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#ctypes">ctypes</a>
+ &#8226;   <a href="#ctypes._endian">ctypes._endian</a>
+ &#8226;   <a href="#cv2">cv2</a>
+ &#8226;   <a href="#cv2.load_config_py2">cv2.load_config_py2</a>
+ &#8226;   <a href="#cv2.load_config_py3">cv2.load_config_py3</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#dis">dis</a>
+ &#8226;   <a href="#doctest">doctest</a>
+ &#8226;   <a href="#email._header_value_parser">email._header_value_parser</a>
+ &#8226;   <a href="#email.generator">email.generator</a>
+ &#8226;   <a href="#email.iterators">email.iterators</a>
+ &#8226;   <a href="#email.policy">email.policy</a>
+ &#8226;   <a href="#encodings">encodings</a>
+ &#8226;   <a href="#encodings.rot_13">encodings.rot_13</a>
+ &#8226;   <a href="#encodings.utf_16">encodings.utf_16</a>
+ &#8226;   <a href="#encodings.utf_32">encodings.utf_32</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#fileinput">fileinput</a>
+ &#8226;   <a href="#fractions">fractions</a>
+ &#8226;   <a href="#ftplib">ftplib</a>
+ &#8226;   <a href="#getopt">getopt</a>
+ &#8226;   <a href="#getpass">getpass</a>
+ &#8226;   <a href="#gettext">gettext</a>
+ &#8226;   <a href="#glob">glob</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#http.client">http.client</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.util">importlib.util</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#mimetypes">mimetypes</a>
+ &#8226;   <a href="#multiprocessing">multiprocessing</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.dummy">multiprocessing.dummy</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.popen_spawn_win32">multiprocessing.popen_spawn_win32</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.reduction">multiprocessing.reduction</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#ntpath">ntpath</a>
+ &#8226;   <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy._core">numpy._core</a>
+ &#8226;   <a href="#numpy._core._add_newdocs_scalars">numpy._core._add_newdocs_scalars</a>
+ &#8226;   <a href="#numpy._core._internal">numpy._core._internal</a>
+ &#8226;   <a href="#numpy._core.arrayprint">numpy._core.arrayprint</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.printoptions">numpy._core.printoptions</a>
+ &#8226;   <a href="#numpy._core.strings">numpy._core.strings</a>
+ &#8226;   <a href="#numpy._pytesttester">numpy._pytesttester</a>
+ &#8226;   <a href="#numpy.ctypeslib._ctypeslib">numpy.ctypeslib._ctypeslib</a>
+ &#8226;   <a href="#numpy.f2py">numpy.f2py</a>
+ &#8226;   <a href="#numpy.f2py._backends._meson">numpy.f2py._backends._meson</a>
+ &#8226;   <a href="#numpy.f2py.auxfuncs">numpy.f2py.auxfuncs</a>
+ &#8226;   <a href="#numpy.f2py.cfuncs">numpy.f2py.cfuncs</a>
+ &#8226;   <a href="#numpy.f2py.crackfortran">numpy.f2py.crackfortran</a>
+ &#8226;   <a href="#numpy.f2py.diagnose">numpy.f2py.diagnose</a>
+ &#8226;   <a href="#numpy.f2py.f2py2e">numpy.f2py.f2py2e</a>
+ &#8226;   <a href="#numpy.f2py.rules">numpy.f2py.rules</a>
+ &#8226;   <a href="#numpy.lib._index_tricks_impl">numpy.lib._index_tricks_impl</a>
+ &#8226;   <a href="#numpy.lib._utils_impl">numpy.lib._utils_impl</a>
+ &#8226;   <a href="#numpy.matrixlib.defmatrix">numpy.matrixlib.defmatrix</a>
+ &#8226;   <a href="#numpy.testing._private.extbuild">numpy.testing._private.extbuild</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#pickle">pickle</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#platform">platform</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#pprint">pprint</a>
+ &#8226;   <a href="#py_compile">py_compile</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#pyi_rth__tkinter.py">pyi_rth__tkinter.py</a>
+ &#8226;   <a href="#pyi_rth_inspect.py">pyi_rth_inspect.py</a>
+ &#8226;   <a href="#pyi_rth_multiprocessing.py">pyi_rth_multiprocessing.py</a>
+ &#8226;   <a href="#quopri">quopri</a>
+ &#8226;   <a href="#re._compiler">re._compiler</a>
+ &#8226;   <a href="#runpy">runpy</a>
+ &#8226;   <a href="#selectors">selectors</a>
+ &#8226;   <a href="#shlex">shlex</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#socketserver">socketserver</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#statistics">statistics</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#unittest.main">unittest.main</a>
+ &#8226;   <a href="#unittest.result">unittest.result</a>
+ &#8226;   <a href="#unittest.runner">unittest.runner</a>
+ &#8226;   <a href="#unittest.suite">unittest.suite</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#weakref">weakref</a>
+ &#8226;   <a href="#webbrowser">webbrowser</a>
+ &#8226;   <a href="#xml.parsers.expat">xml.parsers.expat</a>
+ &#8226;   <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+ &#8226;   <a href="#xmlrpc.client">xmlrpc.client</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="sysconfig"></a>
+  <a target="code" href="///C:/Python312/Lib/sysconfig.py" type="text/plain"><tt>sysconfig</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_aix_support">_aix_support</a>
+ &#8226;   <a href="#_imp">_imp</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#os.path">os.path</a>
+ &#8226;   <a href="#pprint">pprint</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_aix_support">_aix_support</a>
+ &#8226;   <a href="#numpy.ctypeslib._ctypeslib">numpy.ctypeslib._ctypeslib</a>
+ &#8226;   <a href="#numpy.testing._private.extbuild">numpy.testing._private.extbuild</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tarfile"></a>
+  <a target="code" href="///C:/Python312/Lib/tarfile.py" type="text/plain"><tt>tarfile</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#argparse">argparse</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#bz2">bz2</a>
+ &#8226;   <a href="#copy">copy</a>
+ &#8226;   <a href="#grp">grp</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#lzma">lzma</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pwd">pwd</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#stat">stat</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#zlib">zlib</a>
+
+  </div>
+  <div class="import">
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+    <a href="#shutil">shutil</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tempfile"></a>
+  <a target="code" href="///C:/Python312/Lib/tempfile.py" type="text/plain"><tt>tempfile</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_thread">_thread</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#weakref">weakref</a>
+
+  </div>
+  <div class="import">
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+    <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#numpy.f2py.diagnose">numpy.f2py.diagnose</a>
+ &#8226;   <a href="#numpy.f2py.f2py2e">numpy.f2py.f2py2e</a>
+ &#8226;   <a href="#numpy.lib._datasource">numpy.lib._datasource</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#urllib.response">urllib.response</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="termios"></a>
+  <a target="code" href="" type="text/plain"><tt>termios</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#getpass">getpass</a>
+ &#8226;   <a href="#tty">tty</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="textwrap"></a>
+  <a target="code" href="///C:/Python312/Lib/textwrap.py" type="text/plain"><tt>textwrap</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#re">re</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#argparse">argparse</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._adapters">importlib.metadata._adapters</a>
+ &#8226;   <a href="#numpy._core._add_newdocs">numpy._core._add_newdocs</a>
+ &#8226;   <a href="#numpy._typing._add_docstring">numpy._typing._add_docstring</a>
+ &#8226;   <a href="#numpy.ma.core">numpy.ma.core</a>
+ &#8226;   <a href="#numpy.testing._private.extbuild">numpy.testing._private.extbuild</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#traceback">traceback</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="threading"></a>
+  <a target="code" href="///C:/Python312/Lib/threading.py" type="text/plain"><tt>threading</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_collections">_collections</a>
+ &#8226;   <a href="#_thread">_thread</a>
+ &#8226;   <a href="#_threading_local">_threading_local</a>
+ &#8226;   <a href="#_weakrefset">_weakrefset</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#_threading_local">_threading_local</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.mixins">asyncio.mixins</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.runners">asyncio.runners</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#concurrent.futures._base">concurrent.futures._base</a>
+ &#8226;   <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#concurrent.futures.thread">concurrent.futures.thread</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#multiprocessing.context">multiprocessing.context</a>
+ &#8226;   <a href="#multiprocessing.dummy">multiprocessing.dummy</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.heap">multiprocessing.heap</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.resource_sharer">multiprocessing.resource_sharer</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#numpy.random.bit_generator">numpy.random.bit_generator</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#socketserver">socketserver</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+ &#8226;   <a href="#webbrowser">webbrowser</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="threadpoolctl"></a>
+  <a target="code" href="" type="text/plain"><tt>threadpoolctl</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#numpy.lib._utils_impl">numpy.lib._utils_impl</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="time"></a>
+  <tt>time</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imports:
+    <a href="#_strptime">_strptime</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_datetime">_datetime</a>
+ &#8226;   <a href="#_pydatetime">_pydatetime</a>
+ &#8226;   <a href="#_strptime">_strptime</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#concurrent.futures._base">concurrent.futures._base</a>
+ &#8226;   <a href="#datetime">datetime</a>
+ &#8226;   <a href="#email._parseaddr">email._parseaddr</a>
+ &#8226;   <a href="#email.generator">email.generator</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#gc">gc</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#multiprocessing.connection">multiprocessing.connection</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.synchronize">multiprocessing.synchronize</a>
+ &#8226;   <a href="#numpy.f2py.rules">numpy.f2py.rules</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#socketserver">socketserver</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.runner">unittest.runner</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#xmlrpc.client">xmlrpc.client</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tkinter"></a>
+  <a target="code" href="///C:/Python312/Lib/tkinter/__init__.py" type="text/plain"><tt>tkinter</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#_tkinter">_tkinter</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#tkinter.commondialog">tkinter.commondialog</a>
+ &#8226;   <a href="#tkinter.constants">tkinter.constants</a>
+ &#8226;   <a href="#tkinter.filedialog">tkinter.filedialog</a>
+ &#8226;   <a href="#tkinter.messagebox">tkinter.messagebox</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#types">types</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#tkinter.commondialog">tkinter.commondialog</a>
+ &#8226;   <a href="#tkinter.constants">tkinter.constants</a>
+ &#8226;   <a href="#tkinter.dialog">tkinter.dialog</a>
+ &#8226;   <a href="#tkinter.filedialog">tkinter.filedialog</a>
+ &#8226;   <a href="#tkinter.messagebox">tkinter.messagebox</a>
+ &#8226;   <a href="#tkinter.simpledialog">tkinter.simpledialog</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tkinter.commondialog"></a>
+  <a target="code" href="///C:/Python312/Lib/tkinter/commondialog.py" type="text/plain"><tt>tkinter.commondialog</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#tkinter">tkinter</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#tkinter.filedialog">tkinter.filedialog</a>
+ &#8226;   <a href="#tkinter.messagebox">tkinter.messagebox</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tkinter.constants"></a>
+  <a target="code" href="///C:/Python312/Lib/tkinter/constants.py" type="text/plain"><tt>tkinter.constants</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#tkinter">tkinter</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#tkinter">tkinter</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tkinter.dialog"></a>
+  <a target="code" href="///C:/Python312/Lib/tkinter/dialog.py" type="text/plain"><tt>tkinter.dialog</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#tkinter">tkinter</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#tkinter.filedialog">tkinter.filedialog</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tkinter.filedialog"></a>
+  <a target="code" href="///C:/Python312/Lib/tkinter/filedialog.py" type="text/plain"><tt>tkinter.filedialog</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#fnmatch">fnmatch</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#tkinter.commondialog">tkinter.commondialog</a>
+ &#8226;   <a href="#tkinter.dialog">tkinter.dialog</a>
+ &#8226;   <a href="#tkinter.simpledialog">tkinter.simpledialog</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#tkinter">tkinter</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tkinter.messagebox"></a>
+  <a target="code" href="///C:/Python312/Lib/tkinter/messagebox.py" type="text/plain"><tt>tkinter.messagebox</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#tkinter.commondialog">tkinter.commondialog</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#tkinter.simpledialog">tkinter.simpledialog</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="///C:/Python312/Lib/tkinter/simpledialog.py" type="text/plain"><tt>tkinter.simpledialog</tt></a>
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+    <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#tkinter.messagebox">tkinter.messagebox</a>
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+    <a href="#inspect">inspect</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#tokenize">tokenize</a>
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+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tokenize"></a>
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+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#_tokenize">_tokenize</a>
+ &#8226;   <a href="#argparse">argparse</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#codecs">codecs</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#token">token</a>
+
+  </div>
+  <div class="import">
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+    <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#numpy.lib._format_impl">numpy.lib._format_impl</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="traceback"></a>
+  <a target="code" href="///C:/Python312/Lib/traceback.py" type="text/plain"><tt>traceback</tt></a>
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+    <a href="#ast">ast</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#textwrap">textwrap</a>
+ &#8226;   <a href="#unicodedata">unicodedata</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_tasks">asyncio.base_tasks</a>
+ &#8226;   <a href="#asyncio.format_helpers">asyncio.format_helpers</a>
+ &#8226;   <a href="#code">code</a>
+ &#8226;   <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#doctest">doctest</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.process">multiprocessing.process</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pdb">pdb</a>
+ &#8226;   <a href="#py_compile">py_compile</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#socketserver">socketserver</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#unittest.result">unittest.result</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="tracemalloc"></a>
+  <a target="code" href="///C:/Python312/Lib/tracemalloc.py" type="text/plain"><tt>tracemalloc</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_tracemalloc">_tracemalloc</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#fnmatch">fnmatch</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#os.path">os.path</a>
+ &#8226;   <a href="#pickle">pickle</a>
+
+  </div>
+  <div class="import">
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+    <a href="#warnings">warnings</a>
+
+  </div>
+
+</div>
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+  <a target="code" href="///C:/Python312/Lib/tty.py" type="text/plain"><tt>tty</tt></a>
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+    <a href="#termios">termios</a>
+
+  </div>
+  <div class="import">
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+    <a href="#pydoc">pydoc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="types"></a>
+  <a target="code" href="///C:/Python312/Lib/types.py" type="text/plain"><tt>types</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#sys">sys</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_weakrefset">_weakrefset</a>
+ &#8226;   <a href="#asyncio.coroutines">asyncio.coroutines</a>
+ &#8226;   <a href="#asyncio.futures">asyncio.futures</a>
+ &#8226;   <a href="#asyncio.queues">asyncio.queues</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.timeouts">asyncio.timeouts</a>
+ &#8226;   <a href="#concurrent.futures._base">concurrent.futures._base</a>
+ &#8226;   <a href="#concurrent.futures.thread">concurrent.futures.thread</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#copy">copy</a>
+ &#8226;   <a href="#csv">csv</a>
+ &#8226;   <a href="#ctypes">ctypes</a>
+ &#8226;   <a href="#dataclasses">dataclasses</a>
+ &#8226;   <a href="#difflib">difflib</a>
+ &#8226;   <a href="#dis">dis</a>
+ &#8226;   <a href="#email.headerregistry">email.headerregistry</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#fileinput">fileinput</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib.metadata._functools">importlib.metadata._functools</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#importlib.resources._legacy">importlib.resources._legacy</a>
+ &#8226;   <a href="#importlib.util">importlib.util</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#multiprocessing.managers">multiprocessing.managers</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.shared_memory">multiprocessing.shared_memory</a>
+ &#8226;   <a href="#multiprocessing.spawn">multiprocessing.spawn</a>
+ &#8226;   <a href="#numpy._core.fromnumeric">numpy._core.fromnumeric</a>
+ &#8226;   <a href="#numpy._core.function_base">numpy._core.function_base</a>
+ &#8226;   <a href="#numpy._core.getlimits">numpy._core.getlimits</a>
+ &#8226;   <a href="#numpy._utils._inspect">numpy._utils._inspect</a>
+ &#8226;   <a href="#numpy.f2py.auxfuncs">numpy.f2py.auxfuncs</a>
+ &#8226;   <a href="#numpy.lib._utils_impl">numpy.lib._utils_impl</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#pickle">pickle</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#pprint">pprint</a>
+ &#8226;   <a href="#queue">queue</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#tkinter">tkinter</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="typing"></a>
+  <a target="code" href="///C:/Python312/Lib/typing.py" type="text/plain"><tt>typing</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_typing">_typing</a>
+ &#8226;   <a href="#abc">abc</a>
+ &#8226;   <a href="#collections">collections</a>
+ &#8226;   <a href="#collections.abc">collections.abc</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#copyreg">copyreg</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#operator">operator</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#asyncio.staggered">asyncio.staggered</a>
+ &#8226;   <a href="#asyncio.timeouts">asyncio.timeouts</a>
+ &#8226;   <a href="#cv2.mat_wrapper">cv2.mat_wrapper</a>
+ &#8226;   <a href="#cv2.typing">cv2.typing</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._meta">importlib.metadata._meta</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#importlib.resources._legacy">importlib.resources._legacy</a>
+ &#8226;   <a href="#importlib.resources.abc">importlib.resources.abc</a>
+ &#8226;   <a href="#numpy._typing._array_like">numpy._typing._array_like</a>
+ &#8226;   <a href="#numpy._typing._char_codes">numpy._typing._char_codes</a>
+ &#8226;   <a href="#numpy._typing._dtype_like">numpy._typing._dtype_like</a>
+ &#8226;   <a href="#numpy._typing._nbit_base">numpy._typing._nbit_base</a>
+ &#8226;   <a href="#numpy._typing._nested_sequence">numpy._typing._nested_sequence</a>
+ &#8226;   <a href="#numpy._typing._scalars">numpy._typing._scalars</a>
+ &#8226;   <a href="#numpy._typing._shape">numpy._typing._shape</a>
+ &#8226;   <a href="#numpy.lib._arraypad_impl">numpy.lib._arraypad_impl</a>
+ &#8226;   <a href="#numpy.lib._arraysetops_impl">numpy.lib._arraysetops_impl</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+ &#8226;   <a href="#numpy.linalg._umath_linalg">numpy.linalg._umath_linalg</a>
+ &#8226;   <a href="#numpy.random._common">numpy.random._common</a>
+ &#8226;   <a href="#numpy.random._generator">numpy.random._generator</a>
+ &#8226;   <a href="#numpy.random._mt19937">numpy.random._mt19937</a>
+ &#8226;   <a href="#numpy.random._pcg64">numpy.random._pcg64</a>
+ &#8226;   <a href="#numpy.random._philox">numpy.random._philox</a>
+ &#8226;   <a href="#numpy.random._sfc64">numpy.random._sfc64</a>
+ &#8226;   <a href="#numpy.random.bit_generator">numpy.random.bit_generator</a>
+ &#8226;   <a href="#numpy.random.mtrand">numpy.random.mtrand</a>
+ &#8226;   <a href="#pdb">pdb</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="typing_extensions"></a>
+  <a target="code" href="" type="text/plain"><tt>typing_extensions</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
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+    <a href="#numpy._typing._nested_sequence">numpy._typing._nested_sequence</a>
+ &#8226;   <a href="#numpy.random.bit_generator">numpy.random.bit_generator</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unicodedata"></a>
+  <tt>unicodedata</tt> <span class="moduletype"><tt>C:\Python312\DLLs\unicodedata.pyd</tt></span>  <div class="import">
+imported by:
+    <a href="#encodings.idna">encodings.idna</a>
+ &#8226;   <a href="#re._parser">re._parser</a>
+ &#8226;   <a href="#stringprep">stringprep</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/__init__.py" type="text/plain"><tt>unittest</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.async_case">unittest.async_case</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#unittest.main">unittest.main</a>
+ &#8226;   <a href="#unittest.result">unittest.result</a>
+ &#8226;   <a href="#unittest.runner">unittest.runner</a>
+ &#8226;   <a href="#unittest.signals">unittest.signals</a>
+ &#8226;   <a href="#unittest.suite">unittest.suite</a>
+ &#8226;   <a href="#unittest.util">unittest.util</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#doctest">doctest</a>
+ &#8226;   <a href="#numpy.testing">numpy.testing</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest._log">unittest._log</a>
+ &#8226;   <a href="#unittest.async_case">unittest.async_case</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#unittest.main">unittest.main</a>
+ &#8226;   <a href="#unittest.result">unittest.result</a>
+ &#8226;   <a href="#unittest.runner">unittest.runner</a>
+ &#8226;   <a href="#unittest.signals">unittest.signals</a>
+ &#8226;   <a href="#unittest.suite">unittest.suite</a>
+ &#8226;   <a href="#unittest.util">unittest.util</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest._log"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/_log.py" type="text/plain"><tt>unittest._log</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#collections">collections</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+
+  </div>
+  <div class="import">
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+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest.async_case"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/async_case.py" type="text/plain"><tt>unittest.async_case</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#asyncio">asyncio</a>
+ &#8226;   <a href="#contextvars">contextvars</a>
+ &#8226;   <a href="#inspect">inspect</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
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+    <a href="#unittest">unittest</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest.case"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/case.py" type="text/plain"><tt>unittest.case</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#difflib">difflib</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#pprint">pprint</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest._log">unittest._log</a>
+ &#8226;   <a href="#unittest.result">unittest.result</a>
+ &#8226;   <a href="#unittest.util">unittest.util</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest._log">unittest._log</a>
+ &#8226;   <a href="#unittest.async_case">unittest.async_case</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#unittest.runner">unittest.runner</a>
+ &#8226;   <a href="#unittest.suite">unittest.suite</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest.loader"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/loader.py" type="text/plain"><tt>unittest.loader</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#fnmatch">fnmatch</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.suite">unittest.suite</a>
+ &#8226;   <a href="#unittest.util">unittest.util</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
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+    <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.main">unittest.main</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest.main"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/main.py" type="text/plain"><tt>unittest.main</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#argparse">argparse</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#unittest.runner">unittest.runner</a>
+ &#8226;   <a href="#unittest.signals">unittest.signals</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#unittest">unittest</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest.result"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/result.py" type="text/plain"><tt>unittest.result</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#functools">functools</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.util">unittest.util</a>
+
+  </div>
+  <div class="import">
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+    <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.runner">unittest.runner</a>
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+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest.runner"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/runner.py" type="text/plain"><tt>unittest.runner</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.result">unittest.result</a>
+ &#8226;   <a href="#unittest.signals">unittest.signals</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
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+    <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.main">unittest.main</a>
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+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest.signals"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/signals.py" type="text/plain"><tt>unittest.signals</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#functools">functools</a>
+ &#8226;   <a href="#signal">signal</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#weakref">weakref</a>
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+  </div>
+  <div class="import">
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+    <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.main">unittest.main</a>
+ &#8226;   <a href="#unittest.runner">unittest.runner</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest.suite"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/suite.py" type="text/plain"><tt>unittest.suite</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#sys">sys</a>
+ &#8226;   <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.util">unittest.util</a>
+
+  </div>
+  <div class="import">
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+    <a href="#unittest">unittest</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="unittest.util"></a>
+  <a target="code" href="///C:/Python312/Lib/unittest/util.py" type="text/plain"><tt>unittest.util</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#collections">collections</a>
+ &#8226;   <a href="#os.path">os.path</a>
+ &#8226;   <a href="#unittest">unittest</a>
+
+  </div>
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+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#unittest.result">unittest.result</a>
+ &#8226;   <a href="#unittest.suite">unittest.suite</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="///C:/Python312/Lib/urllib/__init__.py" type="text/plain"><tt>urllib</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
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+ &#8226;   <a href="#urllib.error">urllib.error</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#urllib.response">urllib.response</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="urllib.error"></a>
+  <a target="code" href="///C:/Python312/Lib/urllib/error.py" type="text/plain"><tt>urllib.error</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#io">io</a>
+ &#8226;   <a href="#urllib">urllib</a>
+ &#8226;   <a href="#urllib.response">urllib.response</a>
+
+  </div>
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+    <a href="#numpy.lib._datasource">numpy.lib._datasource</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="///C:/Python312/Lib/urllib/parse.py" type="text/plain"><tt>urllib.parse</tt></a>
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+    <a href="#collections">collections</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#ipaddress">ipaddress</a>
+ &#8226;   <a href="#math">math</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#types">types</a>
+ &#8226;   <a href="#unicodedata">unicodedata</a>
+ &#8226;   <a href="#urllib">urllib</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
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+    <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#http.client">http.client</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#http.server">http.server</a>
+ &#8226;   <a href="#mimetypes">mimetypes</a>
+ &#8226;   <a href="#nturl2path">nturl2path</a>
+ &#8226;   <a href="#numpy.lib._datasource">numpy.lib._datasource</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+ &#8226;   <a href="#xmlrpc.client">xmlrpc.client</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="urllib.request"></a>
+  <a target="code" href="///C:/Python312/Lib/urllib/request.py" type="text/plain"><tt>urllib.request</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_scproxy">_scproxy</a>
+ &#8226;   <a href="#base64">base64</a>
+ &#8226;   <a href="#bisect">bisect</a>
+ &#8226;   <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#email">email</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#fnmatch">fnmatch</a>
+ &#8226;   <a href="#ftplib">ftplib</a>
+ &#8226;   <a href="#getpass">getpass</a>
+ &#8226;   <a href="#hashlib">hashlib</a>
+ &#8226;   <a href="#http.client">http.client</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#mimetypes">mimetypes</a>
+ &#8226;   <a href="#nturl2path">nturl2path</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#socket">socket</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#string">string</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#urllib">urllib</a>
+ &#8226;   <a href="#urllib.error">urllib.error</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+ &#8226;   <a href="#urllib.response">urllib.response</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#winreg">winreg</a>
+
+  </div>
+  <div class="import">
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+    <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#numpy.lib._datasource">numpy.lib._datasource</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="urllib.response"></a>
+  <a target="code" href="///C:/Python312/Lib/urllib/response.py" type="text/plain"><tt>urllib.response</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#urllib">urllib</a>
+
+  </div>
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+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+<span class="moduletype">MissingModule</span>  <div class="import">
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+    <a href="#platform">platform</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="warnings"></a>
+  <a target="code" href="///C:/Python312/Lib/warnings.py" type="text/plain"><tt>warnings</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_warnings">_warnings</a>
+ &#8226;   <a href="#builtins">builtins</a>
+ &#8226;   <a href="#linecache">linecache</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#traceback">traceback</a>
+ &#8226;   <a href="#tracemalloc">tracemalloc</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#_pydatetime">_pydatetime</a>
+ &#8226;   <a href="#argparse">argparse</a>
+ &#8226;   <a href="#ast">ast</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.base_subprocess">asyncio.base_subprocess</a>
+ &#8226;   <a href="#asyncio.events">asyncio.events</a>
+ &#8226;   <a href="#asyncio.proactor_events">asyncio.proactor_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.sslproto">asyncio.sslproto</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.unix_events">asyncio.unix_events</a>
+ &#8226;   <a href="#asyncio.windows_utils">asyncio.windows_utils</a>
+ &#8226;   <a href="#calendar">calendar</a>
+ &#8226;   <a href="#codeop">codeop</a>
+ &#8226;   <a href="#email.utils">email.utils</a>
+ &#8226;   <a href="#enum">enum</a>
+ &#8226;   <a href="#fileinput">fileinput</a>
+ &#8226;   <a href="#getpass">getpass</a>
+ &#8226;   <a href="#gettext">gettext</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#hmac">hmac</a>
+ &#8226;   <a href="#http.cookiejar">http.cookiejar</a>
+ &#8226;   <a href="#importlib">importlib</a>
+ &#8226;   <a href="#importlib.abc">importlib.abc</a>
+ &#8226;   <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.metadata._adapters">importlib.metadata._adapters</a>
+ &#8226;   <a href="#importlib.resources._common">importlib.resources._common</a>
+ &#8226;   <a href="#importlib.resources._legacy">importlib.resources._legacy</a>
+ &#8226;   <a href="#locale">locale</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#multiprocessing.forkserver">multiprocessing.forkserver</a>
+ &#8226;   <a href="#multiprocessing.pool">multiprocessing.pool</a>
+ &#8226;   <a href="#multiprocessing.resource_tracker">multiprocessing.resource_tracker</a>
+ &#8226;   <a href="#numpy">numpy</a>
+ &#8226;   <a href="#numpy.__config__">numpy.__config__</a>
+ &#8226;   <a href="#numpy._core._internal">numpy._core._internal</a>
+ &#8226;   <a href="#numpy._core._methods">numpy._core._methods</a>
+ &#8226;   <a href="#numpy._core.arrayprint">numpy._core.arrayprint</a>
+ &#8226;   <a href="#numpy._core.function_base">numpy._core.function_base</a>
+ &#8226;   <a href="#numpy._core.numeric">numpy._core.numeric</a>
+ &#8226;   <a href="#numpy._core.records">numpy._core.records</a>
+ &#8226;   <a href="#numpy._utils">numpy._utils</a>
+ &#8226;   <a href="#numpy.char">numpy.char</a>
+ &#8226;   <a href="#numpy.core._utils">numpy.core._utils</a>
+ &#8226;   <a href="#numpy.f2py">numpy.f2py</a>
+ &#8226;   <a href="#numpy.f2py.symbolic">numpy.f2py.symbolic</a>
+ &#8226;   <a href="#numpy.fft._pocketfft">numpy.fft._pocketfft</a>
+ &#8226;   <a href="#numpy.lib">numpy.lib</a>
+ &#8226;   <a href="#numpy.lib._format_impl">numpy.lib._format_impl</a>
+ &#8226;   <a href="#numpy.lib._function_base_impl">numpy.lib._function_base_impl</a>
+ &#8226;   <a href="#numpy.lib._histograms_impl">numpy.lib._histograms_impl</a>
+ &#8226;   <a href="#numpy.lib._nanfunctions_impl">numpy.lib._nanfunctions_impl</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#numpy.lib._polynomial_impl">numpy.lib._polynomial_impl</a>
+ &#8226;   <a href="#numpy.lib._twodim_base_impl">numpy.lib._twodim_base_impl</a>
+ &#8226;   <a href="#numpy.lib._type_check_impl">numpy.lib._type_check_impl</a>
+ &#8226;   <a href="#numpy.lib._ufunclike_impl">numpy.lib._ufunclike_impl</a>
+ &#8226;   <a href="#numpy.linalg._linalg">numpy.linalg._linalg</a>
+ &#8226;   <a href="#numpy.ma.core">numpy.ma.core</a>
+ &#8226;   <a href="#numpy.ma.extras">numpy.ma.extras</a>
+ &#8226;   <a href="#numpy.ma.mrecords">numpy.ma.mrecords</a>
+ &#8226;   <a href="#numpy.matlib">numpy.matlib</a>
+ &#8226;   <a href="#numpy.matrixlib.defmatrix">numpy.matrixlib.defmatrix</a>
+ &#8226;   <a href="#numpy.polynomial.polyutils">numpy.polynomial.polyutils</a>
+ &#8226;   <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+ &#8226;   <a href="#numpy.typing">numpy.typing</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#pkgutil">pkgutil</a>
+ &#8226;   <a href="#pydoc">pydoc</a>
+ &#8226;   <a href="#random">random</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#re._parser">re._parser</a>
+ &#8226;   <a href="#runpy">runpy</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#sre_compile">sre_compile</a>
+ &#8226;   <a href="#sre_constants">sre_constants</a>
+ &#8226;   <a href="#sre_parse">sre_parse</a>
+ &#8226;   <a href="#ssl">ssl</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sysconfig">sysconfig</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#typing">typing</a>
+ &#8226;   <a href="#unittest.async_case">unittest.async_case</a>
+ &#8226;   <a href="#unittest.case">unittest.case</a>
+ &#8226;   <a href="#unittest.loader">unittest.loader</a>
+ &#8226;   <a href="#unittest.main">unittest.main</a>
+ &#8226;   <a href="#unittest.runner">unittest.runner</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#webbrowser">webbrowser</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="weakref"></a>
+  <a target="code" href="///C:/Python312/Lib/weakref.py" type="text/plain"><tt>weakref</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
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+    <a href="#_collections_abc">_collections_abc</a>
+ &#8226;   <a href="#_weakref">_weakref</a>
+ &#8226;   <a href="#_weakrefset">_weakrefset</a>
+ &#8226;   <a href="#atexit">atexit</a>
+ &#8226;   <a href="#copy">copy</a>
+ &#8226;   <a href="#gc">gc</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#sys">sys</a>
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+  <div class="import">
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+    <a href="#MaskReviewer.py">MaskReviewer.py</a>
+ &#8226;   <a href="#_threading_local">_threading_local</a>
+ &#8226;   <a href="#asyncio.base_events">asyncio.base_events</a>
+ &#8226;   <a href="#asyncio.selector_events">asyncio.selector_events</a>
+ &#8226;   <a href="#asyncio.streams">asyncio.streams</a>
+ &#8226;   <a href="#asyncio.tasks">asyncio.tasks</a>
+ &#8226;   <a href="#asyncio.windows_events">asyncio.windows_events</a>
+ &#8226;   <a href="#concurrent.futures.process">concurrent.futures.process</a>
+ &#8226;   <a href="#concurrent.futures.thread">concurrent.futures.thread</a>
+ &#8226;   <a href="#copy">copy</a>
+ &#8226;   <a href="#functools">functools</a>
+ &#8226;   <a href="#logging">logging</a>
+ &#8226;   <a href="#multiprocessing.dummy">multiprocessing.dummy</a>
+ &#8226;   <a href="#multiprocessing.queues">multiprocessing.queues</a>
+ &#8226;   <a href="#multiprocessing.sharedctypes">multiprocessing.sharedctypes</a>
+ &#8226;   <a href="#multiprocessing.util">multiprocessing.util</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#tempfile">tempfile</a>
+ &#8226;   <a href="#unittest.signals">unittest.signals</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="webbrowser"></a>
+  <a target="code" href="///C:/Python312/Lib/webbrowser.py" type="text/plain"><tt>webbrowser</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
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+ &#8226;   <a href="#getopt">getopt</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#shlex">shlex</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#subprocess">subprocess</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#warnings">warnings</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#pydoc">pydoc</a>
+
+  </div>
+
+</div>
+
+<div class="node">
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+  <a target="code" href="" type="text/plain"><tt>win32pdh</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
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+    <a href="#numpy.testing._private.utils">numpy.testing._private.utils</a>
+
+  </div>
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+</div>
+
+<div class="node">
+  <a name="winreg"></a>
+  <tt>winreg</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
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+    <a href="#importlib._bootstrap_external">importlib._bootstrap_external</a>
+ &#8226;   <a href="#mimetypes">mimetypes</a>
+ &#8226;   <a href="#platform">platform</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xml"></a>
+  <a target="code" href="///C:/Python312/Lib/xml/__init__.py" type="text/plain"><tt>xml</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+ &#8226;   <a href="#xml.sax.xmlreader">xml.sax.xmlreader</a>
+
+  </div>
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+imported by:
+    <a href="#xml.parsers">xml.parsers</a>
+ &#8226;   <a href="#xml.sax">xml.sax</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xml.parsers"></a>
+  <a target="code" href="///C:/Python312/Lib/xml/parsers/__init__.py" type="text/plain"><tt>xml.parsers</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#xml">xml</a>
+ &#8226;   <a href="#xml.parsers.expat">xml.parsers.expat</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#xml.parsers.expat">xml.parsers.expat</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+ &#8226;   <a href="#xmlrpc.client">xmlrpc.client</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xml.parsers.expat"></a>
+  <a target="code" href="///C:/Python312/Lib/xml/parsers/expat.py" type="text/plain"><tt>xml.parsers.expat</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#pyexpat">pyexpat</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#xml.parsers">xml.parsers</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#xml.parsers">xml.parsers</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+ &#8226;   <a href="#xmlrpc.client">xmlrpc.client</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xml.sax"></a>
+  <a target="code" href="///C:/Python312/Lib/xml/sax/__init__.py" type="text/plain"><tt>xml.sax</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#xml">xml</a>
+ &#8226;   <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax._exceptions">xml.sax._exceptions</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+ &#8226;   <a href="#xml.sax.handler">xml.sax.handler</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+ &#8226;   <a href="#xml.sax.xmlreader">xml.sax.xmlreader</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax._exceptions">xml.sax._exceptions</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+ &#8226;   <a href="#xml.sax.handler">xml.sax.handler</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+ &#8226;   <a href="#xml.sax.xmlreader">xml.sax.xmlreader</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xml.sax._exceptions"></a>
+  <a target="code" href="///C:/Python312/Lib/xml/sax/_exceptions.py" type="text/plain"><tt>xml.sax._exceptions</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#xml.sax">xml.sax</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+ &#8226;   <a href="#xml.sax.xmlreader">xml.sax.xmlreader</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xml.sax.expatreader"></a>
+  <a target="code" href="///C:/Python312/Lib/xml/sax/expatreader.py" type="text/plain"><tt>xml.sax.expatreader</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_weakref">_weakref</a>
+ &#8226;   <a href="#weakref">weakref</a>
+ &#8226;   <a href="#xml.parsers">xml.parsers</a>
+ &#8226;   <a href="#xml.parsers.expat">xml.parsers.expat</a>
+ &#8226;   <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax._exceptions">xml.sax._exceptions</a>
+ &#8226;   <a href="#xml.sax.handler">xml.sax.handler</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+ &#8226;   <a href="#xml.sax.xmlreader">xml.sax.xmlreader</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#xml">xml</a>
+ &#8226;   <a href="#xml.sax">xml.sax</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xml.sax.handler"></a>
+  <a target="code" href="///C:/Python312/Lib/xml/sax/handler.py" type="text/plain"><tt>xml.sax.handler</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#xml.sax">xml.sax</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+ &#8226;   <a href="#xml.sax.xmlreader">xml.sax.xmlreader</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xml.sax.saxutils"></a>
+  <a target="code" href="///C:/Python312/Lib/xml/sax/saxutils.py" type="text/plain"><tt>xml.sax.saxutils</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#codecs">codecs</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+ &#8226;   <a href="#urllib.request">urllib.request</a>
+ &#8226;   <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax.handler">xml.sax.handler</a>
+ &#8226;   <a href="#xml.sax.xmlreader">xml.sax.xmlreader</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+ &#8226;   <a href="#xml.sax.xmlreader">xml.sax.xmlreader</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xml.sax.xmlreader"></a>
+  <a target="code" href="///C:/Python312/Lib/xml/sax/xmlreader.py" type="text/plain"><tt>xml.sax.xmlreader</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax._exceptions">xml.sax._exceptions</a>
+ &#8226;   <a href="#xml.sax.handler">xml.sax.handler</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#xml">xml</a>
+ &#8226;   <a href="#xml.sax">xml.sax</a>
+ &#8226;   <a href="#xml.sax.expatreader">xml.sax.expatreader</a>
+ &#8226;   <a href="#xml.sax.saxutils">xml.sax.saxutils</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xmlrpc"></a>
+  <a target="code" href="///C:/Python312/Lib/xmlrpc/__init__.py" type="text/plain"><tt>xmlrpc</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imported by:
+    <a href="#xmlrpc.client">xmlrpc.client</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="xmlrpc.client"></a>
+  <a target="code" href="///C:/Python312/Lib/xmlrpc/client.py" type="text/plain"><tt>xmlrpc.client</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#base64">base64</a>
+ &#8226;   <a href="#datetime">datetime</a>
+ &#8226;   <a href="#decimal">decimal</a>
+ &#8226;   <a href="#errno">errno</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#http.client">http.client</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#urllib.parse">urllib.parse</a>
+ &#8226;   <a href="#xml.parsers">xml.parsers</a>
+ &#8226;   <a href="#xml.parsers.expat">xml.parsers.expat</a>
+ &#8226;   <a href="#xmlrpc">xmlrpc</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#multiprocessing.connection">multiprocessing.connection</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="yaml"></a>
+  <a target="code" href="" type="text/plain"><tt>yaml</tt></a>
+<span class="moduletype">MissingModule</span>  <div class="import">
+imported by:
+    <a href="#numpy.__config__">numpy.__config__</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="zipfile"></a>
+  <a target="code" href="///C:/Python312/Lib/zipfile/__init__.py" type="text/plain"><tt>zipfile</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#argparse">argparse</a>
+ &#8226;   <a href="#binascii">binascii</a>
+ &#8226;   <a href="#bz2">bz2</a>
+ &#8226;   <a href="#importlib.util">importlib.util</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#lzma">lzma</a>
+ &#8226;   <a href="#os">os</a>
+ &#8226;   <a href="#py_compile">py_compile</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#stat">stat</a>
+ &#8226;   <a href="#struct">struct</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#threading">threading</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#warnings">warnings</a>
+ &#8226;   <a href="#zipfile._path">zipfile._path</a>
+ &#8226;   <a href="#zlib">zlib</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#importlib.metadata">importlib.metadata</a>
+ &#8226;   <a href="#importlib.resources.readers">importlib.resources.readers</a>
+ &#8226;   <a href="#numpy.lib._npyio_impl">numpy.lib._npyio_impl</a>
+ &#8226;   <a href="#pyi_rth_inspect.py">pyi_rth_inspect.py</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#zipfile._path">zipfile._path</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="zipfile._path"></a>
+  <a target="code" href="///C:/Python312/Lib/zipfile/_path/__init__.py" type="text/plain"><tt>zipfile._path</tt></a>
+<span class="moduletype">Package</span>  <div class="import">
+imports:
+    <a href="#contextlib">contextlib</a>
+ &#8226;   <a href="#io">io</a>
+ &#8226;   <a href="#itertools">itertools</a>
+ &#8226;   <a href="#pathlib">pathlib</a>
+ &#8226;   <a href="#posixpath">posixpath</a>
+ &#8226;   <a href="#re">re</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+ &#8226;   <a href="#zipfile._path.glob">zipfile._path.glob</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#zipfile">zipfile</a>
+ &#8226;   <a href="#zipfile._path.glob">zipfile._path.glob</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="zipfile._path.glob"></a>
+  <a target="code" href="///C:/Python312/Lib/zipfile/_path/glob.py" type="text/plain"><tt>zipfile._path.glob</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#re">re</a>
+ &#8226;   <a href="#zipfile._path">zipfile._path</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#zipfile._path">zipfile._path</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="zipimport"></a>
+  <a target="code" href="///C:/Python312/Lib/zipimport.py" type="text/plain"><tt>zipimport</tt></a>
+<span class="moduletype">SourceModule</span>  <div class="import">
+imports:
+    <a href="#_frozen_importlib">_frozen_importlib</a>
+ &#8226;   <a href="#_frozen_importlib_external">_frozen_importlib_external</a>
+ &#8226;   <a href="#_imp">_imp</a>
+ &#8226;   <a href="#_io">_io</a>
+ &#8226;   <a href="#_warnings">_warnings</a>
+ &#8226;   <a href="#importlib.readers">importlib.readers</a>
+ &#8226;   <a href="#marshal">marshal</a>
+ &#8226;   <a href="#sys">sys</a>
+ &#8226;   <a href="#time">time</a>
+ &#8226;   <a href="#zlib">zlib</a>
+
+  </div>
+  <div class="import">
+imported by:
+    <a href="#pkgutil">pkgutil</a>
+
+  </div>
+
+</div>
+
+<div class="node">
+  <a name="zlib"></a>
+  <tt>zlib</tt> <span class="moduletype"><i>(builtin module)</i></span>  <div class="import">
+imported by:
+    <a href="#encodings.zlib_codec">encodings.zlib_codec</a>
+ &#8226;   <a href="#gzip">gzip</a>
+ &#8226;   <a href="#shutil">shutil</a>
+ &#8226;   <a href="#tarfile">tarfile</a>
+ &#8226;   <a href="#zipfile">zipfile</a>
+ &#8226;   <a href="#zipimport">zipimport</a>
+
+  </div>
+
+</div>
+
+  </body>
+</html>
diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build_windows.bat b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build_windows.bat
new file mode 100644
index 000000000..69112babf
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/build_windows.bat
@@ -0,0 +1,81 @@
+@echo off
+setlocal
+cd /d "%~dp0"
+
+echo ============================================================
+echo BUILD MASKREVIEWER WINDOWS
+echo ============================================================
+echo.
+
+where py >nul 2>nul
+if %errorlevel%==0 (
+    set PY=py
+) else (
+    set PY=python
+)
+
+%PY% --version
+if errorlevel 1 (
+    echo.
+    echo [ERRO] Python nao encontrado.
+    echo Instale Python 3.11 ou 3.12 para Windows e tente novamente.
+    pause
+    exit /b 1
+)
+
+if not exist ".build_venv\Scripts\python.exe" (
+    echo [1/4] Criando ambiente de build...
+    %PY% -m venv .build_venv
+    if errorlevel 1 goto :erro
+)
+
+call ".build_venv\Scripts\activate.bat"
+
+echo [2/4] Instalando dependencias de build...
+python -m pip install --upgrade pip
+python -m pip install pyinstaller numpy opencv-python
+if errorlevel 1 goto :erro
+
+echo [3/4] Limpando builds antigos...
+if exist build rmdir /s /q build
+if exist dist rmdir /s /q dist
+if exist MaskReviewer.spec del /q MaskReviewer.spec
+
+echo [4/4] Gerando EXE...
+python -m PyInstaller ^
+  --noconfirm ^
+  --clean ^
+  --onedir ^
+  --windowed ^
+  --name MaskReviewer ^
+  --collect-all cv2 ^
+  MaskReviewer.py
+
+if errorlevel 1 goto :erro
+
+echo.
+echo ============================================================
+echo BUILD CONCLUIDO
+echo ============================================================
+echo Pasta gerada:
+echo   %CD%\dist\MaskReviewer
+echo.
+echo Coloque ao lado do MaskReviewer.exe:
+echo   labelmap.txt
+echo   group\
+echo.
+echo Estrutura final recomendada:
+echo   MaskReviewer\
+echo     MaskReviewer.exe
+echo     _internal\
+echo     labelmap.txt
+echo     group\
+echo.
+pause
+exit /b 0
+
+:erro
+echo.
+echo [ERRO] Falha no build.
+pause
+exit /b 1
diff --git a/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/executar_sem_exe.bat b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/executar_sem_exe.bat
new file mode 100644
index 000000000..1c2f6fc31
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/MaskReviewer_package/executar_sem_exe.bat
@@ -0,0 +1,19 @@
+@echo off
+setlocal
+cd /d "%~dp0"
+
+where py >nul 2>nul
+if %errorlevel%==0 (
+    set PY=py
+) else (
+    set PY=python
+)
+
+%PY% -c "import cv2, numpy" >nul 2>nul
+if errorlevel 1 (
+    echo Instalando numpy e opencv-python...
+    %PY% -m pip install numpy opencv-python
+)
+
+%PY% MaskReviewer.py
+if errorlevel 1 pause
diff --git a/Python/OAK/datasets/oak-fcc-3/_10_export_onnx.py b/Python/OAK/datasets/oak-fcc-3/_10_export_onnx.py
index d668adce8..f0de57b22 100644
--- a/Python/OAK/datasets/oak-fcc-3/_10_export_onnx.py
+++ b/Python/OAK/datasets/oak-fcc-3/_10_export_onnx.py
@@ -8,7 +8,8 @@ Exporta o checkpoint PyTorch do SegFormer Multi-Head OAK-FCC-3 para ONNX.
 
 Exemplo:
 
-python _10_export_onnx.py --config config.json --checkpoint backup/segformer_b1/2026_08_17/stacked_raw5_ndvi_ndre/best_score.pt --out backup/segformer_b1/2026_08_17/stacked_raw5_ndvi_ndre/best_score.onnx --include-norm --postprocess argmax_fullres
+python .\_10_export_onnx.py --config config.json --checkpoint backup/segformer_b1/2026_08_17/stacked_raw5_ndvi_ndre/best_score.pt --out backup/segformer_b1/2026_08_17/stacked_raw5_ndvi_ndre/best_score.onnx --include-norm --postprocess argmax_fullres
+python .\_10_export_onnx.py --config .\config.json --train-script .\_8_train_multihead_v2.py --opset 17 --device cuda --include-norm --postprocess argmax_fullres
 """
 
 from __future__ import annotations
diff --git a/Python/OAK/datasets/oak-fcc-3/_11_validate_onnx.py b/Python/OAK/datasets/oak-fcc-3/_11_validate_onnx.py
index e376f6ae8..4ca57cd2d 100644
--- a/Python/OAK/datasets/oak-fcc-3/_11_validate_onnx.py
+++ b/Python/OAK/datasets/oak-fcc-3/_11_validate_onnx.py
@@ -13,6 +13,7 @@ para o SegFormer OAK-FCC-3 Multi-Head.
 Exemplo:
 
 python _11_validate_onnx.py --config config.json --max_samples 20 --device cuda --onnx_provider cuda --torch_no_amp
+python .\_11_validate_onnx.py --config .\config.json --train-script .\_8_train_multihead_v2.py --max_samples 50 --device cuda --onnx_provider cuda --torch_no_amp
 
 Para validar o ONNX com saída já redimensionada:
 
diff --git a/Python/OAK/datasets/oak-fcc-3/_12_benchmark_onnx_v2.py b/Python/OAK/datasets/oak-fcc-3/_12_benchmark_onnx_v2.py
new file mode 100644
index 000000000..dabe02c15
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/_12_benchmark_onnx_v2.py
@@ -0,0 +1,1191 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+_12_benchmark_onnx.py
+
+Benchmark PyTorch vs ONNX Runtime para SegFormer OAK-FCC-3 Multi-Head.
+
+Mede:
+  - PyTorch FP32
+  - PyTorch AMP/FP16
+  - ONNX Runtime CUDA ou CPU
+
+Exemplos:
+
+Benchmark ONNX cru 160x256:
+
+python _12_benchmark_onnx_v2.py --config config.json --train-script .\_8_train_multihead_v2.py --max_samples 50 --warmup 10 --repeat 5 --device cuda --onnx_provider cuda
+
+Benchmark ONNX resized 640x1024:
+
+python _12_benchmark_onnx_v2.py --config config.json --train-script .\_8_train_multihead_v2.py --max_samples 50 --warmup 10 --repeat 5 --device cuda --onnx_provider cuda
+
+
+TensorRT
+python .\_12_benchmark_onnx_v2.py --config .\config.json --train-script .\_8_train_multihead_v2.py --max_samples 50 --warmup 10 --repeat 5 --device cuda --onnx_provider tensorrt --skip_torch_fp32 --skip_torch_amp
+
+"""
+
+from __future__ import annotations
+
+import gc
+import csv
+import json
+import time
+import argparse
+import importlib.util
+import inspect
+from pathlib import Path
+from typing import Optional, List, Dict, Sequence, Tuple
+
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+SOURCE_CHANNEL_ORDER = ["R", "G", "B", "RE", "NIR"]
+DERIVED_CHANNEL_ORDER = ["NDVI", "NDRE"]
+SUPPORTED_INPUT_CHANNELS = SOURCE_CHANNEL_ORDER + DERIVED_CHANNEL_ORDER
+
+
+# ============================================================
+# Utils
+# ============================================================
+
+def load_json(path: str | Path) -> dict:
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def save_json(path: str | Path, data: dict):
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+
+def save_csv(path: str | Path, rows: List[dict]):
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    if not rows:
+        return
+
+    keys = list(rows[0].keys())
+
+    with path.open("w", newline="", encoding="utf-8") as f:
+        w = csv.DictWriter(f, fieldnames=keys)
+        w.writeheader()
+        w.writerows(rows)
+
+
+def resolve_path(path_like: Optional[str], base: Optional[Path] = None) -> Optional[Path]:
+    if path_like is None:
+        return None
+
+    p = Path(path_like)
+    if p.is_absolute():
+        return p
+
+    if base is None:
+        base = Path.cwd()
+
+    return (base / p).resolve()
+
+
+def import_train_module(train_script_path: str | Path):
+    train_script_path = Path(train_script_path)
+
+    if not train_script_path.exists():
+        raise FileNotFoundError(f"Script de treino não encontrado: {train_script_path}")
+
+    spec = importlib.util.spec_from_file_location(
+        "train_multihead_module",
+        str(train_script_path.resolve())
+    )
+
+    if spec is None or spec.loader is None:
+        raise RuntimeError(f"Não consegui importar o script: {train_script_path}")
+
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def synchronize_if_cuda(device: torch.device):
+    if device.type == "cuda":
+        torch.cuda.synchronize()
+
+
+def clear_cuda():
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+
+def percentile(values: List[float], p: float) -> float:
+    if not values:
+        return 0.0
+    return float(np.percentile(np.asarray(values, dtype=np.float64), p))
+
+
+def summarize_times(times_ms: List[float]) -> dict:
+    arr = np.asarray(times_ms, dtype=np.float64)
+
+    if arr.size == 0:
+        return {
+            "n": 0,
+            "mean_ms": 0.0,
+            "median_ms": 0.0,
+            "min_ms": 0.0,
+            "max_ms": 0.0,
+            "p95_ms": 0.0,
+            "p99_ms": 0.0,
+            "fps_mean": 0.0,
+            "fps_p95_latency": 0.0,
+        }
+
+    mean_ms = float(arr.mean())
+    p95_ms = float(np.percentile(arr, 95))
+    p99_ms = float(np.percentile(arr, 99))
+
+    return {
+        "n": int(arr.size),
+        "mean_ms": mean_ms,
+        "median_ms": float(np.median(arr)),
+        "min_ms": float(arr.min()),
+        "max_ms": float(arr.max()),
+        "p95_ms": p95_ms,
+        "p99_ms": p99_ms,
+        "fps_mean": float(1000.0 / mean_ms) if mean_ms > 0 else 0.0,
+        "fps_p95_latency": float(1000.0 / p95_ms) if p95_ms > 0 else 0.0,
+    }
+
+
+def experiment_tag(config: dict, channels: int) -> str:
+    explicit = str(config.get("stats_source_tag", "")).strip()
+    if explicit:
+        return explicit
+    return f"{config.get('fusion_mode', 'stacked')}_raw{channels}"
+
+
+def load_export_metadata(onnx_path: Path) -> Tuple[dict, Optional[Path]]:
+    meta_path = onnx_path.with_suffix(".export_meta.json")
+    if meta_path.is_file():
+        return load_json(meta_path), meta_path
+    return {}, None
+
+
+def resolve_model_artifact_paths(
+    args,
+    config: dict,
+    config_dir: Path,
+    channels: int,
+    require_onnx: bool = True,
+) -> Tuple[Path, Path, str]:
+    """
+    Resolve checkpoint e ONNX.
+
+    Se --checkpoint ou --onnx forem informados, usa os caminhos informados.
+    Se ficarem vazios, monta a partir do config:
+
+      backup/{modelo}/{model_name}/{stats_source_tag}/{ckpt_name}.pt
+      backup/{modelo}/{model_name}/{stats_source_tag}/{ckpt_name}.onnx
+
+    ckpt_name vem de:
+      config["ckpt_test"] ou "best_score"
+    """
+    model = config.get("modelo", "segformer_b1")
+    model_name = config.get("model_name", "target_teached")
+    ckpt_name = config.get("ckpt_test", "best_score")
+    base_dir = config_dir / "backup" / model / model_name / experiment_tag(config, channels)
+
+    ckpt_filename = str(ckpt_name)
+    if not ckpt_filename.lower().endswith(".pt"):
+        ckpt_filename += ".pt"
+    onnx_filename = f"{Path(str(ckpt_name)).stem}.onnx"
+
+    if args.checkpoint:
+        checkpoint_path = resolve_path(args.checkpoint, config_dir)
+    else:
+        checkpoint_path = base_dir / ckpt_filename
+
+    if args.onnx:
+        onnx_path = resolve_path(args.onnx, config_dir)
+    else:
+        onnx_path = base_dir / onnx_filename
+
+    if checkpoint_path is None or not checkpoint_path.is_file():
+        raise FileNotFoundError(
+            f"Checkpoint não encontrado: {checkpoint_path}\n"
+            f"Dica: informe --checkpoint ou ajuste config['ckpt_test']."
+        )
+
+    if require_onnx and (onnx_path is None or not onnx_path.is_file()):
+        raise FileNotFoundError(
+            f"ONNX não encontrado: {onnx_path}\n"
+            f"Dica: exporte o checkpoint, informe --onnx, ou use --skip_onnx para benchmark só PyTorch."
+        )
+
+    # Mesmo em --skip_onnx retornamos o caminho esperado, ainda que o arquivo não exista.
+    # Isso mantém nomes de relatório e diretório do experimento previsíveis.
+    return checkpoint_path.resolve(), onnx_path.resolve(), str(ckpt_name)
+
+
+# ============================================================
+# Dataset / normalização
+# ============================================================
+
+def collect_tensor_samples(root: Path, max_samples: int = 50, start_idx: int = 0) -> List[Path]:
+    tensor_paths: List[Path] = []
+
+    direct = root / "tensors"
+    if direct.is_dir():
+        tensor_paths.extend(sorted(direct.glob("*.npy")))
+
+    group_root = root / "group"
+    if group_root.is_dir():
+        for gdir in sorted(group_root.iterdir()):
+            tdir = gdir / "tensors"
+            if tdir.is_dir():
+                tensor_paths.extend(sorted(tdir.glob("*.npy")))
+
+    if not tensor_paths:
+        tensor_paths.extend(sorted(root.glob("**/tensors/*.npy")))
+
+    if not tensor_paths:
+        raise RuntimeError(f"Nenhum tensor .npy encontrado em: {root}")
+
+    start_idx = max(0, int(start_idx))
+    selected = tensor_paths[start_idx:]
+
+    if max_samples > 0:
+        selected = selected[:int(max_samples)]
+
+    return selected
+
+
+def load_tensor(
+    path: Path,
+    requested_channels: Sequence[str],
+    saved_channel_names: Optional[Sequence[str]] = None,
+) -> np.ndarray:
+    arr = np.load(str(path)).astype(np.float32)
+
+    if arr.ndim != 3:
+        raise RuntimeError(f"Tensor inválido {path}: shape={arr.shape}, esperado 3D")
+
+    if 1 <= arr.shape[0] <= len(SUPPORTED_INPUT_CHANNELS) and arr.shape[1] > 8 and arr.shape[2] > 8:
+        chw = arr
+    elif 1 <= arr.shape[-1] <= len(SUPPORTED_INPUT_CHANNELS) and arr.shape[0] > 8 and arr.shape[1] > 8:
+        chw = np.transpose(arr, (2, 0, 1))
+    else:
+        raise RuntimeError(f"Tensor com layout inesperado: {path} shape={arr.shape}")
+
+    requested = [str(x).strip().upper() for x in requested_channels]
+    saved = [str(x).strip().upper() for x in (saved_channel_names or [])]
+
+    if saved:
+        if len(saved) != chw.shape[0] or len(set(saved)) != len(saved):
+            raise RuntimeError(
+                f"Meta/tensor incompatíveis em {path}: channels={saved}, shape={chw.shape}"
+            )
+        missing = [name for name in requested if name not in saved]
+        if missing:
+            raise RuntimeError(f"Tensor {path} não contém {missing}. Disponíveis={saved}")
+        indices = [saved.index(name) for name in requested]
+    elif chw.shape[0] == len(requested):
+        indices = list(range(len(requested)))
+        print(f"[WARN] {path.name}: sem meta.channels; assumindo ordem {requested}")
+    elif chw.shape[0] == len(SOURCE_CHANNEL_ORDER) and all(x in SOURCE_CHANNEL_ORDER for x in requested):
+        indices = [SOURCE_CHANNEL_ORDER.index(name) for name in requested]
+        print(f"[WARN] {path.name}: Raw5 legado; usando ordem {SOURCE_CHANNEL_ORDER}")
+    else:
+        raise RuntimeError(
+            f"Não é seguro inferir canais de {path}: shape={chw.shape}, pedidos={requested}"
+        )
+
+    chw = chw[indices, :, :]
+
+    finite = np.isfinite(chw)
+    if finite.any():
+        mx = float(np.nanmax(chw[finite]))
+        if mx > 2.0 and mx <= 255.0:
+            chw = chw / 255.0
+        elif mx > 255.0:
+            chw = chw / 65535.0
+
+    chw = np.nan_to_num(chw, nan=0.0, posinf=1.0, neginf=-1.0)
+    return np.ascontiguousarray(chw, dtype=np.float32)
+
+
+def load_tensor_channel_names(tensor_path: Path) -> List[str]:
+    meta_path = tensor_path.parent.parent / "metas" / f"{tensor_path.stem}.json"
+    if not meta_path.is_file():
+        return []
+    meta = load_json(meta_path)
+    return list(meta.get("channels") or meta.get("input_channels") or [])
+
+
+def find_norm_stats(config: dict, config_dir: Path, save_dir: Path, explicit: Optional[str]) -> Optional[Path]:
+    if explicit:
+        return resolve_path(explicit, config_dir)
+
+    W, H = config.get("resolucao", [1024, 640])
+    dataset_path = config_dir / "dataset"
+
+    candidates = [
+        save_dir / "norm_stats.json",
+        dataset_path / f"{int(W)}x{int(H)}" / "group" / "norm_stats.json",
+        config_dir / "backup" / config.get("modelo", "segformer_b1") / config.get("model_name", "test") / config.get("stats_source_tag", "stacked_raw5") / "norm_stats.json",
+    ]
+
+    for p in candidates:
+        if p.is_file():
+            return p
+
+    return candidates[0]
+
+
+def load_norm_stats(
+    path: Optional[Path],
+    channel_names: Sequence[str],
+) -> Tuple[Optional[List[float]], Optional[List[float]], Optional[str]]:
+    if path is None or not path.is_file():
+        raise FileNotFoundError(
+            f"norm_stats não encontrado: {path}. Benchmark exige os stats do treinamento."
+        )
+
+    js = load_json(path)
+    mean = js.get("mean")
+    std = js.get("std")
+    names = [str(x).strip().upper() for x in js.get("channels", [])]
+    requested = [str(x).strip().upper() for x in channel_names]
+
+    if mean is None or std is None:
+        raise RuntimeError(f"norm_stats inválido, faltando mean/std: {path}")
+
+    if len(mean) != len(std):
+        raise RuntimeError(f"norm_stats inválido: mean={len(mean)} std={len(std)}")
+
+    if names:
+        if len(names) != len(mean) or len(set(names)) != len(names):
+            raise RuntimeError(f"norm_stats channels inválido: {names}")
+        missing = [name for name in requested if name not in names]
+        if missing:
+            raise RuntimeError(
+                f"norm_stats não contém {missing}. Disponíveis={names}, pedidos={requested}"
+            )
+        indices = [names.index(name) for name in requested]
+    elif len(mean) == len(requested):
+        indices = list(range(len(requested)))
+        print(f"[NORM][WARN] stats sem nomes; assumindo ordem {requested}")
+    elif len(mean) == len(SOURCE_CHANNEL_ORDER) and all(x in SOURCE_CHANNEL_ORDER for x in requested):
+        indices = [SOURCE_CHANNEL_ORDER.index(name) for name in requested]
+        print(f"[NORM][WARN] stats Raw5 legados; usando ordem {SOURCE_CHANNEL_ORDER}")
+    else:
+        raise RuntimeError(
+            f"Não é seguro mapear norm_stats sem nomes: mean={len(mean)}, pedidos={requested}"
+        )
+
+    mean_sel = [float(mean[i]) for i in indices]
+    std_sel = [float(std[i]) for i in indices]
+    if not np.all(np.isfinite(mean_sel)) or not np.all(np.isfinite(std_sel)):
+        raise RuntimeError(f"norm_stats possui NaN/Inf: mean={mean_sel} std={std_sel}")
+    if any(x <= 0.0 for x in std_sel):
+        raise RuntimeError(f"norm_stats possui std inválido: {std_sel}")
+
+    print(f"[NORM] usando {path}")
+    print(f"[NORM] channels={requested}")
+    print(f"[NORM] mean={mean_sel}")
+    print(f"[NORM] std ={std_sel}")
+
+    return mean_sel, std_sel, str(path)
+
+
+def normalize_numpy_chw(chw: np.ndarray, mean: Optional[List[float]], std: Optional[List[float]]) -> np.ndarray:
+    if mean is None or std is None:
+        return chw.astype(np.float32)
+
+    mean_np = np.asarray(mean, dtype=np.float32).reshape(-1, 1, 1)
+    std_np = np.asarray(std, dtype=np.float32).reshape(-1, 1, 1)
+    std_np = np.clip(std_np, 1e-6, None)
+
+    return ((chw.astype(np.float32) - mean_np) / std_np).astype(np.float32)
+
+
+def load_inputs_as_numpy(
+    samples: List[Path],
+    input_channel_names: Sequence[str],
+    mean: Optional[List[float]],
+    std: Optional[List[float]],
+    target_hw: Tuple[int, int],
+    normalize_input: bool = True,
+) -> List[np.ndarray]:
+    H, W = target_hw
+    xs = []
+
+    for p in samples:
+        chw01 = load_tensor(
+            p,
+            requested_channels=input_channel_names,
+            saved_channel_names=load_tensor_channel_names(p),
+        )
+
+        if chw01.shape[-2:] != (H, W):
+            hwc = np.transpose(chw01, (1, 2, 0))
+            hwc = cv2.resize(hwc, (W, H), interpolation=cv2.INTER_LINEAR)
+            chw01 = np.transpose(hwc, (2, 0, 1)).astype(np.float32)
+
+        if normalize_input:
+            chw = normalize_numpy_chw(chw01, mean=mean, std=std)
+        else:
+            chw = chw01.astype(np.float32, copy=False)
+
+        x = np.expand_dims(chw, axis=0).astype(np.float32)
+        xs.append(x)
+
+    return xs
+
+
+# ============================================================
+# PyTorch
+# ============================================================
+
+class TorchTupleWrapper(nn.Module):
+    def __init__(
+        self,
+        model: nn.Module,
+        output_heads: List[str],
+        postprocess: str = "none",
+        include_norm: bool = False,
+        norm_mean: Optional[Sequence[float]] = None,
+        norm_std: Optional[Sequence[float]] = None,
+    ):
+        super().__init__()
+        self.model = model
+        self.output_heads = list(output_heads)
+        self.postprocess = str(postprocess).lower()
+        self.include_norm = bool(include_norm)
+        if self.postprocess not in ("none", "resize_logits", "argmax_lowres", "argmax_fullres"):
+            raise RuntimeError(f"postprocess inválido: {self.postprocess}")
+        if self.include_norm:
+            if norm_mean is None or norm_std is None:
+                raise RuntimeError("include_norm=True requer norm_mean/norm_std")
+            self.register_buffer(
+                "norm_mean",
+                torch.tensor(norm_mean, dtype=torch.float32).view(1, -1, 1, 1),
+            )
+            self.register_buffer(
+                "norm_std",
+                torch.tensor(norm_std, dtype=torch.float32).view(1, -1, 1, 1),
+            )
+        else:
+            self.register_buffer("norm_mean", torch.empty(0))
+            self.register_buffer("norm_std", torch.empty(0))
+
+    def forward(self, pixel_values: torch.Tensor):
+        x = pixel_values
+        if self.include_norm:
+            x = (x - self.norm_mean) / torch.clamp(self.norm_std, min=1e-6)
+        outputs = self.model(pixel_values=x)
+        input_hw = pixel_values.shape[-2:]
+        result = []
+
+        for head in self.output_heads:
+            value = outputs[head]
+            if self.postprocess in ("resize_logits", "argmax_fullres"):
+                value = F.interpolate(
+                    value,
+                    size=input_hw,
+                    mode="bilinear",
+                    align_corners=False,
+                )
+            if self.postprocess in ("argmax_lowres", "argmax_fullres"):
+                value = torch.argmax(value, dim=1).to(torch.uint8)
+            result.append(value)
+
+        return tuple(result)
+
+
+@torch.inference_mode()
+def benchmark_torch(
+    model: nn.Module,
+    inputs_np: List[np.ndarray],
+    device: torch.device,
+    warmup: int,
+    repeat: int,
+    amp: bool,
+    label: str,
+) -> Tuple[dict, List[dict]]:
+    model.eval()
+
+    times = []
+    rows = []
+
+    # Precarrega tensors na GPU para medir só inferência do modelo.
+    inputs_t = [
+        torch.from_numpy(x).to(device, non_blocking=True)
+        for x in inputs_np
+    ]
+
+    if device.type == "cuda":
+        torch.cuda.synchronize()
+
+    print(f"\n[BENCH] {label} | warmup={warmup} repeat={repeat}")
+
+    # Warmup
+    for i in range(max(0, warmup)):
+        x = inputs_t[i % len(inputs_t)]
+        with torch.autocast(device_type="cuda", dtype=torch.float16, enabled=amp and device.type == "cuda"):
+            _ = model(x)
+
+    synchronize_if_cuda(device)
+
+    # Medição
+    total_iter = len(inputs_t) * max(1, repeat)
+    idx = 0
+
+    for r in range(max(1, repeat)):
+        for sample_idx, x in enumerate(inputs_t):
+            synchronize_if_cuda(device)
+            t0 = time.perf_counter()
+
+            with torch.autocast(device_type="cuda", dtype=torch.float16, enabled=amp and device.type == "cuda"):
+                _ = model(x)
+
+            synchronize_if_cuda(device)
+            dt_ms = (time.perf_counter() - t0) * 1000.0
+
+            times.append(dt_ms)
+            rows.append({
+                "engine": label,
+                "repeat": r,
+                "sample_idx": sample_idx,
+                "iter_idx": idx,
+                "latency_ms": dt_ms,
+            })
+
+            idx += 1
+
+            if idx % 25 == 0 or idx == total_iter:
+                print(f"  {idx:04d}/{total_iter:04d} | last={dt_ms:.2f}ms")
+
+    summary = summarize_times(times)
+    summary["engine"] = label
+    summary["timing_scope"] = "model_forward_and_export_postprocess_device_resident"
+
+    return summary, rows
+
+
+# ============================================================
+# ONNX Runtime
+# ============================================================
+
+def create_onnx_session(
+    onnx_path: Path,
+    provider: str,
+    trt_home: Optional[str] = None,
+    trt_fp16: bool = True,
+):
+    import os
+
+    try:
+        import onnxruntime as ort
+    except ImportError:
+        raise ImportError(
+            "onnxruntime não está instalado. Instale com:\n"
+            "  pip install onnxruntime-gpu\n"
+            "ou CPU:\n"
+            "  pip install onnxruntime"
+        )
+
+    available = ort.get_available_providers()
+    print(f"[ONNX] providers disponíveis: {available}")
+
+    provider = provider.lower()
+
+    if provider == "tensorrt":
+        trt_home = trt_home or os.environ.get("TRT_HOME", r"C:\dev\TensorRT-10.10.0.31")
+        dll_dirs = [
+            os.path.join(trt_home, "lib"),
+            os.path.join(trt_home, "bin"),
+        ]
+        cuda_home = os.environ.get("CUDA_PATH")
+        if cuda_home:
+            dll_dirs.append(os.path.join(cuda_home, "bin"))
+        dll_dirs.append(r"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin")
+
+        for dll_dir in dll_dirs:
+            if os.path.isdir(dll_dir):
+                try:
+                    os.add_dll_directory(dll_dir)
+                    print(f"[DLL] add_dll_directory: {dll_dir}")
+                except Exception as e:
+                    print(f"[DLL][WARN] falha em {dll_dir}: {e}")
+
+    sess_options = ort.SessionOptions()
+    sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+
+    if provider == "cuda":
+        providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
+
+    elif provider == "cpu":
+        providers = ["CPUExecutionProvider"]
+
+    elif provider == "tensorrt":
+        cache_dir = onnx_path.parent / "trt_cache"
+        cache_dir.mkdir(parents=True, exist_ok=True)
+
+        trt_options = {
+            "device_id": 0,
+
+            # FP16: o ponto principal do nosso teste.
+            "trt_fp16_enable": bool(trt_fp16),
+
+            # Cache: evita rebuild do engine a cada execução.
+            "trt_engine_cache_enable": True,
+            "trt_engine_cache_path": str(cache_dir),
+
+            # Timing cache ajuda a acelerar builds futuros.
+            "trt_timing_cache_enable": True,
+            "trt_timing_cache_path": str(cache_dir),
+
+            # Workspace. 4GB é razoável para RTX 3070, ajuste se faltar VRAM.
+            "trt_max_workspace_size": 4 * 1024 * 1024 * 1024,
+        }
+
+        providers = [
+            ("TensorrtExecutionProvider", trt_options),
+            "CUDAExecutionProvider",
+            "CPUExecutionProvider",
+        ]
+
+    else:
+        providers = [provider]
+
+    # Checagem de disponibilidade, lidando com provider tuple.
+    requested_names = [
+        p[0] if isinstance(p, tuple) else p
+        for p in providers
+    ]
+
+    providers_ok = [
+        p for p in providers
+        if (p[0] if isinstance(p, tuple) else p) in available
+    ]
+
+    if not providers_ok:
+        raise RuntimeError(
+            f"Nenhum provider solicitado está disponível. "
+            f"Solicitado={requested_names}, disponível={available}"
+        )
+
+    session = ort.InferenceSession(
+        str(onnx_path),
+        sess_options=sess_options,
+        providers=providers_ok,
+    )
+
+    print(f"[ONNX] usando providers: {session.get_providers()}")
+
+    active_providers = session.get_providers()
+
+    if provider == "tensorrt" and "TensorrtExecutionProvider" not in active_providers:
+        raise RuntimeError(
+            "TensorRTExecutionProvider foi solicitado, mas não ficou ativo. "
+            f"Providers ativos: {active_providers}. "
+            "Provável causa: TensorRT não instalado, DLLs fora do PATH, "
+            "ou versão incompatível com onnxruntime-gpu."
+        )
+
+    if provider == "cuda" and "CUDAExecutionProvider" not in active_providers:
+        raise RuntimeError(
+            "CUDAExecutionProvider foi solicitado, mas não ficou ativo. "
+            f"Providers ativos: {active_providers}."
+        )
+
+    return session
+
+
+def benchmark_onnx(
+    session,
+    inputs_np: List[np.ndarray],
+    warmup: int,
+    repeat: int,
+    label: str,
+) -> Tuple[dict, List[dict]]:
+    input_name = session.get_inputs()[0].name
+
+    times = []
+    rows = []
+
+    print(f"\n[BENCH] {label} | warmup={warmup} repeat={repeat}")
+
+    # Warmup
+    for i in range(max(0, warmup)):
+        x = inputs_np[i % len(inputs_np)]
+        _ = session.run(None, {input_name: x})
+
+    # Medição
+    total_iter = len(inputs_np) * max(1, repeat)
+    idx = 0
+
+    for r in range(max(1, repeat)):
+        for sample_idx, x in enumerate(inputs_np):
+            t0 = time.perf_counter()
+            _ = session.run(None, {input_name: x})
+            dt_ms = (time.perf_counter() - t0) * 1000.0
+
+            times.append(dt_ms)
+            rows.append({
+                "engine": label,
+                "repeat": r,
+                "sample_idx": sample_idx,
+                "iter_idx": idx,
+                "latency_ms": dt_ms,
+            })
+
+            idx += 1
+
+            if idx % 25 == 0 or idx == total_iter:
+                print(f"  {idx:04d}/{total_iter:04d} | last={dt_ms:.2f}ms")
+
+    summary = summarize_times(times)
+    summary["engine"] = label
+    summary["timing_scope"] = "onnx_session_run_host_input_and_outputs"
+
+    return summary, rows
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--config", default="config.json")
+    parser.add_argument("--checkpoint", default="")
+    parser.add_argument("--onnx", default="")
+    parser.add_argument("--train-script", default="_8_train_multihead_v2.py")
+    parser.add_argument("--labelmap", default="dataset/labelmap.txt")
+
+    parser.add_argument("--split_folder", default="val", choices=["train", "val", "test"])
+    parser.add_argument("--root_override", default=None)
+    parser.add_argument("--norm_stats", default=None)
+
+    parser.add_argument("--max_samples", type=int, default=50)
+    parser.add_argument("--start_idx", type=int, default=0)
+    parser.add_argument("--warmup", type=int, default=10)
+    parser.add_argument("--repeat", type=int, default=5)
+
+    parser.add_argument("--device", default="cuda", choices=["cuda", "cpu"])
+    parser.add_argument("--onnx_provider", default="cuda", choices=["cuda", "cpu", "tensorrt"])
+    parser.add_argument("--trt_home", default=None)
+    parser.add_argument("--trt_no_fp16", action="store_true")
+
+    parser.add_argument("--skip_torch_fp32", action="store_true")
+    parser.add_argument("--skip_torch_amp", action="store_true")
+    parser.add_argument("--skip_onnx", action="store_true")
+
+    parser.add_argument(
+        "--onnx_has_norm",
+        action="store_true",
+        help="Força ONNX com normalização interna; normalmente detectado pelo export_meta.",
+    )
+    parser.add_argument(
+        "--onnx_no_norm",
+        action="store_true",
+        help="Força ONNX sem normalização interna; normalmente detectado pelo export_meta.",
+    )
+
+    parser.add_argument("--out_dir", default=None)
+
+    args = parser.parse_args()
+
+    if args.skip_torch_fp32 and args.skip_torch_amp and args.skip_onnx:
+        raise RuntimeError("Todos os engines foram desativados; não há o que medir.")
+
+    config_path = resolve_path(args.config, Path.cwd())
+
+    if config_path is None or not config_path.is_file():
+        raise FileNotFoundError(f"Config não encontrado: {config_path}")
+    config_dir = config_path.parent
+    config = load_json(config_path)
+
+    train_script_path = resolve_path(args.train_script, config_dir)
+    labelmap_path = resolve_path(args.labelmap, config_dir)
+    if train_script_path is None or not train_script_path.is_file():
+        raise FileNotFoundError(f"Train script não encontrado: {train_script_path}")
+    if labelmap_path is None or not labelmap_path.is_file():
+        raise FileNotFoundError(f"Labelmap não encontrado: {labelmap_path}")
+
+    train_mod = import_train_module(train_script_path)
+    trainer_version = str(getattr(train_mod, "TRAINER_VERSION", "legacy/unknown"))
+    print(f"[TRAIN MODULE] {train_script_path.name} | trainer_version={trainer_version}")
+
+    W, H = config.get("resolucao", [1024, 640])
+    W = int(W)
+    H = int(H)
+
+    backbone = config.get("backbone", "nvidia/mit-b1")
+    input_channel_names = train_mod.get_input_channel_names(config)
+    input_channel_indices = train_mod.get_input_channel_indices(config)
+    channels = len(input_channel_names)
+
+    checkpoint_path, onnx_path, ckpt_name = resolve_model_artifact_paths(
+        args=args,
+        config=config,
+        config_dir=config_dir,
+        channels=channels,
+        require_onnx=not args.skip_onnx,
+    )
+
+    export_meta, export_meta_path = load_export_metadata(onnx_path) if onnx_path.is_file() else ({}, None)
+    exported_names = [
+        str(x).strip().upper()
+        for x in export_meta.get("input_channel_names", [])
+    ]
+    if exported_names and exported_names != input_channel_names:
+        raise RuntimeError(
+            f"ONNX incompatível: canais exportados={exported_names}, "
+            f"config atual={input_channel_names}."
+        )
+    if args.onnx_has_norm and args.onnx_no_norm:
+        raise RuntimeError("Use apenas uma opção: --onnx_has_norm ou --onnx_no_norm.")
+    input_contract = export_meta.get("input_contract", {}) or {}
+    detected_onnx_has_norm = bool(
+        input_contract.get("normalization_embedded", export_meta.get("include_norm", False))
+    )
+    onnx_has_norm = (
+        True if args.onnx_has_norm
+        else False if args.onnx_no_norm
+        else detected_onnx_has_norm
+    )
+    exported_postprocess = str(export_meta.get("postprocess", "none")).lower()
+    if exported_postprocess == "none" and bool(export_meta.get("resize_to_input", False)):
+        exported_postprocess = "resize_logits"
+    if exported_postprocess not in ("none", "resize_logits", "argmax_lowres", "argmax_fullres"):
+        raise RuntimeError(f"postprocess inválido no export_meta: {exported_postprocess}")
+
+    semantic_id2label, semantic_label2id, ignore_from_labelmap = train_mod.load_labelmap(
+        str(labelmap_path)
+    )
+
+    heads_config = train_mod.build_heads_config(
+        config,
+        ignore_index=int(ignore_from_labelmap)
+    )
+
+    heads_config["semantic"]["num_classes"] = int(len(semantic_id2label))
+    heads_config["semantic"]["ignore_index"] = int(ignore_from_labelmap)
+
+    output_heads = list(heads_config.keys())
+    exported_heads = list(export_meta.get("heads", []) or [])
+    if exported_heads and exported_heads != output_heads:
+        raise RuntimeError(
+            f"Heads do ONNX={exported_heads} diferem do config/modelo={output_heads}."
+        )
+
+    save_dir = (
+        config_dir
+        / "backup"
+        / config.get("modelo", "segformer_b1")
+        / config.get("model_name", "test")
+        / experiment_tag(config, channels)
+    )
+
+    norm_stats_path = find_norm_stats(
+        config=config,
+        config_dir=config_dir,
+        save_dir=save_dir,
+        explicit=args.norm_stats,
+    )
+
+    mean, std, norm_stats_used = load_norm_stats(
+        norm_stats_path,
+        channel_names=input_channel_names,
+    )
+
+    if args.root_override:
+        root = resolve_path(args.root_override, Path.cwd())
+    else:
+        root = (config_dir / "dataset" / "split" / args.split_folder).resolve()
+
+    if root is None or not root.is_dir():
+        raise FileNotFoundError(f"Root de dados não encontrado: {root}")
+
+    samples = collect_tensor_samples(
+        root=root,
+        max_samples=args.max_samples,
+        start_idx=args.start_idx,
+    )
+
+    if args.out_dir:
+        out_dir = resolve_path(args.out_dir, config_dir)
+    else:
+        out_dir = onnx_path.parent / "benchmarks"
+
+    assert out_dir is not None
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    use_cuda = args.device == "cuda" and torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    if args.device == "cuda" and not torch.cuda.is_available():
+        print("[WARN] CUDA indisponível. Usando CPU no PyTorch.")
+
+    print("==========================================")
+    print("Benchmark PyTorch vs ONNX")
+    print(f"Config       : {config_path}")
+    print(f"Checkpoint   : {checkpoint_path}")
+    print(f"ONNX         : {onnx_path}")
+    print(f"Export meta  : {export_meta_path or 'ausente (modo legado)'}")
+    print(f"Root         : {root}")
+    print(f"Samples      : {len(samples)}")
+    print(f"Warmup       : {args.warmup}")
+    print(f"Repeat       : {args.repeat}")
+    print(f"Backbone     : {backbone}")
+    print(f"Input shape  : [1, {channels}, {H}, {W}]")
+    print(f"Channels     : {input_channel_names} idx={input_channel_indices}")
+    print(f"Heads        : {output_heads}")
+    print(f"Device       : {device}")
+    print(f"ONNX provider: {args.onnx_provider}")
+    print(f"ONNX has norm: {onnx_has_norm}")
+    print(f"Postprocess  : {exported_postprocess}")
+    print(f"Out dir      : {out_dir}")
+    print("==========================================")
+    print(
+        "[TIMING] PyTorch mede forward com entrada residente no device; "
+        "ONNX session.run inclui cópia da entrada/saídas do ORT. "
+        "Pré-processamento e leitura de disco ficam fora das duas medições."
+    )
+
+    run_torch_fp32 = not args.skip_torch_fp32
+    run_torch_amp = not args.skip_torch_amp and device.type == "cuda"
+    if not args.skip_torch_amp and device.type != "cuda":
+        print("[WARN] PyTorch AMP/FP16 exige CUDA; medição AMP será pulada.")
+    need_torch = run_torch_fp32 or run_torch_amp
+    if not need_torch and args.skip_onnx:
+        raise RuntimeError("Nenhum engine executável restou para o benchmark.")
+    need_normalized_inputs = (
+        (need_torch or not args.skip_onnx) and not onnx_has_norm
+    )
+
+    inputs_torch_np: List[np.ndarray] = []
+    if need_normalized_inputs:
+        print("\n[DATA] Carregando inputs normalizados...")
+        inputs_torch_np = load_inputs_as_numpy(
+            samples=samples,
+            input_channel_names=input_channel_names,
+            mean=mean,
+            std=std,
+            target_hw=(H, W),
+            normalize_input=True,
+        )
+
+    if (need_torch or not args.skip_onnx) and onnx_has_norm:
+        print("[DATA] Carregando inputs crus para ONNX com normalização embutida...")
+        inputs_onnx_np = load_inputs_as_numpy(
+            samples=samples,
+            input_channel_names=input_channel_names,
+            mean=mean,
+            std=std,
+            target_hw=(H, W),
+            normalize_input=False,
+        )
+    else:
+        inputs_onnx_np = inputs_torch_np
+    if need_torch and onnx_has_norm:
+        inputs_torch_np = inputs_onnx_np
+    print(f"[DATA] Amostras preparadas: {len(samples)}")
+
+    summaries = []
+    all_rows = []
+
+    # ========================================================
+    # PyTorch
+    # ========================================================
+    if need_torch:
+        print("\n[MODEL] Montando PyTorch...")
+        build_kwargs = dict(
+            backbone=backbone,
+            input_channel_names=input_channel_names,
+            heads_config=heads_config,
+            semantic_id2label=semantic_id2label,
+            semantic_label2id=semantic_label2id,
+        )
+        try:
+            if "config" in inspect.signature(train_mod.build_model).parameters:
+                build_kwargs["config"] = config
+        except (TypeError, ValueError):
+            pass
+        model = train_mod.build_model(**build_kwargs)
+
+        ckpt = train_mod.load_checkpoint(
+            str(checkpoint_path),
+            model,
+            map_location="cpu",
+        )
+
+        saved_contract = ckpt.get("model_contract", {}) or {}
+        print(
+            "[MODEL CONTRACT] "
+            f"decoder={saved_contract.get('decoder_mode', 'legacy')} | "
+            f"spectral_init={saved_contract.get('spectral_input_init', 'legacy')} | "
+            f"channels={saved_contract.get('input_channel_names', [])}"
+        )
+        saved_names = [str(x).strip().upper() for x in saved_contract.get("input_channel_names", [])]
+        legacy_names = SOURCE_CHANNEL_ORDER[:channels]
+        if not saved_names and input_channel_names != legacy_names:
+            raise RuntimeError(
+                "Checkpoint sem contrato nominal não pode ser usado com "
+                f"input_channels={input_channel_names}."
+            )
+        model.to(device)
+        model.eval()
+
+        torch_model = TorchTupleWrapper(
+            model=model,
+            output_heads=output_heads,
+            postprocess=exported_postprocess,
+            include_norm=onnx_has_norm,
+            norm_mean=mean,
+            norm_std=std,
+        ).to(device)
+        torch_model.eval()
+
+        clear_cuda()
+
+        if run_torch_fp32:
+            summary, rows = benchmark_torch(
+                model=torch_model,
+                inputs_np=inputs_torch_np,
+                device=device,
+                warmup=args.warmup,
+                repeat=args.repeat,
+                amp=False,
+                label="torch_fp32",
+            )
+            summaries.append(summary)
+            all_rows.extend(rows)
+
+        clear_cuda()
+
+        if run_torch_amp:
+            summary, rows = benchmark_torch(
+                model=torch_model,
+                inputs_np=inputs_torch_np,
+                device=device,
+                warmup=args.warmup,
+                repeat=args.repeat,
+                amp=True,
+                label="torch_amp_fp16",
+            )
+            summaries.append(summary)
+            all_rows.extend(rows)
+
+        del torch_model
+        del model
+        clear_cuda()
+
+    # ========================================================
+    # ONNX
+    # ========================================================
+    if not args.skip_onnx:
+        print("\n[ONNX] Carregando sessão...")
+        session = create_onnx_session(
+            onnx_path=onnx_path,
+            provider=args.onnx_provider,
+            trt_home=args.trt_home,
+            trt_fp16=not args.trt_no_fp16,
+        )
+
+        onnx_input = session.get_inputs()[0]
+        onnx_input_shape = onnx_input.shape
+        if len(onnx_input_shape) != 4:
+            raise RuntimeError(
+                f"Entrada ONNX inesperada: {onnx_input.name} shape={onnx_input_shape}"
+            )
+        onnx_channels = onnx_input_shape[1]
+        if isinstance(onnx_channels, int) and onnx_channels != channels:
+            raise RuntimeError(
+                f"ONNX espera {onnx_channels} canais, config possui {channels}: "
+                f"{input_channel_names}"
+            )
+        print(f"[ONNX] input={onnx_input.name} shape={onnx_input_shape}")
+
+        summary, rows = benchmark_onnx(
+            session=session,
+            inputs_np=inputs_onnx_np,
+            warmup=args.warmup,
+            repeat=args.repeat,
+            label=f"onnx_{args.onnx_provider}",
+        )
+        summaries.append(summary)
+        all_rows.extend(rows)
+
+    # ========================================================
+    # Relatório
+    # ========================================================
+    print("\n========== RESUMO ==========")
+
+    for s in summaries:
+        print(
+            f"{s['engine']:<16} "
+            f"n={s['n']:<4} "
+            f"mean={s['mean_ms']:.3f}ms "
+            f"median={s['median_ms']:.3f}ms "
+            f"p95={s['p95_ms']:.3f}ms "
+            f"p99={s['p99_ms']:.3f}ms "
+            f"fps_mean={s['fps_mean']:.2f} "
+            f"fps_p95={s['fps_p95_latency']:.2f}"
+        )
+
+    base_name = f"{onnx_path.stem}_{args.onnx_provider}"
+    report_json = out_dir / f"{base_name}_benchmark_report.json"
+    report_csv = out_dir / f"{base_name}_benchmark_rows.csv"
+
+    report = {
+        "config": str(config_path),
+        "train_script": str(train_script_path),
+        "trainer_version": trainer_version,
+        "checkpoint": str(checkpoint_path),
+        "ckpt_name": ckpt_name,
+        "onnx": str(onnx_path),
+        "export_meta": str(export_meta_path) if export_meta_path is not None else None,
+        "onnx_has_norm": bool(onnx_has_norm),
+        "onnx_postprocess": exported_postprocess,
+        "root": str(root),
+        "samples": len(samples),
+        "warmup": int(args.warmup),
+        "repeat": int(args.repeat),
+        "input_shape": [1, channels, H, W],
+        "input_channel_names": input_channel_names,
+        "input_channel_indices": input_channel_indices,
+        "heads": output_heads,
+        "norm_stats_used": norm_stats_used,
+        "onnx_provider": args.onnx_provider,
+        "trt_home": args.trt_home,
+        "trt_fp16": bool(not args.trt_no_fp16),
+        "device": str(device),
+        "dataset_preprocessing_in_timing": False,
+        "normalization_in_timing": bool(onnx_has_norm),
+        "summaries": summaries,
+    }
+
+    save_json(report_json, report)
+    save_csv(report_csv, all_rows)
+
+    print(f"\n[OK] JSON salvo em: {report_json}")
+    print(f"[OK] CSV salvo em : {report_csv}")
+    print("\nBenchmark finalizado.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/_3_select_training_samples.py b/Python/OAK/datasets/oak-fcc-3/_3_select_training_samples.py
index 0045791b1..0bd9bb9d5 100644
--- a/Python/OAK/datasets/oak-fcc-3/_3_select_training_samples.py
+++ b/Python/OAK/datasets/oak-fcc-3/_3_select_training_samples.py
@@ -2,675 +2,780 @@
 # -*- coding: utf-8 -*-
 
 """
-_3_select_training_samples.py
+Seleção agrícola inteligente antes do normalize.
 
-Seleciona amostras prontas em dataset/brutas/group e copia para
-dataset/original/group com balanceamento por classe.
+Princípios:
+- máscara é a fonte da verdade para as classes presentes;
+- pasta de origem é apenas uma indicação manual;
+- remove duplicatas/quase-duplicatas;
+- quando há limite, seleciona coreset por diversidade + relevância agrícola;
+- preserva preview/mask/meta/3 bins;
+- gera manifest, audit e summary.
 
-Estrutura de entrada:
+Classes padrão: 0=chao, 1=cana, 2=erva, 255=ignore.
 
-dataset/brutas/group/
-  <classe>/
-    previews/
-    masks/
-    metas/
-    bins/
+python .\_3_select_training_samples.py --src-root .\dataset\brutas\group --dst-root .\dataset\original\group --copy-all chao_cana chao_erva chao_cana_erva cana erva cana_erva --group-limit chao=500 --selection-mode diverse --presence-min-pixels 100 --seed 42 --clear-dst
 
-Estrutura de saída:
-
-dataset/original/group/
-  <classe>/
-    previews/
-    masks/
-    metas/
-    bins/
-
-Cada amostra válida precisa ter:
-  previews/<base>.png
-  masks/<base>.png
-  metas/<base>.json
-  bins/<base>_CAM_A.bin
-  bins/<base>_CAM_B.bin
-  bins/<base>_CAM_C.bin
-
-Também aceita formato antigo:
-  bins/<base>_cam0.bin
-  bins/<base>_cam1.bin
-  bins/<base>_cam2.bin
-
-Exemplo:
-
-python _4_select_training_samples.py ^
-  --src-root dataset/brutas/group ^
-  --dst-root dataset/original/group ^
-  --copy-all chao_cana chao_erva chao_cana_erva ^
-  --max-ratio 2.0 ^
-  --seed 42
 """
 
 from __future__ import annotations
 
 import argparse
 import csv
+import hashlib
 import json
+import math
 import random
 import re
 import shutil
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from pathlib import Path
 from statistics import median
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Sequence, Tuple
 
+import cv2
+import numpy as np
+
+from helpers import (
+    carregar_labelmap_completo,
+    converter_mask_rgb_para_ids,
+    _infer_ignore_id,
+)
 
 PREVIEW_EXTS = (".png", ".jpg", ".jpeg")
-
-BIN_RE_OAK = re.compile(
-    r"^(?P<prefixo>.+)_(?P<cam>CAM_[ABC])(?P<sufixo>.*)\.bin$",
-    re.IGNORECASE,
-)
-
-BIN_RE_OLD = re.compile(
-    r"^(?P<prefixo>.+)_cam(?P<cam>\d+)(?P<sufixo>.*)\.bin$",
-    re.IGNORECASE,
-)
-
-CAM_ORDER = {
-    "CAM_A": 0,
-    "CAM_B": 1,
-    "CAM_C": 2,
-    "cam0": 0,
-    "cam1": 1,
-    "cam2": 2,
+CLASS_IDS = (0, 1, 2)
+GROUP_BY_SET = {
+    frozenset({0}): "chao",
+    frozenset({1}): "cana",
+    frozenset({2}): "erva",
+    frozenset({0, 1}): "chao_cana",
+    frozenset({0, 2}): "chao_erva",
+    frozenset({1, 2}): "cana_erva",
+    frozenset({0, 1, 2}): "chao_cana_erva",
 }
 
+BIN_RE_OAK = re.compile(r"^(?P<prefixo>.+)_(?P<cam>CAM_[ABC])(?P<sufixo>.*)\.bin$", re.I)
+BIN_RE_OLD = re.compile(r"^(?P<prefixo>.+)_cam(?P<cam>\d+)(?P<sufixo>.*)\.bin$", re.I)
+CAM_ORDER = {"CAM_A": 0, "CAM_B": 1, "CAM_C": 2, "cam0": 0, "cam1": 1, "cam2": 2}
+
 
 @dataclass
-class SampleBundle:
-    group: str
+class Sample:
+    source_group: str
     base: str
     preview_path: Path
     mask_path: Path
     meta_path: Path
     bin_paths: List[Path]
 
+    actual_group: str = ""
+    present_ids: Tuple[int, ...] = field(default_factory=tuple)
+    frac_chao: float = 0.0
+    frac_cana: float = 0.0
+    frac_erva: float = 0.0
+    comp_cana: int = 0
+    comp_erva: int = 0
+    largest_cana: float = 0.0
+    largest_erva: float = 0.0
+    weed_near_cane: float = 0.0
+    weed_touch_cane: float = 0.0
+    brightness: float = 0.0
+    contrast: float = 0.0
+    saturation: float = 0.0
+    dhash: int = 0
+    content_hash: str = ""
+    feature: Optional[np.ndarray] = None
+    agri_score: float = 0.0
+    diversity_score: float = 0.0
+    duplicate_of: str = ""
+    selected: bool = False
+    reason: str = ""
 
-def ensure_dir(path: Path) -> None:
-    path.mkdir(parents=True, exist_ok=True)
+
+def ensure_dir(p: Path):
+    p.mkdir(parents=True, exist_ok=True)
 
 
 def parse_bin_name(path: Path):
-    name = path.name
-
-    m = BIN_RE_OAK.match(name)
+    m = BIN_RE_OAK.match(path.name)
     if m:
         cam = m.group("cam").upper()
-
-        # Exemplo:
-        # 20_PosProcessamento_CAM_A (2).bin
-        # prefixo = 20_PosProcessamento
-        # sufixo  = " (2)"
-        # base    = 20_PosProcessamento (2)
-        base = m.group("prefixo") + m.group("sufixo")
-
-        return base, cam, CAM_ORDER.get(cam, 99)
-
-    m = BIN_RE_OLD.match(name)
+        return m.group("prefixo") + m.group("sufixo"), cam, CAM_ORDER.get(cam, 99)
+    m = BIN_RE_OLD.match(path.name)
     if m:
         cam = f"cam{int(m.group('cam'))}"
-        base = m.group("prefixo") + m.group("sufixo")
-
-        return base, cam, CAM_ORDER.get(cam, 99)
-
+        return m.group("prefixo") + m.group("sufixo"), cam, CAM_ORDER.get(cam, 99)
     return None
 
 
-def find_preview_files(previews_dir: Path) -> List[Path]:
-    files = []
-
-    if not previews_dir.is_dir():
-        return files
-
-    for p in sorted(previews_dir.iterdir()):
-        if p.is_file() and p.suffix.lower() in PREVIEW_EXTS:
-            files.append(p)
-
-    return files
-
-
-def find_bins_for_base(bins_dir: Path, base: str) -> List[Path]:
+def find_bins(bins_dir: Path, base: str) -> List[Path]:
     found = []
-
     if not bins_dir.is_dir():
-        return found
-
+        return []
     for p in bins_dir.glob("*.bin"):
         parsed = parse_bin_name(p)
-        if parsed is None:
-            continue
-
-        bin_base, _cam, order = parsed
-
-        if bin_base != base:
-            continue
-
-        found.append((order, p))
-
-    found.sort(key=lambda x: x[0])
-    return [p for _, p in found]
+        if parsed and parsed[0] == base:
+            found.append((parsed[2], p))
+    return [p for _, p in sorted(found, key=lambda x: x[0])]
 
 
-def has_required_cameras(
-    bin_paths: List[Path],
-    required_cameras: List[str],
-) -> bool:
-    if not required_cameras:
-        return len(bin_paths) > 0
-
-    counts = {cam.upper(): 0 for cam in required_cameras}
-
+def has_required_cameras(bin_paths: Sequence[Path], required: Sequence[str]) -> bool:
+    if not required:
+        return bool(bin_paths)
+    seen = {x.upper(): 0 for x in required}
     for p in bin_paths:
         parsed = parse_bin_name(p)
-        if parsed is None:
+        if not parsed:
             continue
-
-        _base, cam, _order = parsed
-
-        if cam.lower() == "cam0":
-            normalizada = "CAM_A"
-        elif cam.lower() == "cam1":
-            normalizada = "CAM_B"
-        elif cam.lower() == "cam2":
-            normalizada = "CAM_C"
-        else:
-            normalizada = cam.upper()
-
-        if normalizada in counts:
-            counts[normalizada] += 1
-
-    return all(quantidade == 1 for quantidade in counts.values())
+        cam = parsed[1]
+        cam = {"cam0": "CAM_A", "cam1": "CAM_B", "cam2": "CAM_C"}.get(cam.lower(), cam.upper())
+        if cam in seen:
+            seen[cam] += 1
+    return all(v == 1 for v in seen.values())
 
 
-def collect_valid_samples_from_group(
-    group_dir: Path,
-    required_cameras: List[str],
-) -> List[SampleBundle]:
-    group = group_dir.name
-
-    previews_dir = group_dir / "previews"
-    masks_dir = group_dir / "masks"
-    metas_dir = group_dir / "metas"
-    bins_dir = group_dir / "bins"
-
-    samples = []
-
-    if not previews_dir.is_dir():
-        return samples
-
-    for preview_path in find_preview_files(previews_dir):
-        base = preview_path.stem
-
-        mask_path = masks_dir / f"{base}.png"
-        meta_path = metas_dir / f"{base}.json"
-        bin_paths = find_bins_for_base(bins_dir, base)
-
-        if not mask_path.exists():
+def collect_samples(src_root: Path, required: Sequence[str]) -> List[Sample]:
+    out = []
+    for gdir in sorted(src_root.iterdir()):
+        if not gdir.is_dir():
             continue
-
-        if not meta_path.exists():
+        pdir, mdir, jdir, bdir = gdir / "previews", gdir / "masks", gdir / "metas", gdir / "bins"
+        if not pdir.is_dir():
             continue
-
-        if not bin_paths:
-            continue
-
-        if not has_required_cameras(bin_paths, required_cameras):
-            continue
-
-        samples.append(
-            SampleBundle(
-                group=group,
-                base=base,
-                preview_path=preview_path,
-                mask_path=mask_path,
-                meta_path=meta_path,
-                bin_paths=bin_paths,
-            )
-        )
-
-    return samples
+        for preview in sorted(pdir.iterdir()):
+            if not preview.is_file() or preview.suffix.lower() not in PREVIEW_EXTS:
+                continue
+            base = preview.stem
+            mask = mdir / f"{base}.png"
+            meta = jdir / f"{base}.json"
+            bins = find_bins(bdir, base)
+            if not mask.is_file() or not meta.is_file() or not bins:
+                continue
+            if not has_required_cameras(bins, required):
+                continue
+            out.append(Sample(gdir.name, base, preview, mask, meta, bins))
+    return out
 
 
-def collect_all_groups(
-    src_root: Path,
-    required_cameras: List[str],
-) -> Dict[str, List[SampleBundle]]:
-    groups = {}
+def read_mask(
+    path: Path,
+    cor_para_id: dict,
+    ignore_id: int,
+) -> np.ndarray:
+    """
+    Lê a máscara bruta usando o MESMO contrato do normalize.
 
-    for group_dir in sorted(src_root.iterdir()):
-        if not group_dir.is_dir():
-            continue
+    Aceita:
+      1) id-map grayscale já em 0/1/2/ignore;
+      2) máscara RGB colorida definida pelo dataset/labelmap.txt.
 
-        valid = collect_valid_samples_from_group(group_dir, required_cameras)
-        groups[group_dir.name] = valid
+    Nas máscaras coloridas:
+      OpenCV lê BGR -> convertemos para RGB -> helpers.converter_mask_rgb_para_ids.
+    """
+    raw = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)
 
-    return groups
+    if raw is None:
+        raise RuntimeError(f"falha ao ler máscara: {path}")
 
+    allowed = set(CLASS_IDS) | {int(ignore_id)}
 
-def compute_reference_count(
-    all_samples: Dict[str, List[SampleBundle]],
-    copy_all: List[str],
-    fallback_mode: str,
-) -> int:
-    counts = []
+    # Caso já seja uma máscara de IDs grayscale.
+    if raw.ndim == 2:
+        unique = set(int(v) for v in np.unique(raw).tolist())
+        if unique.issubset(allowed):
+            return np.asarray(raw)
 
-    for group in copy_all:
-        n = len(all_samples.get(group, []))
-        if n > 0:
-            counts.append(n)
+    # Algumas máscaras podem estar salvas em 3 canais, mas já conter IDs iguais
+    # nos três canais. Aceitamos esse caso também.
+    if raw.ndim == 3 and raw.shape[2] >= 3:
+        c0 = raw[..., 0]
+        c1 = raw[..., 1]
+        c2 = raw[..., 2]
 
-    if counts:
-        return int(round(median(counts)))
+        if np.array_equal(c0, c1) and np.array_equal(c0, c2):
+            unique = set(int(v) for v in np.unique(c0).tolist())
+            if unique.issubset(allowed):
+                return np.asarray(c0)
 
-    # Fallback caso não tenha copy_all válido
-    all_counts = [len(v) for v in all_samples.values() if len(v) > 0]
+    # Contrato normal do dataset bruto: máscara COLORIDA segundo labelmap.txt.
+    bgr = cv2.imread(str(path), cv2.IMREAD_COLOR)
 
-    if not all_counts:
-        return 0
+    if bgr is None:
+        raise RuntimeError(f"falha ao abrir máscara RGB: {path}")
 
-    if fallback_mode == "min":
-        return min(all_counts)
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
 
-    if fallback_mode == "median":
-        return int(round(median(all_counts)))
-
-    if fallback_mode == "mean":
-        return int(round(sum(all_counts) / len(all_counts)))
-
-    return min(all_counts)
-
-
-def select_samples(
-    all_samples: Dict[str, List[SampleBundle]],
-    copy_all: List[str],
-    max_ratio: float,
-    target_per_other: Optional[int],
-    max_per_class: Optional[int],
-    seed: int,
-    fallback_mode: str,
-) -> Dict[str, List[SampleBundle]]:
-    rng = random.Random(seed)
-
-    copy_all_set = set(copy_all)
-
-    reference_count = compute_reference_count(
-        all_samples=all_samples,
-        copy_all=copy_all,
-        fallback_mode=fallback_mode,
+    ids = converter_mask_rgb_para_ids(
+        rgb,
+        cor_para_id,
+        int(ignore_id),
     )
 
-    if target_per_other is not None:
-        other_limit = int(target_per_other)
-    else:
-        other_limit = int(round(reference_count * float(max_ratio)))
+    ids = np.asarray(ids)
 
-    if other_limit < 1:
-        other_limit = 1
+    if ids.ndim != 2:
+        raise RuntimeError(
+            f"conversão RGB->IDs retornou shape inválido {ids.shape}: {path}"
+        )
 
-    selected = {}
+    unique = set(int(v) for v in np.unique(ids).tolist())
+    unknown = sorted(unique - allowed)
 
-    for group, samples in all_samples.items():
-        samples_sorted = list(samples)
-        rng.shuffle(samples_sorted)
+    if unknown:
+        raise RuntimeError(
+            f"IDs desconhecidos após converter labelmap: {unknown} em {path}"
+        )
 
-        if group in copy_all_set:
-            chosen = samples_sorted
-        else:
-            limit = other_limit
-
-            if max_per_class is not None:
-                limit = min(limit, int(max_per_class))
-
-            chosen = samples_sorted[: min(len(samples_sorted), limit)]
-
-        # Ordena por nome depois do sorteio, só para copiar organizado
-        chosen.sort(key=lambda s: s.base)
-        selected[group] = chosen
-
-    return selected
+    return ids
 
 
-def dest_bin_name(new_base: str, src_bin: Path) -> str:
-    parsed = parse_bin_name(src_bin)
+def infer_group(mask: np.ndarray, ignore_ids: Sequence[int], min_pixels: int, min_frac: float):
+    allowed = set(CLASS_IDS) | set(ignore_ids)
+    unknown = sorted(set(int(v) for v in np.unique(mask)) - allowed)
+    if unknown:
+        raise RuntimeError(f"IDs desconhecidos na máscara: {unknown}")
 
-    if parsed is None:
-        return src_bin.name
+    present = []
+    for cid in CLASS_IDS:
+        count = int(np.count_nonzero(mask == cid))
+        frac = count / max(mask.size, 1)
+        if count >= max(1, min_pixels) and frac >= min_frac:
+            present.append(cid)
 
-    _old_base, cam, _order = parsed
-
-    if cam.upper().startswith("CAM_"):
-        return f"{new_base}_{cam.upper()}.bin"
-
-    return f"{new_base}_{cam}.bin"
+    group = GROUP_BY_SET.get(frozenset(present))
+    if group is None:
+        raise RuntimeError(f"combinação de classes não mapeada: {present}")
+    return group, tuple(present)
 
 
-def unique_base_for_dest(dst_group_dir: Path, base: str, preview_ext: str) -> str:
-    previews_dir = dst_group_dir / "previews"
-    masks_dir = dst_group_dir / "masks"
-    metas_dir = dst_group_dir / "metas"
-    bins_dir = dst_group_dir / "bins"
+def comp_stats(binary: np.ndarray):
+    n, _, stats, _ = cv2.connectedComponentsWithStats(binary.astype(np.uint8), 8)
+    if n <= 1:
+        return 0, 0.0
+    areas = stats[1:, cv2.CC_STAT_AREA]
+    return int(len(areas)), float(np.max(areas) / max(binary.size, 1))
 
-    def occupied(candidate: str) -> bool:
-        if (previews_dir / f"{candidate}{preview_ext}").exists():
-            return True
-        if (masks_dir / f"{candidate}.png").exists():
-            return True
-        if (metas_dir / f"{candidate}.json").exists():
-            return True
 
-        for suffix in ("CAM_A", "CAM_B", "CAM_C", "cam0", "cam1", "cam2"):
-            if (bins_dir / f"{candidate}_{suffix}.bin").exists():
-                return True
+def weed_cane_relation(mask: np.ndarray, near_frac: float):
+    cane = mask == 1
+    weed = mask == 2
+    if not np.any(cane) or not np.any(weed):
+        return 0.0, 0.0
 
-        return False
+    radius = max(1, int(round(min(mask.shape[:2]) * near_frac)))
+    dist = cv2.distanceTransform((~cane).astype(np.uint8), cv2.DIST_L2, 3)
+    near = float(np.mean(dist[weed] <= radius))
 
-    if not occupied(base):
-        return base
+    dil = cv2.dilate(cane.astype(np.uint8), np.ones((3, 3), np.uint8), iterations=1) > 0
+    touch = float(np.mean(dil[weed])) if np.any(weed) else 0.0
+    return near, touch
 
+
+def grid_fraction(binary: np.ndarray, rows=4, cols=6):
+    h, w = binary.shape[:2]
+    vals = []
+    for gy in range(rows):
+        y0, y1 = int(gy*h/rows), int((gy+1)*h/rows)
+        for gx in range(cols):
+            x0, x1 = int(gx*w/cols), int((gx+1)*w/cols)
+            tile = binary[y0:y1, x0:x1]
+            vals.append(float(np.mean(tile)) if tile.size else 0.0)
+    return np.asarray(vals, np.float32)
+
+
+def dhash64(img: np.ndarray) -> int:
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    small = cv2.resize(gray, (9, 8), interpolation=cv2.INTER_AREA)
+    diff = small[:, 1:] > small[:, :-1]
+    v = 0
+    for bit in diff.flatten():
+        v = (v << 1) | int(bool(bit))
+    return v
+
+
+def hist_features(img: np.ndarray):
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    parts = []
+    for ch, bins, hi in ((0, 12, 180), (1, 8, 256), (2, 8, 256)):
+        h = cv2.calcHist([hsv], [ch], None, [bins], [0, hi]).flatten().astype(np.float32)
+        if h.sum() > 0:
+            h /= h.sum()
+        parts.append(h)
+    return np.concatenate(parts)
+
+
+def agricultural_score(s: Sample) -> float:
+    has_chao, has_cana, has_erva = 0 in s.present_ids, 1 in s.present_ids, 2 in s.present_ids
+    score = 0.0
+    if has_erva: score += 0.20
+    if has_cana: score += 0.12
+    if has_chao and (has_cana or has_erva): score += 0.08
+    if has_cana and has_erva: score += 0.28
+    score += 0.18 * s.weed_near_cane
+    score += 0.10 * s.weed_touch_cane
+    return float(min(1.0, score))
+
+
+def analyze(
+    s: Sample,
+    trust_folder: bool,
+    ignore_ids: Sequence[int],
+    min_pixels: int,
+    min_frac: float,
+    near_frac: float,
+    cor_para_id: dict,
+    ignore_id: int,
+):
+    mask = read_mask(
+        s.mask_path,
+        cor_para_id=cor_para_id,
+        ignore_id=ignore_id,
+    )
+    inferred, present = infer_group(mask, ignore_ids, min_pixels, min_frac)
+    s.actual_group = s.source_group if trust_folder else inferred
+    s.present_ids = present
+    s.frac_chao = float(np.mean(mask == 0))
+    s.frac_cana = float(np.mean(mask == 1))
+    s.frac_erva = float(np.mean(mask == 2))
+    s.comp_cana, s.largest_cana = comp_stats(mask == 1)
+    s.comp_erva, s.largest_erva = comp_stats(mask == 2)
+    s.weed_near_cane, s.weed_touch_cane = weed_cane_relation(mask, near_frac)
+
+    img = cv2.imread(str(s.preview_path), cv2.IMREAD_COLOR)
+    if img is None:
+        raise RuntimeError(f"falha ao ler preview: {s.preview_path}")
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    s.brightness = float(np.mean(hsv[..., 2]) / 255.0)
+    s.saturation = float(np.mean(hsv[..., 1]) / 255.0)
+    s.contrast = float(np.std(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)) / 255.0)
+    s.dhash = dhash64(img)
+
+    img_small = cv2.resize(img, (64, 40), interpolation=cv2.INTER_AREA)
+    mask_small = cv2.resize(mask.astype(np.uint8), (64, 40), interpolation=cv2.INTER_NEAREST)
+    h = hashlib.sha1(); h.update(img_small.tobytes()); h.update(mask_small.tobytes())
+    s.content_hash = h.hexdigest()
+
+    low = cv2.resize(img, (8, 5), interpolation=cv2.INTER_AREA).astype(np.float32).flatten() / 255.0
+    s.feature = np.concatenate([
+        np.asarray([
+            s.frac_chao, s.frac_cana, s.frac_erva,
+            math.log1p(s.comp_cana)/6.0, math.log1p(s.comp_erva)/6.0,
+            s.largest_cana, s.largest_erva,
+            s.weed_near_cane, s.weed_touch_cane,
+            s.brightness, s.contrast, s.saturation,
+        ], np.float32),
+        grid_fraction(mask == 1),
+        grid_fraction(mask == 2),
+        hist_features(img),
+        low,
+    ]).astype(np.float32)
+    s.agri_score = agricultural_score(s)
+
+
+def hamming(a: int, b: int) -> int:
+    return (a ^ b).bit_count()
+
+
+def deduplicate(samples: Sequence[Sample], max_hamming: int, max_mask_l1: float):
+    ordered = sorted(samples, key=lambda s: (-s.agri_score, s.base))
+    kept, dups = [], []
+    exact = {}
+    for s in ordered:
+        key = (s.actual_group, s.content_hash)
+        if key in exact:
+            s.duplicate_of = exact[key].base; s.reason = "duplicate_exact"; dups.append(s); continue
+        sf = np.asarray([s.frac_chao, s.frac_cana, s.frac_erva])
+        rep = None
+        for k in kept:
+            if k.actual_group != s.actual_group or hamming(k.dhash, s.dhash) > max_hamming:
+                continue
+            kf = np.asarray([k.frac_chao, k.frac_cana, k.frac_erva])
+            if float(np.sum(np.abs(kf - sf))) <= max_mask_l1:
+                rep = k; break
+        if rep is not None:
+            s.duplicate_of = rep.base; s.reason = "duplicate_near"; dups.append(s); continue
+        kept.append(s); exact[key] = s
+    return kept, dups
+
+
+def robust_z(x: np.ndarray):
+    med = np.median(x, axis=0)
+    q25, q75 = np.percentile(x, 25, axis=0), np.percentile(x, 75, axis=0)
+    scale = q75 - q25
+    std = np.std(x, axis=0)
+    scale = np.where(scale > 1e-6, scale, std)
+    scale = np.where(scale > 1e-6, scale, 1.0)
+    return np.clip((x - med) / scale, -8, 8).astype(np.float32)
+
+
+def select_diverse(samples: Sequence[Sample], k: int, seed: int, agri_weight: float):
+    items = list(samples)
+    if k >= len(items):
+        for s in items: s.reason = s.reason or "all"; s.diversity_score = 1.0
+        return items
+
+    x = robust_z(np.stack([s.feature for s in items]))
+    agri = np.asarray([s.agri_score for s in items], np.float32)
+    rng = np.random.default_rng(seed)
+    jitter = rng.uniform(0, 1e-6, len(items)).astype(np.float32)
+
+    first = int(np.argmax(agri + jitter))
+    chosen = [first]
+    used = np.zeros(len(items), bool); used[first] = True
+    d = np.sum((x - x[first])**2, axis=1)
+
+    while len(chosen) < k:
+        dist = np.sqrt(np.maximum(d, 0))
+        priority = dist * (1.0 + agri_weight * agri)
+        priority[used] = -1
+        idx = int(np.argmax(priority + jitter))
+        chosen.append(idx); used[idx] = True
+        d = np.minimum(d, np.sum((x - x[idx])**2, axis=1))
+
+    out = []
+    for idx in chosen:
+        s = items[idx]; s.reason = "diverse_coreset"; s.diversity_score = 1.0; out.append(s)
+    return out
+
+
+def parse_group_limits(values: Sequence[str]) -> Dict[str, int]:
+    out = {}
+    for v in values:
+        if "=" not in v:
+            raise ValueError(f"--group-limit inválido: {v}; use grupo=N")
+        g, n = v.split("=", 1); out[g.strip()] = int(n)
+    return out
+
+
+def group_by_actual(samples: Sequence[Sample]):
+    out: Dict[str, List[Sample]] = {}
+    for s in samples: out.setdefault(s.actual_group, []).append(s)
+    return out
+
+
+def reference_count(by_group, copy_all, fallback):
+    vals = [len(by_group[g]) for g in copy_all if g in by_group and by_group[g]]
+    if vals: return int(round(median(vals)))
+    vals = [len(v) for v in by_group.values() if v]
+    if not vals: return 0
+    if fallback == "median": return int(round(median(vals)))
+    if fallback == "mean": return int(round(sum(vals)/len(vals)))
+    return min(vals)
+
+
+def resolve_limit(group, n, copy_all, group_limits, ref, max_ratio, target_other, max_per_class):
+    if group in group_limits: return min(n, max(0, group_limits[group]))
+    if group in set(copy_all): return n
+    lim = target_other if target_other is not None else int(round(ref * max_ratio))
+    if max_per_class is not None: lim = min(lim, max_per_class)
+    return min(n, max(0, lim))
+
+
+def dest_bin_name(base: str, src: Path):
+    parsed = parse_bin_name(src)
+    if not parsed: return src.name
+    cam = parsed[1]
+    return f"{base}_{cam.upper() if cam.upper().startswith('CAM_') else cam}.bin"
+
+
+def unique_base(dst_group: Path, base: str, ext: str):
+    def occupied(c):
+        if (dst_group/"previews"/f"{c}{ext}").exists() or (dst_group/"masks"/f"{c}.png").exists() or (dst_group/"metas"/f"{c}.json").exists(): return True
+        return any((dst_group/"bins"/f"{c}_{s}.bin").exists() for s in ("CAM_A","CAM_B","CAM_C","cam0","cam1","cam2"))
+    if not occupied(base): return base
     i = 1
-    while True:
-        candidate = f"{base}_{i:03d}"
-        if not occupied(candidate):
-            return candidate
-        i += 1
+    while occupied(f"{base}_{i:03d}"): i += 1
+    return f"{base}_{i:03d}"
 
 
-def copy_sample(
-    sample: SampleBundle,
-    dst_root: Path,
-    overwrite: bool,
-) -> dict:
-    dst_group_dir = dst_root / sample.group
-
-    dst_previews = dst_group_dir / "previews"
-    dst_masks = dst_group_dir / "masks"
-    dst_metas = dst_group_dir / "metas"
-    dst_bins = dst_group_dir / "bins"
-
-    for d in (dst_previews, dst_masks, dst_metas, dst_bins):
-        ensure_dir(d)
-
-    preview_ext = sample.preview_path.suffix.lower()
-
-    if overwrite:
-        new_base = sample.base
-    else:
-        new_base = unique_base_for_dest(dst_group_dir, sample.base, preview_ext)
-
-    dst_preview = dst_previews / f"{new_base}{preview_ext}"
-    dst_mask = dst_masks / f"{new_base}.png"
-    dst_meta = dst_metas / f"{new_base}.json"
-
-    shutil.copy2(sample.preview_path, dst_preview)
-    shutil.copy2(sample.mask_path, dst_mask)
-    shutil.copy2(sample.meta_path, dst_meta)
-
-    dst_bins_paths = []
-
-    for src_bin in sample.bin_paths:
-        dst_bin = dst_bins / dest_bin_name(new_base, src_bin)
-        shutil.copy2(src_bin, dst_bin)
-        dst_bins_paths.append(dst_bin)
-
+def copy_sample(s: Sample, dst_root: Path, overwrite: bool):
+    g = dst_root / s.actual_group
+    for d in (g/"previews", g/"masks", g/"metas", g/"bins"): ensure_dir(d)
+    ext = s.preview_path.suffix.lower()
+    base = s.base if overwrite else unique_base(g, s.base, ext)
+    dp, dm, dj = g/"previews"/f"{base}{ext}", g/"masks"/f"{base}.png", g/"metas"/f"{base}.json"
+    shutil.copy2(s.preview_path, dp); shutil.copy2(s.mask_path, dm); shutil.copy2(s.meta_path, dj)
+    dbins = []
+    for b in s.bin_paths:
+        d = g/"bins"/dest_bin_name(base, b); shutil.copy2(b, d); dbins.append(d)
     return {
-        "group": sample.group,
-        "src_base": sample.base,
-        "dst_base": new_base,
-        "src_preview": str(sample.preview_path),
-        "src_mask": str(sample.mask_path),
-        "src_meta": str(sample.meta_path),
-        "src_bins": json.dumps([str(p) for p in sample.bin_paths], ensure_ascii=False),
-        "dst_preview": str(dst_preview),
-        "dst_mask": str(dst_mask),
-        "dst_meta": str(dst_meta),
-        "dst_bins": json.dumps([str(p) for p in dst_bins_paths], ensure_ascii=False),
-        "bin_count": len(dst_bins_paths),
+        "source_group": s.source_group, "actual_group": s.actual_group,
+        "src_base": s.base, "dst_base": base,
+        "src_preview": str(s.preview_path), "src_mask": str(s.mask_path), "src_meta": str(s.meta_path),
+        "src_bins": json.dumps([str(x) for x in s.bin_paths], ensure_ascii=False),
+        "dst_preview": str(dp), "dst_mask": str(dm), "dst_meta": str(dj),
+        "dst_bins": json.dumps([str(x) for x in dbins], ensure_ascii=False),
+        "frac_chao": s.frac_chao, "frac_cana": s.frac_cana, "frac_erva": s.frac_erva,
+        "agricultural_score": s.agri_score, "selection_reason": s.reason,
     }
 
 
-def write_manifest(path: Path, rows: List[dict]) -> None:
+def write_csv(path: Path, rows: List[dict]):
+    if not rows: return
     ensure_dir(path.parent)
-
-    fieldnames = [
-        "group",
-        "src_base",
-        "dst_base",
-        "src_preview",
-        "src_mask",
-        "src_meta",
-        "src_bins",
-        "dst_preview",
-        "dst_mask",
-        "dst_meta",
-        "dst_bins",
-        "bin_count",
-    ]
-
     with path.open("w", newline="", encoding="utf-8") as f:
-        w = csv.DictWriter(f, fieldnames=fieldnames)
-        w.writeheader()
-        w.writerows(rows)
+        w = csv.DictWriter(f, fieldnames=list(rows[0].keys()), extrasaction="ignore"); w.writeheader(); w.writerows(rows)
 
 
-def write_summary(path: Path, summary: dict) -> None:
-    ensure_dir(path.parent)
-
-    with path.open("w", encoding="utf-8") as f:
-        json.dump(summary, f, ensure_ascii=False, indent=2)
-
-
-def maybe_clear_dest(dst_root: Path) -> None:
-    if dst_root.exists():
-        shutil.rmtree(dst_root)
-    ensure_dir(dst_root)
+def audit_row(s: Sample):
+    return {
+        "source_group": s.source_group, "actual_group": s.actual_group,
+        "group_mismatch": s.source_group != s.actual_group,
+        "base": s.base, "selected": s.selected, "reason": s.reason,
+        "duplicate_of": s.duplicate_of,
+        "present_ids": ",".join(map(str, s.present_ids)),
+        "frac_chao": s.frac_chao, "frac_cana": s.frac_cana, "frac_erva": s.frac_erva,
+        "comp_cana": s.comp_cana, "comp_erva": s.comp_erva,
+        "largest_cana": s.largest_cana, "largest_erva": s.largest_erva,
+        "weed_near_cane": s.weed_near_cane, "weed_touch_cane": s.weed_touch_cane,
+        "brightness": s.brightness, "contrast": s.contrast, "saturation": s.saturation,
+        "agri_score": s.agri_score, "dhash": f"{s.dhash:016x}", "content_hash": s.content_hash,
+        "preview": str(s.preview_path), "mask": str(s.mask_path), "meta": str(s.meta_path),
+    }
 
 
 def main():
-    parser = argparse.ArgumentParser(
-        description="Seleciona amostras prontas de dataset/brutas/group para dataset/original/group com balanceamento."
-    )
-
-    parser.add_argument(
-        "--src-root",
-        default="dataset/brutas/group",
-        help="Raiz dos grupos preparados.",
-    )
-
-    parser.add_argument(
-        "--dst-root",
-        default="dataset/original/group",
-        help="Raiz de saída dos grupos selecionados.",
-    )
-
-    parser.add_argument(
-        "--copy-all",
-        nargs="*",
-        default=[],
-        help="Classes/grupos que serão copiados integralmente.",
-    )
-
-    parser.add_argument(
-        "--max-ratio",
-        type=float,
-        default=2.0,
-        help="Para classes não listadas em --copy-all, copia no máximo reference_count * max_ratio.",
-    )
-
-    parser.add_argument(
-        "--target-per-other",
-        type=int,
-        default=None,
-        help="Se definido, ignora --max-ratio e usa este limite para classes fora de --copy-all.",
-    )
-
-    parser.add_argument(
-        "--max-per-class",
-        type=int,
-        default=None,
-        help="Limite absoluto por classe para classes fora de --copy-all.",
-    )
-
-    parser.add_argument(
-        "--required-cameras",
-        nargs="*",
-        default=["CAM_A", "CAM_B", "CAM_C"],
-        help="Câmeras obrigatórias nos bins. Use vazio para aceitar qualquer bin.",
-    )
-
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=42,
-        help="Seed para seleção aleatória reprodutível.",
-    )
-
-    parser.add_argument(
-        "--fallback-mode",
-        choices=["min", "median", "mean"],
-        default="min",
-        help="Referência caso nenhum grupo de --copy-all tenha amostras.",
-    )
-
-    parser.add_argument(
-        "--manifest",
+    ap = argparse.ArgumentParser(description="Seleção agrícola inteligente por máscara + diversidade + relevância operacional")
+    ap.add_argument("--src-root", default="dataset/brutas/group")
+    ap.add_argument("--dst-root", default="dataset/original/group")
+    ap.add_argument(
+        "--labelmap",
         default="",
-        help="CSV de manifesto. Default: <dst-root>/selection_manifest.csv",
+        help=(
+            "Caminho do labelmap.txt. "
+            "Default: tenta <dataset>/labelmap.txt a partir de --src-root."
+        ),
     )
+    ap.add_argument("--copy-all", nargs="*", default=[])
+    ap.add_argument("--group-limit", nargs="*", default=[], metavar="GRUPO=N")
+    ap.add_argument("--selection-mode", choices=["diverse", "random"], default="diverse")
+    ap.add_argument("--agricultural-weight", type=float, default=0.75)
+    ap.add_argument("--max-ratio", type=float, default=2.0)
+    ap.add_argument("--target-per-other", type=int, default=None)
+    ap.add_argument("--max-per-class", type=int, default=None)
+    ap.add_argument("--required-cameras", nargs="*", default=["CAM_A","CAM_B","CAM_C"])
+    ap.add_argument("--seed", type=int, default=42)
+    ap.add_argument("--fallback-mode", choices=["min","median","mean"], default="min")
+    ap.add_argument("--trust-folder-group", action="store_true")
+    ap.add_argument("--presence-min-pixels", type=int, default=1)
+    ap.add_argument("--presence-min-fraction", type=float, default=0.0)
+    ap.add_argument("--ignore-ids", nargs="*", type=int, default=[255])
+    ap.add_argument("--near-radius-frac", type=float, default=0.02)
+    ap.add_argument("--no-dedup", action="store_true")
+    ap.add_argument("--dedup-hamming", type=int, default=3)
+    ap.add_argument("--dedup-mask-l1", type=float, default=0.03)
+    ap.add_argument("--manifest", default="")
+    ap.add_argument("--audit", default="")
+    ap.add_argument("--summary", default="")
+    ap.add_argument("--dry-run", action="store_true")
+    ap.add_argument("--clear-dst", action="store_true")
+    ap.add_argument("--overwrite", action="store_true")
+    args = ap.parse_args()
 
-    parser.add_argument(
-        "--summary",
-        default="",
-        help="JSON de resumo. Default: <dst-root>/selection_summary.json",
-    )
-
-    parser.add_argument(
-        "--dry-run",
-        action="store_true",
-        help="Só mostra o plano, não copia nada.",
-    )
-
-    parser.add_argument(
-        "--clear-dst",
-        action="store_true",
-        help="Apaga dst-root antes de copiar.",
-    )
-
-    parser.add_argument(
-        "--overwrite",
-        action="store_true",
-        help="Sobrescreve nomes existentes no destino. Sem isso, deduplica com _001, _002...",
-    )
-
-    args = parser.parse_args()
-
-    src_root = Path(args.src_root)
-    dst_root = Path(args.dst_root)
-
-    if not src_root.is_dir():
-        raise SystemExit(f"[ERRO] src-root não encontrado: {src_root}")
-
+    src, dst = Path(args.src_root), Path(args.dst_root)
+    if not src.is_dir(): raise SystemExit(f"[ERRO] src-root não encontrado: {src}")
     if args.clear_dst and not args.dry_run:
-        maybe_clear_dest(dst_root)
+        if dst.exists(): shutil.rmtree(dst)
+        ensure_dir(dst)
 
-    all_samples = collect_all_groups(
-        src_root=src_root,
-        required_cameras=args.required_cameras,
-    )
+    limits = parse_group_limits(args.group_limit)
 
-    selected = select_samples(
-        all_samples=all_samples,
-        copy_all=args.copy_all,
-        max_ratio=args.max_ratio,
-        target_per_other=args.target_per_other,
-        max_per_class=args.max_per_class,
-        seed=args.seed,
-        fallback_mode=args.fallback_mode,
-    )
+    # --------------------------------------------------------
+    # Labelmap: mesmo contrato usado pelo normalize
+    # src típico: dataset/brutas/group -> dataset/labelmap.txt
+    # --------------------------------------------------------
+    if args.labelmap:
+        labelmap_path = Path(args.labelmap)
+    else:
+        # dataset/brutas/group -> parents[1] == dataset
+        try:
+            dataset_root = src.parents[1]
+        except Exception:
+            dataset_root = Path("dataset")
 
-    reference_count = compute_reference_count(
-        all_samples=all_samples,
-        copy_all=args.copy_all,
-        fallback_mode=args.fallback_mode,
+        labelmap_path = dataset_root / "labelmap.txt"
+
+        if not labelmap_path.is_file():
+            fallback = Path("dataset") / "labelmap.txt"
+            if fallback.is_file():
+                labelmap_path = fallback
+
+    if not labelmap_path.is_file():
+        raise SystemExit(
+            f"[ERRO] labelmap não encontrado: {labelmap_path}\n"
+            "Passe explicitamente --labelmap caminho/labelmap.txt"
+        )
+
+    try:
+        cor_para_id, _, classes_labelmap, ignore_rgb = carregar_labelmap_completo(
+            str(labelmap_path)
+        )
+        ignore_id = int(_infer_ignore_id(ignore_rgb, 255))
+    except Exception as exc:
+        raise SystemExit(
+            f"[ERRO] Falha ao carregar labelmap {labelmap_path}: {exc}"
+        )
+
+    # O ignore detectado no labelmap entra sempre na lista permitida.
+    effective_ignore_ids = sorted(
+        set(int(x) for x in args.ignore_ids) | {ignore_id}
     )
 
     print("==============================================")
-    print("Seleção de amostras para treinamento")
-    print(f"Origem        : {src_root}")
-    print(f"Destino       : {dst_root}")
-    print(f"Copy all      : {args.copy_all}")
-    print(f"Reference     : {reference_count}")
-    print(f"Max ratio     : {args.max_ratio}")
-    print(f"Target other  : {args.target_per_other}")
-    print(f"Seed          : {args.seed}")
-    print(f"Dry-run       : {args.dry_run}")
+    print("SELETOR AGRÍCOLA INTELIGENTE")
+    print(f"Origem              : {src}")
+    print(f"Destino             : {dst}")
+    print(f"Modo                 : {args.selection_mode}")
+    print(f"Copy all             : {args.copy_all}")
+    print(f"Group limits         : {limits}")
+    print(f"Máscara é verdade    : {not args.trust_folder_group}")
+    print(f"Dedup                : {not args.no_dedup}")
+    print(f"Labelmap             : {labelmap_path}")
+    print(f"Ignore ID            : {ignore_id}")
+    print(f"Classes labelmap     : {classes_labelmap}")
     print("==============================================")
 
-    print("\nPlano por grupo:")
-    for group in sorted(all_samples.keys()):
-        total_valid = len(all_samples[group])
-        total_selected = len(selected.get(group, []))
-        mode = "ALL" if group in set(args.copy_all) else "BALANCED"
-        print(f"  {group:24s} validos={total_valid:5d}  selecionados={total_selected:5d}  modo={mode}")
+    samples = collect_samples(src, args.required_cameras)
+    print(f"[1/4] bundles válidos: {len(samples)}")
 
+    analyzed, rejected = [], []
+    for i, s in enumerate(samples, 1):
+        try:
+            analyze(
+                s,
+                args.trust_folder_group,
+                effective_ignore_ids,
+                args.presence_min_pixels,
+                args.presence_min_fraction,
+                args.near_radius_frac,
+                cor_para_id,
+                ignore_id,
+            )
+            analyzed.append(s)
+
+        except Exception as e:
+            error_text = str(e)
+            rejected.append({
+                "source_group": s.source_group,
+                "base": s.base,
+                "mask": str(s.mask_path),
+                "error": error_text,
+            })
+
+            # Mostra cedo os primeiros erros para não esperar milhares de samples
+            # antes de perceber um problema sistêmico.
+            if len(rejected) <= 10:
+                print(
+                    f"[REJECT {len(rejected):02d}] "
+                    f"{s.source_group}/{s.base}: {error_text}"
+                )
+
+        if i % 250 == 0 or i == len(samples):
+            print(
+                f"  auditados={i}/{len(samples)} "
+                f"ok={len(analyzed)} rejeitados={len(rejected)}"
+            )
+
+    mismatches = [s for s in analyzed if s.source_group != s.actual_group]
+    print(
+        f"[2/4] auditados={len(analyzed)} "
+        f"mismatch_pasta_mascara={len(mismatches)} "
+        f"rejeitados={len(rejected)}"
+    )
+
+    if rejected:
+        error_counts: Dict[str, int] = {}
+        for item in rejected:
+            # Agrupa pela mensagem completa; para nosso volume já é suficiente
+            # e deixa o erro sistêmico imediatamente visível.
+            key = str(item.get("error", "erro_desconhecido"))
+            error_counts[key] = error_counts.get(key, 0) + 1
+
+        print("\nPrincipais motivos de rejeição:")
+        for error, count in sorted(
+            error_counts.items(),
+            key=lambda kv: (-kv[1], kv[0]),
+        )[:10]:
+            print(f"  {count:5d}x  {error}")
+
+    if not analyzed:
+        raise SystemExit(
+            "[ERRO] Nenhuma amostra passou pela auditoria. "
+            "Veja os [REJECT] acima antes de continuar."
+        )
+
+    if args.no_dedup:
+        unique, dups = analyzed, []
+    else:
+        unique, dups = deduplicate(analyzed, args.dedup_hamming, args.dedup_mask_l1)
+    print(f"[3/4] únicos={len(unique)} duplicatas/quase={len(dups)}")
+
+    by_group = group_by_actual(unique)
+    ref = reference_count(by_group, args.copy_all, args.fallback_mode)
+    selected_by_group = {}
+
+    print("\nPlano por grupo REAL (máscara):")
+    for group in sorted(by_group):
+        items = by_group[group]
+        lim = resolve_limit(group, len(items), args.copy_all, limits, ref, args.max_ratio, args.target_per_other, args.max_per_class)
+        if lim >= len(items):
+            chosen = list(items)
+            for s in chosen: s.reason = s.reason or "all"; s.diversity_score = 1.0
+        elif args.selection_mode == "diverse":
+            chosen = select_diverse(items, lim, args.seed, args.agricultural_weight)
+        else:
+            rng = random.Random(args.seed); chosen = list(items); rng.shuffle(chosen); chosen = chosen[:lim]
+            for s in chosen: s.reason = "random"
+        for s in chosen: s.selected = True
+        selected_by_group[group] = chosen
+        print(f"  {group:24s} únicos={len(items):5d} selecionados={len(chosen):5d} modo={'ALL' if lim >= len(items) else args.selection_mode.upper()}")
+
+    total_selected = sum(len(v) for v in selected_by_group.values())
     manifest_rows = []
 
     if not args.dry_run:
-        for group in sorted(selected.keys()):
-            for sample in selected[group]:
-                row = copy_sample(
-                    sample=sample,
-                    dst_root=dst_root,
-                    overwrite=args.overwrite,
-                )
-                manifest_rows.append(row)
+        for group in sorted(selected_by_group):
+            for s in selected_by_group[group]:
+                manifest_rows.append(copy_sample(s, dst, args.overwrite))
 
-        manifest_path = Path(args.manifest) if args.manifest else dst_root / "selection_manifest.csv"
-        summary_path = Path(args.summary) if args.summary else dst_root / "selection_summary.json"
+        manifest = Path(args.manifest) if args.manifest else dst / "selection_manifest.csv"
+        audit = Path(args.audit) if args.audit else dst / "selection_audit.csv"
+        summary = Path(args.summary) if args.summary else dst / "selection_summary.json"
+        write_csv(manifest, manifest_rows)
+        write_csv(audit, [audit_row(s) for s in sorted(analyzed, key=lambda x:(x.actual_group,x.source_group,x.base))])
 
-        write_manifest(manifest_path, manifest_rows)
-
-        summary = {
-            "src_root": str(src_root),
-            "dst_root": str(dst_root),
-            "copy_all": args.copy_all,
-            "max_ratio": args.max_ratio,
-            "target_per_other": args.target_per_other,
-            "max_per_class": args.max_per_class,
-            "required_cameras": args.required_cameras,
-            "seed": args.seed,
-            "reference_count": reference_count,
-            "groups": {
-                group: {
-                    "valid": len(all_samples[group]),
-                    "selected": len(selected.get(group, [])),
-                    "mode": "all" if group in set(args.copy_all) else "balanced",
-                }
-                for group in sorted(all_samples.keys())
-            },
-            "total_selected": sum(len(v) for v in selected.values()),
+        data = {
+            "src_root": str(src), "dst_root": str(dst), "selection_mode": args.selection_mode,
+            "copy_all": args.copy_all, "group_limits": limits, "seed": args.seed,
+            "labelmap": str(labelmap_path),
+            "ignore_id": ignore_id,
+            "effective_ignore_ids": effective_ignore_ids,
+            "reclassify_by_mask": not args.trust_folder_group,
+            "dedup_enabled": not args.no_dedup,
+            "total_collected": len(samples), "total_analyzed": len(analyzed),
+            "total_rejected": len(rejected), "total_group_mismatches": len(mismatches),
+            "total_unique_after_dedup": len(unique), "total_duplicates": len(dups),
+            "total_selected": total_selected,
+            "groups": {g:{"unique_available":len(by_group[g]),"selected":len(selected_by_group[g])} for g in sorted(by_group)},
+            "folder_mask_mismatches": [{"base":s.base,"source_group":s.source_group,"actual_group":s.actual_group,"frac_chao":s.frac_chao,"frac_cana":s.frac_cana,"frac_erva":s.frac_erva} for s in mismatches],
+            "rejected": rejected,
         }
-
-        write_summary(summary_path, summary)
-
-        print("\n[OK] Cópia concluída.")
-        print(f"Manifest : {manifest_path}")
-        print(f"Summary  : {summary_path}")
-
+        ensure_dir(summary.parent)
+        summary.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
+        print(f"\n[OK] Manifest: {manifest}\n[OK] Audit   : {audit}\n[OK] Summary : {summary}")
     else:
         print("\n[DRY-RUN] Nada foi copiado.")
 
-    print("\nResumo final:")
-    print(f"Total válido     : {sum(len(v) for v in all_samples.values())}")
-    print(f"Total selecionado: {sum(len(v) for v in selected.values())}")
+    print("\n==============================================")
+    print(f"Válidos coletados        : {len(samples)}")
+    print(f"Auditados OK             : {len(analyzed)}")
+    print(f"Rejeitados               : {len(rejected)}")
+    print(f"Mismatch pasta x máscara : {len(mismatches)}")
+    print(f"Duplicatas/quase         : {len(dups)}")
+    print(f"Únicos após dedup        : {len(unique)}")
+    print(f"Selecionados             : {total_selected}")
+    print("==============================================")
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/_7_split.py b/Python/OAK/datasets/oak-fcc-3/_7_split.py
index 648a2a930..efb328082 100644
--- a/Python/OAK/datasets/oak-fcc-3/_7_split.py
+++ b/Python/OAK/datasets/oak-fcc-3/_7_split.py
@@ -1,23 +1,32 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
+
 """
-_7_split_multi_source.py
+_7_split_agri_teacher_v2.py
 
-Split estratificado por grupo/família para OAK-FCC-3, com suporte a múltiplas
-raízes de dataset e proteção contra vazamento de dados sintéticos/augmentados.
+Split agrícola inteligente para OAK-FCC-3 / Teacher V2.
 
-Por que este script existe?
----------------------------
-No fluxo atual temos, por exemplo:
+Objetivos:
+- VAL/TEST 100% reais.
+- Sintéticos/augment/copy-paste somente no TRAIN.
+- Máscara semântica é a fonte da verdade para o grupo real.
+- Proteção por família para impedir leakage de augmentações.
+- Agrupamento conservador de famílias visualmente quase idênticas.
+- Estratificação por problema agrícola, não apenas pelo nome da pasta:
+    * grupo semântico real
+    * quantidade de erva
+    * proximidade/contato cana x erva
+- Seleção DIVERSA das famílias de VAL/TEST (coreset/farthest-point),
+  para que a "prova" cubra o espaço de situações em vez de ser um random puro.
+- Descritores usam máscara + preview +, opcionalmente, tensor multiespectral
+  já normalizado/preparado pelo pipeline.
+- Gera auditoria completa para sabermos exatamente por que cada família caiu
+  em TRAIN/VAL/TEST.
 
-  dataset/1024x640/group              -> dados reais normalizados
-  dataset/copypaste/group             -> dados sintéticos copy/paste pós-normalização
-
-A validação precisa continuar 100% real. Então este script:
-  - usa dados reais para decidir o split por família
-  - manda aug/copy-paste/sintéticos somente para TRAIN
-  - opcionalmente só inclui sintéticos cuja família real caiu no TRAIN
-  - copia tensores, máscaras, masks auxiliares, metas, previews e visuals
+A ideia pedagógica:
+  TRAIN = sala de aula ampla e diversa.
+  VAL   = prova real, representativa e cobrindo os pontos difíceis.
+  TEST  = prova final opcional, também 100% real.
 
 Estrutura esperada:
   <root>/<grupo>/tensors/*.npy
@@ -28,34 +37,41 @@ Estrutura esperada:
   <root>/<grupo>/previews/*.png
   <root>/<grupo>/visuals/*.png
 
-Uso recomendado para real + copy/paste:
----------------------------------------
-python _7_split_multi_source.py ^
-  --src-roots dataset/1024x640/group,dataset/copypaste/group ^
+  
+Teste
+python .\_7_split.py --train 0.85 --val 0.15 --test 0 --strategy agri_diverse --merge-near-duplicate-families --dry-run
+python .\_7_split.py --train 0.85 --val 0.15 --test 0 --strategy agri_diverse --merge-near-duplicate-families --use-tensor-features --dry-run
+
+Exemplo recomendado, somente dados reais:
+python _7_split.py --train 0.85 --val 0.15 --test 0 --strategy agri_diverse --use-tensor-features --merge-near-duplicate-families --clear-dst
+
+Real + copy/paste:
+python _7_split.py ^
+  --src-roots dataset/1280x800/group,dataset/copypaste/group ^
   --train-only-roots dataset/copypaste/group ^
-  --train 0.7 --val 0.3 --test 0.0 ^
+  --train 0.85 --val 0.15 --test 0 ^
+  --strategy agri_diverse ^
   --synthetic-train-only ^
   --synthetic-respect-family-split ^
   --clear-dst
-
-Com isso:
-  - dados reais vão para train/val conforme split
-  - copy/paste vai apenas para train
-  - copy/paste derivado de família que caiu em val/test é ignorado por padrão
 """
 
 from __future__ import annotations
 
-import os
-import re
+import argparse
 import csv
 import json
-import shutil
+import math
+import os
 import random
-import argparse
-from dataclasses import dataclass
+import re
+import shutil
+from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple, Any
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
+
+import cv2
+import numpy as np
 
 
 # ============================================================
@@ -72,92 +88,88 @@ RESOLUCAO = tuple(CONFIG.get("resolucao", [1024, 640]))
 MULTI_HEAD = bool(CONFIG.get("multi_head", False))
 
 TENSOR_EXT = ".npy"
-MASK_NPY_SUFFIX = ".npy"
+MASK_SUFFIX = ".npy"
 
 AUX_MASK_DIRS = [
     "masks_vegetation",
     "masks_cana",
 ]
 
-COPY_OPTIONAL_DIRS = [
-    "metas",
-    "previews",
-    "visuals",
-]
+CLASS_ID_TO_NAME = {
+    0: "chao",
+    1: "cana",
+    2: "erva",
+}
 
-IGNORE_FILENAMES = {
-    "normalize_manifest.csv",
-    "copy_paste_manifest.csv",
-    "copy_paste_rejected.csv",
-    "copy_paste_summary.json",
-    "audit_summary.json",
-    "audit_health.json",
+GROUP_BY_CLASS_SET = {
+    frozenset({0}): "chao",
+    frozenset({1}): "cana",
+    frozenset({2}): "erva",
+    frozenset({0, 1}): "chao_cana",
+    frozenset({0, 2}): "chao_erva",
+    frozenset({1, 2}): "cana_erva",
+    frozenset({0, 1, 2}): "chao_cana_erva",
 }
 
 
 # ============================================================
-# Regex de família/origem
+# Regex origem/família
 # ============================================================
 
 RE_ORIGINAL_PREFIX = re.compile(r"^original_(.+)$", re.IGNORECASE)
 
-# augmented_abc_augx_00 -> family abc
 RE_AUGMENTED_FAMILY = re.compile(
     r"^augmented_(.+?)(?:_aug[a-zA-Z0-9]*_\d+)?$",
     re.IGNORECASE,
 )
-RE_AUG_SUFFIX = re.compile(r"_aug[a-zA-Z0-9]*_\d+$", re.IGNORECASE)
 
-# copy-paste: abc_cp_00 -> family abc
-RE_COPYPASTE_SUFFIX = re.compile(r"(.+)_cp_\d+$", re.IGNORECASE)
+RE_AUG_SUFFIX = re.compile(
+    r"_aug[a-zA-Z0-9]*_\d+$",
+    re.IGNORECASE,
+)
 
-# grupos copy/paste: chao_cana_copypaste -> split_group chao_cana
-RE_GROUP_COPYPASTE_SUFFIX = re.compile(r"(.+)_copypaste$", re.IGNORECASE)
+RE_COPYPASTE_SUFFIX = re.compile(
+    r"(.+)_cp_\d+$",
+    re.IGNORECASE,
+)
+
+RE_GROUP_COPYPASTE_SUFFIX = re.compile(
+    r"(.+)_copypaste$",
+    re.IGNORECASE,
+)
 
 
 # ============================================================
 # Utilidades
 # ============================================================
 
-def garantir(p: str | Path) -> None:
-    os.makedirs(str(p), exist_ok=True)
+def garantir(path: str | Path) -> None:
+    os.makedirs(str(path), exist_ok=True)
 
 
-def limpar_dir(p: str | Path) -> None:
-    if os.path.isdir(str(p)):
-        shutil.rmtree(str(p))
-    garantir(p)
+def limpar_dir(path: str | Path) -> None:
+    if os.path.isdir(str(path)):
+        shutil.rmtree(str(path))
+    garantir(path)
 
 
-def norm_path(p: str | Path) -> str:
-    return os.path.normcase(os.path.abspath(str(p)))
+def norm_path(path: str | Path) -> str:
+    return os.path.normcase(os.path.abspath(str(path)))
 
 
-def parse_csv_list(s: Optional[str]) -> List[str]:
-    if not s:
+def parse_csv_list(value: Optional[str]) -> List[str]:
+    if not value:
         return []
-    return [x.strip() for x in str(s).split(",") if x.strip()]
+    return [x.strip() for x in str(value).split(",") if x.strip()]
 
 
-def parse_map(s: str, value_type=int) -> Dict[str, Any]:
-    out: Dict[str, Any] = {}
-    if not s:
-        return out
-    for item in s.split(","):
-        item = item.strip()
-        if not item:
-            continue
-        if ":" not in item:
-            raise ValueError(f"Item de mapa inválido: {item}. Use grupo:valor")
-        k, v = item.split(":", 1)
-        out[k.strip()] = value_type(v.strip())
-    return out
-
-
-def load_json_safe(path: str | Path) -> Dict[str, Any]:
+def load_json_safe(path: Optional[str | Path]) -> Dict[str, Any]:
+    if not path:
+        return {}
     try:
         with open(path, "r", encoding="utf-8") as f:
-            return json.load(f)
+            data = json.load(f)
+        return data if isinstance(data, dict) else {}
     except Exception:
         return {}
 
@@ -170,34 +182,36 @@ def save_json(path: str | Path, data: Dict[str, Any]) -> None:
 
 def lista_grupos(root: str | Path) -> List[str]:
     root = str(root)
+
     if not os.path.isdir(root):
         return []
 
-    out = []
-    for g in sorted(os.listdir(root)):
-        gdir = os.path.join(root, g)
-        if not os.path.isdir(gdir):
+    result = []
+
+    for group in sorted(os.listdir(root)):
+        group_dir = os.path.join(root, group)
+
+        if not os.path.isdir(group_dir):
             continue
+
         if (
-            os.path.isdir(os.path.join(gdir, "tensors"))
-            and os.path.isdir(os.path.join(gdir, "masks"))
+            os.path.isdir(os.path.join(group_dir, "tensors"))
+            and os.path.isdir(os.path.join(group_dir, "masks"))
         ):
-            out.append(g)
-    return out
+            result.append(group)
+
+    return result
 
 
 def listar_tensors(tensor_dir: str | Path) -> List[str]:
     if not os.path.isdir(str(tensor_dir)):
         return []
-    return sorted([
-        f for f in os.listdir(str(tensor_dir))
+
+    return sorted(
+        f
+        for f in os.listdir(str(tensor_dir))
         if f.lower().endswith(TENSOR_EXT)
-    ])
-
-
-def mask_npy_from_tensor_name(tensor_name: str) -> str:
-    base, _ = os.path.splitext(tensor_name)
-    return base + MASK_NPY_SUFFIX
+    )
 
 
 def base_no_ext(filename: str) -> str:
@@ -205,49 +219,34 @@ def base_no_ext(filename: str) -> str:
 
 
 def normalize_group_for_split(group_name: str) -> str:
-    """
-    chao_cana_copypaste -> chao_cana
-    outros ficam iguais.
-    """
     m = RE_GROUP_COPYPASTE_SUFFIX.match(group_name)
     if m:
         return m.group(1)
     return group_name
 
 
-def output_group_name(source_group: str, split_group: str, is_synthetic: bool, mode: str) -> str:
-    """
-    mode:
-      source: mantém nome original do grupo da fonte
-      base: usa grupo normalizado do split
-      suffix: sintético vai para <base>_synthetic, real fica base
-    """
+def output_group_name(
+    source_group: str,
+    actual_group: str,
+    is_synthetic: bool,
+    mode: str,
+) -> str:
     if mode == "source":
         return source_group
+
     if mode == "base":
-        return split_group
+        return actual_group
+
     if mode == "suffix":
-        if is_synthetic:
-            return f"{split_group}_synthetic"
-        return split_group
-    return source_group
+        return f"{actual_group}_synthetic" if is_synthetic else actual_group
+
+    return actual_group
 
 
-def classify_source_and_family(filename_no_ext: str, meta: Optional[Dict[str, Any]] = None) -> Tuple[str, str, bool]:
-    """
-    Retorna:
-      source: original | augmented | copypaste | synthetic | unknown
-      family: id da família para impedir vazamento
-      is_synthetic: True para aug/copy/synthetic
-
-    Regras:
-      original_abc           -> original, abc
-      augmented_abc_aug_00   -> augmented, abc
-      abc_aug_00             -> augmented, abc
-      abc_cp_00              -> copypaste, abc
-      meta.synthetic == true -> synthetic/copypaste, tenta family pelo receiver/base
-      abc                    -> unknown, abc, tratado como original real
-    """
+def classify_source_and_family(
+    filename_no_ext: str,
+    meta: Optional[Dict[str, Any]] = None,
+) -> Tuple[str, str, bool]:
     meta = meta or {}
 
     m = RE_ORIGINAL_PREFIX.match(filename_no_ext)
@@ -259,20 +258,19 @@ def classify_source_and_family(filename_no_ext: str, meta: Optional[Dict[str, An
         return "augmented", m.group(1), True
 
     if RE_AUG_SUFFIX.search(filename_no_ext):
-        fam = RE_AUG_SUFFIX.sub("", filename_no_ext)
-        return "augmented", fam, True
+        family = RE_AUG_SUFFIX.sub("", filename_no_ext)
+        return "augmented", family, True
 
     m = RE_COPYPASTE_SUFFIX.match(filename_no_ext)
     if m:
         return "copypaste", m.group(1), True
 
-    # Meta do copy/paste v1/v2.
     cp = meta.get("copy_paste_augmentation")
     if isinstance(cp, dict):
-        recv = cp.get("receiver")
-        if isinstance(recv, dict):
-            fam = str(recv.get("base") or filename_no_ext)
-            return "copypaste", fam, True
+        receiver = cp.get("receiver")
+        if isinstance(receiver, dict):
+            family = str(receiver.get("base") or filename_no_ext)
+            return "copypaste", family, True
         return "copypaste", filename_no_ext, True
 
     if bool(meta.get("synthetic", False)):
@@ -281,6 +279,69 @@ def classify_source_and_family(filename_no_ext: str, meta: Optional[Dict[str, An
     return "unknown", filename_no_ext, False
 
 
+def read_mask(path: str | Path) -> np.ndarray:
+    mask = np.load(str(path), mmap_mode="r")
+    arr = np.asarray(mask)
+
+    if arr.ndim > 2:
+        arr = np.squeeze(arr)
+
+    if arr.ndim != 2:
+        raise RuntimeError(
+            f"Máscara semântica deve ser HxW; veio {arr.shape}: {path}"
+        )
+
+    return arr
+
+
+def infer_actual_group(
+    mask: np.ndarray,
+    presence_min_pixels: int,
+    presence_min_fraction: float,
+    ignore_ids: Sequence[int],
+) -> Tuple[str, Tuple[int, ...]]:
+    ignore_set = set(int(x) for x in ignore_ids)
+
+    unique_ids = set(int(x) for x in np.unique(mask).tolist())
+
+    unknown = sorted(
+        x
+        for x in unique_ids
+        if x not in CLASS_ID_TO_NAME and x not in ignore_set
+    )
+
+    if unknown:
+        raise RuntimeError(
+            f"Máscara possui IDs desconhecidos: {unknown}"
+        )
+
+    present = []
+    n = int(mask.size)
+
+    for class_id in sorted(CLASS_ID_TO_NAME):
+        count = int(np.count_nonzero(mask == class_id))
+        frac = count / max(n, 1)
+
+        if (
+            count >= max(1, int(presence_min_pixels))
+            and frac >= float(presence_min_fraction)
+        ):
+            present.append(class_id)
+
+    group = GROUP_BY_CLASS_SET.get(frozenset(present))
+
+    if group is None:
+        raise RuntimeError(
+            f"Combinação de classes não mapeável: {present}"
+        )
+
+    return group, tuple(present)
+
+
+# ============================================================
+# Dataclasses
+# ============================================================
+
 @dataclass
 class Sample:
     src_root: str
@@ -288,14 +349,13 @@ class Sample:
     source_root_train_only: bool
 
     source_group: str
-    split_group: str
-    output_group_default: str
+    split_group_folder: str
 
     tensor_path: str
     tensor_name: str
     base: str
-
     mask_path: str
+
     source: str
     family: str
     is_synthetic: bool
@@ -304,26 +364,77 @@ class Sample:
     preview_path: Optional[str]
     visual_path: Optional[str]
 
+    actual_group: str = ""
+    present_ids: Tuple[int, ...] = field(default_factory=tuple)
+
+    frac_chao: float = 0.0
+    frac_cana: float = 0.0
+    frac_erva: float = 0.0
+    frac_ignore: float = 0.0
+
+    comp_cana: int = 0
+    comp_erva: int = 0
+    largest_cana_frac: float = 0.0
+    largest_erva_frac: float = 0.0
+
+    weed_near_cane_frac: float = 0.0
+    weed_contact_cane_frac: float = 0.0
+
+    brightness: float = 0.0
+    contrast: float = 0.0
+    saturation: float = 0.0
+    dhash64: int = 0
+
+    feature_vector: Optional[np.ndarray] = None
+    stratum: str = ""
+    agri_risk: float = 0.0
+    audit_error: str = ""
+
+
+@dataclass
+class FamilyUnit:
+    actual_group: str
+    family: str
+    leakage_id: str
+    samples: List[Sample]
+
+    feature_vector: np.ndarray
+    stratum: str
+    agri_risk: float
+
+    dhash64: int
+    frac_chao: float
+    frac_cana: float
+    frac_erva: float
+
+    assigned_split: Optional[str] = None
+    assignment_reason: str = ""
+
 
 # ============================================================
-# Coleta de amostras
+# Coleta
 # ============================================================
 
 def collect_samples_from_root(
     src_root: str | Path,
     train_only_roots_norm: set[str],
-    label: str = "",
-    groups_filter: Optional[set[str]] = None,
+    label: str,
+    groups_filter: Optional[set[str]],
 ) -> List[Sample]:
     src_root = str(src_root)
-    src_root_norm = norm_path(src_root)
-    root_train_only = src_root_norm in train_only_roots_norm
-    root_label = label or os.path.basename(os.path.normpath(src_root)) or src_root
+    root_norm = norm_path(src_root)
+    root_train_only = root_norm in train_only_roots_norm
 
     samples: List[Sample] = []
 
     for group_name in lista_grupos(src_root):
-        if groups_filter and group_name not in groups_filter and normalize_group_for_split(group_name) not in groups_filter:
+        split_group_folder = normalize_group_for_split(group_name)
+
+        if (
+            groups_filter
+            and group_name not in groups_filter
+            and split_group_folder not in groups_filter
+        ):
             continue
 
         group_dir = os.path.join(src_root, group_name)
@@ -333,333 +444,1532 @@ def collect_samples_from_root(
         preview_dir = os.path.join(group_dir, "previews")
         visual_dir = os.path.join(group_dir, "visuals")
 
-        split_group = normalize_group_for_split(group_name)
-
         for tensor_name in listar_tensors(tensor_dir):
             base = base_no_ext(tensor_name)
-            mask_name = mask_npy_from_tensor_name(tensor_name)
-            tensor_path = os.path.join(tensor_dir, tensor_name)
-            mask_path = os.path.join(mask_dir, mask_name)
 
-            if not os.path.exists(mask_path):
+            tensor_path = os.path.join(tensor_dir, tensor_name)
+            mask_path = os.path.join(mask_dir, base + MASK_SUFFIX)
+
+            if not os.path.isfile(mask_path):
                 continue
 
             meta_path = os.path.join(meta_dir, base + ".json")
-            if not os.path.exists(meta_path):
+            if not os.path.isfile(meta_path):
                 meta_path = None
 
-            meta = load_json_safe(meta_path) if meta_path else {}
-            source, family, is_synthetic = classify_source_and_family(base, meta)
+            meta = load_json_safe(meta_path)
+            source, family, is_synthetic = classify_source_and_family(
+                base,
+                meta,
+            )
 
             preview_path = None
             for ext in (".png", ".jpg", ".jpeg"):
-                cand = os.path.join(preview_dir, base + ext)
-                if os.path.exists(cand):
-                    preview_path = cand
+                candidate = os.path.join(preview_dir, base + ext)
+                if os.path.isfile(candidate):
+                    preview_path = candidate
                     break
 
             visual_path = None
             for ext in (".png", ".jpg", ".jpeg"):
-                cand = os.path.join(visual_dir, base + "_debug" + ext)
-                if os.path.exists(cand):
-                    visual_path = cand
-                    break
-                cand2 = os.path.join(visual_dir, base + ext)
-                if os.path.exists(cand2):
-                    visual_path = cand2
+                candidate = os.path.join(
+                    visual_dir,
+                    base + "_debug" + ext,
+                )
+                if os.path.isfile(candidate):
+                    visual_path = candidate
                     break
 
-            samples.append(Sample(
-                src_root=src_root,
-                source_root_label=root_label,
-                source_root_train_only=root_train_only,
+                candidate = os.path.join(
+                    visual_dir,
+                    base + ext,
+                )
+                if os.path.isfile(candidate):
+                    visual_path = candidate
+                    break
 
-                source_group=group_name,
-                split_group=split_group,
-                output_group_default=group_name,
-
-                tensor_path=tensor_path,
-                tensor_name=tensor_name,
-                base=base,
-
-                mask_path=mask_path,
-                source=source,
-                family=family,
-                is_synthetic=is_synthetic,
-
-                meta_path=meta_path,
-                preview_path=preview_path,
-                visual_path=visual_path,
-            ))
+            samples.append(
+                Sample(
+                    src_root=src_root,
+                    source_root_label=label,
+                    source_root_train_only=root_train_only,
+                    source_group=group_name,
+                    split_group_folder=split_group_folder,
+                    tensor_path=tensor_path,
+                    tensor_name=tensor_name,
+                    base=base,
+                    mask_path=mask_path,
+                    source=source,
+                    family=family,
+                    is_synthetic=is_synthetic,
+                    meta_path=meta_path,
+                    preview_path=preview_path,
+                    visual_path=visual_path,
+                )
+            )
 
     return samples
 
 
 def collect_all_samples(
-    src_roots: List[str],
-    train_only_roots: List[str],
-    groups: Optional[List[str]] = None,
+    src_roots: Sequence[str],
+    train_only_roots: Sequence[str],
+    groups: Optional[Sequence[str]],
 ) -> List[Sample]:
-    train_only_norm = {norm_path(p) for p in train_only_roots}
+    train_only_norm = {
+        norm_path(x)
+        for x in train_only_roots
+    }
+
     groups_filter = set(groups) if groups else None
 
-    all_samples: List[Sample] = []
+    result = []
+
     for i, root in enumerate(src_roots):
-        label = f"root{i}"
-        samples = collect_samples_from_root(
-            src_root=root,
-            train_only_roots_norm=train_only_norm,
-            label=label,
-            groups_filter=groups_filter,
+        result.extend(
+            collect_samples_from_root(
+                src_root=root,
+                train_only_roots_norm=train_only_norm,
+                label=f"root{i}",
+                groups_filter=groups_filter,
+            )
         )
-        all_samples.extend(samples)
 
-    return all_samples
+    return result
 
 
 # ============================================================
-# Split por família real
+# Features agrícolas
 # ============================================================
 
-def allocate_counts(n: int, p_train: float, p_val: float, p_test: float, min_train: int, min_val: int, min_test: int) -> Tuple[int, int, int]:
-    n_train = int(round(n * p_train))
-    n_val = int(round(n * p_val))
-    n_test = n - n_train - n_val
+def connected_component_stats(binary: np.ndarray) -> Tuple[int, float]:
+    binary = np.ascontiguousarray(
+        binary.astype(np.uint8)
+    )
 
-    if n_test < 0:
-        excesso = -n_test
-        take_train = min(excesso, max(0, n_train))
-        n_train -= take_train
-        excesso -= take_train
-        if excesso > 0:
-            take_val = min(excesso, max(0, n_val))
-            n_val -= take_val
-            excesso -= take_val
-        n_test = 0
+    n, _labels, stats, _centroids = cv2.connectedComponentsWithStats(
+        binary,
+        connectivity=8,
+    )
 
-    min_sum = min_train + min_val + min_test
+    if n <= 1:
+        return 0, 0.0
 
-    if n >= min_sum:
-        n_train = max(n_train, min_train)
-        n_val = max(n_val, min_val)
-        n_test = max(n_test, min_test)
+    areas = stats[1:, cv2.CC_STAT_AREA].astype(np.float64)
 
-        total = n_train + n_val + n_test
-        while total > n:
-            if n_test > min_test:
-                n_test -= 1
-            elif n_val > min_val:
-                n_val -= 1
-            elif n_train > min_train:
-                n_train -= 1
+    return (
+        int(len(areas)),
+        float(np.max(areas) / max(binary.size, 1)),
+    )
+
+
+def weed_cane_relation(
+    mask_small: np.ndarray,
+    near_radius_frac: float,
+) -> Tuple[float, float]:
+    cane = mask_small == 1
+    weed = mask_small == 2
+
+    if not np.any(cane) or not np.any(weed):
+        return 0.0, 0.0
+
+    h, w = mask_small.shape
+    radius = max(
+        1,
+        int(round(min(h, w) * float(near_radius_frac))),
+    )
+
+    dist = cv2.distanceTransform(
+        (~cane).astype(np.uint8),
+        cv2.DIST_L2,
+        3,
+    )
+
+    weed_dist = dist[weed]
+
+    near_frac = (
+        float(np.mean(weed_dist <= radius))
+        if weed_dist.size
+        else 0.0
+    )
+
+    kernel = np.ones((3, 3), np.uint8)
+    cane_dilated = cv2.dilate(
+        cane.astype(np.uint8),
+        kernel,
+        iterations=1,
+    ) > 0
+
+    weed_count = int(np.count_nonzero(weed))
+    contact = int(np.count_nonzero(weed & cane_dilated))
+
+    contact_frac = contact / max(weed_count, 1)
+
+    return float(near_frac), float(contact_frac)
+
+
+def grid_fraction(
+    binary: np.ndarray,
+    rows: int = 4,
+    cols: int = 6,
+) -> np.ndarray:
+    h, w = binary.shape
+    result = []
+
+    for gy in range(rows):
+        y0 = int(round(gy * h / rows))
+        y1 = int(round((gy + 1) * h / rows))
+
+        for gx in range(cols):
+            x0 = int(round(gx * w / cols))
+            x1 = int(round((gx + 1) * w / cols))
+
+            tile = binary[y0:y1, x0:x1]
+
+            result.append(
+                float(np.mean(tile))
+                if tile.size
+                else 0.0
+            )
+
+    return np.asarray(
+        result,
+        dtype=np.float32,
+    )
+
+
+def dhash64(img_bgr: np.ndarray) -> int:
+    gray = cv2.cvtColor(
+        img_bgr,
+        cv2.COLOR_BGR2GRAY,
+    )
+
+    small = cv2.resize(
+        gray,
+        (9, 8),
+        interpolation=cv2.INTER_AREA,
+    )
+
+    diff = small[:, 1:] > small[:, :-1]
+
+    value = 0
+    for bit in diff.flatten():
+        value = (value << 1) | int(bool(bit))
+
+    return int(value)
+
+
+def preview_features(
+    preview_path: Optional[str],
+) -> Tuple[np.ndarray, float, float, float, int]:
+    if not preview_path:
+        return (
+            np.zeros(35, dtype=np.float32),
+            0.0,
+            0.0,
+            0.0,
+            0,
+        )
+
+    img = cv2.imread(
+        preview_path,
+        cv2.IMREAD_COLOR,
+    )
+
+    if img is None:
+        return (
+            np.zeros(35, dtype=np.float32),
+            0.0,
+            0.0,
+            0.0,
+            0,
+        )
+
+    hsv = cv2.cvtColor(
+        img,
+        cv2.COLOR_BGR2HSV,
+    )
+
+    brightness = float(
+        np.mean(hsv[..., 2]) / 255.0
+    )
+
+    saturation = float(
+        np.mean(hsv[..., 1]) / 255.0
+    )
+
+    gray = cv2.cvtColor(
+        img,
+        cv2.COLOR_BGR2GRAY,
+    )
+
+    contrast = float(
+        np.std(gray) / 255.0
+    )
+
+    hist_h = cv2.calcHist(
+        [hsv],
+        [0],
+        None,
+        [12],
+        [0, 180],
+    ).flatten()
+
+    hist_s = cv2.calcHist(
+        [hsv],
+        [1],
+        None,
+        [8],
+        [0, 256],
+    ).flatten()
+
+    hist_v = cv2.calcHist(
+        [hsv],
+        [2],
+        None,
+        [8],
+        [0, 256],
+    ).flatten()
+
+    hist = np.concatenate([
+        hist_h,
+        hist_s,
+        hist_v,
+    ]).astype(np.float32)
+
+    hist_sum = float(hist.sum())
+    if hist_sum > 0:
+        hist /= hist_sum
+
+    low = cv2.resize(
+        gray,
+        (7, 1),
+        interpolation=cv2.INTER_AREA,
+    ).astype(np.float32).flatten() / 255.0
+
+    feat = np.concatenate([
+        hist,
+        low,
+    ]).astype(np.float32)
+
+    return (
+        feat,
+        brightness,
+        contrast,
+        saturation,
+        dhash64(img),
+    )
+
+
+def tensor_summary_features(
+    tensor_path: str,
+    mask: np.ndarray,
+    enabled: bool,
+    stride: int,
+) -> np.ndarray:
+    """
+    Features multiespectrais baratas.
+
+    Usa mmap + subamostragem espacial.
+    Para cada canal disponível:
+      mean, std, p10, p50, p90
+
+    Para os 5 primeiros canais físicos, acrescenta:
+      média na CANA e média na ERVA
+
+    Também calcula NDVI/NDRE aproximados a partir de:
+      R=0, RE=3, NIR=4
+    quando C>=5.
+
+    Não depende de o treino usar 5 ou 7 canais.
+    """
+    if not enabled:
+        return np.zeros(49, dtype=np.float32)
+
+    try:
+        arr = np.load(
+            tensor_path,
+            mmap_mode="r",
+        )
+
+        x = np.asarray(arr)
+
+        if x.ndim == 4 and x.shape[0] == 1:
+            x = x[0]
+
+        if x.ndim != 3:
+            raise RuntimeError(
+                f"tensor não CHW: {x.shape}"
+            )
+
+        c, h, w = x.shape
+
+        step = max(1, int(stride))
+
+        ys = np.arange(
+            0,
+            h,
+            step,
+            dtype=np.int64,
+        )
+
+        xs = np.arange(
+            0,
+            w,
+            step,
+            dtype=np.int64,
+        )
+
+        x_small = np.asarray(
+            x[:, ys[:, None], xs[None, :]],
+            dtype=np.float32,
+        )
+
+        mask_small = cv2.resize(
+            mask.astype(np.uint8),
+            (len(xs), len(ys)),
+            interpolation=cv2.INTER_NEAREST,
+        )
+
+        feats = []
+
+        max_channels = min(c, 7)
+
+        for ch in range(7):
+            if ch < max_channels:
+                values = x_small[ch].reshape(-1)
+                values = values[np.isfinite(values)]
+
+                if values.size:
+                    feats.extend([
+                        float(np.mean(values)),
+                        float(np.std(values)),
+                        float(np.percentile(values, 10)),
+                        float(np.percentile(values, 50)),
+                        float(np.percentile(values, 90)),
+                    ])
+                else:
+                    feats.extend([0.0] * 5)
             else:
-                break
-            total = n_train + n_val + n_test
+                feats.extend([0.0] * 5)
 
-        while total < n:
-            if n_train - min_train <= n_val - min_val:
-                n_train += 1
-            else:
-                n_val += 1
-            total = n_train + n_val + n_test
-    else:
-        n_train = min(n, max(1, min_train))
-        resto = n - n_train
-        n_val = max(0, min(resto, min_val))
-        n_test = max(0, resto - n_val)
+        # médias por classe dos 5 canais físicos
+        for class_id in (1, 2):
+            class_mask = mask_small == class_id
 
-    diff = n - (n_train + n_val + n_test)
-    if diff != 0:
-        if diff > 0:
-            take = min(diff, n - n_train)
-            n_train += take
-            diff -= take
-            if diff > 0:
-                n_val += diff
+            for ch in range(5):
+                if ch < c and np.any(class_mask):
+                    vals = x_small[ch][class_mask]
+                    vals = vals[np.isfinite(vals)]
+                    feats.append(
+                        float(np.mean(vals))
+                        if vals.size
+                        else 0.0
+                    )
+                else:
+                    feats.append(0.0)
+
+        # NDVI / NDRE globais do Raw5 físico.
+        if c >= 5:
+            r = x_small[0]
+            re = x_small[3]
+            nir = x_small[4]
+
+            def nd(a, b):
+                den = a + b
+                out = np.zeros_like(a, dtype=np.float32)
+                np.divide(
+                    a - b,
+                    den,
+                    out=out,
+                    where=np.abs(den) > 1e-6,
+                )
+                return np.nan_to_num(
+                    out,
+                    nan=0.0,
+                    posinf=1.0,
+                    neginf=-1.0,
+                )
+
+            ndvi = nd(nir, r)
+            ndre = nd(nir, re)
+
+            feats.extend([
+                float(np.mean(ndvi)),
+                float(np.std(ndvi)),
+                float(np.mean(ndre)),
+                float(np.std(ndre)),
+            ])
         else:
-            diff = -diff
-            take = min(diff, n_test)
-            n_test -= take
-            diff -= take
-            if diff > 0:
-                n_val -= diff
+            feats.extend([0.0] * 4)
 
-    return n_train, n_val, n_test
+        # 35 + 10 + 4 = 49
+        return np.asarray(
+            feats,
+            dtype=np.float32,
+        )
+
+    except Exception:
+        return np.zeros(
+            49,
+            dtype=np.float32,
+        )
 
 
-def build_family_split(
-    samples: List[Sample],
+def target_size_bin(
+    frac_erva: float,
+    tiny: float,
+    small: float,
+    medium: float,
+) -> str:
+    if frac_erva <= 0.0:
+        return "none"
+    if frac_erva < tiny:
+        return "tiny"
+    if frac_erva < small:
+        return "small"
+    if frac_erva < medium:
+        return "medium"
+    return "large"
+
+
+def compute_agri_risk(
+    frac_cana: float,
+    frac_erva: float,
+    weed_near_cane_frac: float,
+    weed_contact_cane_frac: float,
+) -> float:
+    """
+    Risco agrícola relativo.
+
+    Não é score de qualidade.
+    É somente uma forma de marcar casos que são "prova difícil":
+      - cana + erva simultâneas;
+      - erva perto/encostada na cana;
+      - target pequeno em presença de cana.
+    """
+    has_cana = frac_cana > 0.0
+    has_erva = frac_erva > 0.0
+
+    score = 0.0
+
+    if has_cana and has_erva:
+        score += 0.35
+
+    score += 0.30 * float(weed_near_cane_frac)
+    score += 0.20 * float(weed_contact_cane_frac)
+
+    if has_cana and has_erva and frac_erva < 0.02:
+        score += 0.15
+
+    return float(
+        max(0.0, min(score, 1.0))
+    )
+
+
+def analyze_sample(
+    sample: Sample,
+    args,
+) -> None:
+    mask = read_mask(
+        sample.mask_path
+    )
+
+    actual_group, present_ids = infer_actual_group(
+        mask=mask,
+        presence_min_pixels=args.presence_min_pixels,
+        presence_min_fraction=args.presence_min_fraction,
+        ignore_ids=args.ignore_ids,
+    )
+
+    sample.actual_group = actual_group
+    sample.present_ids = present_ids
+
+    sample.frac_chao = float(
+        np.mean(mask == 0)
+    )
+    sample.frac_cana = float(
+        np.mean(mask == 1)
+    )
+    sample.frac_erva = float(
+        np.mean(mask == 2)
+    )
+
+    sample.frac_ignore = float(
+        sum(
+            np.mean(mask == int(i))
+            for i in args.ignore_ids
+        )
+    )
+
+    # Para morfologia, reduz bastante. A feature é relativa, não pixel-exata.
+    small_w = min(320, mask.shape[1])
+    small_h = max(
+        1,
+        int(round(
+            mask.shape[0]
+            * small_w
+            / max(mask.shape[1], 1)
+        )),
+    )
+
+    mask_small = cv2.resize(
+        mask.astype(np.uint8),
+        (small_w, small_h),
+        interpolation=cv2.INTER_NEAREST,
+    )
+
+    (
+        sample.comp_cana,
+        sample.largest_cana_frac,
+    ) = connected_component_stats(
+        mask_small == 1
+    )
+
+    (
+        sample.comp_erva,
+        sample.largest_erva_frac,
+    ) = connected_component_stats(
+        mask_small == 2
+    )
+
+    (
+        sample.weed_near_cane_frac,
+        sample.weed_contact_cane_frac,
+    ) = weed_cane_relation(
+        mask_small,
+        args.near_radius_frac,
+    )
+
+    (
+        visual_feat,
+        sample.brightness,
+        sample.contrast,
+        sample.saturation,
+        sample.dhash64,
+    ) = preview_features(
+        sample.preview_path
+    )
+
+    tensor_feat = tensor_summary_features(
+        tensor_path=sample.tensor_path,
+        mask=mask,
+        enabled=bool(args.use_tensor_features),
+        stride=args.tensor_feature_stride,
+    )
+
+    cana_grid = grid_fraction(
+        mask_small == 1,
+    )
+
+    erva_grid = grid_fraction(
+        mask_small == 2,
+    )
+
+    size_bin = target_size_bin(
+        sample.frac_erva,
+        args.target_tiny_frac,
+        args.target_small_frac,
+        args.target_medium_frac,
+    )
+
+    if (
+        sample.frac_cana > 0
+        and sample.frac_erva > 0
+        and sample.weed_near_cane_frac >= args.close_weed_cane_threshold
+    ):
+        proximity_bin = "close"
+    elif sample.frac_cana > 0 and sample.frac_erva > 0:
+        proximity_bin = "separate"
+    else:
+        proximity_bin = "na"
+
+    sample.stratum = (
+        f"{sample.actual_group}"
+        f"|target={size_bin}"
+        f"|caneweed={proximity_bin}"
+    )
+
+    sample.agri_risk = compute_agri_risk(
+        frac_cana=sample.frac_cana,
+        frac_erva=sample.frac_erva,
+        weed_near_cane_frac=sample.weed_near_cane_frac,
+        weed_contact_cane_frac=sample.weed_contact_cane_frac,
+    )
+
+    scalar_feat = np.asarray([
+        sample.frac_chao,
+        sample.frac_cana,
+        sample.frac_erva,
+        sample.frac_ignore,
+        math.log1p(sample.comp_cana) / 6.0,
+        math.log1p(sample.comp_erva) / 6.0,
+        sample.largest_cana_frac,
+        sample.largest_erva_frac,
+        sample.weed_near_cane_frac,
+        sample.weed_contact_cane_frac,
+        sample.brightness,
+        sample.contrast,
+        sample.saturation,
+        sample.agri_risk,
+    ], dtype=np.float32)
+
+    sample.feature_vector = np.concatenate([
+        scalar_feat,
+        cana_grid,
+        erva_grid,
+        visual_feat,
+        tensor_feat,
+    ]).astype(np.float32)
+
+
+# ============================================================
+# Família / near-duplicate leakage
+# ============================================================
+
+def hamming64(a: int, b: int) -> int:
+    return int(
+        (int(a) ^ int(b)).bit_count()
+    )
+
+
+class UnionFind:
+    def __init__(self, n: int):
+        self.parent = list(range(n))
+        self.rank = [0] * n
+
+    def find(self, x: int) -> int:
+        while self.parent[x] != x:
+            self.parent[x] = self.parent[
+                self.parent[x]
+            ]
+            x = self.parent[x]
+        return x
+
+    def union(self, a: int, b: int) -> None:
+        ra = self.find(a)
+        rb = self.find(b)
+
+        if ra == rb:
+            return
+
+        if self.rank[ra] < self.rank[rb]:
+            ra, rb = rb, ra
+
+        self.parent[rb] = ra
+
+        if self.rank[ra] == self.rank[rb]:
+            self.rank[ra] += 1
+
+
+def build_real_family_units(
+    samples: Sequence[Sample],
+) -> List[FamilyUnit]:
+    grouped: Dict[Tuple[str, str], List[Sample]] = {}
+
+    for s in samples:
+        if s.is_synthetic:
+            continue
+        if s.source_root_train_only:
+            continue
+
+        grouped.setdefault(
+            (s.actual_group, s.family),
+            [],
+        ).append(s)
+
+    units = []
+
+    for (group, family), members in grouped.items():
+        vectors = np.stack([
+            s.feature_vector
+            for s in members
+        ], axis=0)
+
+        representative = max(
+            members,
+            key=lambda s: s.agri_risk,
+        )
+
+        stratum_counts: Dict[str, int] = {}
+        for s in members:
+            stratum_counts[s.stratum] = (
+                stratum_counts.get(s.stratum, 0) + 1
+            )
+
+        stratum = max(
+            stratum_counts.items(),
+            key=lambda kv: (kv[1], kv[0]),
+        )[0]
+
+        units.append(
+            FamilyUnit(
+                actual_group=group,
+                family=family,
+                leakage_id=family,
+                samples=list(members),
+                feature_vector=np.mean(
+                    vectors,
+                    axis=0,
+                ).astype(np.float32),
+                stratum=stratum,
+                agri_risk=float(
+                    max(s.agri_risk for s in members)
+                ),
+                dhash64=representative.dhash64,
+                frac_chao=float(
+                    np.mean([s.frac_chao for s in members])
+                ),
+                frac_cana=float(
+                    np.mean([s.frac_cana for s in members])
+                ),
+                frac_erva=float(
+                    np.mean([s.frac_erva for s in members])
+                ),
+            )
+        )
+
+    return units
+
+
+def merge_near_duplicate_family_units(
+    units: Sequence[FamilyUnit],
+    enabled: bool,
+    max_hamming: int,
+    mask_l1: float,
+) -> Tuple[List[FamilyUnit], Dict[Tuple[str, str], str], int]:
+    """
+    Une famílias reais muito semelhantes para que não possam cair uma no
+    TRAIN e outra no VAL.
+
+    É propositalmente conservador.
+    Só compara famílias do mesmo grupo real.
+    """
+    units = list(units)
+
+    if not enabled or len(units) <= 1:
+        mapping = {
+            (u.actual_group, u.family): u.leakage_id
+            for u in units
+        }
+        return units, mapping, 0
+
+    result_units = []
+    family_to_leakage: Dict[Tuple[str, str], str] = {}
+    merged_count = 0
+
+    by_group: Dict[str, List[FamilyUnit]] = {}
+
+    for u in units:
+        by_group.setdefault(
+            u.actual_group,
+            [],
+        ).append(u)
+
+    for group, group_units in by_group.items():
+        n = len(group_units)
+        uf = UnionFind(n)
+
+        # bucket conservador por frações de classes para evitar O(n²) total
+        buckets: Dict[Tuple[int, int, int], List[int]] = {}
+
+        for i, u in enumerate(group_units):
+            key = (
+                int(round(u.frac_chao / 0.02)),
+                int(round(u.frac_cana / 0.02)),
+                int(round(u.frac_erva / 0.02)),
+            )
+            buckets.setdefault(key, []).append(i)
+
+        for key, indices in buckets.items():
+            # compara bucket e vizinhos imediatos de composição
+            candidate_indices = []
+
+            a0, a1, a2 = key
+
+            for d0 in (-1, 0, 1):
+                for d1 in (-1, 0, 1):
+                    for d2 in (-1, 0, 1):
+                        candidate_indices.extend(
+                            buckets.get(
+                                (a0 + d0, a1 + d1, a2 + d2),
+                                [],
+                            )
+                        )
+
+            candidate_indices = sorted(
+                set(candidate_indices)
+            )
+
+            for i in indices:
+                ui = group_units[i]
+
+                for j in candidate_indices:
+                    if j <= i:
+                        continue
+
+                    uj = group_units[j]
+
+                    if (
+                        hamming64(
+                            ui.dhash64,
+                            uj.dhash64,
+                        )
+                        > int(max_hamming)
+                    ):
+                        continue
+
+                    l1 = (
+                        abs(ui.frac_chao - uj.frac_chao)
+                        + abs(ui.frac_cana - uj.frac_cana)
+                        + abs(ui.frac_erva - uj.frac_erva)
+                    )
+
+                    if l1 > float(mask_l1):
+                        continue
+
+                    uf.union(i, j)
+
+        clusters: Dict[int, List[FamilyUnit]] = {}
+
+        for i, u in enumerate(group_units):
+            clusters.setdefault(
+                uf.find(i),
+                [],
+            ).append(u)
+
+        for cluster_index, members in enumerate(clusters.values()):
+            if len(members) > 1:
+                merged_count += len(members) - 1
+
+            leakage_id = (
+                members[0].family
+                if len(members) == 1
+                else f"near_{group}_{cluster_index:05d}"
+            )
+
+            all_samples = []
+            for m in members:
+                all_samples.extend(
+                    m.samples
+                )
+                family_to_leakage[
+                    (group, m.family)
+                ] = leakage_id
+
+            vectors = np.stack([
+                m.feature_vector
+                for m in members
+            ], axis=0)
+
+            hardest = max(
+                members,
+                key=lambda m: m.agri_risk,
+            )
+
+            stratum_counts: Dict[str, int] = {}
+            for m in members:
+                stratum_counts[m.stratum] = (
+                    stratum_counts.get(m.stratum, 0) + 1
+                )
+
+            stratum = max(
+                stratum_counts.items(),
+                key=lambda kv: (kv[1], kv[0]),
+            )[0]
+
+            result_units.append(
+                FamilyUnit(
+                    actual_group=group,
+                    family=members[0].family,
+                    leakage_id=leakage_id,
+                    samples=all_samples,
+                    feature_vector=np.mean(
+                        vectors,
+                        axis=0,
+                    ).astype(np.float32),
+                    stratum=stratum,
+                    agri_risk=float(
+                        max(m.agri_risk for m in members)
+                    ),
+                    dhash64=hardest.dhash64,
+                    frac_chao=float(
+                        np.mean([m.frac_chao for m in members])
+                    ),
+                    frac_cana=float(
+                        np.mean([m.frac_cana for m in members])
+                    ),
+                    frac_erva=float(
+                        np.mean([m.frac_erva for m in members])
+                    ),
+                )
+            )
+
+    return (
+        result_units,
+        family_to_leakage,
+        merged_count,
+    )
+
+
+# ============================================================
+# Seleção diversa de VAL/TEST
+# ============================================================
+
+def robust_standardize(
+    features: np.ndarray,
+) -> np.ndarray:
+    x = np.asarray(
+        features,
+        dtype=np.float32,
+    )
+
+    med = np.median(
+        x,
+        axis=0,
+    )
+
+    q25 = np.percentile(
+        x,
+        25,
+        axis=0,
+    )
+
+    q75 = np.percentile(
+        x,
+        75,
+        axis=0,
+    )
+
+    scale = q75 - q25
+    std = np.std(
+        x,
+        axis=0,
+    )
+
+    scale = np.where(
+        scale > 1e-6,
+        scale,
+        std,
+    )
+
+    scale = np.where(
+        scale > 1e-6,
+        scale,
+        1.0,
+    )
+
+    z = (x - med) / scale
+
+    return np.clip(
+        z,
+        -8.0,
+        8.0,
+    ).astype(np.float32)
+
+
+def diverse_order(
+    units: Sequence[FamilyUnit],
+    seed: int,
+) -> List[int]:
+    """
+    Ordem k-center:
+    começa pela unidade mais próxima do centro do estrato
+    e depois vai pegando a mais distante do conjunto já coberto.
+
+    Isso produz uma prova representativa + ampla, não somente casos extremos.
+    """
+    units = list(units)
+
+    if len(units) <= 1:
+        return list(range(len(units)))
+
+    x = np.stack([
+        u.feature_vector
+        for u in units
+    ], axis=0)
+
+    z = robust_standardize(x)
+
+    centroid = np.mean(
+        z,
+        axis=0,
+    )
+
+    dist_center = np.sum(
+        (z - centroid) ** 2,
+        axis=1,
+    )
+
+    rng = np.random.default_rng(
+        int(seed)
+    )
+
+    jitter = rng.uniform(
+        0.0,
+        1e-8,
+        size=len(units),
+    )
+
+    first = int(
+        np.argmin(
+            dist_center + jitter
+        )
+    )
+
+    order = [first]
+
+    selected = np.zeros(
+        len(units),
+        dtype=bool,
+    )
+    selected[first] = True
+
+    delta = z - z[first]
+
+    min_dist_sq = np.sum(
+        delta * delta,
+        axis=1,
+    )
+
+    min_dist_sq[first] = 0.0
+
+    while len(order) < len(units):
+        score = min_dist_sq.copy()
+        score[selected] = -1.0
+
+        idx = int(
+            np.argmax(
+                score + jitter
+            )
+        )
+
+        order.append(idx)
+        selected[idx] = True
+
+        delta = z - z[idx]
+
+        dist_sq = np.sum(
+            delta * delta,
+            axis=1,
+        )
+
+        min_dist_sq = np.minimum(
+            min_dist_sq,
+            dist_sq,
+        )
+
+    return order
+
+
+def allocate_stratum_counts(
+    n: int,
+    p_train: float,
+    p_val: float,
+    p_test: float,
+) -> Tuple[int, int, int]:
+    if n <= 0:
+        return 0, 0, 0
+
+    n_val = int(round(
+        n * p_val
+    ))
+
+    n_test = int(round(
+        n * p_test
+    ))
+
+    # Garante pelo menos uma família de val em estratos minimamente grandes.
+    if p_val > 0 and n >= 5:
+        n_val = max(
+            1,
+            n_val,
+        )
+
+    if p_test > 0 and n >= 10:
+        n_test = max(
+            1,
+            n_test,
+        )
+
+    while n_val + n_test >= n:
+        if n_test > 0:
+            n_test -= 1
+        elif n_val > 0:
+            n_val -= 1
+        else:
+            break
+
+    n_train = n - n_val - n_test
+
+    return (
+        n_train,
+        n_val,
+        n_test,
+    )
+
+
+def assign_agri_diverse_split(
+    units: Sequence[FamilyUnit],
     p_train: float,
     p_val: float,
     p_test: float,
     seed: int,
-    mins: Dict[str, int],
-    caps_map: Optional[Dict[str, int]] = None,
-) -> Tuple[Dict[str, Dict[str, str]], Dict[str, Dict[str, int]]]:
+) -> Dict[Tuple[str, str], str]:
     """
-    Usa apenas amostras reais/originais para decidir a partição das famílias.
+    Estratifica por "situação agrícola" e escolhe VAL/TEST por diversidade.
 
-    Retorna:
-      family_split[split_group][family] = train|val|test
-      group_summary[split_group] = contagens de famílias
+    Dentro de cada estrato:
+    - escolhe representantes diversos para VAL;
+    - escolhe representantes ainda não usados para TEST;
+    - restante vai para TRAIN.
     """
-    caps_map = caps_map or {}
+    strata: Dict[str, List[FamilyUnit]] = {}
 
-    families_by_group: Dict[str, set[str]] = {}
+    for u in units:
+        strata.setdefault(
+            u.stratum,
+            [],
+        ).append(u)
 
-    for s in samples:
-        if s.source_root_train_only:
-            continue
-        if s.is_synthetic:
-            continue
-        if s.source in ("augmented", "copypaste", "synthetic"):
-            continue
+    assignment: Dict[Tuple[str, str], str] = {}
 
-        families_by_group.setdefault(s.split_group, set()).add(s.family)
+    for stratum_index, stratum in enumerate(sorted(strata)):
+        members = strata[stratum]
 
-    family_split: Dict[str, Dict[str, str]] = {}
-    group_summary: Dict[str, Dict[str, int]] = {}
-
-    for group_name in sorted(families_by_group.keys()):
-        fams = sorted(families_by_group[group_name])
-        rng = random.Random(seed)
-        rng.shuffle(fams)
-
-        total_familias = len(fams)
-        if total_familias == 0:
-            group_summary[group_name] = {"familias": 0, "train_families": 0, "val_families": 0, "test_families": 0}
-            continue
-
-        n_tr, n_va, n_te = allocate_counts(
-            total_familias,
+        n_train, n_val, n_test = allocate_stratum_counts(
+            len(members),
             p_train,
             p_val,
             p_test,
-            mins["train"],
-            mins["val"],
-            mins["test"],
         )
 
-        fam_train = set(fams[:n_tr])
-        fam_val = set(fams[n_tr:n_tr + n_va])
-        fam_test = set(fams[n_tr + n_va:n_tr + n_va + n_te])
+        order = diverse_order(
+            members,
+            seed=seed + stratum_index * 9973,
+        )
 
-        if group_name in caps_map:
-            cap = int(caps_map[group_name])
-            if len(fam_train) > cap:
-                fam_list = list(fam_train)
-                rng.shuffle(fam_list)
-                kept = set(fam_list[:cap])
-                dropped = set(fam_list[cap:])
-                fam_train = kept
-                print(
-                    f"[{group_name}] cap-train-families={cap} → "
-                    f"mantidas {len(kept)} famílias, descartadas {len(dropped)} do TRAIN"
+        # Distribui as posições mais representativas/diversas para a prova.
+        val_idx = set(
+            order[:n_val]
+        )
+
+        test_idx = set(
+            order[n_val:n_val + n_test]
+        )
+
+        for i, unit in enumerate(members):
+            if i in val_idx:
+                split = "val"
+                reason = "agri_diverse_val"
+            elif i in test_idx:
+                split = "test"
+                reason = "agri_diverse_test"
+            else:
+                split = "train"
+                reason = "agri_diverse_train"
+
+            unit.assigned_split = split
+            unit.assignment_reason = reason
+
+            assignment[
+                (unit.actual_group, unit.leakage_id)
+            ] = split
+
+    return assignment
+
+
+def assign_random_split(
+    units: Sequence[FamilyUnit],
+    p_train: float,
+    p_val: float,
+    p_test: float,
+    seed: int,
+) -> Dict[Tuple[str, str], str]:
+    by_group: Dict[str, List[FamilyUnit]] = {}
+
+    for u in units:
+        by_group.setdefault(
+            u.actual_group,
+            [],
+        ).append(u)
+
+    assignment = {}
+
+    for group_index, group in enumerate(sorted(by_group)):
+        members = list(
+            by_group[group]
+        )
+
+        rng = random.Random(
+            seed + group_index * 1009
+        )
+        rng.shuffle(members)
+
+        n = len(members)
+
+        n_train = int(round(
+            n * p_train
+        ))
+
+        n_val = int(round(
+            n * p_val
+        ))
+
+        n_test = (
+            n
+            - n_train
+            - n_val
+        )
+
+        if n_test < 0:
+            n_test = 0
+            n_train = max(
+                0,
+                n - n_val,
+            )
+
+        for i, unit in enumerate(members):
+            if i < n_train:
+                split = "train"
+            elif i < n_train + n_val:
+                split = "val"
+            else:
+                split = "test"
+
+            unit.assigned_split = split
+            unit.assignment_reason = "random_group"
+
+            assignment[
+                (unit.actual_group, unit.leakage_id)
+            ] = split
+
+    return assignment
+
+
+# ============================================================
+# Aplicar split aos samples
+# ============================================================
+
+def decide_sample_split(
+    sample: Sample,
+    family_to_leakage: Dict[Tuple[str, str], str],
+    assignment: Dict[Tuple[str, str], str],
+    args,
+) -> Tuple[Optional[str], str]:
+    # Para dados reais, a máscara define o grupo/família de leakage.
+    # Para sintéticos/copy-paste, a família deve respeitar o split da
+    # FAMÍLIA REAL DE ORIGEM/receiver. O grupo sintético pode mudar depois
+    # do copy-paste (ex.: chao_cana -> chao_cana_erva), então usar
+    # actual_group aqui faria o filho perder o vínculo com o pai real.
+    family_lookup_group = (
+        sample.actual_group
+        if not sample.is_synthetic
+        else normalize_group_for_split(sample.source_group)
+    )
+
+    leakage_id = family_to_leakage.get(
+        (family_lookup_group, sample.family),
+        sample.family,
+    )
+
+    assigned = assignment.get(
+        (family_lookup_group, leakage_id)
+    )
+
+    is_train_only_candidate = (
+        sample.source_root_train_only
+        or (
+            bool(args.synthetic_train_only)
+            and sample.is_synthetic
+        )
+        or sample.source in (
+            "augmented",
+            "copypaste",
+            "synthetic",
+        )
+    )
+
+    if is_train_only_candidate:
+        if bool(args.synthetic_respect_family_split):
+            if assigned is None:
+                if bool(args.allow_orphan_synthetic_train):
+                    return (
+                        "train",
+                        "train_only_orphan_allowed",
+                    )
+
+                return (
+                    None,
+                    "skip_train_only_orphan_no_real_family",
                 )
 
-        group_map: Dict[str, str] = {}
-        for f in fam_train:
-            group_map[f] = "train"
-        for f in fam_val:
-            group_map[f] = "val"
-        for f in fam_test:
-            group_map[f] = "test"
+            if assigned != "train":
+                return (
+                    None,
+                    f"skip_train_only_family_assigned_{assigned}",
+                )
 
-        family_split[group_name] = group_map
-        group_summary[group_name] = {
-            "familias": total_familias,
-            "train_families": len(fam_train),
-            "val_families": len(fam_val),
-            "test_families": len(fam_test),
-        }
+            return (
+                "train",
+                "train_only_family_train",
+            )
 
-    return family_split, group_summary
+        return (
+            "train",
+            "train_only_forced",
+        )
+
+    if assigned is None:
+        return (
+            None,
+            "skip_real_no_family_assignment",
+        )
+
+    return (
+        assigned,
+        f"real_assigned_{assigned}",
+    )
 
 
 # ============================================================
 # Cópia
 # ============================================================
 
-def copy_optional_file(src_path: Optional[str], dst_dir: str, dst_base: str, suffix: str = "") -> Optional[str]:
-    if not src_path or not os.path.exists(src_path):
-        return None
-
-    garantir(dst_dir)
-    ext = os.path.splitext(src_path)[1]
-    dst = os.path.join(dst_dir, dst_base + suffix + ext)
-    shutil.copy2(src_path, dst)
-    return dst
-
-
-def copy_named_optional(src_dir: str, dst_dir: str, base: str, ext: str) -> Optional[str]:
-    src = os.path.join(src_dir, base + ext)
-    if not os.path.exists(src):
-        return None
-    garantir(dst_dir)
-    dst = os.path.join(dst_dir, base + ext)
-    shutil.copy2(src, dst)
-    return dst
-
-
 def copiar_sample(
-    s: Sample,
+    sample: Sample,
     dst_root: str,
     split_name: str,
     out_group_name: str,
-    copy_meta_preview: bool = True,
-    copy_visuals: bool = True,
+    copy_meta_preview: bool,
+    copy_visuals: bool,
 ) -> Optional[Dict[str, Any]]:
-    dst_group_dir = os.path.join(dst_root, split_name, "group", out_group_name)
+    dst_group_dir = os.path.join(
+        dst_root,
+        split_name,
+        "group",
+        out_group_name,
+    )
 
-    dst_tensor_dir = os.path.join(dst_group_dir, "tensors")
-    dst_mask_dir = os.path.join(dst_group_dir, "masks")
-    dst_meta_dir = os.path.join(dst_group_dir, "metas")
-    dst_preview_dir = os.path.join(dst_group_dir, "previews")
-    dst_visual_dir = os.path.join(dst_group_dir, "visuals")
+    dst_tensor_dir = os.path.join(
+        dst_group_dir,
+        "tensors",
+    )
 
-    garantir(dst_tensor_dir)
-    garantir(dst_mask_dir)
+    dst_mask_dir = os.path.join(
+        dst_group_dir,
+        "masks",
+    )
 
-    tensor_dst = os.path.join(dst_tensor_dir, s.tensor_name)
-    mask_dst = os.path.join(dst_mask_dir, os.path.basename(s.mask_path))
+    garantir(
+        dst_tensor_dir
+    )
+    garantir(
+        dst_mask_dir
+    )
 
-    if not (os.path.exists(s.tensor_path) and os.path.exists(s.mask_path)):
+    if not (
+        os.path.isfile(sample.tensor_path)
+        and os.path.isfile(sample.mask_path)
+    ):
         return None
 
-    shutil.copy2(s.tensor_path, tensor_dst)
-    shutil.copy2(s.mask_path, mask_dst)
+    tensor_dst = os.path.join(
+        dst_tensor_dir,
+        sample.tensor_name,
+    )
 
-    base = s.base
+    mask_dst = os.path.join(
+        dst_mask_dir,
+        os.path.basename(sample.mask_path),
+    )
+
+    shutil.copy2(
+        sample.tensor_path,
+        tensor_dst,
+    )
+
+    shutil.copy2(
+        sample.mask_path,
+        mask_dst,
+    )
+
+    base = sample.base
+
+    mask_png_src = os.path.join(
+        os.path.dirname(sample.mask_path),
+        base + ".png",
+    )
 
-    # mask debug PNG opcional
-    mask_png_src = os.path.join(os.path.dirname(s.mask_path), base + ".png")
     mask_png_dst = None
-    if os.path.exists(mask_png_src):
-        mask_png_dst = os.path.join(dst_mask_dir, base + ".png")
-        shutil.copy2(mask_png_src, mask_png_dst)
 
-    # aux masks
+    if os.path.isfile(mask_png_src):
+        mask_png_dst = os.path.join(
+            dst_mask_dir,
+            base + ".png",
+        )
+
+        shutil.copy2(
+            mask_png_src,
+            mask_png_dst,
+        )
+
     aux_masks: Dict[str, Dict[str, Optional[str]]] = {}
-    src_group_dir = os.path.join(s.src_root, s.source_group)
+
+    src_group_dir = os.path.join(
+        sample.src_root,
+        sample.source_group,
+    )
 
     for aux_dir in AUX_MASK_DIRS:
-        aux_src_dir = os.path.join(src_group_dir, aux_dir)
-        aux_dst_dir = os.path.join(dst_group_dir, aux_dir)
+        aux_src_dir = os.path.join(
+            src_group_dir,
+            aux_dir,
+        )
 
-        aux_npy_src = os.path.join(aux_src_dir, base + ".npy")
-        aux_png_src = os.path.join(aux_src_dir, base + ".png")
+        aux_dst_dir = os.path.join(
+            dst_group_dir,
+            aux_dir,
+        )
+
+        aux_npy_src = os.path.join(
+            aux_src_dir,
+            base + ".npy",
+        )
+
+        aux_png_src = os.path.join(
+            aux_src_dir,
+            base + ".png",
+        )
 
         aux_npy_dst = None
         aux_png_dst = None
 
-        if os.path.exists(aux_npy_src):
-            garantir(aux_dst_dir)
-            aux_npy_dst = os.path.join(aux_dst_dir, base + ".npy")
-            shutil.copy2(aux_npy_src, aux_npy_dst)
+        if os.path.isfile(aux_npy_src):
+            garantir(
+                aux_dst_dir
+            )
 
-            if os.path.exists(aux_png_src):
-                aux_png_dst = os.path.join(aux_dst_dir, base + ".png")
-                shutil.copy2(aux_png_src, aux_png_dst)
+            aux_npy_dst = os.path.join(
+                aux_dst_dir,
+                base + ".npy",
+            )
+
+            shutil.copy2(
+                aux_npy_src,
+                aux_npy_dst,
+            )
+
+            if os.path.isfile(aux_png_src):
+                aux_png_dst = os.path.join(
+                    aux_dst_dir,
+                    base + ".png",
+                )
+
+                shutil.copy2(
+                    aux_png_src,
+                    aux_png_dst,
+                )
 
         aux_masks[aux_dir] = {
             "npy": aux_npy_dst,
@@ -668,8 +1978,8 @@ def copiar_sample(
 
         if MULTI_HEAD and aux_npy_dst is None:
             raise RuntimeError(
-                f"multi_head=true, mas máscara auxiliar ausente: "
-                f"{aux_dir}/{base}.npy em {src_group_dir}"
+                f"multi_head=true e máscara auxiliar ausente: "
+                f"{aux_dir}/{base}.npy"
             )
 
     meta_dst = None
@@ -677,146 +1987,215 @@ def copiar_sample(
     visual_dst = None
 
     if copy_meta_preview:
-        if s.meta_path and os.path.exists(s.meta_path):
-            garantir(dst_meta_dir)
-            meta_dst = os.path.join(dst_meta_dir, base + ".json")
-            shutil.copy2(s.meta_path, meta_dst)
+        if (
+            sample.meta_path
+            and os.path.isfile(sample.meta_path)
+        ):
+            dst_meta_dir = os.path.join(
+                dst_group_dir,
+                "metas",
+            )
 
-        if s.preview_path and os.path.exists(s.preview_path):
-            garantir(dst_preview_dir)
-            preview_dst = os.path.join(dst_preview_dir, base + os.path.splitext(s.preview_path)[1])
-            shutil.copy2(s.preview_path, preview_dst)
+            garantir(
+                dst_meta_dir
+            )
 
-    if copy_visuals and s.visual_path and os.path.exists(s.visual_path):
-        garantir(dst_visual_dir)
-        visual_dst = os.path.join(dst_visual_dir, os.path.basename(s.visual_path))
-        shutil.copy2(s.visual_path, visual_dst)
+            meta_dst = os.path.join(
+                dst_meta_dir,
+                base + ".json",
+            )
+
+            shutil.copy2(
+                sample.meta_path,
+                meta_dst,
+            )
+
+        if (
+            sample.preview_path
+            and os.path.isfile(sample.preview_path)
+        ):
+            dst_preview_dir = os.path.join(
+                dst_group_dir,
+                "previews",
+            )
+
+            garantir(
+                dst_preview_dir
+            )
+
+            ext = os.path.splitext(
+                sample.preview_path
+            )[1]
+
+            preview_dst = os.path.join(
+                dst_preview_dir,
+                base + ext,
+            )
+
+            shutil.copy2(
+                sample.preview_path,
+                preview_dst,
+            )
+
+    if (
+        copy_visuals
+        and sample.visual_path
+        and os.path.isfile(sample.visual_path)
+    ):
+        dst_visual_dir = os.path.join(
+            dst_group_dir,
+            "visuals",
+        )
+
+        garantir(
+            dst_visual_dir
+        )
+
+        visual_dst = os.path.join(
+            dst_visual_dir,
+            os.path.basename(sample.visual_path),
+        )
+
+        shutil.copy2(
+            sample.visual_path,
+            visual_dst,
+        )
 
     return {
         "split": split_name,
         "group": out_group_name,
-        "source_group": s.source_group,
-        "split_group": s.split_group,
+        "source_group": sample.source_group,
+        "actual_group": sample.actual_group,
         "base": base,
-        "family": s.family,
-        "source": s.source,
-        "is_synthetic": int(bool(s.is_synthetic)),
-        "source_root": s.src_root,
-        "source_root_label": s.source_root_label,
-        "source_root_train_only": int(bool(s.source_root_train_only)),
-
+        "family": sample.family,
+        "source": sample.source,
+        "is_synthetic": int(sample.is_synthetic),
+        "stratum": sample.stratum,
+        "agri_risk": sample.agri_risk,
+        "frac_chao": sample.frac_chao,
+        "frac_cana": sample.frac_cana,
+        "frac_erva": sample.frac_erva,
+        "weed_near_cane_frac": sample.weed_near_cane_frac,
+        "weed_contact_cane_frac": sample.weed_contact_cane_frac,
+        "source_root": sample.src_root,
         "tensor": tensor_dst,
         "mask_npy": mask_dst,
         "mask_png": mask_png_dst,
-
-        "mask_vegetation_npy": aux_masks.get("masks_vegetation", {}).get("npy"),
-        "mask_vegetation_png": aux_masks.get("masks_vegetation", {}).get("png"),
-
-        "mask_cana_npy": aux_masks.get("masks_cana", {}).get("npy"),
-        "mask_cana_png": aux_masks.get("masks_cana", {}).get("png"),
-
+        "mask_vegetation_npy": aux_masks.get(
+            "masks_vegetation",
+            {},
+        ).get("npy"),
+        "mask_cana_npy": aux_masks.get(
+            "masks_cana",
+            {},
+        ).get("npy"),
         "meta": meta_dst,
         "preview": preview_dst,
         "visual_debug": visual_dst,
     }
 
 
-def decide_sample_split(
-    s: Sample,
-    family_split: Dict[str, Dict[str, str]],
-    args,
-) -> Tuple[Optional[str], str]:
-    """
-    Retorna (split, reason)
-      split None = ignorado
-    """
-    group_map = family_split.get(s.split_group, {})
-    assigned = group_map.get(s.family)
+# ============================================================
+# Relatórios
+# ============================================================
 
-    is_train_only_candidate = (
-        s.source_root_train_only
-        or (bool(args.synthetic_train_only) and s.is_synthetic)
-        or s.source in ("augmented", "copypaste", "synthetic")
+def write_csv(
+    path: str | Path,
+    rows: Sequence[Dict[str, Any]],
+    fieldnames: Sequence[str],
+) -> None:
+    garantir(
+        os.path.dirname(str(path))
     )
 
-    if is_train_only_candidate:
-        if bool(args.synthetic_respect_family_split):
-            if assigned is None:
-                if bool(args.allow_orphan_synthetic_train):
-                    return "train", "train_only_orphan_allowed"
-                return None, "skip_train_only_orphan_no_real_family"
+    with open(
+        path,
+        "w",
+        newline="",
+        encoding="utf-8",
+    ) as f:
+        writer = csv.DictWriter(
+            f,
+            fieldnames=list(fieldnames),
+            extrasaction="ignore",
+        )
 
-            if assigned != "train":
-                return None, f"skip_train_only_family_assigned_{assigned}"
-
-            return "train", "train_only_family_train"
-
-        return "train", "train_only_forced"
-
-    # real/original
-    if assigned is None:
-        return None, "skip_real_no_family_assignment"
-
-    return assigned, f"real_assigned_{assigned}"
+        writer.writeheader()
+        writer.writerows(rows)
 
 
-# ============================================================
-# Manifestos
-# ============================================================
-
-def write_manifest(path: str, rows: List[Dict[str, Any]]) -> None:
-    garantir(os.path.dirname(path))
-
-    fieldnames = [
-        "split",
-        "group",
-        "source_group",
-        "split_group",
-        "base",
-        "family",
-        "source",
-        "is_synthetic",
-        "source_root",
-        "source_root_label",
-        "source_root_train_only",
-
-        "tensor",
-        "mask_npy",
-        "mask_png",
-
-        "mask_vegetation_npy",
-        "mask_vegetation_png",
-
-        "mask_cana_npy",
-        "mask_cana_png",
-
-        "meta",
-        "preview",
-        "visual_debug",
-    ]
-
-    with open(path, "w", newline="", encoding="utf-8") as f:
-        w = csv.DictWriter(f, fieldnames=fieldnames, extrasaction="ignore")
-        w.writeheader()
-        w.writerows(rows)
+def sample_audit_row(
+    sample: Sample,
+    split_name: Optional[str],
+    reason: str,
+) -> Dict[str, Any]:
+    return {
+        "source_group": sample.source_group,
+        "actual_group": sample.actual_group,
+        "folder_mask_mismatch": (
+            normalize_group_for_split(sample.source_group)
+            != sample.actual_group
+        ),
+        "base": sample.base,
+        "family": sample.family,
+        "source": sample.source,
+        "is_synthetic": int(sample.is_synthetic),
+        "split": split_name or "",
+        "reason": reason,
+        "stratum": sample.stratum,
+        "agri_risk": sample.agri_risk,
+        "present_ids": ",".join(
+            map(str, sample.present_ids)
+        ),
+        "frac_chao": sample.frac_chao,
+        "frac_cana": sample.frac_cana,
+        "frac_erva": sample.frac_erva,
+        "frac_ignore": sample.frac_ignore,
+        "comp_cana": sample.comp_cana,
+        "comp_erva": sample.comp_erva,
+        "largest_cana_frac": sample.largest_cana_frac,
+        "largest_erva_frac": sample.largest_erva_frac,
+        "weed_near_cane_frac": sample.weed_near_cane_frac,
+        "weed_contact_cane_frac": sample.weed_contact_cane_frac,
+        "brightness": sample.brightness,
+        "contrast": sample.contrast,
+        "saturation": sample.saturation,
+        "dhash64": f"{sample.dhash64:016x}",
+        "tensor": sample.tensor_path,
+        "mask": sample.mask_path,
+        "preview": sample.preview_path or "",
+        "audit_error": sample.audit_error,
+    }
 
 
-def write_skipped(path: str, rows: List[Dict[str, Any]]) -> None:
-    garantir(os.path.dirname(path))
-    fieldnames = [
-        "source_root",
-        "source_group",
-        "split_group",
-        "base",
-        "family",
-        "source",
-        "is_synthetic",
-        "reason",
-    ]
-    with open(path, "w", newline="", encoding="utf-8") as f:
-        w = csv.DictWriter(f, fieldnames=fieldnames, extrasaction="ignore")
-        w.writeheader()
-        w.writerows(rows)
+def family_audit_rows(
+    units: Sequence[FamilyUnit],
+) -> List[Dict[str, Any]]:
+    rows = []
+
+    for u in sorted(
+        units,
+        key=lambda x: (
+            x.actual_group,
+            x.stratum,
+            x.leakage_id,
+        ),
+    ):
+        rows.append({
+            "actual_group": u.actual_group,
+            "family": u.family,
+            "leakage_id": u.leakage_id,
+            "samples_in_unit": len(u.samples),
+            "stratum": u.stratum,
+            "agri_risk": u.agri_risk,
+            "frac_chao": u.frac_chao,
+            "frac_cana": u.frac_cana,
+            "frac_erva": u.frac_erva,
+            "assigned_split": u.assigned_split or "",
+            "assignment_reason": u.assignment_reason,
+        })
+
+    return rows
 
 
 # ============================================================
@@ -825,208 +2204,384 @@ def write_skipped(path: str, rows: List[Dict[str, Any]]) -> None:
 
 def main() -> None:
     ap = argparse.ArgumentParser(
-        description="Split multi-source OAK-FCC-3 sem vazamento, com sintéticos/aug apenas no train."
+        description=(
+            "Split agrícola Teacher V2: família, máscara, diversidade e "
+            "prova real sem leakage."
+        )
     )
 
-    ap.add_argument("--train", type=float, default=0.70)
-    ap.add_argument("--val", type=float, default=0.29)
-    ap.add_argument("--test", type=float, default=0.01)
-    ap.add_argument("--seed", type=int, default=42)
+    ap.add_argument(
+        "--train",
+        type=float,
+        default=0.85,
+    )
 
-    ap.add_argument("--min-train", type=int, default=1)
-    ap.add_argument("--min-val", type=int, default=1)
-    ap.add_argument("--min-test", type=int, default=0)
+    ap.add_argument(
+        "--val",
+        type=float,
+        default=0.15,
+    )
+
+    ap.add_argument(
+        "--test",
+        type=float,
+        default=0.0,
+    )
+
+    ap.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+    )
+
+    ap.add_argument(
+        "--strategy",
+        choices=[
+            "agri_diverse",
+            "random",
+        ],
+        default="agri_diverse",
+    )
 
     ap.add_argument(
         "--resolucao",
-        type=str,
         default=None,
-        help="Sobrescreve resolução no formato WxH. Ex: 1024x640.",
+        help="WxH, ex. 1280x800",
     )
 
     ap.add_argument(
         "--src-root",
-        type=str,
         default=None,
-        help="Compatibilidade: uma raiz normalizada. Default: dataset/<resolucao>/group",
     )
 
     ap.add_argument(
         "--src-roots",
-        type=str,
         default="",
-        help="Lista de raízes separadas por vírgula. Ex: dataset/1024x640/group,dataset/copypaste/group",
     )
 
     ap.add_argument(
         "--train-only-roots",
-        type=str,
         default="",
-        help="Raízes que devem entrar somente no TRAIN. Ex: dataset/copypaste/group",
     )
 
     ap.add_argument(
         "--dst-root",
-        type=str,
         default="dataset/split",
-        help="Raiz do split.",
     )
 
     ap.add_argument(
         "--groups",
-        type=str,
         default=None,
-        help="Lista de grupos/split_groups separados por vírgula.",
-    )
-
-    ap.add_argument(
-        "--cap-train-families",
-        type=str,
-        default="",
-        help="Mapa 'grupo:cap,...' para limitar famílias reais no TRAIN. Ex: 'chao:50'",
     )
 
     ap.add_argument(
         "--clear-dst",
         action="store_true",
-        help="Apaga dst-root antes de copiar.",
     )
 
     ap.add_argument(
         "--no-meta-preview",
         action="store_true",
-        help="Não copia metas/previews para o split.",
     )
 
     ap.add_argument(
         "--no-visuals",
         action="store_true",
-        help="Não copia pasta visuals/debug.",
     )
 
+    ap.add_argument(
+        "--output-group-mode",
+        choices=[
+            "source",
+            "base",
+            "suffix",
+        ],
+        default="base",
+        help=(
+            "base é recomendado: o grupo final segue a máscara real."
+        ),
+    )
+
+    # máscara
+    ap.add_argument(
+        "--presence-min-pixels",
+        type=int,
+        default=1,
+    )
+
+    ap.add_argument(
+        "--presence-min-fraction",
+        type=float,
+        default=0.0,
+    )
+
+    ap.add_argument(
+        "--ignore-ids",
+        nargs="*",
+        type=int,
+        default=[255],
+    )
+
+    # Teacher/agri strata
+    ap.add_argument(
+        "--target-tiny-frac",
+        type=float,
+        default=0.002,
+    )
+
+    ap.add_argument(
+        "--target-small-frac",
+        type=float,
+        default=0.010,
+    )
+
+    ap.add_argument(
+        "--target-medium-frac",
+        type=float,
+        default=0.050,
+    )
+
+    ap.add_argument(
+        "--near-radius-frac",
+        type=float,
+        default=0.02,
+    )
+
+    ap.add_argument(
+        "--close-weed-cane-threshold",
+        type=float,
+        default=0.25,
+    )
+
+    # features
+    ap.add_argument(
+        "--use-tensor-features",
+        action="store_true",
+        default=False,
+        help=(
+            "Inclui diversidade espectral do tensor no split. "
+            "Recomendado após validar custo no dry-run."
+        ),
+    )
+
+    ap.add_argument(
+        "--tensor-feature-stride",
+        type=int,
+        default=16,
+        help=(
+            "Subamostragem espacial para features do tensor. "
+            "16 é leve mesmo em 1280x800."
+        ),
+    )
+
+    # near duplicates
+    ap.add_argument(
+        "--merge-near-duplicate-families",
+        action="store_true",
+        default=False,
+        help=(
+            "Une famílias reais quase idênticas para impedir leakage."
+        ),
+    )
+
+    ap.add_argument(
+        "--near-dup-hamming",
+        type=int,
+        default=2,
+    )
+
+    ap.add_argument(
+        "--near-dup-mask-l1",
+        type=float,
+        default=0.02,
+    )
+
+    # synthetic
     ap.add_argument(
         "--synthetic-train-only",
         action="store_true",
         default=True,
-        help="Força samples synthetic/augmented/copypaste para TRAIN apenas.",
     )
 
     ap.add_argument(
         "--allow-synthetic-val",
         dest="synthetic_train_only",
         action="store_false",
-        help="Permite sintéticos no val/test. Não recomendado.",
     )
 
     ap.add_argument(
         "--synthetic-respect-family-split",
         action="store_true",
         default=True,
-        help="Só inclui sintético no TRAIN se a família real correspondente caiu no TRAIN.",
     )
 
     ap.add_argument(
         "--no-synthetic-respect-family-split",
         dest="synthetic_respect_family_split",
         action="store_false",
-        help="Inclui sintéticos no TRAIN mesmo sem checar a família real. Mais arriscado.",
     )
 
     ap.add_argument(
         "--allow-orphan-synthetic-train",
         action="store_true",
         default=False,
-        help="Permite sintético no TRAIN mesmo sem família real encontrada.",
-    )
-
-    ap.add_argument(
-        "--output-group-mode",
-        choices=["source", "base", "suffix"],
-        default="source",
-        help="Nome do grupo de saída. source mantém chao_cana_copypaste; base junta em chao_cana; suffix cria <base>_synthetic.",
     )
 
+    # relatórios
     ap.add_argument(
         "--manifest",
-        type=str,
         default="",
-        help="CSV de manifesto. Default: <dst-root>/split_manifest.csv",
+    )
+
+    ap.add_argument(
+        "--audit",
+        default="",
+    )
+
+    ap.add_argument(
+        "--families",
+        default="",
     )
 
     ap.add_argument(
         "--summary",
-        type=str,
         default="",
-        help="JSON de resumo. Default: <dst-root>/split_summary.json",
     )
 
     ap.add_argument(
         "--skipped",
-        type=str,
         default="",
-        help="CSV de samples ignorados. Default: <dst-root>/split_skipped.csv",
+    )
+
+    ap.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Audita e decide split, mas não copia arquivos.",
     )
 
     args = ap.parse_args()
 
+    # resolução
     if args.resolucao:
         try:
             w, h = args.resolucao.lower().split("x")
-            resolucao = (int(w), int(h))
+            resolution = (
+                int(w),
+                int(h),
+            )
         except Exception:
-            resolucao = RESOLUCAO
+            raise SystemExit(
+                f"[ERRO] resolução inválida: {args.resolucao}"
+            )
     else:
-        resolucao = RESOLUCAO
+        resolution = RESOLUCAO
 
-    default_src = os.path.join("dataset", f"{resolucao[0]}x{resolucao[1]}", "group")
+    default_src = os.path.join(
+        "dataset",
+        f"{resolution[0]}x{resolution[1]}",
+        "group",
+    )
+
+    src_roots = parse_csv_list(
+        args.src_roots
+    )
 
-    src_roots = parse_csv_list(args.src_roots)
     if args.src_root:
-        src_roots.insert(0, args.src_root)
-    if not src_roots:
-        src_roots = [default_src]
+        src_roots.insert(
+            0,
+            args.src_root,
+        )
+
+    if not src_roots:
+        src_roots = [
+            default_src
+        ]
 
-    # Remove duplicatas preservando ordem.
     seen = set()
-    src_roots_unique = []
-    for r in src_roots:
-        nr = norm_path(r)
+    unique_src_roots = []
+
+    for root in src_roots:
+        nr = norm_path(root)
+
         if nr not in seen:
             seen.add(nr)
-            src_roots_unique.append(r)
-    src_roots = src_roots_unique
+            unique_src_roots.append(root)
 
-    train_only_roots = parse_csv_list(args.train_only_roots)
+    src_roots = unique_src_roots
 
-    for r in src_roots:
-        if not os.path.isdir(r):
-            raise SystemExit(f"[ERRO] src-root não encontrado: {r}")
+    train_only_roots = parse_csv_list(
+        args.train_only_roots
+    )
 
-    dst_root = args.dst_root
+    for root in src_roots:
+        if not os.path.isdir(root):
+            raise SystemExit(
+                f"[ERRO] src-root não encontrado: {root}"
+            )
+
+    soma = (
+        args.train
+        + args.val
+        + args.test
+    )
 
-    soma = args.train + args.val + args.test
     if soma <= 0:
-        raise ValueError("Soma de proporções deve ser > 0.")
+        raise SystemExit(
+            "[ERRO] soma train+val+test deve ser > 0"
+        )
 
     p_train = args.train / soma
     p_val = args.val / soma
     p_test = args.test / soma
 
-    mins = {
-        "train": max(0, args.min_train),
-        "val": max(0, args.min_val),
-        "test": max(0, args.min_test),
-    }
+    groups = (
+        parse_csv_list(args.groups)
+        if args.groups
+        else None
+    )
 
-    groups = parse_csv_list(args.groups) if args.groups else None
-    caps_map = parse_map(args.cap_train_families, int)
+    dst_root = args.dst_root
 
-    if args.clear_dst:
-        print(f"[INFO] Limpando destino: {dst_root}")
-        limpar_dir(dst_root)
+    if (
+        args.clear_dst
+        and not args.dry_run
+    ):
+        print(
+            f"[INFO] Limpando destino: {dst_root}"
+        )
+        limpar_dir(
+            dst_root
+        )
     else:
-        garantir(dst_root)
+        garantir(
+            dst_root
+        )
 
+    print("==========================================")
+    print("SPLIT AGRÍCOLA TEACHER V2")
+    print(f"Resolution : {resolution[0]}x{resolution[1]}")
+    print(f"Strategy   : {args.strategy}")
+    print(
+        f"Split      : train={p_train:.3f} "
+        f"val={p_val:.3f} test={p_test:.3f}"
+    )
+    print(f"Tensor feat: {args.use_tensor_features}")
+    print(
+        f"Near-dups  : {args.merge_near_duplicate_families}"
+    )
+    print(f"Dry-run    : {args.dry_run}")
+    print("SRC:")
+    for root in src_roots:
+        marker = (
+            " [TRAIN_ONLY]"
+            if norm_path(root)
+            in {norm_path(x) for x in train_only_roots}
+            else ""
+        )
+        print(f"  - {root}{marker}")
+    print("==========================================")
+
+    print("[1/6] Coletando samples...")
     samples = collect_all_samples(
         src_roots=src_roots,
         train_only_roots=train_only_roots,
@@ -1034,41 +2589,135 @@ def main() -> None:
     )
 
     if not samples:
-        print("[WARN] Nenhuma amostra encontrada.")
-        return
-
-    family_split, family_summary = build_family_split(
-        samples=samples,
-        p_train=p_train,
-        p_val=p_val,
-        p_test=p_test,
-        seed=args.seed,
-        mins=mins,
-        caps_map=caps_map,
-    )
-
-    print("==========================================")
-    print("Split OAK-FCC-3 Multi-source")
-    print("SRC_ROOTS:")
-    for r in src_roots:
-        marker = " [TRAIN_ONLY]" if norm_path(r) in {norm_path(x) for x in train_only_roots} else ""
-        print(f"  - {r}{marker}")
-    print(f"DST       : {dst_root}")
-    print(f"Split     : train={p_train:.3f}, val={p_val:.3f}, test={p_test:.3f}")
-    print(f"Mínimos   : train={mins['train']} val={mins['val']} test={mins['test']}")
-    print(f"Seed      : {args.seed}")
-    print(f"Synthetic : train_only={args.synthetic_train_only} respect_family={args.synthetic_respect_family_split}")
-    print("==========================================")
-
-    print("\nFamílias reais por split_group:")
-    for g, info in family_summary.items():
-        print(
-            f"[{g}] famílias={info['familias']} → "
-            f"train={info['train_families']}, val={info['val_families']}, test={info['test_families']}"
+        raise SystemExit(
+            "[ERRO] Nenhum sample encontrado."
         )
 
-    all_rows: List[Dict[str, Any]] = []
-    skipped_rows: List[Dict[str, Any]] = []
+    print(
+        f"[OK] samples encontrados: {len(samples)}"
+    )
+
+    print("[2/6] Auditando máscaras + features agrícolas...")
+    analyzed: List[Sample] = []
+    rejected: List[Sample] = []
+
+    for i, sample in enumerate(
+        samples,
+        start=1,
+    ):
+        try:
+            analyze_sample(
+                sample,
+                args,
+            )
+            analyzed.append(
+                sample
+            )
+        except Exception as exc:
+            sample.audit_error = str(exc)
+            rejected.append(
+                sample
+            )
+
+        if (
+            i % 100 == 0
+            or i == len(samples)
+        ):
+            print(
+                f"  {i}/{len(samples)} "
+                f"ok={len(analyzed)} "
+                f"rejeitados={len(rejected)}"
+            )
+
+    real_analyzed = [
+        s
+        for s in analyzed
+        if not s.is_synthetic
+        and not s.source_root_train_only
+    ]
+
+    mismatches = [
+        s
+        for s in real_analyzed
+        if normalize_group_for_split(
+            s.source_group
+        ) != s.actual_group
+    ]
+
+    print(
+        f"[OK] reais auditados={len(real_analyzed)} "
+        f"mismatch pasta/máscara={len(mismatches)}"
+    )
+
+    print("[3/6] Construindo famílias reais...")
+    base_units = build_real_family_units(
+        real_analyzed
+    )
+
+    (
+        units,
+        family_to_leakage,
+        merged_count,
+    ) = merge_near_duplicate_family_units(
+        units=base_units,
+        enabled=args.merge_near_duplicate_families,
+        max_hamming=args.near_dup_hamming,
+        mask_l1=args.near_dup_mask_l1,
+    )
+
+    # Garante mapping mesmo sem merge.
+    for unit in units:
+        for sample in unit.samples:
+            family_to_leakage.setdefault(
+                (sample.actual_group, sample.family),
+                unit.leakage_id,
+            )
+
+    print(
+        f"[OK] famílias base={len(base_units)} "
+        f"unidades leakage={len(units)} "
+        f"famílias mescladas={merged_count}"
+    )
+
+    print("[4/6] Montando a prova...")
+    if args.strategy == "agri_diverse":
+        assignment = assign_agri_diverse_split(
+            units=units,
+            p_train=p_train,
+            p_val=p_val,
+            p_test=p_test,
+            seed=args.seed,
+        )
+    else:
+        assignment = assign_random_split(
+            units=units,
+            p_train=p_train,
+            p_val=p_val,
+            p_test=p_test,
+            seed=args.seed,
+        )
+
+    family_counts = {
+        "train": 0,
+        "val": 0,
+        "test": 0,
+    }
+
+    for unit in units:
+        if unit.assigned_split:
+            family_counts[
+                unit.assigned_split
+            ] += 1
+
+    print(
+        f"[OK] famílias/unidades → "
+        f"train={family_counts['train']} "
+        f"val={family_counts['val']} "
+        f"test={family_counts['test']}"
+    )
+
+    print("[5/6] Aplicando split aos samples...")
+    decisions: Dict[int, Tuple[Optional[str], str]] = {}
 
     totals = {
         "train": 0,
@@ -1081,121 +2730,306 @@ def main() -> None:
         "real_test": 0,
     }
 
-    by_group: Dict[str, Dict[str, int]] = {}
+    stratum_counts: Dict[str, Dict[str, int]] = {}
 
-    for s in samples:
-        split_name, reason = decide_sample_split(s, family_split, args)
+    for sample in analyzed:
+        split_name, reason = decide_sample_split(
+            sample=sample,
+            family_to_leakage=family_to_leakage,
+            assignment=assignment,
+            args=args,
+        )
+
+        decisions[id(sample)] = (
+            split_name,
+            reason,
+        )
 
         if split_name is None:
             totals["skipped"] += 1
-            skipped_rows.append({
-                "source_root": s.src_root,
-                "source_group": s.source_group,
-                "split_group": s.split_group,
-                "base": s.base,
-                "family": s.family,
-                "source": s.source,
-                "is_synthetic": int(s.is_synthetic),
-                "reason": reason,
-            })
             continue
 
-        out_group = output_group_name(
-            source_group=s.source_group,
-            split_group=s.split_group,
-            is_synthetic=s.is_synthetic,
-            mode=args.output_group_mode,
-        )
-
-        row = copiar_sample(
-            s=s,
-            dst_root=dst_root,
-            split_name=split_name,
-            out_group_name=out_group,
-            copy_meta_preview=not args.no_meta_preview,
-            copy_visuals=not args.no_visuals,
-        )
-
-        if row is None:
-            totals["skipped"] += 1
-            skipped_rows.append({
-                "source_root": s.src_root,
-                "source_group": s.source_group,
-                "split_group": s.split_group,
-                "base": s.base,
-                "family": s.family,
-                "source": s.source,
-                "is_synthetic": int(s.is_synthetic),
-                "reason": "copy_failed_missing_tensor_or_mask",
-            })
-            continue
-
-        all_rows.append(row)
-
         totals[split_name] += 1
-        if split_name == "train" and s.is_synthetic:
+
+        if (
+            split_name == "train"
+            and sample.is_synthetic
+        ):
             totals["synthetic_train"] += 1
+
         elif split_name == "train":
             totals["real_train"] += 1
+
         elif split_name == "val":
             totals["real_val"] += 1
+
         elif split_name == "test":
             totals["real_test"] += 1
 
-        gsum = by_group.setdefault(out_group, {"train": 0, "val": 0, "test": 0, "synthetic_train": 0, "skipped": 0})
-        gsum[split_name] = gsum.get(split_name, 0) + 1
-        if split_name == "train" and s.is_synthetic:
-            gsum["synthetic_train"] += 1
+        ss = stratum_counts.setdefault(
+            sample.stratum,
+            {
+                "train": 0,
+                "val": 0,
+                "test": 0,
+            },
+        )
 
-    manifest_path = args.manifest or os.path.join(dst_root, "split_manifest.csv")
-    summary_path = args.summary or os.path.join(dst_root, "split_summary.json")
-    skipped_path = args.skipped or os.path.join(dst_root, "split_skipped.csv")
+        ss[split_name] += 1
 
-    write_manifest(manifest_path, all_rows)
-    write_skipped(skipped_path, skipped_rows)
+    print(
+        f"[OK] samples → "
+        f"train={totals['train']} "
+        f"val={totals['val']} "
+        f"test={totals['test']} "
+        f"skipped={totals['skipped']}"
+    )
+
+    print("[6/6] Copiando / relatórios...")
+    manifest_rows = []
+
+    if not args.dry_run:
+        for sample in analyzed:
+            split_name, reason = decisions[
+                id(sample)
+            ]
+
+            if split_name is None:
+                continue
+
+            out_group = output_group_name(
+                source_group=sample.source_group,
+                actual_group=sample.actual_group,
+                is_synthetic=sample.is_synthetic,
+                mode=args.output_group_mode,
+            )
+
+            row = copiar_sample(
+                sample=sample,
+                dst_root=dst_root,
+                split_name=split_name,
+                out_group_name=out_group,
+                copy_meta_preview=not args.no_meta_preview,
+                copy_visuals=not args.no_visuals,
+            )
+
+            if row is not None:
+                row["decision_reason"] = reason
+                manifest_rows.append(
+                    row
+                )
+
+    manifest_path = (
+        args.manifest
+        or os.path.join(
+            dst_root,
+            "split_manifest.csv",
+        )
+    )
+
+    audit_path = (
+        args.audit
+        or os.path.join(
+            dst_root,
+            "split_audit.csv",
+        )
+    )
+
+    families_path = (
+        args.families
+        or os.path.join(
+            dst_root,
+            "split_families.csv",
+        )
+    )
+
+    skipped_path = (
+        args.skipped
+        or os.path.join(
+            dst_root,
+            "split_skipped.csv",
+        )
+    )
+
+    summary_path = (
+        args.summary
+        or os.path.join(
+            dst_root,
+            "split_summary.json",
+        )
+    )
+
+    audit_rows = []
+
+    skipped_rows = []
+
+    for sample in analyzed:
+        split_name, reason = decisions[
+            id(sample)
+        ]
+
+        row = sample_audit_row(
+            sample,
+            split_name,
+            reason,
+        )
+
+        audit_rows.append(
+            row
+        )
+
+        if split_name is None:
+            skipped_rows.append(
+                row
+            )
+
+    for sample in rejected:
+        audit_rows.append(
+            sample_audit_row(
+                sample,
+                None,
+                "audit_rejected",
+            )
+        )
+
+    if audit_rows:
+        write_csv(
+            audit_path,
+            audit_rows,
+            list(audit_rows[0].keys()),
+        )
+
+    family_rows = family_audit_rows(
+        units
+    )
+
+    if family_rows:
+        write_csv(
+            families_path,
+            family_rows,
+            list(family_rows[0].keys()),
+        )
+
+    if skipped_rows:
+        write_csv(
+            skipped_path,
+            skipped_rows,
+            list(skipped_rows[0].keys()),
+        )
+
+    if (
+        not args.dry_run
+        and manifest_rows
+    ):
+        write_csv(
+            manifest_path,
+            manifest_rows,
+            list(manifest_rows[0].keys()),
+        )
 
     summary = {
-        "schema": "oak_fcc3_multi_source_split_v1",
+        "schema": "oak_fcc3_agri_teacher_v2_split_v1",
+        "resolution": list(resolution),
+        "strategy": args.strategy,
         "src_roots": src_roots,
         "train_only_roots": train_only_roots,
         "dst_root": dst_root,
-        "resolution": list(resolucao),
         "proportions": {
             "train": p_train,
             "val": p_val,
             "test": p_test,
         },
-        "mins": mins,
-        "seed": args.seed,
-        "multi_head": MULTI_HEAD,
         "options": {
+            "use_tensor_features": args.use_tensor_features,
+            "tensor_feature_stride": args.tensor_feature_stride,
+            "merge_near_duplicate_families": args.merge_near_duplicate_families,
+            "near_dup_hamming": args.near_dup_hamming,
+            "near_dup_mask_l1": args.near_dup_mask_l1,
             "synthetic_train_only": args.synthetic_train_only,
             "synthetic_respect_family_split": args.synthetic_respect_family_split,
-            "allow_orphan_synthetic_train": args.allow_orphan_synthetic_train,
             "output_group_mode": args.output_group_mode,
         },
-        "family_summary": family_summary,
-        "groups": by_group,
-        "total": totals,
-        "samples_seen": len(samples),
-        "manifest": manifest_path,
-        "skipped": skipped_path,
+        "teacher_bins": {
+            "target_tiny_frac": args.target_tiny_frac,
+            "target_small_frac": args.target_small_frac,
+            "target_medium_frac": args.target_medium_frac,
+            "near_radius_frac": args.near_radius_frac,
+            "close_weed_cane_threshold": args.close_weed_cane_threshold,
+        },
+        "counts": {
+            "samples_seen": len(samples),
+            "samples_analyzed": len(analyzed),
+            "samples_rejected": len(rejected),
+            "real_samples": len(real_analyzed),
+            "folder_mask_mismatches": len(mismatches),
+            "base_families": len(base_units),
+            "leakage_units": len(units),
+            "near_duplicate_families_merged": merged_count,
+            "family_split": family_counts,
+            "samples_split": totals,
+        },
+        "strata": stratum_counts,
+        "paths": {
+            "manifest": (
+                manifest_path
+                if not args.dry_run
+                else None
+            ),
+            "audit": audit_path,
+            "families": families_path,
+            "skipped": skipped_path,
+        },
+        "dry_run": bool(args.dry_run),
     }
 
-    save_json(summary_path, summary)
+    save_json(
+        summary_path,
+        summary,
+    )
 
-    print("\nResumo global:")
-    print(f"  train total:       {totals['train']}")
-    print(f"    real train:      {totals['real_train']}")
-    print(f"    synthetic train: {totals['synthetic_train']}")
-    print(f"  val real:          {totals['real_val']}")
-    print(f"  test real:         {totals['real_test']}")
-    print(f"  skipped:           {totals['skipped']}")
-    print(f"\nManifest: {manifest_path}")
-    print(f"Skipped : {skipped_path}")
-    print(f"Summary : {summary_path}")
+    print("==========================================")
+    print("RESULTADO")
+    print(
+        f"Samples reais      : {len(real_analyzed)}"
+    )
+    print(
+        f"Mismatch pasta/GT  : {len(mismatches)}"
+    )
+    print(
+        f"Famílias base      : {len(base_units)}"
+    )
+    print(
+        f"Unidades leakage   : {len(units)}"
+    )
+    print(
+        f"Near-dups mescladas: {merged_count}"
+    )
+    print(
+        f"TRAIN              : {totals['train']} "
+        f"(real={totals['real_train']} synthetic={totals['synthetic_train']})"
+    )
+    print(
+        f"VAL real           : {totals['real_val']}"
+    )
+    print(
+        f"TEST real          : {totals['real_test']}"
+    )
+    print(
+        f"Skipped            : {totals['skipped']}"
+    )
+    print(f"Audit              : {audit_path}")
+    print(f"Families           : {families_path}")
+    print(f"Summary            : {summary_path}")
 
-    print("\n✅ Split multi-source sem vazamento concluído!")
+    if not args.dry_run:
+        print(f"Manifest           : {manifest_path}")
+
+    print("==========================================")
+
+    if args.dry_run:
+        print("✅ DRY-RUN concluído. Nenhum dataset foi copiado.")
+    else:
+        print("✅ Split agrícola Teacher V2 concluído.")
 
 
 if __name__ == "__main__":
diff --git a/Python/OAK/datasets/oak-fcc-3/_8_train_multihead_v2.py b/Python/OAK/datasets/oak-fcc-3/_8_train_multihead_v2.py
new file mode 100644
index 000000000..ef34ac788
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/_8_train_multihead_v2.py
@@ -0,0 +1,3038 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+_8_train_multihead_v2.py
+
+Agri Teacher V2 para SegFormer multiespectral OAK-FCC-3.
+
+Objetivo operacional: distinguir chão/cana/erva e maximizar a decisão
+pulverizável (vegetação não-cana), com proteção explícita da cana.
+
+Recursos principais:
+- Raw3/4/5/7 por nomes de canais, incluindo NDVI/NDRE;
+- augmentation multiespectral sincronizado e fisicamente coerente;
+- sampler agrícola de batches diversos;
+- decoder separado ou decoder compartilhado + classifiers leves;
+- inicialização multiespectral configurável;
+- CE + Dice por imagem, boundary/OHEM opcionais e safety loss;
+- AdamW com LR discriminativo, no-decay em norm/bias;
+- warmup + poly/cosine por optimizer step;
+- AMP, gradient accumulation correto e gradient clipping;
+- métricas agrícolas: target precision/recall/F1, cana safety, spray rate,
+  weed miss rate, macro IoU, threshold sweep, ECE e cenários por área;
+- checkpoint/resume completo com optimizer, scheduler, scaler, RNG e global step.
+
+Mantém as funções públicas usadas por _10_export_onnx.py e _11_validate_onnx.py.
+"""
+
+from __future__ import annotations
+
+import os
+os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "max_split_size_mb:128")
+
+import csv
+import copy
+import json
+import time
+import math
+import argparse
+import random
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader, Sampler, WeightedRandomSampler
+from torch.amp import autocast, GradScaler
+
+from transformers import SegformerForSemanticSegmentation
+
+
+# ============================================================
+# Seed / util
+# ============================================================
+
+def set_seed(seed: int = 42):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def ensure_dir(path: Path):
+    path.mkdir(parents=True, exist_ok=True)
+
+
+def load_json(path: str | Path) -> dict:
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def save_json(path: Path, data: dict):
+    ensure_dir(path.parent)
+    with path.open("w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+
+def safe_float(x, default=0.0) -> float:
+    try:
+        return float(x)
+    except Exception:
+        return float(default)
+
+
+# ============================================================
+# Labelmap
+# ============================================================
+
+def load_labelmap(labelmap_path: str):
+    """
+    Tenta usar helpers.carregar_labelmap_completo.
+    Fallback simples para labelmap com uma classe por linha.
+    """
+    try:
+        from helpers import carregar_labelmap_completo, _infer_ignore_id
+
+        _cor_para_id, _colormap_rgb, id_para_nome, ignore_rgb = carregar_labelmap_completo(labelmap_path)
+        ignore_id = _infer_ignore_id(ignore_rgb, 255)
+
+        id2label = {int(k): str(v) for k, v in id_para_nome.items()}
+        label2id = {v.lower(): k for k, v in id2label.items()}
+
+        return id2label, label2id, int(ignore_id)
+
+    except Exception as e:
+        print(f"[WARN] Não consegui usar helpers.carregar_labelmap_completo: {e}")
+        print("[WARN] Usando parser simples: uma classe por linha.")
+
+    id2label = {}
+    with open(labelmap_path, "r", encoding="utf-8") as f:
+        for line in f:
+            s = line.strip()
+            if not s or s.startswith("#"):
+                continue
+
+            parts = s.replace(",", " ").split()
+
+            if len(parts) >= 2 and parts[0].isdigit():
+                cid = int(parts[0])
+                name = parts[1]
+            else:
+                cid = len(id2label)
+                name = parts[0]
+
+            if name.lower() in ("ignore", "void", "background_ignore"):
+                continue
+
+            id2label[cid] = name
+
+    label2id = {v.lower(): k for k, v in id2label.items()}
+    return id2label, label2id, 255
+
+
+# ============================================================
+# Head config
+# ============================================================
+
+DEFAULT_HEADS = {
+    "semantic": {
+        "enabled": True,
+        "type": "multiclass",
+        "num_classes": 3,
+        "mask_dir": "masks",
+        "classes": {
+            "chao": 0,
+            "cana": 1,
+            "erva": 2,
+        },
+        "ignore_index": 255,
+        "loss_weight": 0.10,
+    },
+    "vegetation": {
+        "enabled": True,
+        "type": "binary",
+        "num_classes": 2,
+        "mask_dir": "masks_vegetation",
+        "classes": {
+            "background": 0,
+            "vegetation": 1,
+        },
+        "ignore_index": 255,
+        "loss_weight": 0.20,
+    },
+    "cana": {
+        "enabled": True,
+        "type": "binary",
+        "num_classes": 2,
+        "mask_dir": "masks_cana",
+        "classes": {
+            "not_cana": 0,
+            "cana": 1,
+        },
+        "ignore_index": 255,
+        "loss_weight": 0.25,
+    },
+    "target": {
+        "enabled": True,
+        "type": "binary",
+        "num_classes": 2,
+        "mask_dir": "__derived_target__",
+        "classes": {
+            "background": 0,
+            "target": 1,
+        },
+        "ignore_index": 255,
+        "loss_weight": 0.45,
+        "derived_from": ["vegetation", "cana"],
+    },
+}
+
+
+def merge_dict(dst: dict, src: dict) -> dict:
+    out = copy.deepcopy(dst)
+
+    def rec(a, b):
+        for k, v in b.items():
+            if isinstance(v, dict) and isinstance(a.get(k), dict):
+                rec(a[k], v)
+            else:
+                a[k] = v
+
+    if isinstance(src, dict):
+        rec(out, src)
+
+    return out
+
+
+def build_heads_config(config: dict, ignore_index: int) -> Dict[str, dict]:
+    cfg = merge_dict(DEFAULT_HEADS, config.get("heads", {}) or {})
+
+    # Compatibilidade com configs antigas.
+    if not bool(config.get("multi_head", True)):
+        print("[WARN] config.multi_head não está true. Este script vai treinar multi-head mesmo assim.")
+
+    for name, hcfg in cfg.items():
+        hcfg.setdefault("enabled", True)
+        hcfg.setdefault("ignore_index", ignore_index)
+        hcfg["num_classes"] = int(hcfg.get("num_classes", 2))
+        hcfg["loss_weight"] = float(hcfg.get("loss_weight", 1.0))
+        hcfg["mask_dir"] = str(hcfg.get("mask_dir", "masks"))
+
+    active = {k: v for k, v in cfg.items() if bool(v.get("enabled", True))}
+
+    if "semantic" not in active:
+        raise RuntimeError("A head 'semantic' deve estar habilitada neste contrato.")
+    if "vegetation" not in active:
+        raise RuntimeError("A head 'vegetation' deve estar habilitada neste contrato.")
+    if "cana" not in active:
+        raise RuntimeError("A head 'cana' deve estar habilitada neste contrato.")
+
+    total_w = sum(float(v.get("loss_weight", 0.0)) for v in active.values())
+    if total_w <= 0:
+        raise RuntimeError("Soma de loss_weight das heads deve ser > 0.")
+
+    # Normaliza os pesos para não bagunçar magnitude da loss.
+    for v in active.values():
+        v["loss_weight_norm"] = float(v.get("loss_weight", 0.0)) / total_w
+
+    return active
+
+
+
+# ============================================================
+# Training V2 defaults / helpers
+# ============================================================
+
+TRAINER_VERSION = "agri_teacher_v2.2"
+SOURCE_CHANNEL_ORDER = ["R", "G", "B", "RE", "NIR"]
+DERIVED_CHANNEL_ORDER = ["NDVI", "NDRE"]
+SUPPORTED_INPUT_CHANNELS = SOURCE_CHANNEL_ORDER + DERIVED_CHANNEL_ORDER
+
+DEFAULT_TRAINING_V2 = {
+    "model": {
+        # separate = 1 decoder SegFormer completo por head (legado)
+        # shared_light = 1 decoder compartilhado + classifiers 1x1 leves
+        "decoder_mode": "shared_light",
+        # mean_rgb | zero_extra | scaled_mean
+        "spectral_input_init": "zero_extra",
+    },
+    "data": {
+        # Valida conteúdo real de tensors/masks .npy antes do treino.
+        # Arquivos truncados podem existir e ainda assim passar pelo split por existência.
+        "validate_npy_content": True,
+        "skip_corrupt_samples": True,
+        # Mesmo com skip=true, aborta se a corrupção deixar de ser pontual.
+        "max_corrupt_fraction": 0.005,
+    },
+    "augmentation": {
+        "enabled": True,
+        "horizontal_flip_p": 0.50,
+        "vertical_flip_p": 0.00,
+        "affine_p": 0.70,
+        "rotate_deg": 5.0,
+        "scale_min": 0.90,
+        "scale_max": 1.10,
+        "translate_frac": 0.04,
+        "crop_p": 0.45,
+        "crop_scale_min": 0.70,
+        "crop_scale_max": 1.00,
+        "crop_focus_target_p": 0.55,
+        "crop_focus_cana_p": 0.25,
+        "global_gain_p": 0.35,
+        "global_gain_min": 0.92,
+        "global_gain_max": 1.08,
+        "band_gain_p": 0.25,
+        "band_gain_min": 0.96,
+        "band_gain_max": 1.04,
+        "rgb_gamma_p": 0.20,
+        "rgb_gamma_min": 0.94,
+        "rgb_gamma_max": 1.06,
+        "noise_p": 0.20,
+        "noise_sigma_min": 0.001,
+        "noise_sigma_max": 0.008,
+        "blur_p": 0.12,
+        "blur_kernel": 3,
+        "sensor_channel_dropout_p": 0.00,
+        "clip_physical": True,
+    },
+    "sampler": {
+        # random | diverse | weighted
+        "mode": "diverse",
+        "samples_per_epoch": 0,
+        "tiny_target_pct": 0.005,
+        "small_target_pct": 0.02,
+        "medium_target_pct": 0.10,
+    },
+    "class_weighting": {
+        # log_inverse = comportamento robusto semelhante ao legado, agora com dataset inteiro
+        # sqrt_inverse = inverso de frequência suavizado
+        "method": "log_inverse",
+        "log_offset": 1.02,
+        "power": 0.50,
+        "min_weight": 0.25,
+        "max_weight": 4.00,
+    },
+    "loss": {
+        "dice_reduction": "per_image",
+        "dice_smooth": 1.0,
+        "boundary_weight": 0.00,
+        "ohem_ratio": 0.00,
+        "safety": {
+            "enabled": True,
+            "weight": 0.08,
+            "cana_weight": 1.00,
+            "ground_weight": 0.20,
+        },
+    },
+    "optimizer": {
+        "encoder_lr": None,          # None => usa --lr
+        "patch_lr_mult": 2.0,
+        "heads_lr_mult": 5.0,
+        "weight_decay": None,        # None => usa --wd
+        "no_decay_bias": True,
+        "no_decay_norm": True,
+        "betas": [0.9, 0.999],
+        "eps": 1e-8,
+    },
+    "scheduler": {
+        # poly | cosine | constant
+        "mode": "poly",
+        "warmup_ratio": 0.05,
+        "warmup_start_factor": 0.10,
+        "poly_power": 1.0,
+        "min_lr_ratio": 0.02,
+    },
+    "optimization": {
+        "grad_clip_norm": 1.0,
+        "matmul_precision": "high",
+        "cudnn_benchmark": True,
+        "persistent_workers": True,
+        "prefetch_factor": 2,
+    },
+    "target_distillation": {
+        # Compatível com config.target_distillation legado.
+        "enabled": False,
+        "mode": "cross_head",
+        "start_epoch": 8,
+        "rampup_epochs": 12,
+        "hard_weight": 0.75,
+        "distill_weight": 0.25,
+        "teacher_confidence_min": 0.60,
+        "detach_teacher": True,
+        "w_sem_erva": 0.45,
+        "w_veg_not_cana": 0.35,
+        "w_veg_suppressed": 0.20,
+        "cana_suppression_power": 1.5,
+    },
+    "metrics": {
+        # Sweep mais amplo para separar qualidade do modelo de calibração do threshold.
+        "target_thresholds": [
+            0.30,
+            0.35,
+            0.40,
+            0.45,
+            0.50,
+            0.55,
+            0.60,
+            0.65,
+            0.70,
+            0.75,
+            0.80,
+            0.85,
+            0.90,
+            0.95,
+        ],
+        "ece_bins": 15,
+        "scenario_metrics": True,
+        "group_metrics": True,
+        # No train mantemos as métricas básicas, mas pulamos sweep/ECE/cenários
+        # pesados. A validação continua completa. Não altera gradientes.
+        "rich_train_metrics": False,
+        "operational_threshold": {
+            # Restrições são usadas somente para recomendar threshold e salvar
+            # best_operational. Não alteram a loss nem o score principal.
+            "max_cana_spray_rate": 0.02,
+            "max_ground_spray_rate": 0.03,
+            "max_weed_miss_rate": 0.20,
+            "score_weights": {
+                "target_iou": 0.35,
+                "target_f1": 0.20,
+                "cana_safety": 0.25,
+                "ground_safety": 0.10,
+                "weed_recall": 0.10
+            }
+        },
+    },
+    "selection_score": {
+        "target_iou": 0.40,
+        "cana_iou": 0.20,
+        "target_f1": 0.10,
+        "vegetation_miou": 0.10,
+        "semantic_miou": 0.05,
+        "cana_safety": 0.15,
+    },
+    "checkpoint": {
+        "early_stop_min_delta": 5e-4,
+        "save_best_safety": True,
+        # Mantém duas réguas extras: a régua histórica do trainer antigo e
+        # uma régua operacional que escolhe threshold sob restrições agrícolas.
+        "save_best_legacy": True,
+        "save_best_operational": True,
+    },
+}
+
+_LAST_BUILD_CONFIG: Optional[dict] = None
+
+
+def get_training_v2_config(config: dict) -> dict:
+    cfg = merge_dict(DEFAULT_TRAINING_V2, config.get("training_v2", {}) or {})
+
+    # Compatibilidade com target_distillation no nível raiz.
+    # Se training_v2.target_distillation foi declarado explicitamente, ele manda.
+    if (
+        "target_distillation" not in (config.get("training_v2", {}) or {})
+        and isinstance(config.get("target_distillation"), dict)
+    ):
+        cfg["target_distillation"] = merge_dict(
+            cfg.get("target_distillation", {}),
+            config.get("target_distillation", {}),
+        )
+
+    return cfg
+
+
+def seed_worker(worker_id: int):
+    seed = torch.initial_seed() % (2**32)
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+def capture_rng_state() -> dict:
+    state = {
+        "python": random.getstate(),
+        "numpy": np.random.get_state(),
+        "torch": torch.get_rng_state(),
+    }
+    if torch.cuda.is_available():
+        state["cuda"] = torch.cuda.get_rng_state_all()
+    return state
+
+
+def restore_rng_state(state: Optional[dict]):
+    if not state:
+        return
+    try:
+        if "python" in state:
+            random.setstate(state["python"])
+        if "numpy" in state:
+            np.random.set_state(state["numpy"])
+        if "torch" in state:
+            torch.set_rng_state(state["torch"])
+        if torch.cuda.is_available() and "cuda" in state:
+            torch.cuda.set_rng_state_all(state["cuda"])
+    except Exception as exc:
+        print(f"[WARN] Não consegui restaurar RNG state: {exc}")
+
+
+# ============================================================
+# Channels
+# ============================================================
+
+def get_input_channel_names(config: dict) -> List[str]:
+    if "input_channels" in config:
+        configured = config["input_channels"]
+        if isinstance(configured, str):
+            names = [str(c).strip().upper() for c in configured.split(",") if str(c).strip()]
+        else:
+            names = [str(c).strip().upper() for c in configured if str(c).strip()]
+    else:
+        n = int(config.get("channels", 5))
+        if n < 1 or n > len(SOURCE_CHANNEL_ORDER):
+            raise RuntimeError(
+                "Config antiga sem input_channels só suporta channels entre 1 e 5. "
+                "Para NDVI/NDRE, declare input_channels explicitamente."
+            )
+        names = SOURCE_CHANNEL_ORDER[:n]
+
+    if not names:
+        raise RuntimeError("input_channels não pode ser vazio.")
+    duplicates = sorted({name for name in names if names.count(name) > 1})
+    if duplicates:
+        raise RuntimeError(f"Canais duplicados em input_channels: {duplicates}")
+    invalid = [c for c in names if c not in SUPPORTED_INPUT_CHANNELS]
+    if invalid:
+        raise RuntimeError(f"Canais inválidos em input_channels: {invalid}. Suportados={SUPPORTED_INPUT_CHANNELS}")
+
+    configured_count = config.get("channels")
+    if configured_count is not None and int(configured_count) != len(names):
+        raise RuntimeError(
+            f"config.channels={configured_count} incompatível com input_channels={names} ({len(names)})."
+        )
+    return names
+
+
+def get_input_channel_indices(config: dict) -> List[int]:
+    return list(range(len(get_input_channel_names(config))))
+
+
+# ============================================================
+# Multispectral augmentation
+# ============================================================
+
+class MultiSpectralAugmenter:
+    """Augmentation sincronizado para imagem multiespectral + todas as masks."""
+
+    def __init__(self, channel_names: List[str], cfg: dict, derived_cfg: Optional[dict] = None):
+        self.channel_names = [str(x).upper() for x in channel_names]
+        self.cfg = cfg or {}
+        self.derived_cfg = derived_cfg or {}
+        self._warned_derived_missing = False
+
+    def _rand(self):
+        return random.random()
+
+    def _uniform(self, a, b):
+        return random.uniform(float(a), float(b))
+
+    def _apply_crop(self, x: torch.Tensor, masks: Dict[str, torch.Tensor]):
+        cfg = self.cfg
+        if self._rand() >= float(cfg.get("crop_p", 0.0)):
+            return x, masks
+
+        h, w = x.shape[-2:]
+        scale = self._uniform(cfg.get("crop_scale_min", 0.70), cfg.get("crop_scale_max", 1.0))
+        crop_h = max(16, min(h, int(round(h * scale))))
+        crop_w = max(16, min(w, int(round(w * scale))))
+
+        focus_xy = None
+        r = self._rand()
+        p_target = float(cfg.get("crop_focus_target_p", 0.55))
+        p_cana = float(cfg.get("crop_focus_cana_p", 0.25))
+
+        focus_mask = None
+        if r < p_target and "target" in masks:
+            focus_mask = masks["target"] == 1
+        elif r < p_target + p_cana and "cana" in masks:
+            focus_mask = masks["cana"] == 1
+
+        if focus_mask is not None and bool(focus_mask.any()):
+            coords = torch.nonzero(focus_mask, as_tuple=False)
+            pos = coords[random.randrange(coords.shape[0])]
+            cy, cx = int(pos[0]), int(pos[1])
+            focus_xy = (cx, cy)
+
+        if focus_xy is None:
+            cx = random.randint(0, max(0, w - 1))
+            cy = random.randint(0, max(0, h - 1))
+        else:
+            cx, cy = focus_xy
+
+        x0 = max(0, min(w - crop_w, cx - crop_w // 2))
+        y0 = max(0, min(h - crop_h, cy - crop_h // 2))
+        x1, y1 = x0 + crop_w, y0 + crop_h
+
+        x_crop = x[:, y0:y1, x0:x1].unsqueeze(0)
+        x = F.interpolate(x_crop, size=(h, w), mode="bilinear", align_corners=False).squeeze(0)
+
+        out_masks = {}
+        for name, m in masks.items():
+            mc = m[y0:y1, x0:x1].unsqueeze(0).unsqueeze(0).float()
+            out_masks[name] = F.interpolate(mc, size=(h, w), mode="nearest").squeeze(0).squeeze(0).long()
+        return x, out_masks
+
+    def _apply_affine(self, x: torch.Tensor, masks: Dict[str, torch.Tensor]):
+        cfg = self.cfg
+        if self._rand() >= float(cfg.get("affine_p", 0.0)):
+            return x, masks
+
+        h, w = x.shape[-2:]
+        angle = math.radians(self._uniform(-cfg.get("rotate_deg", 5.0), cfg.get("rotate_deg", 5.0)))
+        scale = self._uniform(cfg.get("scale_min", 0.90), cfg.get("scale_max", 1.10))
+        trans = float(cfg.get("translate_frac", 0.04))
+        tx = self._uniform(-trans, trans) * 2.0
+        ty = self._uniform(-trans, trans) * 2.0
+
+        c = math.cos(angle) / max(scale, 1e-6)
+        s = math.sin(angle) / max(scale, 1e-6)
+        theta = x.new_tensor([[c, -s, tx], [s, c, ty]]).unsqueeze(0)
+        grid = F.affine_grid(theta, size=(1, x.shape[0], h, w), align_corners=False)
+
+        x = F.grid_sample(
+            x.unsqueeze(0), grid, mode="bilinear", padding_mode="reflection", align_corners=False
+        ).squeeze(0)
+
+        valid = ((grid[..., 0].abs() <= 1.0) & (grid[..., 1].abs() <= 1.0)).squeeze(0)
+        out_masks = {}
+        for name, m in masks.items():
+            mi = F.grid_sample(
+                m.unsqueeze(0).unsqueeze(0).float(),
+                grid,
+                mode="nearest",
+                padding_mode="zeros",
+                align_corners=False,
+            ).squeeze(0).squeeze(0).long()
+            ignore_index = 255
+            mi[~valid] = ignore_index
+            out_masks[name] = mi
+        return x, out_masks
+
+    def _can_recompute_derived(self) -> bool:
+        names = set(self.channel_names)
+        if "NDVI" in names and not {"NIR", "R"}.issubset(names):
+            return False
+        if "NDRE" in names and not {"NIR", "RE"}.issubset(names):
+            return False
+        return True
+
+    def _recompute_derived(self, x: torch.Tensor):
+        idx = {name: i for i, name in enumerate(self.channel_names)}
+        eps = float(self.derived_cfg.get("epsilon", 1e-6))
+        clip_min = float(self.derived_cfg.get("clip_min", -1.0))
+        clip_max = float(self.derived_cfg.get("clip_max", 1.0))
+
+        def nd(a, b):
+            denom = a + b
+            out = torch.zeros_like(a)
+            valid = denom.abs() > eps
+            out[valid] = (a[valid] - b[valid]) / denom[valid]
+            return out.clamp(clip_min, clip_max)
+
+        if "NDVI" in idx and "NIR" in idx and "R" in idx:
+            x[idx["NDVI"]] = nd(x[idx["NIR"]], x[idx["R"]])
+        if "NDRE" in idx and "NIR" in idx and "RE" in idx:
+            x[idx["NDRE"]] = nd(x[idx["NIR"]], x[idx["RE"]])
+        return x
+
+    def _blur(self, x: torch.Tensor, k: int):
+        k = int(k)
+        if k < 3:
+            return x
+        if k % 2 == 0:
+            k += 1
+        return F.avg_pool2d(x.unsqueeze(0), kernel_size=k, stride=1, padding=k // 2).squeeze(0)
+
+    def _apply_radiometric(self, x: torch.Tensor):
+        cfg = self.cfg
+        idx = {name: i for i, name in enumerate(self.channel_names)}
+        physical_idx = [idx[n] for n in SOURCE_CHANNEL_ORDER if n in idx]
+        has_derived = any(n in idx for n in DERIVED_CHANNEL_ORDER)
+        can_recompute = self._can_recompute_derived()
+
+        if has_derived and not can_recompute and not self._warned_derived_missing:
+            print(
+                "[AUG][WARN] Há NDVI/NDRE sem todas as bandas físicas necessárias. "
+                "Jitters espectrais independentes serão pulados para preservar coerência."
+            )
+            self._warned_derived_missing = True
+
+        # Ganho global é seguro para índices de diferença normalizada se aplicado
+        # igualmente às bandas físicas relevantes.
+        if physical_idx and self._rand() < float(cfg.get("global_gain_p", 0.0)):
+            gain = self._uniform(cfg.get("global_gain_min", 0.92), cfg.get("global_gain_max", 1.08))
+            x[physical_idx] *= gain
+
+        if physical_idx and (not has_derived or can_recompute):
+            if self._rand() < float(cfg.get("band_gain_p", 0.0)):
+                lo = float(cfg.get("band_gain_min", 0.96))
+                hi = float(cfg.get("band_gain_max", 1.04))
+                for i in physical_idx:
+                    x[i] *= self._uniform(lo, hi)
+
+            if self._rand() < float(cfg.get("rgb_gamma_p", 0.0)):
+                gamma = self._uniform(cfg.get("rgb_gamma_min", 0.94), cfg.get("rgb_gamma_max", 1.06))
+                for name in ("R", "G", "B"):
+                    if name in idx:
+                        x[idx[name]] = torch.clamp(x[idx[name]], min=0.0).pow(gamma)
+
+            if self._rand() < float(cfg.get("noise_p", 0.0)):
+                sigma = self._uniform(cfg.get("noise_sigma_min", 0.001), cfg.get("noise_sigma_max", 0.008))
+                noise = torch.randn_like(x[physical_idx]) * float(sigma)
+                x[physical_idx] += noise
+
+            p_drop = float(cfg.get("sensor_channel_dropout_p", 0.0))
+            if p_drop > 0 and self._rand() < p_drop:
+                candidates = [i for i in physical_idx if self.channel_names[i] not in ("R", "G", "B")]
+                if candidates:
+                    x[random.choice(candidates)] = 0.0
+
+        if self._rand() < float(cfg.get("blur_p", 0.0)):
+            x = self._blur(x, int(cfg.get("blur_kernel", 3)))
+
+        if bool(cfg.get("clip_physical", True)):
+            for i in physical_idx:
+                x[i].clamp_(0.0, 1.0)
+
+        if has_derived and can_recompute:
+            x = self._recompute_derived(x)
+
+        return x
+
+    def __call__(self, x: torch.Tensor, masks: Dict[str, torch.Tensor]):
+        if not bool(self.cfg.get("enabled", True)):
+            return x, masks
+
+        x, masks = self._apply_crop(x, masks)
+        x, masks = self._apply_affine(x, masks)
+
+        if self._rand() < float(self.cfg.get("horizontal_flip_p", 0.0)):
+            x = torch.flip(x, dims=[2])
+            masks = {k: torch.flip(v, dims=[1]) for k, v in masks.items()}
+
+        if self._rand() < float(self.cfg.get("vertical_flip_p", 0.0)):
+            x = torch.flip(x, dims=[1])
+            masks = {k: torch.flip(v, dims=[0]) for k, v in masks.items()}
+
+        x = self._apply_radiometric(x)
+        return x.contiguous(), {k: v.contiguous() for k, v in masks.items()}
+
+
+def inspect_npy_integrity(path: str | Path, expected_ndim: Optional[int] = None) -> dict:
+    """
+    Valida header + tamanho do payload de um .npy sem criar memory-map.
+
+    Isso evita esgotar recursos de mapeamento do Windows durante auditorias de
+    milhares de arquivos e ainda detecta truncamento real do payload.
+    """
+    p = Path(path)
+    if not p.exists():
+        raise FileNotFoundError(str(p))
+
+    file_size = int(p.stat().st_size)
+    with p.open("rb") as f:
+        version = np.lib.format.read_magic(f)
+        if version == (1, 0):
+            shape, fortran_order, dtype = np.lib.format.read_array_header_1_0(f)
+        elif version in ((2, 0), (3, 0)):
+            # v2/v3 usam header longo. Em caso de implementação NumPy antiga,
+            # o fallback abaixo ainda faz uma leitura convencional sem mmap.
+            try:
+                shape, fortran_order, dtype = np.lib.format.read_array_header_2_0(f)
+            except Exception:
+                arr = np.load(str(p), allow_pickle=False)
+                shape, dtype = tuple(arr.shape), arr.dtype
+                fortran_order = bool(arr.flags.f_contiguous and not arr.flags.c_contiguous)
+                if expected_ndim is not None and arr.ndim != int(expected_ndim):
+                    raise ValueError(f"ndim={arr.ndim}, esperado={expected_ndim}, shape={arr.shape}")
+                return {"shape": tuple(shape), "dtype": str(dtype), "fortran_order": fortran_order, "file_size": file_size}
+        else:
+            arr = np.load(str(p), allow_pickle=False)
+            shape, dtype = tuple(arr.shape), arr.dtype
+            fortran_order = bool(arr.flags.f_contiguous and not arr.flags.c_contiguous)
+            if expected_ndim is not None and arr.ndim != int(expected_ndim):
+                raise ValueError(f"ndim={arr.ndim}, esperado={expected_ndim}, shape={arr.shape}")
+            return {"shape": tuple(shape), "dtype": str(dtype), "fortran_order": fortran_order, "file_size": file_size}
+
+        shape = tuple(int(v) for v in shape)
+        dtype = np.dtype(dtype)
+        data_offset = int(f.tell())
+
+    if expected_ndim is not None and len(shape) != int(expected_ndim):
+        raise ValueError(f"ndim={len(shape)}, esperado={expected_ndim}, shape={shape}")
+    if any(v <= 0 for v in shape):
+        raise ValueError(f"shape inválido={shape}")
+    if dtype.hasobject:
+        # Dataset numérico não deveria usar object. Leitura convencional para
+        # produzir diagnóstico explícito em vez de confiar no tamanho bruto.
+        arr = np.load(str(p), allow_pickle=False)
+        return {"shape": tuple(arr.shape), "dtype": str(arr.dtype), "fortran_order": fortran_order, "file_size": file_size}
+
+    expected_payload = int(np.prod(shape, dtype=np.int64)) * int(dtype.itemsize)
+    actual_payload = max(0, file_size - data_offset)
+    if actual_payload < expected_payload:
+        raise ValueError(
+            f"payload truncado: shape={shape} dtype={dtype} "
+            f"esperado={expected_payload} bytes, encontrado={actual_payload} bytes"
+        )
+
+    return {
+        "shape": shape,
+        "dtype": str(dtype),
+        "fortran_order": bool(fortran_order),
+        "file_size": file_size,
+        "payload_bytes": actual_payload,
+        "expected_payload_bytes": expected_payload,
+    }
+
+
+# ============================================================
+# Dataset OAK-FCC-3 multi-head
+# ============================================================
+
+class OakFcc3TensorMultiHeadDataset(Dataset):
+    def __init__(
+        self,
+        root: str | Path,
+        heads_config: Dict[str, dict],
+        channels: int = 5,
+        input_channel_names: Optional[List[str]] = None,
+        strict_channels: bool = False,
+        resize_hw: Optional[Tuple[int, int]] = None,
+        augment: bool = False,
+        augmentation_config: Optional[dict] = None,
+        derived_config: Optional[dict] = None,
+        data_validation_config: Optional[dict] = None,
+    ):
+        self.root = Path(root)
+        self.heads_config = heads_config
+        self.channels = int(channels)
+        self.input_channel_names = list(input_channel_names or SOURCE_CHANNEL_ORDER[:self.channels])
+        self.strict_channels = bool(strict_channels)
+        self.resize_hw = resize_hw
+        self.augment = bool(augment)
+        self.augmenter = MultiSpectralAugmenter(
+            self.input_channel_names,
+            augmentation_config or {},
+            derived_cfg=derived_config or {},
+        ) if self.augment else None
+        self.data_validation_config = data_validation_config or {}
+
+        self.samples = self._collect_samples()
+        if not self.samples:
+            raise RuntimeError(f"Nenhuma amostra encontrada em: {self.root}")
+        if bool(self.data_validation_config.get("validate_npy_content", True)):
+            self._validate_and_filter_npy_samples()
+        self._validate_tensor_contract()
+        self._add_sample_class_stats()
+
+    def _collect_samples(self):
+        samples = []
+        group_root = self.root / "group"
+        if not group_root.is_dir():
+            raise RuntimeError(f"Pasta group não encontrada em: {self.root}")
+
+        for group_dir in sorted(group_root.iterdir()):
+            if not group_dir.is_dir():
+                continue
+            tensors_dir = group_dir / "tensors"
+            metas_dir = group_dir / "metas"
+            previews_dir = group_dir / "previews"
+            if not tensors_dir.is_dir():
+                continue
+
+            for tensor_path in sorted(tensors_dir.glob("*.npy")):
+                base = tensor_path.stem
+                masks, missing = {}, []
+                for head_name, hcfg in self.heads_config.items():
+                    mask_dir_name = str(hcfg.get("mask_dir"))
+                    if mask_dir_name == "__derived_target__" or bool(hcfg.get("derived", False)):
+                        masks[head_name] = None
+                        continue
+                    mask_path = group_dir / mask_dir_name / f"{base}.npy"
+                    if not mask_path.exists():
+                        missing.append(f"{head_name}:{mask_path}")
+                    else:
+                        masks[head_name] = mask_path
+                if missing:
+                    print(f"[WARN] Pulando {tensor_path}, masks ausentes: {missing}")
+                    continue
+
+                meta_path = metas_dir / f"{base}.json"
+                preview_path = previews_dir / f"{base}.png"
+                tensor_channels = None
+                if meta_path.exists():
+                    meta = load_json(meta_path)
+                    meta_channels = meta.get("channels")
+                    if isinstance(meta_channels, list) and meta_channels:
+                        tensor_channels = [str(c).strip().upper() for c in meta_channels]
+
+                samples.append({
+                    "group": group_dir.name,
+                    "base": base,
+                    "tensor": tensor_path,
+                    "masks": masks,
+                    "meta": meta_path if meta_path.exists() else None,
+                    "preview": preview_path if preview_path.exists() else None,
+                    "tensor_channels": tensor_channels,
+                })
+        return samples
+
+    def _resolve_tensor_channel_indices(self, sample: dict, tensor_c: int) -> List[int]:
+        saved_names = sample.get("tensor_channels")
+        if saved_names:
+            if len(saved_names) != tensor_c:
+                raise RuntimeError(
+                    f"Meta/tensor incompatíveis em {sample['tensor']}: meta.channels={saved_names}, tensor C={tensor_c}."
+                )
+            if len(set(saved_names)) != len(saved_names):
+                raise RuntimeError(f"Meta possui canais duplicados em {sample['tensor']}: {saved_names}")
+            missing = [name for name in self.input_channel_names if name not in saved_names]
+            if missing:
+                raise RuntimeError(
+                    f"Tensor {sample['tensor']} não possui canais {missing}. Salvos={saved_names}, pedidos={self.input_channel_names}."
+                )
+            return [saved_names.index(name) for name in self.input_channel_names]
+
+        if tensor_c == self.channels:
+            return list(range(self.channels))
+        if tensor_c == len(SOURCE_CHANNEL_ORDER) and all(name in SOURCE_CHANNEL_ORDER for name in self.input_channel_names):
+            return [SOURCE_CHANNEL_ORDER.index(name) for name in self.input_channel_names]
+        raise RuntimeError(
+            f"Não foi possível inferir canais de {sample['tensor']}: tensor C={tensor_c}, "
+            f"pedidos={self.input_channel_names}, meta.channels ausente."
+        )
+
+    def _validate_and_filter_npy_samples(self):
+        """
+        Audita .npy por header + tamanho de payload, sem mmap.
+
+        A versão anterior usava milhares de memory-maps e podia gerar WinError 8
+        no Windows por falta de recursos de mapeamento, classificando um arquivo
+        saudável como "corrupt". Aqui só tratamos como corrupção problemas
+        estruturais reais do arquivo.
+        """
+        cfg = self.data_validation_config or {}
+        skip_corrupt = bool(cfg.get("skip_corrupt_samples", True))
+        max_corrupt_fraction = max(0.0, float(cfg.get("max_corrupt_fraction", 0.005)))
+
+        original_count = len(self.samples)
+        good_samples = []
+        bad_samples = []
+
+        for s in self.samples:
+            failures = []
+            try:
+                info = inspect_npy_integrity(s["tensor"], expected_ndim=3)
+                shape = tuple(info["shape"])
+                if int(shape[0]) <= 0 or int(shape[1]) <= 0 or int(shape[2]) <= 0:
+                    failures.append(f"tensor:invalid_shape={shape}")
+            except Exception as exc:
+                failures.append(f"tensor:{type(exc).__name__}:{exc} | path={s['tensor']}")
+
+            for head_name, path in (s.get("masks") or {}).items():
+                if path is None:
+                    continue
+                try:
+                    info = inspect_npy_integrity(path, expected_ndim=2)
+                    shape = tuple(info["shape"])
+                    if int(shape[0]) <= 0 or int(shape[1]) <= 0:
+                        failures.append(f"{head_name}:invalid_shape={shape} | path={path}")
+                except Exception as exc:
+                    failures.append(f"{head_name}:{type(exc).__name__}:{exc} | path={path}")
+
+            if failures:
+                bad_samples.append({
+                    "group": s.get("group"),
+                    "base": s.get("base"),
+                    "tensor": str(s.get("tensor")),
+                    "failures": failures,
+                })
+            else:
+                good_samples.append(s)
+
+        if not bad_samples:
+            print(f"[DATA][INTEGRITY] root={self.root} OK | samples={original_count}")
+            return
+
+        print(
+            f"[DATA][CORRUPT][WARN] root={self.root} "
+            f"corrupt={len(bad_samples)}/{original_count}"
+        )
+        for item in bad_samples[:50]:
+            print(
+                f"[DATA][CORRUPT] group={item['group']} base={item['base']} "
+                f"tensor={item['tensor']}"
+            )
+            for failure in item["failures"]:
+                print(f"    - {failure}")
+        if len(bad_samples) > 50:
+            print(f"[DATA][CORRUPT] ... +{len(bad_samples)-50} amostras não exibidas")
+
+        corrupt_fraction = len(bad_samples) / max(1, original_count)
+        if corrupt_fraction > max_corrupt_fraction:
+            raise RuntimeError(
+                "Corrupção de dataset acima do limite de segurança: "
+                f"{len(bad_samples)}/{original_count} ({corrupt_fraction*100:.3f}%) > "
+                f"{max_corrupt_fraction*100:.3f}%. Corrija o dataset antes de treinar."
+            )
+
+        if not skip_corrupt:
+            first = bad_samples[0]
+            raise RuntimeError(
+                "Amostra .npy corrompida encontrada e skip_corrupt_samples=false: "
+                f"group={first['group']} base={first['base']} failures={first['failures']}"
+            )
+
+        self.samples = good_samples
+        print(
+            f"[DATA][CORRUPT][SKIP] root={self.root} removidas={len(bad_samples)} "
+            f"restantes={len(self.samples)} | limite={max_corrupt_fraction*100:.3f}%"
+        )
+        if not self.samples:
+            raise RuntimeError(f"Todas as amostras foram removidas por corrupção em: {self.root}")
+
+    def _validate_tensor_contract(self):
+        """
+        Valida o contrato EFETIVO dos tensores.
+
+        Importante:
+        - metas novos podem trazer meta.channels explicitamente;
+        - metas legados podem não trazer essa lista;
+        - quando C == self.channels, _resolve_tensor_channel_indices() já define
+          que a ordem implícita é exatamente self.input_channel_names.
+
+        Portanto, strict_channels deve comparar o contrato resolvido, e não
+        simplesmente diferenciar "meta com channels" de "meta sem channels".
+        """
+        contracts = set()
+        spatial = set()
+        missing_channel_meta = 0
+
+        for sample in self.samples:
+            info = inspect_npy_integrity(sample["tensor"], expected_ndim=3)
+            shape = tuple(info["shape"])
+            tensor_c = int(shape[0])
+            indices = self._resolve_tensor_channel_indices(sample, tensor_c)
+            if len(indices) != self.channels:
+                raise RuntimeError(
+                    f"Contrato de canais inválido em {sample['tensor']}: indices={indices}"
+                )
+
+            saved_names = sample.get("tensor_channels")
+            if saved_names:
+                # Contrato nominal explícito no meta, na ordem efetivamente usada.
+                effective_names = tuple(saved_names[i] for i in indices)
+            else:
+                # Contrato legado implícito já validado por
+                # _resolve_tensor_channel_indices().
+                missing_channel_meta += 1
+                effective_names = tuple(self.input_channel_names)
+
+            contracts.add((effective_names, len(indices)))
+            spatial.add(tuple(shape[-2:]))
+
+        if self.strict_channels and len(contracts) > 1:
+            raise RuntimeError(
+                f"Contratos de tensor efetivos diferentes no mesmo split: "
+                f"{sorted(contracts, key=str)}"
+            )
+
+        if missing_channel_meta > 0:
+            print(
+                f"[DATA][CHANNELS][WARN] root={self.root} "
+                f"amostras_sem_meta_channels={missing_channel_meta}/{len(self.samples)} | "
+                f"assumindo ordem={self.input_channel_names} porque C={self.channels}."
+            )
+
+        print(
+            f"[DATA][CHANNELS] root={self.root} pedidos={self.input_channel_names} "
+            f"contratos_efetivos={sorted(contracts, key=str)}"
+        )
+        print(
+            f"[DATA][SPATIAL] root={self.root} shapes={sorted(spatial)} "
+            f"resize_hw={self.resize_hw}"
+        )
+
+    def __len__(self):
+        return len(self.samples)
+
+    def _resize_tensor(self, x: torch.Tensor):
+        if self.resize_hw is None:
+            return x
+        h, w = self.resize_hw
+        if x.shape[-2:] != (h, w):
+            x = F.interpolate(x.unsqueeze(0), size=(h, w), mode="bilinear", align_corners=False).squeeze(0)
+        return x
+
+    def _resize_mask(self, y: torch.Tensor):
+        if self.resize_hw is None:
+            return y
+        h, w = self.resize_hw
+        if y.shape[-2:] != (h, w):
+            y = F.interpolate(y.unsqueeze(0).unsqueeze(0).float(), size=(h, w), mode="nearest").squeeze().long()
+        return y
+
+    def _load_masks_for_sample(self, s: dict, do_resize: bool = True) -> Dict[str, torch.Tensor]:
+        masks = {}
+        for head_name, path in s["masks"].items():
+            if path is None:
+                continue
+            y = np.load(str(path)).astype(np.int64)
+            yt = torch.from_numpy(np.ascontiguousarray(y)).long()
+            if do_resize:
+                yt = self._resize_mask(yt)
+            masks[head_name] = yt
+
+        if "target" in self.heads_config:
+            if "vegetation" not in masks or "cana" not in masks:
+                raise RuntimeError("Head target requer masks vegetation e cana.")
+            ignore_index = int(self.heads_config["target"].get("ignore_index", 255))
+            veg, cana = masks["vegetation"], masks["cana"]
+            valid = (veg != ignore_index) & (cana != ignore_index)
+            target = torch.zeros_like(veg, dtype=torch.long)
+            target[(veg == 1) & (cana == 0)] = 1
+            target[~valid] = ignore_index
+            masks["target"] = target
+        return masks
+
+    def load_mask_numpy(self, sample_idx: int, head_name: str) -> np.ndarray:
+        s = self.samples[int(sample_idx)]
+        if head_name == "target":
+            masks = self._load_masks_for_sample(s, do_resize=False)
+            return masks["target"].numpy()
+        path = s["masks"].get(head_name)
+        if path is None:
+            raise RuntimeError(f"Mask física ausente para head={head_name}")
+        return np.load(str(path)).astype(np.int64)
+
+    def __getitem__(self, idx):
+        s = self.samples[idx]
+        x = np.load(str(s["tensor"])).astype(np.float32)
+        if x.ndim != 3:
+            raise RuntimeError(f"Tensor inválido {s['tensor']}: shape={x.shape}")
+        indices = self._resolve_tensor_channel_indices(s, int(x.shape[0]))
+        x = x[indices, :, :]
+        xt = self._resize_tensor(torch.from_numpy(np.ascontiguousarray(x)).float())
+        masks = self._load_masks_for_sample(s, do_resize=True)
+
+        if self.augmenter is not None:
+            xt, masks = self.augmenter(xt, masks)
+
+        return {"image": xt, "masks": masks, "group": s["group"], "base": s["base"]}
+
+    def _add_sample_class_stats(self):
+        sem_cfg = self.heads_config.get("semantic", {})
+        classes = sem_cfg.get("classes", {}) or {}
+        chao_id = int(classes.get("chao", 0))
+        cana_id = int(classes.get("cana", 1))
+        erva_id = int(classes.get("erva", 2))
+        ignore = int(sem_cfg.get("ignore_index", 255))
+
+        for s in self.samples:
+            stats = {
+                "pixels_total": 0, "pixels_chao": 0, "pixels_cana": 0, "pixels_erva": 0,
+                "pixels_vegetation": 0, "pixels_target": 0,
+                "pct_cana": 0.0, "pct_erva": 0.0, "pct_target": 0.0,
+                "has_cana": False, "has_erva": False, "has_target": False,
+            }
+            sem_path = s["masks"].get("semantic")
+            veg_path = s["masks"].get("vegetation")
+            cana_path = s["masks"].get("cana")
+
+            if sem_path is not None and Path(sem_path).exists():
+                sem = np.load(str(sem_path)).astype(np.int64)
+                valid = sem != ignore
+                total = int(valid.sum())
+                stats["pixels_total"] = total
+                if total > 0:
+                    stats["pixels_chao"] = int(((sem == chao_id) & valid).sum())
+                    stats["pixels_cana"] = int(((sem == cana_id) & valid).sum())
+                    stats["pixels_erva"] = int(((sem == erva_id) & valid).sum())
+                    stats["pct_cana"] = stats["pixels_cana"] / total
+                    stats["pct_erva"] = stats["pixels_erva"] / total
+
+            if veg_path is not None and cana_path is not None:
+                veg = np.load(str(veg_path)).astype(np.int64)
+                cana = np.load(str(cana_path)).astype(np.int64)
+                valid = (veg != ignore) & (cana != ignore)
+                total = int(valid.sum())
+                if total > 0:
+                    target = (veg == 1) & (cana == 0) & valid
+                    stats["pixels_vegetation"] = int(((veg == 1) & valid).sum())
+                    stats["pixels_target"] = int(target.sum())
+                    stats["pct_target"] = stats["pixels_target"] / total
+
+            stats["has_cana"] = stats["pixels_cana"] > 0
+            stats["has_erva"] = stats["pixels_erva"] > 0
+            stats["has_target"] = stats["pixels_target"] > 0
+            s["class_stats"] = stats
+
+
+def collate_fn(batch):
+    imgs = torch.stack([b["image"] for b in batch], dim=0)
+    head_names = list(batch[0]["masks"].keys())
+    masks = {h: torch.stack([b["masks"][h] for b in batch], dim=0) for h in head_names}
+    meta = {"group": [b["group"] for b in batch], "base": [b["base"] for b in batch]}
+    return imgs, masks, meta
+
+
+# ============================================================
+# Diverse agricultural batch sampler
+# ============================================================
+
+class DiverseAgriculturalBatchSampler(Sampler[List[int]]):
+    """Monta batches com target, cana, mistos e negativos sempre que possível."""
+
+    def __init__(self, ds: OakFcc3TensorMultiHeadDataset, batch_size: int, cfg: dict, seed: int = 42):
+        self.ds = ds
+        self.batch_size = int(batch_size)
+        self.cfg = cfg or {}
+        self.seed = int(seed)
+        self.epoch = 0
+        self.samples_per_epoch = int(self.cfg.get("samples_per_epoch", 0) or 0) or len(ds)
+        self.num_batches = max(1, math.ceil(self.samples_per_epoch / max(1, self.batch_size)))
+        self.pools = self._build_pools()
+        self._print_summary()
+
+    def _build_pools(self):
+        tiny = float(self.cfg.get("tiny_target_pct", 0.005))
+        small = float(self.cfg.get("small_target_pct", 0.02))
+        medium = float(self.cfg.get("medium_target_pct", 0.10))
+        pools = {k: [] for k in [
+            "mixed", "target_only", "cana_only", "ground",
+            "target_tiny", "target_small", "target_medium", "target_large", "any"
+        ]}
+        for i, s in enumerate(self.ds.samples):
+            st = s.get("class_stats", {})
+            has_t = bool(st.get("has_target", False))
+            has_c = bool(st.get("has_cana", False))
+            pct = float(st.get("pct_target", 0.0))
+            pools["any"].append(i)
+            if has_t and has_c:
+                pools["mixed"].append(i)
+            elif has_t:
+                pools["target_only"].append(i)
+            elif has_c:
+                pools["cana_only"].append(i)
+            else:
+                pools["ground"].append(i)
+            if has_t:
+                if pct <= tiny:
+                    pools["target_tiny"].append(i)
+                elif pct <= small:
+                    pools["target_small"].append(i)
+                elif pct <= medium:
+                    pools["target_medium"].append(i)
+                else:
+                    pools["target_large"].append(i)
+        return pools
+
+    def _print_summary(self):
+        txt = " | ".join(f"{k}={len(v)}" for k, v in self.pools.items() if k != "any")
+        print(f"[SAMPLER:DIVERSE] batches={self.num_batches} batch={self.batch_size} | {txt}")
+
+    def set_epoch(self, epoch: int):
+        self.epoch = int(epoch)
+
+    def __len__(self):
+        return self.num_batches
+
+    def __iter__(self):
+        rng = random.Random(self.seed + self.epoch * 100003)
+        queues = {}
+        pos = {}
+        for k, vals in self.pools.items():
+            q = list(vals)
+            rng.shuffle(q)
+            queues[k] = q
+            pos[k] = 0
+
+        def draw(pool_name: str, used: set):
+            q = queues.get(pool_name) or []
+            if not q:
+                return None
+            for _ in range(max(1, len(q) * 2)):
+                if pos[pool_name] >= len(q):
+                    rng.shuffle(q)
+                    pos[pool_name] = 0
+                idx = q[pos[pool_name]]
+                pos[pool_name] += 1
+                if idx not in used:
+                    return idx
+            return None
+
+        pattern = [
+            "mixed", "target_tiny", "cana_only", "ground",
+            "target_small", "target_only", "target_medium", "target_large",
+        ]
+
+        for b in range(self.num_batches):
+            used, batch = set(), []
+            offset = b % len(pattern)
+            desired = pattern[offset:] + pattern[:offset]
+            for pool_name in desired:
+                if len(batch) >= self.batch_size:
+                    break
+                idx = draw(pool_name, used)
+                if idx is not None:
+                    batch.append(idx)
+                    used.add(idx)
+
+            while len(batch) < self.batch_size:
+                idx = draw("any", used)
+                if idx is None:
+                    idx = rng.randrange(len(self.ds))
+                batch.append(idx)
+                used.add(idx)
+            yield batch
+
+
+def build_sample_weights(ds, mode="target_focus"):
+    weights = []
+    for s in ds.samples:
+        st = s.get("class_stats", {})
+        has_cana = bool(st.get("has_cana", False))
+        has_target = bool(st.get("has_target", False))
+        pct_target = float(st.get("pct_target", 0.0))
+        w = 1.0
+        if not has_cana and not has_target:
+            w *= 0.55
+        if has_cana:
+            w *= 1.4
+        if has_target:
+            w *= 2.4
+        w *= 1.0 + min(2.0, 30.0 * pct_target)
+        weights.append(max(0.20, min(w, 8.0)))
+    return torch.tensor(weights, dtype=torch.double)
+
+
+# ============================================================
+# Normalization
+# ============================================================
+
+class FixedNormalizer(nn.Module):
+    def __init__(self, mean: List[float], std: List[float]):
+        super().__init__()
+        self.register_buffer("mean", torch.tensor(mean, dtype=torch.float32).view(1, -1, 1, 1))
+        self.register_buffer("std", torch.clamp(torch.tensor(std, dtype=torch.float32).view(1, -1, 1, 1), min=1e-6))
+    def forward(self, x):
+        return (x - self.mean) / self.std
+
+
+def normalize_per_batch(x: torch.Tensor, eps: float = 1e-6):
+    mean = x.mean(dim=(0, 2, 3), keepdim=True)
+    std = x.std(dim=(0, 2, 3), keepdim=True).clamp_min(eps)
+    return (x - mean) / std
+
+
+def build_normalizer(config: dict, args, device: torch.device):
+    input_channel_names = get_input_channel_names(config)
+    channels = len(input_channel_names)
+    model_family = config.get("modelo", "segformer")
+    model_name = config.get("model_name", "model")
+    stats_source_tag = config.get("stats_source_tag", f"stacked_raw{channels}")
+    candidates = []
+    if args.norm_stats:
+        candidates.append(Path(args.norm_stats))
+    candidates.append(Path("dataset") / f"{config['resolucao'][0]}x{config['resolucao'][1]}" / "group" / "norm_stats.json")
+    candidates.append(Path("backup") / model_family / model_name / stats_source_tag / "norm_stats.json")
+
+    for p in candidates:
+        if not p.exists():
+            continue
+        stats = load_json(p)
+        mean, std = stats.get("mean", []), stats.get("std", [])
+        raw_channels = stats.get("channels", [])
+        stat_channels = [str(c).strip().upper() for c in raw_channels] if isinstance(raw_channels, list) else []
+        if len(mean) != len(std):
+            raise RuntimeError(f"norm_stats incompatível: {p} | mean={len(mean)} std={len(std)}")
+        if stat_channels:
+            if len(stat_channels) != len(mean) or len(set(stat_channels)) != len(stat_channels):
+                raise RuntimeError(f"norm_stats channels inválidos: {p} | {stat_channels}")
+            missing = [n for n in input_channel_names if n not in stat_channels]
+            if missing:
+                raise RuntimeError(f"norm_stats {p} não possui {missing}. Disponíveis={stat_channels}")
+            ids = [stat_channels.index(n) for n in input_channel_names]
+            mean, std = [mean[i] for i in ids], [std[i] for i in ids]
+            selected = [stat_channels[i] for i in ids]
+        elif len(mean) == channels:
+            selected = list(input_channel_names)
+        elif len(mean) == len(SOURCE_CHANNEL_ORDER) and all(n in SOURCE_CHANNEL_ORDER for n in input_channel_names):
+            ids = [SOURCE_CHANNEL_ORDER.index(n) for n in input_channel_names]
+            mean, std = [mean[i] for i in ids], [std[i] for i in ids]
+            selected = list(input_channel_names)
+        else:
+            raise RuntimeError(f"norm_stats sem nomes incompatível: {p} mean/std={len(mean)} pedidos={input_channel_names}")
+
+        ma, sa = np.asarray(mean, np.float64), np.asarray(std, np.float64)
+        if not np.isfinite(ma).all() or not np.isfinite(sa).all() or np.any(sa <= 0):
+            raise RuntimeError(f"norm_stats contém valores inválidos: {p}")
+        print(f"[NORM] usando stats fixos: {p}")
+        print(f"[NORM] selected_channels={selected}")
+        return FixedNormalizer(mean, std).to(device), str(p)
+
+    if bool(getattr(args, "allow_missing_norm_stats", False)):
+        print("[NORM][WARN] norm_stats ausente; usando normalize_per_batch.")
+        return None, None
+    raise RuntimeError(
+        "norm_stats não encontrado. Use --norm_stats ou --allow-missing-norm-stats conscientemente. "
+        f"Candidatos={[str(p) for p in candidates]}"
+    )
+
+
+# ============================================================
+# Model: separate decoders or shared-light decoder
+# ============================================================
+
+def patch_segformer_encoder_input_channels(segformer_encoder: nn.Module, input_channel_names: List[str], init_mode: str = "zero_extra"):
+    names = [str(c).strip().upper() for c in input_channel_names]
+    if names == ["R", "G", "B"]:
+        return segformer_encoder
+
+    # Transformers <= versões usadas hoje no projeto: .encoder.patch_embeddings[0].proj
+    # Transformers recentes: .stages[0].patch_embeddings.proj
+    if hasattr(segformer_encoder, "encoder") and hasattr(segformer_encoder.encoder, "patch_embeddings"):
+        patch_owner = segformer_encoder.encoder.patch_embeddings[0]
+    elif hasattr(segformer_encoder, "stages") and len(segformer_encoder.stages) > 0:
+        patch_owner = segformer_encoder.stages[0].patch_embeddings
+    else:
+        raise RuntimeError("Não encontrei o primeiro patch embedding do SegFormer nesta versão do Transformers.")
+
+    proj = patch_owner.proj
+    old_weight = proj.weight.data.clone()
+    old_bias = proj.bias.data.clone() if proj.bias is not None else None
+    new_proj = nn.Conv2d(
+        in_channels=len(names), out_channels=proj.out_channels,
+        kernel_size=proj.kernel_size, stride=proj.stride, padding=proj.padding,
+        dilation=proj.dilation, groups=proj.groups, bias=proj.bias is not None,
+        padding_mode=proj.padding_mode,
+    )
+
+    mode = str(init_mode or "zero_extra").lower()
+    with torch.no_grad():
+        pretrained = {"R": 0, "G": 1, "B": 2}
+        mean_rgb = old_weight[:, :3].mean(dim=1)
+        has_rgb = any(n in pretrained for n in names)
+        for dst, name in enumerate(names):
+            if name in pretrained:
+                new_proj.weight[:, dst].copy_(old_weight[:, pretrained[name]])
+            elif mode == "mean_rgb":
+                new_proj.weight[:, dst].copy_(mean_rgb)
+            elif mode == "scaled_mean":
+                new_proj.weight[:, dst].copy_(mean_rgb * (3.0 / max(3.0, float(len(names)))))
+            elif mode == "zero_extra" and has_rgb:
+                new_proj.weight[:, dst].zero_()
+            else:
+                new_proj.weight[:, dst].copy_(mean_rgb)
+        if old_bias is not None:
+            new_proj.bias.copy_(old_bias)
+
+    patch_owner.proj = new_proj
+    print(f"[MODEL] input patch RGB(3) -> {names} | init={mode}")
+    return segformer_encoder
+
+
+def replace_segformer_decode_classifier(decode_head: nn.Module, num_classes: int):
+    old = getattr(decode_head, "classifier", None)
+    if not isinstance(old, nn.Conv2d):
+        raise RuntimeError(f"decode_head.classifier inesperado: {type(old)}")
+    new = nn.Conv2d(old.in_channels, int(num_classes), kernel_size=old.kernel_size, stride=old.stride,
+                    padding=old.padding, dilation=old.dilation, groups=old.groups,
+                    bias=old.bias is not None, padding_mode=old.padding_mode)
+    nn.init.xavier_uniform_(new.weight)
+    if new.bias is not None:
+        nn.init.zeros_(new.bias)
+    decode_head.classifier = new
+    return decode_head
+
+
+class MultiHeadSegFormer(nn.Module):
+    def __init__(self, backbone: str, input_channel_names: List[str], heads_config: Dict[str, dict],
+                 semantic_id2label: Dict[int, str], semantic_label2id: Dict[str, int],
+                 model_cfg: Optional[dict] = None):
+        super().__init__()
+        model_cfg = model_cfg or {}
+        semantic_classes = int(heads_config["semantic"].get("num_classes", len(semantic_id2label)))
+        names = [str(c).strip().upper() for c in input_channel_names]
+        base = SegformerForSemanticSegmentation.from_pretrained(
+            backbone, num_labels=semantic_classes,
+            id2label={int(k): str(v) for k, v in semantic_id2label.items()},
+            label2id={str(k): int(v) for k, v in semantic_label2id.items()},
+            ignore_mismatched_sizes=True,
+        )
+        self.decoder_mode = str(model_cfg.get("decoder_mode", "shared_light")).lower()
+        self.spectral_input_init = str(model_cfg.get("spectral_input_init", "zero_extra")).lower()
+        patch_segformer_encoder_input_channels(base.segformer, names, self.spectral_input_init)
+        base.config.num_channels = len(names)
+        base.config.input_channel_names = list(names)
+        base.config.agri_decoder_mode = self.decoder_mode
+        base.config.agri_trainer_version = TRAINER_VERSION
+        base.config.agri_spectral_input_init = self.spectral_input_init
+
+        self.segformer = base.segformer
+        self.heads_config = heads_config
+
+        if self.decoder_mode == "separate":
+            self.decode_heads = nn.ModuleDict()
+            for head_name, hcfg in heads_config.items():
+                h = copy.deepcopy(base.decode_head)
+                self.decode_heads[head_name] = replace_segformer_decode_classifier(h, int(hcfg["num_classes"]))
+            self.shared_decode_head = None
+            self.classifiers = None
+
+        elif self.decoder_mode == "shared_light":
+            self.decode_heads = None
+            self.shared_decode_head = copy.deepcopy(base.decode_head)
+            old_classifier = self.shared_decode_head.classifier
+            if not isinstance(old_classifier, nn.Conv2d):
+                raise RuntimeError("SegFormer decode_head.classifier não é Conv2d.")
+            feature_channels = int(old_classifier.in_channels)
+            self.shared_decode_head.classifier = nn.Identity()
+            self.classifiers = nn.ModuleDict()
+            for head_name, hcfg in heads_config.items():
+                clf = nn.Conv2d(feature_channels, int(hcfg["num_classes"]), kernel_size=1, bias=True)
+                nn.init.xavier_uniform_(clf.weight)
+                nn.init.zeros_(clf.bias)
+                self.classifiers[head_name] = clf
+        else:
+            raise RuntimeError(f"training_v2.model.decoder_mode inválido: {self.decoder_mode}")
+
+        self.config = base.config
+        print(f"[MODEL] decoder_mode={self.decoder_mode}")
+
+    def forward(self, pixel_values: torch.Tensor) -> Dict[str, torch.Tensor]:
+        outputs = self.segformer(pixel_values=pixel_values, output_hidden_states=True, return_dict=True)
+        hidden_states = outputs.hidden_states
+        if self.decoder_mode == "separate":
+            return {name: head(hidden_states) for name, head in self.decode_heads.items()}
+        features = self.shared_decode_head(hidden_states)
+        return {name: clf(features) for name, clf in self.classifiers.items()}
+
+
+def build_model(backbone: str, input_channel_names: List[str], heads_config: Dict[str, dict],
+                semantic_id2label: Dict[int, str], semantic_label2id: Dict[str, int], config: Optional[dict] = None):
+    global _LAST_BUILD_CONFIG
+    effective_config = config or _LAST_BUILD_CONFIG or {}
+    tcfg = get_training_v2_config(effective_config)
+    return MultiHeadSegFormer(
+        backbone=backbone,
+        input_channel_names=input_channel_names,
+        heads_config=heads_config,
+        semantic_id2label=semantic_id2label,
+        semantic_label2id=semantic_label2id,
+        model_cfg=tcfg.get("model", {}),
+    )
+
+
+# Override build_heads_config only to keep export/validate aware of the config
+_build_heads_config_base = build_heads_config
+
+def build_heads_config(config: dict, ignore_index: int) -> Dict[str, dict]:
+    global _LAST_BUILD_CONFIG
+    _LAST_BUILD_CONFIG = copy.deepcopy(config)
+    return _build_heads_config_base(config, ignore_index)
+
+
+# ============================================================
+# Class weighting / optimizer / scheduler
+# ============================================================
+
+def estimate_head_class_weights(ds: OakFcc3TensorMultiHeadDataset, head_name: str, num_classes: int,
+                                ignore_index: int, method_cfg: dict):
+    counts = np.zeros(num_classes, dtype=np.float64)
+    for i in range(len(ds)):
+        m = ds.load_mask_numpy(i, head_name).reshape(-1)
+        m = m[(m != ignore_index) & (m >= 0) & (m < num_classes)]
+        if m.size:
+            counts += np.bincount(m, minlength=num_classes)[:num_classes]
+    freq = counts / max(counts.sum(), 1.0)
+    freq = np.clip(freq, 1e-12, 1.0)
+    method = str(method_cfg.get("method", "log_inverse")).lower()
+    if method == "sqrt_inverse":
+        power = float(method_cfg.get("power", 0.5))
+        weights = np.power(1.0 / freq, power)
+    elif method == "none":
+        weights = np.ones_like(freq)
+    else:
+        offset = float(method_cfg.get("log_offset", 1.02))
+        weights = 1.0 / np.log(offset + freq)
+    weights = weights / max(weights.mean(), 1e-12)
+    weights = np.clip(weights, float(method_cfg.get("min_weight", 0.25)), float(method_cfg.get("max_weight", 4.0)))
+    weights = weights / max(weights.mean(), 1e-12)
+    return torch.tensor(weights, dtype=torch.float32), counts
+
+
+def parse_class_weights_string(s: str) -> Dict[str, List[float]]:
+    out, txt = {}, str(s).strip()
+    if not txt:
+        return out
+    if "=" not in txt:
+        out["__single__"] = [float(x) for x in txt.split(",") if x.strip()]
+        return out
+    for block in txt.split(";"):
+        if not block.strip():
+            continue
+        k, v = block.split("=", 1)
+        out[k.strip()] = [float(x) for x in v.split(",") if x.strip()]
+    return out
+
+
+def build_criterions(ds_train, heads_config, class_weights_mode, device, seed, weighting_cfg: Optional[dict] = None):
+    criterions, debug = {}, {}
+    weighting_cfg = weighting_cfg or {}
+    parsed_manual = parse_class_weights_string(class_weights_mode) if str(class_weights_mode).lower() not in ("auto", "none") else {}
+    for head_name, hcfg in heads_config.items():
+        ncls, ignore = int(hcfg["num_classes"]), int(hcfg.get("ignore_index", 255))
+        weights, counts = None, None
+        mode = str(class_weights_mode).lower()
+        if mode == "auto":
+            w, counts = estimate_head_class_weights(ds_train, head_name, ncls, ignore, weighting_cfg)
+            weights = w.to(device)
+        elif mode != "none":
+            vals = parsed_manual.get(head_name, parsed_manual.get("__single__") if head_name == "semantic" else None)
+            if vals is not None:
+                if len(vals) != ncls:
+                    raise RuntimeError(f"Pesos de {head_name} precisam ter {ncls} valores: {vals}")
+                weights = torch.tensor(vals, dtype=torch.float32, device=device)
+        criterions[head_name] = nn.CrossEntropyLoss(weight=weights, ignore_index=ignore)
+        debug[head_name] = {
+            "weights": weights.detach().cpu().tolist() if weights is not None else None,
+            "counts": counts.astype(np.int64).tolist() if counts is not None else None,
+        }
+        print(f"[LOSS:{head_name}] counts={debug[head_name]['counts']} weights={debug[head_name]['weights']}")
+    return criterions, debug
+
+
+def _normalization_parameter_names(model: nn.Module) -> set:
+    norm_types = (nn.LayerNorm, nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.GroupNorm, nn.InstanceNorm1d, nn.InstanceNorm2d, nn.InstanceNorm3d)
+    names = set()
+    for module_name, module in model.named_modules():
+        if isinstance(module, norm_types):
+            for pn, _ in module.named_parameters(recurse=False):
+                names.add(f"{module_name}.{pn}" if module_name else pn)
+    return names
+
+
+def build_optimizer(model: nn.Module, base_lr: float, base_wd: float, cfg: dict):
+    encoder_lr = float(cfg.get("encoder_lr") if cfg.get("encoder_lr") is not None else base_lr)
+    wd = float(cfg.get("weight_decay") if cfg.get("weight_decay") is not None else base_wd)
+    patch_mult = float(cfg.get("patch_lr_mult", 2.0))
+    heads_mult = float(cfg.get("heads_lr_mult", 5.0))
+    norm_names = _normalization_parameter_names(model)
+    groups = {}
+
+    def group_name(name: str):
+        is_patch = (
+            name.startswith("segformer.encoder.patch_embeddings.0") or
+            name.startswith("segformer.stages.0.patch_embeddings")
+        )
+        is_encoder = name.startswith("segformer.")
+        if is_patch:
+            section, lr = "patch", encoder_lr * patch_mult
+        elif is_encoder:
+            section, lr = "encoder", encoder_lr
+        else:
+            section, lr = "heads", encoder_lr * heads_mult
+        no_decay = (
+            (bool(cfg.get("no_decay_bias", True)) and name.endswith(".bias")) or
+            (bool(cfg.get("no_decay_norm", True)) and name in norm_names)
+        )
+        return section, lr, (0.0 if no_decay else wd)
+
+    for name, p in model.named_parameters():
+        if not p.requires_grad:
+            continue
+        section, lr, group_wd = group_name(name)
+        key = (section, lr, group_wd)
+        groups.setdefault(key, []).append(p)
+
+    param_groups = []
+    for (section, lr, group_wd), params in groups.items():
+        param_groups.append({"params": params, "lr": lr, "weight_decay": group_wd, "group_name": f"{section}_{'nodecay' if group_wd == 0 else 'decay'}"})
+        print(f"[OPT] {param_groups[-1]['group_name']}: params={sum(p.numel() for p in params):,} lr={lr:.3e} wd={group_wd:.3e}")
+
+    betas = cfg.get("betas", [0.9, 0.999])
+    return torch.optim.AdamW(param_groups, lr=encoder_lr, betas=(float(betas[0]), float(betas[1])), eps=float(cfg.get("eps", 1e-8)))
+
+
+class WarmupDecayScheduler:
+    """Step scheduler determinístico, salvo integralmente no checkpoint."""
+    def __init__(self, optimizer, total_steps: int, cfg: dict):
+        self.optimizer = optimizer
+        self.total_steps = max(1, int(total_steps))
+        self.cfg = copy.deepcopy(cfg or {})
+        self.base_lrs = [float(g["lr"]) for g in optimizer.param_groups]
+        self.step_num = 0
+        self._apply(self.step_num)
+
+    def _factor(self, step: int) -> float:
+        mode = str(self.cfg.get("mode", "poly")).lower()
+        warmup = int(round(self.total_steps * float(self.cfg.get("warmup_ratio", 0.05))))
+        warmup = max(0, min(warmup, self.total_steps - 1))
+        start = float(self.cfg.get("warmup_start_factor", 0.10))
+        min_ratio = float(self.cfg.get("min_lr_ratio", 0.02))
+        if warmup > 0 and step < warmup:
+            if warmup == 1:
+                return 1.0
+            t = step / float(warmup - 1)
+            return start + (1.0 - start) * t
+        if mode == "constant":
+            return 1.0
+        decay_steps = max(1, self.total_steps - warmup)
+        t = min(1.0, max(0.0, (step - warmup) / float(max(1, decay_steps - 1))))
+        if mode == "cosine":
+            raw = 0.5 * (1.0 + math.cos(math.pi * t))
+        else:
+            raw = (1.0 - t) ** float(self.cfg.get("poly_power", 1.0))
+        return min_ratio + (1.0 - min_ratio) * raw
+
+    def _apply(self, step: int):
+        f = self._factor(step)
+        for g, base in zip(self.optimizer.param_groups, self.base_lrs):
+            g["lr"] = float(base * f)
+
+    def step(self):
+        self.step_num = min(self.total_steps, self.step_num + 1)
+        self._apply(self.step_num)
+
+    def state_dict(self):
+        return {"step_num": self.step_num, "total_steps": self.total_steps, "base_lrs": self.base_lrs, "cfg": self.cfg}
+
+    def load_state_dict(self, state: dict):
+        self.step_num = int(state.get("step_num", 0))
+        saved_total = int(state.get("total_steps", self.total_steps))
+        if saved_total != self.total_steps:
+            print(f"[SCHED][WARN] total_steps checkpoint={saved_total}, atual={self.total_steps}; mantendo atual.")
+        self._apply(self.step_num)
+
+    def get_last_lr(self):
+        return [float(g["lr"]) for g in self.optimizer.param_groups]
+
+
+# ============================================================
+# Metrics / losses
+# ============================================================
+
+@torch.no_grad()
+def update_confusion_matrix(cm, preds, labels, num_classes, ignore_index=255):
+    preds, labels = preds.reshape(-1), labels.reshape(-1)
+    valid = labels != ignore_index
+    preds, labels = preds[valid], labels[valid]
+    valid2 = (labels >= 0) & (labels < num_classes) & (preds >= 0) & (preds < num_classes)
+    preds, labels = preds[valid2], labels[valid2]
+    if labels.numel() == 0:
+        return
+    idx = labels * num_classes + preds
+    cm += torch.bincount(idx, minlength=num_classes * num_classes).view(num_classes, num_classes)
+
+
+@torch.no_grad()
+def compute_iou_from_cm(cm, eps=1e-6):
+    cm = cm.float(); tp = torch.diag(cm); fp = cm.sum(0) - tp; fn = cm.sum(1) - tp
+    iou = (tp / (tp + fp + fn + eps)).cpu().tolist()
+    return float(np.mean(iou)), iou
+
+
+@torch.no_grad()
+def compute_pixel_acc_from_cm(cm, eps=1e-6):
+    cm = cm.float()
+    return float(torch.diag(cm).sum() / (cm.sum() + eps))
+
+
+def binary_stats_from_cm(cm, eps=1e-9):
+    c = cm.detach().float().cpu()
+    if c.numel() != 4:
+        return {}
+    tn, fp, fn, tp = [float(x) for x in c.reshape(-1)]
+    precision = tp / (tp + fp + eps)
+    recall = tp / (tp + fn + eps)
+    specificity = tn / (tn + fp + eps)
+    f1 = 2 * precision * recall / (precision + recall + eps)
+    return {"precision": precision, "recall": recall, "specificity": specificity, "f1": f1,
+            "tp": tp, "fp": fp, "fn": fn, "tn": tn}
+
+
+def dice_loss(logits, target, num_classes, ignore_index=255, smooth=1.0, reduction_mode="per_image"):
+    probs = torch.softmax(logits, dim=1)
+    valid = target != ignore_index
+    if not bool(valid.any()):
+        return logits.new_tensor(0.0)
+    tc = target.clone(); tc[~valid] = 0
+    onehot = F.one_hot(tc.long(), num_classes=num_classes).permute(0, 3, 1, 2).float()
+    vf = valid.unsqueeze(1).float(); probs = probs * vf; onehot = onehot * vf
+    if str(reduction_mode).lower() == "global":
+        dims = (0, 2, 3)
+        inter = (probs * onehot).sum(dims); den = probs.sum(dims) + onehot.sum(dims)
+    else:
+        dims = (2, 3)
+        inter = (probs * onehot).sum(dims); den = probs.sum(dims) + onehot.sum(dims)
+    dice = (2.0 * inter + smooth) / (den + smooth)
+    return 1.0 - dice.mean()
+
+
+def _boundary_mask(target: torch.Tensor, ignore_index: int):
+    valid = target != ignore_index
+    t = target.clone(); t[~valid] = 0
+    b = torch.zeros_like(valid)
+    b[:, 1:] |= valid[:, 1:] & valid[:, :-1] & (t[:, 1:] != t[:, :-1])
+    b[:, :-1] |= valid[:, :-1] & valid[:, 1:] & (t[:, :-1] != t[:, 1:])
+    b[:, :, 1:] |= valid[:, :, 1:] & valid[:, :, :-1] & (t[:, :, 1:] != t[:, :, :-1])
+    b[:, :, :-1] |= valid[:, :, :-1] & valid[:, :, 1:] & (t[:, :, :-1] != t[:, :, 1:])
+    return b
+
+
+def get_target_distill_ramp_factor(config: dict, epoch: Optional[int]) -> float:
+    cfg = get_training_v2_config(config).get("target_distillation", {})
+    if not bool(cfg.get("enabled", False)):
+        return 0.0
+    if epoch is None:
+        return 1.0
+    start, ramp = int(cfg.get("start_epoch", 8)), int(cfg.get("rampup_epochs", 12))
+    if epoch < start:
+        return 0.0
+    if ramp <= 0:
+        return 1.0
+    t = max(0.0, min(1.0, (epoch - start + 1) / float(ramp)))
+    return float(t * t * (3.0 - 2.0 * t))
+
+
+def build_target_teacher_from_heads(logits_by_head, target_shape, config, erva_id=2):
+    cfg = get_training_v2_config(config).get("target_distillation", {})
+    if not bool(cfg.get("enabled", False)) or str(cfg.get("mode", "cross_head")) != "cross_head":
+        return None
+    if any(k not in logits_by_head for k in ("semantic", "vegetation", "cana")):
+        return None
+    def rs(x):
+        return F.interpolate(x, size=target_shape, mode="bilinear", align_corners=False) if x.shape[-2:] != target_shape else x
+    sem, veg, cana = map(rs, (logits_by_head["semantic"], logits_by_head["vegetation"], logits_by_head["cana"]))
+    ps, pv, pc = torch.softmax(sem, 1), torch.softmax(veg, 1), torch.softmax(cana, 1)
+    not_cana = (1.0 - pc[:, 1]).clamp(0, 1)
+    teacher = (
+        float(cfg.get("w_sem_erva", 0.45)) * ps[:, int(erva_id)] +
+        float(cfg.get("w_veg_not_cana", 0.35)) * pv[:, 1] * not_cana +
+        float(cfg.get("w_veg_suppressed", 0.20)) * pv[:, 1] * not_cana.pow(float(cfg.get("cana_suppression_power", 1.5)))
+    ).clamp(0, 1)
+    if bool(cfg.get("detach_teacher", True)):
+        teacher = teacher.detach()
+    return teacher
+
+
+def compute_losses_for_batch(logits_by_head, masks_by_head, heads_config, criterions,
+                             dice_weight=0.30, config=None, epoch=None):
+    cfg = config or {}
+    tv2 = get_training_v2_config(cfg)
+    lcfg = tv2.get("loss", {})
+    total, parts = None, {}
+    resized_logits = {}
+
+    for head_name, logits in logits_by_head.items():
+        target = masks_by_head[head_name]
+        hcfg = heads_config[head_name]
+        ncls, ignore = int(hcfg["num_classes"]), int(hcfg.get("ignore_index", 255))
+        hw = float(hcfg.get("loss_weight_norm", 1.0))
+        if logits.shape[-2:] != target.shape[-2:]:
+            logits = F.interpolate(logits, size=target.shape[-2:], mode="bilinear", align_corners=False)
+        resized_logits[head_name] = logits
+        ce = criterions[head_name](logits, target)
+        dl = dice_loss(logits, target, ncls, ignore, float(lcfg.get("dice_smooth", 1.0)), str(lcfg.get("dice_reduction", "per_image")))
+        head_loss = (1.0 - dice_weight) * ce + dice_weight * dl
+
+        bw = float(lcfg.get("boundary_weight", 0.0))
+        if bw > 0:
+            boundary = _boundary_mask(target, ignore)
+            if bool(boundary.any()):
+                cem = F.cross_entropy(logits, target, weight=criterions[head_name].weight, ignore_index=ignore, reduction="none")
+                bl = cem[boundary].mean()
+                head_loss = head_loss + bw * bl
+                parts[f"{head_name}_boundary"] = bl.detach()
+
+        ohem_ratio = float(lcfg.get("ohem_ratio", 0.0))
+        if 0 < ohem_ratio < 1:
+            cem = F.cross_entropy(logits, target, weight=criterions[head_name].weight, ignore_index=ignore, reduction="none")
+            valid = target != ignore
+            vals = cem[valid]
+            if vals.numel() > 0:
+                k = max(1, int(vals.numel() * ohem_ratio))
+                ohem = torch.topk(vals, k=k, largest=True).values.mean()
+                head_loss = 0.8 * head_loss + 0.2 * ohem
+                parts[f"{head_name}_ohem"] = ohem.detach()
+
+        if head_name == "target":
+            ramp = get_target_distill_ramp_factor(cfg, epoch)
+            dcfg = tv2.get("target_distillation", {})
+            if ramp > 0:
+                teacher = build_target_teacher_from_heads(logits_by_head, target.shape[-2:], cfg,
+                    erva_id=int(cfg.get("heads", {}).get("semantic", {}).get("classes", {}).get("erva", 2)))
+                if teacher is not None:
+                    binary_logits = logits[:, 1] - logits[:, 0]
+                    valid = target != ignore
+                    conf = torch.maximum(teacher, 1.0 - teacher)
+                    valid &= conf >= float(dcfg.get("teacher_confidence_min", 0.60))
+                    if bool(valid.any()):
+                        distill = F.binary_cross_entropy_with_logits(binary_logits[valid], teacher[valid])
+                        hard_w = float(dcfg.get("hard_weight", 0.75))
+                        dist_w = float(dcfg.get("distill_weight", 0.25)) * ramp
+                        denom = max(1e-6, hard_w + dist_w)
+                        head_loss = (hard_w / denom) * head_loss + (dist_w / denom) * distill
+                        parts["target_distill"] = distill.detach()
+                        parts["target_distill_ramp"] = logits.new_tensor(ramp).detach()
+
+        weighted = hw * head_loss
+        total = weighted if total is None else total + weighted
+        parts[f"{head_name}_loss"] = head_loss.detach()
+        parts[f"{head_name}_ce"] = ce.detach()
+        parts[f"{head_name}_dice"] = dl.detach()
+
+    # Domain safety loss: target em cana custa mais que target em chão.
+    scfg = lcfg.get("safety", {}) or {}
+    if bool(scfg.get("enabled", True)) and "target" in resized_logits:
+        logits = resized_logits["target"]
+        binary = logits[:, 1] - logits[:, 0]
+        cana_gt = masks_by_head.get("cana")
+        sem_gt = masks_by_head.get("semantic")
+        safety_terms = []
+        if cana_gt is not None:
+            valid_cana = cana_gt == 1
+            if bool(valid_cana.any()):
+                lc = F.binary_cross_entropy_with_logits(binary[valid_cana], torch.zeros_like(binary[valid_cana]))
+                safety_terms.append(float(scfg.get("cana_weight", 1.0)) * lc)
+                parts["safety_cana"] = lc.detach()
+        if sem_gt is not None:
+            chao_id = int(heads_config.get("semantic", {}).get("classes", {}).get("chao", 0))
+            valid_ground = sem_gt == chao_id
+            if bool(valid_ground.any()):
+                lg = F.binary_cross_entropy_with_logits(binary[valid_ground], torch.zeros_like(binary[valid_ground]))
+                safety_terms.append(float(scfg.get("ground_weight", 0.20)) * lg)
+                parts["safety_ground"] = lg.detach()
+        if safety_terms:
+            safety = sum(safety_terms) / max(1.0, sum([float(scfg.get("cana_weight", 1.0)), float(scfg.get("ground_weight", 0.20))]))
+            total = total + float(scfg.get("weight", 0.08)) * safety
+            parts["safety_loss"] = safety.detach()
+
+    return total, parts
+
+
+def resize_logits_to_target_if_needed(logits, target):
+    return F.interpolate(logits, size=target.shape[-2:], mode="bilinear", align_corners=False) if logits.shape[-2:] != target.shape[-2:] else logits
+
+
+class AgriculturalMetricsMeter:
+    def __init__(
+        self,
+        thresholds: List[float],
+        ece_bins: int,
+        device: torch.device,
+        rich: bool = True,
+        scenario_metrics: bool = True,
+        group_metrics: bool = True,
+        operational_cfg: Optional[dict] = None,
+    ):
+        thresholds = [float(t) for t in thresholds]
+        if 0.5 not in thresholds:
+            thresholds.append(0.5)
+        self.thresholds = sorted(set(thresholds))
+        self.ece_bins = max(2, int(ece_bins))
+        self.device = device
+        self.rich = bool(rich)
+        self.scenario_metrics = bool(scenario_metrics) and self.rich
+        self.group_metrics = bool(group_metrics) and self.rich
+        self.operational_cfg = operational_cfg or {}
+
+        self.cana_total = self.cana_sprayed = 0
+        self.ground_total = self.ground_sprayed = 0
+        self.target_total = self.target_missed = 0
+        self.valid_total = self.predicted_target_total = 0
+
+        self.macro_target_iou_sum = 0.0
+        self.macro_target_iou_count = 0
+
+        # Estatísticas por threshold. Cana/chão usam os mesmos GTs globais.
+        self.threshold_counts = {
+            t: {
+                "tp": 0, "fp": 0, "fn": 0, "tn": 0,
+                "cana_sprayed": 0,
+                "ground_sprayed": 0,
+                "predicted_target": 0,
+            }
+            for t in self.thresholds
+        }
+
+        # Calibração. Acumuladores em float64; entradas são convertidas para
+        # float32 antes de somar, evitando overflow FP16 com --amp_val.
+        self.ece_count = np.zeros(self.ece_bins, np.float64)
+        self.ece_conf = np.zeros(self.ece_bins, np.float64)
+        self.ece_acc = np.zeros(self.ece_bins, np.float64)
+        self.brier_sum = 0.0
+        self.nll_sum = 0.0
+        self.calibration_count = 0
+
+        self.scenario = {
+            k: torch.zeros((2, 2), dtype=torch.int64, device=device)
+            for k in ["none", "tiny", "small", "medium", "large"]
+        }
+        self.group_stats: Dict[str, dict] = {}
+
+        # Métricas por frame, úteis porque uma única mancha errada de target
+        # sobre cana já pode ser operacionalmente relevante.
+        self.frames_total = 0
+        self.frames_with_target = 0
+        self.frames_target_detected = 0
+        self.frames_target_completely_missed = 0
+        self.frames_with_cana = 0
+        self.frames_cana_clean = 0
+        self.frames_with_ground = 0
+        self.frames_ground_clean = 0
+
+        # Consistência entre target direta e target composta
+        # vegetation==1 AND cana==0.
+        self.composed_valid = 0
+        self.composed_disagree = 0
+        self.composed_intersection = 0
+        self.composed_union = 0
+
+    @staticmethod
+    def _empty_group_stats() -> dict:
+        return {
+            "frames": 0,
+            "tp": 0, "fp": 0, "fn": 0, "tn": 0,
+            "cana_total": 0, "cana_sprayed": 0,
+            "ground_total": 0, "ground_sprayed": 0,
+            "target_total": 0, "target_missed": 0,
+        }
+
+    @staticmethod
+    def _binary_metrics(tp: int, fp: int, fn: int, tn: int = 0) -> dict:
+        eps = 1e-9
+        precision = tp / (tp + fp + eps)
+        recall = tp / (tp + fn + eps)
+        f1 = 2 * precision * recall / (precision + recall + eps)
+        iou = tp / (tp + fp + fn + eps)
+        specificity = tn / (tn + fp + eps)
+        return {
+            "precision": precision,
+            "recall": recall,
+            "f1": f1,
+            "iou": iou,
+            "specificity": specificity,
+            "tp": int(tp), "fp": int(fp), "fn": int(fn), "tn": int(tn),
+        }
+
+    @torch.no_grad()
+    def update(self, target_logits, masks, heads_config, meta: Optional[dict] = None, composed_pred: Optional[torch.Tensor] = None):
+        gt = masks.get("target")
+        if gt is None:
+            return
+
+        ignore = int(heads_config["target"].get("ignore_index", 255))
+        logits = resize_logits_to_target_if_needed(target_logits, gt).float()
+        prob = torch.softmax(logits, dim=1)[:, 1].float()
+        pred = (prob >= 0.5).long()
+        valid = gt != ignore
+
+        cana = masks.get("cana")
+        sem = masks.get("semantic")
+        chao_id = int(heads_config.get("semantic", {}).get("classes", {}).get("chao", 0))
+
+        cana_mask = ((cana == 1) & valid) if cana is not None else torch.zeros_like(valid)
+        ground_mask = ((sem == chao_id) & valid) if sem is not None else torch.zeros_like(valid)
+        target_mask = (gt == 1) & valid
+
+        self.cana_total += int(cana_mask.sum())
+        self.cana_sprayed += int(((pred == 1) & cana_mask).sum())
+        self.ground_total += int(ground_mask.sum())
+        self.ground_sprayed += int(((pred == 1) & ground_mask).sum())
+        self.target_total += int(target_mask.sum())
+        self.target_missed += int(((pred == 0) & target_mask).sum())
+        self.valid_total += int(valid.sum())
+        self.predicted_target_total += int(((pred == 1) & valid).sum())
+
+        if composed_pred is not None:
+            cp = composed_pred
+            if cp.shape[-2:] != gt.shape[-2:]:
+                cp = F.interpolate(cp.unsqueeze(1).float(), size=gt.shape[-2:], mode="nearest").squeeze(1).long()
+            dv = valid
+            direct = pred[dv]
+            comp = cp[dv]
+            self.composed_valid += int(direct.numel())
+            self.composed_disagree += int((direct != comp).sum())
+            self.composed_intersection += int(((direct == 1) & (comp == 1)).sum())
+            self.composed_union += int(((direct == 1) | (comp == 1)).sum())
+
+        groups = []
+        if isinstance(meta, dict):
+            groups = list(meta.get("group", []) or [])
+
+        # Métricas frame-a-frame, cenários e grupos.
+        for b in range(gt.shape[0]):
+            vb = valid[b]
+            if not bool(vb.any()):
+                continue
+            gb = gt[b][vb]
+            pb = pred[b][vb]
+            tp = int(((gb == 1) & (pb == 1)).sum())
+            fp = int(((gb == 0) & (pb == 1)).sum())
+            fn = int(((gb == 1) & (pb == 0)).sum())
+            tn = int(((gb == 0) & (pb == 0)).sum())
+
+            self.frames_total += 1
+            has_target = bool((gb == 1).any())
+            if has_target:
+                self.frames_with_target += 1
+                if tp > 0:
+                    self.frames_target_detected += 1
+                else:
+                    self.frames_target_completely_missed += 1
+
+            if cana is not None:
+                cb = cana_mask[b]
+                if bool(cb.any()):
+                    self.frames_with_cana += 1
+                    if not bool(((pred[b] == 1) & cb).any()):
+                        self.frames_cana_clean += 1
+
+            if sem is not None:
+                grb = ground_mask[b]
+                if bool(grb.any()):
+                    self.frames_with_ground += 1
+                    if not bool(((pred[b] == 1) & grb).any()):
+                        self.frames_ground_clean += 1
+
+            denom = tp + fp + fn
+            if denom > 0:
+                self.macro_target_iou_sum += tp / denom
+                self.macro_target_iou_count += 1
+
+            if self.scenario_metrics:
+                pct = float((gb == 1).float().mean())
+                if pct == 0:
+                    bucket = "none"
+                elif pct <= 0.005:
+                    bucket = "tiny"
+                elif pct <= 0.02:
+                    bucket = "small"
+                elif pct <= 0.10:
+                    bucket = "medium"
+                else:
+                    bucket = "large"
+                update_confusion_matrix(self.scenario[bucket], pb, gb, 2, ignore_index=255)
+
+            if self.group_metrics:
+                group = str(groups[b] if b < len(groups) else "unknown")
+                gs = self.group_stats.setdefault(group, self._empty_group_stats())
+                gs["frames"] += 1
+                gs["tp"] += tp; gs["fp"] += fp; gs["fn"] += fn; gs["tn"] += tn
+                if cana is not None:
+                    cb = cana_mask[b]
+                    gs["cana_total"] += int(cb.sum())
+                    gs["cana_sprayed"] += int(((pred[b] == 1) & cb).sum())
+                if sem is not None:
+                    grb = ground_mask[b]
+                    gs["ground_total"] += int(grb.sum())
+                    gs["ground_sprayed"] += int(((pred[b] == 1) & grb).sum())
+                tb = target_mask[b]
+                gs["target_total"] += int(tb.sum())
+                gs["target_missed"] += int(((pred[b] == 0) & tb).sum())
+
+        if not self.rich:
+            return
+
+        pv = prob[valid].float()
+        gv = gt[valid].long()
+        cv = cana_mask[valid]
+        grv = ground_mask[valid]
+
+        for t in self.thresholds:
+            pp = pv >= float(t)
+            st = self.threshold_counts[t]
+            st["tp"] += int(((pp == 1) & (gv == 1)).sum())
+            st["fp"] += int(((pp == 1) & (gv == 0)).sum())
+            st["fn"] += int(((pp == 0) & (gv == 1)).sum())
+            st["tn"] += int(((pp == 0) & (gv == 0)).sum())
+            st["predicted_target"] += int(pp.sum())
+            if cana is not None:
+                st["cana_sprayed"] += int((pp & cv).sum())
+            if sem is not None:
+                st["ground_sprayed"] += int((pp & grv).sum())
+
+        # ECE em FP32, evitando overflow de soma em FP16 durante amp_val.
+        conf = torch.maximum(pv, 1.0 - pv).float()
+        correct = ((pv >= 0.5).long() == gv).float()
+        bins = torch.clamp((conf * self.ece_bins).long(), 0, self.ece_bins - 1)
+        for i in range(self.ece_bins):
+            m = bins == i
+            if bool(m.any()):
+                n = int(m.sum())
+                self.ece_count[i] += n
+                self.ece_conf[i] += float(conf[m].float().sum().cpu())
+                self.ece_acc[i] += float(correct[m].float().sum().cpu())
+
+        gvf = gv.float()
+        self.brier_sum += float(torch.sum((pv - gvf) ** 2).cpu())
+        pclip = torch.clamp(pv, 1e-6, 1.0 - 1e-6)
+        self.nll_sum += float(torch.sum(-(gvf * torch.log(pclip) + (1.0 - gvf) * torch.log(1.0 - pclip))).cpu())
+        self.calibration_count += int(pv.numel())
+
+    def _operational_score(self, row: dict) -> float:
+        cfg = self.operational_cfg or {}
+        w = cfg.get("score_weights", {}) or {}
+        vals = {
+            "target_iou": float(row.get("iou", 0.0)),
+            "target_f1": float(row.get("f1", 0.0)),
+            "cana_safety": float(row.get("cana_safety", 0.0)),
+            "ground_safety": 1.0 - float(row.get("ground_spray_rate", 1.0)),
+            "weed_recall": 1.0 - float(row.get("weed_miss_rate", 1.0)),
+        }
+        defaults = {"target_iou":0.35, "target_f1":0.20, "cana_safety":0.25, "ground_safety":0.10, "weed_recall":0.10}
+        total_w = 0.0
+        score = 0.0
+        for k, dv in defaults.items():
+            wk = float(w.get(k, dv))
+            total_w += wk
+            score += wk * vals[k]
+        return score / max(total_w, 1e-9)
+
+    def compute(self):
+        eps = 1e-9
+        out = {
+            "cana_spray_rate": self.cana_sprayed / max(1, self.cana_total),
+            "cana_safety": 1.0 - self.cana_sprayed / max(1, self.cana_total),
+            "ground_spray_rate": self.ground_sprayed / max(1, self.ground_total),
+            "ground_safety": 1.0 - self.ground_sprayed / max(1, self.ground_total),
+            "weed_miss_rate": self.target_missed / max(1, self.target_total),
+            "weed_recall": 1.0 - self.target_missed / max(1, self.target_total),
+            "predicted_target_rate": self.predicted_target_total / max(1, self.valid_total),
+            "macro_target_iou": self.macro_target_iou_sum / max(1, self.macro_target_iou_count),
+            "macro_target_iou_positive_union": self.macro_target_iou_sum / max(1, self.macro_target_iou_count),
+            "frame_target_detection_rate": self.frames_target_detected / max(1, self.frames_with_target),
+            "frame_target_complete_miss_rate": self.frames_target_completely_missed / max(1, self.frames_with_target),
+            "frame_cana_clean_rate": self.frames_cana_clean / max(1, self.frames_with_cana),
+            "frame_ground_clean_rate": self.frames_ground_clean / max(1, self.frames_with_ground),
+            "target_composed_disagreement_rate": self.composed_disagree / max(1, self.composed_valid),
+            "target_composed_agreement": 1.0 - self.composed_disagree / max(1, self.composed_valid),
+            "target_composed_iou": self.composed_intersection / max(1, self.composed_union),
+            "counts": {
+                "cana_total": self.cana_total, "cana_sprayed": self.cana_sprayed,
+                "ground_total": self.ground_total, "ground_sprayed": self.ground_sprayed,
+                "target_total": self.target_total, "target_missed": self.target_missed,
+                "valid_total": self.valid_total, "predicted_target_total": self.predicted_target_total,
+                "frames_total": self.frames_total, "frames_with_target": self.frames_with_target,
+                "frames_with_cana": self.frames_with_cana, "frames_with_ground": self.frames_with_ground,
+            },
+        }
+
+        sweep = {}
+        best_f1 = {"threshold": 0.5, "f1": -1.0, "iou": -1.0}
+
+        # Contrato:
+        # best_operational_threshold só existe quando há um threshold SAFE.
+        best_operational = {
+            "threshold": None,
+            "operational_score": -1.0,
+            "constraints_met": False,
+        }
+        feasible_rows = []
+
+        if self.rich:
+            max_cana = float(self.operational_cfg.get("max_cana_spray_rate", 0.02))
+            max_ground = float(self.operational_cfg.get("max_ground_spray_rate", 0.03))
+            max_miss = float(self.operational_cfg.get("max_weed_miss_rate", 0.20))
+
+            for t, st in self.threshold_counts.items():
+                bm = self._binary_metrics(st["tp"], st["fp"], st["fn"], st["tn"])
+                cana_spray = st["cana_sprayed"] / max(1, self.cana_total)
+                ground_spray = st["ground_sprayed"] / max(1, self.ground_total)
+                weed_miss = st["fn"] / max(1, self.target_total)
+                row = {
+                    **bm,
+                    "threshold": float(t),
+                    "cana_spray_rate": cana_spray,
+                    "cana_safety": 1.0 - cana_spray,
+                    "ground_spray_rate": ground_spray,
+                    "ground_safety": 1.0 - ground_spray,
+                    "weed_miss_rate": weed_miss,
+                    "weed_recall": 1.0 - weed_miss,
+                    "predicted_target_rate": st["predicted_target"] / max(1, self.valid_total),
+                }
+                has_required_domains = (
+                    self.cana_total > 0
+                    and self.ground_total > 0
+                    and self.target_total > 0
+                )
+                row["constraints_met"] = bool(
+                    has_required_domains and
+                    cana_spray <= max_cana and ground_spray <= max_ground and weed_miss <= max_miss
+                )
+                row["operational_score"] = self._operational_score(row)
+                sweep[str(t)] = row
+                if row["constraints_met"]:
+                    feasible_rows.append(row)
+                if row["f1"] > best_f1["f1"]:
+                    best_f1 = {"threshold": float(t), "f1": row["f1"], "iou": row["iou"]}
+
+            if feasible_rows:
+                chosen = max(
+                    feasible_rows,
+                    key=lambda r: r["operational_score"],
+                )
+                best_operational = dict(chosen)
+                best_operational["constraints_met"] = True
+
+        out["threshold_sweep"] = sweep
+        out["best_threshold"] = best_f1
+        out["best_operational_threshold"] = best_operational
+
+        total = self.ece_count.sum()
+        ece = 0.0
+        if self.rich and total > 0:
+            for n, cs, acs in zip(self.ece_count, self.ece_conf, self.ece_acc):
+                if n > 0:
+                    ece += (n / total) * abs((acs / n) - (cs / n))
+        out["target_ece"] = float(ece) if self.rich else None
+        out["target_brier"] = (self.brier_sum / max(1, self.calibration_count)) if self.rich else None
+        out["target_nll"] = (self.nll_sum / max(1, self.calibration_count)) if self.rich else None
+
+        out["scenario_target"] = {}
+        if self.scenario_metrics:
+            for name, cm in self.scenario.items():
+                miou, ious = compute_iou_from_cm(cm)
+                bs = binary_stats_from_cm(cm)
+                out["scenario_target"][name] = {
+                    "miou": miou,
+                    "iou_target": ious[1],
+                    **bs,
+                    "cm": cm.detach().cpu().tolist(),
+                }
+
+        out["group_target"] = {}
+        if self.group_metrics:
+            for group, gs in sorted(self.group_stats.items()):
+                bm = self._binary_metrics(gs["tp"], gs["fp"], gs["fn"], gs["tn"])
+                out["group_target"][group] = {
+                    **bm,
+                    "frames": int(gs["frames"]),
+                    "cana_spray_rate": gs["cana_sprayed"] / max(1, gs["cana_total"]),
+                    "cana_safety": 1.0 - gs["cana_sprayed"] / max(1, gs["cana_total"]),
+                    "ground_spray_rate": gs["ground_sprayed"] / max(1, gs["ground_total"]),
+                    "weed_miss_rate": gs["target_missed"] / max(1, gs["target_total"]),
+                    "counts": dict(gs),
+                }
+        return out
+
+
+@torch.no_grad()
+def update_operational_target_cm(cm_target, pred_veg, pred_cana, gt_veg, gt_cana, ignore_index=255):
+    valid = (gt_veg != ignore_index) & (gt_cana != ignore_index)
+    gt = ((gt_veg == 1) & (gt_cana == 0)).long()[valid]
+    pred = ((pred_veg == 1) & (pred_cana == 0)).long()[valid]
+    if gt.numel():
+        cm_target += torch.bincount(gt.reshape(-1) * 2 + pred.reshape(-1), minlength=4).view(2, 2)
+
+
+# ============================================================
+# Train / Val
+# ============================================================
+
+def run_one_epoch(model, loader, optimizer, device, heads_config, criterions, amp, scaler, train,
+                  grad_accum=1, normalizer=None, dice_weight=0.30, config=None, epoch=None,
+                  scheduler=None, grad_clip_norm=1.0, global_step=0):
+    model.train(train)
+    total_loss, n_batches, loss_sums = 0.0, 0, {}
+    cms = {name: torch.zeros((int(h["num_classes"]), int(h["num_classes"])), dtype=torch.int64, device=device) for name, h in heads_config.items()}
+    cm_op = torch.zeros((2, 2), dtype=torch.int64, device=device)
+    tv2 = get_training_v2_config(config or {})
+    mm = tv2.get("metrics", {})
+    rich_metrics = (not train) or bool(mm.get("rich_train_metrics", False))
+    agri = AgriculturalMetricsMeter(
+        mm.get("target_thresholds", [0.3,0.4,0.5,0.6,0.7]),
+        mm.get("ece_bins", 12),
+        device,
+        rich=rich_metrics,
+        scenario_metrics=bool(mm.get("scenario_metrics", True)),
+        group_metrics=bool(mm.get("group_metrics", True)),
+        operational_cfg=mm.get("operational_threshold", {}) or {},
+    )
+    grad_norm_sum = grad_norm_max = 0.0; grad_norm_count = 0; optimizer_steps = 0; skipped_steps = 0
+    t0 = time.time()
+
+    if train and optimizer is not None:
+        optimizer.zero_grad(set_to_none=True)
+
+    with torch.set_grad_enabled(train):
+        total_loader_steps = len(loader)
+        for step, (imgs, masks, _meta) in enumerate(loader):
+            imgs = imgs.to(device, non_blocking=True)
+            masks = {k: v.to(device, non_blocking=True) for k, v in masks.items()}
+            imgs = normalizer(imgs) if normalizer is not None else normalize_per_batch(imgs)
+
+            window_start = (step // max(1, grad_accum)) * max(1, grad_accum)
+            window_end = min(window_start + max(1, grad_accum), total_loader_steps)
+            accum_divisor = max(1, window_end - window_start)
+
+            with autocast(device_type="cuda", enabled=amp and device.type == "cuda"):
+                logits_by_head = model(pixel_values=imgs)
+                loss_raw, loss_parts = compute_losses_for_batch(
+                    logits_by_head, masks, heads_config, criterions,
+                    dice_weight=dice_weight, config=config, epoch=epoch,
+                )
+                loss_backward = loss_raw / accum_divisor if train else loss_raw
+
+            if train and optimizer is not None:
+                if amp and scaler is not None and device.type == "cuda":
+                    scaler.scale(loss_backward).backward()
+                else:
+                    loss_backward.backward()
+
+                boundary = ((step + 1) % max(1, grad_accum) == 0) or ((step + 1) == total_loader_steps)
+                if boundary:
+                    if amp and scaler is not None and device.type == "cuda":
+                        scaler.unscale_(optimizer)
+                    gn = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=float(grad_clip_norm)) if grad_clip_norm and grad_clip_norm > 0 else torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=float("inf"))
+                    gn_val = float(gn.detach().cpu()) if torch.is_tensor(gn) else float(gn)
+                    grad_norm_sum += gn_val; grad_norm_max = max(grad_norm_max, gn_val); grad_norm_count += 1
+
+                    did_step = True
+                    if amp and scaler is not None and device.type == "cuda":
+                        before = float(scaler.get_scale())
+                        scaler.step(optimizer); scaler.update()
+                        after = float(scaler.get_scale())
+                        did_step = after >= before
+                    else:
+                        optimizer.step()
+
+                    optimizer.zero_grad(set_to_none=True)
+                    if did_step:
+                        optimizer_steps += 1; global_step += 1
+                        if scheduler is not None:
+                            scheduler.step()
+                    else:
+                        skipped_steps += 1
+
+            total_loss += float(loss_raw.detach().item()); n_batches += 1
+            for k, v in loss_parts.items():
+                loss_sums[k] = loss_sums.get(k, 0.0) + float(v.item())
+
+            with torch.no_grad():
+                preds = {}
+                for head_name, logits in logits_by_head.items():
+                    if head_name not in masks:
+                        continue
+                    lm = resize_logits_to_target_if_needed(logits, masks[head_name])
+                    pr = torch.argmax(lm, dim=1); preds[head_name] = pr
+                    update_confusion_matrix(cms[head_name], pr, masks[head_name], int(heads_config[head_name]["num_classes"]), int(heads_config[head_name].get("ignore_index", 255)))
+                composed_pred = None
+                if "vegetation" in preds and "cana" in preds:
+                    update_operational_target_cm(cm_op, preds["vegetation"], preds["cana"], masks["vegetation"], masks["cana"], int(heads_config["vegetation"].get("ignore_index", 255)))
+                    composed_pred = ((preds["vegetation"] == 1) & (preds["cana"] == 0)).long()
+                if "target" in logits_by_head:
+                    agri.update(logits_by_head["target"], masks, heads_config, meta=_meta, composed_pred=composed_pred)
+
+    metrics = {
+        "loss": total_loss / max(1, n_batches), "time_s": time.time() - t0,
+        "heads": {}, "loss_parts": {k: v/max(1,n_batches) for k,v in loss_sums.items()},
+        "optimization": {"optimizer_steps": optimizer_steps, "skipped_steps": skipped_steps, "global_step": global_step,
+                         "grad_norm_mean": grad_norm_sum/max(1,grad_norm_count), "grad_norm_max": grad_norm_max},
+    }
+    for name, cm in cms.items():
+        miou, ious = compute_iou_from_cm(cm); acc = compute_pixel_acc_from_cm(cm)
+        hm = {"miou": miou, "iou_per_class": ious, "acc": acc, "cm": cm.detach().cpu().tolist()}
+        if cm.shape == (2,2):
+            hm.update(binary_stats_from_cm(cm))
+        metrics["heads"][name] = hm
+    op_miou, op_iou = compute_iou_from_cm(cm_op)
+    metrics["operational_target"] = {"miou": op_miou, "iou_background": op_iou[0], "iou_target": op_iou[1], "acc": compute_pixel_acc_from_cm(cm_op), "cm": cm_op.detach().cpu().tolist(), **binary_stats_from_cm(cm_op)}
+    metrics["agricultural"] = agri.compute()
+
+    # Confusões semanticamente importantes para campo: cana confundida com
+    # erva é muito mais crítica que uma simples queda de mIoU.
+    if "semantic" in cms:
+        scm = cms["semantic"].detach().float().cpu()
+        classes = heads_config.get("semantic", {}).get("classes", {}) or {}
+        cid = int(classes.get("cana", 1)); eid = int(classes.get("erva", 2)); gid = int(classes.get("chao", 0))
+        def _row_rate(src, dst):
+            if src < 0 or dst < 0 or src >= scm.shape[0] or dst >= scm.shape[1]:
+                return 0.0
+            den = float(scm[src, :].sum())
+            return float(scm[src, dst]) / max(den, 1e-9)
+        metrics["agricultural"].update({
+            "semantic_cana_as_erva_rate": _row_rate(cid, eid),
+            "semantic_erva_as_cana_rate": _row_rate(eid, cid),
+            "semantic_cana_as_ground_rate": _row_rate(cid, gid),
+            "semantic_erva_as_ground_rate": _row_rate(eid, gid),
+        })
+    return metrics
+
+
+# ============================================================
+# Selection / checkpoints / logs
+# ============================================================
+
+def compute_selection_score(va: dict, config: Optional[dict] = None) -> dict:
+    heads, ag = va.get("heads", {}), va.get("agricultural", {})
+    target_vals = heads.get("target", {}).get("iou_per_class", []) or []
+    cana_vals = heads.get("cana", {}).get("iou_per_class", []) or []
+    target_iou = safe_float(target_vals[1] if len(target_vals) > 1 else va.get("operational_target", {}).get("iou_target"), 0.0)
+    cana_iou = safe_float(cana_vals[1] if len(cana_vals) > 1 else 0.0, 0.0)
+    target_f1 = safe_float(heads.get("target", {}).get("f1"), 0.0)
+    veg_miou = safe_float(heads.get("vegetation", {}).get("miou"), 0.0)
+    sem_miou = safe_float(heads.get("semantic", {}).get("miou"), 0.0)
+    cana_safety = safe_float(ag.get("cana_safety"), 0.0)
+
+    weights = get_training_v2_config(config or {}).get("selection_score", {})
+    score = (
+        float(weights.get("target_iou", 0.40)) * target_iou +
+        float(weights.get("cana_iou", 0.20)) * cana_iou +
+        float(weights.get("target_f1", 0.10)) * target_f1 +
+        float(weights.get("vegetation_miou", 0.10)) * veg_miou +
+        float(weights.get("semantic_miou", 0.05)) * sem_miou +
+        float(weights.get("cana_safety", 0.15)) * cana_safety
+    )
+
+    # Régua histórica exata do trainer anterior. Isso NÃO torna datasets,
+    # resolução ou preprocessamento diferentes diretamente comparáveis, mas
+    # permite olhar o mesmo score quando fizermos um A/B controlado.
+    legacy_score = (
+        0.50 * target_iou +
+        0.25 * cana_iou +
+        0.15 * veg_miou +
+        0.10 * sem_miou
+    )
+
+    best_op = ag.get("best_operational_threshold", {}) or {}
+    operational_score = safe_float(best_op.get("operational_score"), -1.0)
+
+    return {
+        "score": float(score),
+        "legacy_score": float(legacy_score),
+        "operational_score": float(operational_score),
+        "operational_threshold": best_op.get("threshold"),
+        "operational_constraints_met": bool(best_op.get("constraints_met", False)),
+        "target_iou": target_iou,
+        "target_head_iou": target_iou,
+        "operational_target_iou": safe_float(va.get("operational_target", {}).get("iou_target"), 0.0),
+        "cana_head_iou": cana_iou,
+        "target_f1": target_f1,
+        "vegetation_miou": veg_miou,
+        "semantic_miou": sem_miou,
+        "cana_safety": cana_safety,
+        "cana_spray_rate": safe_float(ag.get("cana_spray_rate"), 1.0),
+    }
+
+
+def atomic_torch_save(obj, path: Path):
+    ensure_dir(path.parent)
+    tmp = path.with_suffix(path.suffix + ".tmp")
+    torch.save(obj, tmp)
+    os.replace(tmp, path)
+
+
+def save_checkpoint(path, model, optimizer=None, scaler=None, epoch=0, best=None, extra=None,
+                    scheduler=None, epochs_without_improve=0, global_step=0):
+    cfg = getattr(model, "config", None)
+    ckpt = {
+        "epoch": int(epoch), "model": model.state_dict(), "best": best or {},
+        "trainer_version": TRAINER_VERSION,
+        "model_contract": {
+            "num_channels": int(getattr(cfg, "num_channels", 0) or 0),
+            "input_channel_names": list(getattr(cfg, "input_channel_names", []) or []),
+            "decoder_mode": str(getattr(cfg, "agri_decoder_mode", "separate")),
+            "spectral_input_init": str(getattr(cfg, "agri_spectral_input_init", "mean_rgb")),
+        },
+        "epochs_without_improve": int(epochs_without_improve),
+        "global_step": int(global_step),
+        "rng_state": capture_rng_state(),
+    }
+    if optimizer is not None: ckpt["optimizer"] = optimizer.state_dict()
+    if scheduler is not None: ckpt["scheduler"] = scheduler.state_dict()
+    if scaler is not None: ckpt["scaler"] = scaler.state_dict()
+    if extra: ckpt["extra"] = extra
+    atomic_torch_save(ckpt, Path(path))
+
+
+def load_checkpoint(path, model, optimizer=None, scaler=None, map_location="cpu", scheduler=None, restore_rng=False):
+    ckpt = torch.load(path, map_location=map_location, weights_only=False)
+    saved = ckpt.get("model_contract", {}) or {}; cfg = getattr(model, "config", None)
+    cur_n = int(getattr(cfg, "num_channels", 0) or 0); cur_names = list(getattr(cfg, "input_channel_names", []) or [])
+    if int(saved.get("num_channels", 0) or 0) not in (0, cur_n):
+        raise RuntimeError(f"Checkpoint canais incompatíveis: saved={saved.get('num_channels')} atual={cur_n}")
+    if saved.get("input_channel_names") and cur_names and list(saved["input_channel_names"]) != cur_names:
+        raise RuntimeError(f"Checkpoint canais nominais incompatíveis: saved={saved['input_channel_names']} atual={cur_names}")
+    saved_decoder = str(saved.get("decoder_mode", ""))
+    cur_decoder = str(getattr(cfg, "agri_decoder_mode", ""))
+    if saved_decoder and cur_decoder and saved_decoder != cur_decoder:
+        raise RuntimeError(f"Checkpoint decoder_mode={saved_decoder}, modelo atual={cur_decoder}")
+    model.load_state_dict(ckpt["model"], strict=True)
+    if optimizer is not None and "optimizer" in ckpt:
+        optimizer.load_state_dict(ckpt["optimizer"])
+    if scheduler is not None and "scheduler" in ckpt:
+        scheduler.load_state_dict(ckpt["scheduler"])
+    if scaler is not None and "scaler" in ckpt:
+        scaler.load_state_dict(ckpt["scaler"])
+    if restore_rng:
+        restore_rng_state(ckpt.get("rng_state"))
+    return ckpt
+
+
+def append_train_log(path: Path, row: dict):
+    ensure_dir(path.parent); exists = path.exists()
+    with path.open("a", newline="", encoding="utf-8") as f:
+        w = csv.DictWriter(f, fieldnames=list(row.keys()))
+        if not exists: w.writeheader()
+        w.writerow(row)
+
+
+def append_jsonl(path: Path, obj: dict):
+    ensure_dir(path.parent)
+    with path.open("a", encoding="utf-8") as f:
+        f.write(json.dumps(obj, ensure_ascii=False, default=str) + "\n")
+
+
+def flatten_epoch_log(epoch, lr, tr, va, best, score_now: Optional[dict] = None):
+    score_now = score_now or {}
+    row = {
+        "epoch": epoch, "lr": lr,
+        "train_loss": tr["loss"], "val_loss": va["loss"],
+        "score": score_now.get("score"),
+        "legacy_score": score_now.get("legacy_score"),
+        "operational_score": score_now.get("operational_score"),
+        "operational_threshold": score_now.get("operational_threshold"),
+        "operational_constraints_met": score_now.get("operational_constraints_met"),
+        "best_score": best.get("score", -1),
+        "best_legacy_score": best.get("legacy_score", -1),
+        "best_operational_score": best.get("operational_score", -1),
+        "best_target_iou": best.get("target_iou", -1),
+        "best_cana_head_iou": best.get("cana_head_iou", -1),
+        "best_semantic_miou": best.get("semantic_miou", -1),
+        "best_cana_safety": best.get("cana_safety", -1),
+    }
+    for prefix, obj in (("train", tr), ("val", va)):
+        for hn, hm in obj.get("heads", {}).items():
+            row[f"{prefix}_{hn}_miou"] = hm.get("miou")
+            row[f"{prefix}_{hn}_acc"] = hm.get("acc")
+            for k in ("precision", "recall", "f1", "specificity"):
+                if k in hm:
+                    row[f"{prefix}_{hn}_{k}"] = hm[k]
+            for i, v in enumerate(hm.get("iou_per_class", []) or []):
+                row[f"{prefix}_{hn}_iou_{i}"] = v
+
+        op = obj.get("operational_target", {}) or {}
+        row[f"{prefix}_op_target_iou"] = op.get("iou_target")
+        row[f"{prefix}_op_target_f1"] = op.get("f1")
+
+        ag = obj.get("agricultural", {}) or {}
+        for k in (
+            "cana_spray_rate", "cana_safety", "ground_spray_rate", "ground_safety",
+            "weed_miss_rate", "weed_recall", "macro_target_iou",
+            "frame_target_detection_rate", "frame_target_complete_miss_rate",
+            "frame_cana_clean_rate", "frame_ground_clean_rate",
+            "target_composed_disagreement_rate", "target_composed_agreement", "target_composed_iou",
+            "semantic_cana_as_erva_rate", "semantic_erva_as_cana_rate",
+            "semantic_cana_as_ground_rate", "semantic_erva_as_ground_rate",
+            "target_ece", "target_brier", "target_nll",
+        ):
+            row[f"{prefix}_{k}"] = ag.get(k)
+
+        bt = ag.get("best_threshold", {}) or {}
+        row[f"{prefix}_best_threshold"] = bt.get("threshold")
+        row[f"{prefix}_best_threshold_f1"] = bt.get("f1")
+        row[f"{prefix}_best_threshold_iou"] = bt.get("iou")
+
+        bop = ag.get("best_operational_threshold", {}) or {}
+        row[f"{prefix}_best_op_threshold"] = bop.get("threshold")
+        row[f"{prefix}_best_op_score"] = bop.get("operational_score")
+        row[f"{prefix}_best_op_constraints_met"] = bop.get("constraints_met")
+        row[f"{prefix}_best_op_spray_cana"] = bop.get("cana_spray_rate")
+        row[f"{prefix}_best_op_spray_ground"] = bop.get("ground_spray_rate")
+        row[f"{prefix}_best_op_weed_miss"] = bop.get("weed_miss_rate")
+
+        # Sweep completo em CSV para análise posterior sem precisar parsear JSONL.
+        for thr, tm in sorted((ag.get("threshold_sweep", {}) or {}).items(), key=lambda kv: float(kv[0])):
+            tag = str(thr).replace(".", "p")
+            for k in ("iou", "f1", "precision", "recall", "cana_spray_rate", "ground_spray_rate", "weed_miss_rate", "operational_score", "constraints_met"):
+                row[f"{prefix}_thr_{tag}_{k}"] = tm.get(k)
+
+        for group, gm in sorted((ag.get("group_target", {}) or {}).items()):
+            gtag = str(group).replace(" ", "_")
+            for k in ("iou", "f1", "precision", "recall", "cana_spray_rate", "ground_spray_rate", "weed_miss_rate"):
+                row[f"{prefix}_group_{gtag}_{k}"] = gm.get(k)
+
+        for scen, sm in sorted((ag.get("scenario_target", {}) or {}).items()):
+            for k in ("iou_target", "f1", "precision", "recall"):
+                row[f"{prefix}_scenario_{scen}_{k}"] = sm.get(k)
+
+        optm = obj.get("optimization", {}) or {}
+        for k in ("global_step", "optimizer_steps", "skipped_steps", "grad_norm_mean", "grad_norm_max"):
+            row[f"{prefix}_{k}"] = optm.get(k)
+        for k, v in obj.get("loss_parts", {}).items():
+            row[f"{prefix}_{k}"] = v
+    return row
+
+
+def pretty_iou(names: Dict[int, str], vals: List[float]):
+    return " | ".join(f"{names.get(i,i)}:{v:.3f}" for i,v in enumerate(vals))
+
+
+def binary_iou_text(head_name, vals):
+    names = {"vegetation": {0:"bg",1:"veg"}, "cana": {0:"not_cana",1:"cana"}, "target": {0:"bg",1:"target"}}.get(head_name,{0:"0",1:"1"})
+    return pretty_iou(names, vals)
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default="config.json")
+    parser.add_argument("--epochs", type=int, default=80)
+    parser.add_argument("--batch", type=int, default=1)
+    parser.add_argument("--lr", type=float, default=3e-5)
+    parser.add_argument("--wd", type=float, default=0.01)
+    parser.add_argument("--num_workers", type=int, default=2)
+    parser.add_argument("--amp", action="store_true")
+    parser.add_argument("--amp_val", action="store_true")
+    parser.add_argument("--grad_accum", type=int, default=4)
+    parser.add_argument("--grad_ckpt", action="store_true")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--ignore_index", type=int, default=None)
+    parser.add_argument("--class_weights", default="auto")
+    parser.add_argument("--dice_weight", type=float, default=0.30)
+    parser.add_argument("--norm_stats", default=None)
+    parser.add_argument("--allow-missing-norm-stats", dest="allow_missing_norm_stats", action="store_true")
+    parser.add_argument("--src-root", default="dataset/split")
+    parser.add_argument("--save-every", type=int, default=10)
+    parser.add_argument("--resume", action="store_true")
+    parser.add_argument("--resume-ckpt", default=None)
+    parser.add_argument("--early-stop", type=int, default=25)
+    # Legado + overrides V2
+    parser.add_argument("--balanced_sampler", action="store_true")
+    parser.add_argument("--samples_per_epoch", type=int, default=0)
+    parser.add_argument("--sampler", choices=["random","diverse","weighted"], default=None)
+    parser.add_argument("--decoder-mode", choices=["separate","shared_light"], default=None)
+    parser.add_argument("--spectral-init", choices=["mean_rgb","zero_extra","scaled_mean"], default=None)
+    parser.add_argument("--no-augment", action="store_true")
+    parser.add_argument("--grad-clip", type=float, default=None)
+    args = parser.parse_args()
+
+    set_seed(args.seed)
+    config = load_json(args.config)
+    tv2 = get_training_v2_config(config)
+    if args.decoder_mode: tv2["model"]["decoder_mode"] = args.decoder_mode
+    if args.spectral_init: tv2["model"]["spectral_input_init"] = args.spectral_init
+    if args.no_augment: tv2["augmentation"]["enabled"] = False
+    if args.sampler: tv2["sampler"]["mode"] = args.sampler
+    if args.balanced_sampler: tv2["sampler"]["mode"] = "weighted"
+    if args.samples_per_epoch > 0: tv2["sampler"]["samples_per_epoch"] = args.samples_per_epoch
+    if args.grad_clip is not None: tv2["optimization"]["grad_clip_norm"] = args.grad_clip
+    config["training_v2"] = tv2
+
+    optcfg = tv2.get("optimization", {})
+    try: torch.set_float32_matmul_precision(str(optcfg.get("matmul_precision", "high")))
+    except Exception: pass
+    if torch.backends.cudnn.is_available():
+        torch.backends.cudnn.benchmark = bool(optcfg.get("cudnn_benchmark", True))
+
+    W, H = config["resolucao"]
+    backbone = config.get("backbone", "nvidia/mit-b1")
+    input_channel_names = get_input_channel_names(config); channels = len(input_channel_names)
+    if config.get("fusion_mode", "stacked") != "stacked": raise RuntimeError("Este script exige fusion_mode='stacked'.")
+    model_family = config.get("modelo", "segformer"); model_name = config.get("model_name", "test")
+    stats_source_tag = config.get("stats_source_tag", f"stacked_raw{channels}")
+    save_dir = Path("backup") / model_family / model_name / stats_source_tag; ensure_dir(save_dir)
+
+    labelmap_path = Path("dataset") / "labelmap.txt"
+    semantic_id2label, semantic_label2id, ignore_from_labelmap = load_labelmap(str(labelmap_path))
+    ignore_index = int(args.ignore_index if args.ignore_index is not None else ignore_from_labelmap)
+    heads_config = build_heads_config(config, ignore_index)
+    heads_config["semantic"]["num_classes"] = len(semantic_id2label); heads_config["semantic"]["ignore_index"] = ignore_index
+
+    print("="*70)
+    print(f"Agri Teacher V2 | {TRAINER_VERSION}")
+    print(f"Backbone={backbone} | resolution={W}x{H} | channels={input_channel_names}")
+    print(f"decoder={tv2['model']['decoder_mode']} | spectral_init={tv2['model']['spectral_input_init']}")
+    print(f"augmentation={tv2['augmentation']['enabled']} | sampler={tv2['sampler']['mode']}")
+    print(f"save_dir={save_dir}")
+    for name,h in heads_config.items(): print(f"HEAD {name}: classes={h['num_classes']} weight={h['loss_weight']} norm={h['loss_weight_norm']:.3f}")
+    print("="*70)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu"); print(f"Device={device}")
+    train_root, val_root = Path(args.src_root)/"train", Path(args.src_root)/"val"
+    resize_hw = (H, W)
+    ds_train = OakFcc3TensorMultiHeadDataset(
+        train_root, heads_config, channels, input_channel_names, True, resize_hw,
+        augment=bool(tv2["augmentation"].get("enabled", True)), augmentation_config=tv2["augmentation"],
+        derived_config=config.get("derived_channels", {}) or {},
+        data_validation_config=tv2.get("data", {}) or {},
+    )
+    ds_val = OakFcc3TensorMultiHeadDataset(
+        val_root, heads_config, channels, input_channel_names, True, resize_hw,
+        augment=False, augmentation_config=None, derived_config=config.get("derived_channels", {}) or {},
+        data_validation_config=tv2.get("data", {}) or {},
+    )
+    print(f"[DATA] train={len(ds_train)} val={len(ds_val)}")
+
+    sampler_mode = str(tv2["sampler"].get("mode", "diverse")).lower()
+    common_dl = dict(num_workers=args.num_workers, pin_memory=True, collate_fn=collate_fn, worker_init_fn=seed_worker)
+    if args.num_workers > 0:
+        common_dl["persistent_workers"] = bool(optcfg.get("persistent_workers", True))
+        common_dl["prefetch_factor"] = int(optcfg.get("prefetch_factor", 2))
+
+    diverse_batch_sampler = None
+    if sampler_mode == "diverse":
+        diverse_batch_sampler = DiverseAgriculturalBatchSampler(ds_train, args.batch, tv2["sampler"], args.seed)
+        dl_train = DataLoader(ds_train, batch_sampler=diverse_batch_sampler, **common_dl)
+    elif sampler_mode == "weighted":
+        weights = build_sample_weights(ds_train)
+        ns = int(tv2["sampler"].get("samples_per_epoch", 0) or 0) or len(ds_train)
+        sampler = WeightedRandomSampler(weights, num_samples=ns, replacement=True)
+        dl_train = DataLoader(ds_train, batch_size=args.batch, sampler=sampler, shuffle=False, drop_last=False, **common_dl)
+        print(f"[SAMPLER:WEIGHTED] min={float(weights.min()):.3f} max={float(weights.max()):.3f} mean={float(weights.mean()):.3f}")
+    else:
+        dl_train = DataLoader(ds_train, batch_size=args.batch, shuffle=True, drop_last=False, **common_dl)
+
+    val_workers = max(0, args.num_workers // 2)
+    dl_val_kwargs = dict(batch_size=1, shuffle=False, num_workers=val_workers, pin_memory=True, collate_fn=collate_fn, worker_init_fn=seed_worker, drop_last=False)
+    if val_workers > 0:
+        dl_val_kwargs["persistent_workers"] = bool(optcfg.get("persistent_workers", True)); dl_val_kwargs["prefetch_factor"] = int(optcfg.get("prefetch_factor", 2))
+    dl_val = DataLoader(ds_val, **dl_val_kwargs)
+
+    normalizer, norm_stats_path = build_normalizer(config, args, device)
+    experiment_stats_path = None
+    if normalizer is not None:
+        source_stats = load_json(norm_stats_path) if norm_stats_path else {}
+        selected = {"channels": input_channel_names, "mean": normalizer.mean.detach().cpu().view(-1).tolist(),
+                    "std": normalizer.std.detach().cpu().view(-1).tolist(), "pixels": source_stats.get("pixels"),
+                    "source_norm_stats": norm_stats_path, "stats_source_tag": stats_source_tag}
+        experiment_stats_path = save_dir/"norm_stats.json"; save_json(experiment_stats_path, selected)
+
+    model = build_model(backbone, input_channel_names, heads_config, semantic_id2label, semantic_label2id, config=config)
+    if args.grad_ckpt:
+        try: model.segformer.gradient_checkpointing_enable(); print("[MODEL] gradient checkpointing enabled")
+        except Exception as e: print(f"[WARN] gradient checkpointing: {e}")
+    model.to(device)
+
+    criterions, weight_debug = build_criterions(ds_train, heads_config, args.class_weights, device, args.seed, tv2.get("class_weighting", {}))
+    optimizer = build_optimizer(model, args.lr, args.wd, tv2.get("optimizer", {}))
+    optimizer_steps_per_epoch = max(1, math.ceil(len(dl_train) / max(1, args.grad_accum)))
+    total_optimizer_steps = optimizer_steps_per_epoch * args.epochs
+    scheduler = WarmupDecayScheduler(optimizer, total_optimizer_steps, tv2.get("scheduler", {}))
+    scaler = GradScaler(enabled=args.amp and device.type == "cuda")
+
+    snapshot = {
+        "trainer_version": TRAINER_VERSION, "config": config, "args": vars(args), "training_v2": tv2,
+        "semantic_id2label": semantic_id2label, "heads_config": heads_config, "ignore_index": ignore_index,
+        "norm_stats_path": norm_stats_path, "experiment_norm_stats_path": str(experiment_stats_path) if experiment_stats_path else None,
+        "class_weight_debug": weight_debug, "input_channel_names": input_channel_names,
+        "tensor_contract": "normalized_v2_final_channel_order", "stats_source_tag": stats_source_tag,
+        "optimizer_steps_per_epoch": optimizer_steps_per_epoch, "total_optimizer_steps": total_optimizer_steps,
+    }
+    snapshot_default = save_dir/"train_config_snapshot.json"
+    snapshot_path = snapshot_default if not snapshot_default.exists() else save_dir/f"train_config_snapshot_{TRAINER_VERSION}.json"
+    save_json(snapshot_path, snapshot)
+
+    last_path=save_dir/"last.pt"; best_score_path=save_dir/"best_score.pt"; best_target_path=save_dir/"best_target.pt"
+    best_cana_path=save_dir/"best_cana_head.pt"; best_sem_path=save_dir/"best_semantic_miou.pt"; best_safety_path=save_dir/"best_cana_safety.pt"
+    best_legacy_path=save_dir/"best_legacy_score.pt"; best_operational_path=save_dir/"best_operational.pt"
+
+    # Se o diretório já contém logs V2.0, não misturamos headers CSV diferentes.
+    legacy_train_log = save_dir/"train_log.csv"
+    legacy_jsonl = save_dir/"train_metrics.jsonl"
+    train_log_path = legacy_train_log if not legacy_train_log.exists() else save_dir/f"train_log_{TRAINER_VERSION}.csv"
+    jsonl_path = legacy_jsonl if not legacy_jsonl.exists() else save_dir/f"train_metrics_{TRAINER_VERSION}.jsonl"
+
+    start_epoch=1; global_step=0; epochs_without_improve=0
+    best={
+        "score":-1.0, "legacy_score":-1.0, "operational_score":-1.0,
+        "target_iou":-1.0, "cana_head_iou":-1.0, "semantic_miou":-1.0,
+        "vegetation_miou":-1.0, "cana_safety":-1.0
+    }
+    resume_path = Path(args.resume_ckpt) if args.resume_ckpt else last_path
+    if args.resume and resume_path.exists():
+        ckpt = load_checkpoint(resume_path, model, optimizer, scaler, "cpu", scheduler=scheduler, restore_rng=True)
+        start_epoch=int(ckpt.get("epoch",0))+1; best=ckpt.get("best",best); global_step=int(ckpt.get("global_step",0)); epochs_without_improve=int(ckpt.get("epochs_without_improve",0))
+        print(f"[RESUME] {resume_path} -> epoch={start_epoch} global_step={global_step}")
+
+    min_delta = float(tv2.get("checkpoint",{}).get("early_stop_min_delta",5e-4))
+    best_score_for_es = float(best.get("score", -1.0))
+
+    for epoch in range(start_epoch, args.epochs+1):
+        if diverse_batch_sampler is not None: diverse_batch_sampler.set_epoch(epoch)
+        lr_now = [float(g["lr"]) for g in optimizer.param_groups]
+        print(f"\n==== Epoch {epoch}/{args.epochs} | lr={[f'{x:.2e}' for x in lr_now]} ====")
+        if device.type=="cuda": torch.cuda.reset_peak_memory_stats()
+
+        tr = run_one_epoch(model, dl_train, optimizer, device, heads_config, criterions, args.amp, scaler, True,
+                           max(1,args.grad_accum), normalizer, args.dice_weight, config, epoch,
+                           scheduler=scheduler, grad_clip_norm=float(tv2["optimization"].get("grad_clip_norm",1.0)), global_step=global_step)
+        global_step = int(tr.get("optimization",{}).get("global_step",global_step))
+        va = run_one_epoch(model, dl_val, None, device, heads_config, criterions, args.amp_val, None, False,
+                           1, normalizer, args.dice_weight, config, epoch, scheduler=None, grad_clip_norm=0, global_step=global_step)
+        score_now = compute_selection_score(va, config)
+
+        th = va["heads"].get("target",{}); ag = va.get("agricultural",{})
+        tr_th = tr["heads"].get("target", {})
+        print(
+            f"TRAIN loss={tr['loss']:.4f} "
+            f"sem_mIoU={tr['heads']['semantic']['miou']:.4f} "
+            f"cana_mIoU={tr['heads']['cana']['miou']:.4f} "
+            f"targetIoU={tr_th.get('iou_per_class',[0,0])[1]:.4f} "
+            f"t={tr['time_s']:.1f}s"
+        )
+        print(
+            f"VAL   loss={va['loss']:.4f} "
+            f"sem_mIoU={va['heads']['semantic']['miou']:.4f} "
+            f"canaIoU={va['heads']['cana']['iou_per_class'][1]:.4f} "
+            f"targetIoU={th.get('iou_per_class',[0,0])[1]:.4f} "
+            f"P={th.get('precision',0):.4f} R={th.get('recall',0):.4f} F1={th.get('f1',0):.4f} "
+            f"score={score_now['score']:.4f}"
+        )
+        print(
+            f"LEGACY score={score_now.get('legacy_score',0):.4f} "
+            f"opTargetIoU={score_now.get('operational_target_iou',0):.4f}"
+        )
+        print(
+            f"AGRI@0.50 cana_safety={ag.get('cana_safety',0):.5f} "
+            f"spray_cana={100*ag.get('cana_spray_rate',0):.3f}% "
+            f"spray_ground={100*ag.get('ground_spray_rate',0):.3f}% "
+            f"weed_miss={100*ag.get('weed_miss_rate',0):.3f}% "
+            f"macroIoU={ag.get('macro_target_iou',0):.4f}"
+        )
+        bt = ag.get("best_threshold", {}) or {}
+        print(f"THR-F1 best={bt.get('threshold')} F1={bt.get('f1',0):.4f} IoU={bt.get('iou',0):.4f}")
+        bop = ag.get("best_operational_threshold", {}) or {}
+        print(
+            f"THR-OP  best={bop.get('threshold')} score={bop.get('operational_score',0):.4f} "
+            f"safe={'YES' if bop.get('constraints_met',False) else 'NO'} "
+            f"spray_cana={100*safe_float(bop.get('cana_spray_rate'),0):.3f}% "
+            f"spray_ground={100*safe_float(bop.get('ground_spray_rate'),0):.3f}% "
+            f"weed_miss={100*safe_float(bop.get('weed_miss_rate'),0):.3f}%"
+        )
+        print(
+            f"CAL   ECE={safe_float(ag.get('target_ece'),0):.4f} "
+            f"Brier={safe_float(ag.get('target_brier'),0):.5f} "
+            f"NLL={safe_float(ag.get('target_nll'),0):.5f}"
+        )
+        print(
+            f"SAFE  cana->erva={100*ag.get('semantic_cana_as_erva_rate',0):.3f}% "
+            f"erva->cana={100*ag.get('semantic_erva_as_cana_rate',0):.3f}% "
+            f"target/composed disagree={100*ag.get('target_composed_disagreement_rate',0):.3f}% "
+            f"IoU={ag.get('target_composed_iou',0):.4f}"
+        )
+        print(
+            f"FRAME target_detect={100*ag.get('frame_target_detection_rate',0):.2f}% "
+            f"complete_miss={100*ag.get('frame_target_complete_miss_rate',0):.2f}% "
+            f"cana_clean={100*ag.get('frame_cana_clean_rate',0):.2f}% "
+            f"ground_clean={100*ag.get('frame_ground_clean_rate',0):.2f}%"
+        )
+
+        sweep = ag.get("threshold_sweep", {}) or {}
+        if sweep:
+            print("THR-SWEEP:")
+            for tkey, tm in sorted(sweep.items(), key=lambda kv: float(kv[0])):
+                flag = "OK" if tm.get("constraints_met", False) else "--"
+                print(
+                    f"  t={float(tkey):.2f} IoU={tm.get('iou',0):.4f} F1={tm.get('f1',0):.4f} "
+                    f"P={tm.get('precision',0):.4f} R={tm.get('recall',0):.4f} "
+                    f"cana={100*tm.get('cana_spray_rate',0):.2f}% "
+                    f"ground={100*tm.get('ground_spray_rate',0):.2f}% "
+                    f"miss={100*tm.get('weed_miss_rate',0):.2f}% op={tm.get('operational_score',0):.4f} {flag}"
+                )
+
+        scen = ag.get("scenario_target", {}) or {}
+        if scen:
+            parts = []
+            for name in ("none", "tiny", "small", "medium", "large"):
+                sm = scen.get(name, {}) or {}
+                if name == "none":
+                    parts.append(f"none FP={100*(1-safe_float(sm.get('specificity'),1)):.2f}%")
+                else:
+                    parts.append(f"{name} IoU={sm.get('iou_target',0):.3f}/R={sm.get('recall',0):.3f}")
+            print("SCEN  " + " | ".join(parts))
+
+        groups = ag.get("group_target", {}) or {}
+        if groups:
+            print("GROUP:")
+            for gname, gm in sorted(groups.items()):
+                print(
+                    f"  {gname}: IoU={gm.get('iou',0):.4f} F1={gm.get('f1',0):.4f} "
+                    f"R={gm.get('recall',0):.4f} cana={100*gm.get('cana_spray_rate',0):.2f}% "
+                    f"ground={100*gm.get('ground_spray_rate',0):.2f}% miss={100*gm.get('weed_miss_rate',0):.2f}%"
+                )
+
+        print("IoU semantic:", pretty_iou(semantic_id2label, va["heads"]["semantic"]["iou_per_class"]))
+        print("IoU vegetation:", binary_iou_text("vegetation", va["heads"]["vegetation"]["iou_per_class"]))
+        print("IoU cana:", binary_iou_text("cana", va["heads"]["cana"]["iou_per_class"]))
+        print("IoU target:", binary_iou_text("target", va["heads"]["target"]["iou_per_class"]))
+        if device.type=="cuda":
+            print(f"GPU peak={torch.cuda.max_memory_allocated()/1024**3:.2f} GiB")
+
+        old = copy.deepcopy(best)
+        op_score = safe_float(
+            score_now.get("operational_score"),
+            -1.0,
+        )
+        op_safe = bool(
+            score_now.get("operational_constraints_met", False)
+        )
+        best["score"] = max(float(best.get("score",-1)), score_now["score"])
+        best["legacy_score"] = max(float(best.get("legacy_score",-1)), score_now.get("legacy_score",-1))
+        # O histórico "best operational" só aceita epochs SAFE.
+        if op_safe:
+            best["operational_score"] = max(float(best.get("operational_score", -1.0)), op_score)
+        best["target_iou"] = max(float(best.get("target_iou",-1)), score_now["target_iou"])
+        best["cana_head_iou"] = max(float(best.get("cana_head_iou",-1)), score_now["cana_head_iou"])
+        best["semantic_miou"] = max(float(best.get("semantic_miou",-1)), score_now["semantic_miou"])
+        best["vegetation_miou"] = max(float(best.get("vegetation_miou",-1)), score_now["vegetation_miou"])
+        best["cana_safety"] = max(float(best.get("cana_safety",-1)), score_now["cana_safety"])
+
+        extra={"train":tr,"val":va,"score_now":score_now,"lr_groups":[float(g['lr']) for g in optimizer.param_groups]}
+        if score_now["score"] > float(old.get("score",-1)):
+            save_checkpoint(best_score_path,model,optimizer,scaler,epoch,best,extra,scheduler,epochs_without_improve,global_step); print(f"[BEST SCORE] {score_now['score']:.4f}")
+        if bool(tv2.get("checkpoint",{}).get("save_best_legacy",True)) and score_now.get("legacy_score",-1) > float(old.get("legacy_score",-1)):
+            save_checkpoint(best_legacy_path,model,optimizer,scaler,epoch,best,extra,scheduler,epochs_without_improve,global_step); print(f"[BEST LEGACY] {score_now.get('legacy_score',-1):.4f}")
+        if (bool(tv2.get("checkpoint", {}).get("save_best_operational", True)) and op_safe and op_score > float(old.get("operational_score", -1.0))):
+            save_checkpoint(best_operational_path, model, optimizer, scaler, epoch, best, extra, scheduler, epochs_without_improve, global_step); print(f"[BEST OPERATIONAL SAFE] {op_score:.4f} @thr={score_now.get('operational_threshold')}")
+        if score_now["target_iou"] > float(old.get("target_iou",-1)):
+            save_checkpoint(best_target_path,model,optimizer,scaler,epoch,best,extra,scheduler,epochs_without_improve,global_step); print(f"[BEST TARGET] {score_now['target_iou']:.4f}")
+        if score_now["cana_head_iou"] > float(old.get("cana_head_iou",-1)):
+            save_checkpoint(best_cana_path,model,optimizer,scaler,epoch,best,extra,scheduler,epochs_without_improve,global_step); print(f"[BEST CANA] {score_now['cana_head_iou']:.4f}")
+        if score_now["semantic_miou"] > float(old.get("semantic_miou",-1)):
+            save_checkpoint(best_sem_path,model,optimizer,scaler,epoch,best,extra,scheduler,epochs_without_improve,global_step); print(f"[BEST SEM] {score_now['semantic_miou']:.4f}")
+        if bool(tv2.get("checkpoint",{}).get("save_best_safety",True)) and score_now["cana_safety"] > float(old.get("cana_safety",-1)):
+            save_checkpoint(best_safety_path,model,optimizer,scaler,epoch,best,extra,scheduler,epochs_without_improve,global_step); print(f"[BEST SAFETY] {score_now['cana_safety']:.5f}")
+
+        # Early stop is driven only by the principal agricultural score.
+        if score_now["score"] > best_score_for_es + min_delta:
+            best_score_for_es = score_now["score"]; epochs_without_improve = 0
+        else:
+            epochs_without_improve += 1
+
+        save_checkpoint(last_path,model,optimizer,scaler,epoch,best,extra,scheduler,epochs_without_improve,global_step)
+        if args.save_every>0 and epoch%args.save_every==0:
+            save_checkpoint(save_dir/f"epoch_{epoch:04d}.pt",model,optimizer,scaler,epoch,best,extra,scheduler,epochs_without_improve,global_step)
+
+        append_train_log(train_log_path, flatten_epoch_log(epoch, lr_now[0], tr, va, best, score_now))
+        append_jsonl(jsonl_path, {"epoch":epoch,"train":tr,"val":va,"score_now":score_now,"best":best})
+
+        if args.early_stop>0 and epochs_without_improve>=args.early_stop:
+            print(f"[EARLY STOP] {epochs_without_improve} épocas sem ganho de score >= {min_delta}.")
+            break
+
+    print("\nTreino finalizado.")
+    print(json.dumps(best, indent=2, ensure_ascii=False))
+    print(f"Save dir: {save_dir}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/_9_review_dataset_multihead_v2.py b/Python/OAK/datasets/oak-fcc-3/_9_review_dataset_multihead_v2.py
new file mode 100644
index 000000000..069c1339c
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/_9_review_dataset_multihead_v2.py
@@ -0,0 +1,1478 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+r"""
+_9_review_dataset_multihead_v2.py
+
+Auditoria/model-mining do dataset usando o mesmo contrato de inferência do
+_9_test_multihead_v2.py.
+
+Objetivo
+--------
+Rodar um checkpoint sobre TODO o dataset normalizado (ex.: dataset/960x600/group),
+medir onde GT e modelo discordam e separar automaticamente os casos que merecem
+revisão humana.
+
+IMPORTANTE: discordância modelo x GT NÃO é tratada automaticamente como erro do
+modelo. Em um dataset com rótulos imperfeitos, um conflito de alta confiança pode
+ser justamente um forte candidato a máscara incorreta.
+
+Saída de revisão (quando não usa --report_only):
+
+  dataset/revisao/group/<grupo>/
+      previews/<base>.png
+      masks/<base>.png              # GT semantic colorido
+      predictions/<base>.png        # predição semantic colorida
+      panels/<base>.png             # opcional, --save_panels
+      final_masks/<base>.png        # NÃO é tocado pelo auditor
+      review_order.csv
+
+Modos de exportação:
+  --export_mode flagged
+      Exporta casos acionados pelos critérios técnicos.
+  --export_mode score --min_suspicion_pct 80
+      Exporta casos com review_score*100 >= 80.
+  --export_mode all
+      Exporta todas as amostras.
+
+suspicion_pct = review_score*100 é uma escala heurística de ranking,
+não uma probabilidade estatística de a máscara estar errada.
+
+Relatórios por execução/checkpoint:
+
+  dataset/revisao/reports/<run_name>/
+      review_report.csv             # TODAS as amostras
+      review_candidates.csv         # somente selecionadas
+      review_summary.json
+      confusion_semantic.csv
+      confusion_vegetation.csv
+      confusion_cana.csv
+      confusion_target.csv
+
+Exemplos
+--------
+
+1) Gerar revisão física dos casos suspeitos no dataset 960x600:
+
+python .\_9_review_dataset_multihead_v2.py ^
+  --config .\config.json ^
+  --root dataset\960x600\group ^
+  --ckpt backup\segformer_b1\2026_08_27_linear_demosaic\stacked_raw5\best_score.pt ^
+  --target_threshold auto ^
+  --export_mode score --min_suspicion_pct 80
+  --clear_review
+
+2) Somente relatório, sem copiar previews/masks/predictions:
+
+python .\_9_review_dataset_multihead_v2.py ^
+  --config .\config.json ^
+  --root dataset\960x600\group ^
+  --ckpt backup\...\best_cana_head.pt ^
+  --report_only ^
+  --run_name best_cana
+
+3) Forçar os 100 casos mais suspeitos de cada grupo para revisão:
+
+python .\_9_review_dataset_multihead_v2.py ^
+  --config .\config.json ^
+  --root dataset\960x600\group ^
+  --ckpt backup\...\best_score.pt ^
+  --top_k_per_group 100 ^
+  --clear_review
+
+4) Mesmo auditor usando ONNX Runtime + TensorRT (logits):
+
+python .\_9_review_dataset_multihead_v2.py ^
+  --config .\config.json ^
+  --root dataset\960x600\group ^
+  --onnx backup\...\best_score_audit.onnx ^
+  --onnx_provider tensorrt ^
+  --target_threshold auto ^
+  --export_mode flagged ^
+  --clear_review
+
+IMPORTANTE para ONNX:
+- O auditor precisa de LOGITS/probabilidades para medir alta confiança.
+- Exporte com --postprocess none ou --postprocess resize_logits.
+- ONNX mask-only (argmax_lowres/argmax_fullres) é recusado de propósito.
+- Normalização embutida é detectada pelo .export_meta.json para evitar dupla normalização.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import importlib.util
+import json
+import math
+import os
+import shutil
+import sys
+import time
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, Iterable, List, Optional, Sequence, Tuple
+
+import cv2
+import numpy as np
+import torch
+
+
+# ============================================================
+# Utilidades gerais
+# ============================================================
+
+
+def load_json(path: Path) -> dict:
+    with path.open("r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def ensure_dir(path: Path) -> None:
+    path.mkdir(parents=True, exist_ok=True)
+
+
+def resolve_path(path_like: Optional[str], base: Optional[Path] = None) -> Optional[Path]:
+    if path_like is None:
+        return None
+    p = Path(path_like)
+    if p.is_absolute():
+        return p
+    return ((base or Path.cwd()) / p).resolve()
+
+
+def safe_float(v, default: float = 0.0) -> float:
+    try:
+        x = float(v)
+        return x if math.isfinite(x) else default
+    except Exception:
+        return default
+
+
+def pct(x: float) -> float:
+    return float(x) * 100.0
+
+
+def clear_dir(path: Path) -> None:
+    if path.exists():
+        shutil.rmtree(path)
+    path.mkdir(parents=True, exist_ok=True)
+
+
+def clear_generated_review(review_group_root: Path) -> None:
+    """Limpa somente artefatos regeneráveis e PRESERVA final_masks/."""
+    if not review_group_root.exists():
+        review_group_root.mkdir(parents=True, exist_ok=True)
+        return
+
+    for group_dir in review_group_root.iterdir():
+        if not group_dir.is_dir():
+            continue
+
+        for name in ("previews", "masks", "predictions", "panels"):
+            p = group_dir / name
+            if p.exists():
+                shutil.rmtree(p)
+
+        order_csv = group_dir / "review_order.csv"
+        if order_csv.exists():
+            order_csv.unlink()
+
+
+def load_test_module(script_path: Path):
+    if not script_path.is_file():
+        raise FileNotFoundError(
+            f"Script base não encontrado: {script_path}\n"
+            "Coloque este arquivo ao lado de _9_test_multihead_v2.py ou informe --test_script."
+        )
+    spec = importlib.util.spec_from_file_location("agri_test_multihead_v2", str(script_path))
+    if spec is None or spec.loader is None:
+        raise RuntimeError(f"Não consegui carregar módulo de teste: {script_path}")
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+
+def load_onnx_export_meta(onnx_path: Path) -> Tuple[dict, Optional[Path]]:
+    """Carrega o sidecar gerado por _10_export_onnx.py, quando existir."""
+    candidates = [
+        onnx_path.with_suffix(".export_meta.json"),
+        onnx_path.parent / f"{onnx_path.stem}.export_meta.json",
+    ]
+    for p in candidates:
+        if p.is_file():
+            try:
+                return load_json(p), p
+            except Exception as exc:
+                raise RuntimeError(f"Falha ao ler metadata ONNX: {p}: {exc}") from exc
+    return {}, None
+
+
+def onnx_normalization_embedded(meta: dict, mode: str = "auto") -> bool:
+    mode = str(mode or "auto").strip().lower()
+    if mode == "embedded":
+        return True
+    if mode == "external":
+        return False
+
+    contract = meta.get("input_contract", {}) if isinstance(meta, dict) else {}
+    if isinstance(contract, dict) and "normalization_embedded" in contract:
+        return bool(contract.get("normalization_embedded"))
+    if isinstance(meta, dict) and "include_norm" in meta:
+        return bool(meta.get("include_norm"))
+    return False
+
+
+def _extract_operational_threshold_from_ckpt_dict(ckpt: dict) -> Optional[float]:
+    if not isinstance(ckpt, dict):
+        return None
+
+    candidates = []
+    best = ckpt.get("best", {}) or {}
+    if isinstance(best, dict):
+        candidates.extend([
+            best.get("operational_threshold"),
+            best.get("best_operational_threshold"),
+        ])
+
+    extra = ckpt.get("extra", {}) or {}
+    if isinstance(extra, dict):
+        score_now = extra.get("score_now", {}) or {}
+        if isinstance(score_now, dict):
+            candidates.append(score_now.get("operational_threshold"))
+        val = extra.get("val", {}) or {}
+        if isinstance(val, dict):
+            agri = val.get("agricultural", {}) or {}
+            if isinstance(agri, dict):
+                bop = agri.get("best_operational_threshold", {}) or {}
+                if isinstance(bop, dict):
+                    candidates.append(bop.get("threshold"))
+
+    for value in candidates:
+        try:
+            if value is not None:
+                v = float(value)
+                if 0.0 <= v <= 1.0:
+                    return v
+        except Exception:
+            pass
+    return None
+
+
+def resolve_onnx_operational_threshold(meta: dict, meta_path: Optional[Path], onnx_path: Path) -> Tuple[Optional[float], str]:
+    """
+    Resolve o threshold operacional sem precisar executar PyTorch.
+
+    Ordem:
+    1) metadata ONNX explícita;
+    2) checkpoint_best do sidecar;
+    3) checkpoint .pt referenciado no sidecar, carregado apenas para ler metadata.
+    """
+    if isinstance(meta, dict):
+        direct_candidates = [
+            meta.get("operational_threshold"),
+            meta.get("checkpoint_operational_threshold"),
+        ]
+        best = meta.get("checkpoint_best", {}) or {}
+        if isinstance(best, dict):
+            direct_candidates.extend([
+                best.get("operational_threshold"),
+                best.get("best_operational_threshold"),
+            ])
+        for value in direct_candidates:
+            try:
+                if value is not None:
+                    v = float(value)
+                    if 0.0 <= v <= 1.0:
+                        return v, "onnx_export_meta"
+            except Exception:
+                pass
+
+        ckpt_txt = meta.get("checkpoint")
+        if ckpt_txt:
+            raw = Path(str(ckpt_txt))
+            candidates = [raw]
+            if not raw.is_absolute():
+                if meta_path is not None:
+                    candidates.append((meta_path.parent / raw).resolve())
+                candidates.append((onnx_path.parent / raw).resolve())
+                candidates.append((Path.cwd() / raw).resolve())
+            ckpt_path = next((p for p in candidates if p.is_file()), None)
+            if ckpt_path is not None:
+                try:
+                    # Carrega apenas para metadata. Nenhum modelo PyTorch é construído/executado.
+                    ckpt = torch.load(str(ckpt_path), map_location="cpu", weights_only=False)
+                    thr = _extract_operational_threshold_from_ckpt_dict(ckpt)
+                    if thr is not None:
+                        return thr, f"checkpoint_metadata:{ckpt_path}"
+                except Exception as exc:
+                    print(f"[ONNX][WARN] não consegui ler threshold do checkpoint do sidecar: {exc}")
+
+    return None, "none"
+
+
+def validate_onnx_for_audit(tester, meta: dict, onnx_path: Path) -> None:
+    """
+    Auditoria precisa de probabilidades reais. Portanto, o ONNX deve devolver logits.
+    Modelos exportados apenas com argmax/mask não servem para high-confidence mining.
+    """
+    postprocess = str((meta or {}).get("postprocess", "")).strip().lower()
+    output_names = [str(x) for x in getattr(tester, "output_names", [])]
+
+    mask_only_by_meta = postprocess in ("argmax_lowres", "argmax_fullres")
+    mask_only_by_name = bool(output_names) and all(
+        name.lower().endswith("_mask") or "mask" in name.lower()
+        for name in output_names
+    )
+
+    if mask_only_by_meta or mask_only_by_name:
+        raise RuntimeError(
+            "Este ONNX devolve máscaras/argmax, mas o auditor precisa de LOGITS para "
+            "calcular probabilidades, high-confidence conflict e uncertainty.\n"
+            f"ONNX: {onnx_path}\n"
+            f"postprocess={postprocess or 'desconhecido'} outputs={output_names}\n"
+            "Exporte um ONNX de auditoria com --postprocess none (recomendado) "
+            "ou --postprocess resize_logits."
+        )
+
+    missing = []
+    for required in ("semantic", "vegetation", "cana"):
+        if not any(required in name.lower() for name in output_names):
+            missing.append(required)
+    if missing:
+        raise RuntimeError(
+            f"ONNX não expõe todas as heads necessárias para auditoria: faltando={missing}, "
+            f"outputs={output_names}"
+        )
+
+
+def write_csv(path: Path, rows: Sequence[dict], fieldnames: Optional[Sequence[str]] = None) -> None:
+    ensure_dir(path.parent)
+    if fieldnames is None:
+        keys = []
+        seen = set()
+        for row in rows:
+            for key in row.keys():
+                if key not in seen:
+                    seen.add(key)
+                    keys.append(key)
+        fieldnames = keys
+
+    with path.open("w", newline="", encoding="utf-8-sig") as f:
+        writer = csv.DictWriter(f, fieldnames=list(fieldnames), extrasaction="ignore")
+        writer.writeheader()
+        for row in rows:
+            writer.writerow(row)
+
+
+def write_cm_csv(path: Path, cm: np.ndarray, labels: Sequence[str]) -> None:
+    rows = []
+    for gi, gt_name in enumerate(labels):
+        row = {"gt\\pred": gt_name}
+        for pi, pred_name in enumerate(labels):
+            row[pred_name] = int(cm[gi, pi])
+        rows.append(row)
+    write_csv(path, rows, ["gt\\pred", *labels])
+
+
+# ============================================================
+# Métricas
+# ============================================================
+
+
+def resize_ids(mask: np.ndarray, hw: Tuple[int, int]) -> np.ndarray:
+    h, w = hw
+    if mask.shape[:2] == (h, w):
+        return mask
+    return cv2.resize(mask.astype(np.uint8), (w, h), interpolation=cv2.INTER_NEAREST)
+
+
+def class_metrics_from_cm(cm: np.ndarray, labels: Sequence[str]) -> dict:
+    cm = cm.astype(np.float64)
+    tp = np.diag(cm)
+    support = cm.sum(axis=1)
+    pred_count = cm.sum(axis=0)
+    fp = pred_count - tp
+    fn = support - tp
+
+    precision = np.divide(tp, tp + fp, out=np.zeros_like(tp), where=(tp + fp) > 0)
+    recall = np.divide(tp, tp + fn, out=np.zeros_like(tp), where=(tp + fn) > 0)
+    f1 = np.divide(2 * precision * recall, precision + recall, out=np.zeros_like(tp), where=(precision + recall) > 0)
+    iou = np.divide(tp, tp + fp + fn, out=np.zeros_like(tp), where=(tp + fp + fn) > 0)
+
+    total = max(float(cm.sum()), 1.0)
+    out = {
+        "accuracy": float(tp.sum() / total),
+        "miou": float(np.mean(iou)) if len(iou) else 0.0,
+        "classes": {},
+    }
+    for i, name in enumerate(labels):
+        out["classes"][str(name)] = {
+            "support_pixels": int(support[i]),
+            "pred_pixels": int(pred_count[i]),
+            "precision": float(precision[i]),
+            "recall": float(recall[i]),
+            "f1": float(f1[i]),
+            "iou": float(iou[i]),
+        }
+    return out
+
+
+def directional_rate(cm: np.ndarray, gt_idx: int, pred_idx: int) -> float:
+    den = int(cm[gt_idx].sum())
+    if den <= 0:
+        return 0.0
+    return float(cm[gt_idx, pred_idx] / den)
+
+
+def binary_target_stats(pred: np.ndarray, gt: np.ndarray, ignore_id: int) -> dict:
+    valid = gt != ignore_id
+    gt1 = (gt == 1) & valid
+    pr1 = (pred == 1) & valid
+    tp = int((gt1 & pr1).sum())
+    fp = int((~gt1 & pr1 & valid).sum())
+    fn = int((gt1 & ~pr1).sum())
+    tn = int((~gt1 & ~pr1 & valid).sum())
+
+    precision = tp / max(tp + fp, 1)
+    recall = tp / max(tp + fn, 1)
+    f1 = 2 * precision * recall / max(precision + recall, 1e-12)
+    iou = tp / max(tp + fp + fn, 1)
+    return {
+        "tp": tp,
+        "fp": fp,
+        "fn": fn,
+        "tn": tn,
+        "precision": float(precision),
+        "recall": float(recall),
+        "f1": float(f1),
+        "iou": float(iou),
+    }
+
+
+def semantic_sample_signals(
+    pred: np.ndarray,
+    probs: np.ndarray,
+    gt: np.ndarray,
+    ignore_id: int,
+    high_confidence: float,
+    uncertainty_margin: float,
+) -> dict:
+    if pred.shape != gt.shape:
+        pred = resize_ids(pred, gt.shape[:2])
+
+    valid = gt != ignore_id
+    valid_n = int(valid.sum())
+    if valid_n <= 0:
+        return {
+            "valid_pixels": 0,
+            "wrong_pixels": 0,
+            "highconf_wrong_pixels": 0,
+            "uncertain_pixels": 0,
+            "gt_chao_pixels": 0,
+            "gt_cana_pixels": 0,
+            "gt_erva_pixels": 0,
+            "hc_gt_chao_pred_cana_pixels": 0,
+            "hc_gt_chao_pred_erva_pixels": 0,
+            "hc_gt_cana_pred_erva_pixels": 0,
+            "hc_gt_erva_pred_cana_pixels": 0,
+            "semantic_disagree_frac": 0.0,
+            "highconf_disagree_frac": 0.0,
+            "uncertain_frac": 0.0,
+            "mean_conf_correct": 0.0,
+            "mean_conf_wrong": 0.0,
+            "gt_chao_pred_cana_frac": 0.0,
+            "gt_chao_pred_erva_frac": 0.0,
+            "gt_cana_pred_erva_frac": 0.0,
+            "gt_erva_pred_cana_frac": 0.0,
+            "hc_gt_chao_pred_cana_frac": 0.0,
+            "hc_gt_chao_pred_erva_frac": 0.0,
+            "hc_gt_cana_pred_erva_frac": 0.0,
+            "hc_gt_erva_pred_cana_frac": 0.0,
+        }
+
+    top1 = np.max(probs, axis=0)
+    if probs.shape[0] >= 2:
+        part = np.partition(probs, kth=probs.shape[0] - 2, axis=0)
+        top2 = part[-2]
+    else:
+        top2 = np.zeros_like(top1)
+    margin = top1 - top2
+
+    wrong = valid & (pred != gt)
+    correct = valid & (pred == gt)
+    highconf_wrong = wrong & (top1 >= high_confidence)
+    uncertain = valid & (margin <= uncertainty_margin)
+
+    def frac(mask: np.ndarray) -> float:
+        return float(mask.sum() / valid_n)
+
+    def conf_mean(mask: np.ndarray) -> float:
+        n = int(mask.sum())
+        return float(top1[mask].mean()) if n else 0.0
+
+    g0p1 = valid & (gt == 0) & (pred == 1)
+    g0p2 = valid & (gt == 0) & (pred == 2)
+    g1p2 = valid & (gt == 1) & (pred == 2)
+    g2p1 = valid & (gt == 2) & (pred == 1)
+
+    return {
+        "valid_pixels": valid_n,
+        "wrong_pixels": int(wrong.sum()),
+        "highconf_wrong_pixels": int(highconf_wrong.sum()),
+        "uncertain_pixels": int(uncertain.sum()),
+        "gt_chao_pixels": int((valid & (gt == 0)).sum()),
+        "gt_cana_pixels": int((valid & (gt == 1)).sum()),
+        "gt_erva_pixels": int((valid & (gt == 2)).sum()),
+        "hc_gt_chao_pred_cana_pixels": int((g0p1 & (top1 >= high_confidence)).sum()),
+        "hc_gt_chao_pred_erva_pixels": int((g0p2 & (top1 >= high_confidence)).sum()),
+        "hc_gt_cana_pred_erva_pixels": int((g1p2 & (top1 >= high_confidence)).sum()),
+        "hc_gt_erva_pred_cana_pixels": int((g2p1 & (top1 >= high_confidence)).sum()),
+        "semantic_disagree_frac": frac(wrong),
+        "highconf_disagree_frac": frac(highconf_wrong),
+        "uncertain_frac": frac(uncertain),
+        "mean_conf_correct": conf_mean(correct),
+        "mean_conf_wrong": conf_mean(wrong),
+        "gt_chao_pred_cana_frac": frac(g0p1),
+        "gt_chao_pred_erva_frac": frac(g0p2),
+        "gt_cana_pred_erva_frac": frac(g1p2),
+        "gt_erva_pred_cana_frac": frac(g2p1),
+        "hc_gt_chao_pred_cana_frac": frac(g0p1 & (top1 >= high_confidence)),
+        "hc_gt_chao_pred_erva_frac": frac(g0p2 & (top1 >= high_confidence)),
+        "hc_gt_cana_pred_erva_frac": frac(g1p2 & (top1 >= high_confidence)),
+        "hc_gt_erva_pred_cana_frac": frac(g2p1 & (top1 >= high_confidence)),
+    }
+
+
+def review_score(signals: dict, target_disagree_frac: float) -> float:
+    """
+    Score apenas para RANKING, não é probabilidade de máscara errada.
+
+    Dá mais peso para conflito de alta confiança que para simples incerteza.
+    Cada termo é saturado numa escala prática para evitar que um único tipo de
+    erro domine completamente a ordenação.
+    """
+    sem = min(safe_float(signals.get("semantic_disagree_frac")) / 0.20, 1.0)
+    hc = min(safe_float(signals.get("highconf_disagree_frac")) / 0.05, 1.0)
+    cross = min(
+        (
+            safe_float(signals.get("hc_gt_cana_pred_erva_frac"))
+            + safe_float(signals.get("hc_gt_erva_pred_cana_frac"))
+        ) / 0.02,
+        1.0,
+    )
+    tgt = min(safe_float(target_disagree_frac) / 0.15, 1.0)
+    unc = min(safe_float(signals.get("uncertain_frac")) / 0.25, 1.0)
+    return float(0.30 * sem + 0.40 * hc + 0.15 * cross + 0.10 * tgt + 0.05 * unc)
+
+
+def build_reasons(
+    row: dict,
+    min_disagree_frac: float,
+    min_highconf_disagree_frac: float,
+    min_cross_frac: float,
+    min_target_disagree_frac: float,
+    min_conflict_pixels: int,
+) -> List[str]:
+    reasons: List[str] = []
+    valid_pixels = max(int(row.get("valid_pixels", 0)), 1)
+    hc_pixels = int(round(float(row.get("highconf_disagree_frac", 0.0)) * valid_pixels))
+
+    if float(row.get("semantic_disagree_frac", 0.0)) >= min_disagree_frac:
+        reasons.append("SEMANTIC_DISAGREE")
+
+    if (
+        float(row.get("highconf_disagree_frac", 0.0)) >= min_highconf_disagree_frac
+        and hc_pixels >= min_conflict_pixels
+    ):
+        reasons.append("HIGH_CONF_GT_CONFLICT")
+
+    if (
+        float(row.get("hc_gt_chao_pred_cana_frac", 0.0))
+        + float(row.get("hc_gt_chao_pred_erva_frac", 0.0))
+        >= min_highconf_disagree_frac
+    ):
+        reasons.append("GT_CHAO_PRED_VEGETATION")
+
+    if (
+        float(row.get("hc_gt_cana_pred_erva_frac", 0.0))
+        + float(row.get("hc_gt_erva_pred_cana_frac", 0.0))
+        >= min_cross_frac
+    ):
+        reasons.append("CANA_ERVA_CROSS")
+
+    if float(row.get("target_disagree_frac", 0.0)) >= min_target_disagree_frac:
+        reasons.append("TARGET_DISAGREE")
+
+    return reasons
+
+
+# ============================================================
+# Visualização/copiar casos de revisão
+# ============================================================
+
+
+def load_preview_rgb(sample, chw: np.ndarray, base_module, input_channel_names: Sequence[str]) -> np.ndarray:
+    if getattr(sample, "preview_path", None) is not None and sample.preview_path.is_file():
+        bgr = cv2.imread(str(sample.preview_path), cv2.IMREAD_COLOR)
+        if bgr is not None:
+            return cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+    return base_module.tensor_to_preview_rgb(chw, input_channel_names)
+
+
+def save_review_item(
+    row: dict,
+    sample,
+    chw: np.ndarray,
+    gt_sem: np.ndarray,
+    pred_sem: np.ndarray,
+    review_group_root: Path,
+    base_module,
+    semantic_cmap: Dict[int, Tuple[int, int, int]],
+    ignore_id: int,
+    input_channel_names: Sequence[str],
+    save_panels: bool,
+) -> None:
+    group_dir = review_group_root / sample.group
+    previews_dir = group_dir / "previews"
+    masks_dir = group_dir / "masks"
+    predictions_dir = group_dir / "predictions"
+    panels_dir = group_dir / "panels"
+
+    ensure_dir(previews_dir)
+    ensure_dir(masks_dir)
+    ensure_dir(predictions_dir)
+    if save_panels:
+        ensure_dir(panels_dir)
+
+    # Revisão humana usa somente PNG. Mesmo se o preview fonte for JPG,
+    # a saída é normalizada para <base>.png.
+    preview_rgb = load_preview_rgb(sample, chw, base_module, input_channel_names)
+    cv2.imwrite(
+        str(previews_dir / f"{sample.base}.png"),
+        cv2.cvtColor(preview_rgb, cv2.COLOR_RGB2BGR),
+    )
+
+    gt_rgb = base_module.ids_to_rgb(gt_sem, semantic_cmap, ignore_id)
+    pred_rgb = base_module.ids_to_rgb(pred_sem, semantic_cmap, ignore_id)
+
+    cv2.imwrite(
+        str(masks_dir / f"{sample.base}.png"),
+        cv2.cvtColor(gt_rgb, cv2.COLOR_RGB2BGR),
+    )
+    cv2.imwrite(
+        str(predictions_dir / f"{sample.base}.png"),
+        cv2.cvtColor(pred_rgb, cv2.COLOR_RGB2BGR),
+    )
+
+    if save_panels:
+        # Normaliza tamanho do preview para GT/pred.
+        h, w = gt_sem.shape[:2]
+        if preview_rgb.shape[:2] != (h, w):
+            preview_rgb = cv2.resize(preview_rgb, (w, h), interpolation=cv2.INTER_AREA)
+
+        diff = np.zeros_like(gt_rgb)
+        valid = gt_sem != ignore_id
+        bad = valid & (gt_sem != pred_sem)
+        diff[bad] = (255, 40, 40)
+
+        overlay_gt = base_module.overlay_rgb(preview_rgb, gt_rgb, 0.45)
+        overlay_pred = base_module.overlay_rgb(preview_rgb, pred_rgb, 0.45)
+        panels = [
+            ("Preview", preview_rgb, sample.base),
+            ("GT semantic", overlay_gt, f"grupo={sample.group}"),
+            ("Pred semantic", overlay_pred, f"score={float(row['review_score']):.3f}"),
+            ("GT != Pred", diff, str(row.get("review_reasons", ""))),
+        ]
+        panel = base_module.compose_grid(panels, cols=2, max_width=1600)
+        cv2.imwrite(str(panels_dir / f"{sample.base}.png"), cv2.cvtColor(panel, cv2.COLOR_RGB2BGR))
+
+
+# ============================================================
+# Main
+# ============================================================
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Audita masks usando um checkpoint multi-head e separa casos suspeitos para revisão humana."
+    )
+    parser.add_argument("--config", default="config.json")
+    parser.add_argument("--root", default=None, help="Default: dataset/<WxH>/group conforme config.resolucao")
+    parser.add_argument("--ckpt", default=None, help="Checkpoint .pt. Se --onnx for usado, não informe --ckpt.")
+    parser.add_argument("--onnx", default=None, help="Modelo ONNX alternativo ao .pt para auditoria acelerada.")
+    parser.add_argument(
+        "--onnx_provider",
+        default="tensorrt",
+        choices=["tensorrt", "cuda", "cpu"],
+        help="Provider do ONNX Runtime. Default: tensorrt.",
+    )
+    parser.add_argument(
+        "--onnx_norm",
+        default="auto",
+        choices=["auto", "external", "embedded"],
+        help="auto usa .export_meta.json; external aplica norm_stats fora; embedded assume normalização dentro do ONNX.",
+    )
+    parser.add_argument("--trt_home", default=None, help="Pasta do TensorRT. Opcional; usa TRT_HOME/default do test script.")
+    parser.add_argument("--trt_no_fp16", action="store_true", help="Desliga FP16 no TensorRT.")
+    parser.add_argument("--onnx_warmup", type=int, default=3, help="Warmups antes de medir auditoria ONNX/TRT. Default=3.")
+    parser.add_argument("--norm_stats", default=None)
+    parser.add_argument("--channels", type=int, default=None)
+    parser.add_argument("--resize_w", type=int, default=None)
+    parser.add_argument("--resize_h", type=int, default=None)
+    parser.add_argument("--ignore_id", type=int, default=None)
+    parser.add_argument("--no_amp", action="store_true")
+    parser.add_argument("--target_threshold", default="auto", help="Número ou auto")
+    parser.add_argument("--test_script", default=None, help="Default: _9_test_multihead_v2.py ao lado deste script")
+
+    parser.add_argument("--out_root", default="dataset/revisao")
+    parser.add_argument("--run_name", default=None, help="Nome do relatório. Default: stem do checkpoint")
+    parser.add_argument("--report_only", action="store_true", help="Não copia casos para dataset/revisao/group")
+    parser.add_argument(
+        "--export_mode",
+        choices=["flagged", "score", "all"],
+        default="flagged",
+        help="flagged=critérios técnicos; score=score >= min_suspicion_pct; all=todas.",
+    )
+    parser.add_argument(
+        "--min_suspicion_pct",
+        type=float,
+        default=80.0,
+        help="Usado com --export_mode score. Escala heurística 0..100, não probabilidade.",
+    )
+    parser.add_argument(
+        "--clear_review",
+        action="store_true",
+        help="Limpa previews/masks/predictions/panels e PRESERVA final_masks.",
+    )
+    parser.add_argument(
+        "--clear_review_all",
+        action="store_true",
+        help="PERIGOSO: apaga toda a árvore group, inclusive final_masks.",
+    )
+    parser.add_argument("--save_panels", action="store_true", help="Salva painel preview/GT/pred/diff dos candidatos")
+
+    # Critérios de revisão. São deliberadamente conservadores.
+    parser.add_argument("--high_confidence", type=float, default=0.85)
+    parser.add_argument("--uncertainty_margin", type=float, default=0.12)
+    parser.add_argument("--min_disagree_frac", type=float, default=0.05,
+                        help="Flag se >=5%% dos pixels semantic discordarem do GT")
+    parser.add_argument("--min_highconf_disagree_frac", type=float, default=0.002,
+                        help="Flag se >=0.2%% dos pixels divergirem com alta confiança")
+    parser.add_argument("--min_cross_frac", type=float, default=0.001,
+                        help="Flag para cana<->erva de alta confiança")
+    parser.add_argument("--min_target_disagree_frac", type=float, default=0.05)
+    parser.add_argument("--min_conflict_pixels", type=int, default=200)
+    parser.add_argument("--min_review_score", type=float, default=0.0,
+                        help="Opcional: também seleciona por score de ranking. 0 desliga.")
+    parser.add_argument("--top_k_per_group", type=int, default=0,
+                        help="Se >0, ignora flags como filtro final e pega os K scores maiores de cada grupo")
+    parser.add_argument("--max_review_per_group", type=int, default=0,
+                        help="Cap dos candidatos por grupo após filtros. 0=sem limite")
+    parser.add_argument("--max_samples", type=int, default=0, help="Debug. 0=todos")
+    args = parser.parse_args()
+
+    if not (0.0 <= float(args.min_suspicion_pct) <= 100.0):
+        parser.error("--min_suspicion_pct deve ficar entre 0 e 100.")
+    if args.clear_review and args.clear_review_all:
+        parser.error("Use apenas um entre --clear_review e --clear_review_all.")
+    if args.ckpt and args.onnx:
+        parser.error("Use apenas um backend por execução: --ckpt OU --onnx.")
+    if args.onnx_warmup < 0:
+        parser.error("--onnx_warmup deve ser >= 0.")
+
+    script_here = Path(__file__).resolve()
+    test_script = resolve_path(args.test_script, Path.cwd()) if args.test_script else script_here.with_name("_9_test_multihead_v2.py")
+    base = load_test_module(test_script)
+
+    config_path = resolve_path(args.config, Path.cwd())
+    if config_path is None or not config_path.is_file():
+        raise FileNotFoundError(f"Config não encontrado: {config_path}")
+    config_dir = config_path.parent
+    config = load_json(config_path)
+    config["runtime_mode"] = "all"
+
+    input_channel_names = base.get_input_channel_names(config, args.channels)
+    channels = len(input_channel_names)
+
+    res = config.get("resolucao", [960, 600])
+    default_w, default_h = int(res[0]), int(res[1])
+    target_w = int(args.resize_w or default_w)
+    target_h = int(args.resize_h or default_h)
+
+    dataset_path = config_dir / "dataset"
+    labelmap_path = dataset_path / "labelmap.txt"
+    semantic_id2label, semantic_label2id, labelmap_ignore_id, loaded_cmap = base.load_labelmap(labelmap_path)
+    ignore_id = int(args.ignore_id if args.ignore_id is not None else labelmap_ignore_id)
+
+    heads_config = base.build_heads_config(config, ignore_index=ignore_id)
+    heads_config["semantic"]["num_classes"] = int(len(semantic_id2label))
+    heads_config["semantic"]["ignore_index"] = ignore_id
+
+    semantic_cmap = dict(base.SEMANTIC_COLORS_RGB)
+    semantic_cmap.update({int(k): tuple(map(int, v)) for k, v in loaded_cmap.items()})
+
+    save_dir = base.infer_save_dir(config, config_dir, channels)
+
+    backend = "onnx" if args.onnx else "pytorch"
+    ckpt_path: Optional[Path] = None
+    onnx_path: Optional[Path] = None
+    onnx_meta: dict = {}
+    onnx_meta_path: Optional[Path] = None
+
+    if backend == "onnx":
+        onnx_path = resolve_path(args.onnx, Path.cwd())
+        if onnx_path is None or not onnx_path.is_file():
+            raise FileNotFoundError(f"ONNX não encontrado: {onnx_path}")
+        onnx_meta, onnx_meta_path = load_onnx_export_meta(onnx_path)
+        model_path = onnx_path
+    else:
+        ckpt_path = base.find_checkpoint(save_dir, args.ckpt)
+        model_path = ckpt_path
+
+    if args.norm_stats:
+        norm_stats_path = resolve_path(args.norm_stats, Path.cwd())
+    else:
+        candidates = [
+            save_dir / "norm_stats.json",
+            dataset_path / f"{default_w}x{default_h}" / "group" / "norm_stats.json",
+        ]
+        norm_stats_path = next((p for p in candidates if p.is_file()), candidates[0])
+
+    mean, std = base.load_norm_stats(norm_stats_path, channel_names=input_channel_names)
+    if mean is None or std is None:
+        raise FileNotFoundError(
+            f"norm_stats obrigatório para auditoria comparável ao treino: {norm_stats_path}"
+        )
+
+    root = resolve_path(args.root, Path.cwd()) if args.root else (dataset_path / f"{default_w}x{default_h}" / "group").resolve()
+    if root is None or not root.is_dir():
+        raise FileNotFoundError(f"Root não encontrado: {root}")
+
+    samples, has_gt = base.collect_samples(root, heads_config=heads_config, require_masks=True)
+    if not has_gt:
+        raise RuntimeError("Dataset não possui GT. Este script precisa das masks para medir conflito.")
+    if args.max_samples > 0:
+        samples = samples[: args.max_samples]
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    threshold_from_model: Optional[float] = None
+    threshold_source = "none"
+    onnx_norm_embedded = False
+
+    if backend == "onnx":
+        if not hasattr(base, "OnnxMultiHeadTester"):
+            raise RuntimeError(
+                "Seu _9_test_multihead_v2.py não possui OnnxMultiHeadTester. "
+                "Use a versão atualizada do script de teste."
+            )
+
+        onnx_norm_embedded = onnx_normalization_embedded(onnx_meta, args.onnx_norm)
+        if args.onnx_norm == "auto" and not onnx_meta:
+            print(
+                "[ONNX][WARN] .export_meta.json não encontrado. "
+                "Assumindo normalização EXTERNA via norm_stats. "
+                "Use --onnx_norm embedded se o grafo já incluir normalização."
+            )
+
+        tester = base.OnnxMultiHeadTester(
+            config=config,
+            onnx_path=onnx_path,
+            provider=args.onnx_provider,
+            input_channel_names=input_channel_names,
+            heads_config=heads_config,
+            mean=None if onnx_norm_embedded else mean,
+            std=None if onnx_norm_embedded else std,
+            trt_home=args.trt_home,
+            trt_fp16=not args.trt_no_fp16,
+        )
+        validate_onnx_for_audit(tester, onnx_meta, onnx_path)
+        threshold_from_model, threshold_source = resolve_onnx_operational_threshold(
+            onnx_meta, onnx_meta_path, onnx_path
+        )
+    else:
+        tester = base.MultiHeadTester(
+            config=config,
+            ckpt_path=ckpt_path,
+            device=device,
+            input_channel_names=input_channel_names,
+            heads_config=heads_config,
+            semantic_id2label=semantic_id2label,
+            semantic_label2id=semantic_label2id,
+            mean=mean,
+            std=std,
+            use_amp=not args.no_amp,
+        )
+        threshold_from_model = getattr(tester, "ckpt_operational_threshold", None)
+        threshold_source = "checkpoint" if threshold_from_model is not None else "none"
+
+    threshold_arg = str(args.target_threshold).strip().lower()
+    if threshold_arg == "auto":
+        if threshold_from_model is not None:
+            tester.target_threshold = float(threshold_from_model)
+            print(f"[TARGET] auto -> {tester.target_threshold:.3f} ({threshold_source})")
+        else:
+            tester.target_threshold = 0.5
+            print("[TARGET][WARN] threshold operacional não encontrado; usando 0.5")
+    else:
+        tester.target_threshold = float(threshold_arg)
+        threshold_source = "cli"
+    if not 0.0 <= tester.target_threshold <= 1.0:
+        raise RuntimeError("--target_threshold deve ficar entre 0 e 1")
+
+    module_params_config = config.get("module_params_json")
+    if module_params_config:
+        p = Path(str(module_params_config))
+        if not p.is_absolute():
+            p = config_dir / p
+        module_params_config = str(p.resolve())
+
+    out_root = resolve_path(args.out_root, Path.cwd())
+    if out_root is None:
+        raise RuntimeError("out_root inválido")
+    review_group_root = out_root / "group"
+    run_name = args.run_name or (model_path.stem + (f"_{args.onnx_provider}" if backend == "onnx" else ""))
+    report_dir = out_root / "reports" / run_name
+    ensure_dir(report_dir)
+
+    if not args.report_only:
+        if args.clear_review_all:
+            print(f"[WARN] Limpando revisão COMPLETA, inclusive final_masks: {review_group_root}")
+            clear_dir(review_group_root)
+        elif args.clear_review:
+            print(f"[INFO] Limpando artefatos regeneráveis e PRESERVANDO final_masks: {review_group_root}")
+            clear_generated_review(review_group_root)
+
+    # Confusion matrices globais.
+    cms_total = {
+        name: np.zeros((int(hcfg["num_classes"]), int(hcfg["num_classes"])), dtype=np.int64)
+        for name, hcfg in heads_config.items()
+    }
+
+    rows: List[dict] = []
+    cache_for_copy: Dict[Tuple[str, str], tuple] = {}
+
+    n = len(samples)
+    print("=" * 72)
+    print("DATASET REVIEW / MODEL MINING")
+    print(f"Root        : {root}")
+    print(f"Backend     : {backend}")
+    print(f"Model       : {model_path}")
+    if backend == "onnx":
+        print(f"Provider    : {args.onnx_provider}")
+        print(f"Norm ONNX   : {'embedded' if onnx_norm_embedded else 'external'}")
+        print(f"Export meta : {onnx_meta_path}")
+        print(f"Outputs     : {getattr(tester, 'output_names', [])}")
+    print(f"Samples     : {n}")
+    print(f"Resolution  : {target_w}x{target_h}")
+    print(f"Threshold   : {tester.target_threshold:.3f}")
+    print(f"High conf   : {args.high_confidence:.3f}")
+    print(f"Report only : {args.report_only}")
+    print(f"Export mode : {args.export_mode}")
+    if args.export_mode == "score":
+        print(f"Suspicion >=: {args.min_suspicion_pct:.1f}/100")
+    print("=" * 72)
+
+    warmup_seconds = 0.0
+    if backend == "onnx" and args.onnx_warmup > 0 and samples:
+        print(f"[ONNX] warmup={args.onnx_warmup} (fora das métricas de tempo)")
+        warm_sample = samples[0]
+        warm_chw = base.load_sample_tensor(
+            warm_sample,
+            requested_channels=input_channel_names,
+            derived_config=config.get("derived_channels", {}) or {},
+            target_size=(target_w, target_h),
+            module_params_path=module_params_config,
+        )
+        if (warm_chw.shape[2], warm_chw.shape[1]) != (target_w, target_h):
+            hwc = np.transpose(warm_chw, (1, 2, 0))
+            hwc = cv2.resize(hwc, (target_w, target_h), interpolation=cv2.INTER_LINEAR)
+            warm_chw = np.transpose(hwc, (2, 0, 1)).astype(np.float32)
+        tw = time.perf_counter()
+        for _ in range(int(args.onnx_warmup)):
+            tester.infer(warm_chw)
+        warmup_seconds = time.perf_counter() - tw
+        print(f"[ONNX] warmup concluído em {warmup_seconds:.2f}s")
+
+    t0_all = time.perf_counter()
+    infer_ms_sum = 0.0
+    infer_total_ms_sum = 0.0
+
+    for i, sample in enumerate(samples, 1):
+        chw = base.load_sample_tensor(
+            sample,
+            requested_channels=input_channel_names,
+            derived_config=config.get("derived_channels", {}) or {},
+            target_size=(target_w, target_h),
+            module_params_path=module_params_config,
+        )
+
+        if (chw.shape[2], chw.shape[1]) != (target_w, target_h):
+            hwc = np.transpose(chw, (1, 2, 0))
+            hwc = cv2.resize(hwc, (target_w, target_h), interpolation=cv2.INTER_LINEAR)
+            chw = np.transpose(hwc, (2, 0, 1)).astype(np.float32)
+
+        t_infer_wall0 = time.perf_counter()
+        preds, probs, t_inf = tester.infer(chw)
+        t_inf_total = (time.perf_counter() - t_infer_wall0) * 1000.0
+        infer_ms_sum += t_inf
+        infer_total_ms_sum += t_inf_total
+
+        gt_masks = {name: base.load_mask(path) for name, path in sample.masks.items()}
+        pred_sem = preds.get("semantic")
+        prob_sem = probs.get("semantic")
+        gt_sem = gt_masks.get("semantic")
+        if pred_sem is None or prob_sem is None or gt_sem is None:
+            raise RuntimeError(f"Amostra sem semantic necessário: {sample.base}")
+
+        gt_sem = resize_ids(gt_sem, pred_sem.shape[:2])
+        gt_masks["semantic"] = gt_sem
+
+        for head_name, pred in preds.items():
+            gt = gt_masks.get(head_name)
+            if gt is None:
+                continue
+            gt = resize_ids(gt, pred.shape[:2])
+            gt_masks[head_name] = gt
+            cms_total[head_name] += base.confusion_matrix_np(
+                pred,
+                gt,
+                int(heads_config[head_name]["num_classes"]),
+                ignore_id,
+            )
+
+        # GT target derivado, exatamente como no teste/treino.
+        gt_target = gt_masks.get("target")
+        if gt_target is None and gt_masks.get("vegetation") is not None and gt_masks.get("cana") is not None:
+            gt_v = resize_ids(gt_masks["vegetation"], pred_sem.shape[:2])
+            gt_c = resize_ids(gt_masks["cana"], pred_sem.shape[:2])
+            gt_target = base.operational_target_mask(gt_v, gt_c, ignore_id=ignore_id)
+
+        pred_target = preds.get("target")
+        if pred_target is None and preds.get("vegetation") is not None and preds.get("cana") is not None:
+            pred_target = base.operational_target_mask(
+                preds["vegetation"], preds["cana"], ignore_id=ignore_id
+            )
+
+        target_stats = None
+        target_disagree_frac = 0.0
+        if gt_target is not None and pred_target is not None:
+            gt_target = resize_ids(gt_target, pred_target.shape[:2])
+            target_stats = binary_target_stats(pred_target, gt_target, ignore_id)
+            valid_t = gt_target != ignore_id
+            den_t = int(valid_t.sum())
+            if den_t > 0:
+                target_disagree_frac = float(((pred_target != gt_target) & valid_t).sum() / den_t)
+            if "target" in cms_total:
+                cms_total["target"] += base.confusion_matrix_np(
+                    pred_target, gt_target, 2, ignore_id
+                )
+
+        signals = semantic_sample_signals(
+            pred=pred_sem,
+            probs=prob_sem,
+            gt=gt_sem,
+            ignore_id=ignore_id,
+            high_confidence=args.high_confidence,
+            uncertainty_margin=args.uncertainty_margin,
+        )
+
+        cm_sem_sample = base.confusion_matrix_np(pred_sem, gt_sem, len(semantic_id2label), ignore_id)
+        iou_sem, miou_sem, acc_sem = base.metrics_from_cm(cm_sem_sample)
+
+        score = review_score(signals, target_disagree_frac)
+
+        row = {
+            "index": i - 1,
+            "group": sample.group,
+            "base": sample.base,
+            "tensor_path": str(sample.tensor_path or ""),
+            "preview_path": str(sample.preview_path or ""),
+            "semantic_mask_path": str(sample.masks.get("semantic") or ""),
+            "inference_backend": backend,
+            "inference_provider": args.onnx_provider if backend == "onnx" else str(device),
+            "inference_ms": float(t_inf),
+            "inference_total_ms": float(t_inf_total),
+            "target_threshold": float(tester.target_threshold),
+            "review_score": score,
+            "suspicion_pct": float(score * 100.0),
+            "semantic_miou": float(miou_sem),
+            "semantic_acc": float(acc_sem),
+            "iou_chao": float(iou_sem[0]) if len(iou_sem) > 0 else 0.0,
+            "iou_cana": float(iou_sem[1]) if len(iou_sem) > 1 else 0.0,
+            "iou_erva": float(iou_sem[2]) if len(iou_sem) > 2 else 0.0,
+            **signals,
+            "target_disagree_frac": float(target_disagree_frac),
+            "target_iou": float(target_stats["iou"]) if target_stats else 0.0,
+            "target_f1": float(target_stats["f1"]) if target_stats else 0.0,
+            "target_precision": float(target_stats["precision"]) if target_stats else 0.0,
+            "target_recall": float(target_stats["recall"]) if target_stats else 0.0,
+        }
+
+        reasons = build_reasons(
+            row,
+            min_disagree_frac=args.min_disagree_frac,
+            min_highconf_disagree_frac=args.min_highconf_disagree_frac,
+            min_cross_frac=args.min_cross_frac,
+            min_target_disagree_frac=args.min_target_disagree_frac,
+            min_conflict_pixels=args.min_conflict_pixels,
+        )
+        if args.min_review_score > 0 and score >= args.min_review_score:
+            reasons.append("REVIEW_SCORE")
+        row["review_reasons"] = ";".join(dict.fromkeys(reasons))
+        row["flagged"] = int(bool(reasons))
+        rows.append(row)
+
+        # Cache somente do que já pode ser exportado. Em --export_mode all,
+        # não guardamos o dataset inteiro em RAM; a cópia refaz a inferência.
+        cache_now = False
+        if not args.report_only and args.top_k_per_group <= 0:
+            if args.export_mode == "flagged":
+                cache_now = bool(reasons)
+            elif args.export_mode == "score":
+                cache_now = (score * 100.0) >= float(args.min_suspicion_pct)
+
+        if cache_now:
+            cache_for_copy[(sample.group, sample.base)] = (sample, chw, gt_sem, pred_sem)
+
+        if i % 50 == 0 or i == n:
+            flagged_now = sum(int(r["flagged"]) for r in rows)
+            print(
+                f"[{i:5d}/{n}] flagged={flagged_now:4d} "
+                f"avg_inf={infer_ms_sum / i:.1f}ms "
+                f"avg_infer_total={infer_total_ms_sum / i:.1f}ms "
+                f"last_score={score:.3f} {sample.group}/{sample.base}"
+            )
+
+    elapsed = time.perf_counter() - t0_all
+
+    # ========================================================
+    # Seleção final para revisão física
+    # ========================================================
+    by_group: Dict[str, List[dict]] = defaultdict(list)
+    for row in rows:
+        by_group[str(row["group"])].append(row)
+
+    selected_rows: List[dict] = []
+    for group, grows in sorted(by_group.items()):
+        ordered = sorted(grows, key=lambda r: float(r["review_score"]), reverse=True)
+
+        if args.top_k_per_group > 0:
+            # top_k tem precedência sobre export_mode.
+            chosen = ordered[: args.top_k_per_group]
+            for r in chosen:
+                if not r["review_reasons"]:
+                    r["review_reasons"] = "TOP_K"
+                r["flagged"] = 1
+        elif args.export_mode == "all":
+            chosen = ordered
+            for r in chosen:
+                if not r["review_reasons"]:
+                    r["review_reasons"] = "EXPORT_ALL"
+        elif args.export_mode == "score":
+            threshold_score = float(args.min_suspicion_pct) / 100.0
+            chosen = [r for r in ordered if float(r["review_score"]) >= threshold_score]
+            for r in chosen:
+                if not r["review_reasons"]:
+                    r["review_reasons"] = "SUSPICION_SCORE"
+        else:
+            chosen = [r for r in ordered if int(r["flagged"]) == 1]
+
+        if args.max_review_per_group > 0:
+            chosen = chosen[: args.max_review_per_group]
+
+        for rank, r in enumerate(chosen, 1):
+            r["review_rank_group"] = rank
+        selected_rows.extend(chosen)
+
+    selected_keys = {(r["group"], r["base"]) for r in selected_rows}
+
+    # Copia apenas depois de finalizar ranking/caps.
+    if not args.report_only:
+        print(f"\n[COPY] candidatos finais: {len(selected_rows)}")
+        sample_by_key = {(s.group, s.base): s for s in samples}
+
+        for j, row in enumerate(selected_rows, 1):
+            key = (str(row["group"]), str(row["base"]))
+            sample = sample_by_key[key]
+
+            cached = cache_for_copy.get((sample.group, sample.base))
+            if cached is not None and cached[0].group == sample.group:
+                _, chw, gt_sem, pred_sem = cached
+            else:
+                chw = base.load_sample_tensor(
+                    sample,
+                    requested_channels=input_channel_names,
+                    derived_config=config.get("derived_channels", {}) or {},
+                    target_size=(target_w, target_h),
+                    module_params_path=module_params_config,
+                )
+                if (chw.shape[2], chw.shape[1]) != (target_w, target_h):
+                    hwc = np.transpose(chw, (1, 2, 0))
+                    hwc = cv2.resize(hwc, (target_w, target_h), interpolation=cv2.INTER_LINEAR)
+                    chw = np.transpose(hwc, (2, 0, 1)).astype(np.float32)
+                preds, _, _ = tester.infer(chw)
+                pred_sem = preds["semantic"]
+                gt_sem = resize_ids(base.load_mask(sample.masks["semantic"]), pred_sem.shape[:2])
+
+            save_review_item(
+                row=row,
+                sample=sample,
+                chw=chw,
+                gt_sem=gt_sem,
+                pred_sem=pred_sem,
+                review_group_root=review_group_root,
+                base_module=base,
+                semantic_cmap=semantic_cmap,
+                ignore_id=ignore_id,
+                input_channel_names=input_channel_names,
+                save_panels=args.save_panels,
+            )
+
+            if j % 50 == 0 or j == len(selected_rows):
+                print(f"  copiados {j}/{len(selected_rows)}")
+
+        # Ordem de revisão por grupo.
+        selected_by_group: Dict[str, List[dict]] = defaultdict(list)
+        for r in selected_rows:
+            selected_by_group[str(r["group"])].append(r)
+        for group, grows in selected_by_group.items():
+            order_path = review_group_root / group / "review_order.csv"
+            write_csv(
+                order_path,
+                sorted(grows, key=lambda r: int(r.get("review_rank_group", 999999))),
+            )
+
+    # ========================================================
+    # Relatórios globais
+    # ========================================================
+    report_path = report_dir / "review_report.csv"
+    candidates_path = report_dir / "review_candidates.csv"
+    write_csv(report_path, rows)
+    write_csv(candidates_path, selected_rows)
+
+    semantic_labels = [semantic_id2label[i] for i in sorted(semantic_id2label)]
+    head_labels = {
+        "semantic": semantic_labels,
+        "vegetation": ["background", "vegetation"],
+        "cana": ["not_cana", "cana"],
+        "target": ["background", "target"],
+    }
+
+    metrics_global = {}
+    for head_name, cm in cms_total.items():
+        if cm.sum() <= 0:
+            continue
+        labels = head_labels.get(head_name, [str(i) for i in range(cm.shape[0])])
+        metrics_global[head_name] = class_metrics_from_cm(cm, labels)
+        write_cm_csv(report_dir / f"confusion_{head_name}.csv", cm, labels)
+
+    cm_sem = cms_total.get("semantic")
+    directional = {}
+    if cm_sem is not None and cm_sem.shape[0] >= 3 and cm_sem.sum() > 0:
+        directional = {
+            "chao_to_cana": directional_rate(cm_sem, 0, 1),
+            "chao_to_erva": directional_rate(cm_sem, 0, 2),
+            "cana_to_chao": directional_rate(cm_sem, 1, 0),
+            "cana_to_erva": directional_rate(cm_sem, 1, 2),
+            "erva_to_chao": directional_rate(cm_sem, 2, 0),
+            "erva_to_cana": directional_rate(cm_sem, 2, 1),
+        }
+
+    # Estatísticas por grupo.
+    group_summary = {}
+    selected_set = {(str(r["group"]), str(r["base"])) for r in selected_rows}
+    for group, grows in sorted(by_group.items()):
+        scores = np.array([float(r["review_score"]) for r in grows], dtype=np.float64)
+        sem_dis = np.array([float(r["semantic_disagree_frac"]) for r in grows], dtype=np.float64)
+        hc_dis = np.array([float(r["highconf_disagree_frac"]) for r in grows], dtype=np.float64)
+        tgt_dis = np.array([float(r["target_disagree_frac"]) for r in grows], dtype=np.float64)
+        selected_n = sum((group, str(r["base"])) in selected_set for r in grows)
+        group_summary[group] = {
+            "samples": len(grows),
+            "selected_for_review": int(selected_n),
+            "selected_pct": float(100.0 * selected_n / max(len(grows), 1)),
+            "review_score_mean": float(scores.mean()) if len(scores) else 0.0,
+            "review_score_p95": float(np.percentile(scores, 95)) if len(scores) else 0.0,
+            "semantic_disagree_mean": float(sem_dis.mean()) if len(sem_dis) else 0.0,
+            "highconf_disagree_mean": float(hc_dis.mean()) if len(hc_dis) else 0.0,
+            "target_disagree_mean": float(tgt_dis.mean()) if len(tgt_dis) else 0.0,
+        }
+
+    all_scores = np.array([float(r["review_score"]) for r in rows], dtype=np.float64)
+    all_sem_dis = np.array([float(r["semantic_disagree_frac"]) for r in rows], dtype=np.float64)
+    all_hc = np.array([float(r["highconf_disagree_frac"]) for r in rows], dtype=np.float64)
+    all_unc = np.array([float(r["uncertain_frac"]) for r in rows], dtype=np.float64)
+
+    correct_pixels_total = sum(max(int(r.get("valid_pixels", 0)) - int(r.get("wrong_pixels", 0)), 0) for r in rows)
+    wrong_pixels_total = sum(int(r.get("wrong_pixels", 0)) for r in rows)
+    mean_conf_correct_global = (
+        sum(float(r.get("mean_conf_correct", 0.0)) * max(int(r.get("valid_pixels", 0)) - int(r.get("wrong_pixels", 0)), 0) for r in rows)
+        / max(correct_pixels_total, 1)
+    )
+    mean_conf_wrong_global = (
+        sum(float(r.get("mean_conf_wrong", 0.0)) * int(r.get("wrong_pixels", 0)) for r in rows)
+        / max(wrong_pixels_total, 1)
+    )
+
+    gt_chao_total = sum(int(r.get("gt_chao_pixels", 0)) for r in rows)
+    gt_cana_total = sum(int(r.get("gt_cana_pixels", 0)) for r in rows)
+    gt_erva_total = sum(int(r.get("gt_erva_pixels", 0)) for r in rows)
+    highconf_directional = {
+        "chao_to_cana": sum(int(r.get("hc_gt_chao_pred_cana_pixels", 0)) for r in rows) / max(gt_chao_total, 1),
+        "chao_to_erva": sum(int(r.get("hc_gt_chao_pred_erva_pixels", 0)) for r in rows) / max(gt_chao_total, 1),
+        "cana_to_erva": sum(int(r.get("hc_gt_cana_pred_erva_pixels", 0)) for r in rows) / max(gt_cana_total, 1),
+        "erva_to_cana": sum(int(r.get("hc_gt_erva_pred_cana_pixels", 0)) for r in rows) / max(gt_erva_total, 1),
+    }
+
+    summary = {
+        "schema": "oak_fcc3_dataset_review_v1",
+        "note": (
+            "Model-vs-GT disagreement is a review signal, not automatic proof of model error. "
+            "High-confidence disagreement can indicate label noise or a systematic model mistake."
+        ),
+        "run_name": run_name,
+        "config": str(config_path),
+        "root": str(root),
+        "backend": backend,
+        "model_path": str(model_path),
+        "checkpoint": str(ckpt_path) if ckpt_path is not None else None,
+        "onnx": str(onnx_path) if onnx_path is not None else None,
+        "onnx_provider": args.onnx_provider if backend == "onnx" else None,
+        "onnx_active_providers": list(getattr(getattr(tester, "session", None), "get_providers", lambda: [])()) if backend == "onnx" else None,
+        "onnx_export_meta": str(onnx_meta_path) if onnx_meta_path is not None else None,
+        "onnx_normalization_embedded": bool(onnx_norm_embedded) if backend == "onnx" else None,
+        "target_threshold_source": threshold_source,
+        "norm_stats": str(norm_stats_path),
+        "resolution": [target_w, target_h],
+        "input_channels": list(input_channel_names),
+        "target_threshold": float(tester.target_threshold),
+        "samples": len(rows),
+        "selected_for_review": len(selected_rows),
+        "selected_pct": float(100.0 * len(selected_rows) / max(len(rows), 1)),
+        "export": {
+            "mode": str(args.export_mode),
+            "min_suspicion_pct": float(args.min_suspicion_pct),
+            "report_only": bool(args.report_only),
+            "top_k_per_group": int(args.top_k_per_group),
+            "max_review_per_group": int(args.max_review_per_group),
+            "png_only": True,
+        },
+        "elapsed_seconds": float(elapsed),
+        "warmup_seconds_excluded": float(warmup_seconds),
+        "mean_inference_ms": float(infer_ms_sum / max(len(rows), 1)),
+        "mean_inference_total_ms": float(infer_total_ms_sum / max(len(rows), 1)),
+        "inference_timing_scope": (
+            "onnx_session_run" if backend == "onnx" else "pytorch_tester_reported"
+        ),
+        "review_thresholds": {
+            "high_confidence": args.high_confidence,
+            "uncertainty_margin": args.uncertainty_margin,
+            "min_disagree_frac": args.min_disagree_frac,
+            "min_highconf_disagree_frac": args.min_highconf_disagree_frac,
+            "min_cross_frac": args.min_cross_frac,
+            "min_target_disagree_frac": args.min_target_disagree_frac,
+            "min_conflict_pixels": args.min_conflict_pixels,
+            "min_review_score": args.min_review_score,
+            "top_k_per_group": args.top_k_per_group,
+            "max_review_per_group": args.max_review_per_group,
+        },
+        "review_score": {
+            "mean": float(all_scores.mean()) if len(all_scores) else 0.0,
+            "median": float(np.median(all_scores)) if len(all_scores) else 0.0,
+            "p95": float(np.percentile(all_scores, 95)) if len(all_scores) else 0.0,
+            "max": float(all_scores.max()) if len(all_scores) else 0.0,
+        },
+        "annotation_health_signals": {
+            "semantic_disagree_mean": float(all_sem_dis.mean()) if len(all_sem_dis) else 0.0,
+            "semantic_disagree_p95": float(np.percentile(all_sem_dis, 95)) if len(all_sem_dis) else 0.0,
+            "highconf_disagree_mean": float(all_hc.mean()) if len(all_hc) else 0.0,
+            "highconf_disagree_p95": float(np.percentile(all_hc, 95)) if len(all_hc) else 0.0,
+            "uncertain_mean": float(all_unc.mean()) if len(all_unc) else 0.0,
+            "mean_model_confidence_on_gt_agreement": float(mean_conf_correct_global),
+            "mean_model_confidence_on_gt_disagreement": float(mean_conf_wrong_global),
+        },
+        "directional_semantic_confusion": directional,
+        "high_confidence_directional_conflict": highconf_directional,
+        "metrics_global": metrics_global,
+        "groups": group_summary,
+        "paths": {
+            "review_report": str(report_path),
+            "review_candidates": str(candidates_path),
+            "review_group_root": str(review_group_root),
+        },
+    }
+
+    summary_path = report_dir / "review_summary.json"
+    with summary_path.open("w", encoding="utf-8") as f:
+        json.dump(summary, f, ensure_ascii=False, indent=2)
+
+    # README curto para não esquecer o significado do score.
+    readme = report_dir / "README_REVIEW.txt"
+    readme.write_text(
+        "\n".join([
+            "DATASET REVIEW / MODEL MINING",
+            "",
+            "IMPORTANTE:",
+            "- Discordancia modelo x GT nao prova que o modelo errou.",
+            "- Conflito de alta confianca e um candidato forte para revisao humana.",
+            "- review_score serve apenas para RANKING de suspeitos.",
+            "- suspicion_pct = review_score*100 e NAO e probabilidade de mascara errada.",
+            f"- export_mode={args.export_mode} min_suspicion_pct={args.min_suspicion_pct:.1f}",
+            "- Exportacao fisica usa somente PNG.",
+            "- --clear_review PRESERVA final_masks; --clear_review_all apaga tudo.",
+            "- Para comparar checkpoints, use --report_only e run_name diferentes.",
+            "",
+            f"Backend: {backend}",
+            f"Model: {model_path}",
+            f"Provider: {args.onnx_provider if backend == 'onnx' else device}",
+            f"Root: {root}",
+            f"Samples: {len(rows)}",
+            f"Selecionados: {len(selected_rows)}",
+        ]),
+        encoding="utf-8",
+    )
+
+    print("\n" + "=" * 72)
+    print("REVISÃO CONCLUÍDA")
+    print(f"Samples                  : {len(rows)}")
+    print(f"Selecionados p/ revisão  : {len(selected_rows)} ({100.0 * len(selected_rows) / max(len(rows), 1):.2f}%)")
+    print(f"Export mode              : {args.export_mode}")
+    if args.export_mode == "score":
+        print(f"Suspicion mínima         : {args.min_suspicion_pct:.1f}/100")
+    print(f"Tempo total              : {elapsed:.1f}s")
+    print(f"Backend                   : {backend}")
+    print(f"Inferência backend média  : {infer_ms_sum / max(len(rows), 1):.1f}ms")
+    print(f"Inferência total média    : {infer_total_ms_sum / max(len(rows), 1):.1f}ms")
+    if backend == "onnx":
+        print(f"ONNX provider             : {args.onnx_provider}")
+        print(f"Warmup excluído           : {warmup_seconds:.2f}s")
+    print(f"Report                   : {report_path}")
+    print(f"Candidates               : {candidates_path}")
+    print(f"Summary                  : {summary_path}")
+    if not args.report_only:
+        print(f"Revisão física           : {review_group_root}")
+
+    if directional:
+        print("\nConfusão semântica direcional global:")
+        print(f"  chão -> cana : {100.0 * directional['chao_to_cana']:.3f}%")
+        print(f"  chão -> erva : {100.0 * directional['chao_to_erva']:.3f}%")
+        print(f"  cana -> chão : {100.0 * directional['cana_to_chao']:.3f}%")
+        print(f"  cana -> erva : {100.0 * directional['cana_to_erva']:.3f}%")
+        print(f"  erva -> chão : {100.0 * directional['erva_to_chao']:.3f}%")
+        print(f"  erva -> cana : {100.0 * directional['erva_to_cana']:.3f}%")
+
+    sem_global = metrics_global.get("semantic")
+    if sem_global:
+        print(f"\nSemantic global: acc={sem_global['accuracy']:.4f} mIoU={sem_global['miou']:.4f}")
+    target_global = metrics_global.get("target")
+    if target_global:
+        print(f"Target global  : acc={target_global['accuracy']:.4f} mIoU={target_global['miou']:.4f}")
+
+    print("=" * 72)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/_9_test_multihead.py b/Python/OAK/datasets/oak-fcc-3/_9_test_multihead.py
index 4ad0a6fc3..8927b1bc7 100644
--- a/Python/OAK/datasets/oak-fcc-3/_9_test_multihead.py
+++ b/Python/OAK/datasets/oak-fcc-3/_9_test_multihead.py
@@ -25,10 +25,7 @@ Mostra:
 
 Exemplo:
 
-python .\\_9_test_infer_multihead.py ^
-  --config config.json ^
-  --split_folder val ^
-  --ckpt backup\segformer_b1\test_multi\stacked_raw5_multihead\best_score.pt
+python .\\_9_test_multihead.py --config config.json --split_folder val --ckpt backup\segformer_b1\test_multi\stacked_raw5_multihead\best_score.pt
 
 Controles:
   D / seta direita : próxima amostra
@@ -634,7 +631,20 @@ def collect_samples(root: Path, heads_config: Dict[str, dict], require_masks: bo
             missing = []
 
             for head_name, hcfg in heads_config.items():
-                mask_dir = group_dir / str(hcfg.get("mask_dir", "masks"))
+                mask_dir_name = str(hcfg.get("mask_dir", "masks"))
+                is_derived = (
+                    bool(hcfg.get("derived", False))
+                    or mask_dir_name == "__derived_target__"
+                )
+
+                # Heads derivadas não possuem máscara física.
+                # O GT target será construído posteriormente a partir de:
+                # vegetation == 1 AND cana == 0.
+                if is_derived:
+                    masks[head_name] = None
+                    continue
+
+                mask_dir = group_dir / mask_dir_name
                 mask_path = mask_dir / f"{base}.npy"
 
                 if mask_path.exists():
@@ -2002,14 +2012,25 @@ def main():
                 sample_metrics[head_name] = {"iou": iou, "miou": miou, "acc": acc}
                 metric_lines.append(f"{head_name}: mIoU={miou:.3f} acc={acc:.3f}")
 
-        if gt_target is not None:
-            cm_t = confusion_matrix_np(pred_target, gt_target, 2, ignore_id)
+        if gt_target is not None and pred_target_op is not None:
+            cm_t = confusion_matrix_np(pred_target_op, gt_target, 2, ignore_id)
             iou_t, miou_t, acc_t = metrics_from_cm(cm_t)
-            sample_metrics["target_op"] = {"iou": iou_t, "miou": miou_t, "acc": acc_t}
-            metric_lines.append(f"target_op: IoU_alvo={iou_t[1]:.3f} acc={acc_t:.3f}")
+
+            sample_metrics["target_op"] = {
+                "iou": iou_t,
+                "miou": miou_t,
+                "acc": acc_t,
+            }
+
+            metric_lines.append(
+                f"target_op: IoU_alvo={iou_t[1]:.3f} acc={acc_t:.3f}"
+            )
+
             if "target" in sample_metrics:
                 iou_head = sample_metrics["target"]["iou"]
-                metric_lines.append(f"target_head: IoU_alvo={iou_head[1]:.3f}")
+                metric_lines.append(
+                    f"target_head: IoU_alvo={iou_head[1]:.3f}"
+                )
 
         if idx not in visited:
             for head_name, pred in preds.items():
diff --git a/Python/OAK/datasets/oak-fcc-3/_9_test_multihead_v2.py b/Python/OAK/datasets/oak-fcc-3/_9_test_multihead_v2.py
new file mode 100644
index 000000000..2f1a526d5
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/_9_test_multihead_v2.py
@@ -0,0 +1,2406 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+r"""
+_9_test_multihead_v2_1.py
+
+Teste/visualização do SegFormer OAK-FCC-3 Multi-Head.
+
+Contrato esperado após normalize + split:
+
+  dataset/split/val/group/<grupo>/
+    tensors/<base>.npy              # CHW float32 conforme meta/config
+    masks/<base>.npy                # semantic: 0=chao, 1=cana, 2=erva, 255=ignore
+    masks_vegetation/<base>.npy     # vegetation: 0=background, 1=vegetation, 255=ignore
+    masks_cana/<base>.npy           # cana: 0=not_cana, 1=cana, 255=ignore
+    metas/<base>.json
+    previews/<base>.png
+
+Mostra:
+  - RGB preview do tensor
+  - GT/pred/overlay semantic
+  - GT/pred/overlay vegetation
+  - GT/pred/overlay cana
+  - alvo operacional = vegetation == 1 AND cana == 0
+  - mapas de confiança das heads binárias
+
+Exemplo:
+
+python .\\_9_test_multihead.py --config config.json --split_folder val --ckpt backup\segformer_b1\test_multi\stacked_raw5_multihead\best_score.pt
+
+Controles:
+  D / seta direita : próxima amostra
+  A / seta esquerda: amostra anterior
+  S               : salvar painel atual em --out_dir
+  SPACE           : alterna modo compacto/detalhado
+  Q / ESC         : sair
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import json
+import time
+import os
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Optional, Sequence, Tuple
+
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import SegformerForSemanticSegmentation
+
+from core.raw_processor_core import RawProcessorCore
+
+
+# ============================================================
+# Config default
+# ============================================================
+
+DEFAULT_HEADS = {
+    "semantic": {
+        "enabled": True,
+        "type": "multiclass",
+        "num_classes": 3,
+        "mask_dir": "masks",
+        "classes": {"chao": 0, "cana": 1, "erva": 2},
+        "ignore_index": 255,
+    },
+    "vegetation": {
+        "enabled": True,
+        "type": "binary",
+        "num_classes": 2,
+        "mask_dir": "masks_vegetation",
+        "classes": {"background": 0, "vegetation": 1},
+        "ignore_index": 255,
+    },
+    "cana": {
+        "enabled": True,
+        "type": "binary",
+        "num_classes": 2,
+        "mask_dir": "masks_cana",
+        "classes": {"not_cana": 0, "cana": 1},
+        "ignore_index": 255,
+    },
+    "target": {
+        "enabled": True,
+        "type": "binary",
+        "num_classes": 2,
+        "mask_dir": "__derived_target__",
+        "classes": {
+            "background": 0,
+            "target": 1,
+        },
+        "ignore_index": 255,
+        "derived_from": ["vegetation", "cana"],
+    },
+}
+
+SEMANTIC_COLORS_RGB = {
+    0: (85, 85, 85),      # chao
+    1: (0, 190, 0),       # cana
+    2: (230, 55, 55),     # erva
+}
+
+BINARY_COLORS_RGB = {
+    0: (30, 30, 30),
+    1: (0, 220, 80),
+}
+
+CANA_COLORS_RGB = {
+    0: (30, 30, 30),
+    1: (40, 210, 255),
+}
+
+TARGET_COLORS_RGB = {
+    0: (30, 30, 30),
+    1: (255, 70, 30),
+}
+
+
+@dataclass
+class SampleItem:
+    group: str
+    base: str
+    tensor_path: Optional[Path]
+    masks: Dict[str, Optional[Path]]
+    meta_path: Optional[Path] = None
+    preview_path: Optional[Path] = None
+
+    # novo
+    source_kind: str = "tensor"  # "tensor" ou "raw_native_multi"
+    raw_group: Optional[dict] = None
+
+
+# ============================================================
+# Util
+# ============================================================
+
+def load_json(path: str | Path) -> dict:
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def ensure_dir(path: Path):
+    path.mkdir(parents=True, exist_ok=True)
+
+
+def resolve_path(path_like: Optional[str], base: Optional[Path] = None) -> Optional[Path]:
+    if path_like is None:
+        return None
+    p = Path(path_like)
+    if p.is_absolute():
+        return p
+    if base is None:
+        base = Path.cwd()
+    return (base / p).resolve()
+
+
+def merge_dict(dst: dict, src: dict) -> dict:
+    out = copy.deepcopy(dst)
+
+    def rec(a, b):
+        for k, v in b.items():
+            if isinstance(v, dict) and isinstance(a.get(k), dict):
+                rec(a[k], v)
+            else:
+                a[k] = v
+
+    if isinstance(src, dict):
+        rec(out, src)
+    return out
+
+
+def build_heads_config(config: dict, ignore_index: int) -> Dict[str, dict]:
+    cfg = merge_dict(DEFAULT_HEADS, config.get("heads", {}) or {})
+    active = {}
+    for name, hcfg in cfg.items():
+        if not bool(hcfg.get("enabled", True)):
+            continue
+        hcfg.setdefault("ignore_index", ignore_index)
+        hcfg["ignore_index"] = int(hcfg.get("ignore_index", ignore_index))
+        hcfg["num_classes"] = int(hcfg.get("num_classes", 2))
+        hcfg["mask_dir"] = str(hcfg.get("mask_dir", "masks"))
+        active[name] = hcfg
+
+    for required in ("semantic", "vegetation", "cana"):
+        if required not in active:
+            raise RuntimeError(f"Head obrigatória ausente no config: {required}")
+
+    return active
+
+
+def load_labelmap(labelmap_path: Path) -> Tuple[Dict[int, str], Dict[str, int], int, Dict[int, Tuple[int, int, int]]]:
+    try:
+        from helpers import carregar_labelmap_completo, _infer_ignore_id
+
+        _cor_para_id, _colormap_rgb, id_para_nome, ignore_rgb = carregar_labelmap_completo(str(labelmap_path))
+        ignore_id = int(_infer_ignore_id(ignore_rgb, 255))
+
+        id2label = {
+            int(k): str(v)
+            for k, v in id_para_nome.items()
+            if str(v).lower() not in ("ignore", "void", "background_ignore")
+        }
+        label2id = {v.lower(): k for k, v in id2label.items()}
+
+        colormap_rgb = {}
+        if isinstance(_colormap_rgb, dict):
+            for k, v in _colormap_rgb.items():
+                ik = int(k)
+                if ik in id2label:
+                    colormap_rgb[ik] = tuple(map(int, v[:3]))
+        elif isinstance(_colormap_rgb, (list, tuple)):
+            for ik, v in enumerate(_colormap_rgb):
+                if ik in id2label:
+                    colormap_rgb[ik] = tuple(map(int, v[:3]))
+
+        return id2label, label2id, ignore_id, colormap_rgb
+
+    except Exception as e:
+        print(f"[WARN] Não consegui usar helpers.carregar_labelmap_completo: {e}")
+        print("[WARN] Usando parser simples do labelmap.")
+
+    id2label: Dict[int, str] = {}
+    colormap_rgb: Dict[int, Tuple[int, int, int]] = {}
+    ignore_id = 255
+    next_id = 0
+
+    with labelmap_path.open("r", encoding="utf-8") as f:
+        for raw_line in f:
+            s = raw_line.strip()
+            if not s or s.startswith("#"):
+                continue
+
+            name = None
+            color = None
+            cid = None
+
+            if ":" in s and not s.split(":", 1)[0].strip().isdigit():
+                name_part, rest = s.split(":", 1)
+                name = name_part.strip()
+                color_txt = rest.split("::", 1)[0].strip().strip(":")
+                rgb_parts = [p.strip() for p in color_txt.split(",") if p.strip()]
+                if len(rgb_parts) >= 3:
+                    color = tuple(int(float(p)) for p in rgb_parts[:3])
+            else:
+                parts = s.replace(",", " ").replace(":", " ").split()
+                if len(parts) >= 2 and parts[0].isdigit():
+                    cid = int(parts[0])
+                    name = parts[1]
+                    if len(parts) >= 5:
+                        color = tuple(int(float(p)) for p in parts[2:5])
+                elif len(parts) >= 1:
+                    name = parts[0]
+
+            if not name:
+                continue
+
+            if name.lower() in ("ignore", "void", "background_ignore"):
+                ignore_id = 255
+                continue
+
+            if cid is None:
+                cid = next_id
+            next_id = max(next_id, cid + 1)
+
+            id2label[int(cid)] = str(name)
+            if color is not None:
+                colormap_rgb[int(cid)] = color
+
+    if not id2label:
+        id2label = {0: "chao", 1: "cana", 2: "erva"}
+
+    label2id = {v.lower(): k for k, v in id2label.items()}
+    for cid in id2label:
+        colormap_rgb.setdefault(cid, SEMANTIC_COLORS_RGB.get(cid, (255, 255, 255)))
+
+    return id2label, label2id, int(ignore_id), colormap_rgb
+
+
+def find_raw_dataset_layout_root(path: Path) -> Optional[Path]:
+    """
+    Detecta layout bruto:
+      root/metas
+      root/previews
+      root/bins
+      root/masks opcional
+
+    Aceita root, root/metas, root/bins, root/previews ou arquivo dentro deles.
+    """
+    p = path.resolve()
+    candidates = []
+
+    if p.is_file():
+        candidates.append(p.parent)
+        candidates.append(p.parent.parent)
+    else:
+        candidates.append(p)
+        candidates.append(p.parent)
+
+    for c in candidates:
+        if not c:
+            continue
+
+        if c.name.lower() in ("metas", "metadata", "jsons", "previews", "bins", "masks"):
+            root = c.parent
+        else:
+            root = c
+
+        if (root / "metas").is_dir() and (root / "previews").is_dir() and (root / "bins").is_dir():
+            return root
+
+    return None
+
+
+def resolve_raw_sibling_file(root: Path, subdir: str, stem: str, exts: Tuple[str, ...]) -> Optional[Path]:
+    folder = root / subdir
+    if not folder.is_dir():
+        return None
+
+    for ext in exts:
+        p = folder / f"{stem}{ext}"
+        if p.exists():
+            return p
+
+    return None
+
+
+def resolve_raw_capture_group_from_json(json_path: Path, dataset_root: Path) -> dict:
+    """
+    Resolve uma captura bruta:
+      metas/<base>.json
+      previews/<base>.png
+      bins/<arquivos das cameras>
+      masks/<base> opcional
+    """
+    json_path = json_path.resolve()
+    meta = load_json(json_path)
+    base_name = json_path.stem
+    bins_dir = dataset_root / "bins"
+
+    preview_path = resolve_raw_sibling_file(
+        dataset_root,
+        "previews",
+        base_name,
+        (".png", ".jpg", ".jpeg"),
+    )
+
+    mask_path = resolve_raw_sibling_file(
+        dataset_root,
+        "masks",
+        base_name,
+        (".npy", ".png", ".tif", ".tiff"),
+    )
+
+    group = {
+        "json": json_path,
+        "png": preview_path,
+        "mask": mask_path,
+        "final_raw": None,
+        "cameras": {},
+        "dataset_root": dataset_root,
+    }
+
+    if "saved_payload_paths" in meta:
+        for cam_id, fname in meta["saved_payload_paths"].items():
+            fname_path = Path(fname)
+
+            candidates = [
+                bins_dir / fname_path.name,
+                json_path.parent / fname,
+                dataset_root / fname,
+                bins_dir / f"{base_name}_{cam_id}.bin",
+                bins_dir / f"{base_name}_{cam_id}.raw",
+                bins_dir / f"{base_name}_{cam_id.lower()}.bin",
+                bins_dir / f"{base_name}_{cam_id.lower()}.raw",
+            ]
+
+            found = next((c for c in candidates if c.exists()), None)
+            if found is not None:
+                group["cameras"][cam_id] = found
+            else:
+                print(f"[WARN] bin não encontrado para {cam_id}: {fname}")
+
+    return group
+
+
+def build_multispec_from_raw_native_multi_for_infer(
+    group: dict,
+    meta: dict,
+    target_size: Optional[Tuple[int, int]] = None,
+    module_params_path: Optional[str] = None,
+) -> Tuple[np.ndarray, dict]:
+    """
+    Gera tensor MULTISPEC [R,G,B,RE,NIR] em CHW float32 0..1
+    a partir dos bins RAW_BRUTO salvos.
+    """
+    if meta.get("saved_payload_type") != "raw_native_multi":
+        raise RuntimeError(
+            f"Captura bruta não suportada aqui: saved_payload_type={meta.get('saved_payload_type')}"
+        )
+
+    stream_meta = meta.get("stream_meta", {}) or {}
+    saved_dtypes = meta.get("saved_payload_dtypes", {}) or {}
+    saved_shapes = meta.get("saved_payload_shapes", {}) or {}
+
+    frame = {}
+
+    for cam_id, path in group["cameras"].items():
+        saved_dtype = saved_dtypes.get(cam_id)
+        saved_shape = saved_shapes.get(cam_id)
+
+        if saved_dtype is None or saved_shape is None:
+            raise RuntimeError(f"Faltam dtype/shape para {cam_id} no JSON: {group['json']}")
+
+        arr = np.fromfile(str(path), dtype=np.dtype(saved_dtype)).reshape(tuple(saved_shape))
+        frame[cam_id] = arr
+
+    if not frame:
+        raise RuntimeError(f"Nenhum bin de câmera encontrado para: {group['json']}")
+
+    sensor_width = int(meta.get("sensor_width", 1280))
+    sensor_height = int(meta.get("sensor_height", 800))
+    bayer = meta.get("bayer_pattern", "BGGR")
+
+    json_dir = Path(group["json"]).parent
+    calib_path = None
+    for candidate in (
+        module_params_path,
+        meta.get("camera_params_json"),
+        "calibration/module_params.json",
+    ):
+        if not candidate:
+            continue
+        p = Path(str(candidate))
+        for resolved in (p, Path.cwd() / p, json_dir / p):
+            if resolved.is_file():
+                calib_path = str(resolved.resolve())
+                break
+        if calib_path is not None:
+            break
+
+    if calib_path is None:
+        raise RuntimeError(
+            "module_params.json não encontrado. A inferência RAW deve usar a mesma "
+            "calibração do normalize; confira config.module_params_json."
+        )
+
+    core = RawProcessorCore(
+        sensor_width=sensor_width,
+        sensor_height=sensor_height,
+        bayer_pattern=bayer,
+        calibration_json_path=calib_path,
+    )
+
+    processing_meta = dict(stream_meta)
+    processing_meta["frame_type"] = "RAW_BRUTO"
+
+    if meta.get("actual_camera_controls") is not None:
+        processing_meta["actual_camera_controls"] = meta.get("actual_camera_controls")
+
+    if meta.get("startup_camera_controls") is not None:
+        processing_meta["startup_camera_controls"] = meta.get("startup_camera_controls")
+
+    tensor = core.build_infer_tensor_from_stream(
+        frame=frame,
+        meta=processing_meta,
+        channels_expected=5,
+        target_size=target_size,
+    )
+
+    if tensor is None:
+        raise RuntimeError(f"RawProcessorCore retornou tensor None para: {group['json']}")
+
+    tensor = np.asarray(tensor, dtype=np.float32)
+    tensor = np.nan_to_num(tensor, nan=0.0, posinf=1.0, neginf=0.0)
+
+    processing_info = {
+        "fusion_result": getattr(core, "last_fusion_result", None),
+        "fusion_config_used": getattr(core, "fusion_config", None),
+        "radiometric_normalization_result": getattr(core, "last_radiometric_normalization_result", None),
+        "patch_normalization_result": getattr(core, "last_patch_normalization_result", None),
+        "frame_quality": getattr(core, "last_frame_quality_result", None),
+    }
+
+    return tensor, processing_info
+
+
+SOURCE_CHANNEL_ORDER = ["R", "G", "B", "RE", "NIR"]
+DERIVED_CHANNEL_ORDER = ["NDVI", "NDRE"]
+SUPPORTED_INPUT_CHANNELS = SOURCE_CHANNEL_ORDER + DERIVED_CHANNEL_ORDER
+
+def get_input_channel_names(config: dict, channels_override: Optional[int] = None) -> List[str]:
+    if "input_channels" in config:
+        configured = config["input_channels"]
+        if isinstance(configured, str):
+            names = [str(c).strip().upper() for c in configured.split(",") if str(c).strip()]
+        else:
+            names = [str(c).strip().upper() for c in configured if str(c).strip()]
+    else:
+        n = int(channels_override or config.get("channels", 5))
+        if n < 1 or n > len(SOURCE_CHANNEL_ORDER):
+            raise RuntimeError(
+                "Config sem input_channels só suporta channels entre 1 e 5. "
+                "Para NDVI/NDRE, declare input_channels explicitamente."
+            )
+        names = SOURCE_CHANNEL_ORDER[:n]
+
+    if not names:
+        raise RuntimeError("input_channels não pode ser vazio.")
+
+    duplicates = sorted({name for name in names if names.count(name) > 1})
+    if duplicates:
+        raise RuntimeError(f"Canais duplicados em input_channels: {duplicates}")
+
+    invalid = [c for c in names if c not in SUPPORTED_INPUT_CHANNELS]
+    if invalid:
+        raise RuntimeError(
+            f"Canais inválidos em input_channels: {invalid}. "
+            f"Suportados: {SUPPORTED_INPUT_CHANNELS}"
+        )
+
+    expected_count = int(channels_override) if channels_override is not None else config.get("channels")
+    if expected_count is not None and int(expected_count) != len(names):
+        raise RuntimeError(
+            "channels incompatível com input_channels: "
+            f"channels={expected_count}, input_channels={names} ({len(names)} canais)."
+        )
+
+    return names
+
+
+def get_input_channel_indices(config: dict, channels_override: Optional[int] = None) -> List[int]:
+    """Indices do tensor normalizado v2, já salvo na ordem final."""
+    return list(range(len(get_input_channel_names(config, channels_override))))
+
+
+def build_model_input_tensor(
+    source_tensor: np.ndarray,
+    requested_channels: Sequence[str],
+    derived_config: Optional[dict] = None,
+) -> np.ndarray:
+    """Replica exatamente a derivação de canais usada pelo normalize."""
+    derived_config = derived_config or {}
+    eps = float(derived_config.get("epsilon", 1e-6))
+    clip_min = float(derived_config.get("clip_min", -1.0))
+    clip_max = float(derived_config.get("clip_max", 1.0))
+
+    if not np.isfinite(eps) or eps <= 0.0:
+        raise RuntimeError(f"derived_channels.epsilon inválido: {eps}")
+    if not np.isfinite(clip_min) or not np.isfinite(clip_max) or clip_min >= clip_max:
+        raise RuntimeError(
+            f"Faixa derived_channels inválida: clip_min={clip_min}, clip_max={clip_max}"
+        )
+
+    source = np.nan_to_num(
+        np.asarray(source_tensor, dtype=np.float32),
+        nan=0.0,
+        posinf=1.0,
+        neginf=0.0,
+    )
+    if source.ndim != 3 or source.shape[0] != len(SOURCE_CHANNEL_ORDER):
+        raise RuntimeError(
+            f"Tensor fonte inválido: esperado {SOURCE_CHANNEL_ORDER}, shape={source.shape}"
+        )
+
+    available = {
+        name: source[index]
+        for index, name in enumerate(SOURCE_CHANNEL_ORDER)
+    }
+
+    def normalized_difference(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+        denominator = a + b
+        result = np.zeros_like(a, dtype=np.float32)
+        np.divide(
+            a - b,
+            denominator,
+            out=result,
+            where=np.abs(denominator) > eps,
+        )
+        return np.clip(result, clip_min, clip_max).astype(np.float32, copy=False)
+
+    if "NDVI" in requested_channels:
+        available["NDVI"] = normalized_difference(available["NIR"], available["R"])
+    if "NDRE" in requested_channels:
+        available["NDRE"] = normalized_difference(available["NIR"], available["RE"])
+
+    return np.ascontiguousarray(
+        np.stack([available[str(name).upper()] for name in requested_channels], axis=0),
+        dtype=np.float32,
+    )
+
+
+# ============================================================
+# Dataset
+# ============================================================
+
+def collect_samples(root: Path, heads_config: Dict[str, dict], require_masks: bool = False) -> Tuple[List[SampleItem], bool]:
+    tensor_paths: List[Path] = []
+
+    direct = root / "tensors"
+    if direct.is_dir():
+        tensor_paths.extend(sorted(direct.glob("*.npy")))
+
+    group_root = root / "group"
+    if group_root.is_dir():
+        for gdir in sorted(group_root.iterdir()):
+            tdir = gdir / "tensors"
+            if tdir.is_dir():
+                tensor_paths.extend(sorted(tdir.glob("*.npy")))
+
+    if not tensor_paths:
+        tensor_paths.extend(sorted(root.glob("**/tensors/*.npy")))
+
+    # ============================================================
+    # MODO 1: dataset já normalizado com tensors/*.npy
+    # ============================================================
+    if tensor_paths:
+        samples: List[SampleItem] = []
+        has_any_gt = False
+
+        for tp in tensor_paths:
+            base = tp.stem
+            group_name = tp.parent.parent.name if tp.parent.name == "tensors" else "default"
+            group_dir = tp.parent.parent if tp.parent.name == "tensors" else tp.parent
+
+            masks: Dict[str, Optional[Path]] = {}
+            missing = []
+
+            for head_name, hcfg in heads_config.items():
+                mask_dir_name = str(hcfg.get("mask_dir", "masks"))
+                is_derived = (
+                    bool(hcfg.get("derived", False))
+                    or mask_dir_name == "__derived_target__"
+                )
+
+                # Heads derivadas não possuem máscara física.
+                # O GT target será construído posteriormente a partir de:
+                # vegetation == 1 AND cana == 0.
+                if is_derived:
+                    masks[head_name] = None
+                    continue
+
+                mask_dir = group_dir / mask_dir_name
+                mask_path = mask_dir / f"{base}.npy"
+
+                if mask_path.exists():
+                    masks[head_name] = mask_path
+                    has_any_gt = True
+                else:
+                    masks[head_name] = None
+                    missing.append(f"{head_name}:{mask_path}")
+
+            if require_masks and missing:
+                raise RuntimeError(f"Masks ausentes para {tp}: {missing}")
+
+            meta_path = group_dir / "metas" / f"{base}.json"
+            preview_path = group_dir / "previews" / f"{base}.png"
+
+            samples.append(SampleItem(
+                group=group_name,
+                base=base,
+                tensor_path=tp,
+                masks=masks,
+                meta_path=meta_path if meta_path.exists() else None,
+                preview_path=preview_path if preview_path.exists() else None,
+                source_kind="tensor",
+                raw_group=None,
+            ))
+
+        return samples, has_any_gt
+
+    # ============================================================
+    # MODO 2: dataset bruto com bins/metas/previews/masks
+    # ============================================================
+    raw_root = find_raw_dataset_layout_root(root)
+
+    if raw_root is None:
+        raise RuntimeError(
+            f"Nenhum tensor .npy encontrado e também não detectei layout bruto "
+            f"com bins/metas/previews em: {root}"
+        )
+
+    meta_paths = sorted((raw_root / "metas").glob("*.json"))
+    if not meta_paths:
+        raise RuntimeError(f"Nenhum meta .json encontrado em: {raw_root / 'metas'}")
+
+    samples: List[SampleItem] = []
+    has_any_gt = False
+
+    for mp in meta_paths:
+        base = mp.stem
+        meta = load_json(mp)
+
+        if meta.get("saved_payload_type") != "raw_native_multi":
+            print(f"[WARN] pulando {mp.name}: saved_payload_type={meta.get('saved_payload_type')}")
+            continue
+
+        raw_group = resolve_raw_capture_group_from_json(mp, raw_root)
+
+        masks: Dict[str, Optional[Path]] = {}
+
+        # Tenta masks específicas multi-head primeiro.
+        for head_name, hcfg in heads_config.items():
+            mask_dir = raw_root / str(hcfg.get("mask_dir", "masks"))
+            mask_path = None
+
+            mask_dir_name = str(hcfg.get("mask_dir", "masks"))
+
+            if mask_dir_name == "__derived_target__" or bool(hcfg.get("derived", False)):
+                masks[head_name] = None
+                continue
+
+            for ext in (".npy", ".png", ".tif", ".tiff"):
+                p = mask_dir / f"{base}{ext}"
+                if p.exists():
+                    mask_path = p
+                    break
+
+            masks[head_name] = mask_path
+            if mask_path is not None:
+                has_any_gt = True
+
+        # Se só existir masks/<base>.png ou .npy semantic, conecta na head semantic.
+        if masks.get("semantic") is None:
+            semantic_mask = resolve_raw_sibling_file(
+                raw_root,
+                "masks",
+                base,
+                (".npy", ".png", ".tif", ".tiff"),
+            )
+            if semantic_mask is not None:
+                masks["semantic"] = semantic_mask
+                has_any_gt = True
+
+        if require_masks and not any(p is not None for p in masks.values()):
+            raise RuntimeError(f"Mask ausente para captura bruta: {mp}")
+
+        preview_path = resolve_raw_sibling_file(
+            raw_root,
+            "previews",
+            base,
+            (".png", ".jpg", ".jpeg"),
+        )
+
+        samples.append(SampleItem(
+            group=raw_root.name,
+            base=base,
+            tensor_path=None,
+            masks=masks,
+            meta_path=mp,
+            preview_path=preview_path,
+            source_kind="raw_native_multi",
+            raw_group=raw_group,
+        ))
+
+    if not samples:
+        raise RuntimeError(f"Nenhuma captura raw_native_multi válida encontrada em: {raw_root}")
+
+    return samples, has_any_gt
+
+
+def load_tensor(
+    path: Path,
+    requested_channels: Sequence[str],
+    saved_channel_names: Optional[Sequence[str]] = None,
+) -> np.ndarray:
+    arr = np.load(str(path)).astype(np.float32)
+
+    if arr.ndim != 3:
+        raise RuntimeError(f"Tensor inválido {path}: shape={arr.shape}, esperado 3D")
+
+    # Normaliza para CHW. Os tensores atuais podem ter Raw5 + NDVI + NDRE.
+    if 1 <= arr.shape[0] <= len(SUPPORTED_INPUT_CHANNELS) and arr.shape[1] > 8 and arr.shape[2] > 8:
+        chw = arr
+    elif 1 <= arr.shape[-1] <= len(SUPPORTED_INPUT_CHANNELS) and arr.shape[0] > 8 and arr.shape[1] > 8:
+        chw = np.transpose(arr, (2, 0, 1))
+    else:
+        raise RuntimeError(f"Tensor com layout inesperado: {path} shape={arr.shape}")
+
+    requested = [str(x).strip().upper() for x in requested_channels]
+    saved = [str(x).strip().upper() for x in (saved_channel_names or [])]
+
+    if saved:
+        if len(saved) != chw.shape[0]:
+            raise RuntimeError(
+                f"Meta/tensor incompatíveis em {path}: meta channels={saved}, shape={chw.shape}"
+            )
+        if len(set(saved)) != len(saved):
+            raise RuntimeError(f"Meta possui canais duplicados em {path}: {saved}")
+        missing = [name for name in requested if name not in saved]
+        if missing:
+            raise RuntimeError(
+                f"Tensor {path} não contém canais pedidos {missing}. Disponíveis={saved}"
+            )
+        indices = [saved.index(name) for name in requested]
+    elif chw.shape[0] == len(requested):
+        # Compatibilidade com tensores antigos sem metadata de canais.
+        indices = list(range(len(requested)))
+        print(f"[WARN] {path.name}: sem channels no meta; assumindo ordem do config {requested}")
+    elif chw.shape[0] == len(SOURCE_CHANNEL_ORDER) and all(name in SOURCE_CHANNEL_ORDER for name in requested):
+        indices = [SOURCE_CHANNEL_ORDER.index(name) for name in requested]
+        print(f"[WARN] {path.name}: tensor Raw5 legado; usando ordem física {SOURCE_CHANNEL_ORDER}")
+    else:
+        raise RuntimeError(
+            f"Não é seguro inferir a ordem dos canais de {path}: shape={chw.shape}, "
+            f"pedidos={requested}. Garanta metas/<base>.json com o campo channels."
+        )
+
+    chw = chw[indices, :, :]
+
+    finite = np.isfinite(chw)
+    if finite.any():
+        mx = float(np.nanmax(chw[finite]))
+        if mx > 2.0 and mx <= 255.0:
+            chw = chw / 255.0
+        elif mx > 255.0:
+            chw = chw / 65535.0
+
+    # Não aplicar clip 0..1: NDVI/NDRE possuem faixa nominal -1..1.
+    chw = np.nan_to_num(chw, nan=0.0, posinf=1.0, neginf=-1.0)
+    return np.ascontiguousarray(chw, dtype=np.float32)
+
+
+def load_sample_tensor(
+    sample: SampleItem,
+    requested_channels: Sequence[str],
+    derived_config: Optional[dict] = None,
+    target_size: Optional[Tuple[int, int]] = None,
+    module_params_path: Optional[str] = None,
+) -> np.ndarray:
+    """
+    Carrega tensor de uma amostra.
+    - Se for tensor pronto: lê .npy.
+    - Se for RAW_BRUTO: gera MULTISPEC em tempo real a partir dos bins.
+    """
+    if sample.source_kind == "tensor":
+        if sample.tensor_path is None:
+            raise RuntimeError(f"Sample tensor sem tensor_path: {sample.base}")
+        meta = load_json(sample.meta_path) if sample.meta_path is not None else {}
+        saved_names = meta.get("channels") or meta.get("input_channels") or []
+        return load_tensor(sample.tensor_path, requested_channels, saved_names)
+
+    if sample.source_kind == "raw_native_multi":
+        if sample.raw_group is None or sample.meta_path is None:
+            raise RuntimeError(f"Sample raw sem raw_group/meta_path: {sample.base}")
+
+        meta = load_json(sample.meta_path)
+        tensor, _processing_info = build_multispec_from_raw_native_multi_for_infer(
+            sample.raw_group,
+            meta,
+            target_size=target_size,
+            module_params_path=module_params_path,
+        )
+
+        if tensor.ndim != 3:
+            raise RuntimeError(f"Tensor RAW gerado inválido: {sample.base} shape={tensor.shape}")
+
+        return build_model_input_tensor(tensor, requested_channels, derived_config)
+
+    raise RuntimeError(f"source_kind desconhecido: {sample.source_kind}")
+
+
+def load_mask(path: Optional[Path]) -> Optional[np.ndarray]:
+    if path is None:
+        return None
+    if path.suffix.lower() == ".npy":
+        mask = np.load(str(path))
+    else:
+        mask = cv2.imread(str(path), cv2.IMREAD_UNCHANGED)
+        if mask is None:
+            raise RuntimeError(f"Falha ao ler mask: {path}")
+        if mask.ndim == 3:
+            mask = mask[:, :, 0]
+    return mask.astype(np.int64)
+
+
+# ============================================================
+# Normalização
+# ============================================================
+
+def load_norm_stats(
+    path: Optional[Path],
+    channel_names: Sequence[str],
+) -> Tuple[Optional[List[float]], Optional[List[float]]]:
+    if path is None or not path.is_file():
+        if path is not None:
+            print(f"[NORM] não encontrei norm_stats em {path}. Usando tensor 0..1 sem padronização.")
+        return None, None
+
+    js = load_json(path)
+    mean = js.get("mean", None)
+    std = js.get("std", None)
+    names = [str(x).strip().upper() for x in js.get("channels", [])]
+    requested = [str(x).strip().upper() for x in channel_names]
+
+    if mean is None or std is None:
+        raise RuntimeError(f"norm_stats inválido, faltando mean/std: {path}")
+    if len(mean) != len(std):
+        raise RuntimeError(f"norm_stats inválido: mean={len(mean)} std={len(std)} em {path}")
+
+    if names:
+        if len(names) != len(mean):
+            raise RuntimeError(
+                f"norm_stats incompatível: channels={len(names)} mean={len(mean)} em {path}"
+            )
+        if len(set(names)) != len(names):
+            raise RuntimeError(f"norm_stats possui canais duplicados: {names}")
+        missing = [name for name in requested if name not in names]
+        if missing:
+            raise RuntimeError(
+                f"norm_stats não contém canais pedidos {missing}. Disponíveis={names}"
+            )
+        indices = [names.index(name) for name in requested]
+    elif len(mean) == len(requested):
+        indices = list(range(len(requested)))
+        print(f"[NORM][WARN] stats sem nomes; assumindo ordem do config {requested}")
+    elif len(mean) == len(SOURCE_CHANNEL_ORDER) and all(name in SOURCE_CHANNEL_ORDER for name in requested):
+        indices = [SOURCE_CHANNEL_ORDER.index(name) for name in requested]
+        print(f"[NORM][WARN] stats Raw5 legados; usando ordem {SOURCE_CHANNEL_ORDER}")
+    else:
+        raise RuntimeError(
+            f"Não é seguro mapear norm_stats sem nomes: mean={len(mean)}, pedidos={requested}"
+        )
+
+    mean = [float(mean[i]) for i in indices]
+    std = [float(std[i]) for i in indices]
+    if not np.all(np.isfinite(mean)) or not np.all(np.isfinite(std)) or any(x <= 0.0 for x in std):
+        raise RuntimeError(f"norm_stats contém valores inválidos: mean={mean} std={std}")
+
+    print(f"[NORM] usando {path}")
+    print(f"[NORM] channels={requested}")
+    print(f"[NORM] mean={mean}")
+    print(f"[NORM] std ={std}")
+    return mean, std
+
+
+# ============================================================
+# Modelo multi-head, igual ao treino
+# ============================================================
+
+def patch_segformer_encoder_input_channels(
+    segformer_encoder: nn.Module,
+    input_channel_names: Sequence[str],
+    init_mode: str = "zero_extra",
+):
+    """Replica a inicialização espectral do Agri Teacher V2/V2.1."""
+    names = [str(c).strip().upper() for c in input_channel_names]
+    if names == ["R", "G", "B"]:
+        return segformer_encoder
+
+    # Compatibilidade entre versões do transformers.
+    if hasattr(segformer_encoder, "encoder") and hasattr(segformer_encoder.encoder, "patch_embeddings"):
+        patch_owner = segformer_encoder.encoder.patch_embeddings[0]
+    elif hasattr(segformer_encoder, "stages") and len(segformer_encoder.stages) > 0:
+        patch_owner = segformer_encoder.stages[0].patch_embeddings
+    else:
+        raise RuntimeError("Não encontrei o primeiro patch embedding do SegFormer nesta versão do Transformers.")
+
+    proj = patch_owner.proj
+    old_weight = proj.weight.data.clone()
+    old_bias = proj.bias.data.clone() if proj.bias is not None else None
+
+    new_proj = nn.Conv2d(
+        in_channels=len(names),
+        out_channels=proj.out_channels,
+        kernel_size=proj.kernel_size,
+        stride=proj.stride,
+        padding=proj.padding,
+        dilation=proj.dilation,
+        groups=proj.groups,
+        bias=proj.bias is not None,
+        padding_mode=proj.padding_mode,
+    )
+
+    mode = str(init_mode or "zero_extra").lower()
+    with torch.no_grad():
+        if old_weight.shape[1] < 3:
+            raise RuntimeError(f"Patch embedding pré-treinado inesperado: weight={tuple(old_weight.shape)}")
+
+        pretrained = {"R": 0, "G": 1, "B": 2}
+        mean_rgb = old_weight[:, :3].mean(dim=1)
+        has_rgb = any(n in pretrained for n in names)
+
+        for dst, name in enumerate(names):
+            if name in pretrained:
+                new_proj.weight[:, dst].copy_(old_weight[:, pretrained[name]])
+            elif mode == "mean_rgb":
+                new_proj.weight[:, dst].copy_(mean_rgb)
+            elif mode == "scaled_mean":
+                new_proj.weight[:, dst].copy_(mean_rgb * (3.0 / max(3.0, float(len(names)))))
+            elif mode == "zero_extra" and has_rgb:
+                new_proj.weight[:, dst].zero_()
+            else:
+                new_proj.weight[:, dst].copy_(mean_rgb)
+
+        if old_bias is not None:
+            new_proj.bias.copy_(old_bias)
+
+    patch_owner.proj = new_proj
+    print(f"[MODEL] input patch RGB(3) -> {names} | init={mode}")
+    return segformer_encoder
+
+
+def replace_segformer_decode_classifier(decode_head: nn.Module, num_classes: int):
+    old = getattr(decode_head, "classifier", None)
+    if not isinstance(old, nn.Conv2d):
+        raise RuntimeError(f"decode_head.classifier inesperado: {type(old)}")
+
+    new = nn.Conv2d(
+        old.in_channels,
+        int(num_classes),
+        kernel_size=old.kernel_size,
+        stride=old.stride,
+        padding=old.padding,
+        dilation=old.dilation,
+        groups=old.groups,
+        bias=old.bias is not None,
+        padding_mode=old.padding_mode,
+    )
+    nn.init.xavier_uniform_(new.weight)
+    if new.bias is not None:
+        nn.init.zeros_(new.bias)
+    decode_head.classifier = new
+    return decode_head
+
+
+class MultiHeadSegFormer(nn.Module):
+    """Arquitetura compatível com o trainer antigo e com Agri Teacher V2/V2.1."""
+    def __init__(
+        self,
+        backbone: str,
+        input_channel_names: Sequence[str],
+        heads_config: Dict[str, dict],
+        semantic_id2label: Dict[int, str],
+        semantic_label2id: Dict[str, int],
+        model_cfg: Optional[dict] = None,
+    ):
+        super().__init__()
+        model_cfg = model_cfg or {}
+
+        semantic_classes = int(heads_config["semantic"].get("num_classes", len(semantic_id2label)))
+        names = [str(c).strip().upper() for c in input_channel_names]
+
+        base = SegformerForSemanticSegmentation.from_pretrained(
+            backbone,
+            num_labels=semantic_classes,
+            id2label={int(k): str(v) for k, v in semantic_id2label.items()},
+            label2id={str(k): int(v) for k, v in semantic_label2id.items()},
+            ignore_mismatched_sizes=True,
+        )
+
+        self.decoder_mode = str(model_cfg.get("decoder_mode", "separate")).lower()
+        self.spectral_input_init = str(model_cfg.get("spectral_input_init", "mean_rgb")).lower()
+
+        patch_segformer_encoder_input_channels(
+            base.segformer,
+            names,
+            init_mode=self.spectral_input_init,
+        )
+
+        base.config.num_channels = len(names)
+        base.config.input_channel_names = list(names)
+        base.config.agri_decoder_mode = self.decoder_mode
+        base.config.agri_spectral_input_init = self.spectral_input_init
+
+        self.segformer = base.segformer
+        self.heads_config = heads_config
+
+        if self.decoder_mode == "separate":
+            self.decode_heads = nn.ModuleDict()
+            for head_name, hcfg in heads_config.items():
+                h = copy.deepcopy(base.decode_head)
+                self.decode_heads[head_name] = replace_segformer_decode_classifier(
+                    h, int(hcfg["num_classes"])
+                )
+            self.shared_decode_head = None
+            self.classifiers = None
+
+        elif self.decoder_mode == "shared_light":
+            self.decode_heads = None
+            self.shared_decode_head = copy.deepcopy(base.decode_head)
+            old_classifier = self.shared_decode_head.classifier
+            if not isinstance(old_classifier, nn.Conv2d):
+                raise RuntimeError("SegFormer decode_head.classifier não é Conv2d.")
+
+            feature_channels = int(old_classifier.in_channels)
+            self.shared_decode_head.classifier = nn.Identity()
+            self.classifiers = nn.ModuleDict()
+
+            for head_name, hcfg in heads_config.items():
+                clf = nn.Conv2d(
+                    feature_channels,
+                    int(hcfg["num_classes"]),
+                    kernel_size=1,
+                    bias=True,
+                )
+                nn.init.xavier_uniform_(clf.weight)
+                nn.init.zeros_(clf.bias)
+                self.classifiers[head_name] = clf
+        else:
+            raise RuntimeError(
+                f"training_v2.model.decoder_mode inválido: {self.decoder_mode}"
+            )
+
+        self.config = base.config
+        print(f"[MODEL] decoder_mode={self.decoder_mode}")
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        head_names: Optional[Sequence[str]] = None,
+    ) -> Dict[str, torch.Tensor]:
+        outputs = self.segformer(
+            pixel_values=pixel_values,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+        hidden_states = outputs.hidden_states
+
+        if self.decoder_mode == "separate":
+            available = list(self.decode_heads.keys())
+            selected = available if head_names is None else [h for h in head_names if h in self.decode_heads]
+            return {name: self.decode_heads[name](hidden_states) for name in selected}
+
+        # shared_light: calcula o decoder compartilhado uma vez e só executa os classifiers pedidos.
+        features = self.shared_decode_head(hidden_states)
+        available = list(self.classifiers.keys())
+        selected = available if head_names is None else [h for h in head_names if h in self.classifiers]
+        return {name: self.classifiers[name](features) for name in selected}
+
+
+class MultiHeadTester:
+    def __init__(
+        self,
+        config: dict,
+        ckpt_path: Path,
+        device: torch.device,
+        input_channel_names: Sequence[str],
+        heads_config: Dict[str, dict],
+        semantic_id2label: Dict[int, str],
+        semantic_label2id: Dict[str, int],
+        mean: Optional[Sequence[float]],
+        std: Optional[Sequence[float]],
+        use_amp: bool = True,
+    ):
+        self.config = config
+        self.ckpt_path = ckpt_path
+        self.device = device
+        self.input_channel_names = [str(c).strip().upper() for c in input_channel_names]
+        self.channels = len(self.input_channel_names)
+        channels = self.channels
+        self.heads_config = heads_config
+        self.use_amp = use_amp and device.type == "cuda"
+        self.runtime_mode = str(config.get("runtime_mode", "all")).lower()
+        self.target_threshold = 0.5
+        self.ckpt_operational_threshold = None
+
+        self.mean = None if mean is None else torch.tensor(mean, dtype=torch.float32).view(1, channels, 1, 1).to(device)
+        self.std = None if std is None else torch.tensor(std, dtype=torch.float32).view(1, channels, 1, 1).to(device)
+
+        backbone = config.get("backbone", config.get("pretrained_model", "nvidia/mit-b1"))
+        print(f"[MODEL] backbone={backbone}")
+        print(f"[MODEL] ckpt={ckpt_path}")
+
+        training_v2 = config.get("training_v2", {}) or {}
+        model_cfg = training_v2.get("model", {}) or {}
+        print(f"[MODEL] config decoder_mode={model_cfg.get('decoder_mode', 'separate')} | spectral_init={model_cfg.get('spectral_input_init', 'mean_rgb')}")
+
+        self.model = MultiHeadSegFormer(
+            backbone=backbone,
+            input_channel_names=self.input_channel_names,
+            heads_config=heads_config,
+            semantic_id2label=semantic_id2label,
+            semantic_label2id=semantic_label2id,
+            model_cfg=model_cfg,
+        )
+
+        self._load_checkpoint(ckpt_path)
+        self.model.to(device)
+        self.model.eval()
+
+    def _load_checkpoint(self, ckpt_path: Path):
+        ckpt = torch.load(str(ckpt_path), map_location="cpu", weights_only=False)
+
+        saved_contract = ckpt.get("model_contract", {}) if isinstance(ckpt, dict) else {}
+        saved_contract = saved_contract or {}
+        saved_channels = int(saved_contract.get("num_channels", 0) or 0)
+        saved_names = [str(x).strip().upper() for x in saved_contract.get("input_channel_names", [])]
+        legacy_default_names = SOURCE_CHANNEL_ORDER[:self.channels]
+        if not saved_names and self.input_channel_names != legacy_default_names:
+            raise RuntimeError(
+                "Checkpoint antigo sem model_contract.input_channel_names não pode ser "
+                f"validado para a ordem atual {self.input_channel_names}. Reexporte/treine com "
+                "o train_multihead atualizado ou use um checkpoint nominal compatível."
+            )
+        if not saved_names:
+            print(
+                f"[MODEL][WARN] checkpoint legado sem nomes; assumindo {legacy_default_names}"
+            )
+        if saved_channels and saved_channels != self.channels:
+            raise RuntimeError(
+                f"Checkpoint incompatível: salvou {saved_channels} canais, "
+                f"config atual pede {self.channels}."
+            )
+        if saved_names and saved_names != self.input_channel_names:
+            raise RuntimeError(
+                f"Checkpoint incompatível: canais salvos={saved_names}, "
+                f"config atual={self.input_channel_names}."
+            )
+
+        saved_decoder = str(saved_contract.get("decoder_mode", "") or "").lower()
+        current_decoder = str(getattr(self.model.config, "agri_decoder_mode", "") or "").lower()
+        if saved_decoder and current_decoder and saved_decoder != current_decoder:
+            raise RuntimeError(
+                f"Checkpoint decoder_mode={saved_decoder}, mas config/teste criou decoder_mode={current_decoder}. "
+                "Use o mesmo training_v2.model.decoder_mode do treinamento."
+            )
+
+        saved_spectral = str(saved_contract.get("spectral_input_init", "") or "").lower()
+        current_spectral = str(getattr(self.model.config, "agri_spectral_input_init", "") or "").lower()
+        if saved_spectral and current_spectral and saved_spectral != current_spectral:
+            raise RuntimeError(
+                f"Checkpoint spectral_input_init={saved_spectral}, mas config/teste usa {current_spectral}."
+            )
+
+        extra = ckpt.get("extra", {}) if isinstance(ckpt, dict) else {}
+        score_now = extra.get("score_now", {}) if isinstance(extra, dict) else {}
+        try:
+            op_thr = score_now.get("operational_threshold")
+            if op_thr is not None:
+                self.ckpt_operational_threshold = float(op_thr)
+        except Exception:
+            self.ckpt_operational_threshold = None
+
+        if isinstance(ckpt, dict):
+            for key in ("model", "model_state", "model_state_dict", "state_dict"):
+                if key in ckpt and isinstance(ckpt[key], dict):
+                    state = ckpt[key]
+                    break
+            else:
+                state = ckpt
+        else:
+            raise RuntimeError(f"Checkpoint em formato inesperado: {type(ckpt)}")
+
+        clean = {}
+        for k, v in state.items():
+            nk = k
+            for prefix in ("module.", "model."):
+                if nk.startswith(prefix):
+                    nk = nk[len(prefix):]
+            clean[nk] = v
+
+        try:
+            self.model.load_state_dict(clean, strict=True)
+        except RuntimeError as exc:
+            raise RuntimeError(
+                "Checkpoint incompatível com a arquitetura/canais atuais. "
+                "Use o mesmo input_channels e heads empregados no treinamento.\n"
+                f"Detalhe original: {exc}"
+            ) from exc
+        print("[MODEL] checkpoint carregado com strict=True")
+
+    def _normalize(self, x: torch.Tensor) -> torch.Tensor:
+        if self.mean is not None and self.std is not None:
+            return (x - self.mean) / torch.clamp(self.std, min=1e-6)
+        return x
+
+    @torch.inference_mode()
+    def infer(self, chw_01: np.ndarray) -> Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray], float]:
+        x = torch.from_numpy(chw_01).unsqueeze(0).to(self.device, non_blocking=True)
+        x = self._normalize(x)
+
+        h, w = int(chw_01.shape[1]), int(chw_01.shape[2])
+
+        if self.device.type == "cuda":
+            torch.cuda.synchronize()
+        t0 = time.perf_counter()
+
+        with torch.autocast(device_type="cuda", dtype=torch.float16, enabled=self.use_amp):
+            head_names = None
+            if self.runtime_mode in ("target_direct", "target_head"):
+                head_names = ["target"]
+            elif self.runtime_mode in ("operational", "target_op"):
+                head_names = ["vegetation", "cana"]
+
+            logits_by_head = self.model(pixel_values=x, head_names=head_names)
+
+            preds = {}
+            probs = {}
+
+            for head_name, logits in logits_by_head.items():
+                logits = F.interpolate(logits, size=(h, w), mode="bilinear", align_corners=False)
+                prob = torch.softmax(logits, dim=1)[0]
+                if head_name == "target" and prob.shape[0] > 1:
+                    pred = (prob[1] >= float(self.target_threshold)).to(torch.uint8)
+                else:
+                    pred = torch.argmax(prob, dim=0).to(torch.uint8)
+                preds[head_name] = pred.detach().cpu().numpy().astype(np.uint8)
+                probs[head_name] = prob.detach().cpu().numpy().astype(np.float32)
+
+        if self.device.type == "cuda":
+            torch.cuda.synchronize()
+        t_ms = (time.perf_counter() - t0) * 1000.0
+
+        return preds, probs, t_ms
+
+
+class OnnxMultiHeadTester:
+    def __init__(
+        self,
+        config: dict,
+        onnx_path: Path,
+        provider: str,
+        input_channel_names: Sequence[str],
+        heads_config: Dict[str, dict],
+        mean: Optional[Sequence[float]],
+        std: Optional[Sequence[float]],
+        trt_home: Optional[str] = None,
+        trt_fp16: bool = True,
+    ):
+        self.config = config
+        self.onnx_path = onnx_path
+        self.provider = provider.lower()
+        self.input_channel_names = [str(x).strip().upper() for x in input_channel_names]
+        self.channels = len(self.input_channel_names)
+        channels = self.channels
+        self.heads_config = heads_config
+        self.runtime_mode = str(config.get("runtime_mode", "all")).lower()
+        self.target_threshold = 0.5
+
+        export_meta_path = onnx_path.with_suffix(".export_meta.json")
+        export_meta = load_json(export_meta_path) if export_meta_path.is_file() else {}
+        exported_names = [
+            str(x).strip().upper()
+            for x in export_meta.get("input_channel_names", [])
+        ]
+        if exported_names and exported_names != self.input_channel_names:
+            raise RuntimeError(
+                f"ONNX incompatível: canais exportados={exported_names}, "
+                f"config atual={self.input_channel_names}."
+            )
+
+        postprocess = str(export_meta.get("postprocess", "none")).lower()
+        if postprocess not in ("none", "resize_logits"):
+            raise RuntimeError(
+                f"Este comparador espera logits, mas o ONNX usa postprocess={postprocess}. "
+                "Exporte com --postprocess none ou resize_logits."
+            )
+
+        input_contract = export_meta.get("input_contract", {}) or {}
+        self.normalization_embedded = bool(
+            input_contract.get(
+                "normalization_embedded",
+                export_meta.get("include_norm", False),
+            )
+        )
+        if self.normalization_embedded:
+            self.mean = None
+            self.std = None
+            print("[ONNX_MODEL] normalização já embutida no grafo")
+        else:
+            self.mean = None if mean is None else np.asarray(mean, dtype=np.float32).reshape(1, channels, 1, 1)
+            self.std = None if std is None else np.asarray(std, dtype=np.float32).reshape(1, channels, 1, 1)
+
+        print(f"[ONNX_MODEL] onnx={onnx_path}")
+        print(f"[ONNX_MODEL] provider={provider}")
+
+        self.session = self._create_session(
+            onnx_path=onnx_path,
+            provider=provider,
+            trt_home=trt_home,
+            trt_fp16=trt_fp16,
+        )
+
+        self.input_name = self.session.get_inputs()[0].name
+        input_shape = self.session.get_inputs()[0].shape
+        if len(input_shape) != 4:
+            raise RuntimeError(f"Entrada ONNX inesperada: nome={self.input_name} shape={input_shape}")
+        onnx_channels = input_shape[1]
+        if isinstance(onnx_channels, int) and onnx_channels != self.channels:
+            raise RuntimeError(
+                f"ONNX incompatível: entrada possui {onnx_channels} canais, "
+                f"config atual pede {self.channels}."
+            )
+        self.output_names = [o.name for o in self.session.get_outputs()]
+        print(f"[ONNX_MODEL] input={self.input_name} shape={input_shape}")
+        print(f"[ONNX_MODEL] outputs={self.output_names}")
+
+    def _create_session(
+        self,
+        onnx_path: Path,
+        provider: str,
+        trt_home: Optional[str],
+        trt_fp16: bool,
+    ):
+        try:
+            import onnxruntime as ort
+        except ImportError:
+            raise ImportError(
+                "onnxruntime não está instalado. Use:\n"
+                "  pip install onnxruntime-gpu"
+            )
+
+        provider = provider.lower()
+
+        if provider == "tensorrt":
+            trt_home = trt_home or os.environ.get("TRT_HOME", r"C:\dev\TensorRT-10.10.0.31")
+
+            dll_dirs = [
+                os.path.join(trt_home, "lib"),
+                os.path.join(trt_home, "bin"),
+            ]
+
+            cuda_home = os.environ.get("CUDA_PATH")
+            if cuda_home:
+                dll_dirs.append(os.path.join(cuda_home, "bin"))
+
+            # fallback comum que vocês estão usando
+            dll_dirs.append(r"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.3\bin")
+            dll_dirs.append(r"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin")
+
+            for dll_dir in dll_dirs:
+                if os.path.isdir(dll_dir):
+                    try:
+                        os.add_dll_directory(dll_dir)
+                        print(f"[DLL] add_dll_directory: {dll_dir}")
+                    except Exception as e:
+                        print(f"[DLL][WARN] falha em {dll_dir}: {e}")
+
+        available = ort.get_available_providers()
+        print(f"[ONNX] providers disponíveis: {available}")
+
+        sess_options = ort.SessionOptions()
+        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+
+        if provider == "cpu":
+            providers = ["CPUExecutionProvider"]
+
+        elif provider == "cuda":
+            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
+
+        elif provider == "tensorrt":
+            cache_dir = onnx_path.parent / "trt_cache"
+            cache_dir.mkdir(parents=True, exist_ok=True)
+
+            trt_options = {
+                "device_id": 0,
+                "trt_fp16_enable": bool(trt_fp16),
+                "trt_engine_cache_enable": True,
+                "trt_engine_cache_path": str(cache_dir),
+                "trt_timing_cache_enable": True,
+                "trt_timing_cache_path": str(cache_dir),
+                "trt_max_workspace_size": 4 * 1024 * 1024 * 1024,
+            }
+
+            providers = [
+                ("TensorrtExecutionProvider", trt_options),
+                "CUDAExecutionProvider",
+                "CPUExecutionProvider",
+            ]
+
+        else:
+            raise RuntimeError(f"Provider ONNX inválido: {provider}")
+
+        providers_ok = [
+            p for p in providers
+            if (p[0] if isinstance(p, tuple) else p) in available
+        ]
+
+        if not providers_ok:
+            raise RuntimeError(f"Nenhum provider ONNX disponível. Pedido={providers}, disponíveis={available}")
+
+        session = ort.InferenceSession(
+            str(onnx_path),
+            sess_options=sess_options,
+            providers=providers_ok,
+        )
+
+        active = session.get_providers()
+        print(f"[ONNX] usando providers: {active}")
+
+        if provider == "tensorrt" and "TensorrtExecutionProvider" not in active:
+            raise RuntimeError(f"TensorRT solicitado, mas não ficou ativo. Providers ativos: {active}")
+
+        if provider == "cuda" and "CUDAExecutionProvider" not in active:
+            raise RuntimeError(f"CUDA solicitado, mas não ficou ativo. Providers ativos: {active}")
+
+        return session
+
+    def _normalize(self, x: np.ndarray) -> np.ndarray:
+        if self.mean is not None and self.std is not None:
+            return ((x - self.mean[0]) / np.clip(self.std[0], 1e-6, None)).astype(np.float32)
+        return x.astype(np.float32)
+
+    def _selected_heads(self) -> Optional[List[str]]:
+        if self.runtime_mode in ("target_direct", "target_head"):
+            return ["target"]
+        if self.runtime_mode in ("operational", "target_op"):
+            return ["vegetation", "cana"]
+        return None
+
+    @staticmethod
+    def _softmax_np(logits: np.ndarray, axis: int = 1) -> np.ndarray:
+        x = logits.astype(np.float32)
+        x = x - np.max(x, axis=axis, keepdims=True)
+        e = np.exp(x)
+        return e / np.clip(np.sum(e, axis=axis, keepdims=True), 1e-12, None)
+
+    @staticmethod
+    def _resize_logits_nchw(logits: np.ndarray, target_hw: Tuple[int, int]) -> np.ndarray:
+        n, c, h, w = logits.shape
+        th, tw = target_hw
+
+        if (h, w) == (th, tw):
+            return logits
+
+        out = np.empty((n, c, th, tw), dtype=np.float32)
+        for bi in range(n):
+            for ci in range(c):
+                out[bi, ci] = cv2.resize(
+                    logits[bi, ci].astype(np.float32),
+                    (tw, th),
+                    interpolation=cv2.INTER_LINEAR,
+                )
+        return out
+
+    def _map_outputs(self, outputs: List[np.ndarray]) -> Dict[str, np.ndarray]:
+        raw = {
+            name: arr.astype(np.float32)
+            for name, arr in zip(self.output_names, outputs)
+        }
+
+        mapped = {}
+        for head in self.heads_config.keys():
+            candidates = [
+                head,
+                f"{head}_logits",
+                f"output_{head}",
+            ]
+
+            found = None
+            for c in candidates:
+                if c in raw:
+                    found = c
+                    break
+
+            if found is not None:
+                mapped[head] = raw[found]
+
+        return mapped
+
+    def infer(self, chw_01: np.ndarray) -> Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray], float]:
+        h, w = int(chw_01.shape[1]), int(chw_01.shape[2])
+
+        x = self._normalize(chw_01)
+        x = np.expand_dims(x, axis=0).astype(np.float32)
+
+        t0 = time.perf_counter()
+        outputs = self.session.run(None, {self.input_name: x})
+        t_ms = (time.perf_counter() - t0) * 1000.0
+
+        logits_by_head = self._map_outputs(outputs)
+
+        selected = self._selected_heads()
+        if selected is not None:
+            logits_by_head = {
+                hname: logits
+                for hname, logits in logits_by_head.items()
+                if hname in selected
+            }
+
+        preds = {}
+        probs = {}
+
+        for head_name, logits in logits_by_head.items():
+            logits = self._resize_logits_nchw(logits, (h, w))
+            prob = self._softmax_np(logits, axis=1)[0]
+            if head_name == "target" and prob.shape[0] > 1:
+                pred = (prob[1] >= float(self.target_threshold)).astype(np.uint8)
+            else:
+                pred = np.argmax(prob, axis=0).astype(np.uint8)
+
+            preds[head_name] = pred
+            probs[head_name] = prob.astype(np.float32)
+
+        return preds, probs, t_ms
+
+
+# ============================================================
+# Checkpoints / paths
+# ============================================================
+
+def infer_experiment_tag(config: dict, channels: int) -> str:
+    explicit = str(config.get("stats_source_tag", "")).strip()
+    if explicit:
+        return explicit
+    fusion_mode = str(config.get("fusion_mode", "stacked")).strip()
+    return f"{fusion_mode}_raw{channels}"
+
+
+def infer_save_dir(config: dict, config_dir: Path, channels: int) -> Path:
+    model_name = config.get("model_name", "test_multi")
+    modelo_folder = config.get("modelo", "segformer_b1")
+    exp_tag = infer_experiment_tag(config, channels)
+    return (config_dir / "backup" / modelo_folder / model_name / exp_tag).resolve()
+
+
+def find_checkpoint(save_dir: Path, preferred: Optional[str] = None) -> Path:
+    if preferred is not None:
+        ckpt = Path(preferred)
+        if not ckpt.is_absolute():
+            ckpt_cwd = (Path.cwd() / ckpt).resolve()
+            ckpt_save = (save_dir / ckpt).resolve()
+            ckpt = ckpt_cwd if ckpt_cwd.is_file() else ckpt_save
+        if not ckpt.is_file():
+            raise FileNotFoundError(f"Checkpoint não encontrado: {ckpt}")
+        return ckpt
+
+    candidates = [
+        save_dir / "best_score.pt",
+        save_dir / "best_operational.pt",
+        save_dir / "best_legacy_score.pt",
+        save_dir / "best_target.pt",
+        save_dir / "best_cana_head.pt",
+        save_dir / "best_semantic_miou.pt",
+        save_dir / "last.pt",
+    ]
+    for c in candidates:
+        if c.is_file():
+            return c
+
+    raise FileNotFoundError("Nenhum checkpoint encontrado. Procurei:\n" + "\n".join(str(c) for c in candidates))
+
+
+def find_onnx_model(save_dir: Path, ckpt_path: Path, preferred: Optional[str] = None) -> Path:
+    """
+    Resolve o .onnx.
+
+    Se preferred for informado, usa ele.
+    Caso contrário, usa o mesmo stem do checkpoint:
+      best_score.pt -> best_score.onnx
+    """
+    if preferred:
+        p = Path(preferred)
+        if not p.is_absolute():
+            p_cwd = (Path.cwd() / p).resolve()
+            p_save = (save_dir / p).resolve()
+            p = p_cwd if p_cwd.is_file() else p_save
+
+        if not p.is_file():
+            raise FileNotFoundError(f"ONNX não encontrado: {p}")
+
+        return p.resolve()
+
+    p = ckpt_path.with_suffix(".onnx")
+
+    if not p.is_file():
+        alt = save_dir / f"{ckpt_path.stem}.onnx"
+        p = alt
+
+    if not p.is_file():
+        raise FileNotFoundError(
+            f"ONNX não encontrado para checkpoint {ckpt_path.name}. Procurei:\n"
+            f"  {ckpt_path.with_suffix('.onnx')}\n"
+            f"  {save_dir / (ckpt_path.stem + '.onnx')}\n"
+            f"Informe manualmente com --onnx."
+        )
+
+    return p.resolve()
+
+
+# ============================================================
+# Visualização
+# ============================================================
+
+def tensor_to_preview_rgb(
+    chw: np.ndarray,
+    channel_names: Sequence[str],
+    gamma: float = 0.85,
+) -> np.ndarray:
+    c, h, w = chw.shape
+    names = [str(x).strip().upper() for x in channel_names]
+    if all(name in names for name in ("R", "G", "B")):
+        indices = [names.index(name) for name in ("R", "G", "B")]
+        rgb = np.transpose(chw[indices], (1, 2, 0)).copy()
+    elif c >= 3:
+        rgb = np.transpose(chw[:3], (1, 2, 0)).copy()
+    else:
+        one = chw[0]
+        rgb = np.stack([one, one, one], axis=-1)
+
+    rgb = np.nan_to_num(rgb, nan=0.0, posinf=1.0, neginf=0.0)
+    lo = np.percentile(rgb, 1.0)
+    hi = np.percentile(rgb, 99.0)
+    if hi > lo:
+        rgb = (rgb - lo) / (hi - lo)
+    rgb = np.clip(rgb, 0.0, 1.0)
+    if gamma and gamma > 0:
+        rgb = np.power(rgb, gamma)
+    return (rgb * 255.0).astype(np.uint8)
+
+
+def ids_to_rgb(mask: np.ndarray, colormap_rgb: Dict[int, Tuple[int, int, int]], ignore_id: int = 255) -> np.ndarray:
+    h, w = mask.shape[:2]
+    out = np.zeros((h, w, 3), dtype=np.uint8)
+    for cid, color in colormap_rgb.items():
+        out[mask == cid] = color
+    out[mask == ignore_id] = (0, 0, 0)
+    return out
+
+
+def prob_to_heat_rgb(prob01: np.ndarray) -> np.ndarray:
+    p = np.clip(prob01, 0.0, 1.0)
+    u8 = (p * 255.0).astype(np.uint8)
+    bgr = cv2.applyColorMap(u8, cv2.COLORMAP_TURBO)
+    return cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+
+
+def overlay_rgb(base_rgb: np.ndarray, mask_rgb: np.ndarray, alpha: float) -> np.ndarray:
+    return cv2.addWeighted(base_rgb, 1.0 - alpha, mask_rgb, alpha, 0.0)
+
+
+def put_label(img_rgb: np.ndarray, title: str, subtitle: str = "") -> np.ndarray:
+    out = img_rgb.copy()
+    cv2.putText(out, title, (10, 26), cv2.FONT_HERSHEY_SIMPLEX, 0.68, (0, 0, 0), 4, cv2.LINE_AA)
+    cv2.putText(out, title, (10, 26), cv2.FONT_HERSHEY_SIMPLEX, 0.68, (255, 255, 255), 2, cv2.LINE_AA)
+    if subtitle:
+        cv2.putText(out, subtitle, (10, 52), cv2.FONT_HERSHEY_SIMPLEX, 0.48, (0, 0, 0), 3, cv2.LINE_AA)
+        cv2.putText(out, subtitle, (10, 52), cv2.FONT_HERSHEY_SIMPLEX, 0.48, (0, 255, 90), 1, cv2.LINE_AA)
+    return out
+
+
+def resize_panel(img: np.ndarray, size: Tuple[int, int]) -> np.ndarray:
+    w, h = size
+    if img.shape[1] == w and img.shape[0] == h:
+        return img
+    return cv2.resize(img, (w, h), interpolation=cv2.INTER_NEAREST)
+
+
+def compose_grid(panels: List[Tuple[str, np.ndarray, str]], cols: int = 3, max_width: int = 1800) -> np.ndarray:
+    if not panels:
+        return np.zeros((480, 640, 3), dtype=np.uint8)
+
+    base_h, base_w = panels[0][1].shape[:2]
+    labeled = []
+    for title, img, subtitle in panels:
+        img = resize_panel(img, (base_w, base_h))
+        labeled.append(put_label(img, title, subtitle))
+
+    rows = []
+    blank = np.zeros_like(labeled[0])
+    for i in range(0, len(labeled), cols):
+        row_imgs = labeled[i:i + cols]
+        while len(row_imgs) < cols:
+            row_imgs.append(blank.copy())
+        rows.append(np.hstack(row_imgs))
+
+    canvas = np.vstack(rows)
+
+    if canvas.shape[1] > max_width:
+        scale = max_width / canvas.shape[1]
+        canvas = cv2.resize(canvas, (int(canvas.shape[1] * scale), int(canvas.shape[0] * scale)), interpolation=cv2.INTER_AREA)
+
+    return canvas
+
+
+def class_percent(mask: np.ndarray, class_id: int, ignore_id: int = 255) -> float:
+    valid = mask != ignore_id
+    den = int(valid.sum())
+    if den <= 0:
+        return 0.0
+    return float(((mask == class_id) & valid).sum() * 100.0 / den)
+
+
+def compare_pred_equal_percent(a: Optional[np.ndarray], b: Optional[np.ndarray]) -> Optional[float]:
+    if a is None or b is None:
+        return None
+
+    if a.shape != b.shape:
+        b = cv2.resize(
+            b.astype(np.uint8),
+            (a.shape[1], a.shape[0]),
+            interpolation=cv2.INTER_NEAREST,
+        )
+
+    return float(np.mean(a == b) * 100.0)
+
+
+def diff_mask_rgb(a: Optional[np.ndarray], b: Optional[np.ndarray]) -> Optional[np.ndarray]:
+    if a is None or b is None:
+        return None
+
+    if a.shape != b.shape:
+        b = cv2.resize(
+            b.astype(np.uint8),
+            (a.shape[1], a.shape[0]),
+            interpolation=cv2.INTER_NEAREST,
+        )
+
+    diff = (a != b).astype(np.uint8) * 255
+    rgb = np.zeros((diff.shape[0], diff.shape[1], 3), dtype=np.uint8)
+    rgb[:, :, 0] = diff  # vermelho em RGB
+    return rgb
+
+
+# ============================================================
+# Métricas numpy
+# ============================================================
+
+def confusion_matrix_np(pred: np.ndarray, gt: np.ndarray, num_classes: int, ignore_id: int) -> np.ndarray:
+    if pred.shape != gt.shape:
+        pred = cv2.resize(pred.astype(np.uint8), (gt.shape[1], gt.shape[0]), interpolation=cv2.INTER_NEAREST)
+
+    valid = gt != ignore_id
+    valid &= gt >= 0
+    valid &= gt < num_classes
+    gt_v = gt[valid].astype(np.int64)
+    pred_v = pred[valid].astype(np.int64)
+    pred_v = np.clip(pred_v, 0, num_classes - 1)
+
+    cm = np.bincount(num_classes * gt_v + pred_v, minlength=num_classes * num_classes)
+    return cm.reshape(num_classes, num_classes).astype(np.int64)
+
+
+def metrics_from_cm(cm: np.ndarray) -> Tuple[np.ndarray, float, float]:
+    tp = np.diag(cm).astype(np.float64)
+    fp = cm.sum(axis=0).astype(np.float64) - tp
+    fn = cm.sum(axis=1).astype(np.float64) - tp
+    denom = tp + fp + fn
+    iou = np.divide(tp, denom, out=np.zeros_like(tp), where=denom > 0)
+    miou = float(np.mean(iou)) if len(iou) else 0.0
+    acc = float(tp.sum() / max(cm.sum(), 1))
+    return iou, miou, acc
+
+
+def operational_target_mask(veg_mask: np.ndarray, cana_mask: np.ndarray, ignore_id: int = 255) -> np.ndarray:
+    out = np.zeros_like(veg_mask, dtype=np.uint8)
+    ignore = (veg_mask == ignore_id) | (cana_mask == ignore_id)
+    out[(veg_mask == 1) & (cana_mask == 0)] = 1
+    out[ignore] = ignore_id
+    return out
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default="config.json")
+    parser.add_argument("--split_folder", default="val", choices=["train", "val", "test"])
+    parser.add_argument("--root_override", default=None)
+    parser.add_argument("--test_folder", default=None)
+    parser.add_argument("--ckpt", default=None)
+    parser.add_argument("--norm_stats", default=None)
+    parser.add_argument(
+        "--allow_missing_norm_stats",
+        action="store_true",
+        help="Permite inferência sem padronização (não recomendado).",
+    )
+    parser.add_argument("--channels", type=int, default=None)
+    parser.add_argument("--resize_w", type=int, default=None)
+    parser.add_argument("--resize_h", type=int, default=None)
+    parser.add_argument("--alpha", type=float, default=0.45)
+    parser.add_argument("--ignore_id", type=int, default=None)
+    parser.add_argument("--no_amp", action="store_true")
+    parser.add_argument("--require_masks", action="store_true")
+    parser.add_argument("--out_dir", default="outputs_test_multihead")
+    parser.add_argument("--start_idx", type=int, default=0)
+    parser.add_argument("--max_width", type=int, default=1800)
+    parser.add_argument("--runtime_mode", default="all", choices=["all", "target_direct", "operational"])
+    parser.add_argument(
+        "--target_threshold",
+        default="0.5",
+        help="Threshold da head target direta. Use número (ex. 0.7) ou 'auto' para usar o operational_threshold salvo no checkpoint.",
+    )
+    parser.add_argument("--onnx", default="")
+    parser.add_argument("--onnx_provider", default="", choices=["", "cpu", "cuda", "tensorrt"])
+    parser.add_argument("--trt_home", default=None)
+    parser.add_argument("--trt_no_fp16", action="store_true")
+    args = parser.parse_args()
+
+    config_path = resolve_path(args.config, Path.cwd())
+    if config_path is None or not config_path.is_file():
+        raise FileNotFoundError(f"Config não encontrado: {config_path}")
+
+    config_dir = config_path.parent
+    config = load_json(config_path)
+    config["runtime_mode"] = args.runtime_mode
+
+    input_channel_names = get_input_channel_names(config, args.channels)
+    input_channel_indices = get_input_channel_indices(config, args.channels)
+    channels = len(input_channel_names)
+
+    print(f"Input channels: {input_channel_names} idx={input_channel_indices}")
+    res = config.get("resolucao", [1024, 640])
+    default_w, default_h = int(res[0]), int(res[1])
+    target_w = int(args.resize_w or default_w)
+    target_h = int(args.resize_h or default_h)
+    module_params_config = config.get("module_params_json")
+    if module_params_config:
+        module_params_candidate = Path(str(module_params_config))
+        if not module_params_candidate.is_absolute():
+            module_params_candidate = config_dir / module_params_candidate
+        module_params_config = str(module_params_candidate.resolve())
+
+    dataset_path = config_dir / "dataset"
+    labelmap_path = dataset_path / "labelmap.txt"
+    semantic_id2label, semantic_label2id, labelmap_ignore_id, loaded_colormap_rgb = load_labelmap(labelmap_path)
+
+    ignore_id = int(args.ignore_id if args.ignore_id is not None else labelmap_ignore_id)
+    heads_config = build_heads_config(config, ignore_index=ignore_id)
+    heads_config["semantic"]["num_classes"] = int(len(semantic_id2label))
+    heads_config["semantic"]["ignore_index"] = int(ignore_id)
+
+    semantic_cmap = dict(SEMANTIC_COLORS_RGB)
+    semantic_cmap.update({int(k): tuple(map(int, v)) for k, v in loaded_colormap_rgb.items()})
+
+    save_dir = infer_save_dir(config, config_dir, channels)
+    ckpt_path = find_checkpoint(save_dir, args.ckpt)
+
+    if args.norm_stats is not None:
+        norm_stats_path = resolve_path(args.norm_stats, Path.cwd())
+    else:
+        # O treino multihead usa stats do dataset normalizado, mas também tentamos alguns fallbacks.
+        candidates = [
+            save_dir / "norm_stats.json",
+            dataset_path / f"{default_w}x{default_h}" / "group" / "norm_stats.json",
+            config_dir / "backup" / config.get("modelo", "segformer_b1") / config.get("model_name", "test_multi") / config.get("stats_source_tag", "stacked_raw5") / "norm_stats.json",
+        ]
+        norm_stats_path = next((p for p in candidates if p.is_file()), candidates[0])
+
+    mean, std = load_norm_stats(norm_stats_path, channel_names=input_channel_names)
+    if (mean is None or std is None) and not args.allow_missing_norm_stats:
+        raise FileNotFoundError(
+            f"norm_stats não encontrado em {norm_stats_path}. A inferência deve usar os "
+            "mesmos stats do treinamento. Informe --norm_stats ou, conscientemente, "
+            "use --allow_missing_norm_stats."
+        )
+
+    if args.test_folder is not None:
+        root = resolve_path(args.test_folder, Path.cwd())
+    elif args.root_override is not None:
+        root = resolve_path(args.root_override, Path.cwd())
+    else:
+        root = (dataset_path / "split" / args.split_folder).resolve()
+
+    if root is None or not root.is_dir():
+        raise FileNotFoundError(f"Root de dados não encontrado: {root}")
+
+    samples, has_gt = collect_samples(root, heads_config=heads_config, require_masks=args.require_masks)
+    n = len(samples)
+
+    print("==========================================")
+    print("Teste SegFormer OAK-FCC-3 Multi-Head")
+    print(f"Root        : {root}")
+    print(f"Samples     : {n}")
+    print(f"GT          : {'sim' if has_gt else 'não'}")
+    print(f"Resolution  : {target_w}x{target_h}")
+    print(f"Channels    : {channels}")
+    print(f"Semantic    : {semantic_id2label}")
+    print(f"Ignore index: {ignore_id}")
+    print("Heads:")
+    for name, hcfg in heads_config.items():
+        print(f"  - {name}: classes={hcfg['num_classes']} mask_dir={hcfg['mask_dir']}")
+    print("==========================================")
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Device: {device}")
+
+    tester = MultiHeadTester(
+        config=config,
+        ckpt_path=ckpt_path,
+        device=device,
+        input_channel_names=input_channel_names,
+        heads_config=heads_config,
+        semantic_id2label=semantic_id2label,
+        semantic_label2id=semantic_label2id,
+        mean=mean,
+        std=std,
+        use_amp=not args.no_amp,
+    )
+
+    target_threshold_arg = str(args.target_threshold).strip().lower()
+    if target_threshold_arg == "auto":
+        if tester.ckpt_operational_threshold is None:
+            tester.target_threshold = 0.5
+            print("[TARGET][WARN] checkpoint não possui operational_threshold; usando 0.5")
+        else:
+            tester.target_threshold = float(tester.ckpt_operational_threshold)
+            print(f"[TARGET] threshold=auto -> {tester.target_threshold:.3f} (salvo no checkpoint)")
+    else:
+        try:
+            tester.target_threshold = float(target_threshold_arg)
+        except ValueError as exc:
+            raise RuntimeError(f"--target_threshold inválido: {args.target_threshold}") from exc
+        if not (0.0 <= tester.target_threshold <= 1.0):
+            raise RuntimeError("--target_threshold deve ficar entre 0 e 1.")
+        print(f"[TARGET] threshold direto={tester.target_threshold:.3f}")
+
+    onnx_tester = None
+    onnx_path = None
+
+    if args.onnx_provider:
+        onnx_path = find_onnx_model(
+            save_dir=save_dir,
+            ckpt_path=ckpt_path,
+            preferred=args.onnx,
+        )
+
+        onnx_tester = OnnxMultiHeadTester(
+            config=config,
+            onnx_path=onnx_path,
+            provider=args.onnx_provider,
+            input_channel_names=input_channel_names,
+            heads_config=heads_config,
+            mean=mean,
+            std=std,
+            trt_home=args.trt_home,
+            trt_fp16=not args.trt_no_fp16,
+        )
+        onnx_tester.target_threshold = float(tester.target_threshold)
+
+    if onnx_tester is not None:
+        print(f"ONNX        : {onnx_path}")
+        print(f"ONNX provider: {args.onnx_provider}")
+
+    out_dir = Path(args.out_dir)
+    ensure_dir(out_dir)
+
+    idx = max(0, min(args.start_idx, n - 1))
+    detailed = False
+
+    cms_total = {
+        name: np.zeros((int(hcfg["num_classes"]), int(hcfg["num_classes"])), dtype=np.int64)
+        for name, hcfg in heads_config.items()
+    }
+    cm_target_total = np.zeros((2, 2), dtype=np.int64)
+    visited = set()
+
+    win_name = "OAK-FCC-3 MultiHead Test | D/A navega | S salva | SPACE detalhado | Q sai"
+    cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)
+    win_name_onnx = None
+    if onnx_tester is not None:
+        win_name_onnx = "ONNX/TensorRT MultiHead Test | comparação visual"
+        cv2.namedWindow(win_name_onnx, cv2.WINDOW_NORMAL)
+
+    while True:
+        sample = samples[idx]
+        chw = load_sample_tensor(
+            sample,
+            requested_channels=input_channel_names,
+            derived_config=config.get("derived_channels", {}) or {},
+            target_size=(target_w, target_h),
+            module_params_path=module_params_config,
+        )
+
+        if (chw.shape[2], chw.shape[1]) != (target_w, target_h):
+            hwc = np.transpose(chw, (1, 2, 0))
+            hwc = cv2.resize(hwc, (target_w, target_h), interpolation=cv2.INTER_LINEAR)
+            chw = np.transpose(hwc, (2, 0, 1)).astype(np.float32)
+
+        preds, probs, t_inf = tester.infer(chw)
+        preview_rgb = tensor_to_preview_rgb(chw, input_channel_names)
+
+        onnx_preds = None
+        onnx_probs = None
+        t_onnx = None
+
+        if onnx_tester is not None:
+            onnx_preds, onnx_probs, t_onnx = onnx_tester.infer(chw)
+
+        first_pred = next(iter(preds.values()))
+        pred_h, pred_w = first_pred.shape[:2]
+
+        pred_sem = preds.get("semantic")
+        pred_veg = preds.get("vegetation")
+        pred_cana = preds.get("cana")
+
+        # Target operacional antigo: vegetation AND not cana
+        pred_target_op = None
+        if pred_veg is not None and pred_cana is not None:
+            pred_target_op = operational_target_mask(pred_veg, pred_cana, ignore_id=ignore_id)
+
+        # Target direta nova: saída da 4ª cabeça
+        pred_target_head = preds.get("target")
+
+        # Fallback visual: se não tiver head target, mostra operacional
+        pred_target = pred_target_head if pred_target_head is not None else pred_target_op
+
+        prob_veg = probs["vegetation"][1] if "vegetation" in probs and probs["vegetation"].shape[0] > 1 else None
+        prob_cana = probs["cana"][1] if "cana" in probs and probs["cana"].shape[0] > 1 else None
+
+        prob_target_op = None
+        if prob_veg is not None and prob_cana is not None:
+            prob_target_op = np.clip(prob_veg * (1.0 - prob_cana), 0.0, 1.0)
+
+        prob_target_head = None
+        if "target" in probs:
+            prob_target_head = probs["target"][1] if probs["target"].shape[0] > 1 else probs["target"][0]
+
+        prob_target = prob_target_head if prob_target_head is not None else prob_target_op
+
+        gt_masks = {name: load_mask(path) for name, path in sample.masks.items()}
+        for name, gt in list(gt_masks.items()):
+            if gt is not None and gt.shape != (pred_h, pred_w):
+                gt_masks[name] = cv2.resize(
+                    gt.astype(np.uint8),
+                    (pred_w, pred_h),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+        gt_target = None
+        if gt_masks.get("target") is not None:
+            gt_target = gt_masks["target"]
+        elif gt_masks.get("vegetation") is not None and gt_masks.get("cana") is not None:
+            gt_target = operational_target_mask(
+                gt_masks["vegetation"],
+                gt_masks["cana"],
+                ignore_id=ignore_id,
+            )
+            gt_masks["target"] = gt_target
+
+        # Métricas da amostra.
+        metric_lines = []
+        sample_metrics = {}
+        for head_name, pred in preds.items():
+            gt = gt_masks.get(head_name)
+            if gt is not None:
+                cm = confusion_matrix_np(pred, gt, int(heads_config[head_name]["num_classes"]), ignore_id)
+                iou, miou, acc = metrics_from_cm(cm)
+                sample_metrics[head_name] = {"iou": iou, "miou": miou, "acc": acc}
+                metric_lines.append(f"{head_name}: mIoU={miou:.3f} acc={acc:.3f}")
+
+        if gt_target is not None and pred_target_op is not None:
+            cm_t = confusion_matrix_np(pred_target_op, gt_target, 2, ignore_id)
+            iou_t, miou_t, acc_t = metrics_from_cm(cm_t)
+
+            sample_metrics["target_op"] = {
+                "iou": iou_t,
+                "miou": miou_t,
+                "acc": acc_t,
+            }
+
+            metric_lines.append(
+                f"target_op: IoU_alvo={iou_t[1]:.3f} acc={acc_t:.3f}"
+            )
+
+            if "target" in sample_metrics:
+                iou_head = sample_metrics["target"]["iou"]
+                metric_lines.append(
+                    f"target_head: IoU_alvo={iou_head[1]:.3f}"
+                )
+
+        if idx not in visited:
+            for head_name, pred in preds.items():
+                gt = gt_masks.get(head_name)
+                if gt is not None:
+                    cms_total[head_name] += confusion_matrix_np(pred, gt, int(heads_config[head_name]["num_classes"]), ignore_id)
+            if gt_target is not None and pred_target_op is not None:
+                cm_target_total += confusion_matrix_np(pred_target_op, gt_target, 2, ignore_id)
+            visited.add(idx)
+
+        # Visuals.
+        panels: List[Tuple[str, np.ndarray, str]] = []
+        if pred_sem is not None:
+            pred_sem_rgb = ids_to_rgb(pred_sem, semantic_cmap, ignore_id)
+            if gt_masks.get("semantic") is not None:
+                panels.append(("GT semantic", ids_to_rgb(gt_masks["semantic"], semantic_cmap, ignore_id), "chao/cana/erva"))
+            panels.append(("Pred semantic", pred_sem_rgb, f"erva={class_percent(pred_sem, 2, ignore_id):.1f}% cana={class_percent(pred_sem, 1, ignore_id):.1f}%"))
+            panels.append(("Overlay semantic", overlay_rgb(preview_rgb, pred_sem_rgb, args.alpha), ""))
+
+        if pred_veg is not None:
+            pred_veg_rgb = ids_to_rgb(pred_veg, BINARY_COLORS_RGB, ignore_id)
+            if gt_masks.get("vegetation") is not None:
+                panels.append(("GT vegetation", ids_to_rgb(gt_masks["vegetation"], BINARY_COLORS_RGB, ignore_id), "0=fundo 1=veg"))
+            panels.append(("Pred vegetation", pred_veg_rgb, f"veg={class_percent(pred_veg, 1, ignore_id):.1f}%"))
+            if prob_veg is not None:
+                panels.append(("P vegetation", prob_to_heat_rgb(prob_veg), f"mean={float(prob_veg.mean()):.3f}"))
+
+        if pred_cana is not None:
+            pred_cana_rgb = ids_to_rgb(pred_cana, CANA_COLORS_RGB, ignore_id)
+            if gt_masks.get("cana") is not None:
+                panels.append(("GT cana", ids_to_rgb(gt_masks["cana"], CANA_COLORS_RGB, ignore_id), "0=not_cana 1=cana"))
+            panels.append(("Pred cana", pred_cana_rgb, f"cana={class_percent(pred_cana, 1, ignore_id):.1f}%"))
+            if prob_cana is not None:
+                panels.append(("P cana", prob_to_heat_rgb(prob_cana), f"mean={float(prob_cana.mean()):.3f}"))
+
+        if detailed:
+            if gt_target is not None:
+                panels.append(("GT target", ids_to_rgb(gt_target, TARGET_COLORS_RGB, ignore_id), "derivado"))
+
+            if pred_target_head is not None:
+                panels.append(("Pred target HEAD", ids_to_rgb(pred_target_head, TARGET_COLORS_RGB, ignore_id), f"thr={tester.target_threshold:.2f} alvo={class_percent(pred_target_head, 1, ignore_id):.1f}%"))
+                if prob_target_head is not None:
+                    panels.append(("P target HEAD", prob_to_heat_rgb(prob_target_head), f"mean={float(prob_target_head.mean()):.3f}"))
+
+            if pred_target_op is not None:
+                panels.append(("Pred target OP", ids_to_rgb(pred_target_op, TARGET_COLORS_RGB, ignore_id), f"alvo={class_percent(pred_target_op, 1, ignore_id):.1f}%"))
+                if prob_target_op is not None:
+                    panels.append(("P target OP", prob_to_heat_rgb(prob_target_op), f"mean={float(prob_target_op.mean()):.3f}"))
+        else:
+            if pred_target_head is not None:
+                rgb = ids_to_rgb(pred_target_head, TARGET_COLORS_RGB, ignore_id)
+                panels.append(("Pred target HEAD", rgb, f"thr={tester.target_threshold:.2f} alvo={class_percent(pred_target_head, 1, ignore_id):.1f}%"))
+                panels.append(("Overlay target HEAD", overlay_rgb(preview_rgb, rgb, args.alpha), "head direta"))
+            elif pred_target_op is not None:
+                rgb = ids_to_rgb(pred_target_op, TARGET_COLORS_RGB, ignore_id)
+                panels.append(("Pred target OP", rgb, f"alvo={class_percent(pred_target_op, 1, ignore_id):.1f}%"))
+                panels.append(("Overlay target OP", overlay_rgb(preview_rgb, rgb, args.alpha), "veg & !cana"))
+
+        canvas = compose_grid(panels, cols=3, max_width=args.max_width)
+
+        header_h = 78
+        header = np.zeros((header_h, canvas.shape[1], 3), dtype=np.uint8)
+        header[:] = (25, 25, 25)
+        source_name = sample.tensor_path.name if sample.tensor_path is not None else f"{sample.base}.json [{sample.source_kind}]"
+        h1 = f"idx {idx + 1}/{n} | {source_name} | inf={t_inf:.1f}ms | {'detalhado' if detailed else 'compacto'}"
+        h2 = " | ".join(metric_lines[:3]) if metric_lines else "sem GT"
+        cv2.putText(header, h1, (12, 28), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (235, 235, 235), 1, cv2.LINE_AA)
+        cv2.putText(header, h2, (12, 58), cv2.FONT_HERSHEY_SIMPLEX, 0.52, (0, 255, 120), 1, cv2.LINE_AA)
+        canvas = np.vstack([header, canvas])
+
+        onnx_canvas = None
+        if onnx_tester is not None and onnx_preds is not None and onnx_probs is not None:
+            onnx_pred_sem = onnx_preds.get("semantic")
+            onnx_pred_veg = onnx_preds.get("vegetation")
+            onnx_pred_cana = onnx_preds.get("cana")
+
+            onnx_pred_target_op = None
+            if onnx_pred_veg is not None and onnx_pred_cana is not None:
+                onnx_pred_target_op = operational_target_mask(
+                    onnx_pred_veg,
+                    onnx_pred_cana,
+                    ignore_id=ignore_id,
+                )
+
+            onnx_pred_target_head = onnx_preds.get("target")
+            onnx_pred_target = onnx_pred_target_head if onnx_pred_target_head is not None else onnx_pred_target_op
+
+            onnx_prob_veg = onnx_probs["vegetation"][1] if "vegetation" in onnx_probs and onnx_probs["vegetation"].shape[0] > 1 else None
+            onnx_prob_cana = onnx_probs["cana"][1] if "cana" in onnx_probs and onnx_probs["cana"].shape[0] > 1 else None
+
+            onnx_prob_target_op = None
+            if onnx_prob_veg is not None and onnx_prob_cana is not None:
+                onnx_prob_target_op = np.clip(onnx_prob_veg * (1.0 - onnx_prob_cana), 0.0, 1.0)
+
+            onnx_prob_target_head = None
+            if "target" in onnx_probs:
+                onnx_prob_target_head = onnx_probs["target"][1] if onnx_probs["target"].shape[0] > 1 else onnx_probs["target"][0]
+
+            onnx_prob_target = onnx_prob_target_head if onnx_prob_target_head is not None else onnx_prob_target_op
+
+            eq_sem = compare_pred_equal_percent(pred_sem, onnx_pred_sem)
+            eq_veg = compare_pred_equal_percent(pred_veg, onnx_pred_veg)
+            eq_cana = compare_pred_equal_percent(pred_cana, onnx_pred_cana)
+            eq_target = compare_pred_equal_percent(pred_target, onnx_pred_target)
+
+            onnx_panels: List[Tuple[str, np.ndarray, str]] = []
+
+            if onnx_pred_sem is not None:
+                onnx_sem_rgb = ids_to_rgb(onnx_pred_sem, semantic_cmap, ignore_id)
+                onnx_panels.append((
+                    "ONNX semantic",
+                    onnx_sem_rgb,
+                    "" if eq_sem is None else f"igual PT={eq_sem:.3f}%"
+                ))
+                onnx_panels.append((
+                    "ONNX overlay semantic",
+                    overlay_rgb(preview_rgb, onnx_sem_rgb, args.alpha),
+                    ""
+                ))
+
+                d = diff_mask_rgb(pred_sem, onnx_pred_sem)
+                if d is not None:
+                    onnx_panels.append(("Diff semantic", d, "vermelho=diferente"))
+
+            if onnx_pred_veg is not None:
+                onnx_veg_rgb = ids_to_rgb(onnx_pred_veg, BINARY_COLORS_RGB, ignore_id)
+                onnx_panels.append((
+                    "ONNX vegetation",
+                    onnx_veg_rgb,
+                    "" if eq_veg is None else f"igual PT={eq_veg:.3f}%"
+                ))
+
+                if onnx_prob_veg is not None:
+                    onnx_panels.append((
+                        "ONNX P vegetation",
+                        prob_to_heat_rgb(onnx_prob_veg),
+                        f"mean={float(onnx_prob_veg.mean()):.3f}"
+                    ))
+
+            if onnx_pred_cana is not None:
+                onnx_cana_rgb = ids_to_rgb(onnx_pred_cana, CANA_COLORS_RGB, ignore_id)
+                onnx_panels.append((
+                    "ONNX cana",
+                    onnx_cana_rgb,
+                    "" if eq_cana is None else f"igual PT={eq_cana:.3f}%"
+                ))
+
+                if onnx_prob_cana is not None:
+                    onnx_panels.append((
+                        "ONNX P cana",
+                        prob_to_heat_rgb(onnx_prob_cana),
+                        f"mean={float(onnx_prob_cana.mean()):.3f}"
+                    ))
+
+            if onnx_pred_target is not None:
+                onnx_target_rgb = ids_to_rgb(onnx_pred_target, TARGET_COLORS_RGB, ignore_id)
+                title = "ONNX target HEAD" if onnx_pred_target_head is not None else "ONNX target OP"
+
+                onnx_panels.append((
+                    title,
+                    onnx_target_rgb,
+                    "" if eq_target is None else f"igual PT={eq_target:.3f}%"
+                ))
+                onnx_panels.append((
+                    "ONNX overlay target",
+                    overlay_rgb(preview_rgb, onnx_target_rgb, args.alpha),
+                    f"inf={t_onnx:.1f}ms"
+                ))
+
+                if onnx_prob_target is not None:
+                    onnx_panels.append((
+                        "ONNX P target",
+                        prob_to_heat_rgb(onnx_prob_target),
+                        f"mean={float(onnx_prob_target.mean()):.3f}"
+                    ))
+
+                d = diff_mask_rgb(pred_target, onnx_pred_target)
+                if d is not None:
+                    onnx_panels.append(("Diff target", d, "vermelho=diferente"))
+
+            onnx_canvas = compose_grid(onnx_panels, cols=3, max_width=args.max_width)
+
+            header_h_onnx = 78
+            header_onnx = np.zeros((header_h_onnx, onnx_canvas.shape[1], 3), dtype=np.uint8)
+            header_onnx[:] = (18, 18, 35)
+
+            h1_onnx = f"ONNX {args.onnx_provider} | {source_name} | inf={t_onnx:.1f}ms"
+            h2_parts = []
+            if eq_sem is not None:
+                h2_parts.append(f"sem={eq_sem:.3f}%")
+            if eq_veg is not None:
+                h2_parts.append(f"veg={eq_veg:.3f}%")
+            if eq_cana is not None:
+                h2_parts.append(f"cana={eq_cana:.3f}%")
+            if eq_target is not None:
+                h2_parts.append(f"target={eq_target:.3f}%")
+            h2_onnx = "igual PyTorch: " + " | ".join(h2_parts) if h2_parts else "comparação indisponível"
+
+            cv2.putText(header_onnx, h1_onnx, (12, 28), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (235, 235, 235), 1, cv2.LINE_AA)
+            cv2.putText(header_onnx, h2_onnx, (12, 58), cv2.FONT_HERSHEY_SIMPLEX, 0.52, (0, 255, 120), 1, cv2.LINE_AA)
+
+            onnx_canvas = np.vstack([header_onnx, onnx_canvas])
+
+        cv2.imshow(win_name, cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR))
+        if onnx_canvas is not None and win_name_onnx is not None:
+            cv2.imshow(win_name_onnx, cv2.cvtColor(onnx_canvas, cv2.COLOR_RGB2BGR))
+        
+        k = cv2.waitKey(0) & 0xFF
+
+        if k in (ord("q"), ord("Q"), 27):
+            break
+        elif k in (ord("d"), ord("D"), 83):
+            idx = (idx + 1) % n
+        elif k in (ord("a"), ord("A"), 81):
+            idx = (idx - 1 + n) % n
+        elif k == ord(" "):
+            detailed = not detailed
+        elif k in (ord("s"), ord("S")):
+            out_path = out_dir / f"multihead_pytorch_{idx:05d}_{sample.base}.png"
+            cv2.imwrite(str(out_path), cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR))
+            print(f"[SAVE] {out_path}")
+
+            if onnx_canvas is not None:
+                out_path_onnx = out_dir / f"multihead_onnx_{args.onnx_provider}_{idx:05d}_{sample.base}.png"
+                cv2.imwrite(str(out_path_onnx), cv2.cvtColor(onnx_canvas, cv2.COLOR_RGB2BGR))
+                print(f"[SAVE] {out_path_onnx}")
+
+    cv2.destroyAllWindows()
+
+    if visited:
+        print("\n========== RESUMO DOS SAMPLES VISITADOS ==========")
+        print(f"visitados={len(visited)}/{n}")
+        for head_name, cm in cms_total.items():
+            if cm.sum() <= 0:
+                continue
+            iou, miou, acc = metrics_from_cm(cm)
+            print(f"\n[{head_name}] acc={acc:.4f} mIoU={miou:.4f}")
+            for i, v in enumerate(iou):
+                print(f"  IoU {i} = {v:.4f}")
+
+        if cm_target_total.sum() > 0:
+            iou, miou, acc = metrics_from_cm(cm_target_total)
+            print(f"\n[target operacional] acc={acc:.4f} mIoU={miou:.4f}")
+            print(f"  IoU background = {iou[0]:.4f}")
+            print(f"  IoU alvo       = {iou[1]:.4f}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/config.json b/Python/OAK/datasets/oak-fcc-3/config.json
index 8eeb400e9..574570075 100644
--- a/Python/OAK/datasets/oak-fcc-3/config.json
+++ b/Python/OAK/datasets/oak-fcc-3/config.json
@@ -1,18 +1,18 @@
 {
   "camera": "oak-fcc-3",
   "modelo": "segformer_b1",
-  "model_name": "2026_08_18",
+  "model_name": "2026_08_28_linear_demosaic",
   "main_class_name": "erva",
   "es_classes": "",
   "model_to_use": "geral",
   "raw_size": [1280, 800],
-  "resolucao": [640, 400],
+  "resolucao": [960, 600],
   "roi_inicio": 0.0,
   "roi_tamanho": 1.0,
   "shaves": 3,
   "source_channels": ["R", "G", "B", "RE", "NIR"],
-  "channels": 7,
-  "input_channels": ["R", "G", "B", "RE", "NIR", "NDVI", "NDRE"],
+  "channels": 5,
+  "input_channels": ["R", "G", "B", "RE", "NIR"],
   "derived_channels": {
     "epsilon": 1e-6,
     "clip_min": -1.0,
@@ -20,7 +20,7 @@
   },
   "backbone": "nvidia/mit-b1",
   "fusion_mode": "stacked",
-  "stats_source_tag": "stacked_raw5_ndvi_ndre",
+  "stats_source_tag": "stacked_raw5",
   "module_params_json": "calibration/module_params.json",
   "ckpt_test": "best_score",
   "multi_head": true,
@@ -41,7 +41,7 @@
       "mask_dir": "masks_vegetation",
       "classes": {"background": 0, "vegetation": 1},
       "ignore_index": 255,
-      "loss_weight": 0.20
+      "loss_weight": 0.15
     },
     "cana": {
       "enabled": true,
@@ -50,7 +50,7 @@
       "mask_dir": "masks_cana",
       "classes": {"not_cana": 0, "cana": 1},
       "ignore_index": 255,
-      "loss_weight": 0.25
+      "loss_weight": 0.35
     },
     "target": {
       "enabled": true,
@@ -59,23 +59,158 @@
       "mask_dir": "__derived_target__",
       "classes": {"background": 0, "target": 1},
       "ignore_index": 255,
-      "loss_weight": 0.35,
+      "loss_weight": 0.30,
       "derived": true
     }
   },
-  "target_distillation": {
-    "enabled": true,
-    "hard_weight": 0.85,
-    "distill_weight": 0.15,
-    "rampup_enabled": true,
-    "start_epoch": 8,
-    "rampup_epochs": 12,
-    "w_sem_erva": 0.45,
-    "w_veg_not_cana": 0.35,
-    "w_veg_suppressed": 0.20,
-    "cana_suppression_power": 1.5,
-    "teacher_min": 0.0,
-    "teacher_max": 1.0,
-    "detach_teacher": true
+  "training_v2": {
+    "model": {
+      "decoder_mode": "shared_light",
+      "spectral_input_init": "zero_extra"
+    },
+    "augmentation": {
+      "enabled": true,
+      "horizontal_flip_p": 0.5,
+      "vertical_flip_p": 0.0,
+      "affine_p": 0.7,
+      "rotate_deg": 5.0,
+      "scale_min": 0.9,
+      "scale_max": 1.1,
+      "translate_frac": 0.04,
+      "crop_p": 0.45,
+      "crop_scale_min": 0.7,
+      "crop_scale_max": 1.0,
+      "crop_focus_target_p": 0.55,
+      "crop_focus_cana_p": 0.25,
+      "global_gain_p": 0.35,
+      "global_gain_min": 0.92,
+      "global_gain_max": 1.08,
+      "band_gain_p": 0.25,
+      "band_gain_min": 0.96,
+      "band_gain_max": 1.04,
+      "rgb_gamma_p": 0.2,
+      "rgb_gamma_min": 0.94,
+      "rgb_gamma_max": 1.06,
+      "noise_p": 0.2,
+      "noise_sigma_min": 0.001,
+      "noise_sigma_max": 0.008,
+      "blur_p": 0.12,
+      "blur_kernel": 3,
+      "sensor_channel_dropout_p": 0.0,
+      "clip_physical": true
+    },
+    "sampler": {
+      "mode": "diverse",
+      "samples_per_epoch": 0,
+      "tiny_target_pct": 0.005,
+      "small_target_pct": 0.02,
+      "medium_target_pct": 0.1
+    },
+    "class_weighting": {
+      "method": "log_inverse",
+      "log_offset": 1.02,
+      "power": 0.5,
+      "min_weight": 0.25,
+      "max_weight": 4.0
+    },
+    "loss": {
+      "dice_reduction": "per_image",
+      "dice_smooth": 1.0,
+      "boundary_weight": 0.0,
+      "ohem_ratio": 0.0,
+      "safety": {
+        "enabled": true,
+        "weight": 0.08,
+        "cana_weight": 1.0,
+        "ground_weight": 0.2
+      }
+    },
+    "optimizer": {
+      "encoder_lr": null,
+      "patch_lr_mult": 2.0,
+      "heads_lr_mult": 5.0,
+      "weight_decay": null,
+      "no_decay_bias": true,
+      "no_decay_norm": true,
+      "betas": [
+        0.9,
+        0.999
+      ],
+      "eps": 1e-08
+    },
+    "scheduler": {
+      "mode": "poly",
+      "warmup_ratio": 0.05,
+      "warmup_start_factor": 0.1,
+      "poly_power": 1.0,
+      "min_lr_ratio": 0.02
+    },
+    "optimization": {
+      "grad_clip_norm": 1.0,
+      "matmul_precision": "high",
+      "cudnn_benchmark": true,
+      "persistent_workers": true,
+      "prefetch_factor": 2
+    },
+    "target_distillation": {
+      "enabled": false,
+      "mode": "cross_head",
+      "start_epoch": 8,
+      "rampup_epochs": 12,
+      "hard_weight": 0.75,
+      "distill_weight": 0.25,
+      "teacher_confidence_min": 0.6,
+      "detach_teacher": true,
+      "w_sem_erva": 0.45,
+      "w_veg_not_cana": 0.35,
+      "w_veg_suppressed": 0.2,
+      "cana_suppression_power": 1.5
+    },
+    "metrics": {
+      "target_thresholds": [
+        0.3,
+        0.4,
+        0.5,
+        0.6,
+        0.7,
+        0.8,
+        0.9
+      ],
+      "ece_bins": 15,
+      "scenario_metrics": true,
+      "group_metrics": true,
+      "rich_train_metrics": false,
+      "operational_threshold": {
+        "max_cana_spray_rate": 0.02,
+        "max_ground_spray_rate": 0.03,
+        "max_weed_miss_rate": 0.2,
+        "score_weights": {
+          "target_iou": 0.35,
+          "target_f1": 0.2,
+          "cana_safety": 0.25,
+          "ground_safety": 0.1,
+          "weed_recall": 0.1
+        }
+      }
+    },
+    "selection_score": {
+      "target_iou": 0.4,
+      "cana_iou": 0.2,
+      "target_f1": 0.1,
+      "vegetation_miou": 0.1,
+      "semantic_miou": 0.05,
+      "cana_safety": 0.15
+    },
+    "checkpoint": {
+      "early_stop_min_delta": 0.0005,
+      "save_best_safety": true,
+      "save_best_legacy": true,
+      "save_best_operational": true
+    },
+    "data": {
+      "validate_npy_content": true,
+      "skip_corrupt_samples": true,
+      "max_corrupt_fraction": 0.005
+    }
   }
 }
\ No newline at end of file
diff --git a/Python/OAK/datasets/oak-fcc-3/core/benchmark.py b/Python/OAK/datasets/oak-fcc-3/core/benchmark.py
deleted file mode 100644
index 755895987..000000000
--- a/Python/OAK/datasets/oak-fcc-3/core/benchmark.py
+++ /dev/null
@@ -1,620 +0,0 @@
-import argparse
-import json
-import sys
-import time
-from pathlib import Path
-
-import cv2
-import numpy as np
-
-try:
-    import torch
-except Exception:
-    torch = None
-
-
-# ============================================================
-# Ajuste de import local
-# ============================================================
-
-THIS_FILE = Path(__file__).resolve()
-
-# Esperado:
-# .../Python/Scripts/workers/camera_worker/oak_fcc3_core/benchmark_raw_bruto_scientific.py
-WORKERS_DIR = THIS_FILE.parents[2]
-
-if str(WORKERS_DIR) not in sys.path:
-    sys.path.insert(0, str(WORKERS_DIR))
-
-from camera_worker.oak_fcc3_core.oak_fcc3_client import OakFcc3Client
-
-try:
-    from camera_worker.oak_fcc3_core.segformer_service import MultiSpecSegformerService
-except Exception:
-    MultiSpecSegformerService = None
-
-
-# ============================================================
-# Utils
-# ============================================================
-
-def now_ms():
-    return time.perf_counter() * 1000.0
-
-
-def mean(xs):
-    return float(np.mean(xs)) if xs else 0.0
-
-
-def p95(xs):
-    return float(np.percentile(xs, 95)) if xs else 0.0
-
-
-def maxv(xs):
-    return float(np.max(xs)) if xs else 0.0
-
-
-def last_mean(xs, n=30):
-    return float(np.mean(xs[-n:])) if xs else 0.0
-
-
-def last_max(xs, n=30):
-    return float(np.max(xs[-n:])) if xs else 0.0
-
-
-def load_json_if_exists(path):
-    if not path:
-        return None
-    with open(path, "r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-def parse_float_list(s, expected=5, default=None):
-    if s is None:
-        return default
-
-    vals = [float(x.strip()) for x in str(s).split(",") if x.strip() != ""]
-
-    if len(vals) != expected:
-        raise ValueError(f"Esperado {expected} valores, veio {len(vals)}: {s}")
-
-    return np.array(vals, dtype=np.float32)
-
-
-def extract_mean_std_from_model_config(cfg):
-    if not isinstance(cfg, dict):
-        return None, None
-
-    mean_cfg = cfg.get("mean") or cfg.get("channel_mean") or cfg.get("norm_mean")
-    std_cfg = cfg.get("std") or cfg.get("channel_std") or cfg.get("norm_std")
-
-    norm = cfg.get("normalization") or cfg.get("norm") or {}
-
-    if mean_cfg is None and isinstance(norm, dict):
-        mean_cfg = norm.get("mean") or norm.get("channel_mean")
-
-    if std_cfg is None and isinstance(norm, dict):
-        std_cfg = norm.get("std") or norm.get("channel_std")
-
-    if mean_cfg is None or std_cfg is None:
-        return None, None
-
-    mean_arr = np.array(mean_cfg, dtype=np.float32)
-    std_arr = np.array(std_cfg, dtype=np.float32)
-
-    if mean_arr.size != 5 or std_arr.size != 5:
-        return None, None
-
-    return mean_arr, std_arr
-
-
-def tensor_stats(tensor):
-    names = ["R", "G", "B", "RE", "NIR"]
-    out = {}
-
-    for i, name in enumerate(names):
-        ch = tensor[i].astype(np.float32)
-        out[name] = {
-            "min": float(np.min(ch)),
-            "p01": float(np.percentile(ch, 1)),
-            "p50": float(np.percentile(ch, 50)),
-            "p99": float(np.percentile(ch, 99)),
-            "max": float(np.max(ch)),
-            "mean": float(np.mean(ch)),
-            "std": float(np.std(ch)),
-        }
-
-    return out
-
-
-def print_tensor_stats(label, tensor):
-    print("============================================")
-    print(f"[{label}] TENSOR")
-    print(f"shape={tensor.shape} dtype={tensor.dtype}")
-
-    stats = tensor_stats(tensor)
-    for ch, s in stats.items():
-        print(
-            f"{ch:>3} | "
-            f"min={s['min']:.4f} "
-            f"p01={s['p01']:.4f} "
-            f"p50={s['p50']:.4f} "
-            f"p99={s['p99']:.4f} "
-            f"max={s['max']:.4f} "
-            f"mean={s['mean']:.4f} "
-            f"std={s['std']:.4f}"
-        )
-
-    print("============================================")
-
-
-def to_u8_01(arr):
-    arr = np.asarray(arr, dtype=np.float32)
-    arr = np.nan_to_num(arr, nan=0.0, posinf=1.0, neginf=0.0)
-    arr = np.clip(arr, 0.0, 1.0)
-    return (arr * 255.0).astype(np.uint8)
-
-
-def make_panel(tensor):
-    rgb = np.stack([tensor[0], tensor[1], tensor[2]], axis=2)
-    rgb_bgr = cv2.cvtColor(to_u8_01(rgb), cv2.COLOR_RGB2BGR)
-
-    re_bgr = cv2.cvtColor(to_u8_01(tensor[3]), cv2.COLOR_GRAY2BGR)
-    nir_bgr = cv2.cvtColor(to_u8_01(tensor[4]), cv2.COLOR_GRAY2BGR)
-
-    false_rgb = np.stack(
-        [
-            tensor[4],  # visual R = NIR
-            tensor[3],  # visual G = RE
-            tensor[0],  # visual B = R real
-        ],
-        axis=2,
-    )
-    false_bgr = cv2.cvtColor(to_u8_01(false_rgb), cv2.COLOR_RGB2BGR)
-
-    def title(img, text):
-        h, w = img.shape[:2]
-        bar_h = 34
-        bar = np.zeros((bar_h, w, 3), dtype=np.uint8)
-        cv2.putText(
-            bar,
-            text,
-            (10, 24),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.7,
-            (255, 255, 255),
-            2,
-            cv2.LINE_AA,
-        )
-        return np.vstack([bar, img])
-
-    rgb_bgr = title(rgb_bgr, "RGB")
-    re_bgr = title(re_bgr, "RE")
-    nir_bgr = title(nir_bgr, "NIR")
-    false_bgr = title(false_bgr, "Falso color NIR/RE/R")
-
-    top = np.hstack([rgb_bgr, re_bgr])
-    bottom = np.hstack([nir_bgr, false_bgr])
-
-    return np.vstack([top, bottom])
-
-
-# ============================================================
-# Benchmark processor
-# ============================================================
-
-class ScientificBenchmark:
-    def __init__(self, args):
-        self.args = args
-        self.target_size = (int(args.width), int(args.height))
-
-        self.model_cfg = load_json_if_exists(args.model_config_json)
-        mean_cfg, std_cfg = extract_mean_std_from_model_config(self.model_cfg)
-
-        if args.model_mean is not None:
-            self.mean = parse_float_list(args.model_mean, expected=5)
-        elif mean_cfg is not None:
-            self.mean = mean_cfg
-        else:
-            self.mean = np.array([0.5, 0.5, 0.5, 0.5, 0.5], dtype=np.float32)
-
-        if args.model_std is not None:
-            self.std = parse_float_list(args.model_std, expected=5)
-        elif std_cfg is not None:
-            self.std = std_cfg
-        else:
-            self.std = np.array([0.25, 0.25, 0.25, 0.25, 0.25], dtype=np.float32)
-
-        self.mean_chw = self.mean[:, None, None].astype(np.float32)
-        self.std_chw = self.std[:, None, None].astype(np.float32)
-
-        self.device = None
-        if torch is not None and torch.cuda.is_available():
-            self.device = torch.device("cuda")
-        elif torch is not None:
-            self.device = torch.device("cpu")
-
-        self.model_svc = None
-
-        if args.run_model:
-            if MultiSpecSegformerService is None:
-                raise RuntimeError("MultiSpecSegformerService não pôde ser importado.")
-
-            if not isinstance(self.model_cfg, dict):
-                raise RuntimeError("--run_model requer --model_config_json válido.")
-
-            self.model_svc = MultiSpecSegformerService(
-                model_config=self.model_cfg,
-                mostrar_log=print,
-            )
-
-            dummy = np.zeros((5, args.height, args.width), dtype=np.float32)
-
-            for _ in range(max(0, int(args.warmup_model))):
-                self.model_svc.infer_tensor_fast(dummy, keep_probs=False)
-
-            print(f"[BENCH] Warmup modelo concluído: {args.warmup_model}x")
-
-    def process_once(self, client):
-        """
-        Mede uma iteração completa do fluxo científico.
-        """
-        times = {
-            "capture_ms": 0.0,
-            "decode_ms": 0.0,
-            "controller_ms": 0.0,
-            "fuse_total_ms": 0.0,
-            "fuse_dark_ms": 0.0,
-            "fuse_radnorm_ms": 0.0,
-            "fuse_flat_ms": 0.0,
-            "fuse_prepare_ms": 0.0,
-            "fuse_warp_ms": 0.0,
-            "fuse_crop_resize_ms": 0.0,
-            "fuse_concat_ms": 0.0,
-            "model_norm_ms": 0.0,
-            "torch_ms": 0.0,
-            "infer_ms": 0.0,
-            "total_ms": 0.0,
-            "sync_dt_ms": 0.0,
-        }
-
-        t_total0 = now_ms()
-
-        # ========================================================
-        # 1. Captura RAW_BRUTO
-        # ========================================================
-        t0 = now_ms()
-        raw_frame, raw_meta = client.get_next_raw_frame(timeout=self.args.timeout)
-        times["capture_ms"] = now_ms() - t0
-
-        #cp = raw_meta.get("capture_perf", {})
-        #print(
-        #    "[CAP_ASYNC] "
-        #    f"get_wait={cp.get('async_get_wait_ms',0):.2f}ms "
-        #    f"age={cp.get('async_packet_age_ms',0):.2f}ms "
-        #    f"seq={cp.get('async_packet_seq')} "
-        #    f"thread_wait={cp.get('wait_total_ms',0):.2f}ms "
-        #    f"sleep={cp.get('sleep_ms',0):.2f}ms/{cp.get('sleep_count',0)} "
-        #    f"drain={cp.get('drain_total_ms',0):.2f}ms "
-        #    f"copy={cp.get('drain_frombuffer_copy_ms',0):.2f}ms "
-        #    f"status={cp.get('async_status',{})}"
-        #)
-
-        times["sync_dt_ms"] = float(raw_meta.get("sync_dt_ms", 0.0) or 0.0)
-
-        # ========================================================
-        # 2. Decode RAW10 packed -> float científico por câmera
-        # ========================================================
-        t0 = now_ms()
-        decoded = client.decode_stream_cameras(raw_frame, raw_meta)
-        times["decode_ms"] = now_ms() - t0
-
-        # ========================================================
-        # 3. RadiometricController update, se ativo
-        #    Isso NÃO é a radiometric_normalization do tensor.
-        #    É o controlador de exposição/ganho.
-        # ========================================================
-        t0 = now_ms()
-        client.update_radiometry(decoded, raw_meta)
-        times["controller_ms"] = now_ms() - t0
-
-        # ========================================================
-        # 4. Fusão científica no RawProcessorCore
-        #    dark + radnorm + flat + homografia + crop/resize + concat
-        # ========================================================
-        t0 = now_ms()
-        tensor = client.build_infer_tensor_from_decoded(
-            decoded=decoded,
-            meta=raw_meta,
-            channels_expected=5,
-            target_size=self.target_size,
-        )
-        times["fuse_total_ms"] = now_ms() - t0
-
-        # Pega detalhamento interno do core
-        try:
-            perf = (client.core.last_fusion_result or {}).get("perf", {}) or {}
-            times["fuse_dark_ms"] = float(perf.get("dark_ms", 0.0) or 0.0)
-            times["fuse_radnorm_ms"] = float(perf.get("radnorm_ms", 0.0) or 0.0)
-            times["fuse_flat_ms"] = float(perf.get("flat_ms", 0.0) or 0.0)
-            times["fuse_prepare_ms"] = float(perf.get("prepare_ms", 0.0) or 0.0)
-            times["fuse_warp_ms"] = float(perf.get("warp_total_ms", 0.0) or 0.0)
-            times["fuse_crop_resize_ms"] = float(perf.get("crop_resize_ms", 0.0) or 0.0)
-            times["fuse_concat_ms"] = float(perf.get("concat_ms", 0.0) or 0.0)
-        except Exception:
-            pass
-
-        # ========================================================
-        # 5. Normalização do modelo, opcional
-        # ========================================================
-        if self.args.simulate_model_norm:
-            t0 = now_ms()
-            tensor = (tensor - self.mean_chw) / np.maximum(self.std_chw, 1e-6)
-            tensor = np.ascontiguousarray(tensor, dtype=np.float32)
-            times["model_norm_ms"] = now_ms() - t0
-
-        # ========================================================
-        # 6. Transferência para torch/cuda, opcional
-        # ========================================================
-        if self.args.to_torch:
-            if torch is None:
-                raise RuntimeError("--to_torch requer torch instalado.")
-
-            t0 = now_ms()
-            x = torch.from_numpy(tensor).unsqueeze(0).to(self.device, non_blocking=True)
-
-            if self.device is not None and self.device.type == "cuda":
-                torch.cuda.synchronize()
-
-            times["torch_ms"] = now_ms() - t0
-
-        # ========================================================
-        # 7. Inferência real, opcional
-        # ========================================================
-        pred = None
-        if self.args.run_model:
-            t0 = now_ms()
-            pred = self.model_svc.infer_tensor_fast(tensor, keep_probs=False)
-            times["infer_ms"] = now_ms() - t0
-
-        times["total_ms"] = now_ms() - t_total0
-
-        return tensor, pred, raw_frame, raw_meta, decoded, times
-
-
-# ============================================================
-# Main
-# ============================================================
-
-def main():
-    ap = argparse.ArgumentParser(
-        description="Benchmark científico OAK-FCC-3 RAW_BRUTO -> tensor multiespectral final."
-    )
-
-    ap.add_argument(
-        "--module_params",
-        default=r"C:\ZendionInc\agrobot_base\Python\OAK\datasets\oak-fcc-3\calibration\module_params.json",
-        help="Caminho do module_params.json.",
-    )
-
-    ap.add_argument("--width", type=int, default=1024, help="Largura final do tensor.")
-    ap.add_argument("--height", type=int, default=640, help="Altura final do tensor.")
-    ap.add_argument("--fps", type=float, default=30.0)
-    ap.add_argument("--seconds", type=float, default=20.0)
-    ap.add_argument("--timeout", type=float, default=3.0)
-    ap.add_argument("--warmup", type=int, default=5)
-    ap.add_argument("--mx_id", default=None)
-    ap.add_argument("--sync_tolerance_ms", type=float, default=25.0)
-    ap.add_argument("--buffer_size", type=int, default=8)
-
-    ap.add_argument("--simulate_model_norm", action="store_true")
-    ap.add_argument("--model_mean", default=None)
-    ap.add_argument("--model_std", default=None)
-    ap.add_argument("--to_torch", action="store_true")
-
-    ap.add_argument("--run_model", action="store_true")
-    ap.add_argument("--model_config_json", default=None)
-    ap.add_argument("--warmup_model", type=int, default=3)
-
-    ap.add_argument("--save_debug", action="store_true")
-    ap.add_argument("--debug_dir", default="raw_bruto_scientific_benchmark")
-    ap.add_argument("--show", action="store_true")
-    ap.add_argument("--display_scale", type=float, default=0.65)
-
-    args = ap.parse_args()
-
-    bench = ScientificBenchmark(args)
-
-    client = OakFcc3Client(
-        width=args.width,
-        height=args.height,
-        fps=args.fps,
-        frame_type="RAW_BRUTO",
-        output_dtype="uint8",
-        capture_mode="TRIPLE",
-        raw_policy="require_triple",
-        module_calibration_json=args.module_params,
-        sync_tolerance_ms=args.sync_tolerance_ms,
-        buffer_size=args.buffer_size,
-        mx_id=args.mx_id,
-    )
-
-    client.core.warmup_numba_raw10_decode()
-
-    samples = {
-        "capture_ms": [],
-        "decode_ms": [],
-        "controller_ms": [],
-        "fuse_total_ms": [],
-        "fuse_dark_ms": [],
-        "fuse_radnorm_ms": [],
-        "fuse_flat_ms": [],
-        "fuse_prepare_ms": [],
-        "fuse_warp_ms": [],
-        "fuse_crop_resize_ms": [],
-        "fuse_concat_ms": [],
-        "model_norm_ms": [],
-        "torch_ms": [],
-        "infer_ms": [],
-        "total_ms": [],
-        "sync_dt_ms": [],
-    }
-
-    n_frames = 0
-    t_start = time.perf_counter()
-    t_last_log = t_start
-
-    last_tensor = None
-    last_meta = None
-
-    try:
-        client.start(print_debug=True)
-
-        # Warmup de câmera/controlador/filas
-        print(f"[BENCH] Warmup frames: {args.warmup}")
-        for _ in range(max(0, int(args.warmup))):
-            try:
-                bench.process_once(client)
-            except Exception as e:
-                print(f"[WARN] warmup falhou: {type(e).__name__}: {e}")
-            time.sleep(0.02)
-
-        print("============================================")
-        print("[BENCH] Iniciando benchmark científico RAW_BRUTO")
-        print(f"target tensor : (5,{args.height},{args.width})")
-        print(f"duration      : {args.seconds}s")
-        print("============================================")
-
-        while True:
-            now = time.perf_counter()
-            elapsed = now - t_start
-
-            if elapsed >= args.seconds:
-                break
-
-            try:
-                tensor, pred, raw_frame, raw_meta, decoded, times = bench.process_once(client)
-            except TimeoutError as e:
-                print(f"[TIMEOUT] {e}")
-                continue
-
-            n_frames += 1
-            last_tensor = tensor
-            last_meta = raw_meta
-
-            for k in samples:
-                samples[k].append(float(times.get(k, 0.0) or 0.0))
-
-            if now - t_last_log >= 1.0:
-                elapsed = now - t_start
-                fps = n_frames / max(elapsed, 1e-6)
-
-                print(
-                    "[RAW_SCI_PERF] "
-                    f"elapsed={elapsed:.1f}s "
-                    f"frames={n_frames} "
-                    f"fps={fps:.2f} "
-                    f"sync={last_mean(samples['sync_dt_ms']):.2f}ms "
-                    f"capture={last_mean(samples['capture_ms']):.2f}ms "
-                    f"decode={last_mean(samples['decode_ms']):.2f}ms "
-                    f"controller={last_mean(samples['controller_ms']):.2f}ms "
-                    f"fuse={last_mean(samples['fuse_total_ms']):.2f}ms "
-                    f"radnorm={last_mean(samples['fuse_radnorm_ms']):.2f}ms "
-                    f"flat={last_mean(samples['fuse_flat_ms']):.2f}ms "
-                    f"warp={last_mean(samples['fuse_warp_ms']):.2f}ms "
-                    f"crop_resize={last_mean(samples['fuse_crop_resize_ms']):.2f}ms "
-                    f"concat={last_mean(samples['fuse_concat_ms']):.2f}ms "
-                    f"model_norm={last_mean(samples['model_norm_ms']):.2f}ms "
-                    f"torch={last_mean(samples['torch_ms']):.2f}ms "
-                    f"infer={last_mean(samples['infer_ms']):.2f}ms "
-                    f"total={last_mean(samples['total_ms']):.2f}ms "
-                    f"tensor_shape={tuple(tensor.shape)}"
-                )
-
-                t_last_log = now
-
-        elapsed_total = time.perf_counter() - t_start
-        fps_total = n_frames / max(elapsed_total, 1e-6)
-
-        print("============================================")
-        print("RESULTADO FINAL RAW_BRUTO CIENTÍFICO")
-        print(f"elapsed : {elapsed_total:.2f}s")
-        print(f"frames  : {n_frames}")
-        print(f"fps     : {fps_total:.2f}")
-        print("--------------------------------------------")
-
-        def print_metric(name):
-            xs = samples[name]
-            print(
-                f"{name:18s} "
-                f"mean={mean(xs):8.2f}ms "
-                f"p95={p95(xs):8.2f}ms "
-                f"max={maxv(xs):8.2f}ms"
-            )
-
-        for name in [
-            "sync_dt_ms",
-            "capture_ms",
-            "decode_ms",
-            "controller_ms",
-            "fuse_total_ms",
-            "fuse_dark_ms",
-            "fuse_radnorm_ms",
-            "fuse_flat_ms",
-            "fuse_prepare_ms",
-            "fuse_warp_ms",
-            "fuse_crop_resize_ms",
-            "fuse_concat_ms",
-            "model_norm_ms",
-            "torch_ms",
-            "infer_ms",
-            "total_ms",
-        ]:
-            print_metric(name)
-
-        print("============================================")
-
-        if last_tensor is not None:
-            print_tensor_stats("LAST RAW_BRUTO SCI", last_tensor)
-
-            if args.save_debug:
-                debug_dir = Path(args.debug_dir)
-                debug_dir.mkdir(parents=True, exist_ok=True)
-
-                np.save(str(debug_dir / "last_tensor.npy"), last_tensor)
-
-                stats_path = debug_dir / "last_tensor_stats.json"
-                with open(stats_path, "w", encoding="utf-8") as f:
-                    json.dump(tensor_stats(last_tensor), f, indent=2, ensure_ascii=False)
-
-                panel = make_panel(last_tensor)
-                cv2.imwrite(str(debug_dir / "last_tensor_panel.png"), panel)
-
-                if last_meta is not None:
-                    with open(debug_dir / "last_meta.json", "w", encoding="utf-8") as f:
-                        json.dump(last_meta, f, indent=2, ensure_ascii=False)
-
-                print(f"[SAVE] Debug salvo em: {debug_dir}")
-
-            if args.show:
-                panel = make_panel(last_tensor)
-
-                if args.display_scale and abs(args.display_scale - 1.0) > 1e-6:
-                    new_w = max(1, int(panel.shape[1] * args.display_scale))
-                    new_h = max(1, int(panel.shape[0] * args.display_scale))
-                    panel = cv2.resize(panel, (new_w, new_h), interpolation=cv2.INTER_AREA)
-
-                cv2.imshow("RAW_BRUTO scientific tensor", panel)
-                print("[INFO] Pressione qualquer tecla para fechar.")
-                cv2.waitKey(0)
-                cv2.destroyAllWindows()
-
-    finally:
-        try:
-            client.stop()
-        except Exception:
-            pass
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/Python/OAK/datasets/oak-fcc-3/core/benchmark_preview.py b/Python/OAK/datasets/oak-fcc-3/core/benchmark_preview.py
deleted file mode 100644
index 407a3bbbe..000000000
--- a/Python/OAK/datasets/oak-fcc-3/core/benchmark_preview.py
+++ /dev/null
@@ -1,645 +0,0 @@
-import argparse
-import json
-import sys
-import time
-import threading
-from pathlib import Path
-
-import cv2
-import numpy as np
-
-# ============================================================
-# Ajuste de import local
-# ============================================================
-THIS_FILE = Path(__file__).resolve()
-WORKERS_DIR = THIS_FILE.parents[2]
-if str(WORKERS_DIR) not in sys.path:
-    sys.path.insert(0, str(WORKERS_DIR))
-
-from camera_worker.oak_fcc3_core.oak_fcc3_client import OakFcc3Client
-
-try:
-    from camera_worker.oak_fcc3_core.segformer_service import MultiSpecSegformerService
-except Exception:
-    MultiSpecSegformerService = None
-
-
-# ============================================================
-# FPS / utilidades
-# ============================================================
-class FpsMeter:
-    def __init__(self, alpha=0.15):
-        self.alpha = float(alpha)
-        self.last_ts = None
-        self.fps = 0.0
-
-    def tick(self):
-        now = time.perf_counter()
-        if self.last_ts is not None:
-            dt = now - self.last_ts
-            if dt > 1e-9:
-                inst = 1.0 / dt
-                self.fps = inst if self.fps <= 0 else (1.0 - self.alpha) * self.fps + self.alpha * inst
-        self.last_ts = now
-        return self.fps
-
-
-def load_json_if_exists(path):
-    if not path:
-        return None
-    with open(path, "r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-def init_model_service(args):
-    """
-    Mesmo contrato do benchmark científico:
-      --run_model exige --model_config_json
-      MultiSpecSegformerService(model_config=cfg).infer_tensor_fast(tensor, keep_probs=False)
-    """
-    if not args.run_model:
-        return None
-
-    if MultiSpecSegformerService is None:
-        raise RuntimeError("MultiSpecSegformerService não pôde ser importado.")
-
-    model_cfg = load_json_if_exists(args.model_config_json)
-    if not isinstance(model_cfg, dict):
-        raise RuntimeError("--run_model requer --model_config_json válido.")
-
-    svc = MultiSpecSegformerService(
-        model_config=model_cfg,
-        mostrar_log=print,
-    )
-
-    dummy = np.zeros((5, int(args.height), int(args.width)), dtype=np.float32)
-    for _ in range(max(0, int(args.warmup_model))):
-        svc.infer_tensor_fast(dummy, keep_probs=False)
-
-    print(f"[MODEL] Warmup concluído: {args.warmup_model}x")
-    return svc
-
-
-def to_u8_01(arr, auto_level=False):
-    arr = np.asarray(arr, dtype=np.float32)
-    arr = np.nan_to_num(arr, nan=0.0, posinf=1.0, neginf=0.0)
-
-    if auto_level:
-        p1 = float(np.percentile(arr, 1))
-        p99 = float(np.percentile(arr, 99))
-        den = max(p99 - p1, 1e-6)
-        arr = (arr - p1) / den
-
-    arr = np.clip(arr, 0.0, 1.0)
-    return (arr * 255.0).astype(np.uint8)
-
-
-def ensure_bgr(img, auto_level=False):
-    img = np.asarray(img)
-
-    if img.ndim == 2:
-        g = to_u8_01(img, auto_level=auto_level)
-        return cv2.cvtColor(g, cv2.COLOR_GRAY2BGR)
-
-    if img.ndim == 3 and img.shape[2] == 3:
-        u8 = to_u8_01(img, auto_level=auto_level)
-        # decoded/tensor RGB vem em RGB; OpenCV mostra BGR
-        return cv2.cvtColor(u8, cv2.COLOR_RGB2BGR)
-
-    raise RuntimeError(f"Imagem inválida para visualização: shape={img.shape}")
-
-
-def tensor_rgb_to_bgr(tensor, auto_level=False):
-    rgb = np.stack([tensor[0], tensor[1], tensor[2]], axis=2)
-    return ensure_bgr(rgb, auto_level=auto_level)
-
-
-def tensor_channel_to_bgr(tensor, idx, auto_level=False):
-    return ensure_bgr(tensor[idx], auto_level=auto_level)
-
-
-def add_title(img, title, color=(255, 255, 255)):
-    out = img.copy()
-    h, w = out.shape[:2]
-    bar_h = 34
-    bar = np.zeros((bar_h, w, 3), dtype=np.uint8)
-    cv2.putText(bar, str(title), (10, 23), cv2.FONT_HERSHEY_SIMPLEX, 0.62, color, 2, cv2.LINE_AA)
-    return np.vstack([bar, out])
-
-
-def add_hud(panel, lines):
-    out = panel.copy()
-    x, y = 12, 45
-    for line in lines:
-        cv2.putText(out, line, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.62, (0, 255, 255), 2, cv2.LINE_AA)
-        y += 24
-    return out
-
-
-def resize_tile(img, tile_w, tile_h):
-    return cv2.resize(img, (int(tile_w), int(tile_h)), interpolation=cv2.INTER_AREA)
-
-
-def get_cam_by_role(decoded, role):
-    role = str(role).lower()
-    for cam_id, item in decoded.items():
-        r = str(item.get("role") or item.get("meta", {}).get("role") or "").lower()
-        if r == role:
-            return cam_id
-    return None
-
-
-def decoded_raw_tiles(decoded, tile_w, tile_h, auto_level=False):
-    """
-    Retorna tiles RAW/decoded para RGB, RE, NIR antes da fusão final.
-    Aqui 'RAW_BRUTO' significa o conteúdo decodificado vindo das câmeras, ainda no espaço nativo.
-    """
-    tiles = {}
-
-    rgb_id = get_cam_by_role(decoded, "rgb")
-    re_id = get_cam_by_role(decoded, "re")
-    nir_id = get_cam_by_role(decoded, "nir")
-
-    if rgb_id is not None:
-        img = decoded[rgb_id]["image"]
-        tiles["rgb"] = resize_tile(ensure_bgr(img, auto_level=auto_level), tile_w, tile_h)
-    else:
-        tiles["rgb"] = np.zeros((tile_h, tile_w, 3), dtype=np.uint8)
-
-    if re_id is not None:
-        img = decoded[re_id]["image"]
-        tiles["re"] = resize_tile(ensure_bgr(img, auto_level=auto_level), tile_w, tile_h)
-    else:
-        tiles["re"] = np.zeros((tile_h, tile_w, 3), dtype=np.uint8)
-
-    if nir_id is not None:
-        img = decoded[nir_id]["image"]
-        tiles["nir"] = resize_tile(ensure_bgr(img, auto_level=auto_level), tile_w, tile_h)
-    else:
-        tiles["nir"] = np.zeros((tile_h, tile_w, 3), dtype=np.uint8)
-
-    return tiles
-
-
-def tensor_tiles(tensor, tile_w, tile_h, auto_level=False):
-    return {
-        "rgb": resize_tile(tensor_rgb_to_bgr(tensor, auto_level=auto_level), tile_w, tile_h),
-        "re": resize_tile(tensor_channel_to_bgr(tensor, 3, auto_level=auto_level), tile_w, tile_h),
-        "nir": resize_tile(tensor_channel_to_bgr(tensor, 4, auto_level=auto_level), tile_w, tile_h),
-    }
-
-
-def colorize_label_map(label_map, num_classes=None):
-    label = np.asarray(label_map)
-    if label.ndim == 3:
-        label = np.argmax(label, axis=0)
-    label = label.astype(np.int32)
-
-    if num_classes is None:
-        num_classes = int(max(1, label.max() + 1))
-
-    # Paleta simples e estável. BGR.
-    palette = np.array([
-        [40, 40, 40],
-        [60, 180, 60],
-        [60, 60, 220],
-        [220, 180, 60],
-        [180, 60, 180],
-        [180, 180, 60],
-        [60, 180, 180],
-        [220, 220, 220],
-    ], dtype=np.uint8)
-
-    out = palette[label % len(palette)]
-    return out
-
-
-def try_extract_prediction_tiles(pred, target_w, target_h):
-    """
-    Tentativa genérica. Adapte aqui se o benchmark tiver nomes específicos das cabeças.
-    Retorna até 3 tiles BGR: semântica/head0/head1.
-    """
-    if pred is None:
-        blank = np.zeros((target_h, target_w, 3), dtype=np.uint8)
-        return [blank, blank.copy(), blank.copy()], ["Pred vazio", "Head 1", "Head 2"]
-
-    candidates = []
-    names = []
-
-    if isinstance(pred, dict):
-        # nomes comuns
-        for key in ("mask", "pred_mask", "class_map", "semantic", "semantic_mask", "segmentation"):
-            if key in pred:
-                candidates.append(pred[key])
-                names.append(key)
-
-        heads = pred.get("heads") or pred.get("head_outputs") or pred.get("predictions")
-        if isinstance(heads, dict):
-            for k, v in heads.items():
-                candidates.append(v)
-                names.append(str(k))
-        elif isinstance(heads, (list, tuple)):
-            for i, v in enumerate(heads):
-                candidates.append(v)
-                names.append(f"head_{i}")
-    else:
-        candidates.append(pred)
-        names.append("prediction")
-
-    tiles = []
-    out_names = []
-
-    for name, arr in zip(names, candidates):
-        arr = np.asarray(arr)
-
-        # remove batch se existir
-        if arr.ndim == 4 and arr.shape[0] == 1:
-            arr = arr[0]
-
-        if arr.ndim == 3:
-            # CHW logits/probs ou HWC RGB/probs
-            if arr.shape[0] <= 32:
-                vis = colorize_label_map(np.argmax(arr, axis=0), num_classes=arr.shape[0])
-            elif arr.shape[2] in (1, 3):
-                vis = ensure_bgr(arr[:, :, 0] if arr.shape[2] == 1 else arr, auto_level=True)
-            else:
-                vis = ensure_bgr(np.max(arr, axis=2), auto_level=True)
-        elif arr.ndim == 2:
-            # se parecer label map, colore; se parecer float, cinza auto-level
-            if np.issubdtype(arr.dtype, np.integer) and int(np.max(arr)) <= 64:
-                vis = colorize_label_map(arr)
-            else:
-                vis = ensure_bgr(arr, auto_level=True)
-        elif arr.ndim == 1:
-            # vetor de classe/score: desenha texto
-            vis = np.zeros((target_h, target_w, 3), dtype=np.uint8)
-            txt = np.array2string(arr[:8], precision=2, separator=", ")
-            cv2.putText(vis, txt[:80], (10, target_h // 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
-        else:
-            continue
-
-        vis = resize_tile(vis, target_w, target_h)
-        tiles.append(vis)
-        out_names.append(name)
-
-        if len(tiles) >= 3:
-            break
-
-    while len(tiles) < 3:
-        tiles.append(np.zeros((target_h, target_w, 3), dtype=np.uint8))
-        out_names.append(f"pred_{len(tiles)}")
-
-    return tiles[:3], out_names[:3]
-
-
-def try_run_model(model_svc, tensor):
-    """
-    Inferência real, igual ao benchmark científico.
-    Mantém esta função isolada para adaptar fácil caso o retorno do modelo mude.
-    """
-    if model_svc is None:
-        return None, "model_svc_none"
-
-    if hasattr(model_svc, "infer_tensor_fast"):
-        return model_svc.infer_tensor_fast(tensor, keep_probs=False), None
-
-    if hasattr(model_svc, "infer"):
-        return model_svc.infer(tensor), None
-
-    return None, "model_svc_sem_infer"
-
-
-def build_grid(raw_tiles, final_tiles, pred_tiles=None, pred_names=None, tile_w=420, tile_h=260):
-    rows = []
-    row_defs = [
-        ("RGB", "rgb"),
-        ("RE", "re"),
-        ("NIR", "nir"),
-    ]
-
-    for i, (label, key) in enumerate(row_defs):
-        left = add_title(raw_tiles[key], f"RAW_BRUTO decoded {label}")
-        mid = add_title(final_tiles[key], f"Tensor final {label}")
-
-        cells = [left, mid]
-
-        if pred_tiles is not None:
-            name = pred_names[i] if pred_names and i < len(pred_names) else f"Pred {i}"
-            cells.append(add_title(pred_tiles[i], name))
-
-        # iguala altura após título
-        h_min = min(c.shape[0] for c in cells)
-        norm = [cv2.resize(c, (tile_w, h_min), interpolation=cv2.INTER_AREA) if c.shape[1] != tile_w or c.shape[0] != h_min else c for c in cells]
-        rows.append(np.hstack(norm))
-
-    return np.vstack(rows)
-
-
-def get_core_perf(client):
-    for attr in ("raw_processor", "processor", "core", "raw_processor_core"):
-        obj = getattr(client, attr, None)
-        if obj is not None and getattr(obj, "last_fusion_result", None) is not None:
-            return obj.last_fusion_result.get("perf", {}) or {}
-    return {}
-
-
-
-# ============================================================
-# Estado compartilhado / workers assíncronos
-# ============================================================
-class SharedState:
-    def __init__(self):
-        self.lock = threading.Lock()
-        self.running = True
-        self.latest_decoded = None
-        self.latest_meta = None
-        self.latest_tensor = None
-        self.latest_perf = {}
-        self.latest_pred = None
-        self.latest_model_warn = None
-        self.latest_model_ms = 0.0
-        self.latest_error = None
-        self.tensor_fps = FpsMeter()
-        self.model_fps = FpsMeter()
-        self.preview_fps = FpsMeter()
-        self.tensor_seq = 0
-        self.model_seq = 0
-
-    def stop(self):
-        with self.lock:
-            self.running = False
-
-    def is_running(self):
-        with self.lock:
-            return bool(self.running)
-
-
-def tensor_worker(client, state, args):
-    """
-    Roda no talo: captura RAW_BRUTO, monta tensor final e atualiza cache.
-    Não depende do FPS da janela.
-    """
-    while state.is_running():
-        try:
-            frame, meta, decoded = client.get_next_decoded(timeout=args.timeout)
-            if not isinstance(frame, dict):
-                raise RuntimeError(f"RAW_BRUTO esperado como dict. Veio {type(frame)}")
-
-            tensor = client.build_infer_tensor_from_decoded(
-                decoded=decoded,
-                meta=meta,
-                channels_expected=5,
-                target_size=(args.width, args.height),
-            )
-            tensor = np.ascontiguousarray(tensor.astype(np.float32, copy=False))
-            perf = get_core_perf(client)
-            fps = state.tensor_fps.tick()
-
-            with state.lock:
-                state.latest_decoded = decoded
-                state.latest_meta = meta
-                state.latest_tensor = tensor
-                state.latest_perf = dict(perf or {})
-                state.tensor_seq += 1
-                state.latest_error = None
-
-        except Exception as e:
-            with state.lock:
-                state.latest_error = f"tensor_worker: {type(e).__name__}: {e}"
-            time.sleep(0.02)
-
-
-def model_worker(model_svc, state, args):
-    """
-    Opcional: roda inferência no último tensor disponível.
-    Não bloqueia o worker de tensor nem a janela.
-    """
-    last_seq = -1
-
-    while state.is_running():
-        with state.lock:
-            tensor = None if state.latest_tensor is None else state.latest_tensor.copy()
-            seq = state.tensor_seq
-
-        if tensor is None or seq == last_seq:
-            time.sleep(0.005)
-            continue
-
-        last_seq = seq
-
-        try:
-            t0_model = time.perf_counter()
-            pred, warn = try_run_model(model_svc, tensor)
-            model_ms = (time.perf_counter() - t0_model) * 1000.0
-            if warn is None:
-                state.model_fps.tick()
-
-            with state.lock:
-                state.latest_pred = pred
-                state.latest_model_warn = warn
-                state.latest_model_ms = float(model_ms)
-                state.model_seq += 1
-
-        except Exception as e:
-            with state.lock:
-                state.latest_model_warn = f"model_worker: {type(e).__name__}: {e}"
-            time.sleep(0.02)
-
-
-def snapshot_state(state):
-    """
-    Copia referências do cache para desenhar. A janela só roda na cadência do preview.
-    """
-    with state.lock:
-        return {
-            "decoded": state.latest_decoded,
-            "meta": state.latest_meta,
-            "tensor": state.latest_tensor,
-            "perf": dict(state.latest_perf or {}),
-            "pred": state.latest_pred,
-            "model_warn": state.latest_model_warn,
-            "model_ms": state.latest_model_ms,
-            "error": state.latest_error,
-            "tensor_fps": state.tensor_fps.fps,
-            "model_fps": state.model_fps.fps,
-            "tensor_seq": state.tensor_seq,
-            "model_seq": state.model_seq,
-        }
-
-
-# ============================================================
-# Main loop assíncrono
-# ============================================================
-def main():
-    ap = argparse.ArgumentParser(description="Preview assíncrono RAW_BRUTO decoded vs tensor final multispectral.")
-    ap.add_argument("--module_params", default=r"C:\ZendionInc\agrobot_base\Python\OAK\datasets\oak-fcc-3\calibration\module_params.json")
-    ap.add_argument("--width", type=int, default=1024, help="Largura final do tensor.")
-    ap.add_argument("--height", type=int, default=640, help="Altura final do tensor.")
-    ap.add_argument("--fps", type=float, default=40.0, help="FPS alvo da câmera.")
-    ap.add_argument("--preview_fps", type=float, default=5.0, help="FPS da janela OpenCV apenas.")
-    ap.add_argument("--timeout", type=float, default=2.0)
-    ap.add_argument("--warmup", type=int, default=5)
-    ap.add_argument("--mx_id", default=None)
-    ap.add_argument("--display_scale", type=float, default=0.75)
-    ap.add_argument("--tile_w", type=int, default=420)
-    ap.add_argument("--tile_h", type=int, default=260)
-    ap.add_argument("--auto_level_raw", action="store_true")
-    ap.add_argument("--auto_level_tensor", action="store_true")
-    ap.add_argument("--run_model", action="store_true", help="Roda inferência em thread separada usando o último tensor cacheado.")
-    ap.add_argument("--model_config_json", default=None, help="JSON de configuração do modelo SegFormer, igual ao benchmark.")
-    ap.add_argument("--warmup_model", type=int, default=3, help="Número de inferências dummy para aquecer o modelo.")
-    ap.add_argument("--save_last", default=None)
-    args = ap.parse_args()
-
-    client = OakFcc3Client(
-        width=args.width,
-        height=args.height,
-        fps=args.fps,
-        frame_type="RAW_BRUTO",
-        capture_mode="TRIPLE",
-        raw_policy="require_triple",
-        module_calibration_json=args.module_params,
-        mx_id=args.mx_id,
-    )
-
-    model_svc = init_model_service(args) if args.run_model else None
-
-    state = SharedState()
-    last_panel = None
-    last_warn_ts = 0.0
-    last_seq_drawn = -1
-
-    try:
-        client.start(print_debug=True)
-
-        for _ in range(max(0, int(args.warmup))):
-            try:
-                client.get_next_decoded(timeout=args.timeout)
-            except Exception:
-                pass
-            time.sleep(0.03)
-
-        tw = threading.Thread(target=tensor_worker, args=(client, state, args), daemon=True)
-        tw.start()
-
-        mw = None
-        if args.run_model:
-            mw = threading.Thread(target=model_worker, args=(model_svc, state, args), daemon=True)
-            mw.start()
-
-        min_period = 1.0 / max(float(args.preview_fps), 0.1)
-        next_draw_ts = 0.0
-
-        print("[INFO] Preview assíncrono iniciado. Pressione Q ou ESC para sair.")
-        print("[INFO] Tensor FPS = geração real do tensor. Preview FPS = janela. Model FPS = inferência, se habilitada.")
-
-        while True:
-            now = time.perf_counter()
-            if now < next_draw_ts:
-                time.sleep(min(0.005, next_draw_ts - now))
-                key = cv2.waitKey(1) & 0xFF
-                if key in (27, ord('q'), ord('Q')):
-                    break
-                continue
-
-            next_draw_ts = now + min_period
-            snap = snapshot_state(state)
-
-            if snap["tensor"] is None or snap["decoded"] is None:
-                blank = np.zeros((360, 900, 3), dtype=np.uint8)
-                cv2.putText(blank, "Aguardando primeiro tensor...", (30, 180), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255,255,255), 2, cv2.LINE_AA)
-                cv2.imshow("OAK RAW_BRUTO vs Tensor Final", blank)
-                key = cv2.waitKey(1) & 0xFF
-                if key in (27, ord('q'), ord('Q')):
-                    break
-                continue
-
-            # Desenha só usando o cache. Não captura nem monta tensor aqui.
-            t_draw0 = time.perf_counter()
-            raw_tiles = decoded_raw_tiles(
-                snap["decoded"],
-                tile_w=args.tile_w,
-                tile_h=args.tile_h,
-                auto_level=args.auto_level_raw,
-            )
-            final_tiles = tensor_tiles(
-                snap["tensor"],
-                tile_w=args.tile_w,
-                tile_h=args.tile_h,
-                auto_level=args.auto_level_tensor,
-            )
-
-            pred_tiles = None
-            pred_names = None
-            if args.run_model:
-                pred_tiles, pred_names = try_extract_prediction_tiles(
-                    snap["pred"],
-                    target_w=args.tile_w,
-                    target_h=args.tile_h,
-                )
-                if snap["model_warn"]:
-                    tnow = time.time()
-                    if tnow - last_warn_ts > 2.0:
-                        print(f"[WARN][MODEL] {snap['model_warn']}")
-                        last_warn_ts = tnow
-
-            panel = build_grid(
-                raw_tiles=raw_tiles,
-                final_tiles=final_tiles,
-                pred_tiles=pred_tiles,
-                pred_names=pred_names,
-                tile_w=args.tile_w,
-                tile_h=args.tile_h,
-            )
-
-            preview_fps = state.preview_fps.tick()
-            draw_ms = (time.perf_counter() - t_draw0) * 1000.0
-            perf = snap["perf"]
-            meta = snap["meta"] or {}
-            tensor_seq = int(snap["tensor_seq"])
-            dropped_for_preview = max(0, tensor_seq - last_seq_drawn - 1) if last_seq_drawn >= 0 else 0
-            last_seq_drawn = tensor_seq
-
-            hud = [
-                f"Preview FPS: {preview_fps:.1f} | Tensor FPS: {snap['tensor_fps']:.1f} | Model FPS: {snap['model_fps']:.1f} | infer={snap.get('model_ms', 0.0):.1f}ms",
-                f"draw={draw_ms:.1f}ms flat={perf.get('flat_ms', 0):.1f} warp={perf.get('warp_total_ms', 0):.1f} crop={perf.get('crop_resize_ms', 0):.1f} fuse={perf.get('total_ms', 0):.1f}",
-                f"seq={tensor_seq} skipped_preview={dropped_for_preview} frame_type={meta.get('frame_type')} run_model={args.run_model}",
-            ]
-            if snap["error"]:
-                hud.append(str(snap["error"])[:120])
-
-            panel = add_hud(panel, hud)
-            last_panel = panel
-
-            disp = panel
-            if args.display_scale and abs(args.display_scale - 1.0) > 1e-6:
-                disp = cv2.resize(
-                    disp,
-                    (max(1, int(disp.shape[1] * args.display_scale)), max(1, int(disp.shape[0] * args.display_scale))),
-                    interpolation=cv2.INTER_AREA,
-                )
-
-            cv2.imshow("OAK RAW_BRUTO vs Tensor Final", disp)
-            key = cv2.waitKey(1) & 0xFF
-            if key in (27, ord('q'), ord('Q')):
-                break
-
-    finally:
-        state.stop()
-        time.sleep(0.05)
-
-        if args.save_last and last_panel is not None:
-            out_path = Path(args.save_last)
-            out_path.parent.mkdir(parents=True, exist_ok=True)
-            cv2.imwrite(str(out_path), last_panel)
-            print(f"[SAVE] {out_path}")
-
-        try:
-            client.stop()
-        except Exception:
-            pass
-        cv2.destroyAllWindows()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/core/oak_fcc3_client.py b/Python/OAK/datasets/oak-fcc-3/core/oak_fcc3_client.py
index 3cb60e76c..ba9dc5406 100644
--- a/Python/OAK/datasets/oak-fcc-3/core/oak_fcc3_client.py
+++ b/Python/OAK/datasets/oak-fcc-3/core/oak_fcc3_client.py
@@ -46,6 +46,7 @@ class OakFcc3Client:
         mx_id=None,
         imu_modo="rotation_vector",
         imu_freq_hz=200,
+        evaluate_quality=True,
         **kwargs,
     ):
         self.width = width
@@ -63,6 +64,16 @@ class OakFcc3Client:
         self.imu_modo = str(imu_modo).strip().lower()
         self.imu_freq_hz = int(imu_freq_hz)
 
+        # Auditoria radiométrica completa do Raw5.
+        #
+        # True mantém o comportamento histórico e é útil para captura científica,
+        # normalize/auditoria e ferramentas offline.
+        #
+        # No runtime em tempo real deve ficar False: evaluate_frame_quality()
+        # calcula estatísticas/percentis pesados e não faz parte da montagem
+        # necessária para a inferência.
+        self.evaluate_quality = bool(evaluate_quality)
+
         self.mx_id = str(mx_id) if mx_id else None
 
         self.svc = OakFcc3Service(
@@ -327,8 +338,33 @@ class OakFcc3Client:
             target_size=target_size,
         )
 
-    def build_infer_tensor_from_decoded(self, decoded, meta, channels_expected, target_size=None):
+    def build_infer_tensor_from_decoded(
+        self,
+        decoded,
+        meta,
+        channels_expected,
+        target_size=None,
+        evaluate_quality=None,
+    ):
+        """
+        Monta o Raw5 físico a partir das câmeras já decodificadas.
+
+        evaluate_quality:
+          - None  -> usa self.evaluate_quality
+          - True  -> executa evaluate_frame_quality() e atualiza
+                     core.last_frame_quality_result
+          - False -> não executa a auditoria pesada e limpa
+                     core.last_frame_quality_result
+
+        A flag altera somente a auditoria de qualidade. Não altera decode,
+        radiometria, flat-field, homografia, crop/resize ou patch normalization.
+        """
         channels_expected = self._validate_physical_channel_count(channels_expected)
+
+        if evaluate_quality is None:
+            evaluate_quality = self.evaluate_quality
+        evaluate_quality = bool(evaluate_quality)
+
         tensor = self.core.fuse_multispec_cameras(decoded, meta, channels_expected)
         tensor = self.core.resize_tensor_chw(tensor, target_size=target_size)
 
@@ -338,7 +374,12 @@ class OakFcc3Client:
         if bool(patch_cfg.get("enabled", False)):
             tensor = self.core.apply_patch_normalization_to_tensor(tensor)
 
-        self.core.last_frame_quality_result = self.core.evaluate_frame_quality(tensor)
+        if evaluate_quality:
+            self.core.last_frame_quality_result = self.core.evaluate_frame_quality(tensor)
+        else:
+            # Evita deixar um resultado antigo parecer referente ao frame atual.
+            self.core.last_frame_quality_result = None
+
         return tensor
 
     def decode_stream_cameras(self, frame, meta):
@@ -382,12 +423,19 @@ class OakFcc3Client:
         tensor = np.transpose(rgb01.astype(np.float32), (2, 0, 1))
         return np.ascontiguousarray(tensor.astype(np.float32, copy=False))
 
-    def build_multispec_tensor(self, decoded, meta=None, target_size=None):
+    def build_multispec_tensor(
+        self,
+        decoded,
+        meta=None,
+        target_size=None,
+        evaluate_quality=None,
+    ):
         tensor = self.build_infer_tensor_from_decoded(
             decoded=decoded,
             meta=meta,
             channels_expected=5,
             target_size=target_size,
+            evaluate_quality=evaluate_quality,
         )
         return np.ascontiguousarray(tensor.astype(np.float32, copy=False))
     
diff --git a/Python/OAK/datasets/oak-fcc-3/core/oak_fcc3_manager.py b/Python/OAK/datasets/oak-fcc-3/core/oak_fcc3_manager.py
index 4544dcebf..ad04a1a47 100644
--- a/Python/OAK/datasets/oak-fcc-3/core/oak_fcc3_manager.py
+++ b/Python/OAK/datasets/oak-fcc-3/core/oak_fcc3_manager.py
@@ -9,6 +9,8 @@ import cv2
 import depthai as dai
 import numpy as np
 
+from itertools import product
+
 
 class OakFcc3Manager:
     """
@@ -43,6 +45,8 @@ class OakFcc3Manager:
         raw_policy="allow_single",
         roles=None,
         sync_mode="best",
+        hardware_sync_enabled=True,
+        frame_sync_master="CAM_A",
         sync_tolerance_ms=12.0,
         buffer_size=8,
         only_camera=None,
@@ -52,6 +56,9 @@ class OakFcc3Manager:
         imu_modo="rotation_vector",
         imu_freq_hz=200,
     ):
+        self.hardware_sync_enabled = hardware_sync_enabled
+        self.frame_sync_master = frame_sync_master
+        
         self.fps = fps
 
         # Para compatibilidade, mantemos width/height.
@@ -432,6 +439,49 @@ class OakFcc3Manager:
     # Pipeline creation, MULTISPEC aligned on OAK
     # ============================================================
 
+    def _aplicar_frame_sync(self, cam, cam_id):
+        cam_id_normalizado = str(cam_id).strip().upper()
+        master = str(self.frame_sync_master).strip().upper()
+
+        if not self.hardware_sync_enabled:
+            print(
+                f"[OAK FSYNC] cam={cam_id_normalizado} "
+                f"habilitado=False modo=DISABLED"
+            )
+            return
+
+        cameras_validas = {"CAM_A", "CAM_B", "CAM_C"}
+
+        if master not in cameras_validas:
+            raise ValueError(
+                f"frame_sync_master inválido: {self.frame_sync_master}. "
+                f"Esperado: CAM_A, CAM_B ou CAM_C."
+            )
+
+        if cam_id_normalizado not in cameras_validas:
+            print(
+                f"[OAK FSYNC] Câmera ignorada: "
+                f"cam_id={cam_id_normalizado}"
+            )
+            return
+
+        if cam_id_normalizado == master:
+            modo = dai.CameraControl.FrameSyncMode.OUTPUT
+            nome_modo = "OUTPUT"
+        else:
+            modo = dai.CameraControl.FrameSyncMode.INPUT
+            nome_modo = "INPUT"
+
+        cam.initialControl.setFrameSyncMode(modo)
+
+        print(
+            f"[OAK FSYNC] "
+            f"cam={cam_id_normalizado} "
+            f"master={master} "
+            f"habilitado=True "
+            f"modo={nome_modo}"
+        )
+
     def _create_color_camera_multispec(self, socket):
         cam = self.pipeline.create(dai.node.ColorCamera)
         cam.setBoardSocket(socket)
@@ -591,6 +641,7 @@ class OakFcc3Manager:
                 raise RuntimeError(f"Role não suportada no MULTISPEC: cam_id={cam_id}, role={role}")
 
             self.apply_initial_camera_controls_to_node(cam, cam_id)
+            self._aplicar_frame_sync(cam, cam_id)
 
             xin_ctrl = self.pipeline.create(dai.node.XLinkIn)
             xin_ctrl.setStreamName(f"{cam_id}_ctrl")
@@ -871,6 +922,7 @@ class OakFcc3Manager:
                     )
 
                     self.apply_initial_camera_controls_to_node(cam, socket_name)
+                    self._aplicar_frame_sync(cam, socket_name)
 
                     xin_ctrl = self.pipeline.create(dai.node.XLinkIn)
                     xin_ctrl.setStreamName(f"{socket_name}_ctrl")
@@ -1241,6 +1293,41 @@ class OakFcc3Manager:
             f"Tolerância atual={self.sync_tolerance_ms} ms."
         )
 
+    def _encontrar_melhor_tripleta(self, required_cam_ids):
+        listas = [
+            list(self.buffers[cam_id])
+            for cam_id in required_cam_ids
+        ]
+
+        melhor_selecao = None
+        melhor_score = None
+
+        for combinacao in product(*listas):
+            timestamps = [
+                item["timestamp"]
+                for item in combinacao
+            ]
+
+            menor_ts = min(timestamps)
+            maior_ts = max(timestamps)
+            spread_ms = (maior_ts - menor_ts) * 1000.0
+
+            # Primeiro prioriza menor dispersão.
+            # Em empate, prefere o pacote mais recente.
+            score = (
+                spread_ms,
+                -menor_ts,
+            )
+
+            if melhor_score is None or score < melhor_score:
+                melhor_score = score
+                melhor_selecao = {
+                    cam_id: item
+                    for cam_id, item in zip(required_cam_ids, combinacao)
+                }
+
+        return melhor_selecao
+
 
     def _extract_frame_controls(self, msg):
         controls = {
@@ -1330,9 +1417,19 @@ class OakFcc3Manager:
 
                 t0_ts = self._cap_now_ms()
                 try:
-                    ts = msg.getTimestamp().total_seconds()
+                    ts_start = msg.getTimestampDevice(
+                        dai.CameraExposureOffset.START
+                    ).total_seconds()
+
+                    ts_end = msg.getTimestampDevice(
+                        dai.CameraExposureOffset.END
+                    ).total_seconds()
+
                 except Exception:
-                    ts = time.time()
+                    ts_start = msg.getTimestamp().total_seconds()
+                    ts_end = ts_start
+
+                ts = ts_start
                 if perf is not None:
                     perf["drain_get_timestamp_ms"] += self._cap_now_ms() - t0_ts
 
@@ -1395,7 +1492,9 @@ class OakFcc3Manager:
 
                 self.buffers[cam_id].append({
                     "frame": frame,
-                    "timestamp": ts,
+                    "timestamp": ts_start,
+                    "timestamp_start": ts_start,
+                    "timestamp_end": ts_end,
                     "controls": frame_controls,
                 })
 
@@ -1420,34 +1519,25 @@ class OakFcc3Manager:
     def _try_get_synced_packet(self, perf=None):
         required_cam_ids = self._get_required_cam_ids()
 
+        if not required_cam_ids:
+            if perf is not None:
+                perf["wait_reason"] = "no_required_cameras"
+            return None
+
+        # Todas as câmeras precisam ter pelo menos um frame disponível.
         for cam_id in required_cam_ids:
             if cam_id not in self.buffers or len(self.buffers[cam_id]) == 0:
                 if perf is not None:
                     perf["wait_reason"] = f"empty_buffer:{cam_id}"
                 return None
 
-        ref_cam_id = min(required_cam_ids, key=lambda cid: len(self.buffers[cid]))
-        ref_item = self.buffers[ref_cam_id][0]
-        ref_ts = ref_item["timestamp"]
+        # Procura a melhor combinação entre todos os frames disponíveis.
+        selected = self._encontrar_melhor_tripleta(required_cam_ids)
 
-        selected = {}
-
-        for cam_id in required_cam_ids:
-            best_item = None
-            best_dt = None
-
-            for item in self.buffers[cam_id]:
-                dt = abs(item["timestamp"] - ref_ts)
-                if best_dt is None or dt < best_dt:
-                    best_dt = dt
-                    best_item = item
-
-            if best_item is None:
-                if perf is not None:
-                    perf["wait_reason"] = f"no_best_item:{cam_id}"
-                return None
-
-            selected[cam_id] = best_item
+        if not selected or len(selected) != len(required_cam_ids):
+            if perf is not None:
+                perf["wait_reason"] = "no_valid_selection"
+            return None
 
         timestamps = {
             cam_id: item["timestamp"]
@@ -1459,44 +1549,80 @@ class OakFcc3Manager:
             for cam_id, item in selected.items()
         }
 
+        ts_values = list(timestamps.values())
+
+        sync_dt_ms = (
+            (max(ts_values) - min(ts_values)) * 1000.0
+            if len(ts_values) >= 2
+            else 0.0
+        )
+
+        sync_ok = sync_dt_ms <= self.sync_tolerance_ms
+
         if perf is not None:
-            perf["selected_ts_by_cam"] = {cam_id: float(ts) for cam_id, ts in timestamps.items()}
+            perf["selected_ts_by_cam"] = {
+                cam_id: float(ts)
+                for cam_id, ts in timestamps.items()
+            }
+
             perf["selected_seq_by_cam"] = {
                 cam_id: item.get("controls", {}).get("sequence_num")
                 for cam_id, item in selected.items()
             }
 
-        ts_values = list(timestamps.values())
-        sync_dt_ms = (max(ts_values) - min(ts_values)) * 1000.0 if len(ts_values) >= 2 else 0.0
-        sync_ok = sync_dt_ms <= self.sync_tolerance_ms
+            perf["sync_dt_ms"] = float(sync_dt_ms)
+            perf["sync_ok"] = bool(sync_ok)
+
+        modo_sync = str(self.sync_mode).strip().lower()
+
+        # No modo estrito, nunca entrega uma tripleta fora da tolerância.
+        if not sync_ok and modo_sync == "strict":
+            #Remove o frame globalmente mais antigo entre as cabeças
+            #dos buffers. Frames futuros somente estarão mais distantes
+            #desse frame, então ele não conseguirá formar uma combinação
+            #melhor posteriormente.
+            oldest_cam_id = min(
+                required_cam_ids,
+                key=lambda cam_id: self.buffers[cam_id][0]["timestamp"]
+            )
+
+            dropped_item = self.buffers[oldest_cam_id].popleft()
 
-        if not sync_ok and self.sync_mode == "strict":
-            oldest_cam_id = min(timestamps, key=timestamps.get)
-            if len(self.buffers[oldest_cam_id]) > 0:
-                self.buffers[oldest_cam_id].popleft()
             if perf is not None:
                 perf["wait_reason"] = f"strict_drop_oldest:{oldest_cam_id}"
-                perf["sync_dt_ms"] = float(sync_dt_ms)
-                perf["sync_ok"] = False
+                perf["dropped_timestamp"] = float(dropped_item["timestamp"])
+
             return None
 
+        # Em best/best_effort, entrega a melhor combinação disponível,
+        # mesmo quando estiver fora da tolerância.
         frames = {
             cam_id: item["frame"]
             for cam_id, item in selected.items()
         }
 
+        # Consome todos os frames anteriores e o próprio frame selecionado.
         for cam_id, used_item in selected.items():
-            while len(self.buffers[cam_id]) > 0:
+            while self.buffers[cam_id]:
                 item = self.buffers[cam_id].popleft()
+
                 if item is used_item:
                     break
 
         if perf is not None:
-            perf["wait_reason"] = "synced_selected"
-            perf["sync_dt_ms"] = float(sync_dt_ms)
-            perf["sync_ok"] = bool(sync_ok)
+            perf["wait_reason"] = (
+                "synced_selected"
+                if sync_ok
+                else "best_effort_selected_outside_tolerance"
+            )
 
-        return frames, timestamps, sync_dt_ms, sync_ok, frame_controls
+        return (
+            frames,
+            timestamps,
+            sync_dt_ms,
+            sync_ok,
+            frame_controls,
+        )
 
     def _get_available_cam_ids_ordered(self):
         role_order = ["rgb", "re", "nir"]
diff --git a/Python/OAK/datasets/oak-fcc-3/core/raw_processor_core.py b/Python/OAK/datasets/oak-fcc-3/core/raw_processor_core.py
index c105e7b8f..3a2ba9d24 100644
--- a/Python/OAK/datasets/oak-fcc-3/core/raw_processor_core.py
+++ b/Python/OAK/datasets/oak-fcc-3/core/raw_processor_core.py
@@ -1,9 +1,42 @@
+"""
+RawProcessorCore - Production
+=============================
+
+Canonical multispectral preprocessing core for training and inference.
+
+Product contract:
+- CAM_A RGB: OV9782 1280x800 OR AR0234 1920x1200
+- CAM_B RE : OV9282 1280x800
+- CAM_C NIR: OV9282 1280x800
+- Physical output tensor: [R, G, B, RE, NIR]
+
+Production revision highlights:
+- mixed-resolution homography with independent source/reference spaces;
+- OpenCV pixel-center-aware geometry scaling;
+- native RE/NIR warp without pre-resizing to RGB;
+- strict validation of final module_params and per-frame hardware metadata;
+- product RAW_BRUTO fail-closed input contract;
+- radiometric frame-control fallbacks for saved datasets;
+- single-resample fusion directly to requested tensor size;
+- intrinsics contract loading/validation with runtime undistort gated off;
+- faster Flat-Field saturation guard using fused Numba kernel;
+- lower-allocation in-place Flat-Field path;
+- faster frame-quality statistics while retaining exact critical percentages.
+
+This module intentionally keeps legacy behavior available when a non-product
+module_params is loaded. The strict fail-closed behavior is enabled only for
+module_params generated by the production assembler.
+"""
+
+RAW_PROCESSOR_CORE_VERSION = "production_v1_2026_08_24"
+
 import json
 import os
 import time
 import cv2
 import numpy as np
 import math
+from copy import deepcopy
 from typing import Optional
 
 
@@ -215,6 +248,97 @@ if _HAS_NUMBA:
                 out[y, x] = v
 
 
+    @_numba.njit(cache=True, fastmath=True, parallel=True)
+    def _apply_flat_gain_guard_numba(
+        base,
+        gain,
+        saturation_mask,
+        out,
+        height,
+        width,
+        strength,
+        gain_min_runtime,
+        gain_max_runtime,
+        sat_soft_start,
+        sat_hard,
+        clip_output,
+        mode_code,
+    ):
+        """
+        Flat-field com saturation guard em uma única passada.
+
+        mode_code:
+          1 = fade_strength
+          2 = skip
+          0 = simple
+        """
+        denom = sat_hard - sat_soft_start
+        if denom < 1e-6:
+            denom = 1e-6
+
+        for y in _numba.prange(height):
+            for x in range(width):
+                v = float(base[y, x])
+                g = float(gain[y, x])
+
+                if mode_code == 1:
+                    t = (v - sat_soft_start) / denom
+
+                    if t < 0.0:
+                        t = 0.0
+                    elif t > 1.0:
+                        t = 1.0
+
+                    local_strength = 1.0 - t
+
+                    if saturation_mask[y, x]:
+                        local_strength = 0.0
+
+                    gain_eff = 1.0 + (
+                        strength
+                        * local_strength
+                        * (g - 1.0)
+                    )
+
+                    if gain_eff < gain_min_runtime:
+                        gain_eff = gain_min_runtime
+                    elif gain_eff > gain_max_runtime:
+                        gain_eff = gain_max_runtime
+
+                    value = v * gain_eff
+
+                elif mode_code == 2:
+                    if saturation_mask[y, x]:
+                        value = v
+                    else:
+                        gain_eff = 1.0 + strength * (g - 1.0)
+
+                        if gain_eff < gain_min_runtime:
+                            gain_eff = gain_min_runtime
+                        elif gain_eff > gain_max_runtime:
+                            gain_eff = gain_max_runtime
+
+                        value = v * gain_eff
+
+                else:
+                    gain_eff = 1.0 + strength * (g - 1.0)
+
+                    if gain_eff < gain_min_runtime:
+                        gain_eff = gain_min_runtime
+                    elif gain_eff > gain_max_runtime:
+                        gain_eff = gain_max_runtime
+
+                    value = v * gain_eff
+
+                if clip_output:
+                    if value < 0.0:
+                        value = 0.0
+                    elif value > 1.0:
+                        value = 1.0
+
+                out[y, x] = value
+
+
     @_numba.njit(cache=True, fastmath=True, parallel=True)
     def _apply_tensor_flat_gain_chw_numba(tensor, gain, channels, height, width, clip_output):
         for c in _numba.prange(channels):
@@ -389,6 +513,7 @@ class RawProcessorCore:
             "save_debug": True,
         }
         self.last_radiometric_normalization_result = None
+        self.last_flatfield_result = None
 
         self.radiometric_config = {}
         self.patch_normalization_config = {
@@ -440,37 +565,160 @@ class RawProcessorCore:
         self.last_fusion_result = None
         self.camera_settings = {}
 
+        # Contrato de produto carregado pelo assembler final.
+        self.strict_product_contract = False
+        self.module_params_schema = None
+        self.sensor_size_by_role = {}
+        self.camera_hardware = {}
+        self.intrinsics_config = {
+            "enabled": False,
+            "calibration_space": "native_stream_no_external_undistort",
+            "cameras": {},
+            "runtime_undistort": {"enabled": False},
+        }
+        self.calibration_provenance = {}
+        self.assembly_metadata = {}
+        self._last_frame_controls_source = None
+
         self._flatfield_runtime_cache = {}
 
         self.last_decode_perf = {}
         self._last_decode_perf_log_ts = 0.0
         self.last_rgb_enhancement_result = None
 
+        # QC: percentis são diagnóstico, não parte do tensor.
+        # Mantemos saturação/dark/range exatos e limitamos apenas estatísticas
+        # descritivas para não gastar dezenas/centenas de ms por frame.
+        self.quality_stats_max_samples = 65536
+
         if calibration_json_path:
             self.load_config_json(calibration_json_path)
 
 
     def warmup_numba_raw10_decode(self):
+        """
+        Warmup sensor-aware.
+
+        Numba especializa por dtype/layout, não por shape, mas usar os tamanhos
+        reais também aquece alocações e o caminho OpenCV do RGB correto.
+        """
         if not _HAS_NUMBA:
             return {"ok": False, "reason": "numba_not_available"}
 
-        w, h = 1280, 800
-        packed_w = int(np.ceil(w * 10 / 8))
-        dummy = np.zeros((h, packed_w), dtype=np.uint8)
+        sizes = getattr(self, "sensor_size_by_role", {}) or {}
 
-        _ = self._raw10_mono_to_float01_aggressive(dummy, w, h, 10)
-        _ = self._raw10_rgb_bayer_planes_to_rgb_float01_aggressive(dummy, w, h, "RGGB", 10)
-        _ = self._raw10_to_raw16_aggressive(dummy, w, h)
+        rgb_size = sizes.get("rgb") or [
+            int(self.sensor_width),
+            int(self.sensor_height),
+        ]
+        re_size = sizes.get("re") or [1280, 800]
 
-        raw16 = self._raw10_to_raw16_aggressive(dummy, w, h)
-        rgb16 = cv2.cvtColor(raw16, cv2.COLOR_BayerRG2RGB)
+        rgb_w, rgb_h = int(rgb_size[0]), int(rgb_size[1])
+        spec_w, spec_h = int(re_size[0]), int(re_size[1])
+
+        rgb_packed_w = self._raw10_expected_packed_width_fast(rgb_w)
+        spec_packed_w = self._raw10_expected_packed_width_fast(spec_w)
+
+        dummy_rgb = np.zeros((rgb_h, rgb_packed_w), dtype=np.uint8)
+        dummy_spec = np.zeros((spec_h, spec_packed_w), dtype=np.uint8)
+
+        _ = self._raw10_mono_to_float01_aggressive(
+            dummy_spec,
+            spec_w,
+            spec_h,
+            10,
+        )
+
+        _ = self._raw10_rgb_bayer_planes_to_rgb_float01_aggressive(
+            dummy_rgb,
+            rgb_w,
+            rgb_h,
+            self.bayer_pattern,
+            10,
+        )
+
+        raw16 = self._raw10_to_raw16_aggressive(
+            dummy_rgb,
+            rgb_w,
+            rgb_h,
+        )
+
+        cv_code, _ = self._get_bayer_cv2_code(
+            bayer_pattern=self.bayer_pattern,
+            algorithm=str(
+                (self.rgb_processing_config or {}).get(
+                    "demosaic_algorithm",
+                    "ea",
+                )
+            ),
+        )
+
+        rgb16 = cv2.cvtColor(raw16, cv_code)
         _ = self._rgb16_to_float32_gain_clip_aggressive(rgb16, 10)
 
-        dummy_tensor = np.zeros((5, 640, 1024), dtype=np.float32)
-        dummy_gain = np.ones((5, 640, 1024), dtype=np.float32)
-        _apply_tensor_flat_gain_chw_numba(dummy_tensor, dummy_gain, 5, 640, 1024, True)
+        # Warmup do flat final/Numba com target real, se houver.
+        target = (self.fusion_config or {}).get("target_size") or [
+            max(1, rgb_w // 2),
+            max(1, rgb_h // 2),
+        ]
+        tw, th = int(target[0]), int(target[1])
 
-        return {"ok": True, "backend": "numba", "shape": [h, packed_w]}
+        dummy_tensor = np.zeros((5, th, tw), dtype=np.float32)
+        dummy_gain = np.ones((5, th, tw), dtype=np.float32)
+
+        _apply_tensor_flat_gain_chw_numba(
+            dummy_tensor,
+            dummy_gain,
+            5,
+            th,
+            tw,
+            True,
+        )
+
+        # Warmup do kernel fused do Flat-Field com saturation guard.
+        kh = min(th, 64)
+        kw = min(tw, 64)
+
+        dummy_base = np.zeros(
+            (kh, kw),
+            dtype=np.float32,
+        )
+        dummy_gain_ch = np.ones(
+            (kh, kw),
+            dtype=np.float32,
+        )
+        dummy_mask = np.zeros(
+            (kh, kw),
+            dtype=np.bool_,
+        )
+        dummy_out = np.empty(
+            (kh, kw),
+            dtype=np.float32,
+        )
+
+        _apply_flat_gain_guard_numba(
+            dummy_base,
+            dummy_gain_ch,
+            dummy_mask,
+            dummy_out,
+            kh,
+            kw,
+            1.0,
+            0.75,
+            1.35,
+            0.88,
+            0.97,
+            True,
+            1,
+        )
+
+        return {
+            "ok": True,
+            "backend": "numba",
+            "rgb_native": [rgb_w, rgb_h],
+            "spectral_native": [spec_w, spec_h],
+            "target_size": [tw, th],
+        }
 
     def unpack_raw10_packed(
         self,
@@ -910,8 +1158,19 @@ class RawProcessorCore:
         output_dtype: str = "float32",
         bit_depth: int = 10,
     ) -> np.ndarray:
+        """
+        RAW Bayer uint16 -> RGB CHW usando exatamente o rgb_processing atual.
+
+        Suporta:
+          linear_demosaic
+          linear_demosaic_half
+          bayer_planes
+        """
         rgb_mode = str(
-            (getattr(self, "rgb_processing_config", {}) or {}).get("mode", "linear_demosaic")
+            (getattr(self, "rgb_processing_config", {}) or {}).get(
+                "mode",
+                "linear_demosaic",
+            )
         ).lower()
 
         if rgb_mode in ("linear_demosaic", "demosaic", "full_res"):
@@ -920,6 +1179,23 @@ class RawProcessorCore:
                 bit_depth=bit_depth,
             )
 
+        elif rgb_mode in (
+            "linear_demosaic_half",
+            "demosaic_half",
+            "full_demosaic_half",
+        ):
+            r, g, b = self.demosaic_raw16_to_rgb_linear(
+                raw16,
+                bit_depth=bit_depth,
+            )
+
+            h, w = r.shape[:2]
+            size = (w // 2, h // 2)
+
+            r = cv2.resize(r, size, interpolation=cv2.INTER_AREA)
+            g = cv2.resize(g, size, interpolation=cv2.INTER_AREA)
+            b = cv2.resize(b, size, interpolation=cv2.INTER_AREA)
+
         elif rgb_mode in ("bayer_planes", "bayer", "half_res"):
             r, g, b = self.bayer_planes_to_rgb_linear(
                 raw16,
@@ -927,7 +1203,9 @@ class RawProcessorCore:
             )
 
         else:
-            raise ValueError(f"rgb_processing.mode inválido: {rgb_mode}")
+            raise ValueError(
+                f"rgb_processing.mode inválido: {rgb_mode}"
+            )
 
         rgb_cal = getattr(self, "rgb_calibration", {}) or {}
         if rgb_cal.get("enabled", False):
@@ -936,30 +1214,58 @@ class RawProcessorCore:
             g = g * float(gains.get("G", 1.0))
             b = b * float(gains.get("B", 1.0))
 
-        rgb_hwc = np.stack([r, g, b], axis=2).astype(np.float32)
-        np.clip(rgb_hwc, 0.0, 1.0, out=rgb_hwc)
+        rgb_hwc = np.stack(
+            [r, g, b],
+            axis=2,
+        ).astype(np.float32)
+
+        np.clip(
+            rgb_hwc,
+            0.0,
+            1.0,
+            out=rgb_hwc,
+        )
 
-        # Pós-processamento RGB opcional.
-        # Aplicado aqui para o caminho de treino/offline que chama build_training_rgb().
         rgb_hwc = self.apply_rgb_enhancement_to_hwc_float01(
             rgb_hwc,
             stage="build_training_rgb.after_decode",
             source_kind="raw",
         )
 
-        chw = np.transpose(rgb_hwc, (2, 0, 1)).astype(np.float32, copy=False)
-        np.clip(chw, 0.0, 1.0, out=chw)
+        chw = np.transpose(
+            rgb_hwc,
+            (2, 0, 1),
+        ).astype(np.float32, copy=False)
+
+        np.clip(
+            chw,
+            0.0,
+            1.0,
+            out=chw,
+        )
 
         if output_dtype == "float32":
             return chw
 
         if output_dtype == "uint8":
-            return (chw * 255.0).clip(0, 255).astype(np.uint8)
+            return (
+                chw * 255.0
+            ).clip(
+                0,
+                255,
+            ).astype(np.uint8)
 
         if output_dtype == "uint16":
-            return (chw * 65535.0).clip(0, 65535).astype(np.uint16)
+            return (
+                chw * 65535.0
+            ).clip(
+                0,
+                65535,
+            ).astype(np.uint16)
 
-        raise ValueError(f"output_dtype não suportado: {output_dtype}")
+        raise ValueError(
+            f"output_dtype não suportado: {output_dtype}"
+        )
     
     def _channel_names_from_decoded(self, decoded):
         names = ["R", "G", "B"]
@@ -1049,38 +1355,106 @@ class RawProcessorCore:
 
         return decoded
 
-    def build_multispectral_tensor(self, bins_data, bins_meta, target_size=None):
-        decoded = self.decode_bins_cameras(bins_data, bins_meta)
+    def build_multispectral_tensor(
+        self,
+        bins_data,
+        bins_meta,
+        target_size=None,
+        capture_meta=None,
+    ):
+        """
+        Caminho offline/legado para dados já carregados.
 
-        rgb_cam_id = self._find_cam_by_role(decoded, "rgb")
+        Em module_params de produção, radiometric_normalization é obrigatória.
+        Portanto capture_meta deve acompanhar o sample; caso contrário este método
+        recusará gerar um tensor silenciosamente diferente do runtime.
+
+        Para datasets RAW_BRUTO novos, prefira build_infer_tensor_from_stream().
+        """
+        decoded = self.decode_bins_cameras(
+            bins_data,
+            bins_meta,
+        )
+
+        rgb_cam_id = self._find_cam_by_role(
+            decoded,
+            "rgb",
+        )
         if rgb_cam_id is None:
             raise RuntimeError("RGB obrigatório")
 
-        channel_names = self._channel_names_from_decoded(decoded)
+        channel_names = self._channel_names_from_decoded(
+            decoded
+        )
+
+        if (
+            getattr(self, "strict_product_contract", False)
+            and bool(
+                (self.radiometric_normalization_config or {}).get(
+                    "enabled",
+                    False,
+                )
+            )
+            and not isinstance(capture_meta, dict)
+        ):
+            raise RuntimeError(
+                "build_multispectral_tensor() em modo produto exige capture_meta "
+                "com controles reais da captura. Prefira build_infer_tensor_from_stream()."
+            )
 
         tensor = self.fuse_multispec_cameras(
             decoded,
-            meta=None,
+            meta=capture_meta,
             channels_expected=len(channel_names),
+            target_size=target_size,
         )
 
-        tensor = self.resize_tensor_chw(tensor, target_size=target_size)
+        if bool(
+            (self.patch_normalization_config or {}).get(
+                "enabled",
+                False,
+            )
+        ):
+            tensor = self.apply_patch_normalization_to_tensor(
+                tensor
+            )
 
-        if bool((self.patch_normalization_config or {}).get("enabled", False)):
-            tensor = self.apply_patch_normalization_to_tensor(tensor)
-
-        self.last_frame_quality_result = self.evaluate_frame_quality(tensor)
+        self.last_frame_quality_result = self.evaluate_frame_quality(
+            tensor
+        )
 
         return tensor, channel_names
 
     def build_infer_tensor_from_stream(self, frame, meta, channels_expected, target_size=None):
         channels_expected = self._validate_physical_channel_count(channels_expected)
-        frame_type = meta.get("frame_type")
+        stream_meta = (
+            meta.get("stream_meta", {})
+            if isinstance(meta, dict)
+            else {}
+        ) or {}
+        frame_type = (
+            meta.get("frame_type")
+            if isinstance(meta, dict)
+            else None
+        ) or stream_meta.get("frame_type")
+
+        if (
+            getattr(self, "strict_product_contract", False)
+            and frame_type != "RAW_BRUTO"
+        ):
+            raise RuntimeError(
+                "module_params de produção aceita somente frame_type='RAW_BRUTO' "
+                "neste core. RGB/MULTISPEC pré-processado contornaria calibrações."
+            )
 
         if frame_type == "RAW_BRUTO":
             decoded = self.decode_stream_cameras(frame, meta)
-            tensor = self.fuse_multispec_cameras(decoded, meta, channels_expected)
-            tensor = self.resize_tensor_chw(tensor, target_size=target_size)
+            tensor = self.fuse_multispec_cameras(
+                decoded,
+                meta,
+                channels_expected,
+                target_size=target_size,
+            )
 
             # Sem cartões neste modo novo.
             # patch_normalization deve ficar desligado no JSON.
@@ -1125,42 +1499,92 @@ class RawProcessorCore:
         if not isinstance(frame, dict):
             raise RuntimeError("RAW_BRUTO esperado como dict de câmeras no modo multi")
 
-        camera_frames = meta.get("camera_frames", {}) or {}
-        camera_info = meta.get("camera_info", {}) or {}
+        if not isinstance(meta, dict):
+            raise RuntimeError("RAW_BRUTO exige meta dict.")
+
+        self._validate_stream_camera_contract(frame, meta)
+
+        stream_meta = (
+            meta.get("stream_meta", {})
+            if isinstance(meta.get("stream_meta"), dict)
+            else {}
+        )
+        camera_frames = (
+            meta.get("camera_frames", {})
+            or stream_meta.get("camera_frames", {})
+            or {}
+        )
+        camera_info = (
+            meta.get("camera_info", {})
+            or stream_meta.get("camera_info", {})
+            or {}
+        )
 
         decoded = {}
 
         rgb_mode = str(
-            (getattr(self, "rgb_processing_config", {}) or {}).get("mode", "linear_demosaic")
+            (getattr(self, "rgb_processing_config", {}) or {}).get(
+                "mode",
+                "linear_demosaic",
+            )
         ).lower()
 
         for cam_id, data in frame.items():
-            cam_meta = camera_frames.get(cam_id) or camera_info.get(cam_id) or {}
+            cam_meta = {}
+            if isinstance(camera_info.get(cam_id), dict):
+                cam_meta.update(camera_info.get(cam_id) or {})
+            if isinstance(camera_frames.get(cam_id), dict):
+                cam_meta.update(camera_frames.get(cam_id) or {})
 
             role = str(cam_meta.get("role", "")).lower()
             if not role:
-                raise RuntimeError(f"Meta da câmera {cam_id} sem role. Esperado role='rgb', 'nir' ou 're'.")
+                raise RuntimeError(
+                    f"Meta da câmera {cam_id} sem role. "
+                    "Esperado role='rgb', 'nir' ou 're'."
+                )
 
             bit_depth = int(cam_meta.get("bit_depth", 10))
             raw_format = str(cam_meta.get("raw_format", "")).upper()
-            packed = bool(cam_meta.get("packed", False))
-            is_raw10 = raw_format == "RAW10_PACKED" or packed or bit_depth == 10
+            packed_flag = bool(cam_meta.get("packed", False))
 
             if role == "rgb":
-                if is_raw10 and data.ndim == 2:
-                    sensor_w = int(cam_meta.get("width", self.sensor_width))
-                    sensor_h = int(cam_meta.get("height", self.sensor_height))
+                sensor_w = int(cam_meta.get("width", self.sensor_width))
+                sensor_h = int(cam_meta.get("height", self.sensor_height))
 
-                    bayer = (
-                        cam_meta.get("bayer_pattern")
-                        or cam_meta.get("bayer")
-                        or self.bayer_pattern
-                        or "RGGB"
+                bayer = str(
+                    cam_meta.get("bayer_pattern")
+                    or cam_meta.get("bayer")
+                    or self.bayer_pattern
+                    or "RGGB"
+                ).upper()
+
+                arr = np.asarray(data)
+
+                expected_packed_w = self._raw10_expected_packed_width_fast(sensor_w)
+                packed_width_meta = int(cam_meta.get("packed_width", 0) or 0)
+
+                looks_like_raw10_packed = (
+                    arr.ndim == 2
+                    and arr.dtype == np.uint8
+                    and arr.shape[0] == sensor_h
+                    and (
+                        raw_format == "RAW10_PACKED"
+                        or packed_flag
+                        or (
+                            packed_width_meta > 0
+                            and arr.shape[1] >= packed_width_meta
+                        )
+                        or (
+                            bit_depth == 10
+                            and arr.shape[1] >= expected_packed_w
+                        )
                     )
+                )
 
+                if looks_like_raw10_packed:
                     if rgb_mode in ("bayer_planes", "bayer", "half_res"):
                         rgb_hwc = self._raw10_rgb_bayer_planes_to_rgb_float01_aggressive(
-                            data,
+                            arr,
                             width=sensor_w,
                             height=sensor_h,
                             bayer_pattern=bayer,
@@ -1176,7 +1600,7 @@ class RawProcessorCore:
                         "full_demosaic_half",
                     ):
                         rgb_hwc = self._raw10_rgb_linear_demosaic_to_rgb_float01_fast(
-                            data,
+                            arr,
                             width=sensor_w,
                             height=sensor_h,
                             bayer_pattern=bayer,
@@ -1184,10 +1608,12 @@ class RawProcessorCore:
                         )
 
                     else:
-                        raise ValueError(f"rgb_processing.mode inválido: {rgb_mode}")
+                        raise ValueError(
+                            f"rgb_processing.mode inválido: {rgb_mode}"
+                        )
 
-                    # Pós-processamento RGB opcional.
-                    # Este é o caminho principal de inferência RAW_BRUTO.
+                    # Em produto enhancement é validado como OFF.
+                    # Em modo legado permanece compatível com os experimentos antigos.
                     rgb_hwc = self.apply_rgb_enhancement_to_hwc_float01(
                         rgb_hwc,
                         stage="decode_stream_cameras.after_raw_decode",
@@ -1201,18 +1627,50 @@ class RawProcessorCore:
                         "meta": cam_meta,
                     }
 
+                elif arr.ndim == 2 and arr.dtype == np.uint16:
+                    # RAW Bayer já desempacotado. Útil em fluxo offline/diagnóstico.
+                    old_bayer = self.bayer_pattern
+                    try:
+                        self.bayer_pattern = bayer
+                        rgb_chw = self.build_training_rgb(
+                            arr,
+                            output_dtype="float32",
+                            bit_depth=bit_depth,
+                        )
+                    finally:
+                        self.bayer_pattern = old_bayer
+
+                    rgb_hwc = np.transpose(
+                        rgb_chw,
+                        (1, 2, 0),
+                    ).astype(np.float32, copy=False)
+
+                    decoded[cam_id] = {
+                        "name": "RGB",
+                        "role": "rgb",
+                        "image": rgb_hwc,
+                        "meta": cam_meta,
+                    }
+
                 else:
-                    # Caso preview/processado antigo: BGR HWC uint8.
-                    if data.ndim != 3 or data.shape[2] != 3:
-                        raise RuntimeError(f"{cam_id} RGB inválida: shape={data.shape}")
+                    # Preview/processado antigo: BGR HWC.
+                    if arr.ndim != 3 or arr.shape[2] != 3:
+                        raise RuntimeError(
+                            f"{cam_id} RGB inválida: shape={arr.shape}, "
+                            f"dtype={arr.dtype}"
+                        )
 
-                    rgb = data[:, :, ::-1].astype(np.float32) / 255.0
+                    if arr.dtype == np.uint8:
+                        rgb = arr[:, :, ::-1].astype(np.float32) / 255.0
+                    elif arr.dtype == np.uint16:
+                        rgb = arr[:, :, ::-1].astype(np.float32) / 65535.0
+                    else:
+                        rgb = arr[:, :, ::-1].astype(np.float32)
+                        if np.nanmax(rgb) > 1.5:
+                            rgb /= 255.0
 
-                    rgb = np.clip(rgb, 0.0, 1.0)
+                    np.clip(rgb, 0.0, 1.0, out=rgb)
 
-                    # Por padrão não mexe em preview/RGB já processado.
-                    # Se quiser aplicar também neste caminho, use:
-                    # rgb_processing.enhancement.apply_to_preview_input=true
                     rgb = self.apply_rgb_enhancement_to_hwc_float01(
                         rgb,
                         stage="decode_stream_cameras.preview_input",
@@ -1230,7 +1688,10 @@ class RawProcessorCore:
                 decoded[cam_id] = {
                     "name": "RE",
                     "role": "re",
-                    "image": self._decode_spectral_frame_to_float01(data, cam_meta),
+                    "image": self._decode_spectral_frame_to_float01(
+                        data,
+                        cam_meta,
+                    ),
                     "meta": cam_meta,
                 }
 
@@ -1238,12 +1699,17 @@ class RawProcessorCore:
                 decoded[cam_id] = {
                     "name": "NIR",
                     "role": "nir",
-                    "image": self._decode_spectral_frame_to_float01(data, cam_meta),
+                    "image": self._decode_spectral_frame_to_float01(
+                        data,
+                        cam_meta,
+                    ),
                     "meta": cam_meta,
                 }
 
             else:
-                raise RuntimeError(f"Role não suportada em {cam_id}: {role}")
+                raise RuntimeError(
+                    f"Role não suportada em {cam_id}: {role}"
+                )
 
         return decoded
     
@@ -1313,7 +1779,13 @@ class RawProcessorCore:
 
         return np.clip(arr01, 0.0, 1.0)
 
-    def fuse_multispec_cameras(self, decoded, meta, channels_expected):
+    def fuse_multispec_cameras(
+        self,
+        decoded,
+        meta,
+        channels_expected,
+        target_size=None,
+    ):
         t_total0 = time.perf_counter()
 
         t0 = time.perf_counter()
@@ -1344,6 +1816,7 @@ class RawProcessorCore:
             decoded,
             meta,
             channels_expected,
+            target_size_override=target_size,
         )
 
         t_flat_ms = float(t_flat_native_ms + float(direct_perf.get("final_flat_ms", 0.0)))
@@ -1465,13 +1938,55 @@ class RawProcessorCore:
         )
 
     def _warp_with_valid_mask(self, img, role, ref_shape, meta):
-        ref_h, ref_w = ref_shape
+        """
+        Legacy spatial path mantido correto para mixed-resolution.
 
-        if img.shape[:2] != (ref_h, ref_w):
-            img = cv2.resize(img, (ref_w, ref_h), interpolation=cv2.INTER_LINEAR)
+        Em homography, RE/NIR permanecem no tamanho nativo até o warp.
+        Nos modos identity/manual antigos, resize para a referência continua
+        permitido apenas por compatibilidade.
+        """
+        ref_h, ref_w = int(ref_shape[0]), int(ref_shape[1])
 
         cfg = self.fusion_config
-        mode = cfg.get("alignment_mode", "identity")
+        mode = str(cfg.get("alignment_mode", "identity")).lower()
+
+        if mode == "homography":
+            H = self._direct_fusion_get_role_homography_fast(
+                role,
+                meta,
+                (ref_h, ref_w),
+                source_size=img.shape[:2],
+            )
+
+            source_mask = np.ones(img.shape[:2], dtype=np.uint8) * 255
+
+            warped = cv2.warpPerspective(
+                img,
+                H,
+                (ref_w, ref_h),
+                flags=cv2.INTER_LINEAR,
+                borderMode=cv2.BORDER_CONSTANT,
+                borderValue=0,
+            )
+
+            warped_mask = cv2.warpPerspective(
+                source_mask,
+                H,
+                (ref_w, ref_h),
+                flags=cv2.INTER_NEAREST,
+                borderMode=cv2.BORDER_CONSTANT,
+                borderValue=0,
+            )
+
+            return warped, (warped_mask > 0).astype(np.uint8)
+
+        # Modos legados operavam em uma grade comum.
+        if img.shape[:2] != (ref_h, ref_w):
+            img = cv2.resize(
+                img,
+                (ref_w, ref_h),
+                interpolation=cv2.INTER_LINEAR,
+            )
 
         mask = np.ones((ref_h, ref_w), dtype=np.uint8) * 255
 
@@ -1496,36 +2011,10 @@ class RawProcessorCore:
             warped = self._affine_image(img, dx, dy, theta_deg)
             warped_mask = self._affine_image(mask, dx, dy, theta_deg)
 
-        elif mode == "homography":
-            H, calib_size, profile_name = self._resolve_homography_entry_for_role(role)
-            H = self._scale_homography_to_runtime(
-                H,
-                calib_size=calib_size,
-                runtime_size=(ref_w, ref_h),
-            )
-
-            if H.shape != (3, 3):
-                raise RuntimeError(f"Homografia inválida para {role}: shape={H.shape}")
-
-            warped = cv2.warpPerspective(
-                img, H, (ref_w, ref_h),
-                flags=cv2.INTER_LINEAR,
-                borderMode=cv2.BORDER_CONSTANT,
-                borderValue=0
-            )
-
-            warped_mask = cv2.warpPerspective(
-                mask, H, (ref_w, ref_h),
-                flags=cv2.INTER_NEAREST,
-                borderMode=cv2.BORDER_CONSTANT,
-                borderValue=0
-            )
-
         else:
             raise RuntimeError(f"alignment_mode inválido: {mode}")
 
-        warped_mask = (warped_mask > 0).astype(np.uint8)
-        return warped, warped_mask
+        return warped, (warped_mask > 0).astype(np.uint8)
 
     def _compute_common_crop_box(self, masks):
         if not masks:
@@ -1599,56 +2088,157 @@ class RawProcessorCore:
 
         return np.stack(chans, axis=0)
 
-    def _scale_homography_to_runtime(self, H, calib_size, runtime_size):
+    def _scale_homography_to_runtime(
+        self,
+        H,
+        calib_size=None,
+        runtime_size=None,
+        *,
+        calib_source_size=None,
+        calib_reference_size=None,
+        runtime_source_size=None,
+        runtime_reference_size=None,
+    ):
         """
-        Ajusta uma homografia calculada em calib_size para ser aplicada em runtime_size.
+        Escala H entre dois espaços independentes usando convenção de centro
+        de pixel compatível com cv2.resize:
 
-        H original:
-            ponto_spec_calib -> ponto_rgb_calib
+            x_dst = (x_src + 0.5) * scale - 0.5
 
-        H runtime:
-            ponto_spec_runtime -> ponto_rgb_runtime
+        Caso geral:
+            H_calib   : source_calib  -> reference_calib
+            H_runtime : source_runtime -> reference_runtime
+
+            H_runtime = A_dst @ H_calib @ inv(A_src)
+
+        Chamadas antigas com calib_size/runtime_size seguem compatíveis.
         """
         if H is None:
             return None
 
-        H = np.asarray(H, dtype=np.float32)
+        H = np.asarray(
+            H,
+            dtype=np.float64,
+        )
 
-        if calib_size is None:
-            return H
+        if H.shape != (3, 3):
+            raise RuntimeError(
+                f"Homografia inválida: shape={H.shape}"
+            )
 
-        calib_w, calib_h = calib_size
-        runtime_w, runtime_h = runtime_size
+        if calib_source_size is None:
+            calib_source_size = calib_size
 
-        calib_w = float(calib_w)
-        calib_h = float(calib_h)
-        runtime_w = float(runtime_w)
-        runtime_h = float(runtime_h)
+        if calib_reference_size is None:
+            calib_reference_size = calib_size
 
-        if calib_w <= 0 or calib_h <= 0:
-            return H
+        if runtime_source_size is None:
+            runtime_source_size = runtime_size
 
-        sx = runtime_w / calib_w
-        sy = runtime_h / calib_h
+        if runtime_reference_size is None:
+            runtime_reference_size = runtime_size
 
-        S = np.array([
-            [sx, 0.0, 0.0],
-            [0.0, sy, 0.0],
-            [0.0, 0.0, 1.0],
-        ], dtype=np.float32)
+        if (
+            calib_source_size is None
+            or calib_reference_size is None
+            or runtime_source_size is None
+            or runtime_reference_size is None
+        ):
+            out = H.copy()
 
-        S_inv = np.array([
-            [1.0 / sx, 0.0, 0.0],
-            [0.0, 1.0 / sy, 0.0],
-            [0.0, 0.0, 1.0],
-        ], dtype=np.float32)
+            if abs(
+                float(
+                    out[2, 2]
+                )
+            ) > 1e-12:
+                out /= out[2, 2]
 
-        H_runtime = S @ H @ S_inv
+            return out.astype(
+                np.float32
+            )
 
-        if abs(H_runtime[2, 2]) > 1e-9:
-            H_runtime = H_runtime / H_runtime[2, 2]
+        def resize_matrix(
+            src_size,
+            dst_size,
+        ):
+            src_w, src_h = [
+                float(v)
+                for v in src_size
+            ]
+            dst_w, dst_h = [
+                float(v)
+                for v in dst_size
+            ]
 
-        return H_runtime.astype(np.float32)
+            if (
+                src_w <= 0
+                or src_h <= 0
+                or dst_w <= 0
+                or dst_h <= 0
+            ):
+                raise RuntimeError(
+                    f"Tamanho inválido: "
+                    f"src={src_size}, dst={dst_size}"
+                )
+
+            sx = dst_w / src_w
+            sy = dst_h / src_h
+
+            tx = (
+                0.5 * sx
+                - 0.5
+            )
+            ty = (
+                0.5 * sy
+                - 0.5
+            )
+
+            return np.array(
+                [
+                    [sx, 0.0, tx],
+                    [0.0, sy, ty],
+                    [0.0, 0.0, 1.0],
+                ],
+                dtype=np.float64,
+            )
+
+        A_src = resize_matrix(
+            calib_source_size,
+            runtime_source_size,
+        )
+
+        A_dst = resize_matrix(
+            calib_reference_size,
+            runtime_reference_size,
+        )
+
+        H_runtime = (
+            A_dst
+            @ H
+            @ np.linalg.inv(
+                A_src
+            )
+        )
+
+        if abs(
+            float(
+                H_runtime[2, 2]
+            )
+        ) > 1e-12:
+            H_runtime /= H_runtime[2, 2]
+
+        if not np.all(
+            np.isfinite(
+                H_runtime
+            )
+        ):
+            raise RuntimeError(
+                "Homografia runtime contém valores não finitos."
+            )
+
+        return H_runtime.astype(
+            np.float32
+        )
 
 
     def apply_patch_normalization_to_tensor(self, tensor: np.ndarray) -> np.ndarray:
@@ -1941,36 +2531,114 @@ class RawProcessorCore:
 
     def _array01_stats(self, arr: np.ndarray) -> dict:
         """
-        Estatísticas compactas para debug radiométrico de arrays float.
-        Mantém tudo serializável em JSON e leve o bastante para log por frame.
+        Estatísticas compactas para debug/quality.
+
+        Performance:
+        - sat/dark/over/under, min/max e finitude são EXATOS;
+        - percentis, mean e std usam amostragem determinística quando o array
+          ultrapassa quality_stats_max_samples.
+
+        Essa amostragem não altera o tensor nem decisões de saturação/range.
         """
         if arr is None:
             return {}
 
-        vals = np.asarray(arr, dtype=np.float32).reshape(-1)
+        vals = np.asarray(
+            arr,
+            dtype=np.float32,
+        ).reshape(-1)
+
         if vals.size <= 0:
             return {}
 
-        finite = vals[np.isfinite(vals)]
-        if finite.size <= 0:
-            return {"count": int(vals.size), "finite_count": 0}
+        finite_mask = np.isfinite(vals)
+        finite_count = int(
+            np.count_nonzero(finite_mask)
+        )
+
+        if finite_count <= 0:
+            return {
+                "count": int(vals.size),
+                "finite_count": 0,
+            }
+
+        if finite_count == vals.size:
+            finite = vals
+        else:
+            finite = vals[finite_mask]
+
+        n = int(finite.size)
+
+        max_samples = int(
+            getattr(
+                self,
+                "quality_stats_max_samples",
+                65536,
+            )
+            or 65536
+        )
+
+        if max_samples <= 0:
+            max_samples = 65536
+
+        if n > max_samples:
+            stride = int(
+                math.ceil(
+                    n / float(max_samples)
+                )
+            )
+            sample = finite[::stride]
+        else:
+            stride = 1
+            sample = finite
+
+        # Percentis em uma única chamada.
+        p01, p05, p50, p95, p99 = np.percentile(
+            sample,
+            [1, 5, 50, 95, 99],
+        )
+
+        sat_count = int(
+            np.count_nonzero(
+                finite >= 0.98
+            )
+        )
+        dark_count = int(
+            np.count_nonzero(
+                finite <= 0.02
+            )
+        )
+        over_count = int(
+            np.count_nonzero(
+                finite > 1.0
+            )
+        )
+        under_count = int(
+            np.count_nonzero(
+                finite < 0.0
+            )
+        )
+
+        inv_n = 100.0 / float(n)
 
         return {
             "count": int(vals.size),
-            "finite_count": int(finite.size),
+            "finite_count": finite_count,
+            "sample_count": int(sample.size),
+            "sample_stride": int(stride),
             "min": float(np.min(finite)),
-            "p01": float(np.percentile(finite, 1)),
-            "p05": float(np.percentile(finite, 5)),
-            "p50": float(np.percentile(finite, 50)),
-            "p95": float(np.percentile(finite, 95)),
-            "p99": float(np.percentile(finite, 99)),
+            "p01": float(p01),
+            "p05": float(p05),
+            "p50": float(p50),
+            "p95": float(p95),
+            "p99": float(p99),
             "max": float(np.max(finite)),
-            "mean": float(np.mean(finite)),
-            "std": float(np.std(finite)),
-            "sat_pct": float((finite >= 0.98).mean() * 100.0),
-            "dark_pct": float((finite <= 0.02).mean() * 100.0),
-            "over_1_pct": float((finite > 1.0).mean() * 100.0),
-            "under_0_pct": float((finite < 0.0).mean() * 100.0),
+            "mean": float(np.mean(sample)),
+            "std": float(np.std(sample)),
+            "sat_pct": float(sat_count * inv_n),
+            "dark_pct": float(dark_count * inv_n),
+            "over_1_pct": float(over_count * inv_n),
+            "under_0_pct": float(under_count * inv_n),
         }
 
     def _tensor_channel_stats(self, tensor: np.ndarray, channel_names: list) -> dict:
@@ -2503,6 +3171,107 @@ class RawProcessorCore:
                     f"patch_warning:{warning_text}",
                 )
 
+        # ========================================================
+        # Contrato obrigatório de calibração
+        # ========================================================
+
+        rad_norm_cfg = self.radiometric_normalization_config or {}
+        if bool(rad_norm_cfg.get("enabled", False)):
+            rad_result = self.last_radiometric_normalization_result or {}
+
+            if not isinstance(rad_result, dict) or not bool(rad_result.get("applied", False)):
+                mark("bad", "radiometric_normalization_required_but_not_applied")
+            else:
+                roles_done = set((rad_result.get("by_role", {}) or {}).keys())
+                missing_roles = [
+                    role
+                    for role in ("rgb", "re", "nir")
+                    if role not in roles_done
+                ]
+
+                if missing_roles:
+                    mark(
+                        "bad",
+                        "radiometric_normalization_missing_roles:"
+                        + ",".join(missing_roles),
+                    )
+
+                rad_warnings = [
+                    str(x)
+                    for x in (rad_result.get("warnings", []) or [])
+                ]
+
+                hard_rad_tokens = (
+                    "missing_frame_controls",
+                    "invalid_factor",
+                    "unsupported_method",
+                )
+
+                for warning in rad_warnings:
+                    if any(token in warning for token in hard_rad_tokens):
+                        mark("bad", f"radiometric_normalization:{warning}")
+
+            quality["metrics"]["radiometric_normalization"] = rad_result
+
+        flat_cfg = self.flatfield_config or {}
+        if bool(flat_cfg.get("enabled", False)):
+            if not bool(getattr(self, "flatfield_loaded", False)):
+                mark("bad", "flatfield_required_but_not_loaded")
+
+            loaded_channels = sorted(
+                str(x)
+                for x in (getattr(self, "flatfield_maps", {}) or {}).keys()
+            )
+            quality["metrics"]["flatfield_loaded_channels"] = loaded_channels
+
+            required_flat_channels = {"R", "G", "B", "RE", "NIR"}
+            if not required_flat_channels.issubset(set(loaded_channels)):
+                mark(
+                    "bad",
+                    "flatfield_missing_channels:"
+                    + ",".join(
+                        sorted(required_flat_channels - set(loaded_channels))
+                    ),
+                )
+
+            flat_result = self.last_flatfield_result or {}
+            quality["metrics"]["flatfield_result"] = flat_result
+
+            if (
+                str(flat_cfg.get("apply_space", "native_camera_space")).lower()
+                == "native_camera_space"
+                and (
+                    not isinstance(flat_result, dict)
+                    or not bool(flat_result.get("applied", False))
+                )
+            ):
+                mark(
+                    "bad",
+                    "flatfield_required_but_not_applied",
+                )
+
+        if getattr(self, "strict_product_contract", False):
+            fusion_result = self.last_fusion_result or {}
+            profiles_used = fusion_result.get("homography_profiles_used", {}) or {}
+
+            for role in ("re", "nir"):
+                if role not in profiles_used:
+                    mark("bad", f"homography_not_applied:{role}")
+
+            intr = self.intrinsics_config or {}
+            if bool((intr.get("runtime_undistort", {}) or {}).get("enabled", False)):
+                mark("bad", "runtime_undistort_enabled_but_not_supported")
+
+            quality["metrics"]["product_contract"] = {
+                "strict": True,
+                "frame_controls_source": getattr(
+                    self,
+                    "_last_frame_controls_source",
+                    None,
+                ),
+                "homography_profiles_used": profiles_used,
+            }
+
         # ========================================================
         # Resultado final
         # ========================================================
@@ -2861,53 +3630,725 @@ class RawProcessorCore:
             )
     
 
+    def _validate_product_runtime_contract(self, data: dict):
+        """
+        Validação fail-closed do module_params montado pelo assembler de produção.
+
+        Arquivos legados continuam aceitos fora do strict_product_contract.
+        """
+        if not getattr(self, "strict_product_contract", False):
+            return True
+
+        if self.module_params_schema != "multispec_module_params_v3":
+            raise RuntimeError(
+                f"Schema de module_params inválido: {self.module_params_schema!r}"
+            )
+
+        if str(data.get("frame_type", "RAW_BRUTO")).upper() != "RAW_BRUTO":
+            raise RuntimeError(
+                f"Produto exige frame_type='RAW_BRUTO', veio {data.get('frame_type')!r}."
+            )
+
+        if self.bayer_pattern not in ("RGGB", "BGGR", "GRBG", "GBRG"):
+            raise RuntimeError(
+                f"Bayer inválido no module_params: {self.bayer_pattern!r}"
+            )
+
+        if not isinstance(self.sensor_size_by_role, dict):
+            raise RuntimeError("sensor_size_by_role ausente/inválido.")
+
+        if not isinstance(self.camera_hardware, dict):
+            raise RuntimeError("camera_hardware ausente/inválido.")
+
+        for role in ("rgb", "re", "nir"):
+            size = self.sensor_size_by_role.get(role)
+            hw = self.camera_hardware.get(role)
+
+            if not (
+                isinstance(size, (list, tuple))
+                and len(size) == 2
+                and int(size[0]) > 0
+                and int(size[1]) > 0
+            ):
+                raise RuntimeError(
+                    f"sensor_size_by_role.{role} inválido: {size}"
+                )
+
+            if not isinstance(hw, dict):
+                raise RuntimeError(
+                    f"camera_hardware.{role} ausente."
+                )
+
+            hw_size = hw.get("size")
+            if hw_size is not None:
+                hw_size = [
+                    int(hw_size[0]),
+                    int(hw_size[1]),
+                ]
+                expected_size = [
+                    int(size[0]),
+                    int(size[1]),
+                ]
+
+                if hw_size != expected_size:
+                    raise RuntimeError(
+                        f"Hardware/size divergente em {role}: "
+                        f"hw={hw_size}, size={expected_size}"
+                    )
+
+        rgb_size = [
+            int(self.sensor_width),
+            int(self.sensor_height),
+        ]
+        expected_rgb = [
+            int(x)
+            for x in self.sensor_size_by_role["rgb"]
+        ]
+
+        if rgb_size != expected_rgb:
+            raise RuntimeError(
+                f"sensor_width/height root {rgb_size} != RGB {expected_rgb}"
+            )
+
+        rgb_mode = str(
+            (self.rgb_processing_config or {}).get(
+                "mode",
+                "",
+            )
+        ).lower()
+
+        allowed_rgb_modes = {
+            "linear_demosaic",
+            "demosaic",
+            "full_res",
+            "linear_demosaic_half",
+            "demosaic_half",
+            "full_demosaic_half",
+            "bayer_planes",
+            "bayer",
+            "half_res",
+        }
+
+        if rgb_mode not in allowed_rgb_modes:
+            raise RuntimeError(
+                f"rgb_processing.mode inválido no produto: {rgb_mode!r}"
+            )
+
+        if (
+            str(
+                (self.fusion_config or {}).get(
+                    "alignment_mode",
+                    "",
+                )
+            ).lower()
+            != "homography"
+        ):
+            raise RuntimeError(
+                "Produto exige fusion_config.alignment_mode='homography'."
+            )
+
+        if not bool(
+            (self.flatfield_config or {}).get(
+                "enabled",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                "Produto exige flatfield_config.enabled=true."
+            )
+
+        flat_space = str(
+            (self.flatfield_config or {}).get(
+                "apply_space",
+                "native_camera_space",
+            )
+        ).lower()
+
+        if flat_space != "native_camera_space":
+            raise RuntimeError(
+                f"Produto exige Flat-Field no espaço nativo; "
+                f"apply_space={flat_space!r}"
+            )
+
+        if bool(
+            (self.flatfield_config or {}).get(
+                "subtract_dark",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                "Produto atual exige flatfield_config.subtract_dark=false."
+            )
+
+        if not self.flatfield_loaded:
+            raise RuntimeError(
+                "Flat-field produto não foi carregado integralmente."
+            )
+
+        rad_norm = self.radiometric_normalization_config or {}
+
+        if not bool(
+            rad_norm.get(
+                "enabled",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                "Produto exige radiometric_normalization.enabled=true."
+            )
+
+        if (
+            str(
+                rad_norm.get(
+                    "method",
+                    "",
+                )
+            ).lower()
+            != "oak_ae_frame_controls_v1"
+        ):
+            raise RuntimeError(
+                f"Método radiométrico inválido no produto: "
+                f"{rad_norm.get('method')!r}"
+            )
+
+        factor_model = str(
+            rad_norm.get(
+                "factor_model",
+                "exposure_time_us_x_iso",
+            )
+        ).lower()
+
+        if factor_model != "exposure_time_us_x_iso":
+            raise RuntimeError(
+                f"factor_model radiométrico inválido: {factor_model!r}"
+            )
+
+        apply_stage = str(
+            rad_norm.get(
+                "apply_stage",
+                "after_dark_before_flat_gain",
+            )
+        ).lower()
+
+        if apply_stage != "after_dark_before_flat_gain":
+            raise RuntimeError(
+                f"apply_stage radiométrico incompatível: {apply_stage!r}"
+            )
+
+        refs = rad_norm.get(
+            "reference_controls",
+            {},
+        ) or {}
+
+        for role in ("rgb", "re", "nir"):
+            ref = refs.get(role)
+            if not isinstance(ref, dict):
+                raise RuntimeError(
+                    f"radiometric_normalization sem reference_controls.{role}"
+                )
+
+            try:
+                exp = float(
+                    ref.get(
+                        "exposure_time_us",
+                        0,
+                    )
+                )
+                iso = float(
+                    ref.get(
+                        "sensitivity_iso",
+                        0,
+                    )
+                )
+            except Exception as exc:
+                raise RuntimeError(
+                    f"reference_controls inválido em {role}: {ref}"
+                ) from exc
+
+            if exp <= 0 or iso <= 0:
+                raise RuntimeError(
+                    f"reference_controls inválido em {role}: {ref}"
+                )
+
+        if bool(
+            (self.radiometric_config or {}).get(
+                "enabled",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                "radiometric_config controller deve estar desligado no produto."
+            )
+
+        if bool(
+            (self.patch_normalization_config or {}).get(
+                "enabled",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                "patch_normalization deve estar desligado no produto."
+            )
+
+        if bool(
+            (self.rgb_calibration or {}).get(
+                "enabled",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                "rgb_calibration manual deve estar desligado no produto."
+            )
+
+        intr = self.intrinsics_config or {}
+
+        if not bool(
+            intr.get(
+                "enabled",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                "Produto exige intrinsics_config.enabled=true."
+            )
+
+        und_cfg = (
+            intr.get(
+                "runtime_undistort",
+                {},
+            )
+            or {}
+        )
+
+        if bool(
+            und_cfg.get(
+                "enabled",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                "runtime_undistort=true ainda não é permitido neste core. "
+                "Recalibre Homography no espaço undistorted e implemente esse "
+                "estágio antes de ativar."
+            )
+
+        intr_space = str(
+            intr.get(
+                "calibration_space",
+                "native_stream_no_external_undistort",
+            )
+        )
+
+        for role in ("re", "nir"):
+            entry = self._resolve_homography_geometry_entry_for_role(
+                role
+            )
+
+            hom_space = str(
+                entry.get(
+                    "coordinate_space",
+                )
+                or ""
+            )
+
+            if (
+                hom_space
+                and intr_space
+                and hom_space != intr_space
+            ):
+                raise RuntimeError(
+                    f"Domínio geométrico divergente em {role}: "
+                    f"intrinsics={intr_space}, homography={hom_space}"
+                )
+
+        cameras = (
+            intr.get(
+                "cameras",
+                {},
+            )
+            or {}
+        )
+
+        for role in ("rgb", "re", "nir"):
+            cam = cameras.get(role)
+
+            if not isinstance(cam, dict):
+                raise RuntimeError(
+                    f"intrinsics_config.cameras.{role} ausente."
+                )
+
+            image_size = cam.get(
+                "image_size"
+            )
+            expected = [
+                int(x)
+                for x in self.sensor_size_by_role[role]
+            ]
+
+            if not (
+                isinstance(image_size, (list, tuple))
+                and len(image_size) == 2
+                and [
+                    int(image_size[0]),
+                    int(image_size[1]),
+                ]
+                == expected
+            ):
+                raise RuntimeError(
+                    f"Intrinsics size divergente em {role}: "
+                    f"{image_size} != {expected}"
+                )
+
+            K = np.asarray(
+                cam.get(
+                    "camera_matrix"
+                ),
+                dtype=np.float64,
+            )
+
+            D = np.asarray(
+                cam.get(
+                    "dist_coeffs"
+                ),
+                dtype=np.float64,
+            ).reshape(-1)
+
+            if (
+                K.shape != (3, 3)
+                or not np.all(
+                    np.isfinite(K)
+                )
+            ):
+                raise RuntimeError(
+                    f"Intrinsics K inválida em {role}."
+                )
+
+            if (
+                D.size < 4
+                or not np.all(
+                    np.isfinite(D)
+                )
+            ):
+                raise RuntimeError(
+                    f"Intrinsics D inválida em {role}."
+                )
+
+        # Enhancement é não linear e, no caminho legado atual, acontece antes
+        # da normalização radiométrica. Produto calibrado não pode ativá-lo.
+        enh = self._get_rgb_enhancement_config()
+
+        if bool(
+            enh.get(
+                "enabled",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                "rgb_processing.enhancement deve ficar disabled no pipeline "
+                "calibrado de produto."
+            )
+
+        for role in ("rgb", "re", "nir"):
+            if role not in (
+                self.camera_settings
+                or {}
+            ):
+                raise RuntimeError(
+                    f"camera_settings sem role {role}."
+                )
+
+        return True
+
+
+    def _validate_stream_camera_contract(self, frame: dict, meta: dict):
+        """
+        Confere por frame se o RAW recebido pertence ao hardware homologado.
+
+        Em modo produto:
+          - exige CAM_A/CAM_B/CAM_C por role;
+          - exige resolução nativa correta;
+          - exige RAW10 packed uint8 2D;
+          - exige sensor/socket coerentes quando informados;
+          - exige Bayer RGB coerente.
+        """
+        if not getattr(self, "strict_product_contract", False):
+            return True
+
+        if not isinstance(frame, dict) or not isinstance(meta, dict):
+            raise RuntimeError("Contrato stream inválido para produto.")
+
+        stream_meta = (
+            meta.get("stream_meta", {})
+            if isinstance(meta.get("stream_meta"), dict)
+            else {}
+        )
+        camera_frames = (
+            meta.get("camera_frames", {})
+            or stream_meta.get("camera_frames", {})
+            or {}
+        )
+        camera_info = (
+            meta.get("camera_info", {})
+            or stream_meta.get("camera_info", {})
+            or {}
+        )
+
+        seen_roles = {}
+
+        for cam_id, data in frame.items():
+            cam_meta = {}
+
+            if isinstance(camera_info.get(cam_id), dict):
+                cam_meta.update(camera_info.get(cam_id) or {})
+
+            if isinstance(camera_frames.get(cam_id), dict):
+                cam_meta.update(camera_frames.get(cam_id) or {})
+
+            role = str(cam_meta.get("role", "")).lower()
+
+            if role not in ("rgb", "re", "nir"):
+                raise RuntimeError(
+                    f"{cam_id}: role inválida/ausente no stream: {role!r}"
+                )
+
+            if role in seen_roles:
+                raise RuntimeError(
+                    f"Role duplicada no stream: {role} "
+                    f"em {seen_roles[role]} e {cam_id}"
+                )
+
+            seen_roles[role] = cam_id
+
+            expected_size = self.sensor_size_by_role.get(role)
+            if not expected_size:
+                raise RuntimeError(f"MP sem sensor_size_by_role.{role}")
+
+            width = cam_meta.get("width")
+            height = cam_meta.get("height")
+
+            if width is None or height is None:
+                raise RuntimeError(
+                    f"{cam_id}/{role}: metadata sem width/height nativos."
+                )
+
+            got = [int(width), int(height)]
+            expected = [int(expected_size[0]), int(expected_size[1])]
+
+            if got != expected:
+                raise RuntimeError(
+                    f"{cam_id}/{role}: resolução nativa {got} "
+                    f"!= homologada {expected}"
+                )
+
+            arr = np.asarray(data)
+            expected_packed_width = self._raw10_expected_packed_width_fast(
+                expected[0]
+            )
+
+            if arr.ndim != 2 or arr.dtype != np.uint8:
+                raise RuntimeError(
+                    f"{cam_id}/{role}: produto RAW_BRUTO exige "
+                    f"RAW10 packed uint8 2D; shape={arr.shape}, dtype={arr.dtype}"
+                )
+
+            if arr.shape[0] != expected[1]:
+                raise RuntimeError(
+                    f"{cam_id}/{role}: altura packed={arr.shape[0]} "
+                    f"!= nativa={expected[1]}"
+                )
+
+            if arr.shape[1] < expected_packed_width:
+                raise RuntimeError(
+                    f"{cam_id}/{role}: packed_width={arr.shape[1]} "
+                    f"< mínimo RAW10={expected_packed_width}"
+                )
+
+            padding = int(arr.shape[1] - expected_packed_width)
+            if padding > 64:
+                raise RuntimeError(
+                    f"{cam_id}/{role}: padding RAW10 excessivo={padding}"
+                )
+
+            bit_depth = int(cam_meta.get("bit_depth", 10))
+            if bit_depth != 10:
+                raise RuntimeError(
+                    f"{cam_id}/{role}: produto espera bit_depth=10, "
+                    f"veio {bit_depth}"
+                )
+
+            expected_hw = self.camera_hardware.get(role, {}) or {}
+            expected_sensor = str(
+                expected_hw.get("sensor", "") or ""
+            ).upper()
+
+            actual_sensor = str(
+                cam_meta.get("sensor")
+                or cam_meta.get("sensor_name")
+                or ""
+            ).upper()
+
+            if (
+                expected_sensor
+                and actual_sensor
+                and actual_sensor != expected_sensor
+            ):
+                raise RuntimeError(
+                    f"{cam_id}/{role}: sensor={actual_sensor} "
+                    f"!= homologado={expected_sensor}"
+                )
+
+            expected_socket = str(
+                expected_hw.get("socket", "") or ""
+            )
+
+            if (
+                expected_socket
+                and str(cam_id).upper() != expected_socket.upper()
+            ):
+                raise RuntimeError(
+                    f"{role}: cam_id={cam_id} "
+                    f"!= socket homologado={expected_socket}"
+                )
+
+            if role == "rgb":
+                actual_bayer = str(
+                    cam_meta.get("bayer_pattern")
+                    or cam_meta.get("bayer")
+                    or self.bayer_pattern
+                ).upper()
+
+                if actual_bayer != self.bayer_pattern:
+                    raise RuntimeError(
+                        f"RGB Bayer do frame={actual_bayer} "
+                        f"!= MP={self.bayer_pattern}"
+                    )
+
+        missing = [
+            role
+            for role in ("rgb", "re", "nir")
+            if role not in seen_roles
+        ]
+
+        if missing:
+            raise RuntimeError(
+                f"Frame produto incompleto. Faltam roles: {missing}"
+            )
+
+        return True
+
     def load_config_json(self, path: str):
         if not path or not os.path.isfile(path):
-            raise FileNotFoundError(f"Arquivo de calibração não encontrado: {path}")
+            raise FileNotFoundError(
+                f"Arquivo de calibração não encontrado: {path}"
+            )
 
         with open(path, "r", encoding="utf-8") as f:
             data = json.load(f)
 
-        self.bayer_pattern = data.get("bayer_pattern", self.bayer_pattern)
+        if not isinstance(data, dict):
+            raise RuntimeError("module_params root deve ser dict/object.")
+
+        self.calibration_json_path = path
+        self.calibration_base_dir = os.path.dirname(os.path.abspath(path))
+
+        self.strict_product_contract = bool(
+            isinstance(data.get("assembly_metadata"), dict)
+            and isinstance(data.get("camera_hardware"), dict)
+        )
+
+        self.module_params_schema = data.get("schema")
+        self.assembly_metadata = deepcopy(data.get("assembly_metadata", {}) or {})
+        self.calibration_provenance = deepcopy(
+            data.get("calibration_provenance", {}) or {}
+        )
+        self.camera_hardware = deepcopy(data.get("camera_hardware", {}) or {})
+        self.sensor_size_by_role = deepcopy(
+            data.get("sensor_size_by_role", {}) or {}
+        )
+        self.intrinsics_config = deepcopy(data.get("intrinsics_config", {}) or {})
+
+        if data.get("sensor_width") is not None:
+            self.sensor_width = int(data["sensor_width"])
+
+        if data.get("sensor_height") is not None:
+            self.sensor_height = int(data["sensor_height"])
+
+        self.bayer_pattern = str(
+            data.get("bayer_pattern", self.bayer_pattern)
+        ).upper()
 
         rgb_proc = data.get("rgb_processing")
         if isinstance(rgb_proc, dict):
-            self.rgb_processing_config = self._merge_config(self.rgb_processing_config, rgb_proc)
+            self.rgb_processing_config = self._merge_config(
+                self.rgb_processing_config,
+                rgb_proc,
+            )
 
         fusion = data.get("fusion_config")
         if isinstance(fusion, dict):
-            self.fusion_config = self._merge_config(self.fusion_config, fusion)
+            self.fusion_config = self._merge_config(
+                self.fusion_config,
+                fusion,
+            )
+        elif self.strict_product_contract:
+            raise RuntimeError("module_params produto sem fusion_config.")
         else:
             print("[WARN] JSON sem fusion_config. Mantendo config padrão.")
 
         rgb_cal = data.get("rgb_calibration")
         if isinstance(rgb_cal, dict):
-            self.rgb_calibration = self._merge_config(self.rgb_calibration, rgb_cal)
+            self.rgb_calibration = self._merge_config(
+                self.rgb_calibration,
+                rgb_cal,
+            )
+
+        radiometric = data.get("radiometric_config")
+        if isinstance(radiometric, dict):
+            self.radiometric_config = self._merge_config(
+                self.radiometric_config,
+                radiometric,
+            )
+
+        rad_norm = data.get("radiometric_normalization")
+        if isinstance(rad_norm, dict):
+            self.radiometric_normalization_config = self._merge_config(
+                self.radiometric_normalization_config,
+                rad_norm,
+            )
+
+        patch_norm = data.get("patch_normalization")
+        if isinstance(patch_norm, dict):
+            self.patch_normalization_config = self._merge_config(
+                self.patch_normalization_config,
+                patch_norm,
+            )
+
+        cam_set = data.get("camera_settings")
+        if isinstance(cam_set, dict):
+            self.camera_settings = self._merge_config(
+                self.camera_settings,
+                cam_set,
+            )
 
         flatfield = data.get("flatfield_config")
         if isinstance(flatfield, dict):
-            self.flatfield_config = self._merge_config(self.flatfield_config, flatfield)
-            self.load_flatfield_maps()
+            self.flatfield_config = self._merge_config(
+                self.flatfield_config,
+                flatfield,
+            )
         else:
             self.flatfield_config["enabled"] = False
             self.flatfield_maps = {}
             self.flatfield_loaded = False
 
-        radiometric = data.get("radiometric_config")
-        if isinstance(radiometric, dict):
-            self.radiometric_config = self._merge_config(self.radiometric_config, radiometric)
+        self.clear_direct_fusion_geometry_cache()
+        self._flatfield_runtime_cache = {}
 
-        rad_norm_config = data.get("radiometric_normalization")
-        if isinstance(rad_norm_config, dict):
-            self.radiometric_normalization_config = self._merge_config(self.radiometric_normalization_config, rad_norm_config)
+        if self.flatfield_config.get("enabled", False):
+            self.load_flatfield_maps()
 
-        patch_norm = data.get("patch_normalization")
-        if isinstance(patch_norm, dict):
-            self.patch_normalization_config = self._merge_config(self.patch_normalization_config, patch_norm)
-
-        cam_set = data.get("camera_settings")
-        if isinstance(cam_set, dict):
-            self.camera_settings = self._merge_config(self.camera_settings, cam_set)
+        self._validate_product_runtime_contract(data)
+        return data
 
     def _merge_config(self, default_cfg: dict, loaded_cfg: dict) -> dict:
         cfg = json.loads(json.dumps(default_cfg))
@@ -2961,55 +4402,110 @@ class RawProcessorCore:
 
         npz_file = cfg.get("npz_file")
         if not npz_file:
-            print("[WARN] flatfield_config habilitado, mas sem npz_file.")
+            msg = "flatfield_config habilitado, mas sem npz_file."
             self.flatfield_maps = {}
             self.flatfield_loaded = False
+
+            if getattr(self, "strict_product_contract", False):
+                raise RuntimeError(msg)
+
+            print(f"[WARN] {msg}")
             return False
 
         npz_path = self._resolve_calibration_path(npz_file)
 
         if not os.path.isfile(npz_path):
-            print(f"[WARN] Arquivo flat-field não encontrado: {npz_file} -> {npz_path}")
+            msg = f"Arquivo flat-field não encontrado: {npz_file} -> {npz_path}"
             self.flatfield_maps = {}
             self.flatfield_loaded = False
-            return False
 
-        data = np.load(npz_path)
+            if getattr(self, "strict_product_contract", False):
+                raise FileNotFoundError(msg)
+
+            print(f"[WARN] {msg}")
+            return False
 
         maps = {}
         channel_maps = cfg.get("channel_maps", {}) or {}
         channels = cfg.get("channels", ["R", "G", "B", "RE", "NIR"])
 
-        for ch in channels:
-            ch = str(ch).upper()
-            ch_cfg = channel_maps.get(ch, {}) or {}
+        with np.load(npz_path, allow_pickle=False) as data:
+            for ch in channels:
+                ch = str(ch).upper()
+                ch_cfg = channel_maps.get(ch, {}) or {}
 
-            gain_key = ch_cfg.get("gain_key", f"gain_{ch}")
-            dark_key = ch_cfg.get("dark_median_key", f"dark_median_{ch}")
+                if isinstance(ch_cfg, str):
+                    ch_cfg = {"gain_key": ch_cfg}
 
-            if gain_key not in data:
-                print(f"[WARN] Flat-field sem chave {gain_key} para canal {ch}.")
-                continue
+                gain_key = ch_cfg.get("gain_key", f"gain_{ch}")
+                dark_key = ch_cfg.get("dark_median_key", f"dark_median_{ch}")
 
-            entry = {
-                "gain": data[gain_key].astype(np.float32),
-                "gain_key": gain_key,
-            }
+                if gain_key not in data:
+                    if getattr(self, "strict_product_contract", False):
+                        raise RuntimeError(
+                            f"Flat-field sem chave obrigatória {gain_key} para {ch}."
+                        )
 
-            if dark_key and dark_key in data:
-                entry["dark"] = data[dark_key].astype(np.float32)
-                entry["dark_key"] = dark_key
+                    print(f"[WARN] Flat-field sem chave {gain_key} para canal {ch}.")
+                    continue
 
-            maps[ch] = entry
+                gain = np.asarray(data[gain_key], dtype=np.float32)
+
+                if gain.ndim != 2 or gain.size == 0:
+                    raise RuntimeError(
+                        f"Flat-field {ch}/{gain_key} shape inválido: {gain.shape}"
+                    )
+
+                if not np.all(np.isfinite(gain)):
+                    raise RuntimeError(
+                        f"Flat-field {ch}/{gain_key} contém NaN/Inf."
+                    )
+
+                if np.any(gain <= 0):
+                    raise RuntimeError(
+                        f"Flat-field {ch}/{gain_key} contém ganho <= 0."
+                    )
+
+                entry = {
+                    "gain": gain,
+                    "gain_key": gain_key,
+                }
+
+                if dark_key and dark_key in data:
+                    dark = np.asarray(data[dark_key], dtype=np.float32)
+                    if dark.ndim != 2 or not np.all(np.isfinite(dark)):
+                        raise RuntimeError(
+                            f"Dark map {ch}/{dark_key} inválido."
+                        )
+
+                    entry["dark"] = dark
+                    entry["dark_key"] = dark_key
+
+                maps[ch] = entry
+
+        required = {"R", "G", "B", "RE", "NIR"}
 
         self.flatfield_maps = maps
-        self.flatfield_loaded = len(maps) > 0
+        self.flatfield_loaded = required.issubset(set(maps))
+
+        if getattr(self, "strict_product_contract", False) and not self.flatfield_loaded:
+            missing = sorted(required - set(maps))
+            raise RuntimeError(
+                f"Flat-field produto incompleto. Faltam canais: {missing}"
+            )
 
         if self.flatfield_loaded:
-            print(f"[OK] Flat-field carregado: {npz_path} | canais={list(maps.keys())}")
+            print(
+                f"[OK] Flat-field carregado: {npz_path} | "
+                f"canais={list(maps.keys())}"
+            )
         else:
-            print(f"[WARN] Flat-field habilitado, mas nenhum mapa foi carregado: {npz_path}")
+            print(
+                f"[WARN] Flat-field parcial: {npz_path} | "
+                f"canais={list(maps.keys())}"
+            )
 
+        self._flatfield_runtime_cache = {}
         return self.flatfield_loaded
 
     def apply_dark_to_decoded(self, decoded: dict) -> dict:
@@ -3077,50 +4573,97 @@ class RawProcessorCore:
     def apply_flat_gain_to_decoded(self, decoded: dict) -> dict:
         cfg = self.flatfield_config or {}
 
+        result = {
+            "enabled": bool(cfg.get("enabled", False)),
+            "applied": False,
+            "by_role": {},
+            "warnings": [],
+        }
+        self.last_flatfield_result = result
+
         if not cfg.get("enabled", False):
+            result["warnings"].append("flatfield_disabled")
             return decoded
 
         if not self.flatfield_loaded:
             self.load_flatfield_maps()
 
         if not self.flatfield_loaded:
+            result["warnings"].append("flatfield_maps_not_loaded")
             return decoded
 
         clip_output = bool(cfg.get("clip_output", True))
         corrected = {}
 
-        # Guarda anti-roxo / anti-artefato em saturação
-        sat_guard_enabled = bool(cfg.get("saturation_guard_enabled", True))
-        sat_mode = str(cfg.get("saturation_guard_mode", "fade_strength")).lower()
-        sat_threshold = float(cfg.get("saturation_guard_threshold", 0.97))
+        sat_guard_enabled = bool(
+            cfg.get(
+                "saturation_guard_enabled",
+                True,
+            )
+        )
+        sat_mode = str(
+            cfg.get(
+                "saturation_guard_mode",
+                "fade_strength",
+            )
+        ).lower()
+        sat_threshold = float(
+            cfg.get(
+                "saturation_guard_threshold",
+                0.97,
+            )
+        )
 
         for cam_id, item in decoded.items():
-            role = str(item.get("role") or item.get("meta", {}).get("role") or "").lower()
+            role = str(
+                item.get("role")
+                or item.get("meta", {}).get("role")
+                or ""
+            ).lower()
+
             img = item.get("image")
 
             if img is None:
                 corrected[cam_id] = item
+                result["warnings"].append(
+                    f"{cam_id}:missing_image"
+                )
                 continue
 
             new_item = dict(item)
-            new_meta = dict(item.get("meta", {}) or {})
+            new_meta = dict(
+                item.get("meta", {})
+                or {}
+            )
 
             if role == "rgb":
-                if img.ndim != 3 or img.shape[2] < 3:
+                if (
+                    img.ndim != 3
+                    or img.shape[2] < 3
+                ):
                     corrected[cam_id] = item
+                    result["warnings"].append(
+                        f"{cam_id}:invalid_rgb_shape:{img.shape}"
+                    )
                     continue
 
-                out = img.astype(np.float32).copy()
+                out, reused = self._radiometric_get_writable_float32_image(
+                    img
+                )
 
-                # Máscara comum RGB:
-                # se qualquer canal estiver perto de saturar, tratamos os 3 canais juntos.
-                # Isso evita R/G/B receberem correções diferentes e criarem magenta/roxo.
-                saturation_mask = None
+                # Máscara comum sem criar rgb_max float32 full-res.
                 if sat_guard_enabled:
-                    rgb_max = np.max(out[:, :, :3], axis=2)
-                    saturation_mask = rgb_max >= sat_threshold
+                    saturation_mask = (
+                        (out[:, :, 0] >= sat_threshold)
+                        | (out[:, :, 1] >= sat_threshold)
+                        | (out[:, :, 2] >= sat_threshold)
+                    )
+                else:
+                    saturation_mask = None
 
-                for idx, ch in enumerate(("R", "G", "B")):
+                for idx, ch in enumerate(
+                    ("R", "G", "B")
+                ):
                     out[:, :, idx] = self._apply_flat_gain_single_channel(
                         out[:, :, idx],
                         ch,
@@ -3130,29 +4673,61 @@ class RawProcessorCore:
 
                 new_item["image"] = out
 
+                result["by_role"]["rgb"] = {
+                    "camera_id": cam_id,
+                    "channels": ["R", "G", "B"],
+                    "applied": True,
+                    "inplace_reused_input": bool(reused),
+                    "shape": list(out.shape),
+                }
+
             elif role in ("re", "nir"):
-                ch = "RE" if role == "re" else "NIR"
+                ch = (
+                    "RE"
+                    if role == "re"
+                    else "NIR"
+                )
+
+                img_f, reused = self._radiometric_get_writable_float32_image(
+                    img
+                )
 
-                # Para RE/NIR a guarda pode ser por canal mesmo.
-                # Não existe cor roxa aqui, mas ainda evita mexer em pixels clipados.
-                saturation_mask = None
-                img_f = img.astype(np.float32)
                 if sat_guard_enabled:
-                    saturation_mask = img_f >= sat_threshold
+                    saturation_mask = (
+                        img_f >= sat_threshold
+                    )
+                else:
+                    saturation_mask = None
 
-                new_item["image"] = self._apply_flat_gain_single_channel(
+                out = self._apply_flat_gain_single_channel(
                     img_f,
                     ch,
                     clip_output=clip_output,
                     saturation_mask=saturation_mask,
                 )
 
+                new_item["image"] = out
+
+                result["by_role"][role] = {
+                    "camera_id": cam_id,
+                    "channels": [ch],
+                    "applied": True,
+                    "inplace_reused_input": bool(reused),
+                    "shape": list(out.shape),
+                }
+
             else:
                 corrected[cam_id] = item
+                result["warnings"].append(
+                    f"{cam_id}:unsupported_role:{role}"
+                )
                 continue
 
             new_meta["flatfield_applied"] = True
-            new_meta["flatfield_map_type"] = cfg.get("map_type", "gain")
+            new_meta["flatfield_map_type"] = cfg.get(
+                "map_type",
+                "gain",
+            )
             new_meta["flatfield_saturation_guard"] = {
                 "enabled": sat_guard_enabled,
                 "mode": sat_mode,
@@ -3162,6 +4737,45 @@ class RawProcessorCore:
             new_item["meta"] = new_meta
             corrected[cam_id] = new_item
 
+        required_roles = {
+            "rgb",
+            "re",
+            "nir",
+        }
+        applied_roles = {
+            role
+            for role, info in result["by_role"].items()
+            if isinstance(info, dict)
+            and bool(info.get("applied", False))
+        }
+
+        result["applied"] = (
+            required_roles.issubset(
+                applied_roles
+            )
+        )
+        result["applied_roles"] = sorted(
+            applied_roles
+        )
+        result["missing_roles"] = sorted(
+            required_roles
+            - applied_roles
+        )
+
+        if (
+            getattr(
+                self,
+                "strict_product_contract",
+                False,
+            )
+            and not result["applied"]
+        ):
+            raise RuntimeError(
+                "Flat-Field obrigatório não foi aplicado em todas as roles: "
+                f"missing={result['missing_roles']}"
+            )
+
+        self.last_flatfield_result = result
         return corrected
 
     def _subtract_dark_single_channel(
@@ -3289,7 +4903,93 @@ class RawProcessorCore:
             saturation_mask = base >= sat_threshold
 
         # ============================================================
-        # Calcula ganho efetivo
+        # Fast path Numba: saturation guard + gain + clip em uma passada.
+        # ============================================================
+        if (
+            _HAS_NUMBA
+            and sat_guard_enabled
+            and saturation_mask is not None
+            and sat_mode in ("fade_strength", "skip")
+        ):
+            base_c = base
+            if not base_c.flags.c_contiguous:
+                base_c = np.ascontiguousarray(base_c)
+
+            gain_c = gain.astype(np.float32, copy=False)
+            if not gain_c.flags.c_contiguous:
+                gain_c = np.ascontiguousarray(gain_c)
+
+            mask_c = np.asarray(
+                saturation_mask,
+                dtype=np.bool_,
+            )
+            if not mask_c.flags.c_contiguous:
+                mask_c = np.ascontiguousarray(mask_c)
+
+            if mask_c.shape != base_c.shape:
+                raise RuntimeError(
+                    f"saturation_mask shape inválido: "
+                    f"{mask_c.shape} != {base_c.shape}"
+                )
+
+            mode_code = 1 if sat_mode == "fade_strength" else 2
+
+            out = np.empty(
+                base_c.shape,
+                dtype=np.float32,
+            )
+
+            t_numba0 = time.perf_counter()
+
+            _apply_flat_gain_guard_numba(
+                base_c,
+                gain_c,
+                mask_c,
+                out,
+                int(base_c.shape[0]),
+                int(base_c.shape[1]),
+                float(strength),
+                float(gain_min_runtime),
+                float(gain_max_runtime),
+                float(sat_soft_start),
+                float(sat_hard),
+                bool(clip_output),
+                int(mode_code),
+            )
+
+            t_total_ms = (
+                time.perf_counter()
+                - t_total0
+            ) * 1000.0
+
+            if not hasattr(
+                self,
+                "_last_flat_ch_perf_log_ts",
+            ):
+                self._last_flat_ch_perf_log_ts = 0.0
+
+            now = time.time()
+
+            if (
+                now
+                - self._last_flat_ch_perf_log_ts
+                >= 1.0
+            ):
+                self._last_flat_ch_perf_log_ts = now
+
+                print(
+                    "[PERF][FLAT_CH_NUMBA] "
+                    f"ch={ch} "
+                    f"shape={base_c.shape} "
+                    f"total={t_total_ms:.2f}ms "
+                    f"mode={sat_mode} "
+                    f"strength={strength:.2f}"
+                )
+
+            return out
+
+        # ============================================================
+        # Fallback NumPy: calcula ganho efetivo.
         # ============================================================
         t0 = time.perf_counter()
 
@@ -3382,8 +5082,14 @@ class RawProcessorCore:
             result["warnings"].append("missing_frame_controls")
             self.last_radiometric_normalization_result = result
 
-            if str(cfg.get("missing_controls_policy", "skip")).lower() == "raise":
-                raise RuntimeError("radiometric_normalization ativo, mas meta.frame_controls ausente.")
+            if (
+                getattr(self, "strict_product_contract", False)
+                or str(cfg.get("missing_controls_policy", "skip")).lower() == "raise"
+            ):
+                raise RuntimeError(
+                    "radiometric_normalization ativo, mas controles reais "
+                    "da captura estão ausentes."
+                )
 
             return decoded
 
@@ -3422,8 +5128,14 @@ class RawProcessorCore:
                         f"{role}:invalid_factor actual={actual_factor:.6g} ref={ref_factor:.6g}"
                     )
 
-                    if str(cfg.get("invalid_controls_policy", "skip")).lower() == "raise":
-                        raise RuntimeError(f"Controles radiométricos inválidos para role={role}: {actual_ctrl}")
+                    if (
+                        getattr(self, "strict_product_contract", False)
+                        or str(cfg.get("invalid_controls_policy", "skip")).lower() == "raise"
+                    ):
+                        raise RuntimeError(
+                            f"Controles radiométricos inválidos para role={role}: "
+                            f"{actual_ctrl}"
+                        )
 
                     continue
 
@@ -3487,53 +5199,87 @@ class RawProcessorCore:
         self.last_radiometric_normalization_result = result
         return normalized
     
-    def _extract_frame_controls_from_meta_by_role(self, meta: dict | None) -> tuple[dict, dict]:
+    def _extract_frame_controls_from_meta_by_role(
+        self,
+        meta: dict | None,
+    ) -> tuple[dict, dict]:
         """
-        Retorna:
-            controls_by_role = {
-                "rgb": {...},
-                "re": {...},
-                "nir": {...}
-            }
+        Resolve controles reais da captura com compatibilidade histórica.
 
-            controls_by_cam = {
-                "CAM_A": {...},
-                "CAM_B": {...},
-                "CAM_C": {...}
-            }
+        Prioridade:
+          1. frame_controls
+          2. actual_camera_controls
+          3. camera_controls
+          4. startup_camera_controls
 
-        Fonte principal:
-            meta["frame_controls"]
-
-        Também aceita:
-            meta["stream_meta"]["frame_controls"]
+        Aceita chaves CAM_A/B/C ou rgb/re/nir.
         """
         if not isinstance(meta, dict):
             return {}, {}
 
-        stream_meta = meta.get("stream_meta") if isinstance(meta.get("stream_meta"), dict) else None
+        stream_meta = (
+            meta.get("stream_meta")
+            if isinstance(meta.get("stream_meta"), dict)
+            else {}
+        )
 
-        frame_controls = meta.get("frame_controls")
-        if not isinstance(frame_controls, dict) and stream_meta is not None:
-            frame_controls = stream_meta.get("frame_controls")
+        controls = None
+        source_key = None
 
-        if not isinstance(frame_controls, dict) or not frame_controls:
+        for key in (
+            "frame_controls",
+            "actual_camera_controls",
+            "camera_controls",
+            "startup_camera_controls",
+        ):
+            candidate = meta.get(key)
+
+            if not isinstance(candidate, dict) and stream_meta:
+                candidate = stream_meta.get(key)
+
+            if isinstance(candidate, dict) and candidate:
+                controls = candidate
+                source_key = key
+                break
+
+        if not isinstance(controls, dict) or not controls:
             return {}, {}
 
         camera_info = meta.get("camera_info")
-        if not isinstance(camera_info, dict) and stream_meta is not None:
+        if not isinstance(camera_info, dict):
             camera_info = stream_meta.get("camera_info")
 
         camera_info = camera_info if isinstance(camera_info, dict) else {}
 
         controls_by_cam = {}
         controls_by_role = {}
+        role_to_cam = {}
 
-        for cam_id, ctrl in frame_controls.items():
+        for cam_id, info in camera_info.items():
+            if not isinstance(info, dict):
+                continue
+            role = str(info.get("role", "")).lower()
+            if role in ("rgb", "re", "nir"):
+                role_to_cam[role] = str(cam_id)
+
+        for key, ctrl in controls.items():
             if not isinstance(ctrl, dict):
                 continue
 
-            cam_id = str(cam_id)
+            key_str = str(key)
+            key_lower = key_str.lower()
+
+            if key_lower in ("rgb", "re", "nir"):
+                role = key_lower
+                cam_id = role_to_cam.get(role)
+
+                if cam_id:
+                    controls_by_cam[cam_id] = dict(ctrl)
+
+                controls_by_role[role] = dict(ctrl)
+                continue
+
+            cam_id = key_str
             controls_by_cam[cam_id] = dict(ctrl)
 
             role = str(
@@ -3541,12 +5287,12 @@ class RawProcessorCore:
             ).lower()
 
             if not role:
-                # fallback defensivo caso algum meta antigo venha sem camera_info
                 role = self._role_from_cam_id_fallback(cam_id)
 
             if role:
                 controls_by_role[role] = dict(ctrl)
 
+        self._last_frame_controls_source = source_key
         return controls_by_role, controls_by_cam
 
     def _role_from_cam_id_fallback(self, cam_id: str) -> str:
@@ -4256,17 +6002,53 @@ class RawProcessorCore:
 
 
 
-    def _direct_fusion_get_target_size_fast(self, ref_size):
+    def _direct_fusion_get_target_size_fast(
+        self,
+        ref_size,
+        target_size_override=None,
+    ):
         """
-        Resolve target_size final como (target_w, target_h).
-        Usa fusion_config.target_size se existir, senão usa tamanho de referência.
+        Resolve o target final como (W,H).
+
+        Prioridade:
+          1. target_size_override do caller (treino/inferência);
+          2. fusion_config.target_size;
+          3. tamanho RGB/ref.
+
+        Isso evita fuse -> resize extra no caminho canônico RAW_BRUTO.
         """
         ref_h, ref_w = int(ref_size[0]), int(ref_size[1])
-        fusion = getattr(self, "fusion_config", {}) or {}
 
-        target_size = fusion.get("target_size", None)
-        if isinstance(target_size, (list, tuple)) and len(target_size) == 2:
-            return int(target_size[0]), int(target_size[1])
+        if (
+            isinstance(target_size_override, (list, tuple))
+            and len(target_size_override) == 2
+        ):
+            tw = int(target_size_override[0])
+            th = int(target_size_override[1])
+
+            if tw <= 0 or th <= 0:
+                raise RuntimeError(
+                    f"target_size_override inválido: {target_size_override}"
+                )
+
+            return tw, th
+
+        fusion = getattr(self, "fusion_config", {}) or {}
+        target_size = fusion.get("target_size")
+
+        if (
+            isinstance(target_size, (list, tuple))
+            and len(target_size) == 2
+        ):
+            tw = int(target_size[0])
+            th = int(target_size[1])
+
+            if tw <= 0 or th <= 0:
+                raise RuntimeError(
+                    f"fusion_config.target_size inválido: {target_size}"
+                )
+
+            return tw, th
 
         return int(ref_w), int(ref_h)
 
@@ -4285,48 +6067,85 @@ class RawProcessorCore:
 
         return (0, 0, ref_w, ref_h), False
 
-    def _direct_fusion_crop_to_target_matrix_fast(self, crop_box, target_size):
+    def _direct_fusion_crop_to_target_matrix_fast(
+        self,
+        crop_box,
+        target_size,
+    ):
         """
-        Matriz C que leva coordenadas do espaço RGB/ref para a saída final.
+        Matriz C: RGB/reference -> target final.
 
-        crop_box está no espaço da imagem de referência:
-        x0,y0,x1,y1
+        Replica crop + cv2.resize com convenção de centro de pixel:
 
-        Queremos:
-        x=x0 -> 0
-        x=x1 -> target_w
-        y=y0 -> 0
-        y=y1 -> target_h
-
-        Retorna C_ref_to_target.
+            x_crop = x_ref - x0
+            x_target = (x_crop + 0.5) * sx - 0.5
         """
-        x0, y0, x1, y1 = [float(v) for v in crop_box]
-        target_w, target_h = int(target_size[0]), int(target_size[1])
+        x0, y0, x1, y1 = [
+            float(v)
+            for v in crop_box
+        ]
 
-        crop_w = max(1.0, x1 - x0)
-        crop_h = max(1.0, y1 - y0)
+        target_w, target_h = [
+            int(v)
+            for v in target_size
+        ]
 
-        sx = float(target_w) / crop_w
-        sy = float(target_h) / crop_h
+        crop_w = max(
+            1.0,
+            x1 - x0,
+        )
+        crop_h = max(
+            1.0,
+            y1 - y0,
+        )
 
-        C = np.array(
+        sx = (
+            float(target_w)
+            / crop_w
+        )
+        sy = (
+            float(target_h)
+            / crop_h
+        )
+
+        tx = (
+            -x0 * sx
+            + 0.5 * sx
+            - 0.5
+        )
+        ty = (
+            -y0 * sy
+            + 0.5 * sy
+            - 0.5
+        )
+
+        return np.array(
             [
-                [sx, 0.0, -x0 * sx],
-                [0.0, sy, -y0 * sy],
+                [sx, 0.0, tx],
+                [0.0, sy, ty],
                 [0.0, 0.0, 1.0],
             ],
             dtype=np.float32,
         )
 
-        return C
-
-    def _direct_fusion_scale_homography_for_ref_fast(self, H, meta, ref_size):
+    def _direct_fusion_scale_homography_for_ref_fast(
+        self,
+        H,
+        meta,
+        ref_size,
+    ):
         """
-        Escala a homografia calibrada para o runtime da referência RGB.
+        Helper legado sem informação da source.
 
-        Usa a própria função existente _scale_homography_to_runtime() se existir.
-        Isso mantém compatibilidade com o contrato atual do core.
+        Em produto mixed-resolution ele é ambíguo e portanto proibido.
+        Use _direct_fusion_get_role_homography_fast(role, ..., source_size=...).
         """
+        if getattr(self, "strict_product_contract", False):
+            raise RuntimeError(
+                "_direct_fusion_scale_homography_for_ref_fast() não pode ser usado "
+                "no produto mixed-resolution porque não conhece source_size."
+            )
+
         fusion = getattr(self, "fusion_config", {}) or {}
         ref_h, ref_w = int(ref_size[0]), int(ref_size[1])
 
@@ -4337,118 +6156,137 @@ class RawProcessorCore:
             or None
         )
 
-        H = np.asarray(H, dtype=np.float32)
-
-        if hasattr(self, "_scale_homography_to_runtime"):
-            try:
-                return self._scale_homography_to_runtime(
-                    H,
-                    calib_size=calib_size,
-                    runtime_size=(ref_w, ref_h),
-                ).astype(np.float32)
-            except TypeError:
-                try:
-                    return self._scale_homography_to_runtime(
-                        H,
-                        calib_size,
-                        (ref_w, ref_h),
-                    ).astype(np.float32)
-                except Exception:
-                    pass
-            except Exception:
-                pass
-
-        # Fallback local.
-        if calib_size is None:
-            if abs(float(H[2, 2])) > 1e-9:
-                H = H / H[2, 2]
-            return H.astype(np.float32)
-
-        calib_w, calib_h = float(calib_size[0]), float(calib_size[1])
-        if calib_w <= 0 or calib_h <= 0:
-            return H.astype(np.float32)
-
-        sx = float(ref_w) / calib_w
-        sy = float(ref_h) / calib_h
-
-        S = np.array([[sx, 0.0, 0.0], [0.0, sy, 0.0], [0.0, 0.0, 1.0]], dtype=np.float32)
-        S_inv = np.array([[1.0 / sx, 0.0, 0.0], [0.0, 1.0 / sy, 0.0], [0.0, 0.0, 1.0]], dtype=np.float32)
-
-        H_runtime = S @ H @ S_inv
-        if abs(float(H_runtime[2, 2])) > 1e-9:
-            H_runtime = H_runtime / H_runtime[2, 2]
-
-        return H_runtime.astype(np.float32)
-
-    def _direct_fusion_get_role_homography_fast(self, role, meta, ref_size):
-        """
-        Retorna H_role_to_rgb escalada para o espaço da referência RGB.
-
-        Suporta:
-        - contrato antigo: fusion_config.homographies.re_to_rgb/nir_to_rgb
-        - contrato novo: fusion_config.homography_profiles.<perfil>.homographies.*
-        """
-        role = str(role).lower()
-
-        H, calib_size, profile_name = self._resolve_homography_entry_for_role(role)
-
-        ref_h, ref_w = int(ref_size[0]), int(ref_size[1])
-
-        H_scaled = self._scale_homography_to_runtime(
+        return self._scale_homography_to_runtime(
             H,
             calib_size=calib_size,
             runtime_size=(ref_w, ref_h),
+        ).astype(np.float32)
+
+    def _direct_fusion_get_role_homography_fast(
+        self,
+        role,
+        meta,
+        ref_size,
+        source_size=None,
+    ):
+        """
+        Retorna H_role_to_rgb no espaço REAL de runtime.
+
+        ref_size:
+            (ref_h, ref_w) da imagem RGB já decodificada.
+
+        source_size:
+            (src_h, src_w) da banda RE/NIR REAL, sem resize prévio.
+        """
+        role = str(role).lower()
+        entry = self._resolve_homography_geometry_entry_for_role(role)
+
+        ref_h, ref_w = int(ref_size[0]), int(ref_size[1])
+
+        if source_size is None:
+            src_w, src_h = entry["source_calibration_size"]
+        else:
+            src_h, src_w = int(source_size[0]), int(source_size[1])
+
+        H_scaled = self._scale_homography_to_runtime(
+            entry["H"],
+            calib_source_size=entry["source_calibration_size"],
+            calib_reference_size=entry["reference_calibration_size"],
+            runtime_source_size=(int(src_w), int(src_h)),
+            runtime_reference_size=(int(ref_w), int(ref_h)),
         )
 
         if H_scaled is None or H_scaled.shape != (3, 3):
             raise RuntimeError(
-                f"Homografia inválida para role={role}, profile={profile_name}: "
+                f"Homografia inválida para role={role}, "
+                f"profile={entry['profile_name']}: "
                 f"shape={None if H_scaled is None else H_scaled.shape}"
             )
 
         return H_scaled.astype(np.float32)
 
-    def _direct_fusion_resize_spec_to_ref_if_needed_fast(self, img, ref_size):
+    def _direct_fusion_resize_spec_to_ref_if_needed_fast(
+        self,
+        img,
+        ref_size,
+    ):
         """
-        Mantém compatibilidade com o fluxo atual:
-        se RE/NIR não estão no mesmo shape do RGB de referência, redimensiona para ref.
+        Compatibilidade legado.
+
+        No pipeline de produto mixed-resolution, pré-resize da banda antes da
+        homografia é proibido. O caminho ativo não chama mais este método.
+
+        Em MP legado, preserva o comportamento histórico.
         """
         ref_h, ref_w = int(ref_size[0]), int(ref_size[1])
 
         if img.shape[:2] == (ref_h, ref_w):
             return img
 
+        if getattr(self, "strict_product_contract", False):
+            raise RuntimeError(
+                "Pré-resize de RE/NIR para o tamanho RGB é inválido no "
+                "contrato mixed-resolution de produto."
+            )
+
         return cv2.resize(
             img.astype(np.float32, copy=False),
             (ref_w, ref_h),
             interpolation=cv2.INTER_LINEAR,
         )
 
-    def _direct_fusion_compute_valid_masks_fast(self, decoded, role_to_cam, ref_size, meta):
+    def _direct_fusion_compute_valid_masks_fast(
+        self,
+        decoded,
+        role_to_cam,
+        ref_size,
+        meta,
+    ):
         """
-        Calcula máscaras válidas no espaço RGB/ref para crop comum.
-        Usa warpPerspective apenas em máscara uint8, que costuma ser barato.
+        Máscaras válidas no espaço RGB/ref para mixed-resolution.
+
+        A máscara de cada banda nasce no tamanho REAL da própria source.
         """
         ref_h, ref_w = int(ref_size[0]), int(ref_size[1])
 
-        valid_masks = [np.ones((ref_h, ref_w), dtype=np.uint8)]
-
-        base = np.ones((ref_h, ref_w), dtype=np.uint8) * 255
+        valid_masks = [
+            np.ones((ref_h, ref_w), dtype=np.uint8)
+        ]
 
         for role in ("re", "nir"):
-            if role not in role_to_cam:
+            cam_id = role_to_cam.get(role)
+            if cam_id is None:
                 continue
 
-            H = self._direct_fusion_get_role_homography_fast(role, meta, ref_size)
+            img = decoded[cam_id]["image"]
+
+            H = self._direct_fusion_get_role_homography_fast(
+                role,
+                meta,
+                ref_size,
+                source_size=img.shape[:2],
+            )
+
+            source_mask = (
+                np.ones(
+                    img.shape[:2],
+                    dtype=np.uint8,
+                )
+                * 255
+            )
+
             mask = cv2.warpPerspective(
-                base,
+                source_mask,
                 H,
                 (ref_w, ref_h),
                 flags=cv2.INTER_NEAREST,
                 borderMode=cv2.BORDER_CONSTANT,
                 borderValue=0,
             )
-            valid_masks.append(mask)
+
+            valid_masks.append(
+                (mask > 0).astype(np.uint8)
+            )
 
         return valid_masks
 
@@ -4475,23 +6313,34 @@ class RawProcessorCore:
         tensor[1] = rgb_out[:, :, 1]
         tensor[2] = rgb_out[:, :, 2]
 
-    def _direct_fusion_write_spec_fast(self, tensor, channel_index, img, role, C_ref_to_target, ref_size, target_size, meta):
+    def _direct_fusion_write_spec_fast(
+        self,
+        tensor,
+        channel_index,
+        img,
+        role,
+        C_ref_to_target,
+        ref_size,
+        target_size,
+        meta,
+    ):
         """
-        Escreve RE ou NIR direto no tensor final, compondo:
-        M = C_ref_to_target @ H_role_to_rgb
+        RE/NIR native -> target final em um único warp.
 
-        img é primeiro redimensionada para ref_size se necessário, para manter o mesmo
-        comportamento geométrico do fluxo atual.
+        M = C_ref_to_target @ H_runtime(source_real -> RGB/ref_real)
         """
         target_w, target_h = int(target_size[0]), int(target_size[1])
 
-        img_ref = self._direct_fusion_resize_spec_to_ref_if_needed_fast(img, ref_size)
-
-        H_role_to_rgb = self._direct_fusion_get_role_homography_fast(role, meta, ref_size)
+        H_role_to_rgb = self._direct_fusion_get_role_homography_fast(
+            role,
+            meta,
+            ref_size,
+            source_size=img.shape[:2],
+        )
         M_role_to_target = (C_ref_to_target @ H_role_to_rgb).astype(np.float32)
 
         out = cv2.warpPerspective(
-            img_ref.astype(np.float32, copy=False),
+            img.astype(np.float32, copy=False),
             M_role_to_target,
             (target_w, target_h),
             flags=cv2.INTER_LINEAR,
@@ -4501,9 +6350,18 @@ class RawProcessorCore:
 
         tensor[int(channel_index)] = out
 
-    def _fuse_multispec_direct_to_target_fast(self, decoded, meta, channels_expected):
+    def _fuse_multispec_direct_to_target_fast(
+        self,
+        decoded,
+        meta,
+        channels_expected,
+        target_size_override=None,
+    ):
         """
         Fusão direta otimizada com cache de geometria fixa.
+
+        target_size_override permite ir diretamente ao tamanho final pedido
+        pelo treino/inferência, evitando um segundo resize posterior.
         """
         channels_expected = self._validate_physical_channel_count(channels_expected)
         t0 = time.perf_counter()
@@ -4518,7 +6376,10 @@ class RawProcessorCore:
 
         ref_h, ref_w = rgb.shape[:2]
         ref_size = (int(ref_h), int(ref_w))
-        target_size = self._direct_fusion_get_target_size_fast(ref_size)
+        target_size = self._direct_fusion_get_target_size_fast(
+            ref_size,
+            target_size_override=target_size_override,
+        )
         target_w, target_h = int(target_size[0]), int(target_size[1])
 
         role_to_cam = {
@@ -4747,39 +6608,49 @@ class RawProcessorCore:
 
 
 
-    def _direct_fusion_get_geometry_cache_key_fast(self, ref_size, target_size, role_to_cam):
+    def _direct_fusion_get_geometry_cache_key_fast(
+        self,
+        decoded,
+        ref_size,
+        target_size,
+        role_to_cam,
+    ):
         """
-        Chave simples e estável para cache da geometria.
-
-        Considera:
-        - tamanho do RGB/ref
-        - target final
-        - roles presentes
-        - crop/resize
-        - homografia efetivamente selecionada por perfil
-        - calibration_size efetivo por role
+        Mixed-resolution exige que o tamanho REAL de cada source faça parte
+        da chave, pois H_runtime depende de source_size e reference_size.
         """
         ref_h, ref_w = int(ref_size[0]), int(ref_size[1])
         target_w, target_h = int(target_size[0]), int(target_size[1])
 
         fusion = getattr(self, "fusion_config", {}) or {}
-        roles = tuple(sorted([str(r).lower() for r in role_to_cam.keys()]))
+        roles = tuple(sorted(str(r).lower() for r in role_to_cam.keys()))
+
+        role_shapes = []
+        for role in roles:
+            cam_id = role_to_cam.get(role)
+            if cam_id is None:
+                continue
+            img = decoded[cam_id].get("image")
+            if img is None:
+                continue
+            role_shapes.append((role, tuple(int(v) for v in img.shape[:2])))
 
         def h_sig_for_role(role):
             role = str(role).lower()
-
             if role not in role_to_cam:
                 return None
 
             try:
-                H, calib_size, profile_name = self._resolve_homography_entry_for_role(role)
+                e = self._resolve_homography_geometry_entry_for_role(role)
             except Exception:
                 return None
 
-            arr = np.asarray(H, dtype=np.float32).reshape(-1)
+            arr = np.asarray(e["H"], dtype=np.float32).reshape(-1)
             return (
-                str(profile_name),
-                tuple(calib_size or []),
+                str(e["profile_name"]),
+                tuple(e["source_calibration_size"] or []),
+                tuple(e["reference_calibration_size"] or []),
+                str(e.get("coordinate_space") or ""),
                 tuple(np.round(arr, 8).tolist()),
             )
 
@@ -4789,6 +6660,7 @@ class RawProcessorCore:
             target_w,
             target_h,
             roles,
+            tuple(sorted(role_shapes)),
             bool(fusion.get("crop_valid_common", False)),
             bool(fusion.get("resize_after_crop", False)),
             h_sig_for_role("re"),
@@ -4807,26 +6679,31 @@ class RawProcessorCore:
         self._direct_fusion_remap_cache_hits = 0
         self._direct_fusion_remap_cache_misses = 0
 
-    def _direct_fusion_get_geometry_cached_fast(self, decoded, role_to_cam, ref_size, target_size, meta):
+    def _direct_fusion_get_geometry_cached_fast(
+        self,
+        decoded,
+        role_to_cam,
+        ref_size,
+        target_size,
+        meta,
+    ):
         """
-        Retorna geometria cacheada para a fusão direta.
+        Geometria fixa cacheada.
 
-        Saída:
-        geom = {
-            key,
-            crop_box,
-            crop_applied,
-            C_ref_to_target,
-            H_role_to_rgb: {re,nir},
-            M_role_to_target: {re,nir},
-            valid_masks,          # opcional/debug
-            prepare_cache_hit,
-        }
+        H_role_to_rgb sempre mapeia:
+            banda no tamanho REAL atual -> RGB/ref atual.
+
+        Nenhum resize de RE/NIR acontece antes da homografia.
         """
         if not hasattr(self, "_direct_fusion_geometry_cache"):
             self.clear_direct_fusion_geometry_cache()
 
-        key = self._direct_fusion_get_geometry_cache_key_fast(ref_size, target_size, role_to_cam)
+        key = self._direct_fusion_get_geometry_cache_key_fast(
+            decoded,
+            ref_size,
+            target_size,
+            role_to_cam,
+        )
         cache = self._direct_fusion_geometry_cache
 
         if key in cache:
@@ -4841,34 +6718,46 @@ class RawProcessorCore:
 
         ref_h, ref_w = int(ref_size[0]), int(ref_size[1])
 
-        # ------------------------------------------------------------
-        # Homografias escaladas para runtime.
-        # ------------------------------------------------------------
         H_role_to_rgb = {}
         homography_profiles_used = {}
-        for role in ("re", "nir"):
-            if role in role_to_cam:
-                H_raw, calib_size, profile_name = self._resolve_homography_entry_for_role(role)
-                homography_profiles_used[role] = {
-                    "profile": profile_name,
-                    "calib_size": list(calib_size) if calib_size is not None else None,
-                }
-                H_role_to_rgb[role] = self._direct_fusion_get_role_homography_fast(role, meta, ref_size)
-
-        # ------------------------------------------------------------
-        # Máscaras válidas e crop comum.
-        # Essa era uma das partes caras e totalmente fixa.
-        # ------------------------------------------------------------
-        valid_masks = [np.ones((ref_h, ref_w), dtype=np.uint8)]
-        base = np.ones((ref_h, ref_w), dtype=np.uint8) * 255
+        source_shapes = {}
 
         for role in ("re", "nir"):
-            if role not in H_role_to_rgb:
+            if role not in role_to_cam:
                 continue
 
+            cam_id = role_to_cam[role]
+            img = decoded[cam_id]["image"]
+            src_shape = tuple(int(v) for v in img.shape[:2])
+            source_shapes[role] = src_shape
+
+            entry = self._resolve_homography_geometry_entry_for_role(role)
+
+            homography_profiles_used[role] = {
+                "profile": entry["profile_name"],
+                "source_calibration_size": list(entry["source_calibration_size"]),
+                "reference_calibration_size": list(entry["reference_calibration_size"]),
+                "coordinate_space": entry.get("coordinate_space"),
+                "runtime_source_shape": list(src_shape),
+                "runtime_reference_shape": [ref_h, ref_w],
+            }
+
+            H_role_to_rgb[role] = self._direct_fusion_get_role_homography_fast(
+                role,
+                meta,
+                ref_size,
+                source_size=src_shape,
+            )
+
+        valid_masks = [np.ones((ref_h, ref_w), dtype=np.uint8)]
+
+        for role, H in H_role_to_rgb.items():
+            src_h, src_w = source_shapes[role]
+            source_mask = np.ones((src_h, src_w), dtype=np.uint8) * 255
+
             mask = cv2.warpPerspective(
-                base,
-                H_role_to_rgb[role],
+                source_mask,
+                H,
                 (ref_w, ref_h),
                 flags=cv2.INTER_NEAREST,
                 borderMode=cv2.BORDER_CONSTANT,
@@ -4876,19 +6765,20 @@ class RawProcessorCore:
             )
             valid_masks.append(mask)
 
-        crop_box, crop_applied = self._direct_fusion_get_crop_box_fast(valid_masks, ref_size)
+        crop_box, crop_applied = self._direct_fusion_get_crop_box_fast(
+            valid_masks,
+            ref_size,
+        )
 
-        # ------------------------------------------------------------
-        # Matriz crop RGB/ref -> target final.
-        # ------------------------------------------------------------
-        C_ref_to_target = self._direct_fusion_crop_to_target_matrix_fast(crop_box, target_size)
+        C_ref_to_target = self._direct_fusion_crop_to_target_matrix_fast(
+            crop_box,
+            target_size,
+        )
 
-        # ------------------------------------------------------------
-        # Matrizes compostas role original/ref -> target.
-        # ------------------------------------------------------------
-        M_role_to_target = {}
-        for role, H in H_role_to_rgb.items():
-            M_role_to_target[role] = (C_ref_to_target @ H).astype(np.float32)
+        M_role_to_target = {
+            role: (C_ref_to_target @ H).astype(np.float32)
+            for role, H in H_role_to_rgb.items()
+        }
 
         geom = {
             "key": key,
@@ -4897,6 +6787,7 @@ class RawProcessorCore:
             "C_ref_to_target": C_ref_to_target.astype(np.float32),
             "H_role_to_rgb": H_role_to_rgb,
             "M_role_to_target": M_role_to_target,
+            "source_shapes": source_shapes,
             "valid_masks": valid_masks,
             "prepare_cache_hit": False,
             "cache_hits": int(self._direct_fusion_geometry_cache_hits),
@@ -4904,28 +6795,33 @@ class RawProcessorCore:
             "homography_profiles_used": homography_profiles_used,
         }
 
-        # Cache pequeno: normalmente só uma geometria. Se mudar resolução/config,
-        # evita crescimento infinito.
         if len(cache) > 4:
             cache.clear()
 
         cache[key] = geom
         return geom
 
-    def _direct_fusion_write_spec_cached_fast(self, tensor, channel_index, img, role, geom, ref_size, target_size):
+    def _direct_fusion_write_spec_cached_fast(
+        self,
+        tensor,
+        channel_index,
+        img,
+        role,
+        geom,
+        ref_size,
+        target_size,
+    ):
         """
-        Escreve RE/NIR usando matriz composta cacheada.
+        RE/NIR native -> target final em um único warp.
         """
         target_w, target_h = int(target_size[0]), int(target_size[1])
 
-        img_ref = self._direct_fusion_resize_spec_to_ref_if_needed_fast(img, ref_size)
-
         M_role_to_target = geom["M_role_to_target"].get(str(role).lower())
         if M_role_to_target is None:
             raise RuntimeError(f"Matriz composta ausente para role={role}")
 
         out = cv2.warpPerspective(
-            img_ref.astype(np.float32, copy=False),
+            img.astype(np.float32, copy=False),
             M_role_to_target,
             (target_w, target_h),
             flags=cv2.INTER_LINEAR,
@@ -5046,7 +6942,10 @@ class RawProcessorCore:
         target_size: tuple,
     ):
         """
-        Retorna mapas de remap cacheados para RGB, RE e NIR.
+        Retorna remaps cacheados.
+
+        M_role_to_target já nasce no espaço da source REAL.
+        Não existe resize/compensação posterior.
         """
         if not hasattr(self, "_direct_fusion_remap_cache"):
             self._direct_fusion_remap_cache = {}
@@ -5083,25 +6982,16 @@ class RawProcessorCore:
             "cache_misses": int(self._direct_fusion_remap_cache_misses),
         }
 
-        # ------------------------------------------------------------
-        # RGB: matriz C_ref_to_target leva RGB/ref -> target.
-        # ------------------------------------------------------------
         rgb_cam_id = role_to_cam.get("rgb")
         if rgb_cam_id is not None:
             rgb_img = decoded[rgb_cam_id]["image"]
-            C_ref_to_target = geom["C_ref_to_target"]
-
             remap["maps"]["rgb"] = self._direct_fusion_build_remap_from_src_to_dst_fast(
-                M_src_to_dst=C_ref_to_target,
+                M_src_to_dst=geom["C_ref_to_target"],
                 src_shape=rgb_img.shape[:2],
                 dst_size=(target_w, target_h),
                 ref_shape_for_scaled_src=None,
             )
 
-        # ------------------------------------------------------------
-        # RE/NIR: matriz composta M_role_to_target leva role/ref -> target.
-        # Se a imagem fonte não tiver o mesmo tamanho do ref, escalamos o mapa.
-        # ------------------------------------------------------------
         for role in ("re", "nir"):
             cam_id = role_to_cam.get(role)
             if cam_id is None:
@@ -5117,7 +7007,7 @@ class RawProcessorCore:
                 M_src_to_dst=M_role_to_target,
                 src_shape=img.shape[:2],
                 dst_size=(target_w, target_h),
-                ref_shape_for_scaled_src=ref_size,
+                ref_shape_for_scaled_src=None,
             )
 
         if len(cache) > 4:
@@ -5199,30 +7089,24 @@ class RawProcessorCore:
 
         return "default"
 
-    def _resolve_homography_entry_for_role(self, role: str):
+    def _resolve_homography_geometry_entry_for_role(self, role: str) -> dict:
         """
-        Resolve a homografia no contrato novo ou antigo.
+        Resolve H preservando os espaços de calibração de origem e referência.
 
         Contrato novo:
-        fusion_config.homography_profiles.<perfil>.homographies.<role>_to_rgb
-
-        Contrato antigo:
-        fusion_config.homographies.<role>_to_rgb
-
-        Retorna:
-        H, calib_size, profile_name
+          fusion_config.homography_profiles.<perfil>
+              .reference_size
+              .source_size_by_role[re|nir]
+              .homographies.<role>_to_rgb
         """
         role = str(role).lower()
+        if role not in ("re", "nir"):
+            raise ValueError(f"Role espectral inválida: {role}")
+
         fusion = getattr(self, "fusion_config", {}) or {}
-
         key = f"{role}_to_rgb"
-
         selected_profile = self._resolve_homography_profile_name_for_role(role)
 
-        # ------------------------------------------------------------
-        # Futuro: auto por profundidade.
-        # Por enquanto, cai em media/default de forma explícita.
-        # ------------------------------------------------------------
         if selected_profile == "auto":
             profiles = fusion.get("homography_profiles", {}) or {}
             if "media" in profiles:
@@ -5232,15 +7116,12 @@ class RawProcessorCore:
             else:
                 selected_profile = ""
 
-        # ------------------------------------------------------------
-        # Contrato novo: homography_profiles
-        # ------------------------------------------------------------
         profiles = fusion.get("homography_profiles", {}) or {}
+
         if isinstance(profiles, dict) and selected_profile:
             profile = profiles.get(selected_profile)
 
             if profile is None:
-                # tolera nomes com caixa diferente
                 for name, item in profiles.items():
                     if str(name).lower() == selected_profile:
                         profile = item
@@ -5248,45 +7129,109 @@ class RawProcessorCore:
                         break
 
             if isinstance(profile, dict):
-                profile_homographies = profile.get("homographies", {}) or {}
-                H = profile_homographies.get(key)
-
+                homographies = profile.get("homographies", {}) or {}
+                H = homographies.get(key)
                 if H is None:
-                    # fallback curto: "re" ou "nir"
-                    H = profile_homographies.get(role)
+                    H = homographies.get(role)
 
                 if H is not None:
-                    calib_size = (
-                        profile.get("homography_calibration_size")
+                    reference_size = (
+                        profile.get("reference_size")
+                        or profile.get("homography_calibration_size")
                         or profile.get("calibration_size")
                         or fusion.get("homography_calibration_size")
                         or fusion.get("calibration_size")
-                        or None
                     )
-                    return H, calib_size, selected_profile
 
-        # ------------------------------------------------------------
-        # Contrato antigo: homographies direto
-        # ------------------------------------------------------------
+                    source_sizes = profile.get("source_size_by_role", {}) or {}
+                    source_size = (
+                        source_sizes.get(role)
+                        or profile.get("source_size")
+                        or reference_size
+                    )
+
+                    if reference_size is None or source_size is None:
+                        raise RuntimeError(
+                            f"Perfil {selected_profile!r} sem tamanhos geométricos "
+                            f"suficientes para role={role}."
+                        )
+
+                    if len(reference_size) != 2 or len(source_size) != 2:
+                        raise RuntimeError(
+                            f"Tamanhos inválidos no perfil {selected_profile!r}: "
+                            f"source={source_size}, reference={reference_size}"
+                        )
+
+                    coordinate_space = (
+                        profile.get("coordinate_space")
+                        or fusion.get("coordinate_space")
+                        or "native_stream_no_external_undistort"
+                    )
+
+                    H_arr = np.asarray(H, dtype=np.float32)
+                    if H_arr.shape != (3, 3) or not np.all(np.isfinite(H_arr)):
+                        raise RuntimeError(
+                            f"Homografia inválida no perfil={selected_profile} role={role}"
+                        )
+
+                    return {
+                        "H": H_arr,
+                        "source_calibration_size": [
+                            int(source_size[0]),
+                            int(source_size[1]),
+                        ],
+                        "reference_calibration_size": [
+                            int(reference_size[0]),
+                            int(reference_size[1]),
+                        ],
+                        "profile_name": selected_profile,
+                        "coordinate_space": str(coordinate_space),
+                    }
+
+        # Compatibilidade legado, onde as duas grades tinham o mesmo tamanho.
         homographies = fusion.get("homographies", {}) or {}
         H = homographies.get(key)
-
         if H is None:
             H = homographies.get(role)
 
         if H is not None:
-            calib_size = (
+            common_size = (
                 fusion.get("homography_calibration_size")
                 or fusion.get("calibration_size")
-                or None
+                or [self.sensor_width, self.sensor_height]
             )
-            return H, calib_size, "legacy"
+
+            H_arr = np.asarray(H, dtype=np.float32)
+            if H_arr.shape != (3, 3):
+                raise RuntimeError(f"Homografia legacy inválida para {role}: {H_arr.shape}")
+
+            return {
+                "H": H_arr,
+                "source_calibration_size": [int(common_size[0]), int(common_size[1])],
+                "reference_calibration_size": [int(common_size[0]), int(common_size[1])],
+                "profile_name": "legacy",
+                "coordinate_space": "legacy_common_pixel_space",
+            }
 
         raise RuntimeError(
-            f"Homografia ausente para role={role}. "
-            f"Procurei profile='{selected_profile}' em "
-            f"fusion_config.homography_profiles.*.homographies.{key} "
-            f"e fallback fusion_config.homographies.{key}"
+            f"Homografia ausente para role={role}; profile={selected_profile!r}."
+        )
+
+    def _resolve_homography_entry_for_role(self, role: str):
+        """
+        Compatibilidade com callers antigos.
+
+        Retorna:
+            H, reference_calibration_size, profile_name
+
+        A geometria mixed-resolution usa internamente
+        _resolve_homography_geometry_entry_for_role().
+        """
+        entry = self._resolve_homography_geometry_entry_for_role(role)
+        return (
+            entry["H"],
+            entry["reference_calibration_size"],
+            entry["profile_name"],
         )
 
 
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/_1_focus_calibration_tool.py b/Python/OAK/datasets/oak-fcc-3/utils/_1_focus_calibration_tool.py
new file mode 100644
index 000000000..516328ade
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/utils/_1_focus_calibration_tool.py
@@ -0,0 +1,3990 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+focus_calibration_production.py
+===============================
+
+Ferramenta FINAL de produção/QC para ajuste manual de foco do módulo OAK-FFC-3P.
+
+Topologia oficial do produto
+----------------------------
+    CAM_A = RGB = OV9782 (1280x800) OU AR0234 (1920x1200)
+    CAM_B = RE  = OV9282 (1280x800)
+    CAM_C = NIR = OV9282 (1280x800)
+
+Objetivos
+---------
+- Detectar e validar o hardware antes de permitir a calibração.
+- Suportar OV9782 e AR0234 na CAM_A.
+- Mostrar RGB + RE + NIR simultaneamente.
+- Deixar AE estabilizar e depois congelar exposição/ISO.
+- Medir foco SOMENTE quando chega frame novo.
+- Auxiliar o operador a atravessar e reencontrar o pico de foco.
+- Exigir estabilidade próxima ao pico antes do ACCEPT.
+- Medir centro + 4 regiões periféricas para detectar tilt/decentering.
+- Salvar evidência auditável por sessão.
+- Nunca alterar module_params nem qualquer calibração de runtime.
+- Nunca sobrescrever um resultado ativo aprovado com uma sessão FAIL/CANCELLED.
+
+Natureza do artefato
+--------------------
+Foco é um ajuste FÍSICO da lente. Portanto:
+    runtime_effect = "none"
+    module_params_fragment = null
+
+O JSON produzido por esta ferramenta é um relatório de QC/rastreabilidade.
+O assembler final do module_params pode ignorá-lo.
+
+Target recomendado
+------------------
+Para o modo de produção, use um alvo plano com DETALHE FINO distribuído por
+todo o FOV, perpendicular ao eixo óptico. Ex.: chart de foco / Siemens stars /
+checkerboard denso / textura impressa de alta frequência.
+
+Evite avaliar FIELD QA em cena natural com regiões sem textura. A ferramenta
+bloqueia acceptance se alguma das 5 zonas não tiver textura suficiente.
+
+Fluxo
+-----
+1) PRE-FLIGHT
+   - target de foco preenche o FOV
+   - AE estabiliza
+   - valida saturação, escuridão e textura
+   - ENTER congela EXP/ISO das 3 câmeras
+
+2) FOCUS RGB
+   - gire a lente atravessando o pico
+   - volte para >= limiar do melhor score
+   - score deve estabilizar por N frames NOVOS
+   - FIELD QA centro + cantos precisa passar
+   - A solicita acceptance
+
+3) FOCUS RE
+
+4) FOCUS NIR
+
+5) Resultado
+   - PASS: salva candidate + promove relatório QC ativo
+   - FAIL/CANCEL: candidate preservado, ativo anterior mantido
+
+Teclas durante ajuste
+---------------------
+1 / 2 / 3  -> seleciona RGB / RE / NIR
+M          -> troca métrica principal
+D          -> informa sentido atual (rosqueando/desrosqueando)
+C          -> centraliza ROI principal
+L          -> trava/destrava ROI
+E          -> equalize apenas para diagnóstico; ao mudar, reseta histórico
+R          -> reseta sweep/score da câmera ativa
+A          -> tenta ACCEPT da câmera ativa
+S          -> snapshot de diagnóstico
+Q / ESC    -> cancela sessão
+
+IMPORTANTE SOBRE ACCEPTANCE
+---------------------------
+O "best" é relativo à sessão. Para evitar aceitar um foco ruim sem ter
+atravessado o pico, o modo de produção exige:
+  - ter observado um best;
+  - ter caído X% abaixo desse best APÓS o pico (peak bracket);
+  - ter retornado para perto do best;
+  - manter estabilidade por N frames novos;
+  - field QA aprovado.
+
+Há um override de engenharia:
+    --engineering-override
+que permite aceitar sem bracket/field QA, mas isso fica registrado no JSON
+e o resultado não é considerado homologação normal.
+
+Saídas padrão
+-------------
+calibration/focus_candidates/<session_id>/
+    report.json
+    PASS.txt / FAIL.txt / CANCELLED.txt
+    snapshots/
+        rgb_accepted_full.png
+        rgb_accepted_board.png
+        ...
+        re_...
+        nir_...
+
+Se PASS:
+    calibration/focus_qc_active.json
+
+Nenhum NPZ é gerado.
+Nenhuma chave do module_params é alterada.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import shutil
+import time
+from collections import deque
+from dataclasses import dataclass, asdict
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, Optional, Tuple, List, Any
+
+import cv2
+import depthai as dai
+import numpy as np
+
+
+# ============================================================
+# Contrato do produto
+# ============================================================
+
+ROLES = ("rgb", "re", "nir")
+METHODS = ("tenengrad_norm", "laplacian_norm", "tenengrad", "laplacian", "brenner")
+
+PRODUCT_TOPOLOGY = {
+    "rgb": {
+        "socket": "CAM_A",
+        "allowed_sensors": ("OV9782", "AR0234"),
+    },
+    "re": {
+        "socket": "CAM_B",
+        "allowed_sensors": ("OV9282",),
+    },
+    "nir": {
+        "socket": "CAM_C",
+        "allowed_sensors": ("OV9282",),
+    },
+}
+
+RGB_SENSOR_MODES = {
+    "OV9782": {
+        "resolution_enum": "THE_800_P",
+        "width": 1280,
+        "height": 800,
+    },
+    "AR0234": {
+        "resolution_enum": "THE_1200_P",
+        "width": 1920,
+        "height": 1200,
+    },
+}
+
+MONO_SENSOR_MODES = {
+    "OV9282": {
+        "resolution_enum": "THE_800_P",
+        "width": 1280,
+        "height": 800,
+    }
+}
+
+SCHEMA = "multispec_focus_qc_v3"
+
+
+# ============================================================
+# Limiares de produção
+# ============================================================
+
+DEFAULT_QA = {
+    # Preflight imagem
+    "preflight_sat_warning_pct": 0.20,
+    "preflight_sat_bad_pct": 1.00,
+    "preflight_dark_warning_pct": 5.00,
+    "preflight_dark_bad_pct": 15.00,
+
+    # Textura mínima usando std no ROI/zone em escala 0..1
+    "min_texture_std": 0.035,
+    "min_texture_std_warning": 0.050,
+
+    # Bracket do pico
+    "peak_drop_required_pct": 4.0,
+
+    # Se um novo best superar o anterior só por menos que este valor,
+    # tratamos como refinamento/ruído e preservamos o bracket já observado.
+    # Um salto maior significa que achamos uma região de foco nova e exige
+    # atravessar o pico novamente.
+    "new_best_rebracket_pct": 0.020,
+
+    # Retorno ao pico
+    "accept_near_best_ratio": 0.985,
+
+    # Estabilidade do score no lock
+    "accept_stable_frames": 12,
+    "accept_stable_cv": 0.018,
+    "accept_stable_range_pct": 0.050,
+
+    # Field QA é agregado sobre vários frames NOVOS.
+    "field_stable_frames": 8,
+
+    # Field QA, razão do sharpness normalizado da zona / centro
+    "field_corner_ratio_warning": 0.72,
+    "field_corner_ratio_bad": 0.58,
+
+    # Assimetria entre cantos opostos / extremos
+    "field_spread_warning": 0.35,
+    "field_spread_bad": 0.50,
+
+    # Controle manual confirmado
+    "exposure_rel_tolerance": 0.01,
+    "exposure_abs_tolerance_us": 20,
+    "iso_tolerance": 5,
+
+    # FPS
+    "fps_min_ratio": 0.65,
+}
+
+
+# ============================================================
+# Helpers
+# ============================================================
+
+def now_str() -> str:
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+def file_stamp() -> str:
+    return datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+
+
+def ensure_dir(path: str | Path):
+    Path(path).mkdir(parents=True, exist_ok=True)
+
+
+def save_json_atomic(path: str | Path, data: dict):
+    p = Path(path)
+    ensure_dir(p.parent)
+
+    tmp = p.with_suffix(p.suffix + ".tmp")
+    with tmp.open("w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+        f.flush()
+        os.fsync(f.fileno())
+
+    os.replace(tmp, p)
+
+
+def sha256_file(path: str | Path) -> str:
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        while True:
+            b = f.read(1024 * 1024)
+            if not b:
+                break
+            h.update(b)
+    return h.hexdigest()
+
+
+def safe_int(v, default=None):
+    try:
+        if v is None:
+            return default
+        return int(v)
+    except Exception:
+        return default
+
+
+def safe_float(v, default=None):
+    try:
+        if v is None:
+            return default
+        return float(v)
+    except Exception:
+        return default
+
+
+def status_rank(s: str) -> int:
+    return {"good": 0, "warning": 1, "bad": 2}.get(str(s).lower(), 2)
+
+
+def merge_status(a: str, b: str) -> str:
+    return a if status_rank(a) >= status_rank(b) else b
+
+
+def overlay_hud(
+    img,
+    lines,
+    x=12,
+    y=24,
+    font_scale=0.50,
+    line_step=20,
+    color=(255, 255, 255),
+    shadow=True,
+):
+    yy = int(y)
+    h = img.shape[0]
+
+    for line in lines:
+        if yy > h - 6:
+            break
+
+        text = str(line)
+
+        if shadow:
+            cv2.putText(
+                img,
+                text,
+                (int(x), yy),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                font_scale,
+                (0, 0, 0),
+                3,
+                cv2.LINE_AA,
+            )
+
+        cv2.putText(
+            img,
+            text,
+            (int(x), yy),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            font_scale,
+            color,
+            1,
+            cv2.LINE_AA,
+        )
+
+        yy += int(line_step)
+
+
+def build_empty_panel(shape_hw, title, subtitle="sem frame"):
+    h, w = shape_hw
+    img = np.zeros((h, w, 3), dtype=np.uint8)
+    overlay_hud(
+        img,
+        [title, subtitle],
+        x=20,
+        y=46,
+        font_scale=0.72,
+        line_step=30,
+    )
+    return img
+
+
+def socket_name(socket) -> str:
+    name = getattr(socket, "name", None)
+    if name:
+        return str(name)
+
+    s = str(socket)
+    for candidate in ("CAM_A", "CAM_B", "CAM_C", "CAM_D"):
+        if candidate in s:
+            return candidate
+
+    return s
+
+
+def get_socket(name: str):
+    name = str(name).upper().strip()
+
+    mapping = {
+        "CAM_A": dai.CameraBoardSocket.CAM_A,
+        "CAM_B": dai.CameraBoardSocket.CAM_B,
+        "CAM_C": dai.CameraBoardSocket.CAM_C,
+    }
+
+    if name not in mapping:
+        raise ValueError(f"Socket inválido: {name}")
+
+    return mapping[name]
+
+
+def supported_type_strings(feature) -> List[str]:
+    return [
+        str(v).upper()
+        for v in (getattr(feature, "supportedTypes", []) or [])
+    ]
+
+
+def feature_is_color(feature) -> bool:
+    types = supported_type_strings(feature)
+    sensor = str(getattr(feature, "sensorName", "") or "").upper()
+
+    if any("COLOR" in t for t in types):
+        return True
+
+    if any("MONO" in t for t in types):
+        return False
+
+    return sensor in {"OV9782", "AR0234"}
+
+
+def feature_is_mono(feature) -> bool:
+    types = supported_type_strings(feature)
+
+    if any("MONO" in t for t in types):
+        return True
+
+    if any("COLOR" in t for t in types):
+        return False
+
+    return not feature_is_color(feature)
+
+
+def resize_panel(img: np.ndarray, target_hw: Tuple[int, int]) -> np.ndarray:
+    th, tw = target_hw
+    return cv2.resize(img, (tw, th), interpolation=cv2.INTER_AREA)
+
+
+def to_gray01(img_bgr: np.ndarray) -> Optional[np.ndarray]:
+    if img_bgr is None:
+        return None
+
+    if img_bgr.ndim == 2:
+        gray = img_bgr
+
+    elif img_bgr.ndim == 3 and img_bgr.shape[2] == 1:
+        gray = img_bgr[:, :, 0]
+
+    else:
+        gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
+
+    return np.clip(gray.astype(np.float32) / 255.0, 0.0, 1.0)
+
+
+def default_roi_for_shape(shape_hw, frac=0.36):
+    h, w = shape_hw
+
+    rw = max(16, int(round(w * frac)))
+    rh = max(16, int(round(h * frac)))
+
+    x0 = (w - rw) // 2
+    y0 = (h - rh) // 2
+
+    return (x0, y0, x0 + rw, y0 + rh)
+
+
+def sanitize_roi(rect, shape_hw):
+    if rect is None:
+        return None
+
+    h, w = shape_hw
+    x0, y0, x1, y1 = rect
+
+    x0, x1 = sorted((int(x0), int(x1)))
+    y0, y1 = sorted((int(y0), int(y1)))
+
+    x0 = max(0, min(w - 1, x0))
+    x1 = max(1, min(w, x1))
+    y0 = max(0, min(h - 1, y0))
+    y1 = max(1, min(h, y1))
+
+    if x1 - x0 < 12 or y1 - y0 < 12:
+        return None
+
+    return (x0, y0, x1, y1)
+
+
+def crop_rect(img, rect):
+    if img is None:
+        return None
+
+    r = sanitize_roi(rect, img.shape[:2])
+    if r is None:
+        return None
+
+    x0, y0, x1, y1 = r
+    return img[y0:y1, x0:x1]
+
+
+def rect_from_norm(shape_hw, x0, y0, x1, y1):
+    h, w = shape_hw
+    return (
+        int(round(x0 * w)),
+        int(round(y0 * h)),
+        int(round(x1 * w)),
+        int(round(y1 * h)),
+    )
+
+
+# ============================================================
+# Descoberta e contrato de hardware
+# ============================================================
+
+@dataclass
+class SensorSpec:
+    role: str
+    socket_name: str
+    sensor_name: str
+    feature_width: int
+    feature_height: int
+    configured_width: int
+    configured_height: int
+    resolution_name: str
+    stream_name: str
+    control_name: str
+    is_color: bool
+    source: str
+
+
+def discover_cameras(mx_id: Optional[str]):
+    device_info = dai.DeviceInfo(mx_id) if mx_id else None
+
+    ctx = dai.Device(device_info) if device_info is not None else dai.Device()
+
+    with ctx as device:
+        features = list(device.getConnectedCameraFeatures())
+
+        actual_mx = None
+        for attr in ("getMxId", "getDeviceId"):
+            fn = getattr(device, attr, None)
+            if callable(fn):
+                try:
+                    v = fn()
+                    if v:
+                        actual_mx = str(v)
+                        break
+                except Exception:
+                    pass
+
+        usb_speed = None
+        try:
+            usb_speed = str(device.getUsbSpeed())
+        except Exception:
+            pass
+
+        rows = []
+
+        for f in features:
+            rows.append({
+                "socket_obj": f.socket,
+                "socket_name": socket_name(f.socket),
+                "sensor_name": str(getattr(f, "sensorName", "") or "").upper(),
+                "width": int(getattr(f, "width", 0) or 0),
+                "height": int(getattr(f, "height", 0) or 0),
+                "supported_types": supported_type_strings(f),
+                "is_color": feature_is_color(f),
+                "is_mono": feature_is_mono(f),
+                "has_autofocus_ic": int(getattr(f, "hasAutofocusIC", 0) or 0),
+            })
+
+    return rows, actual_mx, usb_speed
+
+
+def enum_if_exists(enum_cls, name: str):
+    return getattr(enum_cls, name, None)
+
+
+def product_sensor_mode(sensor_name: str, is_color: bool):
+    sensor = str(sensor_name).upper()
+
+    table = RGB_SENSOR_MODES if is_color else MONO_SENSOR_MODES
+
+    if sensor not in table:
+        raise RuntimeError(
+            f"Sensor não homologado para esta role: {sensor}"
+        )
+
+    return table[sensor]
+
+
+def validate_product_topology(camera_rows: list[dict], relaxed=False):
+    by_socket = {
+        row["socket_name"]: row
+        for row in camera_rows
+    }
+
+    errors = []
+
+    for role in ROLES:
+        contract = PRODUCT_TOPOLOGY[role]
+        sock = contract["socket"]
+
+        row = by_socket.get(sock)
+
+        if row is None:
+            errors.append(f"{role.upper()}: {sock} ausente")
+            continue
+
+        sensor = row["sensor_name"].upper()
+
+        if sensor not in contract["allowed_sensors"]:
+            errors.append(
+                f"{role.upper()}: {sock} sensor={sensor}, "
+                f"permitidos={contract['allowed_sensors']}"
+            )
+
+        if role == "rgb" and not row["is_color"]:
+            errors.append(f"RGB: {sock}/{sensor} não anunciado como COLOR")
+
+        if role in ("re", "nir") and not row["is_mono"]:
+            errors.append(f"{role.upper()}: {sock}/{sensor} não anunciado como MONO")
+
+    if errors and not relaxed:
+        raise RuntimeError(
+            "Topologia do módulo não corresponde ao produto:\n  - "
+            + "\n  - ".join(errors)
+        )
+
+    return {
+        "valid": len(errors) == 0,
+        "relaxed": bool(relaxed),
+        "errors": errors,
+    }
+
+
+def build_specs(camera_rows) -> Dict[str, SensorSpec]:
+    by_socket = {
+        row["socket_name"]: row
+        for row in camera_rows
+    }
+
+    specs = {}
+
+    for role in ROLES:
+        socket = PRODUCT_TOPOLOGY[role]["socket"]
+        row = by_socket.get(socket)
+
+        if row is None:
+            continue
+
+        is_color = role == "rgb"
+
+        mode = product_sensor_mode(
+            row["sensor_name"],
+            is_color=is_color,
+        )
+
+        # A dimensão anunciada pelo DepthAI deve ser coerente quando disponível.
+        if row["width"] and row["height"]:
+            if (row["width"], row["height"]) != (mode["width"], mode["height"]):
+                raise RuntimeError(
+                    f"{socket}/{row['sensor_name']}: dimensão anunciada "
+                    f"{row['width']}x{row['height']} != homologada "
+                    f"{mode['width']}x{mode['height']}"
+                )
+
+        specs[role] = SensorSpec(
+            role=role,
+            socket_name=socket,
+            sensor_name=row["sensor_name"],
+            feature_width=row["width"],
+            feature_height=row["height"],
+            configured_width=mode["width"],
+            configured_height=mode["height"],
+            resolution_name=mode["resolution_enum"],
+            stream_name=f"focus_{role}",
+            control_name=f"focus_ctrl_{role}",
+            is_color=is_color,
+            source="ISP" if is_color else "MONO_OUT",
+        )
+
+    missing = [r for r in ROLES if r not in specs]
+    if missing:
+        raise RuntimeError(f"Não foi possível montar specs das roles: {missing}")
+
+    return specs
+
+
+# ============================================================
+# Pipeline e controles
+# ============================================================
+
+def try_set_initial_isp_controls(cam, args) -> dict:
+    """
+    ISP determinístico na RGB.
+
+    Esses parâmetros afetam diretamente energia de borda, portanto a ferramenta
+    tenta fixá-los. Se a versão do DepthAI não expuser algum controle, por
+    padrão a calibração aborta. --allow-isp-defaults relaxa isso.
+    """
+    result = {
+        "sharpness": None,
+        "luma_denoise": None,
+        "chroma_denoise": None,
+        "errors": [],
+    }
+
+    operations = (
+        ("sharpness", "setSharpness", int(args.isp_sharpness)),
+        ("luma_denoise", "setLumaDenoise", int(args.isp_luma_denoise)),
+        ("chroma_denoise", "setChromaDenoise", int(args.isp_chroma_denoise)),
+    )
+
+    for key, method_name, value in operations:
+        fn = getattr(cam.initialControl, method_name, None)
+
+        if not callable(fn):
+            result["errors"].append(f"{method_name}:unsupported")
+            continue
+
+        try:
+            fn(value)
+            result[key] = value
+        except Exception as exc:
+            result["errors"].append(f"{method_name}:{exc}")
+
+    if result["errors"] and not args.allow_isp_defaults:
+        raise RuntimeError(
+            "Não foi possível fixar controles ISP necessários para repetibilidade: "
+            + "; ".join(result["errors"])
+            + ". Use --allow-isp-defaults somente como override de engenharia."
+        )
+
+    return result
+
+
+def build_pipeline(specs: Dict[str, SensorSpec], args):
+    pipeline = dai.Pipeline()
+
+    isp_settings = None
+
+    for role in ROLES:
+        spec = specs[role]
+        socket = get_socket(spec.socket_name)
+
+        if spec.is_color:
+            cam = pipeline.createColorCamera()
+            cam.setBoardSocket(socket)
+
+            enum_value = enum_if_exists(
+                dai.ColorCameraProperties.SensorResolution,
+                spec.resolution_name,
+            )
+
+            if enum_value is None:
+                raise RuntimeError(
+                    f"DepthAI sem ColorCamera {spec.resolution_name}"
+                )
+
+            cam.setResolution(enum_value)
+            cam.setFps(float(args.fps))
+            cam.setInterleaved(False)
+
+            try:
+                cam.setColorOrder(
+                    dai.ColorCameraProperties.ColorOrder.BGR
+                )
+            except Exception:
+                pass
+
+            # AE/AWB ativos somente para preflight.
+            try:
+                cam.initialControl.setAutoExposureEnable()
+            except Exception:
+                pass
+
+            try:
+                cam.initialControl.setAutoWhiteBalanceLock(False)
+            except Exception:
+                pass
+
+            isp_settings = try_set_initial_isp_controls(cam, args)
+
+            output = cam.isp
+
+        else:
+            cam = pipeline.createMonoCamera()
+            cam.setBoardSocket(socket)
+
+            enum_value = enum_if_exists(
+                dai.MonoCameraProperties.SensorResolution,
+                spec.resolution_name,
+            )
+
+            if enum_value is None:
+                raise RuntimeError(
+                    f"DepthAI sem MonoCamera {spec.resolution_name}"
+                )
+
+            cam.setResolution(enum_value)
+            cam.setFps(float(args.fps))
+
+            try:
+                cam.initialControl.setAutoExposureEnable()
+            except Exception:
+                pass
+
+            output = cam.out
+
+        xout = pipeline.createXLinkOut()
+        xout.setStreamName(spec.stream_name)
+        output.link(xout.input)
+
+        xin = pipeline.createXLinkIn()
+        xin.setStreamName(spec.control_name)
+
+        if not hasattr(cam, "inputControl"):
+            raise RuntimeError(
+                f"{spec.socket_name}/{spec.sensor_name} sem inputControl"
+            )
+
+        xin.out.link(cam.inputControl)
+
+    return pipeline, isp_settings
+
+
+@dataclass
+class LockedControl:
+    role: str
+    exposure_time_us: int
+    sensitivity_iso: int
+    source: str
+
+
+def packet_controls(packet) -> dict:
+    exp_us = None
+    iso = None
+    color_temp = None
+    seq = None
+    timestamp = None
+
+    try:
+        exp = packet.getExposureTime()
+        if hasattr(exp, "total_seconds"):
+            exp_us = int(round(exp.total_seconds() * 1_000_000))
+        else:
+            exp_us = int(exp)
+    except Exception:
+        pass
+
+    try:
+        iso = int(packet.getSensitivity())
+    except Exception:
+        pass
+
+    try:
+        color_temp = int(packet.getColorTemperature())
+    except Exception:
+        pass
+
+    try:
+        seq = int(packet.getSequenceNum())
+    except Exception:
+        pass
+
+    for method in ("getTimestampDevice", "getTimestamp"):
+        fn = getattr(packet, method, None)
+        if callable(fn):
+            try:
+                ts = fn()
+                if hasattr(ts, "total_seconds"):
+                    timestamp = float(ts.total_seconds())
+                else:
+                    timestamp = float(ts)
+                break
+            except Exception:
+                pass
+
+    return {
+        "exposure_time_us": exp_us,
+        "sensitivity_iso": iso,
+        "color_temperature_k": color_temp,
+        "sequence_num": seq,
+        "timestamp_s": timestamp,
+    }
+
+
+def send_manual_control(queue, lock: LockedControl, rgb=False):
+    ctrl = dai.CameraControl()
+
+    ctrl.setManualExposure(
+        int(lock.exposure_time_us),
+        int(lock.sensitivity_iso),
+    )
+
+    if rgb:
+        try:
+            ctrl.setAutoWhiteBalanceLock(True)
+        except Exception:
+            pass
+
+    queue.send(ctrl)
+
+
+def controls_match(actual: dict, target: LockedControl, qa: dict):
+    exp = safe_int(actual.get("exposure_time_us"), None)
+    iso = safe_int(actual.get("sensitivity_iso"), None)
+
+    if exp is None or iso is None:
+        return False
+
+    exp_tol = max(
+        int(qa["exposure_abs_tolerance_us"]),
+        int(round(target.exposure_time_us * qa["exposure_rel_tolerance"])),
+    )
+
+    if abs(exp - target.exposure_time_us) > exp_tol:
+        return False
+
+    if abs(iso - target.sensitivity_iso) > int(qa["iso_tolerance"]):
+        return False
+
+    return True
+
+
+# ============================================================
+# Métricas de foco
+# ============================================================
+
+def preprocess_gray(gray01: np.ndarray, equalize=False):
+    g = np.clip(gray01 * 255.0, 0, 255).astype(np.uint8)
+
+    if equalize:
+        g = cv2.equalizeHist(g)
+
+    return g
+
+
+def raw_focus_metrics_from_gray(gray01: np.ndarray, equalize=False):
+    if gray01 is None or gray01.size < 64:
+        return {
+            "valid": False,
+            "laplacian": 0.0,
+            "tenengrad": 0.0,
+            "brenner": 0.0,
+            "laplacian_norm": 0.0,
+            "tenengrad_norm": 0.0,
+            "mean": 0.0,
+            "std": 0.0,
+            "p95": 0.0,
+            "pct_saturated": 0.0,
+            "pct_dark": 0.0,
+            "pixels": 0,
+        }
+
+    arr01 = np.clip(gray01.astype(np.float32), 0.0, 1.0)
+    g = preprocess_gray(arr01, equalize=equalize)
+
+    lap = cv2.Laplacian(g, cv2.CV_64F, ksize=3)
+    lap_var = float(lap.var())
+
+    sx = cv2.Sobel(g, cv2.CV_64F, 1, 0, ksize=3)
+    sy = cv2.Sobel(g, cv2.CV_64F, 0, 1, ksize=3)
+    ten = float(np.mean(sx * sx + sy * sy))
+
+    arr_u8 = g.astype(np.float32)
+
+    if arr_u8.shape[1] >= 3:
+        diff = arr_u8[:, 2:] - arr_u8[:, :-2]
+        brenner = float(np.mean(diff * diff))
+    else:
+        brenner = 0.0
+
+    std01 = float(arr01.std())
+
+    # Normalização por contraste local.
+    # Evita que uma zona ganhe score só porque o desenho impresso é mais contrastado.
+    variance_u8 = max(float(np.var(arr_u8)), 1.0)
+
+    return {
+        "valid": True,
+        "laplacian": lap_var,
+        "tenengrad": ten,
+        "brenner": brenner,
+        "laplacian_norm": float(lap_var / variance_u8),
+        "tenengrad_norm": float(ten / variance_u8),
+        "mean": float(arr01.mean()),
+        "std": std01,
+        "p95": float(np.percentile(arr01, 95)),
+        "pct_saturated": float((arr01 >= 0.98).mean() * 100.0),
+        "pct_dark": float((arr01 <= 0.02).mean() * 100.0),
+        "pixels": int(arr01.size),
+    }
+
+
+def compute_focus_metrics(img_bgr, roi_rect, equalize=False):
+    gray = to_gray01(img_bgr)
+
+    if gray is None:
+        return raw_focus_metrics_from_gray(None)
+
+    roi = crop_rect(gray, roi_rect)
+
+    return raw_focus_metrics_from_gray(
+        roi,
+        equalize=equalize,
+    )
+
+
+def metric_value(metrics: dict, method: str) -> float:
+    return float(metrics.get(method, 0.0) or 0.0)
+
+
+def smoothed_score(history, window: int) -> float:
+    if not history:
+        return 0.0
+
+    vals = [
+        float(x["score"])
+        for x in list(history)[-max(1, int(window)):]
+    ]
+
+    return float(np.mean(vals))
+
+
+def stable_stats(history, frames_count: int) -> dict:
+    n = int(frames_count)
+
+    if len(history) < n:
+        return {
+            "ready": False,
+            "count": len(history),
+            "needed": n,
+            "mean": 0.0,
+            "std": 0.0,
+            "cv": float("inf"),
+            "range_pct": float("inf"),
+        }
+
+    vals = np.array(
+        [float(x["smooth"]) for x in list(history)[-n:]],
+        dtype=np.float64,
+    )
+
+    mean = float(np.mean(vals))
+    std = float(np.std(vals))
+
+    cv = std / max(abs(mean), 1e-12)
+    range_pct = float((np.max(vals) - np.min(vals)) / max(abs(mean), 1e-12))
+
+    return {
+        "ready": True,
+        "count": n,
+        "needed": n,
+        "mean": mean,
+        "std": std,
+        "cv": float(cv),
+        "range_pct": range_pct,
+    }
+
+
+# ============================================================
+# Sweep / bracket do pico
+# ============================================================
+
+@dataclass
+class SweepState:
+    best_score: float = 0.0
+    best_raw_score: float = 0.0
+    best_timestamp: Optional[str] = None
+    best_seq: Optional[int] = None
+    best_metrics: Optional[dict] = None
+    best_roi: Optional[List[int]] = None
+
+    peak_drop_observed: bool = False
+    lowest_ratio_after_best: float = 1.0
+
+    frames_scored: int = 0
+
+
+def update_sweep(
+    sweep: SweepState,
+    score: float,
+    smooth: float,
+    metrics: dict,
+    roi,
+    sequence_num,
+    drop_required_pct: float,
+    new_best_rebracket_pct: float,
+):
+    sweep.frames_scored += 1
+
+    if smooth > sweep.best_score:
+        previous_best = float(sweep.best_score)
+        had_bracket = bool(sweep.peak_drop_observed)
+        previous_lowest_ratio = float(sweep.lowest_ratio_after_best)
+
+        improvement = (
+            float("inf")
+            if previous_best <= 0
+            else (float(smooth) / previous_best) - 1.0
+        )
+
+        sweep.best_score = float(smooth)
+        sweep.best_raw_score = float(score)
+        sweep.best_timestamp = now_str()
+        sweep.best_seq = safe_int(sequence_num, None)
+        sweep.best_metrics = dict(metrics)
+        sweep.best_roi = list(map(int, roi)) if roi else None
+
+        # Primeiro best obviamente não tem bracket.
+        if previous_best <= 0:
+            sweep.lowest_ratio_after_best = 1.0
+            sweep.peak_drop_observed = False
+
+        # Pequeno refinamento do mesmo pico: preserva bracket já demonstrado.
+        elif (
+            had_bracket
+            and improvement <= float(new_best_rebracket_pct)
+        ):
+            sweep.lowest_ratio_after_best = previous_lowest_ratio
+            sweep.peak_drop_observed = True
+
+        # Novo patamar significativamente melhor: precisa bracketar de novo.
+        else:
+            sweep.lowest_ratio_after_best = 1.0
+            sweep.peak_drop_observed = False
+
+        return
+
+    if sweep.best_score <= 0:
+        return
+
+    ratio = float(smooth / sweep.best_score)
+    sweep.lowest_ratio_after_best = min(
+        sweep.lowest_ratio_after_best,
+        ratio,
+    )
+
+    required_ratio = 1.0 - float(drop_required_pct) / 100.0
+
+    if ratio <= required_ratio:
+        sweep.peak_drop_observed = True
+
+
+def analyze_trend(
+    history,
+    sweep: SweepState,
+    direction_name: str,
+    drop_warn_pct=3.0,
+):
+    if len(history) < 6:
+        return {
+            "status": "coletando",
+            "instruction": "gire devagar",
+            "pct_of_best": 0.0,
+            "delta": 0.0,
+        }
+
+    recent = [
+        float(x["smooth"])
+        for x in list(history)[-5:]
+    ]
+
+    if len(history) >= 12:
+        old = [
+            float(x["smooth"])
+            for x in list(history)[-12:-7]
+        ]
+    else:
+        old = [
+            float(x["smooth"])
+            for x in list(history)[:5]
+        ]
+
+    recent_mean = float(np.mean(recent))
+    old_mean = float(np.mean(old))
+    delta = recent_mean - old_mean
+
+    best = float(sweep.best_score)
+
+    ratio = 0.0 if best <= 0 else recent_mean / best
+    drop_pct = 100.0 * (1.0 - ratio)
+
+    if best > 0 and drop_pct >= float(drop_warn_pct):
+        return {
+            "status": "passou_do_pico",
+            "instruction": f"volte: contrario de {direction_name}",
+            "pct_of_best": ratio * 100.0,
+            "delta": delta,
+        }
+
+    eps = max(best * 0.002, 1e-6)
+
+    if delta > eps:
+        status = "melhorando"
+        instruction = f"continue {direction_name}"
+
+    elif delta < -eps:
+        status = "piorando"
+        instruction = f"inverta: contrario de {direction_name}"
+
+    else:
+        status = "estavel"
+        instruction = "ajuste fino"
+
+    return {
+        "status": status,
+        "instruction": instruction,
+        "pct_of_best": ratio * 100.0,
+        "delta": delta,
+    }
+
+
+# ============================================================
+# FIELD QA
+# ============================================================
+
+FIELD_ZONES_NORM = {
+    "center": (0.34, 0.34, 0.66, 0.66),
+
+    # Evita os extremos absolutos do FOV.
+    "top_left": (0.08, 0.08, 0.30, 0.30),
+    "top_right": (0.70, 0.08, 0.92, 0.30),
+    "bottom_left": (0.08, 0.70, 0.30, 0.92),
+    "bottom_right": (0.70, 0.70, 0.92, 0.92),
+}
+
+
+def compute_field_qa(
+    img_bgr: np.ndarray,
+    qa: dict,
+    equalize=False,
+):
+    gray = to_gray01(img_bgr)
+
+    if gray is None:
+        return {
+            "status": "bad",
+            "reasons": ["sem_frame"],
+            "zones": {},
+        }
+
+    zones = {}
+
+    for name, nrect in FIELD_ZONES_NORM.items():
+        rect = rect_from_norm(
+            gray.shape[:2],
+            *nrect,
+        )
+
+        roi = crop_rect(gray, rect)
+
+        metrics = raw_focus_metrics_from_gray(
+            roi,
+            equalize=equalize,
+        )
+
+        texture = float(metrics.get("std", 0.0))
+        sharp = float(metrics.get("tenengrad_norm", 0.0))
+
+        zones[name] = {
+            "rect": list(rect),
+            "texture_std": texture,
+            "sharpness": sharp,
+            "metrics": metrics,
+        }
+
+    status = "good"
+    reasons = []
+
+    # Textura suficiente em todas as zonas.
+    for name, item in zones.items():
+        texture = item["texture_std"]
+
+        if texture < qa["min_texture_std"]:
+            status = merge_status(status, "bad")
+            reasons.append(
+                f"{name}:insufficient_texture:{texture:.4f}"
+            )
+
+        elif texture < qa["min_texture_std_warning"]:
+            status = merge_status(status, "warning")
+            reasons.append(
+                f"{name}:low_texture:{texture:.4f}"
+            )
+
+    center = max(
+        float(zones["center"]["sharpness"]),
+        1e-9,
+    )
+
+    corner_names = (
+        "top_left",
+        "top_right",
+        "bottom_left",
+        "bottom_right",
+    )
+
+    corner_ratios = {}
+
+    for name in corner_names:
+        ratio = float(
+            zones[name]["sharpness"] / center
+        )
+
+        corner_ratios[name] = ratio
+        zones[name]["ratio_to_center"] = ratio
+
+        if ratio < qa["field_corner_ratio_bad"]:
+            status = merge_status(status, "bad")
+            reasons.append(
+                f"{name}:corner_ratio_bad:{ratio:.3f}"
+            )
+
+        elif ratio < qa["field_corner_ratio_warning"]:
+            status = merge_status(status, "warning")
+            reasons.append(
+                f"{name}:corner_ratio_warn:{ratio:.3f}"
+            )
+
+    vals = np.array(
+        [corner_ratios[x] for x in corner_names],
+        dtype=np.float64,
+    )
+
+    spread = float(
+        (np.max(vals) - np.min(vals))
+        / max(float(np.median(vals)), 1e-9)
+    )
+
+    if spread > qa["field_spread_bad"]:
+        status = merge_status(status, "bad")
+        reasons.append(
+            f"corner_spread_bad:{spread:.3f}"
+        )
+
+    elif spread > qa["field_spread_warning"]:
+        status = merge_status(status, "warning")
+        reasons.append(
+            f"corner_spread_warn:{spread:.3f}"
+        )
+
+    return {
+        "status": status,
+        "reasons": reasons,
+        "zones": zones,
+        "corner_ratios": corner_ratios,
+        "corner_spread": spread,
+        "samples": 1,
+    }
+
+
+def aggregate_field_qa(
+    reports,
+    qa: dict,
+):
+    """
+    Agrega FIELD QA por mediana sobre vários frames NOVOS.
+
+    Isso evita aprovar/reprovar tilt/decentering por ruído de um frame isolado.
+    """
+    reports = list(reports or [])
+
+    needed = int(qa["field_stable_frames"])
+
+    if len(reports) < needed:
+        return {
+            "status": "collecting",
+            "reasons": [
+                f"field_samples:{len(reports)}/{needed}"
+            ],
+            "zones": {},
+            "corner_ratios": {},
+            "corner_spread": None,
+            "samples": len(reports),
+            "needed": needed,
+        }
+
+    recent = reports[-needed:]
+
+    zone_names = tuple(FIELD_ZONES_NORM.keys())
+    zones = {}
+
+    for name in zone_names:
+        textures = []
+        sharpness = []
+        ratios = []
+
+        for report in recent:
+            item = report.get("zones", {}).get(name, {})
+
+            if item.get("texture_std") is not None:
+                textures.append(
+                    float(item["texture_std"])
+                )
+
+            if item.get("sharpness") is not None:
+                sharpness.append(
+                    float(item["sharpness"])
+                )
+
+            if item.get("ratio_to_center") is not None:
+                ratios.append(
+                    float(item["ratio_to_center"])
+                )
+
+        zones[name] = {
+            "texture_std": (
+                float(np.median(textures))
+                if textures
+                else 0.0
+            ),
+            "sharpness": (
+                float(np.median(sharpness))
+                if sharpness
+                else 0.0
+            ),
+        }
+
+        if name == "center":
+            zones[name]["ratio_to_center"] = 1.0
+        elif ratios:
+            zones[name]["ratio_to_center"] = float(
+                np.median(ratios)
+            )
+
+    status = "good"
+    reasons = []
+
+    for name, item in zones.items():
+        texture = float(
+            item.get("texture_std", 0.0)
+        )
+
+        if texture < qa["min_texture_std"]:
+            status = merge_status(status, "bad")
+            reasons.append(
+                f"{name}:insufficient_texture:{texture:.4f}"
+            )
+
+        elif texture < qa["min_texture_std_warning"]:
+            status = merge_status(status, "warning")
+            reasons.append(
+                f"{name}:low_texture:{texture:.4f}"
+            )
+
+    corner_names = (
+        "top_left",
+        "top_right",
+        "bottom_left",
+        "bottom_right",
+    )
+
+    corner_ratios = {}
+
+    for name in corner_names:
+        ratio = float(
+            zones[name].get("ratio_to_center", 0.0)
+        )
+
+        corner_ratios[name] = ratio
+
+        if ratio < qa["field_corner_ratio_bad"]:
+            status = merge_status(status, "bad")
+            reasons.append(
+                f"{name}:corner_ratio_bad:{ratio:.3f}"
+            )
+
+        elif ratio < qa["field_corner_ratio_warning"]:
+            status = merge_status(status, "warning")
+            reasons.append(
+                f"{name}:corner_ratio_warn:{ratio:.3f}"
+            )
+
+    vals = np.array(
+        list(corner_ratios.values()),
+        dtype=np.float64,
+    )
+
+    spread = float(
+        (np.max(vals) - np.min(vals))
+        / max(float(np.median(vals)), 1e-9)
+    )
+
+    if spread > qa["field_spread_bad"]:
+        status = merge_status(status, "bad")
+        reasons.append(
+            f"corner_spread_bad:{spread:.3f}"
+        )
+
+    elif spread > qa["field_spread_warning"]:
+        status = merge_status(status, "warning")
+        reasons.append(
+            f"corner_spread_warn:{spread:.3f}"
+        )
+
+    return {
+        "status": status,
+        "reasons": reasons,
+        "zones": zones,
+        "corner_ratios": corner_ratios,
+        "corner_spread": spread,
+        "samples": needed,
+        "needed": needed,
+    }
+
+
+# ============================================================
+# Preflight
+# ============================================================
+
+def image_preflight(img_bgr, qa: dict):
+    gray = to_gray01(img_bgr)
+
+    if gray is None:
+        return {
+            "status": "bad",
+            "reasons": ["sem_frame"],
+        }
+
+    arr = gray.reshape(-1)
+
+    mean = float(np.mean(arr))
+    std = float(np.std(arr))
+    p95 = float(np.percentile(arr, 95))
+    sat_pct = float((arr >= 0.98).mean() * 100.0)
+    dark_pct = float((arr <= 0.02).mean() * 100.0)
+
+    status = "good"
+    reasons = []
+
+    if sat_pct > qa["preflight_sat_bad_pct"]:
+        status = "bad"
+        reasons.append(f"sat_bad:{sat_pct:.2f}%")
+
+    elif sat_pct > qa["preflight_sat_warning_pct"]:
+        status = merge_status(status, "warning")
+        reasons.append(f"sat_warn:{sat_pct:.2f}%")
+
+    if dark_pct > qa["preflight_dark_bad_pct"]:
+        status = merge_status(status, "bad")
+        reasons.append(f"dark_bad:{dark_pct:.2f}%")
+
+    elif dark_pct > qa["preflight_dark_warning_pct"]:
+        status = merge_status(status, "warning")
+        reasons.append(f"dark_warn:{dark_pct:.2f}%")
+
+    # Full frame precisa ter alguma textura.
+    if std < qa["min_texture_std"]:
+        status = merge_status(status, "bad")
+        reasons.append(f"texture_bad:{std:.4f}")
+
+    elif std < qa["min_texture_std_warning"]:
+        status = merge_status(status, "warning")
+        reasons.append(f"texture_warn:{std:.4f}")
+
+    return {
+        "status": status,
+        "reasons": reasons,
+        "mean": mean,
+        "std": std,
+        "p95": p95,
+        "sat_pct": sat_pct,
+        "dark_pct": dark_pct,
+    }
+
+
+def merge_preflight_by_role(frames, qa):
+    result = {
+        "status": "good",
+        "roles": {},
+        "reasons": [],
+    }
+
+    for role in ROLES:
+        r = image_preflight(frames.get(role), qa)
+        result["roles"][role] = r
+        result["status"] = merge_status(
+            result["status"],
+            r["status"],
+        )
+        result["reasons"].extend(
+            [f"{role}:{x}" for x in r.get("reasons", [])]
+        )
+
+    return result
+
+
+# ============================================================
+# Estado de runtime
+# ============================================================
+
+class FocusRuntime:
+    def __init__(
+        self,
+        device,
+        specs: Dict[str, SensorSpec],
+        args,
+        qa,
+    ):
+        self.device = device
+        self.specs = specs
+        self.args = args
+        self.qa = qa
+
+        self.output_queues = {
+            role: device.getOutputQueue(
+                name=spec.stream_name,
+                maxSize=2,
+                blocking=False,
+            )
+            for role, spec in specs.items()
+        }
+
+        self.control_queues = {
+            role: device.getInputQueue(
+                spec.control_name
+            )
+            for role, spec in specs.items()
+        }
+
+        self.frames = {
+            role: None
+            for role in ROLES
+        }
+
+        self.controls = {
+            role: {}
+            for role in ROLES
+        }
+
+        self.last_seq = {
+            role: None
+            for role in ROLES
+        }
+
+        self.frame_counts = {
+            role: 0
+            for role in ROLES
+        }
+
+        self.fps = {
+            role: 0.0
+            for role in ROLES
+        }
+
+        self._fps_count = {
+            role: 0
+            for role in ROLES
+        }
+
+        self._fps_t0 = {
+            role: time.time()
+            for role in ROLES
+        }
+
+    def poll(self) -> set[str]:
+        """
+        Retorna as roles que receberam FRAME NOVO nesta iteração.
+
+        Esta informação é usada pelo score. O mesmo frame NUNCA é adicionado
+        duas vezes ao histórico de foco.
+        """
+        fresh = set()
+
+        for role in ROLES:
+            packet = self.output_queues[role].tryGet()
+
+            if packet is None:
+                continue
+
+            ctrl = packet_controls(packet)
+            seq = ctrl.get("sequence_num")
+
+            if seq is not None and seq == self.last_seq[role]:
+                continue
+
+            try:
+                img = packet.getCvFrame()
+            except Exception as exc:
+                raise RuntimeError(
+                    f"{role}: getCvFrame falhou: {exc}"
+                )
+
+            if img is None:
+                continue
+
+            if img.ndim == 2:
+                img = cv2.cvtColor(
+                    img,
+                    cv2.COLOR_GRAY2BGR,
+                )
+
+            self.frames[role] = img
+            self.controls[role] = ctrl
+            self.last_seq[role] = seq
+            self.frame_counts[role] += 1
+
+            fresh.add(role)
+
+            self._fps_count[role] += 1
+
+            dt = time.time() - self._fps_t0[role]
+
+            if dt >= 1.0:
+                self.fps[role] = (
+                    self._fps_count[role] / dt
+                )
+                self._fps_count[role] = 0
+                self._fps_t0[role] = time.time()
+
+        return fresh
+
+    def wait_all(self, timeout=8.0):
+        t0 = time.time()
+
+        while time.time() - t0 < timeout:
+            self.poll()
+
+            if all(
+                self.frames[r] is not None
+                for r in ROLES
+            ):
+                return
+
+            time.sleep(0.003)
+
+        missing = [
+            r for r in ROLES
+            if self.frames[r] is None
+        ]
+
+        raise TimeoutError(
+            f"Timeout aguardando câmeras: {missing}"
+        )
+
+    def send_manual(self, locked):
+        for role in ROLES:
+            send_manual_control(
+                self.control_queues[role],
+                locked[role],
+                rgb=(role == "rgb"),
+            )
+
+    def verify_manual(self, locked, settle_frames):
+        matched = {
+            r: 0
+            for r in ROLES
+        }
+
+        observed_seq = dict(self.last_seq)
+        t0 = time.time()
+
+        while time.time() - t0 < 10.0:
+            fresh = self.poll()
+
+            for role in fresh:
+                seq = self.last_seq[role]
+
+                if seq == observed_seq.get(role):
+                    continue
+
+                observed_seq[role] = seq
+
+                if controls_match(
+                    self.controls[role],
+                    locked[role],
+                    self.qa,
+                ):
+                    matched[role] += 1
+
+                else:
+                    matched[role] = 0
+
+            if all(
+                matched[r] >= int(settle_frames)
+                for r in ROLES
+            ):
+                return
+
+            time.sleep(0.002)
+
+        raise RuntimeError(
+            "Não foi possível confirmar EXP/ISO manual nas três câmeras. "
+            f"matched={matched}"
+        )
+
+
+# ============================================================
+# Seleção de controles
+# ============================================================
+
+def manual_override_from_args(args, role: str):
+    exp = getattr(args, f"{role}_exp_us")
+    iso = getattr(args, f"{role}_iso")
+
+    if exp is None and iso is None:
+        return None
+
+    if exp is None or iso is None:
+        raise ValueError(
+            f"Informe ambos --{role}-exp-us e --{role}-iso"
+        )
+
+    return LockedControl(
+        role=role,
+        exposure_time_us=int(exp),
+        sensitivity_iso=int(iso),
+        source="cli_manual",
+    )
+
+
+def sample_ae_controls(
+    runtime: FocusRuntime,
+    args,
+):
+    locked = {}
+
+    for role in ROLES:
+        override = manual_override_from_args(
+            args,
+            role,
+        )
+
+        if override is not None:
+            locked[role] = override
+            continue
+
+        exp_values = []
+        iso_values = []
+
+        t0 = time.time()
+
+        while time.time() - t0 < 0.8:
+            fresh = runtime.poll()
+
+            if role not in fresh:
+                time.sleep(0.003)
+                continue
+
+            c = runtime.controls[role]
+
+            exp = safe_int(
+                c.get("exposure_time_us"),
+                None,
+            )
+
+            iso = safe_int(
+                c.get("sensitivity_iso"),
+                None,
+            )
+
+            if exp is not None and exp > 0:
+                exp_values.append(exp)
+
+            if iso is not None and iso > 0:
+                iso_values.append(iso)
+
+        if not exp_values or not iso_values:
+            raise RuntimeError(
+                f"Sem metadados EXP/ISO suficientes para {role.upper()}"
+            )
+
+        locked[role] = LockedControl(
+            role=role,
+            exposure_time_us=int(
+                round(np.median(exp_values))
+            ),
+            sensitivity_iso=int(
+                round(np.median(iso_values))
+            ),
+            source="ae_snapshot",
+        )
+
+    return locked
+
+
+# ============================================================
+# Desenho
+# ============================================================
+
+def draw_roi(
+    panel,
+    roi_src,
+    src_shape_hw,
+    color=(0, 255, 255),
+    label="FOCUS ROI",
+    thickness=2,
+):
+    if roi_src is None:
+        return
+
+    ph, pw = panel.shape[:2]
+    sh, sw = src_shape_hw
+
+    x0, y0, x1, y1 = roi_src
+
+    px0 = int(x0 * pw / max(sw, 1))
+    px1 = int(x1 * pw / max(sw, 1))
+    py0 = int(y0 * ph / max(sh, 1))
+    py1 = int(y1 * ph / max(sh, 1))
+
+    cv2.rectangle(
+        panel,
+        (px0, py0),
+        (px1, py1),
+        color,
+        thickness,
+    )
+
+    cv2.putText(
+        panel,
+        label,
+        (px0 + 4, max(18, py0 - 5)),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.40,
+        color,
+        1,
+        cv2.LINE_AA,
+    )
+
+
+def draw_field_zones(panel, src_shape_hw, field_qa=None):
+    color_good = (0, 220, 0)
+    color_warn = (0, 200, 255)
+    color_bad = (0, 0, 255)
+
+    for name, nrect in FIELD_ZONES_NORM.items():
+        rect = rect_from_norm(
+            src_shape_hw,
+            *nrect,
+        )
+
+        color = (150, 150, 150)
+        label = name.upper()
+
+        if field_qa:
+            item = field_qa.get("zones", {}).get(name, {})
+            ratio = item.get("ratio_to_center")
+
+            if name == "center":
+                color = color_good
+                label += " 1.00"
+
+            elif ratio is not None:
+                if ratio < DEFAULT_QA["field_corner_ratio_bad"]:
+                    color = color_bad
+
+                elif ratio < DEFAULT_QA["field_corner_ratio_warning"]:
+                    color = color_warn
+
+                else:
+                    color = color_good
+
+                label += f" {ratio:.2f}"
+
+        draw_roi(
+            panel,
+            rect,
+            src_shape_hw,
+            color=color,
+            label=label,
+            thickness=1,
+        )
+
+
+def draw_history_graph(
+    panel,
+    history,
+    x,
+    y,
+    w,
+    h,
+    best_score,
+):
+    cv2.rectangle(
+        panel,
+        (x, y),
+        (x + w, y + h),
+        (35, 35, 35),
+        -1,
+    )
+
+    cv2.rectangle(
+        panel,
+        (x, y),
+        (x + w, y + h),
+        (110, 110, 110),
+        1,
+    )
+
+    if len(history) < 2:
+        return
+
+    vals = np.array(
+        [
+            float(item["smooth"])
+            for item in history
+        ],
+        dtype=np.float32,
+    )
+
+    vals = vals[-max(2, w):]
+
+    max_val = max(
+        float(np.max(vals)),
+        float(best_score),
+        1e-6,
+    )
+
+    min_val = min(
+        float(np.min(vals)),
+        max_val * 0.88,
+    )
+
+    span = max(
+        max_val - min_val,
+        1e-6,
+    )
+
+    pts = []
+
+    for i, v in enumerate(vals):
+        px = x + int(
+            i / max(1, len(vals) - 1)
+            * (w - 1)
+        )
+
+        py = y + h - 1 - int(
+            (float(v) - min_val)
+            / span
+            * (h - 1)
+        )
+
+        pts.append((px, py))
+
+    for a, b in zip(pts[:-1], pts[1:]):
+        cv2.line(
+            panel,
+            a,
+            b,
+            (255, 255, 255),
+            2,
+            cv2.LINE_AA,
+        )
+
+
+def build_board(
+    runtime: FocusRuntime,
+    specs,
+    selected_role,
+    roi_rects,
+    state,
+    field_qa_by_role,
+    args,
+    qa,
+    status_message="",
+):
+    ph = int(args.panel_height)
+    pw = int(args.panel_width)
+
+    panels = {}
+
+    for role in ROLES:
+        img = runtime.frames[role]
+        spec = specs[role]
+
+        if img is None:
+            panels[role] = build_empty_panel(
+                (ph, pw),
+                role.upper(),
+            )
+            continue
+
+        view = img.copy()
+
+        if role == "re":
+            gray = cv2.cvtColor(
+                view,
+                cv2.COLOR_BGR2GRAY,
+            )
+            z = np.zeros_like(gray)
+            view = np.dstack([z, z, gray])
+
+        elif role == "nir":
+            gray = cv2.cvtColor(
+                view,
+                cv2.COLOR_BGR2GRAY,
+            )
+            z = np.zeros_like(gray)
+            view = np.dstack([gray, gray, z])
+
+        panel = resize_panel(
+            view,
+            (ph, pw),
+        )
+
+        active = role == selected_role
+
+        draw_roi(
+            panel,
+            roi_rects[role],
+            img.shape[:2],
+            color=(0, 255, 255) if active else (0, 170, 255),
+            label="FOCUS ROI",
+            thickness=2 if active else 1,
+        )
+
+        draw_field_zones(
+            panel,
+            img.shape[:2],
+            field_qa_by_role.get(role),
+        )
+
+        c = runtime.controls[role]
+
+        overlay_hud(
+            panel,
+            [
+                f"{role.upper()} | {spec.socket_name} | {spec.sensor_name}",
+                f"{img.shape[1]}x{img.shape[0]} | {'ATIVA' if active else ''}",
+                f"EXP={c.get('exposure_time_us')}us ISO={c.get('sensitivity_iso')}",
+                f"FPS={runtime.fps[role]:.1f}",
+            ],
+            x=10,
+            y=20,
+            font_scale=0.40,
+            line_step=17,
+        )
+
+        panels[role] = panel
+
+    data = np.zeros((ph, pw, 3), dtype=np.uint8)
+
+    role_state = state[selected_role]
+    sweep = role_state["sweep"]
+    history = role_state["history"]
+
+    best = float(sweep.best_score)
+    current = float(role_state.get("smooth", 0.0))
+    ratio = 0.0 if best <= 0 else current / best
+
+    stable = role_state.get("stable", {})
+    field = field_qa_by_role.get(selected_role) or {}
+
+    lines = [
+        "FOCUS CALIBRATION - PRODUCTION",
+        f"ativa={selected_role.upper()} | metodo={args.method}",
+        f"score={role_state.get('score', 0.0):.3f}",
+        f"smooth={current:.3f}",
+        f"best={best:.3f} | atual/best={ratio*100:.2f}%",
+        f"frames NOVOS medidos={sweep.frames_scored}",
+        "",
+        f"peak_bracket={'SIM' if sweep.peak_drop_observed else 'NAO'}",
+        f"stable={'SIM' if stable.get('pass') else 'NAO'} "
+        f"CV={stable.get('cv', 0)*100:.2f}% "
+        f"range={stable.get('range_pct', 0)*100:.2f}%",
+        f"FIELD QA={field.get('status', 'aguardando').upper()}",
+        f"camera accepted={'SIM' if role_state.get('accepted') else 'NAO'}",
+        "",
+        f"trend={role_state.get('trend', {}).get('status', 'coletando')}",
+        f"acao={role_state.get('trend', {}).get('instruction', 'gire devagar')}",
+        f"sentido={role_state.get('direction_name')}",
+        "",
+        "A tenta ACCEPT | R reset sweep",
+        "1/2/3 câmera | M métrica | D sentido",
+        "mouse ROI | C centraliza | L trava",
+        "E equalize | S snapshot | Q cancela",
+    ]
+
+    overlay_hud(
+        data,
+        lines,
+        x=14,
+        y=22,
+        font_scale=0.40,
+        line_step=17,
+    )
+
+    draw_history_graph(
+        data,
+        history,
+        x=16,
+        y=max(260, ph - 130),
+        w=max(100, pw - 32),
+        h=90,
+        best_score=best,
+    )
+
+    board = np.vstack([
+        np.hstack([
+            panels["rgb"],
+            panels["re"],
+        ]),
+        np.hstack([
+            panels["nir"],
+            data,
+        ]),
+    ])
+
+    if status_message:
+        cv2.putText(
+            board,
+            status_message,
+            (18, board.shape[0] - 14),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.55,
+            (0, 255, 0),
+            2,
+            cv2.LINE_AA,
+        )
+
+    return board
+
+
+# ============================================================
+# Reset e acceptance
+# ============================================================
+
+def new_role_state(args):
+    return {
+        "history": deque(
+            maxlen=int(args.history)
+        ),
+        "sweep": SweepState(),
+        "score": 0.0,
+        "smooth": 0.0,
+        "metrics": None,
+        "trend": {},
+        "stable": {},
+        "accepted": False,
+        "accepted_at": None,
+        "acceptance": None,
+        "direction_idx": 0,
+        "direction_name": "rosqueando",
+        "last_scored_seq": None,
+    }
+
+
+def reset_role_state(
+    state,
+    role,
+    args,
+):
+    accepted = state[role].get("accepted", False)
+
+    state[role] = new_role_state(args)
+
+    # Mudar metodologia/ROI invalida o acceptance anterior dessa role.
+    if accepted:
+        state[role]["accepted"] = False
+
+
+def evaluate_stability(
+    history,
+    sweep,
+    qa,
+):
+    stats = stable_stats(
+        history,
+        qa["accept_stable_frames"],
+    )
+
+    if not stats["ready"]:
+        stats["pass"] = False
+        return stats
+
+    near_best = (
+        stats["mean"]
+        >= sweep.best_score
+        * qa["accept_near_best_ratio"]
+    )
+
+    cv_ok = (
+        stats["cv"]
+        <= qa["accept_stable_cv"]
+    )
+
+    range_ok = (
+        stats["range_pct"]
+        <= qa["accept_stable_range_pct"]
+    )
+
+    stats["near_best"] = bool(near_best)
+    stats["cv_ok"] = bool(cv_ok)
+    stats["range_ok"] = bool(range_ok)
+    stats["pass"] = bool(
+        near_best and cv_ok and range_ok
+    )
+
+    return stats
+
+
+def attempt_accept(
+    role,
+    state,
+    field_qa_by_role,
+    runtime,
+    roi_rects,
+    args,
+    qa,
+):
+    s = state[role]
+    sweep = s["sweep"]
+    stable = s.get("stable") or {}
+    field = field_qa_by_role.get(role) or {}
+
+    reasons = []
+
+    if sweep.frames_scored < max(20, qa["accept_stable_frames"]):
+        reasons.append(
+            f"poucos_frames:{sweep.frames_scored}"
+        )
+
+    if not sweep.peak_drop_observed:
+        reasons.append("peak_nao_bracketado")
+
+    if not stable.get("pass"):
+        reasons.append("score_nao_estavel_perto_do_best")
+
+    field_status = field.get("status")
+
+    if field_status == "bad":
+        reasons.append("field_qa_bad")
+
+    elif (
+        field_status == "warning"
+        and not args.allow_warning_field
+    ):
+        reasons.append("field_qa_warning")
+
+    if field_status not in ("good", "warning"):
+        reasons.append("field_qa_ausente")
+
+    engineering_override_used = False
+
+    if reasons and args.engineering_override:
+        engineering_override_used = True
+        reasons_for_record = list(reasons)
+        reasons = []
+    else:
+        reasons_for_record = list(reasons)
+
+    accepted = len(reasons) == 0
+
+    result = {
+        "accepted": accepted,
+        "requested_at": now_str(),
+        "engineering_override_used": engineering_override_used,
+        "blocked_reasons": reasons_for_record,
+        "method": args.method,
+        "equalize": bool(args.equalize_runtime),
+        "best_score": float(sweep.best_score),
+        "accepted_score": float(s.get("smooth", 0.0)),
+        "accepted_ratio_to_best": (
+            0.0
+            if sweep.best_score <= 0
+            else float(
+                s.get("smooth", 0.0)
+                / sweep.best_score
+            )
+        ),
+        "frames_scored": int(sweep.frames_scored),
+        "peak_drop_observed": bool(
+            sweep.peak_drop_observed
+        ),
+        "lowest_ratio_after_best": float(
+            sweep.lowest_ratio_after_best
+        ),
+        "stability": dict(stable),
+        "field_qa": field,
+        "roi": (
+            list(map(int, roi_rects[role]))
+            if roi_rects[role] is not None
+            else None
+        ),
+        "frame_controls": dict(
+            runtime.controls[role]
+        ),
+        "sequence_num": safe_int(
+            runtime.last_seq[role],
+            None,
+        ),
+    }
+
+    if accepted:
+        s["accepted"] = True
+        s["accepted_at"] = now_str()
+        s["acceptance"] = result
+
+    return result
+
+
+# ============================================================
+# Snapshots
+# ============================================================
+
+def save_role_snapshot(
+    role,
+    runtime,
+    board,
+    out_dir,
+    label,
+):
+    ensure_dir(out_dir)
+
+    stamp = file_stamp()
+
+    frame_path = Path(out_dir) / (
+        f"{role}_{label}_full_{stamp}.png"
+    )
+
+    board_path = Path(out_dir) / (
+        f"{role}_{label}_board_{stamp}.png"
+    )
+
+    img = runtime.frames.get(role)
+
+    if img is not None:
+        cv2.imwrite(
+            str(frame_path),
+            img,
+        )
+
+    if board is not None:
+        cv2.imwrite(
+            str(board_path),
+            board,
+        )
+
+    return {
+        "frame_png": (
+            str(frame_path)
+            if frame_path.exists()
+            else None
+        ),
+        "board_png": (
+            str(board_path)
+            if board_path.exists()
+            else None
+        ),
+    }
+
+
+# ============================================================
+# Preflight UI
+# ============================================================
+
+def run_preflight(
+    runtime: FocusRuntime,
+    specs,
+    args,
+    qa,
+):
+    runtime.wait_all()
+
+    print("")
+    print("[PRE-FLIGHT] Use alvo de foco plano, detalhado e preenchendo o FOV.")
+    print("[PRE-FLIGHT] ENTER congela exposição/ISO quando aprovado.")
+
+    last_report = None
+
+    while True:
+        runtime.poll()
+
+        last_report = merge_preflight_by_role(
+            runtime.frames,
+            qa,
+        )
+
+        ph = int(args.panel_height)
+        pw = int(args.panel_width)
+
+        panels = []
+
+        for role in ROLES:
+            img = runtime.frames[role]
+
+            if img is None:
+                panel = build_empty_panel(
+                    (ph, pw),
+                    role.upper(),
+                )
+            else:
+                panel = resize_panel(
+                    img,
+                    (ph, pw),
+                )
+
+                r = last_report["roles"][role]
+
+                overlay_hud(
+                    panel,
+                    [
+                        f"{role.upper()} | {specs[role].sensor_name}",
+                        f"PREFLIGHT={r['status'].upper()}",
+                        f"std={r.get('std', 0):.3f}",
+                        f"sat={r.get('sat_pct', 0):.2f}% dark={r.get('dark_pct', 0):.2f}%",
+                        f"EXP={runtime.controls[role].get('exposure_time_us')}us "
+                        f"ISO={runtime.controls[role].get('sensitivity_iso')}",
+                    ],
+                    x=10,
+                    y=22,
+                    font_scale=0.42,
+                    line_step=18,
+                )
+
+            panels.append(panel)
+
+        data = np.zeros(
+            (ph, pw, 3),
+            dtype=np.uint8,
+        )
+
+        lines = [
+            "FOCUS QC - PRE-FLIGHT",
+            "",
+            f"STATUS GLOBAL: {last_report['status'].upper()}",
+            "",
+            "Use chart DETALHADO em todo o campo.",
+            "Módulo perpendicular ao alvo.",
+            "Evite reflexos e saturação.",
+            "",
+            "ENTER = congelar EXP/ISO",
+            "Q/ESC = cancelar",
+        ]
+
+        for reason in last_report["reasons"][:8]:
+            lines.append(f"! {reason}")
+
+        overlay_hud(
+            data,
+            lines,
+            x=14,
+            y=30,
+            font_scale=0.45,
+            line_step=20,
+        )
+
+        board = np.vstack([
+            np.hstack([
+                panels[0],
+                panels[1],
+            ]),
+            np.hstack([
+                panels[2],
+                data,
+            ]),
+        ])
+
+        cv2.imshow(
+            "Focus Calibration - Production",
+            board,
+        )
+
+        k = cv2.waitKey(1) & 0xFF
+
+        if k in (ord("q"), ord("Q"), 27):
+            raise KeyboardInterrupt(
+                "Cancelado no preflight."
+            )
+
+        if k in (13, 10):
+            status = last_report["status"]
+
+            allowed = (
+                status == "good"
+                or (
+                    status == "warning"
+                    and args.allow_warning_preflight
+                )
+                or args.engineering_override
+            )
+
+            if not allowed:
+                print(
+                    f"[BLOCK] PREFLIGHT={status.upper()}. "
+                    "Corrija alvo/luz antes de continuar."
+                )
+                continue
+
+            locked = sample_ae_controls(
+                runtime,
+                args,
+            )
+
+            runtime.send_manual(locked)
+
+            runtime.verify_manual(
+                locked,
+                settle_frames=args.lock_settle_frames,
+            )
+
+            return last_report, locked
+
+        time.sleep(0.002)
+
+
+# ============================================================
+# Report
+# ============================================================
+
+def state_to_report(state):
+    out = {}
+
+    for role in ROLES:
+        s = state[role]
+        sweep = s["sweep"]
+
+        out[role] = {
+            "accepted": bool(s.get("accepted")),
+            "accepted_at": s.get("accepted_at"),
+            "acceptance": s.get("acceptance"),
+            "sweep": asdict(sweep),
+        }
+
+    return out
+
+
+def build_base_report(
+    args,
+    camera_rows,
+    specs,
+    actual_mx,
+    usb_speed,
+    topology_report,
+    isp_settings,
+    qa,
+    candidate_dir,
+):
+    return {
+        "schema": SCHEMA,
+        "created_at": now_str(),
+        "status": "running",
+        "runtime_effect": "none",
+        "module_params_fragment": None,
+        "module_params_merge_required": False,
+        "purpose": (
+            "physical_optical_focus_qc_only"
+        ),
+        "depthai_version": getattr(
+            dai,
+            "__version__",
+            "unknown",
+        ),
+        "device_mx_id": actual_mx,
+        "usb_speed": usb_speed,
+        "topology": topology_report,
+        "camera_inventory": [
+            {
+                k: v
+                for k, v in row.items()
+                if k != "socket_obj"
+            }
+            for row in camera_rows
+        ],
+        "resolved_setup": {
+            role: asdict(spec)
+            for role, spec in specs.items()
+        },
+        "isp_measurement_settings": isp_settings,
+        "measurement": {
+            "method_initial": args.method,
+            "fps_requested": args.fps,
+            "panel_size": [
+                args.panel_width,
+                args.panel_height,
+            ],
+            "smooth_window": args.smooth_window,
+            "history": args.history,
+            "equalize_initial": bool(
+                args.equalize
+            ),
+            "target_requirement": (
+                "flat_high_detail_target_across_entire_fov"
+            ),
+        },
+        "qa_thresholds": qa,
+        "policy": {
+            "allow_warning_preflight": bool(
+                args.allow_warning_preflight
+            ),
+            "allow_warning_field": bool(
+                args.allow_warning_field
+            ),
+            "engineering_override": bool(
+                args.engineering_override
+            ),
+            "allow_isp_defaults": bool(
+                args.allow_isp_defaults
+            ),
+        },
+        "traceability": {
+            "module_id": args.module_id or None,
+            "operator": args.operator or None,
+            "target_distance_mm": args.target_distance_mm,
+            "lens_by_role": {
+                "rgb": args.lens_rgb or None,
+                "re": args.lens_re or None,
+                "nir": args.lens_nir or None,
+            },
+        },
+        "notes": args.notes or "",
+        "candidate_dir": str(candidate_dir),
+        "preflight": None,
+        "locked_controls": None,
+        "results_by_role": {},
+        "snapshots": [],
+        "promoted": False,
+    }
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Ferramenta FINAL de produção para ajuste/QC de foco "
+            "OV9782 ou AR0234 + 2x OV9282."
+        ),
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    # Hardware
+    parser.add_argument(
+        "--mx-id",
+        default=None,
+    )
+
+    parser.add_argument(
+        "--fps",
+        type=float,
+        default=20.0,
+    )
+
+    parser.add_argument(
+        "--relaxed-topology",
+        action="store_true",
+        help=(
+            "Override de engenharia. Produto normal exige "
+            "CAM_A=RGB, CAM_B=RE, CAM_C=NIR."
+        ),
+    )
+
+    # ISP determinístico RGB
+    parser.add_argument(
+        "--isp-sharpness",
+        type=int,
+        default=0,
+    )
+
+    parser.add_argument(
+        "--isp-luma-denoise",
+        type=int,
+        default=0,
+    )
+
+    parser.add_argument(
+        "--isp-chroma-denoise",
+        type=int,
+        default=0,
+    )
+
+    parser.add_argument(
+        "--allow-isp-defaults",
+        action="store_true",
+        help=(
+            "Permite continuar caso esta versão do DepthAI "
+            "não exponha sharpness/denoise no initialControl."
+        ),
+    )
+
+    # UI / métrica
+    parser.add_argument(
+        "--panel-width",
+        type=int,
+        default=640,
+    )
+
+    parser.add_argument(
+        "--panel-height",
+        type=int,
+        default=400,
+    )
+
+    parser.add_argument(
+        "--method",
+        default="tenengrad_norm",
+        choices=list(METHODS),
+    )
+
+    parser.add_argument(
+        "--history",
+        type=int,
+        default=320,
+    )
+
+    parser.add_argument(
+        "--smooth-window",
+        type=int,
+        default=5,
+    )
+
+    parser.add_argument(
+        "--drop-warn-pct",
+        type=float,
+        default=3.0,
+    )
+
+    parser.add_argument(
+        "--equalize",
+        action="store_true",
+    )
+
+    parser.add_argument(
+        "--lock-settle-frames",
+        type=int,
+        default=8,
+    )
+
+    # Controles manuais opcionais
+    for role in ROLES:
+        parser.add_argument(
+            f"--{role}-exp-us",
+            type=int,
+            default=None,
+        )
+
+        parser.add_argument(
+            f"--{role}-iso",
+            type=int,
+            default=None,
+        )
+
+    # Política
+    parser.add_argument(
+        "--allow-warning-preflight",
+        action="store_true",
+    )
+
+    parser.add_argument(
+        "--allow-warning-field",
+        action="store_true",
+        help=(
+            "Permite aceitar FIELD QA=WARNING. Por padrão produto exige GOOD."
+        ),
+    )
+
+    parser.add_argument(
+        "--engineering-override",
+        action="store_true",
+        help=(
+            "Permite acceptance mesmo sem todos os critérios. "
+            "Fica explicitamente registrado e não representa fluxo normal."
+        ),
+    )
+
+    # Output
+    parser.add_argument(
+        "--candidate-root",
+        default="calibration/focus_candidates",
+    )
+
+    parser.add_argument(
+        "--active-json",
+        default="calibration/focus_qc_active.json",
+    )
+
+    parser.add_argument(
+        "--module-id",
+        default="",
+        help="Identificador/NS do módulo óptico, se houver.",
+    )
+
+    parser.add_argument(
+        "--operator",
+        default="",
+        help="Operador/técnico responsável pela calibração.",
+    )
+
+    parser.add_argument(
+        "--target-distance-mm",
+        type=float,
+        default=None,
+        help="Distância lente-alvo usada no QC de foco.",
+    )
+
+    parser.add_argument(
+        "--lens-rgb",
+        default="",
+        help="PN/ID/lote da lente RGB.",
+    )
+
+    parser.add_argument(
+        "--lens-re",
+        default="",
+        help="PN/ID/lote da lente RE.",
+    )
+
+    parser.add_argument(
+        "--lens-nir",
+        default="",
+        help="PN/ID/lote da lente NIR.",
+    )
+
+    parser.add_argument(
+        "--notes",
+        default="",
+    )
+
+    args = parser.parse_args()
+
+    # Campo mutável apenas em runtime para E/M.
+    args.equalize_runtime = bool(args.equalize)
+
+    qa = dict(DEFAULT_QA)
+
+    sid = file_stamp()
+    candidate_dir = (
+        Path(args.candidate_root)
+        / sid
+    )
+    snapshot_dir = (
+        candidate_dir
+        / "snapshots"
+    )
+    candidate_report = (
+        candidate_dir
+        / "report.json"
+    )
+
+    ensure_dir(snapshot_dir)
+
+    camera_rows = []
+    specs = {}
+    actual_mx = None
+    usb_speed = None
+    report = None
+    device_ctx = None
+    state = None
+
+    cv2.namedWindow(
+        "Focus Calibration - Production",
+        cv2.WINDOW_NORMAL,
+    )
+
+    try:
+        # ----------------------------------------------------
+        # Descoberta
+        # ----------------------------------------------------
+
+        (
+            camera_rows,
+            actual_mx,
+            usb_speed,
+        ) = discover_cameras(
+            args.mx_id
+        )
+
+        topology_report = validate_product_topology(
+            camera_rows,
+            relaxed=args.relaxed_topology,
+        )
+
+        specs = build_specs(
+            camera_rows
+        )
+
+        print("=" * 82)
+        print("FOCUS CALIBRATION - PRODUCTION")
+        print(f"DepthAI : {getattr(dai, '__version__', 'unknown')}")
+        print(f"MX ID   : {actual_mx}")
+        print(f"USB     : {usb_speed}")
+        print("-" * 82)
+
+        for role in ROLES:
+            spec = specs[role]
+
+            print(
+                f"{spec.socket_name} -> "
+                f"{role.upper():3s} | "
+                f"{spec.sensor_name:8s} | "
+                f"{spec.configured_width}x{spec.configured_height} | "
+                f"{spec.resolution_name} | "
+                f"{spec.source}"
+            )
+
+        print("=" * 82)
+
+        # ----------------------------------------------------
+        # Pipeline
+        # ----------------------------------------------------
+
+        pipeline, isp_settings = build_pipeline(
+            specs,
+            args,
+        )
+
+        device_info = (
+            dai.DeviceInfo(actual_mx)
+            if actual_mx
+            else None
+        )
+
+        device_ctx = (
+            dai.Device(
+                pipeline,
+                device_info,
+            )
+            if device_info is not None
+            else dai.Device(pipeline)
+        )
+
+        report = build_base_report(
+            args,
+            camera_rows,
+            specs,
+            actual_mx,
+            usb_speed,
+            topology_report,
+            isp_settings,
+            qa,
+            candidate_dir,
+        )
+
+        with device_ctx as device:
+            runtime = FocusRuntime(
+                device,
+                specs,
+                args,
+                qa,
+            )
+
+            # ------------------------------------------------
+            # Preflight + lock
+            # ------------------------------------------------
+
+            preflight, locked = run_preflight(
+                runtime,
+                specs,
+                args,
+                qa,
+            )
+
+            report["preflight"] = preflight
+
+            report["locked_controls"] = {
+                role: asdict(locked[role])
+                for role in ROLES
+            }
+
+            print("[LOCK] Exposição congelada:")
+            for role in ROLES:
+                c = locked[role]
+                print(
+                    f"  {role.upper()}: "
+                    f"{c.exposure_time_us}us "
+                    f"ISO={c.sensitivity_iso}"
+                )
+
+            # ------------------------------------------------
+            # Estado de foco
+            # ------------------------------------------------
+
+            state = {
+                role: new_role_state(args)
+                for role in ROLES
+            }
+
+            roi_rects = {
+                role: None
+                for role in ROLES
+            }
+
+            roi_locked = False
+            selected_role = "rgb"
+
+            field_qa_by_role = {
+                role: None
+                for role in ROLES
+            }
+
+            field_history_by_role = {
+                role: deque(
+                    maxlen=max(
+                        16,
+                        int(qa["field_stable_frames"]) * 2,
+                    )
+                )
+                for role in ROLES
+            }
+
+            last_board = None
+            last_message = ""
+            last_message_t = 0.0
+
+            panel_rects = {
+                "rgb": None,
+                "re": None,
+                "nir": None,
+                "data": None,
+            }
+
+            # ------------------------------------------------
+            # Mouse ROI
+            # ------------------------------------------------
+
+            dragging = False
+            drag_start = None
+
+            def message(text):
+                nonlocal last_message, last_message_t
+                last_message = str(text)
+                last_message_t = time.time()
+
+            def inside(rect, x, y):
+                if rect is None:
+                    return False
+
+                x0, y0, x1, y1 = rect
+                return (
+                    x0 <= x < x1
+                    and y0 <= y < y1
+                )
+
+            def display_to_source(
+                role,
+                x,
+                y,
+            ):
+                rect = panel_rects.get(role)
+                img = runtime.frames.get(role)
+
+                if rect is None or img is None:
+                    return None
+
+                x0, y0, x1, y1 = rect
+                pw = max(1, x1 - x0)
+                ph = max(1, y1 - y0)
+
+                sh, sw = img.shape[:2]
+
+                lx = max(
+                    0,
+                    min(
+                        pw - 1,
+                        int(x - x0),
+                    ),
+                )
+
+                ly = max(
+                    0,
+                    min(
+                        ph - 1,
+                        int(y - y0),
+                    ),
+                )
+
+                sx = int(lx * sw / pw)
+                sy = int(ly * sh / ph)
+
+                return (
+                    max(0, min(sw - 1, sx)),
+                    max(0, min(sh - 1, sy)),
+                )
+
+            def on_mouse(
+                event,
+                x,
+                y,
+                flags,
+                param,
+            ):
+                nonlocal dragging, drag_start
+
+                if roi_locked:
+                    return
+
+                rect = panel_rects.get(
+                    selected_role
+                )
+
+                if (
+                    rect is None
+                    or not inside(rect, x, y)
+                ):
+                    return
+
+                pt = display_to_source(
+                    selected_role,
+                    x,
+                    y,
+                )
+
+                if pt is None:
+                    return
+
+                if event == cv2.EVENT_LBUTTONDOWN:
+                    dragging = True
+                    drag_start = pt
+
+                elif (
+                    event == cv2.EVENT_MOUSEMOVE
+                    and dragging
+                    and drag_start is not None
+                ):
+                    x0, y0 = drag_start
+                    x1, y1 = pt
+
+                    roi_rects[selected_role] = (
+                        x0,
+                        y0,
+                        x1,
+                        y1,
+                    )
+
+                elif (
+                    event == cv2.EVENT_LBUTTONUP
+                    and dragging
+                    and drag_start is not None
+                ):
+                    x0, y0 = drag_start
+                    x1, y1 = pt
+
+                    img = runtime.frames[
+                        selected_role
+                    ]
+
+                    r = sanitize_roi(
+                        (
+                            x0,
+                            y0,
+                            x1,
+                            y1,
+                        ),
+                        img.shape[:2],
+                    )
+
+                    dragging = False
+                    drag_start = None
+
+                    if r is not None:
+                        roi_rects[selected_role] = r
+
+                        reset_role_state(
+                            state,
+                            selected_role,
+                            args,
+                        )
+                        field_history_by_role[selected_role].clear()
+                        field_qa_by_role[selected_role] = None
+
+                        message(
+                            f"ROI alterada: "
+                            f"{selected_role.upper()} | sweep resetado"
+                        )
+
+            cv2.setMouseCallback(
+                "Focus Calibration - Production",
+                on_mouse,
+            )
+
+            # ------------------------------------------------
+            # Loop principal
+            # ------------------------------------------------
+
+            print("")
+            print("[FOCUS] Ajuste RGB, depois RE e NIR.")
+            print("[FOCUS] atravesse o pico, volte ao melhor e pressione A.")
+
+            while True:
+                fresh_roles = runtime.poll()
+
+                # Inicializa ROIs quando os frames existem.
+                for role in ROLES:
+                    img = runtime.frames[role]
+
+                    if (
+                        img is not None
+                        and roi_rects[role] is None
+                    ):
+                        roi_rects[role] = (
+                            default_roi_for_shape(
+                                img.shape[:2],
+                                frac=0.36,
+                            )
+                        )
+
+                # ------------------------------------------------
+                # SCORE SOMENTE EM FRAME NOVO
+                # ------------------------------------------------
+
+                if (
+                    selected_role in fresh_roles
+                    and runtime.frames[selected_role] is not None
+                    and roi_rects[selected_role] is not None
+                ):
+                    s = state[selected_role]
+
+                    seq = runtime.last_seq[
+                        selected_role
+                    ]
+
+                    # Defesa extra contra duplicate score.
+                    if (
+                        seq is None
+                        or seq != s["last_scored_seq"]
+                    ):
+                        img = runtime.frames[
+                            selected_role
+                        ]
+
+                        metrics = compute_focus_metrics(
+                            img,
+                            roi_rects[selected_role],
+                            equalize=args.equalize_runtime,
+                        )
+
+                        score = metric_value(
+                            metrics,
+                            args.method,
+                        )
+
+                        hist = s["history"]
+
+                        hist.append({
+                            "timestamp": now_str(),
+                            "sequence_num": safe_int(
+                                seq,
+                                None,
+                            ),
+                            "score": float(score),
+                            "smooth": float(score),
+                        })
+
+                        smooth = smoothed_score(
+                            hist,
+                            args.smooth_window,
+                        )
+
+                        hist[-1]["smooth"] = float(
+                            smooth
+                        )
+
+                        update_sweep(
+                            s["sweep"],
+                            score=float(score),
+                            smooth=float(smooth),
+                            metrics=metrics,
+                            roi=roi_rects[selected_role],
+                            sequence_num=seq,
+                            drop_required_pct=qa[
+                                "peak_drop_required_pct"
+                            ],
+                            new_best_rebracket_pct=qa[
+                                "new_best_rebracket_pct"
+                            ],
+                        )
+
+                        s["score"] = float(score)
+                        s["smooth"] = float(smooth)
+                        s["metrics"] = metrics
+
+                        s["trend"] = analyze_trend(
+                            hist,
+                            s["sweep"],
+                            s["direction_name"],
+                            drop_warn_pct=args.drop_warn_pct,
+                        )
+
+                        s["stable"] = evaluate_stability(
+                            hist,
+                            s["sweep"],
+                            qa,
+                        )
+
+                        # FIELD QA entra no histórico apenas em frame novo.
+                        field_history_by_role[
+                            selected_role
+                        ].append(
+                            compute_field_qa(
+                                img,
+                                qa,
+                                equalize=args.equalize_runtime,
+                            )
+                        )
+
+                        field_qa_by_role[
+                            selected_role
+                        ] = aggregate_field_qa(
+                            field_history_by_role[
+                                selected_role
+                            ],
+                            qa,
+                        )
+
+                        s["last_scored_seq"] = seq
+
+                # Atualiza FIELD QA das outras câmeras somente com frames novos,
+                # sem alimentar o histórico do score principal.
+                for role in (
+                    fresh_roles
+                    - {selected_role}
+                ):
+                    img = runtime.frames[role]
+
+                    if img is not None:
+                        field_history_by_role[
+                            role
+                        ].append(
+                            compute_field_qa(
+                                img,
+                                qa,
+                                equalize=args.equalize_runtime,
+                            )
+                        )
+
+                        field_qa_by_role[
+                            role
+                        ] = aggregate_field_qa(
+                            field_history_by_role[
+                                role
+                            ],
+                            qa,
+                        )
+
+                # ------------------------------------------------
+                # FPS watchdog
+                # ------------------------------------------------
+
+                fps_warn = []
+
+                for role in ROLES:
+                    fps = runtime.fps[role]
+
+                    if (
+                        fps > 0
+                        and fps
+                        < args.fps
+                        * qa["fps_min_ratio"]
+                    ):
+                        fps_warn.append(
+                            f"{role.upper()} FPS baixo={fps:.1f}"
+                        )
+
+                if fps_warn:
+                    message(
+                        " | ".join(fps_warn)
+                    )
+
+                # ------------------------------------------------
+                # Board
+                # ------------------------------------------------
+
+                ph = int(args.panel_height)
+                pw = int(args.panel_width)
+
+                panel_rects["rgb"] = (
+                    0,
+                    0,
+                    pw,
+                    ph,
+                )
+
+                panel_rects["re"] = (
+                    pw,
+                    0,
+                    pw * 2,
+                    ph,
+                )
+
+                panel_rects["nir"] = (
+                    0,
+                    ph,
+                    pw,
+                    ph * 2,
+                )
+
+                panel_rects["data"] = (
+                    pw,
+                    ph,
+                    pw * 2,
+                    ph * 2,
+                )
+
+                msg = (
+                    last_message
+                    if (
+                        last_message
+                        and time.time()
+                        - last_message_t
+                        < 3.0
+                    )
+                    else ""
+                )
+
+                last_board = build_board(
+                    runtime,
+                    specs,
+                    selected_role,
+                    roi_rects,
+                    state,
+                    field_qa_by_role,
+                    args,
+                    qa,
+                    status_message=msg,
+                )
+
+                cv2.imshow(
+                    "Focus Calibration - Production",
+                    last_board,
+                )
+
+                # ------------------------------------------------
+                # Teclas
+                # ------------------------------------------------
+
+                k = cv2.waitKey(1) & 0xFF
+
+                if k in (
+                    ord("q"),
+                    ord("Q"),
+                    27,
+                ):
+                    raise KeyboardInterrupt(
+                        "Sessão de foco cancelada."
+                    )
+
+                elif k == ord("1"):
+                    selected_role = "rgb"
+                    message("Selecionada RGB")
+
+                elif k == ord("2"):
+                    selected_role = "re"
+                    message("Selecionada RE")
+
+                elif k == ord("3"):
+                    selected_role = "nir"
+                    message("Selecionada NIR")
+
+                elif k in (
+                    ord("m"),
+                    ord("M"),
+                ):
+                    idx = METHODS.index(
+                        args.method
+                    )
+
+                    args.method = METHODS[
+                        (idx + 1)
+                        % len(METHODS)
+                    ]
+
+                    # Score mudou de domínio: reset de todos os sweeps.
+                    for role in ROLES:
+                        reset_role_state(
+                            state,
+                            role,
+                            args,
+                        )
+                        field_history_by_role[role].clear()
+                        field_qa_by_role[role] = None
+
+                    message(
+                        f"Métrica -> {args.method} | sweeps resetados"
+                    )
+
+                elif k in (
+                    ord("d"),
+                    ord("D"),
+                ):
+                    s = state[selected_role]
+
+                    s["direction_idx"] = (
+                        1 - s["direction_idx"]
+                    )
+
+                    s["direction_name"] = (
+                        "rosqueando"
+                        if s["direction_idx"] == 0
+                        else "desrosqueando"
+                    )
+
+                    message(
+                        f"Sentido -> {s['direction_name']}"
+                    )
+
+                elif k in (
+                    ord("e"),
+                    ord("E"),
+                ):
+                    args.equalize_runtime = (
+                        not args.equalize_runtime
+                    )
+
+                    for role in ROLES:
+                        reset_role_state(
+                            state,
+                            role,
+                            args,
+                        )
+                        field_history_by_role[role].clear()
+                        field_qa_by_role[role] = None
+
+                    message(
+                        f"Equalize -> "
+                        f"{'ON' if args.equalize_runtime else 'OFF'} | "
+                        "sweeps resetados"
+                    )
+
+                elif k in (
+                    ord("l"),
+                    ord("L"),
+                ):
+                    roi_locked = not roi_locked
+
+                    message(
+                        f"ROI -> "
+                        f"{'travada' if roi_locked else 'editavel'}"
+                    )
+
+                elif k in (
+                    ord("c"),
+                    ord("C"),
+                ):
+                    img = runtime.frames[
+                        selected_role
+                    ]
+
+                    if img is not None:
+                        roi_rects[selected_role] = (
+                            default_roi_for_shape(
+                                img.shape[:2],
+                                frac=0.36,
+                            )
+                        )
+
+                        reset_role_state(
+                            state,
+                            selected_role,
+                            args,
+                        )
+                        field_history_by_role[selected_role].clear()
+                        field_qa_by_role[selected_role] = None
+
+                        message(
+                            f"ROI centralizada {selected_role.upper()} | "
+                            "sweep resetado"
+                        )
+
+                elif k in (
+                    ord("r"),
+                    ord("R"),
+                ):
+                    reset_role_state(
+                        state,
+                        selected_role,
+                        args,
+                    )
+                    field_history_by_role[selected_role].clear()
+                    field_qa_by_role[selected_role] = None
+
+                    message(
+                        f"Sweep resetado: {selected_role.upper()}"
+                    )
+
+                elif k in (
+                    ord("s"),
+                    ord("S"),
+                ):
+                    paths = save_role_snapshot(
+                        selected_role,
+                        runtime,
+                        last_board,
+                        snapshot_dir,
+                        "diagnostic",
+                    )
+
+                    report["snapshots"].append({
+                        "timestamp": now_str(),
+                        "role": selected_role,
+                        "type": "diagnostic",
+                        "paths": paths,
+                    })
+
+                    message(
+                        f"Snapshot salvo: {selected_role.upper()}"
+                    )
+
+                elif k in (
+                    ord("a"),
+                    ord("A"),
+                ):
+                    acceptance = attempt_accept(
+                        selected_role,
+                        state,
+                        field_qa_by_role,
+                        runtime,
+                        roi_rects,
+                        args,
+                        qa,
+                    )
+
+                    if acceptance["accepted"]:
+                        paths = save_role_snapshot(
+                            selected_role,
+                            runtime,
+                            last_board,
+                            snapshot_dir,
+                            "accepted",
+                        )
+
+                        acceptance[
+                            "snapshot_paths"
+                        ] = paths
+
+                        state[selected_role][
+                            "acceptance"
+                        ] = acceptance
+
+                        message(
+                            f"{selected_role.upper()} ACCEPTED ✅"
+                        )
+
+                        print(
+                            f"[ACCEPT] {selected_role.upper()} "
+                            f"ratio={acceptance['accepted_ratio_to_best']*100:.2f}% "
+                            f"field={acceptance['field_qa']['status']}"
+                        )
+
+                        # Avança automaticamente para próxima não aceita.
+                        pending = [
+                            r for r in ROLES
+                            if not state[r]["accepted"]
+                        ]
+
+                        if pending:
+                            selected_role = pending[0]
+
+                        else:
+                            break
+
+                    else:
+                        message(
+                            "ACCEPT bloqueado: "
+                            + ", ".join(
+                                acceptance[
+                                    "blocked_reasons"
+                                ]
+                            )
+                        )
+
+                time.sleep(0.001)
+
+            # ------------------------------------------------
+            # Final PASS
+            # ------------------------------------------------
+
+            all_accepted = all(
+                state[r]["accepted"]
+                for r in ROLES
+            )
+
+            if not all_accepted:
+                raise RuntimeError(
+                    "Loop finalizou sem todas as câmeras aceitas."
+                )
+
+            report["results_by_role"] = (
+                state_to_report(state)
+            )
+
+            report["finished_at"] = now_str()
+            report["status"] = "pass"
+
+            # Se engineering override foi usado em qualquer role,
+            # continua PASS operacional, mas fica marcado como não-homologação normal.
+            override_roles = [
+                role
+                for role in ROLES
+                if (
+                    state[role]
+                    .get("acceptance", {})
+                    .get(
+                        "engineering_override_used",
+                        False,
+                    )
+                )
+            ]
+
+            report["engineering_override_roles"] = (
+                override_roles
+            )
+
+            report["normal_production_homologation"] = (
+                len(override_roles) == 0
+            )
+
+            # Override de engenharia pode concluir a sessão para diagnóstico,
+            # mas NUNCA substitui o último QC homologado.
+            report["promoted"] = bool(
+                report["normal_production_homologation"]
+            )
+
+            if report["promoted"]:
+                report["promoted_at"] = now_str()
+
+            save_json_atomic(
+                candidate_report,
+                report,
+            )
+
+            marker_name = (
+                "PASS.txt"
+                if report["normal_production_homologation"]
+                else "PASS_ENGINEERING_OVERRIDE.txt"
+            )
+
+            (
+                candidate_dir
+                / marker_name
+            ).write_text(
+                (
+                    f"status=pass\n"
+                    f"created_at={report['created_at']}\n"
+                    f"finished_at={report['finished_at']}\n"
+                    f"normal_production_homologation="
+                    f"{report['normal_production_homologation']}\n"
+                    f"promoted={report['promoted']}\n"
+                ),
+                encoding="utf-8",
+            )
+
+            if report["promoted"]:
+                active = Path(args.active_json)
+
+                if active.exists():
+                    backup = active.with_name(
+                        active.stem
+                        + f".backup_{sid}"
+                        + active.suffix
+                    )
+
+                    shutil.copy2(
+                        active,
+                        backup,
+                    )
+
+                save_json_atomic(
+                    active,
+                    report,
+                )
+
+            print("")
+            print("=" * 82)
+
+            if report["promoted"]:
+                print("[PASS] FOCUS QC APROVADO E HOMOLOGADO")
+                print(f"[ACTIVE QC] {args.active_json}")
+            else:
+                print("[PASS OVERRIDE] Sessão concluída somente para engenharia")
+                print("[SAFE] QC ativo homologado anterior foi preservado.")
+
+            print(f"[CANDIDATE] {candidate_dir}")
+            print("[RUNTIME] Nenhum parâmetro do module_params foi alterado.")
+            print("=" * 82)
+
+            final = np.zeros(
+                (720, 1280, 3),
+                dtype=np.uint8,
+            )
+
+            lines = [
+                "FOCUS QC - PASS",
+                "",
+                f"RGB: {specs['rgb'].sensor_name} ACCEPTED",
+                f"RE : {specs['re'].sensor_name} ACCEPTED",
+                f"NIR: {specs['nir'].sensor_name} ACCEPTED",
+                "",
+                f"Homologacao normal: "
+                f"{'SIM' if report['normal_production_homologation'] else 'NAO - override usado'}",
+                "",
+                "Nenhum parametro de runtime foi modificado.",
+                (
+                    f"Relatorio ativo: {args.active_json}"
+                    if report["promoted"]
+                    else f"Candidate: {candidate_report}"
+                ),
+                "",
+                "Qualquer tecla fecha.",
+            ]
+
+            overlay_hud(
+                final,
+                lines,
+                x=46,
+                y=70,
+                font_scale=0.72,
+                line_step=34,
+            )
+
+            cv2.imshow(
+                "Focus Calibration - Production",
+                final,
+            )
+
+            cv2.waitKey(0)
+
+    except KeyboardInterrupt as exc:
+        if report is None:
+            report = {
+                "schema": SCHEMA,
+                "created_at": now_str(),
+                "runtime_effect": "none",
+                "module_params_fragment": None,
+            }
+
+        if state is not None:
+            report["results_by_role"] = state_to_report(state)
+
+        report["status"] = "cancelled"
+        report["finished_at"] = now_str()
+        report["error"] = str(exc)
+        report["promoted"] = False
+
+        try:
+            save_json_atomic(
+                candidate_report,
+                report,
+            )
+
+            (
+                candidate_dir
+                / "CANCELLED.txt"
+            ).write_text(
+                (
+                    f"status=cancelled\n"
+                    f"reason={exc}\n"
+                ),
+                encoding="utf-8",
+            )
+        except Exception:
+            pass
+
+        print(f"[CANCELADO] {exc}")
+        print("[SAFE] QC ativo anterior preservado.")
+
+    except Exception as exc:
+        if report is None:
+            report = {
+                "schema": SCHEMA,
+                "created_at": now_str(),
+                "runtime_effect": "none",
+                "module_params_fragment": None,
+            }
+
+        if state is not None:
+            report["results_by_role"] = state_to_report(state)
+
+        report["status"] = "error"
+        report["finished_at"] = now_str()
+        report["error"] = (
+            f"{type(exc).__name__}: {exc}"
+        )
+        report["promoted"] = False
+
+        try:
+            save_json_atomic(
+                candidate_report,
+                report,
+            )
+
+            (
+                candidate_dir
+                / "FAIL.txt"
+            ).write_text(
+                (
+                    f"status=error\n"
+                    f"error={type(exc).__name__}: {exc}\n"
+                ),
+                encoding="utf-8",
+            )
+        except Exception:
+            pass
+
+        print("")
+        print("=" * 82)
+        print("[ERRO] FOCUS QC ABORTADO")
+        print(f"{type(exc).__name__}: {exc}")
+        print("[SAFE] QC ativo anterior preservado.")
+        print("=" * 82)
+
+        raise
+
+    finally:
+        cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/_2_intrinsics_calibration_tool.py b/Python/OAK/datasets/oak-fcc-3/utils/_2_intrinsics_calibration_tool.py
new file mode 100644
index 000000000..37c64d52a
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/utils/_2_intrinsics_calibration_tool.py
@@ -0,0 +1,2088 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+intrinsics_calibration_production.py
+
+Calibração intrínseca + distorção de lente, nível produto, para:
+
+  CAM_A = RGB = OV9782 1280x800 OU AR0234 1920x1200
+  CAM_B = RE  = OV9282 1280x800
+  CAM_C = NIR = OV9282 1280x800
+
+Domínio:
+  RGB = ISP full-resolution
+  RE/NIR = MONO full-resolution
+  sem resize
+  sem undistort externo
+
+Gera:
+  calibration/intrinsics_calibration_v1.json
+
+O artefato contém:
+  module_params_fragment.intrinsics_config
+
+IMPORTANTE:
+  runtime_undistort.enabled = false por padrão.
+  Se no futuro ativarmos undistort no runtime, a homografia precisa ser
+  recalibrada no mesmo espaço geométrico undistorted.
+
+Fluxo:
+  1) Mostre o ChArUco nas 3 câmeras.
+  2) ENTER trava EXP/ISO.
+  3) G captura views variadas.
+  4) Varie X/Y, distância, yaw, pitch e roll.
+  5) Ideal: 18-25 views.
+  6) A calibra e executa acceptance.
+  7) PASS promove o JSON ativo.
+
+Teclas:
+  G      captura view
+  A      calibra/avalia
+  V      limpa views
+  S      snapshot
+  1/2/3  seleciona câmera do preview
+  U      liga/desliga preview undistorted após avaliação
+  Q/ESC  cancela
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+import shutil
+import time
+from dataclasses import dataclass, asdict
+from datetime import datetime
+from pathlib import Path
+from typing import Optional
+
+import cv2
+import depthai as dai
+import numpy as np
+
+
+ROLES = ("rgb", "re", "nir")
+SCHEMA = "multispec_intrinsics_calibration_v1"
+CALIBRATION_SPACE = "native_stream_no_external_undistort"
+
+TOPOLOGY = {
+    "rgb": ("CAM_A", ("OV9782", "AR0234")),
+    "re":  ("CAM_B", ("OV9282",)),
+    "nir": ("CAM_C", ("OV9282",)),
+}
+
+RGB_MODES = {
+    "OV9782": ("THE_800_P", 1280, 800),
+    "AR0234": ("THE_1200_P", 1920, 1200),
+}
+MONO_MODES = {
+    "OV9282": ("THE_800_P", 1280, 800),
+}
+
+QA = {
+    "min_markers": 4,
+    "min_corners": 10,
+    "min_views": 14,
+    "recommended_views": 20,
+    "min_retained_views": 12,
+    "min_unique_ids": 24,
+
+    "duplicate_center_norm": 0.035,
+    "duplicate_area_norm": 0.055,
+
+    "coverage_span_x_warn": 0.72,
+    "coverage_span_x_bad": 0.60,
+    "coverage_span_y_warn": 0.66,
+    "coverage_span_y_bad": 0.54,
+    "coverage_hull_warn": 0.36,
+    "coverage_hull_bad": 0.25,
+
+    "area_range_warn": 0.12,
+    "area_range_bad": 0.07,
+    "center_std_x_warn": 0.18,
+    "center_std_x_bad": 0.11,
+    "center_std_y_warn": 0.16,
+    "center_std_y_bad": 0.10,
+
+    "rms_warn": 0.85,
+    "rms_bad": 1.40,
+    "fit_med_warn": 0.55,
+    "fit_med_bad": 0.90,
+    "fit_p95_warn": 1.20,
+    "fit_p95_bad": 2.00,
+
+    "cv_med_warn": 0.85,
+    "cv_med_bad": 1.35,
+    "cv_p95_warn": 1.80,
+    "cv_p95_bad": 3.00,
+    "cv_view_med_warn": 1.60,
+    "cv_view_med_bad": 2.60,
+
+    "reject_view_med": 1.80,
+    "reject_view_p95": 3.20,
+    "max_reject_rounds": 3,
+
+    "fx_fy_ratio_warn": 1.08,
+    "fx_fy_ratio_bad": 1.18,
+    "principal_margin_frac": 0.12,
+    "focal_min_frac_w": 0.18,
+    "focal_max_frac_w": 8.0,
+
+    "brown_coeff_warn": 1.5,
+    "brown_coeff_bad": 5.0,
+    "rational_coeff_warn": 5.0,
+    "rational_coeff_bad": 20.0,
+
+    "rational_min_cv_improvement": 0.10,
+
+    "edge_shift_warn_px": 8.0,
+    "edge_shift_high_px": 20.0,
+
+    "exp_rel_tol": 0.01,
+    "exp_abs_tol_us": 20,
+    "iso_tol": 5,
+}
+
+
+def now_str():
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+def stamp():
+    return datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+
+
+def ensure_dir(p):
+    Path(p).mkdir(parents=True, exist_ok=True)
+
+
+def save_json_atomic(path, data):
+    p = Path(path)
+    ensure_dir(p.parent)
+    tmp = p.with_suffix(p.suffix + ".tmp")
+    with tmp.open("w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+        f.flush()
+        os.fsync(f.fileno())
+    os.replace(tmp, p)
+
+
+def status_rank(s):
+    return {"good": 0, "warning": 1, "bad": 2}.get(str(s), 2)
+
+
+def merge_status(a, b):
+    return a if status_rank(a) >= status_rank(b) else b
+
+
+def safe_int(v, default=None):
+    try:
+        return int(v) if v is not None else default
+    except Exception:
+        return default
+
+
+def overlay(img, lines, x=12, y=24, scale=0.45, step=19):
+    yy = y
+    for line in lines:
+        if yy > img.shape[0] - 6:
+            break
+        s = str(line)
+        cv2.putText(img, s, (x, yy), cv2.FONT_HERSHEY_SIMPLEX,
+                    scale, (0, 0, 0), 3, cv2.LINE_AA)
+        cv2.putText(img, s, (x, yy), cv2.FONT_HERSHEY_SIMPLEX,
+                    scale, (255, 255, 255), 1, cv2.LINE_AA)
+        yy += step
+
+
+def socket_name(sock):
+    name = getattr(sock, "name", None)
+    if name:
+        return str(name)
+    text = str(sock)
+    for x in ("CAM_A", "CAM_B", "CAM_C", "CAM_D"):
+        if x in text:
+            return x
+    return text
+
+
+def get_socket(name):
+    return {
+        "CAM_A": dai.CameraBoardSocket.CAM_A,
+        "CAM_B": dai.CameraBoardSocket.CAM_B,
+        "CAM_C": dai.CameraBoardSocket.CAM_C,
+    }[name]
+
+
+def feature_types(f):
+    return [str(x).upper() for x in (getattr(f, "supportedTypes", []) or [])]
+
+
+def feature_is_color(f):
+    types = feature_types(f)
+    sensor = str(getattr(f, "sensorName", "") or "").upper()
+    if any("COLOR" in x for x in types):
+        return True
+    if any("MONO" in x for x in types):
+        return False
+    return sensor in {"OV9782", "AR0234"}
+
+
+def feature_is_mono(f):
+    types = feature_types(f)
+    if any("MONO" in x for x in types):
+        return True
+    if any("COLOR" in x for x in types):
+        return False
+    return not feature_is_color(f)
+
+
+@dataclass
+class CameraSpec:
+    role: str
+    socket: str
+    sensor: str
+    width: int
+    height: int
+    resolution_name: str
+    stream: str
+    control: str
+    is_color: bool
+    source: str
+
+
+def discover(mx_id: Optional[str]):
+    info = dai.DeviceInfo(mx_id) if mx_id else None
+    ctx = dai.Device(info) if info is not None else dai.Device()
+
+    with ctx as dev:
+        actual_mx = None
+        for method in ("getMxId", "getDeviceId"):
+            fn = getattr(dev, method, None)
+            if callable(fn):
+                try:
+                    actual_mx = str(fn())
+                    if actual_mx:
+                        break
+                except Exception:
+                    pass
+
+        try:
+            usb = str(dev.getUsbSpeed())
+        except Exception:
+            usb = None
+
+        rows = []
+        for f in dev.getConnectedCameraFeatures():
+            rows.append({
+                "socket": socket_name(f.socket),
+                "sensor": str(f.sensorName or "").upper(),
+                "width": int(getattr(f, "width", 0) or 0),
+                "height": int(getattr(f, "height", 0) or 0),
+                "is_color": feature_is_color(f),
+                "is_mono": feature_is_mono(f),
+                "supported_types": feature_types(f),
+            })
+
+    return rows, actual_mx, usb
+
+
+def validate_specs(rows):
+    by_socket = {r["socket"]: r for r in rows}
+    specs = {}
+    errors = []
+
+    for role in ROLES:
+        sock, allowed = TOPOLOGY[role]
+        row = by_socket.get(sock)
+
+        if row is None:
+            errors.append(f"{role}: {sock} ausente")
+            continue
+
+        sensor = row["sensor"]
+        if sensor not in allowed:
+            errors.append(f"{role}: {sock}={sensor}, esperado={allowed}")
+            continue
+
+        if role == "rgb":
+            mode, w, h = RGB_MODES[sensor]
+            if not row["is_color"]:
+                errors.append(f"{sock}/{sensor} não COLOR")
+        else:
+            mode, w, h = MONO_MODES[sensor]
+            if not row["is_mono"]:
+                errors.append(f"{sock}/{sensor} não MONO")
+
+        if row["width"] and row["height"]:
+            if (row["width"], row["height"]) != (w, h):
+                errors.append(
+                    f"{sock}/{sensor}: {row['width']}x{row['height']} != {w}x{h}"
+                )
+
+        specs[role] = CameraSpec(
+            role=role,
+            socket=sock,
+            sensor=sensor,
+            width=w,
+            height=h,
+            resolution_name=mode,
+            stream=f"intr_{role}",
+            control=f"intr_ctrl_{role}",
+            is_color=(role == "rgb"),
+            source="ISP" if role == "rgb" else "MONO_OUT",
+        )
+
+    if errors:
+        raise RuntimeError("Topologia inválida:\n  - " + "\n  - ".join(errors))
+
+    return specs
+
+
+def hardware_signature(specs):
+    return {
+        role: {
+            "socket": s.socket,
+            "sensor": s.sensor,
+            "size": [s.width, s.height],
+            "stream_source": s.source,
+        }
+        for role, s in specs.items()
+    }
+
+
+def build_pipeline(specs, fps, sharpness, luma, chroma):
+    p = dai.Pipeline()
+    isp = {"sharpness": None, "luma_denoise": None, "chroma_denoise": None,
+           "errors": []}
+
+    for role in ROLES:
+        s = specs[role]
+        sock = get_socket(s.socket)
+
+        if s.is_color:
+            cam = p.createColorCamera()
+            cam.setBoardSocket(sock)
+            enum = getattr(dai.ColorCameraProperties.SensorResolution,
+                           s.resolution_name, None)
+            if enum is None:
+                raise RuntimeError(f"DepthAI sem {s.resolution_name}")
+            cam.setResolution(enum)
+            cam.setFps(float(fps))
+            cam.setInterleaved(False)
+            try:
+                cam.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
+                cam.setIspScale(1, 1)
+            except Exception:
+                pass
+            try:
+                cam.initialControl.setAutoExposureEnable()
+            except Exception:
+                pass
+            try:
+                cam.initialControl.setAutoWhiteBalanceLock(False)
+            except Exception:
+                pass
+
+            for key, method, value in (
+                ("sharpness", "setSharpness", sharpness),
+                ("luma_denoise", "setLumaDenoise", luma),
+                ("chroma_denoise", "setChromaDenoise", chroma),
+            ):
+                fn = getattr(cam.initialControl, method, None)
+                if callable(fn):
+                    try:
+                        fn(int(value))
+                        isp[key] = int(value)
+                    except Exception as exc:
+                        isp["errors"].append(f"{method}:{exc}")
+                else:
+                    isp["errors"].append(f"{method}:unsupported")
+
+            output = cam.isp
+        else:
+            cam = p.createMonoCamera()
+            cam.setBoardSocket(sock)
+            enum = getattr(dai.MonoCameraProperties.SensorResolution,
+                           s.resolution_name, None)
+            if enum is None:
+                raise RuntimeError(f"DepthAI sem {s.resolution_name}")
+            cam.setResolution(enum)
+            cam.setFps(float(fps))
+            try:
+                cam.initialControl.setAutoExposureEnable()
+            except Exception:
+                pass
+            output = cam.out
+
+        xo = p.createXLinkOut()
+        xo.setStreamName(s.stream)
+        output.link(xo.input)
+
+        xi = p.createXLinkIn()
+        xi.setStreamName(s.control)
+        xi.out.link(cam.inputControl)
+
+    return p, isp
+
+
+@dataclass
+class LockedControl:
+    role: str
+    exposure_time_us: int
+    sensitivity_iso: int
+
+
+def packet_controls(pkt):
+    exp = iso = seq = None
+    try:
+        v = pkt.getExposureTime()
+        exp = int(round(v.total_seconds() * 1e6)) if hasattr(v, "total_seconds") else int(v)
+    except Exception:
+        pass
+    try:
+        iso = int(pkt.getSensitivity())
+    except Exception:
+        pass
+    try:
+        seq = int(pkt.getSequenceNum())
+    except Exception:
+        pass
+    return {
+        "exposure_time_us": exp,
+        "sensitivity_iso": iso,
+        "sequence_num": seq,
+    }
+
+
+def controls_match(actual, target):
+    exp = safe_int(actual.get("exposure_time_us"))
+    iso = safe_int(actual.get("sensitivity_iso"))
+    if exp is None or iso is None:
+        return False
+    exp_tol = max(
+        QA["exp_abs_tol_us"],
+        int(round(target.exposure_time_us * QA["exp_rel_tol"])),
+    )
+    return (
+        abs(exp - target.exposure_time_us) <= exp_tol
+        and abs(iso - target.sensitivity_iso) <= QA["iso_tol"]
+    )
+
+
+class Runtime:
+    def __init__(self, dev, specs):
+        self.specs = specs
+        self.oq = {
+            r: dev.getOutputQueue(name=specs[r].stream, maxSize=3, blocking=False)
+            for r in ROLES
+        }
+        self.cq = {
+            r: dev.getInputQueue(specs[r].control)
+            for r in ROLES
+        }
+        self.frames = {r: None for r in ROLES}
+        self.ctrl = {r: {} for r in ROLES}
+        self.seq = {r: None for r in ROLES}
+
+    def poll(self):
+        fresh = set()
+        for r in ROLES:
+            pkt = self.oq[r].tryGet()
+            if pkt is None:
+                continue
+
+            c = packet_controls(pkt)
+            seq = c["sequence_num"]
+
+            if seq is not None and seq == self.seq[r]:
+                continue
+
+            img = pkt.getCvFrame()
+            if img is None:
+                continue
+
+            if img.shape[:2] != (self.specs[r].height, self.specs[r].width):
+                raise RuntimeError(
+                    f"{r}: shape={img.shape[:2]}, esperado="
+                    f"{(self.specs[r].height, self.specs[r].width)}"
+                )
+
+            self.frames[r] = np.ascontiguousarray(img)
+            self.ctrl[r] = c
+            self.seq[r] = seq
+            fresh.add(r)
+
+        return fresh
+
+    def wait_all(self, timeout=8.0):
+        t0 = time.time()
+        while time.time() - t0 < timeout:
+            self.poll()
+            if all(self.frames[r] is not None for r in ROLES):
+                return
+            time.sleep(0.003)
+        raise TimeoutError("Timeout aguardando câmeras.")
+
+    def fresh_triplet(self, timeout=3.0):
+        start = dict(self.seq)
+        frames, ctrls = {}, {}
+        t0 = time.time()
+
+        while time.time() - t0 < timeout:
+            fresh = self.poll()
+            for r in fresh:
+                if r not in frames and self.seq[r] != start.get(r):
+                    frames[r] = self.frames[r].copy()
+                    ctrls[r] = dict(self.ctrl[r])
+
+            if len(frames) == 3:
+                return {"frames": frames, "controls": ctrls}
+
+            time.sleep(0.001)
+
+        raise TimeoutError("Triplet incompleto.")
+
+    def send_manual(self, locked):
+        for r in ROLES:
+            c = dai.CameraControl()
+            c.setManualExposure(
+                int(locked[r].exposure_time_us),
+                int(locked[r].sensitivity_iso),
+            )
+            if r == "rgb":
+                try:
+                    c.setAutoWhiteBalanceLock(True)
+                except Exception:
+                    pass
+            self.cq[r].send(c)
+
+    def verify_manual(self, locked, settle_frames=8, timeout=10.0):
+        seen = dict(self.seq)
+        ok = {r: 0 for r in ROLES}
+        t0 = time.time()
+
+        while time.time() - t0 < timeout:
+            fresh = self.poll()
+            for r in fresh:
+                if self.seq[r] == seen.get(r):
+                    continue
+                seen[r] = self.seq[r]
+                ok[r] = ok[r] + 1 if controls_match(self.ctrl[r], locked[r]) else 0
+
+            if all(ok[r] >= settle_frames for r in ROLES):
+                return
+
+            time.sleep(0.002)
+
+        raise RuntimeError(f"EXP/ISO não estabilizou: {ok}")
+
+
+def lock_current_ae(runtime):
+    locked = {}
+    for role in ROLES:
+        exps, isos = [], []
+        t0 = time.time()
+
+        while time.time() - t0 < 0.8:
+            fresh = runtime.poll()
+            if role not in fresh:
+                time.sleep(0.002)
+                continue
+
+            e = safe_int(runtime.ctrl[role].get("exposure_time_us"))
+            i = safe_int(runtime.ctrl[role].get("sensitivity_iso"))
+
+            if e and e > 0:
+                exps.append(e)
+            if i and i > 0:
+                isos.append(i)
+
+        if not exps or not isos:
+            raise RuntimeError(f"Sem EXP/ISO em {role}")
+
+        locked[role] = LockedControl(
+            role=role,
+            exposure_time_us=int(round(np.median(exps))),
+            sensitivity_iso=int(round(np.median(isos))),
+        )
+
+    runtime.send_manual(locked)
+    runtime.verify_manual(locked)
+    return locked
+
+
+# ============================================================
+# ChArUco
+# ============================================================
+
+def require_aruco():
+    if not hasattr(cv2, "aruco"):
+        raise RuntimeError("Use opencv-contrib-python; cv2.aruco não existe.")
+
+
+def aruco_dictionary(name):
+    require_aruco()
+    if not hasattr(cv2.aruco, name):
+        raise ValueError(f"Dicionário inválido: {name}")
+    enum = getattr(cv2.aruco, name)
+    if hasattr(cv2.aruco, "getPredefinedDictionary"):
+        return cv2.aruco.getPredefinedDictionary(enum)
+    return cv2.aruco.Dictionary_get(enum)
+
+
+def make_board(nx, ny, square, marker, dictionary):
+    if hasattr(cv2.aruco, "CharucoBoard"):
+        try:
+            return cv2.aruco.CharucoBoard(
+                (int(nx), int(ny)), float(square), float(marker), dictionary
+            )
+        except Exception:
+            pass
+
+    if hasattr(cv2.aruco, "CharucoBoard_create"):
+        return cv2.aruco.CharucoBoard_create(
+            int(nx), int(ny), float(square), float(marker), dictionary
+        )
+
+    raise RuntimeError("CharucoBoard incompatível com este OpenCV.")
+
+
+def board_object_points(board):
+    if hasattr(board, "getChessboardCorners"):
+        pts = board.getChessboardCorners()
+    else:
+        pts = getattr(board, "chessboardCorners", None)
+
+    if pts is None:
+        raise RuntimeError("Sem chessboard corners no board.")
+
+    return np.asarray(pts, dtype=np.float32).reshape(-1, 3)
+
+
+def detector_params():
+    if hasattr(cv2.aruco, "DetectorParameters"):
+        p = cv2.aruco.DetectorParameters()
+    else:
+        p = cv2.aruco.DetectorParameters_create()
+
+    try:
+        p.cornerRefinementMethod = getattr(cv2.aruco, "CORNER_REFINE_SUBPIX", 1)
+        p.cornerRefinementWinSize = 5
+        p.cornerRefinementMaxIterations = 40
+        p.cornerRefinementMinAccuracy = 0.01
+    except Exception:
+        pass
+
+    return p
+
+
+def detect_charuco(img, board, dictionary, params):
+    gray = img if img.ndim == 2 else cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    if hasattr(cv2.aruco, "ArucoDetector"):
+        det = cv2.aruco.ArucoDetector(dictionary, params)
+        mc, mi, _ = det.detectMarkers(gray)
+    else:
+        mc, mi, _ = cv2.aruco.detectMarkers(gray, dictionary, parameters=params)
+
+    markers = 0 if mi is None else len(mi)
+    if markers == 0:
+        return {"markers": 0, "corners": 0, "points": {}}
+
+    cc = ci = None
+
+    if hasattr(cv2.aruco, "CharucoDetector"):
+        try:
+            cd = cv2.aruco.CharucoDetector(board)
+            out = cd.detectBoard(
+                image=gray,
+                markerCorners=mc,
+                markerIds=mi,
+            )
+            cc, ci = out[0], out[1]
+        except Exception:
+            pass
+
+    if cc is None and hasattr(cv2.aruco, "interpolateCornersCharuco"):
+        ret, cc, ci = cv2.aruco.interpolateCornersCharuco(
+            markerCorners=mc,
+            markerIds=mi,
+            image=gray,
+            board=board,
+        )
+        if ret is None or ret <= 0:
+            cc = ci = None
+
+    if cc is None or ci is None:
+        return {"markers": markers, "corners": 0, "points": {}}
+
+    pts = np.asarray(cc, dtype=np.float32).reshape(-1, 2)
+    ids = np.asarray(ci).reshape(-1)
+
+    try:
+        cv2.cornerSubPix(
+            gray,
+            pts.reshape(-1, 1, 2),
+            (4, 4),
+            (-1, -1),
+            (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 40, 0.01),
+        )
+    except Exception:
+        pass
+
+    return {
+        "markers": int(markers),
+        "corners": int(len(ids)),
+        "points": {
+            int(cid): [float(pt[0]), float(pt[1])]
+            for cid, pt in zip(ids, pts)
+        },
+    }
+
+
+def detect_triplet(triplet, board, dictionary, params):
+    out = {}
+    reasons = []
+
+    for role in ROLES:
+        d = detect_charuco(triplet["frames"][role], board, dictionary, params)
+        out[role] = d
+
+        if d["markers"] < QA["min_markers"]:
+            reasons.append(f"{role}:markers={d['markers']}")
+        if d["corners"] < QA["min_corners"]:
+            reasons.append(f"{role}:corners={d['corners']}")
+
+    return {
+        "valid": not reasons,
+        "reasons": reasons,
+        "detections": out,
+    }
+
+
+# ============================================================
+# Pose/cobertura
+# ============================================================
+
+def pose_signature(points, shape_hw):
+    pts = np.asarray(list(points.values()), dtype=np.float32)
+    h, w = shape_hw
+
+    x0, x1 = float(pts[:, 0].min()), float(pts[:, 0].max())
+    y0, y1 = float(pts[:, 1].min()), float(pts[:, 1].max())
+
+    return {
+        "cx": ((x0 + x1) * 0.5) / w,
+        "cy": ((y0 + y1) * 0.5) / h,
+        "area": ((x1 - x0) * (y1 - y0)) / float(w * h),
+    }
+
+
+def duplicate_pose(sig, views):
+    if not views:
+        return False
+
+    # Rejeita somente se parecer repetida nas três câmeras.
+    repeated = 0
+
+    for role in ROLES:
+        s = sig[role]
+        role_dup = False
+
+        for v in views:
+            p = v["pose"][role]
+            d = math.hypot(s["cx"] - p["cx"], s["cy"] - p["cy"])
+            da = abs(s["area"] - p["area"])
+
+            if d < QA["duplicate_center_norm"] and da < QA["duplicate_area_norm"]:
+                role_dup = True
+                break
+
+        repeated += int(role_dup)
+
+    return repeated == 3
+
+
+def coverage(points, image_size):
+    pts = np.asarray(points, dtype=np.float32)
+    w, h = image_size
+
+    if len(pts) < 3:
+        return {"span_x": 0.0, "span_y": 0.0, "hull": 0.0}
+
+    hull = cv2.convexHull(pts.reshape(-1, 1, 2))
+
+    return {
+        "span_x": float((pts[:, 0].max() - pts[:, 0].min()) / w),
+        "span_y": float((pts[:, 1].max() - pts[:, 1].min()) / h),
+        "hull": float(cv2.contourArea(hull) / (w * h)),
+    }
+
+
+def diversity(views, role):
+    if not views:
+        return {}
+
+    cx = np.array([v["pose"][role]["cx"] for v in views], dtype=np.float64)
+    cy = np.array([v["pose"][role]["cy"] for v in views], dtype=np.float64)
+    area = np.array([v["pose"][role]["area"] for v in views], dtype=np.float64)
+
+    return {
+        "center_std_x": float(cx.std()),
+        "center_std_y": float(cy.std()),
+        "area_min": float(area.min()),
+        "area_max": float(area.max()),
+        "area_range": float(area.max() - area.min()),
+    }
+
+
+# ============================================================
+# Dataset / calibração
+# ============================================================
+
+def view_object_image(view, role, board_pts):
+    p = view["detections"][role]["points"]
+    ids = sorted(int(x) for x in p.keys())
+
+    obj = np.asarray([board_pts[i] for i in ids], dtype=np.float32).reshape(-1, 1, 3)
+    img = np.asarray([p[str(i)] if str(i) in p else p[i] for i in ids],
+                     dtype=np.float32).reshape(-1, 1, 2)
+
+    return ids, obj, img
+
+
+def build_dataset(views, role, board_pts, retained=None):
+    allowed = None if retained is None else set(int(x) for x in retained)
+    objs, imgs, view_ids, charuco_ids = [], [], [], []
+
+    for v in views:
+        vid = int(v["view_id"])
+        if allowed is not None and vid not in allowed:
+            continue
+
+        ids, obj, img = view_object_image(v, role, board_pts)
+        if len(ids) < 4:
+            continue
+
+        objs.append(obj)
+        imgs.append(img)
+        view_ids.append(vid)
+        charuco_ids.extend(ids)
+
+    return {
+        "objects": objs,
+        "images": imgs,
+        "view_ids": view_ids,
+        "charuco_ids": charuco_ids,
+    }
+
+
+def model_flags(name):
+    if name == "brown5":
+        return cv2.CALIB_FIX_K4 | cv2.CALIB_FIX_K5 | cv2.CALIB_FIX_K6
+    if name == "rational8":
+        return cv2.CALIB_RATIONAL_MODEL
+    raise ValueError(name)
+
+
+def calibrate_model(dataset, image_size, model_name):
+    if len(dataset["objects"]) < 3:
+        raise RuntimeError("Poucas views.")
+
+    w, h = image_size
+    K0 = np.array([
+        [float(w), 0.0, w / 2.0],
+        [0.0, float(w), h / 2.0],
+        [0.0, 0.0, 1.0],
+    ], dtype=np.float64)
+
+    D0 = np.zeros((8 if model_name == "rational8" else 5, 1), dtype=np.float64)
+    flags = model_flags(model_name)
+    criteria = (
+        cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,
+        100,
+        1e-10,
+    )
+
+    if hasattr(cv2, "calibrateCameraExtended"):
+        rms, K, D, rvecs, tvecs, std_i, _, per_view = cv2.calibrateCameraExtended(
+            dataset["objects"],
+            dataset["images"],
+            (w, h),
+            K0,
+            D0,
+            flags=flags,
+            criteria=criteria,
+        )
+    else:
+        rms, K, D, rvecs, tvecs = cv2.calibrateCamera(
+            dataset["objects"],
+            dataset["images"],
+            (w, h),
+            K0,
+            D0,
+            flags=flags,
+            criteria=criteria,
+        )
+        std_i = per_view = None
+
+    model = {
+        "name": model_name,
+        "rms": float(rms),
+        "K": np.asarray(K, dtype=np.float64),
+        "D": np.asarray(D, dtype=np.float64).reshape(-1),
+        "rvecs": [np.asarray(x).reshape(3) for x in rvecs],
+        "tvecs": [np.asarray(x).reshape(3) for x in tvecs],
+        "std_intrinsics": None if std_i is None else np.asarray(std_i).reshape(-1),
+        "opencv_per_view": None if per_view is None else np.asarray(per_view).reshape(-1),
+    }
+
+    return model
+
+
+def reprojection_errors(model, dataset):
+    per_view = []
+    all_e = []
+
+    for idx, (obj, img, rv, tv) in enumerate(zip(
+        dataset["objects"], dataset["images"], model["rvecs"], model["tvecs"]
+    )):
+        proj, _ = cv2.projectPoints(obj, rv, tv, model["K"], model["D"])
+        obs = np.asarray(img).reshape(-1, 2)
+        pred = np.asarray(proj).reshape(-1, 2)
+        e = np.linalg.norm(obs - pred, axis=1)
+
+        all_e.extend(e.tolist())
+        per_view.append({
+            "view_id": int(dataset["view_ids"][idx]),
+            "count": int(len(e)),
+            "mean_px": float(e.mean()),
+            "median_px": float(np.median(e)),
+            "p95_px": float(np.percentile(e, 95)),
+            "max_px": float(e.max()),
+        })
+
+    a = np.asarray(all_e, dtype=np.float64)
+
+    return {
+        "per_view": per_view,
+        "overall": {
+            "count": int(a.size),
+            "mean_px": float(a.mean()) if a.size else None,
+            "median_px": float(np.median(a)) if a.size else None,
+            "p95_px": float(np.percentile(a, 95)) if a.size else None,
+            "max_px": float(a.max()) if a.size else None,
+        },
+    }
+
+
+def iterative_reject(views, role, board_pts, image_size, model_name):
+    retained = [int(v["view_id"]) for v in views]
+    rejected = []
+    rounds = []
+
+    for round_idx in range(QA["max_reject_rounds"] + 1):
+        ds = build_dataset(views, role, board_pts, retained)
+
+        if len(ds["objects"]) < QA["min_retained_views"]:
+            break
+
+        model = calibrate_model(ds, image_size, model_name)
+        err = reprojection_errors(model, ds)
+
+        bad = [
+            x for x in err["per_view"]
+            if (
+                x["median_px"] > QA["reject_view_med"]
+                or x["p95_px"] > QA["reject_view_p95"]
+            )
+        ]
+
+        rounds.append({
+            "round": round_idx,
+            "retained_before": list(retained),
+            "per_view": err["per_view"],
+            "bad": [x["view_id"] for x in bad],
+        })
+
+        if not bad:
+            break
+
+        worst = max(bad, key=lambda x: x["median_px"])
+
+        if len(retained) - 1 < QA["min_retained_views"]:
+            break
+
+        retained.remove(worst["view_id"])
+        rejected.append(worst["view_id"])
+
+    return {
+        "retained": retained,
+        "rejected": rejected,
+        "rounds": rounds,
+    }
+
+
+def leave_one_out(views, role, board_pts, image_size, model_name, retained):
+    retained = sorted(set(int(x) for x in retained))
+    reports = []
+    all_e = []
+
+    if len(retained) < 6:
+        return {"valid": False, "views": [], "overall": {}}
+
+    for held in retained:
+        train_ids = [x for x in retained if x != held]
+        train = build_dataset(views, role, board_pts, train_ids)
+        test = build_dataset(views, role, board_pts, [held])
+
+        if len(train["objects"]) < 5 or len(test["objects"]) != 1:
+            continue
+
+        try:
+            model = calibrate_model(train, image_size, model_name)
+            obj = test["objects"][0]
+            img = test["images"][0]
+
+            ok, rv, tv = cv2.solvePnP(
+                obj, img, model["K"], model["D"],
+                flags=cv2.SOLVEPNP_ITERATIVE,
+            )
+            if not ok:
+                continue
+
+            proj, _ = cv2.projectPoints(obj, rv, tv, model["K"], model["D"])
+            obs = np.asarray(img).reshape(-1, 2)
+            pred = np.asarray(proj).reshape(-1, 2)
+            e = np.linalg.norm(obs - pred, axis=1)
+
+            all_e.extend(e.tolist())
+            reports.append({
+                "held_out_view_id": int(held),
+                "count": int(len(e)),
+                "mean_px": float(e.mean()),
+                "median_px": float(np.median(e)),
+                "p95_px": float(np.percentile(e, 95)),
+                "max_px": float(e.max()),
+            })
+        except Exception:
+            continue
+
+    a = np.asarray(all_e, dtype=np.float64)
+
+    return {
+        "valid": len(reports) == len(retained),
+        "views": reports,
+        "overall": {
+            "count": int(a.size),
+            "mean_px": float(a.mean()) if a.size else None,
+            "median_px": float(np.median(a)) if a.size else None,
+            "p95_px": float(np.percentile(a, 95)) if a.size else None,
+            "max_px": float(a.max()) if a.size else None,
+        },
+    }
+
+
+def distortion_shift(K, D, image_size):
+    w, h = image_size
+    xs = np.linspace(0, w - 1, 21)
+    ys = np.linspace(0, h - 1, 15)
+
+    pts = np.asarray([[x, y] for y in ys for x in xs],
+                     dtype=np.float32).reshape(-1, 1, 2)
+
+    und = cv2.undistortPoints(pts, K, D, P=K).reshape(-1, 2)
+    orig = pts.reshape(-1, 2)
+    shift = np.linalg.norm(und - orig, axis=1)
+
+    edge = (
+        (orig[:, 0] < 0.15 * w)
+        | (orig[:, 0] > 0.85 * w)
+        | (orig[:, 1] < 0.15 * h)
+        | (orig[:, 1] > 0.85 * h)
+    )
+
+    e = shift[edge]
+
+    return {
+        "median_px": float(np.median(shift)),
+        "p95_px": float(np.percentile(shift, 95)),
+        "max_px": float(shift.max()),
+        "edge_median_px": float(np.median(e)),
+        "edge_p95_px": float(np.percentile(e, 95)),
+        "edge_max_px": float(e.max()),
+    }
+
+
+def undistort_products(K, D, image_size):
+    w, h = image_size
+    out = {}
+
+    for alpha in (0.0, 0.5, 1.0):
+        newK, roi = cv2.getOptimalNewCameraMatrix(
+            K, D, (w, h), alpha, (w, h), centerPrincipalPoint=False
+        )
+
+        out[str(alpha)] = {
+            "alpha": alpha,
+            "new_camera_matrix": np.asarray(newK).tolist(),
+            "valid_roi": [int(x) for x in roi],
+        }
+
+    return out
+
+
+def sanity(model, image_size):
+    w, h = image_size
+    K, D = model["K"], model["D"]
+
+    fx, fy = float(K[0, 0]), float(K[1, 1])
+    cx, cy = float(K[0, 2]), float(K[1, 2])
+
+    ratio = max(fx / max(fy, 1e-9), fy / max(fx, 1e-9))
+    margin_x = QA["principal_margin_frac"] * w
+    margin_y = QA["principal_margin_frac"] * h
+
+    status = "good"
+    reasons = []
+
+    if not (QA["focal_min_frac_w"] * w <= fx <= QA["focal_max_frac_w"] * w):
+        status = "bad"
+        reasons.append(f"fx_implausible:{fx:.1f}")
+
+    if not (QA["focal_min_frac_w"] * w <= fy <= QA["focal_max_frac_w"] * w):
+        status = "bad"
+        reasons.append(f"fy_implausible:{fy:.1f}")
+
+    if ratio > QA["fx_fy_ratio_bad"]:
+        status = "bad"
+        reasons.append(f"fx_fy_ratio_bad:{ratio:.3f}")
+    elif ratio > QA["fx_fy_ratio_warn"]:
+        status = merge_status(status, "warning")
+        reasons.append(f"fx_fy_ratio_warn:{ratio:.3f}")
+
+    if not (-margin_x <= cx <= w + margin_x and -margin_y <= cy <= h + margin_y):
+        status = "bad"
+        reasons.append(f"principal_point_bad:{cx:.1f},{cy:.1f}")
+
+    max_abs = float(np.max(np.abs(D))) if len(D) else 0.0
+
+    return {
+        "status": status,
+        "reasons": reasons,
+        "fx": fx,
+        "fy": fy,
+        "cx": cx,
+        "cy": cy,
+        "fx_fy_ratio": ratio,
+        "max_abs_dist_coeff": max_abs,
+    }
+
+
+def evaluate_model(views, role, board_pts, image_size, model_name):
+    rej = iterative_reject(
+        views, role, board_pts, image_size, model_name
+    )
+
+    ds = build_dataset(
+        views, role, board_pts, rej["retained"]
+    )
+
+    if len(ds["objects"]) < QA["min_retained_views"]:
+        return {
+            "status": "bad",
+            "reasons": ["retained_views_insufficient"],
+            "model_name": model_name,
+        }
+
+    model = calibrate_model(ds, image_size, model_name)
+    fit = reprojection_errors(model, ds)
+    cv = leave_one_out(
+        views, role, board_pts, image_size, model_name, rej["retained"]
+    )
+
+    all_points = []
+    for im in ds["images"]:
+        all_points.extend(np.asarray(im).reshape(-1, 2).tolist())
+
+    cov = coverage(all_points, image_size)
+    retained_views = [v for v in views if int(v["view_id"]) in set(rej["retained"])]
+    div = diversity(retained_views, role)
+    sane = sanity(model, image_size)
+    shift = distortion_shift(model["K"], model["D"], image_size)
+    und = undistort_products(model["K"], model["D"], image_size)
+
+    status = sane["status"]
+    reasons = list(sane["reasons"])
+
+    def high(value, warn, bad, name):
+        nonlocal status
+        if value is None:
+            status = "bad"
+            reasons.append(f"{name}_missing")
+        elif value > bad:
+            status = "bad"
+            reasons.append(f"{name}_bad:{value:.4f}")
+        elif value > warn:
+            status = merge_status(status, "warning")
+            reasons.append(f"{name}_warn:{value:.4f}")
+
+    def low(value, warn, bad, name):
+        nonlocal status
+        if value < bad:
+            status = "bad"
+            reasons.append(f"{name}_bad:{value:.4f}")
+        elif value < warn:
+            status = merge_status(status, "warning")
+            reasons.append(f"{name}_warn:{value:.4f}")
+
+    high(model["rms"], QA["rms_warn"], QA["rms_bad"], "rms")
+    high(fit["overall"]["median_px"], QA["fit_med_warn"], QA["fit_med_bad"], "fit_med")
+    high(fit["overall"]["p95_px"], QA["fit_p95_warn"], QA["fit_p95_bad"], "fit_p95")
+
+    if not cv.get("valid"):
+        status = "bad"
+        reasons.append("cross_validation_invalid")
+    else:
+        high(cv["overall"].get("median_px"), QA["cv_med_warn"], QA["cv_med_bad"], "cv_med")
+        high(cv["overall"].get("p95_px"), QA["cv_p95_warn"], QA["cv_p95_bad"], "cv_p95")
+        medians = [x["median_px"] for x in cv["views"]]
+        high(max(medians) if medians else None,
+             QA["cv_view_med_warn"], QA["cv_view_med_bad"], "cv_worst_view_med")
+
+    low(cov["span_x"], QA["coverage_span_x_warn"], QA["coverage_span_x_bad"], "coverage_x")
+    low(cov["span_y"], QA["coverage_span_y_warn"], QA["coverage_span_y_bad"], "coverage_y")
+    low(cov["hull"], QA["coverage_hull_warn"], QA["coverage_hull_bad"], "coverage_hull")
+    low(div.get("area_range", 0), QA["area_range_warn"], QA["area_range_bad"], "area_range")
+    low(div.get("center_std_x", 0), QA["center_std_x_warn"], QA["center_std_x_bad"], "center_std_x")
+    low(div.get("center_std_y", 0), QA["center_std_y_warn"], QA["center_std_y_bad"], "center_std_y")
+
+    max_abs = sane["max_abs_dist_coeff"]
+    if model_name == "brown5":
+        warn, bad = QA["brown_coeff_warn"], QA["brown_coeff_bad"]
+    else:
+        warn, bad = QA["rational_coeff_warn"], QA["rational_coeff_bad"]
+
+    if max_abs > bad:
+        status = "bad"
+        reasons.append(f"dist_coeff_bad:{max_abs:.3f}")
+    elif max_abs > warn:
+        status = merge_status(status, "warning")
+        reasons.append(f"dist_coeff_warn:{max_abs:.3f}")
+
+    unique_ids = sorted(set(ds["charuco_ids"]))
+    if len(unique_ids) < QA["min_unique_ids"]:
+        status = "bad"
+        reasons.append(f"unique_ids_bad:{len(unique_ids)}")
+
+    return {
+        "status": status,
+        "reasons": reasons,
+        "model_name": model_name,
+        "image_size": list(image_size),
+        "camera_matrix": model["K"].tolist(),
+        "dist_coeffs": model["D"].tolist(),
+        "rms_px": model["rms"],
+        "std_intrinsics": None if model["std_intrinsics"] is None
+        else model["std_intrinsics"].tolist(),
+        "retained_view_ids": rej["retained"],
+        "rejected_view_ids": rej["rejected"],
+        "view_rejection": rej,
+        "fit_error": fit,
+        "cross_validation": cv,
+        "coverage": cov,
+        "diversity": div,
+        "unique_charuco_ids": len(unique_ids),
+        "sanity": sane,
+        "distortion_shift": shift,
+        "undistort_products": und,
+    }
+
+
+def choose_model(brown, rational, requested):
+    if requested == "brown5":
+        return brown, {"selected": "brown5", "reason": "forced"}
+    if requested == "rational8":
+        return rational, {"selected": "rational8", "reason": "forced"}
+
+    if rational["status"] == "bad":
+        return brown, {"selected": "brown5", "reason": "rational_bad"}
+    if brown["status"] == "bad":
+        return rational, {"selected": "rational8", "reason": "brown_bad"}
+
+    b = brown["cross_validation"]["overall"].get("p95_px")
+    r = rational["cross_validation"]["overall"].get("p95_px")
+
+    if b is None or r is None:
+        return brown, {"selected": "brown5", "reason": "prefer_simple"}
+
+    improvement = (b - r) / max(b, 1e-9)
+
+    if improvement >= QA["rational_min_cv_improvement"]:
+        return rational, {
+            "selected": "rational8",
+            "reason": "cv_materially_better",
+            "cv_p95_improvement_fraction": float(improvement),
+        }
+
+    return brown, {
+        "selected": "brown5",
+        "reason": "prefer_simple",
+        "cv_p95_improvement_fraction": float(improvement),
+    }
+
+
+def evaluate_all(views, board_pts, specs, requested):
+    cameras = {}
+    overall = "good"
+    reasons = []
+
+    for role in ROLES:
+        size = (specs[role].width, specs[role].height)
+
+        brown = evaluate_model(
+            views, role, board_pts, size, "brown5"
+        )
+        rational = evaluate_model(
+            views, role, board_pts, size, "rational8"
+        )
+
+        selected, selection = choose_model(
+            brown, rational, requested
+        )
+
+        edge = selected.get("distortion_shift", {}).get("edge_p95_px", 0.0)
+
+        if edge >= QA["edge_shift_high_px"]:
+            rec = "strongly_consider_runtime_undistort"
+        elif edge >= QA["edge_shift_warn_px"]:
+            rec = "consider_runtime_undistort"
+        else:
+            rec = "runtime_undistort_optional"
+
+        cameras[role] = {
+            "status": selected["status"],
+            "reasons": selected["reasons"],
+            "model_selection": selection,
+            "selected_model": selected,
+            "candidate_models": {
+                "brown5": brown,
+                "rational8": rational,
+            },
+            "runtime_geometry_recommendation": rec,
+        }
+
+        overall = merge_status(overall, selected["status"])
+        reasons.extend([f"{role}:{x}" for x in selected["reasons"]])
+
+    return {
+        "status": overall,
+        "reasons": reasons,
+        "cameras": cameras,
+    }
+
+
+def module_fragment(evaluation, specs):
+    cameras = {}
+
+    for role in ROLES:
+        cam = evaluation["cameras"][role]
+        m = cam["selected_model"]
+
+        cameras[role] = {
+            "socket": specs[role].socket,
+            "sensor": specs[role].sensor,
+            "stream_source": specs[role].source,
+            "image_size": [specs[role].width, specs[role].height],
+            "distortion_model": cam["model_selection"]["selected"],
+            "camera_matrix": m["camera_matrix"],
+            "dist_coeffs": m["dist_coeffs"],
+            "undistort_products": m["undistort_products"],
+        }
+
+    return {
+        "intrinsics_config": {
+            "enabled": True,
+            "calibration_space": CALIBRATION_SPACE,
+            "cameras": cameras,
+            "runtime_undistort": {
+                "enabled": False,
+                "alpha": 0.0,
+                "interpolation": "linear",
+                "reason": (
+                    "Homography atual foi calibrada no espaço nativo distorcido. "
+                    "Ative undistort somente após recalibrar a homografia no novo espaço."
+                ),
+            },
+        }
+    }
+
+
+# ============================================================
+# UI helpers
+# ============================================================
+
+def draw_points(panel, points, src_shape, color):
+    if not points:
+        return
+
+    ph, pw = panel.shape[:2]
+    sh, sw = src_shape
+
+    for idx, (cid, pt) in enumerate(sorted((int(k), v) for k, v in points.items())):
+        x = int(float(pt[0]) * pw / sw)
+        y = int(float(pt[1]) * ph / sh)
+        cv2.circle(panel, (x, y), 3, color, -1)
+        if idx < 40:
+            cv2.putText(panel, str(cid), (x + 3, y - 3),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.3, color, 1, cv2.LINE_AA)
+
+
+def save_view_preview(path, frames, det):
+    panels = []
+    colors = {"rgb": (0, 255, 0), "re": (0, 255, 255), "nir": (255, 255, 0)}
+
+    for role in ROLES:
+        img = frames[role]
+        bgr = img if img.ndim == 3 else cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        p = cv2.resize(bgr, (480, 300), interpolation=cv2.INTER_AREA)
+        draw_points(p, det["detections"][role]["points"], img.shape[:2], colors[role])
+        overlay(p, [
+            role.upper(),
+            f"markers={det['detections'][role]['markers']}",
+            f"corners={det['detections'][role]['corners']}",
+        ], x=8, y=18, scale=0.36, step=16)
+        panels.append(p)
+
+    cv2.imwrite(str(path), np.hstack(panels))
+
+
+def undistorted(frame, cam_result, alpha):
+    m = cam_result["selected_model"]
+    K = np.asarray(m["camera_matrix"], dtype=np.float64)
+    D = np.asarray(m["dist_coeffs"], dtype=np.float64)
+    product = m["undistort_products"][str(float(alpha))]
+    newK = np.asarray(product["new_camera_matrix"], dtype=np.float64)
+    return cv2.undistort(frame, K, D, None, newK)
+
+
+def build_ui(runtime, specs, views, last_det, evaluation,
+             selected_role, show_undist, args, msg=""):
+    pw, ph = args.panel_width, args.panel_height
+    panels = {}
+    colors = {"rgb": (0, 255, 0), "re": (0, 255, 255), "nir": (255, 255, 0)}
+
+    for role in ROLES:
+        frame = runtime.frames[role]
+
+        if show_undist and evaluation is not None and role == selected_role:
+            frame = undistorted(
+                frame, evaluation["cameras"][role], args.preview_alpha
+            )
+
+        bgr = frame if frame.ndim == 3 else cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
+        p = cv2.resize(bgr, (pw, ph), interpolation=cv2.INTER_AREA)
+
+        if last_det is not None:
+            draw_points(
+                p,
+                last_det["detections"][role]["points"],
+                runtime.frames[role].shape[:2],
+                colors[role],
+            )
+
+        overlay(p, [
+            f"{role.upper()} | {specs[role].sensor}",
+            f"{specs[role].width}x{specs[role].height}",
+            "UNDIST PREVIEW" if (show_undist and role == selected_role) else "NATIVE",
+            f"EXP={runtime.ctrl[role].get('exposure_time_us')}us "
+            f"ISO={runtime.ctrl[role].get('sensitivity_iso')}",
+        ], x=8, y=18, scale=0.36, step=16)
+
+        panels[role] = p
+
+    data = np.zeros((ph, pw, 3), dtype=np.uint8)
+
+    lines = [
+        "INTRINSICS CALIBRATION - PRODUCTION",
+        f"views={len(views)} / min={QA['min_views']} rec={QA['recommended_views']}",
+        f"preview={selected_role.upper()} undist={'ON' if show_undist else 'OFF'}",
+        "",
+        "G capture | A calibrate",
+        "V clear | S snapshot",
+        "1/2/3 select | U undist preview",
+        "Q/ESC cancel",
+    ]
+
+    if evaluation is not None:
+        lines += ["", f"EVAL={evaluation['status'].upper()}"]
+
+        for role in ROLES:
+            c = evaluation["cameras"][role]
+            m = c["selected_model"]
+            cvmed = m["cross_validation"]["overall"].get("median_px")
+            lines.append(
+                f"{role.upper()} {c['model_selection']['selected']} "
+                f"RMS={m['rms_px']:.3f}px "
+                f"CV={0.0 if cvmed is None else cvmed:.3f}px "
+                f"edge={m['distortion_shift']['edge_p95_px']:.1f}px"
+            )
+
+    overlay(data, lines, x=12, y=20, scale=0.39, step=17)
+
+    board = np.vstack([
+        np.hstack([panels["rgb"], panels["re"]]),
+        np.hstack([panels["nir"], data]),
+    ])
+
+    if msg:
+        cv2.putText(
+            board, msg, (16, board.shape[0] - 14),
+            cv2.FONT_HERSHEY_SIMPLEX, 0.52, (0, 255, 0), 2, cv2.LINE_AA,
+        )
+
+    return board
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    ap = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    ap.add_argument("--mx-id", default=None)
+    ap.add_argument("--fps", type=float, default=20.0)
+
+    ap.add_argument("--isp-sharpness", type=int, default=0)
+    ap.add_argument("--isp-luma-denoise", type=int, default=0)
+    ap.add_argument("--isp-chroma-denoise", type=int, default=0)
+
+    ap.add_argument("--charuco-dictionary", default="DICT_4X4_50")
+    ap.add_argument("--charuco-squares-x", type=int, default=13)
+    ap.add_argument("--charuco-squares-y", type=int, default=7)
+    ap.add_argument("--charuco-square-length", type=float, default=0.031)
+    ap.add_argument("--charuco-marker-length", type=float, default=0.023)
+
+    ap.add_argument(
+        "--distortion-model",
+        default="auto",
+        choices=["auto", "brown5", "rational8"],
+    )
+    ap.add_argument("--promote-warning", action="store_true")
+
+    ap.add_argument("--panel-width", type=int, default=640)
+    ap.add_argument("--panel-height", type=int, default=400)
+    ap.add_argument("--preview-alpha", type=float, default=0.0,
+                    choices=[0.0, 0.5, 1.0])
+
+    ap.add_argument(
+        "--candidate-root",
+        default="calibration/intrinsics_candidates",
+    )
+    ap.add_argument(
+        "--active-json",
+        default="calibration/intrinsics_calibration_v1.json",
+    )
+
+    ap.add_argument("--module-id", default="")
+    ap.add_argument("--operator", default="")
+    ap.add_argument("--lens-rgb", default="")
+    ap.add_argument("--lens-re", default="")
+    ap.add_argument("--lens-nir", default="")
+    ap.add_argument("--notes", default="")
+
+    args = ap.parse_args()
+
+    sid = stamp()
+    candidate_dir = Path(args.candidate_root) / sid
+    views_dir = candidate_dir / "views"
+    snapshots_dir = candidate_dir / "snapshots"
+    report_path = candidate_dir / "report.json"
+
+    ensure_dir(views_dir)
+    ensure_dir(snapshots_dir)
+
+    dictionary = aruco_dictionary(args.charuco_dictionary)
+    board = make_board(
+        args.charuco_squares_x,
+        args.charuco_squares_y,
+        args.charuco_square_length,
+        args.charuco_marker_length,
+        dictionary,
+    )
+    board_pts = board_object_points(board)
+    params = detector_params()
+
+    views = []
+    report = None
+    evaluation = None
+    last_det = None
+
+    window = "Intrinsics Calibration - Production"
+    cv2.namedWindow(window, cv2.WINDOW_NORMAL)
+
+    try:
+        rows, actual_mx, usb = discover(args.mx_id)
+        specs = validate_specs(rows)
+
+        pipeline, isp = build_pipeline(
+            specs,
+            args.fps,
+            args.isp_sharpness,
+            args.isp_luma_denoise,
+            args.isp_chroma_denoise,
+        )
+
+        print("=" * 82)
+        print("INTRINSICS CALIBRATION - PRODUCTION")
+        print(f"MX ID: {actual_mx}")
+        print(f"USB  : {usb}")
+
+        for role in ROLES:
+            s = specs[role]
+            print(
+                f"{s.socket} -> {role.upper():3s} | {s.sensor:8s} | "
+                f"{s.width}x{s.height} | {s.source}"
+            )
+
+        print("=" * 82)
+
+        report = {
+            "schema": SCHEMA,
+            "session_id": sid,
+            "created_at": now_str(),
+            "status": "running",
+            "promoted": False,
+            "depthai_version": getattr(dai, "__version__", "unknown"),
+            "opencv_version": cv2.__version__,
+            "device_mx_id": actual_mx,
+            "usb_speed": usb,
+            "hardware_signature": hardware_signature(specs),
+            "calibration_space": CALIBRATION_SPACE,
+            "charuco": {
+                "dictionary": args.charuco_dictionary,
+                "squares_x": args.charuco_squares_x,
+                "squares_y": args.charuco_squares_y,
+                "square_length_m": args.charuco_square_length,
+                "marker_length_m": args.charuco_marker_length,
+            },
+            "distortion_model_policy": {
+                "requested": args.distortion_model,
+                "auto_rule": (
+                    "Prefer Brown5; choose Rational8 only if LOO-CV p95 "
+                    "improves materially."
+                ),
+            },
+            "qa_thresholds": QA,
+            "isp_measurement_settings": isp,
+            "traceability": {
+                "module_id": args.module_id or None,
+                "operator": args.operator or None,
+                "lens_by_role": {
+                    "rgb": args.lens_rgb or None,
+                    "re": args.lens_re or None,
+                    "nir": args.lens_nir or None,
+                },
+            },
+            "notes": args.notes,
+            "locked_controls": None,
+            "views": [],
+            "evaluation": None,
+            "module_params_fragment": None,
+        }
+
+        info = dai.DeviceInfo(actual_mx) if actual_mx else None
+        dev_ctx = dai.Device(pipeline, info) if info is not None else dai.Device(pipeline)
+
+        with dev_ctx as dev:
+            runtime = Runtime(dev, specs)
+            runtime.wait_all()
+
+            # Preflight
+            print("[PRE-FLIGHT] Mostre o ChArUco nas três câmeras.")
+
+            while True:
+                runtime.poll()
+
+                trip = {"frames": runtime.frames}
+                det = detect_triplet(trip, board, dictionary, params)
+
+                panels = []
+
+                for role in ROLES:
+                    img = runtime.frames[role]
+                    bgr = img if img.ndim == 3 else cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+                    p = cv2.resize(
+                        bgr,
+                        (args.panel_width, args.panel_height),
+                        interpolation=cv2.INTER_AREA,
+                    )
+
+                    d = det["detections"][role]
+                    overlay(p, [
+                        f"{role.upper()} | {specs[role].sensor}",
+                        f"markers={d['markers']} corners={d['corners']}",
+                        f"EXP={runtime.ctrl[role].get('exposure_time_us')}us "
+                        f"ISO={runtime.ctrl[role].get('sensitivity_iso')}",
+                    ], x=8, y=18, scale=0.38, step=17)
+
+                    panels.append(p)
+
+                data = np.zeros(
+                    (args.panel_height, args.panel_width, 3),
+                    dtype=np.uint8,
+                )
+
+                overlay(data, [
+                    "INTRINSICS PRE-FLIGHT",
+                    "",
+                    "GOOD" if det["valid"] else "BAD",
+                    "",
+                    "ENTER = lock EXP/ISO",
+                    "Q/ESC = cancel",
+                    "",
+                    *[f"! {x}" for x in det["reasons"][:8]],
+                ], x=14, y=28, scale=0.43, step=20)
+
+                board_ui = np.vstack([
+                    np.hstack([panels[0], panels[1]]),
+                    np.hstack([panels[2], data]),
+                ])
+
+                cv2.imshow(window, board_ui)
+                k = cv2.waitKey(1) & 0xFF
+
+                if k in (ord("q"), ord("Q"), 27):
+                    raise KeyboardInterrupt("Cancelado no preflight.")
+
+                if k in (13, 10):
+                    if not det["valid"]:
+                        print("[BLOCK] ChArUco ainda não GOOD nas 3 câmeras.")
+                        continue
+
+                    locked = lock_current_ae(runtime)
+                    report["locked_controls"] = {
+                        r: asdict(locked[r]) for r in ROLES
+                    }
+                    save_json_atomic(report_path, report)
+                    break
+
+                time.sleep(0.002)
+
+            # Capture loop
+            selected_role = "rgb"
+            show_undist = False
+            message = ""
+            message_t = 0.0
+
+            print(
+                f"[CAPTURE] mínimo={QA['min_views']}, ideal={QA['recommended_views']}. "
+                "Varie posição, distância, yaw, pitch e roll."
+            )
+
+            while True:
+                runtime.poll()
+
+                msg = message if (
+                    message and time.time() - message_t < 4.0
+                ) else ""
+
+                ui = build_ui(
+                    runtime, specs, views, last_det, evaluation,
+                    selected_role, show_undist, args, msg
+                )
+
+                cv2.imshow(window, ui)
+                k = cv2.waitKey(1) & 0xFF
+
+                if k in (ord("q"), ord("Q"), 27):
+                    raise KeyboardInterrupt("Sessão cancelada.")
+
+                if k == ord("1"):
+                    selected_role = "rgb"
+                elif k == ord("2"):
+                    selected_role = "re"
+                elif k == ord("3"):
+                    selected_role = "nir"
+
+                elif k in (ord("u"), ord("U")):
+                    if evaluation is None:
+                        message = "Use A primeiro para calibrar."
+                    else:
+                        show_undist = not show_undist
+                        message = f"Undistort preview={show_undist}"
+                    message_t = time.time()
+
+                elif k in (ord("v"), ord("V")):
+                    views.clear()
+                    last_det = None
+                    evaluation = None
+                    report["views"] = []
+                    report["evaluation"] = None
+                    save_json_atomic(report_path, report)
+                    message = "Views limpas."
+                    message_t = time.time()
+
+                elif k in (ord("s"), ord("S")):
+                    snap = snapshots_dir / f"board_{stamp()}.png"
+                    cv2.imwrite(str(snap), ui)
+                    message = f"Snapshot={snap.name}"
+                    message_t = time.time()
+
+                elif k in (ord("g"), ord("G")):
+                    try:
+                        trip = runtime.fresh_triplet()
+                        det = detect_triplet(
+                            trip, board, dictionary, params
+                        )
+                        last_det = det
+
+                        if not det["valid"]:
+                            message = "REJEITADO: " + " | ".join(det["reasons"])
+                            message_t = time.time()
+                            continue
+
+                        pose = {
+                            role: pose_signature(
+                                det["detections"][role]["points"],
+                                trip["frames"][role].shape[:2],
+                            )
+                            for role in ROLES
+                        }
+
+                        if duplicate_pose(pose, views):
+                            message = (
+                                "REJEITADO: pose repetida. "
+                                "Mude posição/distância/inclinação."
+                            )
+                            message_t = time.time()
+                            continue
+
+                        view_id = len(views) + 1
+
+                        view = {
+                            "view_id": view_id,
+                            "captured_at": now_str(),
+                            "pose": pose,
+                            "controls": trip["controls"],
+                            "detections": {
+                                role: {
+                                    "markers": det["detections"][role]["markers"],
+                                    "corners": det["detections"][role]["corners"],
+                                    "points": {
+                                        str(cid): pt
+                                        for cid, pt in det["detections"][role]["points"].items()
+                                    },
+                                }
+                                for role in ROLES
+                            },
+                        }
+
+                        preview = views_dir / f"view_{view_id:03d}.png"
+                        save_view_preview(
+                            preview, trip["frames"], det
+                        )
+                        view["preview_png"] = str(preview)
+
+                        views.append(view)
+                        report["views"] = views
+                        save_json_atomic(report_path, report)
+
+                        message = (
+                            f"VIEW #{view_id:03d} ACCEPTED | "
+                            f"RGB={view['detections']['rgb']['corners']} "
+                            f"RE={view['detections']['re']['corners']} "
+                            f"NIR={view['detections']['nir']['corners']}"
+                        )
+                        message_t = time.time()
+                        print("[VIEW]", message)
+
+                    except Exception as exc:
+                        message = f"Captura rejeitada: {exc}"
+                        message_t = time.time()
+
+                elif k in (ord("a"), ord("A")):
+                    if len(views) < QA["min_views"]:
+                        message = (
+                            f"Precisa >= {QA['min_views']} views. Atual={len(views)}"
+                        )
+                        message_t = time.time()
+                        continue
+
+                    evaluation = evaluate_all(
+                        views, board_pts, specs, args.distortion_model
+                    )
+
+                    report["evaluation"] = evaluation
+                    report["module_params_fragment"] = module_fragment(
+                        evaluation, specs
+                    )
+                    save_json_atomic(report_path, report)
+
+                    if evaluation["status"] == "bad":
+                        message = (
+                            "EVAL BAD: "
+                            + " | ".join(evaluation["reasons"][:5])
+                        )
+                        message_t = time.time()
+                        print("[BAD]", *evaluation["reasons"], sep="\n  ")
+                        continue
+
+                    if (
+                        evaluation["status"] == "warning"
+                        and not args.promote_warning
+                    ):
+                        message = (
+                            "EVAL WARNING. Capture mais/melhores views."
+                        )
+                        message_t = time.time()
+                        print("[WARNING]", *evaluation["reasons"], sep="\n  ")
+                        continue
+
+                    report["status"] = (
+                        "pass"
+                        if evaluation["status"] == "good"
+                        else "warning_promoted"
+                    )
+                    report["finished_at"] = now_str()
+                    report["promoted"] = True
+                    report["promoted_at"] = now_str()
+
+                    active = Path(args.active_json)
+
+                    if active.exists():
+                        backup = active.with_name(
+                            active.stem + ".backup_" + stamp() + active.suffix
+                        )
+                        shutil.copy2(active, backup)
+
+                    save_json_atomic(active, report)
+                    save_json_atomic(report_path, report)
+
+                    marker = candidate_dir / (
+                        "PASS.txt"
+                        if evaluation["status"] == "good"
+                        else "PASS_WARNING.txt"
+                    )
+                    marker.write_text(
+                        f"status={report['status']}\nactive_json={active}\n",
+                        encoding="utf-8",
+                    )
+
+                    print("=" * 82)
+                    print("[PASS] INTRINSICS PROMOTED")
+
+                    for role in ROLES:
+                        c = evaluation["cameras"][role]
+                        m = c["selected_model"]
+                        print(
+                            f"{role.upper()}: "
+                            f"{c['model_selection']['selected']} | "
+                            f"RMS={m['rms_px']:.3f}px | "
+                            f"CVmed={m['cross_validation']['overall']['median_px']:.3f}px | "
+                            f"edgeP95={m['distortion_shift']['edge_p95_px']:.1f}px | "
+                            f"{c['runtime_geometry_recommendation']}"
+                        )
+
+                    print("[RUNTIME] runtime_undistort permanece false.")
+                    print(
+                        "[IMPORTANTE] Se ativarmos undistort, recalibrar Homography "
+                        "no espaço undistorted."
+                    )
+                    print(f"[ACTIVE] {active}")
+                    print("=" * 82)
+
+                    final = np.zeros((720, 1280, 3), dtype=np.uint8)
+                    lines = [
+                        "INTRINSICS CALIBRATION - PASS",
+                        "",
+                    ]
+
+                    for role in ROLES:
+                        c = evaluation["cameras"][role]
+                        m = c["selected_model"]
+                        lines.append(
+                            f"{role.upper()}: {c['model_selection']['selected']} | "
+                            f"RMS={m['rms_px']:.3f}px | "
+                            f"CV={m['cross_validation']['overall']['median_px']:.3f}px | "
+                            f"edge={m['distortion_shift']['edge_p95_px']:.1f}px"
+                        )
+
+                    lines += [
+                        "",
+                        "runtime_undistort = DISABLED",
+                        f"Active: {active}",
+                        "",
+                        "Qualquer tecla fecha.",
+                    ]
+
+                    overlay(final, lines, x=42, y=62, scale=0.64, step=31)
+                    cv2.imshow(window, final)
+                    cv2.waitKey(0)
+                    break
+
+                time.sleep(0.001)
+
+    except KeyboardInterrupt as exc:
+        if report is None:
+            report = {
+                "schema": SCHEMA,
+                "session_id": sid,
+                "created_at": now_str(),
+            }
+
+        report["status"] = "cancelled"
+        report["promoted"] = False
+        report["finished_at"] = now_str()
+        report["error"] = str(exc)
+        report["views"] = views
+
+        try:
+            save_json_atomic(report_path, report)
+            (candidate_dir / "CANCELLED.txt").write_text(
+                f"reason={exc}\n", encoding="utf-8"
+            )
+        except Exception:
+            pass
+
+        print("[CANCELLED]", exc)
+        print("[SAFE] Intrinsics ativo anterior preservado.")
+
+    except Exception as exc:
+        if report is None:
+            report = {
+                "schema": SCHEMA,
+                "session_id": sid,
+                "created_at": now_str(),
+            }
+
+        report["status"] = "error"
+        report["promoted"] = False
+        report["finished_at"] = now_str()
+        report["error"] = f"{type(exc).__name__}: {exc}"
+        report["views"] = views
+
+        try:
+            save_json_atomic(report_path, report)
+            (candidate_dir / "FAIL.txt").write_text(
+                f"error={type(exc).__name__}: {exc}\n",
+                encoding="utf-8",
+            )
+        except Exception:
+            pass
+
+        print("[ERROR]", type(exc).__name__, exc)
+        print("[SAFE] Intrinsics ativo anterior preservado.")
+        raise
+
+    finally:
+        cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/_3_flatfield_calibration_tool.py b/Python/OAK/datasets/oak-fcc-3/utils/_3_flatfield_calibration_tool.py
new file mode 100644
index 000000000..680844c1e
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/utils/_3_flatfield_calibration_tool.py
@@ -0,0 +1,3089 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+flatfield_calibration_production.py
+===================================
+
+Calibrador de flat-field / dark-frame de PRODUÇÃO para o módulo OAK-FFC-3P.
+
+Setup suportado:
+    CAM_A = RGB = OV9782 (1280x800) OU AR0234 (1920x1200)
+    CAM_B = RE  = OV9282 (1280x800)
+    CAM_C = NIR = OV9282 (1280x800)
+
+Princípios de produto:
+----------------------
+1. Descobre e valida o hardware antes de iniciar.
+2. Configura resolução por SENSOR, não por resolução global.
+3. Captura RAW10 nativo de cada sensor.
+4. Usa o mesmo RawProcessorCore da aplicação para obter R/G/B/RE/NIR.
+5. Deixa AE estabilizar no WHITE e então CONGELA exposição/ISO.
+6. WHITE, DARK e WHITE-VALIDATION usam exatamente os mesmos controles.
+7. Não redimensiona dark para "caber": shape divergente é erro fatal.
+8. Rejeita frames temporalmente anômalos.
+9. Gera o gain a partir do campo suavizado antes da inversão.
+10. Valida o flat numa segunda captura WHITE que não participou do cálculo.
+11. Sempre salva um candidate auditável.
+12. Só promove para "active" se o acceptance test passar.
+13. Preserva calibração ativa anterior em caso de falha.
+14. Gera metadados, hashes, previews e sugestão de flatfield_config.
+
+Dependências:
+-------------
+    pip install depthai opencv-python numpy
+
+    Projeto:
+        core/raw_processor_core.py
+
+Fluxo:
+------
+    A) WHITE PREFLIGHT
+       - alvo branco/cinza fosco uniforme
+       - AE estabiliza
+       - ENTER congela exposição/ISO de cada câmera
+
+    B) WHITE A
+       - captura usada para construir o flat
+
+    C) DARK
+       - lentes totalmente tampadas
+       - MESMOS controles do WHITE A
+
+    D) WHITE B / VALIDATION
+       - destampar e voltar ao mesmo alvo
+       - MESMOS controles
+       - esta captura NÃO participa da construção
+       - mede ganho real de uniformidade antes/depois
+
+Saídas:
+-------
+    calibration/flatfield_candidates/<session_id>/
+        flatfield_maps.npz
+        report.json
+        previews/
+        PASS.txt ou FAIL.txt
+
+    Se PASS:
+        calibration/flatfield_maps_v1.npz
+        calibration/flatfield_maps_v1.json
+
+Observação importante sobre Bayer:
+----------------------------------
+    --rgb-bayer auto usa:
+        OV9782 -> BGGR   (configuração histórica do módulo deste projeto)
+        AR0234 -> GRBG   (CFA nativo usual do AR0234)
+
+    Para produto, o Bayer resolvido fica registrado no report.json.
+    Se o seu módulo AR0234 específico estiver montado com orientação/mirror
+    diferente, use --rgb-bayer RGGB|BGGR|GRBG|GBRG explicitamente.
+
+Exemplo:
+--------
+    python flatfield_calibration_production.py
+
+    python flatfield_calibration_production.py ^
+        --module-params calibration/module_params.json ^
+        --frames 60 ^
+        --validation-frames 30
+
+Teclas:
+-------
+    ENTER = avançar/iniciar etapa
+    Q/ESC = cancelar sem promover
+
+IMPORTANTE:
+-----------
+Este script é fail-closed. Um candidate pode ser salvo mesmo quando falha,
+mas uma calibração FAIL não sobrescreve o arquivo ativo.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import os
+import shutil
+import sys
+import time
+from collections import deque
+from dataclasses import dataclass, asdict
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, Optional, Tuple, List, Any
+
+import cv2
+import depthai as dai
+import numpy as np
+
+from core.raw_processor_core import RawProcessorCore
+
+
+# ============================================================
+# Constantes de produto
+# ============================================================
+
+ROLES = ("rgb", "re", "nir")
+CHANNELS = ("R", "G", "B", "RE", "NIR")
+
+ROLE_TO_SOCKET = {
+    "rgb": "CAM_A",
+    "re": "CAM_B",
+    "nir": "CAM_C",
+}
+
+EXPECTED_MONO_SENSOR = "OV9282"
+
+SUPPORTED_RGB = {
+    "OV9782": {
+        "width": 1280,
+        "height": 800,
+        "resolution_enum": "THE_800_P",
+        "default_bayer": "BGGR",
+    },
+    "AR0234": {
+        "width": 1920,
+        "height": 1200,
+        "resolution_enum": "THE_1200_P",
+        "default_bayer": "GRBG",
+    },
+}
+
+SUPPORTED_MONO = {
+    "OV9282": {
+        "width": 1280,
+        "height": 800,
+        "resolution_enum": "THE_800_P",
+    },
+}
+
+BAYER_PATTERNS = ("RGGB", "BGGR", "GRBG", "GBRG")
+
+# Raw10
+RAW10_MAX = 1023.0
+RAW10_WHITE_SAT = 1018
+RAW10_DARK_FLOOR = 16
+
+SCHEMA = "multispec_flatfield_production_v2"
+
+
+# ============================================================
+# Thresholds de QA
+# ============================================================
+
+DEFAULT_THRESHOLDS = {
+    # WHITE ao vivo / raw por role
+    "white_sat_warning_pct": 0.10,
+    "white_sat_bad_pct": 0.50,
+    "white_dark_warning_pct": 1.00,
+    "white_dark_bad_pct": 4.00,
+
+    # Estabilidade temporal da mediana global por role
+    "temporal_cv_warning": 0.005,   # 0.5%
+    "temporal_cv_bad": 0.015,       # 1.5%
+
+    # Rejeição de frame individual em relação à mediana temporal
+    "frame_global_deviation_reject": 0.05,  # 5%
+
+    # DARK: vazamento de luz / tampa inadequada
+    "dark_p99_warning": 0.08,
+    "dark_p99_bad": 0.16,
+
+    # Gain
+    "gain_p99_warning": 1.75,
+    "gain_p99_bad": 2.20,
+    "gain_max_warning": 2.20,
+    "gain_max_bad": 3.00,
+    "gain_min_warning": 0.55,
+    "gain_min_bad": 0.35,
+
+    # Defeitos locais no sinal branco após remover shading suave
+    "local_dev_p99_warning": 0.12,
+    "local_dev_p99_bad": 0.20,
+    "local_outlier_pct_warning": 1.0,
+    "local_outlier_pct_bad": 3.0,
+
+    # Acceptance WHITE B após flat
+    "corrected_grid_cv_warning": 0.055,
+    "corrected_grid_cv_bad": 0.085,
+
+    # Só exige melhora relativa quando havia shading mensurável antes.
+    "min_before_grid_cv_for_improvement": 0.025,
+    "improvement_warning": 0.25,
+    "improvement_bad": 0.10,
+
+    # Se o flat piorar a uniformidade de forma clara, falha.
+    "worsening_bad": -0.05,
+
+    # Controle travado
+    "exposure_rel_tolerance": 0.01,
+    "exposure_abs_tolerance_us": 20,
+    "iso_tolerance": 5,
+}
+
+
+# ============================================================
+# Utilidades
+# ============================================================
+
+def now_str() -> str:
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+def session_id() -> str:
+    return datetime.now().strftime("%Y%m%d_%H%M%S")
+
+
+def ensure_dir(path: str | Path):
+    Path(path).mkdir(parents=True, exist_ok=True)
+
+
+def load_json(path: Optional[str]) -> dict:
+    if not path:
+        return {}
+    p = Path(path)
+    if not p.is_file():
+        return {}
+    with p.open("r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def save_json(path: str | Path, data: dict):
+    p = Path(path)
+    ensure_dir(p.parent)
+    tmp = p.with_suffix(p.suffix + ".tmp")
+    with tmp.open("w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+        f.flush()
+        os.fsync(f.fileno())
+    os.replace(tmp, p)
+
+
+def sha256_file(path: str | Path) -> str:
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        while True:
+            chunk = f.read(1024 * 1024)
+            if not chunk:
+                break
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def safe_float(v, default=None):
+    try:
+        if v is None:
+            return default
+        return float(v)
+    except Exception:
+        return default
+
+
+def safe_int(v, default=None):
+    try:
+        if v is None:
+            return default
+        return int(v)
+    except Exception:
+        return default
+
+
+def clamp(v, lo, hi):
+    return max(lo, min(hi, v))
+
+
+def finite_percentile(arr: np.ndarray, pct: float, default=0.0) -> float:
+    f = arr[np.isfinite(arr)]
+    if f.size == 0:
+        return float(default)
+    return float(np.percentile(f, pct))
+
+
+def robust_median(arr: np.ndarray, margin_frac=0.04) -> float:
+    a = arr.astype(np.float32)
+    h, w = a.shape[:2]
+    mx = int(w * margin_frac)
+    my = int(h * margin_frac)
+    if w - 2 * mx > 8 and h - 2 * my > 8:
+        a = a[my:h-my, mx:w-mx]
+    f = a[np.isfinite(a)]
+    if f.size == 0:
+        return 0.0
+    return float(np.median(f))
+
+
+def robust_center_reference(arr: np.ndarray, center_frac=0.44) -> float:
+    """
+    Referência do flat no centro óptico, robusta contra pixels ruins.
+    Mantém ganho ~1 no centro.
+    """
+    a = arr.astype(np.float32)
+    h, w = a.shape[:2]
+
+    cw = max(16, int(w * center_frac))
+    ch = max(16, int(h * center_frac))
+
+    x0 = (w - cw) // 2
+    y0 = (h - ch) // 2
+
+    core = a[y0:y0+ch, x0:x0+cw]
+    f = core[np.isfinite(core)]
+
+    if f.size == 0:
+        f = a[np.isfinite(a)]
+
+    if f.size == 0:
+        return 1.0
+
+    lo = np.percentile(f, 10)
+    hi = np.percentile(f, 90)
+    trimmed = f[(f >= lo) & (f <= hi)]
+    if trimmed.size:
+        f = trimmed
+
+    v = float(np.median(f))
+    return v if np.isfinite(v) and v > 1e-8 else 1.0
+
+
+def normalize_display(arr: np.ndarray, low=1.0, high=99.0) -> np.ndarray:
+    a = arr.astype(np.float32)
+    f = a[np.isfinite(a)]
+    if f.size == 0:
+        return np.zeros(a.shape[:2], dtype=np.uint8)
+
+    lo = float(np.percentile(f, low))
+    hi = float(np.percentile(f, high))
+
+    if hi <= lo:
+        hi = lo + 1e-6
+
+    out = (a - lo) / (hi - lo)
+    return np.clip(out * 255.0, 0, 255).astype(np.uint8)
+
+
+def raw10_display(raw16: np.ndarray) -> np.ndarray:
+    u8 = np.clip(raw16.astype(np.float32) * (255.0 / RAW10_MAX), 0, 255).astype(np.uint8)
+    return u8
+
+
+def overlay_hud(
+    img,
+    lines,
+    x=12,
+    y=24,
+    font_scale=0.52,
+    line_step=21,
+    color=(255, 255, 255),
+):
+    yy = int(y)
+    h = img.shape[0]
+
+    for line in lines:
+        if yy > h - 6:
+            break
+
+        s = str(line)
+        cv2.putText(
+            img, s, (int(x), yy),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            font_scale, (0, 0, 0), 3, cv2.LINE_AA,
+        )
+        cv2.putText(
+            img, s, (int(x), yy),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            font_scale, color, 1, cv2.LINE_AA,
+        )
+        yy += int(line_step)
+
+
+def resize_panel(img: np.ndarray, width: int, height: int) -> np.ndarray:
+    return cv2.resize(img, (int(width), int(height)), interpolation=cv2.INTER_AREA)
+
+
+def status_rank(status: str) -> int:
+    return {"good": 0, "warning": 1, "bad": 2}.get(str(status).lower(), 2)
+
+
+def merge_status(a: str, b: str) -> str:
+    return a if status_rank(a) >= status_rank(b) else b
+
+
+def get_timestamp_seconds(pkt) -> float:
+    for name in ("getTimestampDevice", "getTimestamp"):
+        fn = getattr(pkt, name, None)
+        if callable(fn):
+            try:
+                ts = fn()
+                if hasattr(ts, "total_seconds"):
+                    return float(ts.total_seconds())
+                return float(ts)
+            except Exception:
+                pass
+    return time.monotonic()
+
+
+def packet_controls(pkt) -> dict:
+    exp_us = None
+    iso = None
+    color_temp = None
+    seq = None
+
+    try:
+        exp = pkt.getExposureTime()
+        if hasattr(exp, "total_seconds"):
+            exp_us = int(round(exp.total_seconds() * 1_000_000))
+        else:
+            exp_us = int(exp)
+    except Exception:
+        pass
+
+    try:
+        iso = int(pkt.getSensitivity())
+    except Exception:
+        pass
+
+    try:
+        color_temp = int(pkt.getColorTemperature())
+    except Exception:
+        pass
+
+    try:
+        seq = int(pkt.getSequenceNum())
+    except Exception:
+        pass
+
+    return {
+        "exposure_time_us": exp_us,
+        "sensitivity_iso": iso,
+        "color_temperature_k": color_temp,
+        "sequence_num": seq,
+        "timestamp_s": get_timestamp_seconds(pkt),
+    }
+
+
+# ============================================================
+# RAW10 pack/unpack
+# ============================================================
+
+def unpack_raw10(data, width: int, height: int, stride: Optional[int] = None) -> np.ndarray:
+    """
+    Unpack RAW10 MIPI: 4 pixels / 5 bytes.
+    Retorna uint16 0..1023.
+    Vetorizado e com suporte a stride.
+    """
+    width = int(width)
+    height = int(height)
+
+    if width <= 0 or height <= 0:
+        raise ValueError(f"Dimensão RAW inválida: {width}x{height}")
+
+    if width % 4 != 0:
+        raise ValueError(f"RAW10 width precisa ser múltiplo de 4: {width}")
+
+    arr = np.asarray(data, dtype=np.uint8).reshape(-1)
+
+    min_row_bytes = (width // 4) * 5
+
+    if stride is None or int(stride) <= 0:
+        stride = min_row_bytes
+
+    stride = int(stride)
+
+    expected = stride * height
+    if arr.size < expected:
+        raise ValueError(
+            f"RAW10 curto: bytes={arr.size}, esperado>={expected} "
+            f"({width}x{height}, stride={stride})"
+        )
+
+    rows = arr[:expected].reshape(height, stride)
+    payload = rows[:, :min_row_bytes]
+
+    groups = payload.reshape(height, width // 4, 5)
+
+    out = np.empty((height, width // 4, 4), dtype=np.uint16)
+
+    out[:, :, 0] = (groups[:, :, 0].astype(np.uint16) << 2) | (groups[:, :, 4] & 0x03)
+    out[:, :, 1] = (groups[:, :, 1].astype(np.uint16) << 2) | ((groups[:, :, 4] >> 2) & 0x03)
+    out[:, :, 2] = (groups[:, :, 2].astype(np.uint16) << 2) | ((groups[:, :, 4] >> 4) & 0x03)
+    out[:, :, 3] = (groups[:, :, 3].astype(np.uint16) << 2) | ((groups[:, :, 4] >> 6) & 0x03)
+
+    return np.ascontiguousarray(out.reshape(height, width))
+
+
+def pack_raw10(raw16: np.ndarray) -> np.ndarray:
+    """
+    Reempacota uint16 0..1023 no formato esperado pelo RawProcessorCore:
+    shape=(H, W*5/4), uint8.
+    """
+    raw = np.clip(np.rint(raw16), 0, 1023).astype(np.uint16)
+
+    h, w = raw.shape[:2]
+    if w % 4 != 0:
+        raise ValueError(f"RAW10 pack requer width múltiplo de 4: {w}")
+
+    px = raw.reshape(h, w // 4, 4)
+
+    packed = np.empty((h, w // 4, 5), dtype=np.uint8)
+
+    packed[:, :, 0] = (px[:, :, 0] >> 2).astype(np.uint8)
+    packed[:, :, 1] = (px[:, :, 1] >> 2).astype(np.uint8)
+    packed[:, :, 2] = (px[:, :, 2] >> 2).astype(np.uint8)
+    packed[:, :, 3] = (px[:, :, 3] >> 2).astype(np.uint8)
+
+    low = (
+        (px[:, :, 0] & 0x03)
+        | ((px[:, :, 1] & 0x03) << 2)
+        | ((px[:, :, 2] & 0x03) << 4)
+        | ((px[:, :, 3] & 0x03) << 6)
+    )
+
+    packed[:, :, 4] = low.astype(np.uint8)
+
+    return np.ascontiguousarray(packed.reshape(h, (w // 4) * 5))
+
+
+# ============================================================
+# Hardware discovery
+# ============================================================
+
+@dataclass
+class CameraSpec:
+    role: str
+    socket_name: str
+    sensor_name: str
+    width: int
+    height: int
+    resolution_enum: str
+    stream_name: str
+    control_name: str
+    is_color: bool
+    bayer_pattern: Optional[str] = None
+
+
+def device_id_from_info(dev_info) -> Optional[str]:
+    for name in ("getMxId", "getDeviceId"):
+        try:
+            fn = getattr(dev_info, name, None)
+            if callable(fn):
+                v = fn()
+                if v:
+                    return str(v)
+        except Exception:
+            pass
+
+    for name in ("mxid", "deviceId"):
+        try:
+            v = getattr(dev_info, name, None)
+            if v:
+                return str(v)
+        except Exception:
+            pass
+
+    return None
+
+
+def select_device_info(mx_id: Optional[str]):
+    devices = dai.Device.getAllAvailableDevices()
+
+    if not devices:
+        raise RuntimeError("Nenhum dispositivo OAK/DepthAI encontrado.")
+
+    if not mx_id:
+        return devices[0]
+
+    target = str(mx_id).strip()
+
+    for info in devices:
+        if device_id_from_info(info) == target:
+            return info
+
+    available = [device_id_from_info(x) for x in devices]
+    raise RuntimeError(f"MX ID {target!r} não encontrado. Disponíveis: {available}")
+
+
+def discover_features(dev_info):
+    with dai.Device(dev_info) as dev:
+        rows = []
+
+        for f in dev.getConnectedCameraFeatures():
+            rows.append({
+                "socket_name": str(f.socket.name),
+                "sensor_name": str(f.sensorName or ""),
+                "width": int(getattr(f, "width", 0) or 0),
+                "height": int(getattr(f, "height", 0) or 0),
+                "supported_types": [str(x) for x in (getattr(f, "supportedTypes", []) or [])],
+                "has_autofocus_ic": int(getattr(f, "hasAutofocusIC", 0) or 0),
+            })
+
+        usb = None
+        try:
+            usb = str(dev.getUsbSpeed())
+        except Exception:
+            pass
+
+    return rows, usb
+
+
+def get_socket(name: str):
+    name = str(name).upper()
+
+    mapping = {
+        "CAM_A": dai.CameraBoardSocket.CAM_A,
+        "CAM_B": dai.CameraBoardSocket.CAM_B,
+        "CAM_C": dai.CameraBoardSocket.CAM_C,
+    }
+
+    if name not in mapping:
+        raise ValueError(f"Socket não suportado: {name}")
+
+    return mapping[name]
+
+
+def resolve_bayer(sensor_name: str, arg_value: str, module_params: dict) -> str:
+    arg = str(arg_value or "auto").upper()
+
+    if arg != "AUTO":
+        if arg not in BAYER_PATTERNS:
+            raise ValueError(f"Bayer inválido: {arg}")
+        return arg
+
+    # 1) Procura configuração explícita no module_params.
+    candidates = [
+        module_params.get("bayer_pattern"),
+        module_params.get("bayer"),
+    ]
+
+    cam_a_cfg = (
+        module_params.get("camera_info", {}).get("CAM_A", {})
+        if isinstance(module_params.get("camera_info"), dict)
+        else {}
+    )
+
+    if isinstance(cam_a_cfg, dict):
+        candidates.extend([
+            cam_a_cfg.get("bayer_pattern"),
+            cam_a_cfg.get("bayer"),
+        ])
+
+    for c in candidates:
+        if c and str(c).upper() in BAYER_PATTERNS:
+            return str(c).upper()
+
+    # 2) Fallback sensor-aware.
+    sensor = str(sensor_name).upper()
+    if sensor in SUPPORTED_RGB:
+        return SUPPORTED_RGB[sensor]["default_bayer"]
+
+    raise RuntimeError(
+        f"Não foi possível resolver Bayer automaticamente para {sensor_name}. "
+        f"Use --rgb-bayer explicitamente."
+    )
+
+
+def validate_and_build_specs(
+    features: list[dict],
+    rgb_bayer_arg: str,
+    module_params: dict,
+) -> Dict[str, CameraSpec]:
+    by_socket = {x["socket_name"]: x for x in features}
+
+    missing = [sock for sock in ("CAM_A", "CAM_B", "CAM_C") if sock not in by_socket]
+    if missing:
+        raise RuntimeError(f"Módulo incompleto. Sockets ausentes: {missing}")
+
+    rgb_row = by_socket["CAM_A"]
+    re_row = by_socket["CAM_B"]
+    nir_row = by_socket["CAM_C"]
+
+    rgb_sensor = rgb_row["sensor_name"].upper()
+
+    if rgb_sensor not in SUPPORTED_RGB:
+        raise RuntimeError(
+            f"CAM_A deve ser OV9782 ou AR0234. Detectado: {rgb_row['sensor_name']}"
+        )
+
+    for role, row in (("RE", re_row), ("NIR", nir_row)):
+        if row["sensor_name"].upper() != EXPECTED_MONO_SENSOR:
+            raise RuntimeError(
+                f"{role} deve usar OV9282. Detectado em "
+                f"{'CAM_B' if role == 'RE' else 'CAM_C'}: {row['sensor_name']}"
+            )
+
+    rgb_cfg = SUPPORTED_RGB[rgb_sensor]
+    bayer = resolve_bayer(rgb_sensor, rgb_bayer_arg, module_params)
+
+    specs = {
+        "rgb": CameraSpec(
+            role="rgb",
+            socket_name="CAM_A",
+            sensor_name=rgb_sensor,
+            width=rgb_cfg["width"],
+            height=rgb_cfg["height"],
+            resolution_enum=rgb_cfg["resolution_enum"],
+            stream_name="raw_rgb",
+            control_name="ctrl_rgb",
+            is_color=True,
+            bayer_pattern=bayer,
+        ),
+        "re": CameraSpec(
+            role="re",
+            socket_name="CAM_B",
+            sensor_name="OV9282",
+            width=1280,
+            height=800,
+            resolution_enum="THE_800_P",
+            stream_name="raw_re",
+            control_name="ctrl_re",
+            is_color=False,
+        ),
+        "nir": CameraSpec(
+            role="nir",
+            socket_name="CAM_C",
+            sensor_name="OV9282",
+            width=1280,
+            height=800,
+            resolution_enum="THE_800_P",
+            stream_name="raw_nir",
+            control_name="ctrl_nir",
+            is_color=False,
+        ),
+    }
+
+    # Valida o que o device anunciou, sem aceitar silent mismatch.
+    for role, spec in specs.items():
+        row = by_socket[spec.socket_name]
+
+        if row["width"] and row["height"]:
+            if (row["width"], row["height"]) != (spec.width, spec.height):
+                # Alguns firmwares podem anunciar dimensão diferente da mode table.
+                # Produto: não adivinhamos.
+                raise RuntimeError(
+                    f"{spec.socket_name}/{spec.sensor_name}: dimensão anunciada "
+                    f"{row['width']}x{row['height']} diverge da esperada "
+                    f"{spec.width}x{spec.height}."
+                )
+
+    return specs
+
+
+# ============================================================
+# Pipeline DepthAI standalone
+# ============================================================
+
+def resolve_antibanding(mode: str):
+    mode = str(mode or "OFF").upper()
+
+    enum_cls = dai.CameraControl.AntiBandingMode
+
+    names = {
+        "OFF": "OFF",
+        "50": "MAINS_50_HZ",
+        "50HZ": "MAINS_50_HZ",
+        "60": "MAINS_60_HZ",
+        "60HZ": "MAINS_60_HZ",
+    }
+
+    enum_name = names.get(mode)
+    if not enum_name:
+        raise ValueError(f"anti-banding inválido: {mode}")
+
+    return getattr(enum_cls, enum_name)
+
+
+def build_pipeline(specs: Dict[str, CameraSpec], fps: float, anti_banding: str):
+    pipeline = dai.Pipeline()
+
+    anti_enum = resolve_antibanding(anti_banding)
+
+    for role in ROLES:
+        spec = specs[role]
+        socket = get_socket(spec.socket_name)
+
+        if spec.is_color:
+            cam = pipeline.createColorCamera()
+            cam.setBoardSocket(socket)
+
+            enum_value = getattr(
+                dai.ColorCameraProperties.SensorResolution,
+                spec.resolution_enum,
+                None,
+            )
+
+            if enum_value is None:
+                raise RuntimeError(
+                    f"DepthAI instalado não expõe ColorCameraProperties."
+                    f"SensorResolution.{spec.resolution_enum}."
+                )
+
+            cam.setResolution(enum_value)
+            cam.setFps(float(fps))
+            cam.setInterleaved(False)
+
+        else:
+            cam = pipeline.create(dai.node.MonoCamera)
+            cam.setBoardSocket(socket)
+
+            enum_value = getattr(
+                dai.MonoCameraProperties.SensorResolution,
+                spec.resolution_enum,
+                None,
+            )
+
+            if enum_value is None:
+                raise RuntimeError(
+                    f"DepthAI instalado não expõe MonoCameraProperties."
+                    f"SensorResolution.{spec.resolution_enum}."
+                )
+
+            cam.setResolution(enum_value)
+            cam.setFps(float(fps))
+
+        # AE inicia ativo para o preflight WHITE.
+        try:
+            cam.initialControl.setAutoExposureEnable()
+        except Exception:
+            pass
+
+        try:
+            cam.initialControl.setAntiBandingMode(anti_enum)
+        except Exception:
+            pass
+
+        if spec.is_color:
+            try:
+                cam.initialControl.setAutoWhiteBalanceLock(False)
+            except Exception:
+                pass
+
+        # RAW obrigatório.
+        if not hasattr(cam, "raw"):
+            raise RuntimeError(
+                f"{spec.socket_name}/{spec.sensor_name} não expõe saída raw "
+                "nesta versão do DepthAI."
+            )
+
+        xout = pipeline.createXLinkOut()
+        xout.setStreamName(spec.stream_name)
+        cam.raw.link(xout.input)
+
+        xin = pipeline.createXLinkIn()
+        xin.setStreamName(spec.control_name)
+
+        if not hasattr(cam, "inputControl"):
+            raise RuntimeError(
+                f"{spec.socket_name}/{spec.sensor_name} sem inputControl."
+            )
+
+        xin.out.link(cam.inputControl)
+
+    return pipeline
+
+
+# ============================================================
+# Controle manual de exposição
+# ============================================================
+
+@dataclass
+class LockedControl:
+    role: str
+    exposure_time_us: int
+    sensitivity_iso: int
+    source: str
+
+
+def send_manual_control(queue, control: LockedControl, lock_awb=False):
+    ctrl = dai.CameraControl()
+    ctrl.setManualExposure(
+        int(control.exposure_time_us),
+        int(control.sensitivity_iso),
+    )
+
+    if lock_awb:
+        try:
+            ctrl.setAutoWhiteBalanceLock(True)
+        except Exception:
+            pass
+
+    queue.send(ctrl)
+
+
+def controls_match(actual: dict, target: LockedControl, th: dict) -> bool:
+    exp = safe_int(actual.get("exposure_time_us"), None)
+    iso = safe_int(actual.get("sensitivity_iso"), None)
+
+    if exp is None or iso is None:
+        return False
+
+    exp_tol = max(
+        int(th["exposure_abs_tolerance_us"]),
+        int(round(target.exposure_time_us * th["exposure_rel_tolerance"])),
+    )
+
+    if abs(exp - target.exposure_time_us) > exp_tol:
+        return False
+
+    if abs(iso - target.sensitivity_iso) > int(th["iso_tolerance"]):
+        return False
+
+    return True
+
+
+# ============================================================
+# Estatísticas / QA
+# ============================================================
+
+def raw_stats(raw16: np.ndarray) -> dict:
+    a = raw16.astype(np.float32) / RAW10_MAX
+
+    return {
+        "mean": float(np.mean(a)),
+        "std": float(np.std(a)),
+        "p01": float(np.percentile(a, 1)),
+        "p05": float(np.percentile(a, 5)),
+        "p50": float(np.percentile(a, 50)),
+        "p95": float(np.percentile(a, 95)),
+        "p99": float(np.percentile(a, 99)),
+        "sat_pct": float((raw16 >= RAW10_WHITE_SAT).mean() * 100.0),
+        "dark_pct": float((raw16 <= RAW10_DARK_FLOOR).mean() * 100.0),
+    }
+
+
+def temporal_cv(values: List[float]) -> float:
+    if len(values) < 3:
+        return float("inf")
+
+    arr = np.array(values, dtype=np.float64)
+    mean = float(np.mean(arr))
+
+    if abs(mean) <= 1e-12:
+        return float("inf")
+
+    return float(np.std(arr) / abs(mean))
+
+
+def local_defect_stats(
+    img01: np.ndarray,
+    blur_frac: float = 0.18,
+    margin_frac: float = 0.04,
+    outlier_thr: float = 0.10,
+) -> dict:
+    a = img01.astype(np.float32)
+
+    h, w = a.shape[:2]
+    mx = int(w * margin_frac)
+    my = int(h * margin_frac)
+
+    if w - 2 * mx > 32 and h - 2 * my > 32:
+        a = a[my:h-my, mx:w-mx]
+
+    h, w = a.shape[:2]
+
+    k = int(round(min(h, w) * blur_frac))
+    k = max(31, k)
+    if k % 2 == 0:
+        k += 1
+
+    # Kernel OpenCV não pode exceder a imagem.
+    k = min(k, min(h, w) - (1 - min(h, w) % 2))
+    if k < 3:
+        return {"valid": False}
+
+    smooth = cv2.GaussianBlur(
+        a, (k, k), 0,
+        borderType=cv2.BORDER_REFLECT,
+    )
+
+    ratio = a / np.maximum(smooth, 1e-6)
+    resid = np.abs(ratio - 1.0)
+
+    f = resid[np.isfinite(resid)]
+    if f.size == 0:
+        return {"valid": False}
+
+    return {
+        "valid": True,
+        "blur_ksize": int(k),
+        "local_dev_p95": float(np.percentile(f, 95)),
+        "local_dev_p99": float(np.percentile(f, 99)),
+        "local_dev_max": float(np.max(f)),
+        "local_outlier_pct": float((f >= outlier_thr).mean() * 100.0),
+        "outlier_threshold": float(outlier_thr),
+    }
+
+
+def grid_uniformity(img01: np.ndarray, grid_size=5, margin_frac=0.05) -> dict:
+    a = img01.astype(np.float32)
+
+    h, w = a.shape[:2]
+    mx = int(w * margin_frac)
+    my = int(h * margin_frac)
+
+    if w - 2 * mx > 16 and h - 2 * my > 16:
+        a = a[my:h-my, mx:w-mx]
+
+    h, w = a.shape[:2]
+
+    vals = []
+    grid = []
+
+    for gy in range(grid_size):
+        row = []
+        y0 = int(round(gy * h / grid_size))
+        y1 = int(round((gy + 1) * h / grid_size))
+
+        for gx in range(grid_size):
+            x0 = int(round(gx * w / grid_size))
+            x1 = int(round((gx + 1) * w / grid_size))
+
+            cell = a[y0:y1, x0:x1]
+            f = cell[np.isfinite(cell)]
+
+            v = float(np.median(f)) if f.size else 0.0
+            vals.append(v)
+            row.append(v)
+
+        grid.append(row)
+
+    v = np.array(vals, dtype=np.float64)
+    center = float(grid[grid_size // 2][grid_size // 2])
+    med = float(np.median(v))
+
+    den = max(abs(med), 1e-9)
+
+    return {
+        "grid": grid,
+        "median": med,
+        "center": center,
+        "min": float(np.min(v)),
+        "max": float(np.max(v)),
+        "grid_cv": float(np.std(v) / den),
+        "range_over_median": float((np.max(v) - np.min(v)) / den),
+    }
+
+
+def evaluate_live_white(
+    latest_raw: Dict[str, np.ndarray],
+    temporal_medians: Dict[str, deque],
+    th: dict,
+) -> dict:
+    report = {
+        "status": "good",
+        "roles": {},
+        "reasons": [],
+    }
+
+    for role in ROLES:
+        raw = latest_raw.get(role)
+
+        if raw is None:
+            report["status"] = "bad"
+            report["roles"][role] = {
+                "status": "bad",
+                "reasons": ["sem_frame"],
+            }
+            continue
+
+        st = raw_stats(raw)
+        cv = temporal_cv(list(temporal_medians[role]))
+
+        status = "good"
+        reasons = []
+
+        if st["sat_pct"] > th["white_sat_bad_pct"]:
+            status = merge_status(status, "bad")
+            reasons.append(f"sat_bad:{st['sat_pct']:.3f}%")
+        elif st["sat_pct"] > th["white_sat_warning_pct"]:
+            status = merge_status(status, "warning")
+            reasons.append(f"sat_warn:{st['sat_pct']:.3f}%")
+
+        if st["dark_pct"] > th["white_dark_bad_pct"]:
+            status = merge_status(status, "bad")
+            reasons.append(f"dark_bad:{st['dark_pct']:.3f}%")
+        elif st["dark_pct"] > th["white_dark_warning_pct"]:
+            status = merge_status(status, "warning")
+            reasons.append(f"dark_warn:{st['dark_pct']:.3f}%")
+
+        if np.isfinite(cv):
+            if cv > th["temporal_cv_bad"]:
+                status = merge_status(status, "bad")
+                reasons.append(f"temporal_cv_bad:{cv:.4f}")
+            elif cv > th["temporal_cv_warning"]:
+                status = merge_status(status, "warning")
+                reasons.append(f"temporal_cv_warn:{cv:.4f}")
+
+        report["roles"][role] = {
+            "status": status,
+            "stats": st,
+            "temporal_cv": None if not np.isfinite(cv) else cv,
+            "reasons": reasons,
+        }
+
+        report["status"] = merge_status(report["status"], status)
+        report["reasons"].extend([f"{role}:{x}" for x in reasons])
+
+    return report
+
+
+# ============================================================
+# Acumulador streaming
+# ============================================================
+
+class MeanAccumulator:
+    """
+    Média por pixel com rejeição de frames baseada na mediana global.
+    Não guarda N frames full-res em RAM.
+    """
+
+    def __init__(self, shape: Tuple[int, int], reject_fraction: float):
+        self.shape = tuple(shape)
+        self.reject_fraction = float(reject_fraction)
+
+        self.sum = np.zeros(self.shape, dtype=np.float64)
+        self.count = 0
+        self.rejected = 0
+
+        self.global_medians = deque(maxlen=25)
+        self.accepted_medians = []
+        self.frame_controls = []
+        self.frame_meta = []
+
+    def try_add(self, raw16: np.ndarray, ctrl: dict, meta: dict) -> bool:
+        if raw16.shape != self.shape:
+            raise RuntimeError(
+                f"Shape mudou durante captura: esperado={self.shape}, recebido={raw16.shape}"
+            )
+
+        med = robust_median(raw16)
+
+        if len(self.global_medians) >= 5:
+            ref = float(np.median(np.array(self.global_medians, dtype=np.float64)))
+
+            if abs(ref) > 1e-9:
+                dev = abs(med - ref) / abs(ref)
+
+                if dev > self.reject_fraction:
+                    self.rejected += 1
+                    return False
+
+        self.global_medians.append(med)
+        self.accepted_medians.append(float(med))
+
+        self.sum += raw16.astype(np.float64)
+        self.count += 1
+
+        self.frame_controls.append(dict(ctrl))
+        self.frame_meta.append(dict(meta))
+
+        return True
+
+    def mean(self) -> np.ndarray:
+        if self.count <= 0:
+            raise RuntimeError("Nenhum frame aceito no acumulador.")
+        return (self.sum / float(self.count)).astype(np.float32)
+
+    def summary(self) -> dict:
+        vals = np.array(self.accepted_medians, dtype=np.float64)
+
+        return {
+            "accepted": int(self.count),
+            "rejected": int(self.rejected),
+            "global_median_mean": float(np.mean(vals)) if vals.size else None,
+            "global_median_std": float(np.std(vals)) if vals.size else None,
+            "global_median_cv": (
+                float(np.std(vals) / max(abs(np.mean(vals)), 1e-9))
+                if vals.size else None
+            ),
+        }
+
+
+# ============================================================
+# Contexto de aquisição
+# ============================================================
+
+class AcquisitionContext:
+    def __init__(
+        self,
+        device,
+        specs: Dict[str, CameraSpec],
+        thresholds: dict,
+        panel_width: int,
+        panel_height: int,
+        rgb_bayer: str,
+    ):
+        self.device = device
+        self.specs = specs
+        self.thresholds = thresholds
+        self.panel_width = int(panel_width)
+        self.panel_height = int(panel_height)
+        self.rgb_bayer = rgb_bayer
+
+        self.raw_queues = {
+            role: device.getOutputQueue(
+                name=spec.stream_name,
+                maxSize=2,
+                blocking=False,
+            )
+            for role, spec in specs.items()
+        }
+
+        self.control_queues = {
+            role: device.getInputQueue(spec.control_name)
+            for role, spec in specs.items()
+        }
+
+        self.latest_raw: Dict[str, Optional[np.ndarray]] = {r: None for r in ROLES}
+        self.latest_ctrl: Dict[str, dict] = {r: {} for r in ROLES}
+        self.latest_packet_meta: Dict[str, dict] = {r: {} for r in ROLES}
+        self.latest_seq: Dict[str, Optional[int]] = {r: None for r in ROLES}
+
+        self.temporal_medians = {
+            r: deque(maxlen=20)
+            for r in ROLES
+        }
+
+        self.last_ui_time = 0.0
+
+    def decode_packet(self, role: str, pkt):
+        spec = self.specs[role]
+
+        # Produto: RAW10 obrigatório para estes três sensores.
+        raw_type = str(pkt.getType()).upper()
+
+        if "RAW10" not in raw_type:
+            raise RuntimeError(
+                f"{role.upper()}/{spec.sensor_name}: esperado RAW10, recebido {pkt.getType()}"
+            )
+
+        width = int(pkt.getWidth())
+        height = int(pkt.getHeight())
+
+        if (width, height) != (spec.width, spec.height):
+            raise RuntimeError(
+                f"{role.upper()}/{spec.sensor_name}: frame {width}x{height}, "
+                f"esperado {spec.width}x{spec.height}"
+            )
+
+        stride = None
+        try:
+            stride = int(pkt.getStride())
+        except Exception:
+            pass
+
+        raw16 = unpack_raw10(
+            pkt.getData(),
+            width=width,
+            height=height,
+            stride=stride,
+        )
+
+        ctrl = packet_controls(pkt)
+
+        seq = ctrl.get("sequence_num")
+        meta = {
+            "role": role,
+            "socket": spec.socket_name,
+            "sensor": spec.sensor_name,
+            "width": width,
+            "height": height,
+            "stride": stride,
+            "raw_type": raw_type,
+            "sequence_num": seq,
+            "timestamp_s": ctrl.get("timestamp_s"),
+        }
+
+        return raw16, ctrl, meta
+
+    def poll(self):
+        updated = []
+
+        for role in ROLES:
+            pkt = self.raw_queues[role].tryGet()
+            if pkt is None:
+                continue
+
+            raw16, ctrl, meta = self.decode_packet(role, pkt)
+
+            seq = ctrl.get("sequence_num")
+
+            if seq is not None and seq == self.latest_seq.get(role):
+                continue
+
+            self.latest_seq[role] = seq
+            self.latest_raw[role] = raw16
+            self.latest_ctrl[role] = ctrl
+            self.latest_packet_meta[role] = meta
+            self.temporal_medians[role].append(robust_median(raw16))
+
+            updated.append(role)
+
+        return updated
+
+    def wait_until_all_have_frames(self, timeout=8.0):
+        t0 = time.time()
+
+        while time.time() - t0 < timeout:
+            self.poll()
+
+            if all(self.latest_raw[r] is not None for r in ROLES):
+                return
+
+            time.sleep(0.003)
+
+        missing = [r for r in ROLES if self.latest_raw[r] is None]
+        raise TimeoutError(f"Timeout aguardando RAW das roles: {missing}")
+
+    def send_locked_controls(self, locked: Dict[str, LockedControl]):
+        for role in ROLES:
+            send_manual_control(
+                self.control_queues[role],
+                locked[role],
+                lock_awb=(role == "rgb"),
+            )
+
+    def verify_locked_controls(
+        self,
+        locked: Dict[str, LockedControl],
+        settle_frames=10,
+        timeout=10.0,
+    ):
+        matched = {r: 0 for r in ROLES}
+        last_seq = dict(self.latest_seq)
+
+        t0 = time.time()
+
+        while time.time() - t0 < timeout:
+            self.poll()
+
+            for role in ROLES:
+                seq = self.latest_seq.get(role)
+                if seq is None or seq == last_seq.get(role):
+                    continue
+
+                last_seq[role] = seq
+
+                if controls_match(
+                    self.latest_ctrl[role],
+                    locked[role],
+                    self.thresholds,
+                ):
+                    matched[role] += 1
+                else:
+                    matched[role] = 0
+
+            self.show_board(
+                title="TRAVANDO CONTROLES",
+                lines=[
+                    "Verificando exposição/ISO manual...",
+                    *[
+                        f"{r.upper()}: {matched[r]}/{settle_frames} "
+                        f"target={locked[r].exposure_time_us}us ISO{locked[r].sensitivity_iso} "
+                        f"actual={self.latest_ctrl[r].get('exposure_time_us')}us "
+                        f"ISO{self.latest_ctrl[r].get('sensitivity_iso')}"
+                        for r in ROLES
+                    ],
+                    "",
+                    "Não altere iluminação nem cubra as lentes ainda.",
+                ],
+            )
+
+            if all(matched[r] >= settle_frames for r in ROLES):
+                return
+
+            k = cv2.waitKey(1) & 0xFF
+            if k in (ord("q"), ord("Q"), 27):
+                raise KeyboardInterrupt("Cancelado durante lock de controles.")
+
+            time.sleep(0.001)
+
+        raise RuntimeError(
+            "Não foi possível confirmar controles manuais estáveis. "
+            f"matched={matched}"
+        )
+
+    def _rgb_raw_preview(self, raw16: np.ndarray) -> np.ndarray:
+        # Preview somente. A calibração usa RawProcessorCore.
+        code_map = {
+            "RGGB": cv2.COLOR_BayerRG2BGR,
+            "BGGR": cv2.COLOR_BayerBG2BGR,
+            "GRBG": cv2.COLOR_BayerGR2BGR,
+            "GBRG": cv2.COLOR_BayerGB2BGR,
+        }
+
+        u8 = raw10_display(raw16)
+
+        try:
+            bgr = cv2.cvtColor(u8, code_map[self.rgb_bayer])
+        except Exception:
+            bgr = cv2.cvtColor(u8, cv2.COLOR_GRAY2BGR)
+
+        return bgr
+
+    def show_board(
+        self,
+        title: str,
+        lines: List[str],
+        progress: Optional[Dict[str, Tuple[int, int]]] = None,
+        quality: Optional[dict] = None,
+    ):
+        pw = self.panel_width
+        ph = self.panel_height
+
+        panels = {}
+
+        for role in ROLES:
+            raw = self.latest_raw.get(role)
+            spec = self.specs[role]
+
+            if raw is None:
+                panel = np.zeros((ph, pw, 3), dtype=np.uint8)
+                overlay_hud(panel, [role.upper(), "aguardando frame"])
+                panels[role] = panel
+                continue
+
+            if role == "rgb":
+                view = self._rgb_raw_preview(raw)
+            else:
+                u8 = raw10_display(raw)
+                view = cv2.cvtColor(u8, cv2.COLOR_GRAY2BGR)
+
+            panel = resize_panel(view, pw, ph)
+
+            st = raw_stats(raw)
+            ctrl = self.latest_ctrl.get(role, {})
+
+            overlay_hud(
+                panel,
+                [
+                    f"{role.upper()} | {spec.socket_name} | {spec.sensor_name}",
+                    f"RAW10 {spec.width}x{spec.height}",
+                    f"p50={st['p50']:.3f} p99={st['p99']:.3f}",
+                    f"sat={st['sat_pct']:.3f}% dark={st['dark_pct']:.3f}%",
+                    f"EXP={ctrl.get('exposure_time_us')}us ISO={ctrl.get('sensitivity_iso')}",
+                ],
+                x=10,
+                y=22,
+                font_scale=0.46,
+                line_step=19,
+            )
+
+            panels[role] = panel
+
+        data = np.zeros((ph, pw, 3), dtype=np.uint8)
+
+        data_lines = [title, ""] + list(lines)
+
+        if quality:
+            data_lines.append("")
+            data_lines.append(f"PREFLIGHT: {quality.get('status', 'unknown').upper()}")
+
+            for role in ROLES:
+                item = quality.get("roles", {}).get(role, {})
+                st = item.get("status", "unknown").upper()
+                cv = item.get("temporal_cv")
+                cv_txt = "n/a" if cv is None else f"{cv*100:.2f}%"
+                data_lines.append(
+                    f"{role.upper()}: {st} temporal_cv={cv_txt}"
+                )
+
+                for reason in item.get("reasons", [])[:2]:
+                    data_lines.append(f"  ! {reason}")
+
+        if progress:
+            data_lines.append("")
+            for role in ROLES:
+                a, b = progress.get(role, (0, 0))
+                data_lines.append(f"{role.upper()}: {a}/{b}")
+
+        overlay_hud(
+            data,
+            data_lines,
+            x=14,
+            y=24,
+            font_scale=0.44,
+            line_step=18,
+        )
+
+        board = np.vstack([
+            np.hstack([panels["rgb"], panels["re"]]),
+            np.hstack([panels["nir"], data]),
+        ])
+
+        cv2.imshow("Flat Field Calibration - Production", board)
+        return board
+
+
+# ============================================================
+# Preflight / locking
+# ============================================================
+
+def resolve_manual_override(args, role: str):
+    exp = getattr(args, f"{role}_exp_us")
+    iso = getattr(args, f"{role}_iso")
+
+    if exp is None and iso is None:
+        return None
+
+    if exp is None or iso is None:
+        raise ValueError(
+            f"Para override manual de {role}, informe ambos "
+            f"--{role}-exp-us e --{role}-iso."
+        )
+
+    return LockedControl(
+        role=role,
+        exposure_time_us=int(exp),
+        sensitivity_iso=int(iso),
+        source="cli_manual",
+    )
+
+
+def choose_locked_controls(
+    ctx: AcquisitionContext,
+    args,
+) -> Dict[str, LockedControl]:
+    locked = {}
+
+    for role in ROLES:
+        override = resolve_manual_override(args, role)
+
+        if override is not None:
+            locked[role] = override
+            continue
+
+        # Usa mediana das últimas leituras recentes observadas.
+        exp_values = []
+        iso_values = []
+
+        # O estado latest contém a amostra mais recente. Para robustez,
+        # capturamos mais algumas amostras rapidamente.
+        t0 = time.time()
+
+        while time.time() - t0 < 0.7:
+            ctx.poll()
+
+            c = ctx.latest_ctrl.get(role, {})
+            exp = safe_int(c.get("exposure_time_us"), None)
+            iso = safe_int(c.get("sensitivity_iso"), None)
+
+            if exp is not None and exp > 0:
+                exp_values.append(exp)
+            if iso is not None and iso > 0:
+                iso_values.append(iso)
+
+            time.sleep(0.01)
+
+        if not exp_values or not iso_values:
+            raise RuntimeError(
+                f"Não foi possível ler exposição/ISO reais da câmera {role.upper()}."
+            )
+
+        locked[role] = LockedControl(
+            role=role,
+            exposure_time_us=int(round(np.median(exp_values))),
+            sensitivity_iso=int(round(np.median(iso_values))),
+            source="ae_snapshot",
+        )
+
+    return locked
+
+
+def wait_white_preflight(
+    ctx: AcquisitionContext,
+    args,
+    thresholds: dict,
+) -> Tuple[dict, Dict[str, LockedControl]]:
+    print("")
+    print("[ETAPA] WHITE PREFLIGHT")
+    print("Aponte para alvo branco/cinza fosco uniforme, preenchendo todo o FOV.")
+    print("A iluminação deve estar estável e sem reflexos.")
+    print("ENTER congela os controles quando o preflight estiver aprovado.")
+
+    ctx.wait_until_all_have_frames()
+
+    last_quality = None
+
+    while True:
+        ctx.poll()
+
+        last_quality = evaluate_live_white(
+            {r: ctx.latest_raw[r] for r in ROLES},
+            ctx.temporal_medians,
+            thresholds,
+        )
+
+        ctx.show_board(
+            title="ETAPA 0/3 - WHITE PREFLIGHT",
+            lines=[
+                "Alvo branco/cinza fosco uniforme.",
+                "Preencha TODO o campo de visão.",
+                "Aguarde AE estabilizar.",
+                "",
+                "ENTER = congelar EXP/ISO e iniciar",
+                "Q/ESC = cancelar",
+            ],
+            quality=last_quality,
+        )
+
+        k = cv2.waitKey(1) & 0xFF
+
+        if k in (ord("q"), ord("Q"), 27):
+            raise KeyboardInterrupt("Cancelado no WHITE PREFLIGHT.")
+
+        if k in (13, 10):
+            status = last_quality.get("status", "bad")
+
+            allowed = (
+                status == "good"
+                or (status == "warning" and args.allow_warning_preflight)
+                or args.force_preflight
+            )
+
+            if not allowed:
+                print(
+                    f"[BLOCK] WHITE PREFLIGHT={status.upper()}. "
+                    "Ajuste luz/alvo ou use override explícito somente se souber o que está fazendo."
+                )
+                continue
+
+            locked = choose_locked_controls(ctx, args)
+
+            print("[LOCK] Controles escolhidos:")
+            for role in ROLES:
+                c = locked[role]
+                print(
+                    f"  {role.upper()}: {c.exposure_time_us} us | "
+                    f"ISO {c.sensitivity_iso} | source={c.source}"
+                )
+
+            ctx.send_locked_controls(locked)
+            ctx.verify_locked_controls(
+                locked,
+                settle_frames=args.lock_settle_frames,
+            )
+
+            return last_quality, locked
+
+        time.sleep(0.002)
+
+
+# ============================================================
+# Captura de estágio
+# ============================================================
+
+def wait_stage_prompt(
+    ctx: AcquisitionContext,
+    title: str,
+    instructions: List[str],
+):
+    while True:
+        ctx.poll()
+
+        ctx.show_board(
+            title=title,
+            lines=instructions + [
+                "",
+                "ENTER = iniciar captura",
+                "Q/ESC = cancelar",
+            ],
+        )
+
+        k = cv2.waitKey(1) & 0xFF
+
+        if k in (ord("q"), ord("Q"), 27):
+            raise KeyboardInterrupt(f"Cancelado em {title}.")
+
+        if k in (13, 10):
+            return
+
+        time.sleep(0.002)
+
+
+def capture_stage(
+    ctx: AcquisitionContext,
+    stage_name: str,
+    frames: int,
+    discard_frames: int,
+    locked: Dict[str, LockedControl],
+    thresholds: dict,
+) -> Tuple[Dict[str, np.ndarray], dict]:
+    target = int(frames)
+
+    accum = {
+        role: MeanAccumulator(
+            shape=(ctx.specs[role].height, ctx.specs[role].width),
+            reject_fraction=thresholds["frame_global_deviation_reject"],
+        )
+        for role in ROLES
+    }
+
+    discard_left = {role: int(discard_frames) for role in ROLES}
+    last_seq = dict(ctx.latest_seq)
+
+    t0 = time.time()
+
+    while any(accum[r].count < target for r in ROLES):
+        updated = ctx.poll()
+
+        for role in updated:
+            if accum[role].count >= target:
+                continue
+
+            seq = ctx.latest_seq.get(role)
+            if seq is not None and seq == last_seq.get(role):
+                continue
+            last_seq[role] = seq
+
+            ctrl = ctx.latest_ctrl[role]
+
+            if not controls_match(ctrl, locked[role], thresholds):
+                raise RuntimeError(
+                    f"{stage_name}/{role}: controles divergiram do lock. "
+                    f"target={asdict(locked[role])}, actual={ctrl}"
+                )
+
+            if discard_left[role] > 0:
+                discard_left[role] -= 1
+                continue
+
+            raw = ctx.latest_raw[role]
+
+            accum[role].try_add(
+                raw,
+                ctrl=ctrl,
+                meta=ctx.latest_packet_meta[role],
+            )
+
+        progress = {
+            role: (accum[role].count, target)
+            for role in ROLES
+        }
+
+        ctx.show_board(
+            title=f"CAPTURA - {stage_name.upper()}",
+            lines=[
+                "Controles MANUAIS travados.",
+                "Não mova o módulo nem altere iluminação.",
+                f"Tempo: {time.time() - t0:.1f}s",
+                "",
+                "Frames temporalmente anômalos são rejeitados.",
+                "Q/ESC = cancelar",
+            ],
+            progress=progress,
+        )
+
+        k = cv2.waitKey(1) & 0xFF
+
+        if k in (ord("q"), ord("Q"), 27):
+            raise KeyboardInterrupt(f"Captura {stage_name} cancelada.")
+
+        time.sleep(0.001)
+
+    means = {
+        role: accum[role].mean()
+        for role in ROLES
+    }
+
+    report = {
+        role: accum[role].summary()
+        for role in ROLES
+    }
+
+    return means, report
+
+
+# ============================================================
+# Decode usando o RawProcessorCore da aplicação
+# ============================================================
+
+def disable_nonflat_processing(core: RawProcessorCore):
+    """
+    Mantém SOMENTE o decode físico necessário para produzir R/G/B/RE/NIR.
+
+    Durante a geração de um novo flat não podemos deixar uma calibração antiga
+    participar da própria medição. Por isso ficam forçados OFF:
+      - flat-field existente;
+      - normalização radiométrica;
+      - enhancement;
+      - ganhos RGB globais/calibração de cor.
+
+    O único item herdável do module_params é a estratégia de DECODE RGB
+    (mode + demosaic_algorithm), pois o mapa precisa nascer no mesmo domínio
+    espacial em que será aplicado na aplicação.
+    """
+    try:
+        core.flatfield_config["enabled"] = False
+        core.flatfield_config["npz_file"] = None
+    except Exception:
+        pass
+
+    try:
+        core.flatfield_loaded = False
+        core.flatfield_maps = {}
+    except Exception:
+        pass
+
+    for attr in (
+        "radiometric_normalization_config",
+        "radiometric_config",
+        "patch_normalization_config",
+    ):
+        try:
+            cfg = getattr(core, attr, None)
+            if isinstance(cfg, dict):
+                cfg["enabled"] = False
+        except Exception:
+            pass
+
+    try:
+        enh = core.rgb_processing_config.get("enhancement")
+        if isinstance(enh, dict):
+            enh["enabled"] = False
+            auto_stretch = enh.get("auto_stretch")
+            if isinstance(auto_stretch, dict):
+                auto_stretch["enabled"] = False
+    except Exception:
+        pass
+
+    try:
+        core.rgb_calibration["enabled"] = False
+        gains = core.rgb_calibration.get("gains")
+        if isinstance(gains, dict):
+            gains.update({"R": 1.0, "G": 1.0, "B": 1.0})
+    except Exception:
+        pass
+
+
+def create_processing_core(
+    specs: Dict[str, CameraSpec],
+    module_params_path: Optional[str],
+) -> RawProcessorCore:
+    """
+    Cria um RawProcessorCore LIMPO.
+
+    Regra importante:
+    NUNCA passa module_params como calibration_json_path aqui. O construtor do
+    RawProcessorCore pode carregar flatfield_config imediatamente, portanto um
+    flat antigo da OV9782 poderia contaminar ou até quebrar a calibração da
+    AR0234 antes de conseguirmos desligá-lo.
+
+    Do module_params copiamos apenas:
+      rgb_processing.mode
+      rgb_processing.demosaic_algorithm
+    """
+    rgb = specs["rgb"]
+
+    core = RawProcessorCore(
+        sensor_width=rgb.width,
+        sensor_height=rgb.height,
+        bayer_pattern=rgb.bayer_pattern or "BGGR",
+        calibration_json_path=None,
+    )
+
+    module_cfg = load_json(module_params_path)
+
+    rgb_cfg = module_cfg.get("rgb_processing")
+    if isinstance(rgb_cfg, dict):
+        mode = str(
+            rgb_cfg.get("mode", core.rgb_processing_config.get("mode", "linear_demosaic"))
+        ).strip().lower()
+
+        allowed_modes = {
+            "linear_demosaic",
+            "linear_demosaic_half",
+            "bayer_planes",
+        }
+
+        if mode not in allowed_modes:
+            raise RuntimeError(
+                "rgb_processing.mode do module_params não é seguro/compatível "
+                f"com este calibrador: {mode!r}. Permitidos: {sorted(allowed_modes)}"
+            )
+
+        algorithm = str(
+            rgb_cfg.get(
+                "demosaic_algorithm",
+                core.rgb_processing_config.get("demosaic_algorithm", "ea"),
+            )
+        ).strip().lower()
+
+        if algorithm not in {"ea", "bilinear"}:
+            raise RuntimeError(
+                "rgb_processing.demosaic_algorithm inválido no module_params: "
+                f"{algorithm!r}. Use 'ea' ou 'bilinear'."
+            )
+
+        core.rgb_processing_config["mode"] = mode
+        core.rgb_processing_config["demosaic_algorithm"] = algorithm
+
+    # A identidade do sensor recém-descoberto sempre vence qualquer JSON antigo.
+    core.sensor_width = int(rgb.width)
+    core.sensor_height = int(rgb.height)
+    core.bayer_pattern = str(rgb.bayer_pattern or "BGGR").upper()
+
+    disable_nonflat_processing(core)
+    return core
+
+
+def build_decode_meta(specs: Dict[str, CameraSpec]) -> dict:
+    camera_info = {}
+
+    for role in ROLES:
+        spec = specs[role]
+
+        camera_info[spec.socket_name] = {
+            "id": spec.socket_name,
+            "role": role,
+            "sensor": spec.sensor_name,
+            "sensor_name": spec.sensor_name,
+            "width": int(spec.width),
+            "height": int(spec.height),
+            "bit_depth": 10,
+            "raw_format": "RAW10_PACKED",
+            "packed": True,
+            "bayer_pattern": spec.bayer_pattern if role == "rgb" else None,
+        }
+
+    return {
+        "frame_type": "RAW_BRUTO",
+        "camera_info": camera_info,
+        "camera_frames": camera_info,
+    }
+
+
+def decode_reference(
+    core: RawProcessorCore,
+    specs: Dict[str, CameraSpec],
+    raw_means: Dict[str, np.ndarray],
+) -> dict:
+    frame = {}
+
+    for role in ROLES:
+        spec = specs[role]
+        raw = raw_means[role]
+
+        expected = (spec.height, spec.width)
+        if raw.shape != expected:
+            raise RuntimeError(
+                f"decode_reference/{role}: shape={raw.shape}, esperado={expected}"
+            )
+
+        frame[spec.socket_name] = pack_raw10(raw)
+
+    decoded = core.decode_stream_cameras(
+        frame,
+        build_decode_meta(specs),
+    )
+
+    out = {}
+
+    for cam_id, item in decoded.items():
+        role = str(item.get("role", "")).lower()
+        img = item.get("image")
+
+        if role == "rgb":
+            if img is None or img.ndim != 3 or img.shape[2] < 3:
+                raise RuntimeError(f"Decode RGB inválido: {None if img is None else img.shape}")
+
+            out["R"] = img[:, :, 0].astype(np.float32).copy()
+            out["G"] = img[:, :, 1].astype(np.float32).copy()
+            out["B"] = img[:, :, 2].astype(np.float32).copy()
+
+        elif role == "re":
+            if img is None or img.ndim != 2:
+                raise RuntimeError(f"Decode RE inválido: {None if img is None else img.shape}")
+            out["RE"] = img.astype(np.float32).copy()
+
+        elif role == "nir":
+            if img is None or img.ndim != 2:
+                raise RuntimeError(f"Decode NIR inválido: {None if img is None else img.shape}")
+            out["NIR"] = img.astype(np.float32).copy()
+
+    missing = [ch for ch in CHANNELS if ch not in out]
+    if missing:
+        raise RuntimeError(f"RawProcessorCore não produziu canais: {missing}")
+
+    return out
+
+
+# ============================================================
+# Construção do flat
+# ============================================================
+
+def gaussian_smooth_field(signal: np.ndarray, sigma_frac: float) -> Tuple[np.ndarray, float]:
+    """
+    Suaviza o CAMPO luminoso antes de inverter.
+    sigma é proporcional à menor dimensão, portanto AR0234/OV9782 recebem
+    escalas comparáveis no FOV.
+    """
+    a = signal.astype(np.float32)
+
+    sigma = max(1.0, float(min(a.shape[:2])) * float(sigma_frac))
+
+    smooth = cv2.GaussianBlur(
+        a,
+        (0, 0),
+        sigmaX=sigma,
+        sigmaY=sigma,
+        borderType=cv2.BORDER_REFLECT,
+    )
+
+    return smooth.astype(np.float32), float(sigma)
+
+
+def build_gain_maps(
+    white_a: dict,
+    dark: Optional[dict],
+    epsilon: float,
+    sigma_frac: float,
+    min_gain: float,
+    max_gain: float,
+):
+    gain_maps = {}
+    flat_norm = {}
+    white_signal = {}
+    smooth_fields = {}
+    dark_used = {}
+    processing_meta = {}
+
+    for ch in CHANNELS:
+        w = white_a[ch].astype(np.float32)
+
+        if dark is not None:
+            d = dark[ch].astype(np.float32)
+
+            if d.shape != w.shape:
+                raise RuntimeError(
+                    f"{ch}: DARK shape {d.shape} != WHITE shape {w.shape}. "
+                    "Calibração abortada; resize de dark é proibido."
+                )
+        else:
+            d = np.zeros_like(w, dtype=np.float32)
+
+        signal = w - d
+        signal = np.maximum(signal, float(epsilon)).astype(np.float32)
+
+        smooth, sigma = gaussian_smooth_field(signal, sigma_frac=sigma_frac)
+
+        reference = robust_center_reference(smooth)
+        gain_unclipped = reference / np.maximum(smooth, float(epsilon))
+
+        gain = np.clip(
+            gain_unclipped,
+            float(min_gain),
+            float(max_gain),
+        ).astype(np.float32)
+
+        gain_maps[ch] = gain
+        flat_norm[ch] = (smooth / max(reference, epsilon)).astype(np.float32)
+        white_signal[ch] = signal
+        smooth_fields[ch] = smooth
+        dark_used[ch] = d
+
+        processing_meta[ch] = {
+            "reference": float(reference),
+            "sigma_px": float(sigma),
+            "gain_unclipped_min": float(np.min(gain_unclipped)),
+            "gain_unclipped_max": float(np.max(gain_unclipped)),
+            "clipped_low_pct": float((gain_unclipped < min_gain).mean() * 100.0),
+            "clipped_high_pct": float((gain_unclipped > max_gain).mean() * 100.0),
+        }
+
+    return {
+        "gain_maps": gain_maps,
+        "flat_norm": flat_norm,
+        "white_signal": white_signal,
+        "smooth_fields": smooth_fields,
+        "dark_used": dark_used,
+        "processing_meta": processing_meta,
+    }
+
+
+# ============================================================
+# Acceptance test
+# ============================================================
+
+def evaluate_gain_channel(gain: np.ndarray, th: dict) -> dict:
+    f = gain[np.isfinite(gain)]
+
+    if f.size == 0:
+        return {
+            "status": "bad",
+            "reasons": ["gain_invalid"],
+        }
+
+    st = {
+        "min": float(np.min(f)),
+        "p01": float(np.percentile(f, 1)),
+        "p05": float(np.percentile(f, 5)),
+        "p50": float(np.percentile(f, 50)),
+        "p95": float(np.percentile(f, 95)),
+        "p99": float(np.percentile(f, 99)),
+        "max": float(np.max(f)),
+        "mean": float(np.mean(f)),
+        "std": float(np.std(f)),
+    }
+
+    status = "good"
+    reasons = []
+
+    if st["p99"] > th["gain_p99_bad"]:
+        status = merge_status(status, "bad")
+        reasons.append(f"gain_p99_bad:{st['p99']:.3f}")
+    elif st["p99"] > th["gain_p99_warning"]:
+        status = merge_status(status, "warning")
+        reasons.append(f"gain_p99_warn:{st['p99']:.3f}")
+
+    if st["max"] > th["gain_max_bad"]:
+        status = merge_status(status, "bad")
+        reasons.append(f"gain_max_bad:{st['max']:.3f}")
+    elif st["max"] > th["gain_max_warning"]:
+        status = merge_status(status, "warning")
+        reasons.append(f"gain_max_warn:{st['max']:.3f}")
+
+    if st["min"] < th["gain_min_bad"]:
+        status = merge_status(status, "bad")
+        reasons.append(f"gain_min_bad:{st['min']:.3f}")
+    elif st["min"] < th["gain_min_warning"]:
+        status = merge_status(status, "warning")
+        reasons.append(f"gain_min_warn:{st['min']:.3f}")
+
+    return {
+        "status": status,
+        "reasons": reasons,
+        "stats": st,
+    }
+
+
+def evaluate_dark_channels(dark: dict, th: dict) -> dict:
+    result = {
+        "status": "good",
+        "channels": {},
+        "reasons": [],
+    }
+
+    for ch in CHANNELS:
+        a = dark[ch].astype(np.float32)
+        p99 = finite_percentile(a, 99, default=1.0)
+
+        status = "good"
+        reasons = []
+
+        if p99 > th["dark_p99_bad"]:
+            status = "bad"
+            reasons.append(f"dark_p99_bad:{p99:.4f}")
+        elif p99 > th["dark_p99_warning"]:
+            status = "warning"
+            reasons.append(f"dark_p99_warn:{p99:.4f}")
+
+        result["channels"][ch] = {
+            "status": status,
+            "p99": p99,
+            "reasons": reasons,
+        }
+
+        result["status"] = merge_status(result["status"], status)
+        result["reasons"].extend([f"{ch}:{x}" for x in reasons])
+
+    return result
+
+
+def acceptance_test(
+    gain_maps: dict,
+    white_b: dict,
+    dark: Optional[dict],
+    th: dict,
+) -> dict:
+    result = {
+        "status": "good",
+        "channels": {},
+        "reasons": [],
+    }
+
+    for ch in CHANNELS:
+        wb = white_b[ch].astype(np.float32)
+
+        if dark is not None:
+            d = dark[ch].astype(np.float32)
+
+            if wb.shape != d.shape:
+                raise RuntimeError(
+                    f"Acceptance {ch}: WHITE_B {wb.shape} != DARK {d.shape}"
+                )
+        else:
+            d = np.zeros_like(wb)
+
+        signal = np.maximum(wb - d, 1e-8)
+        corrected = signal * gain_maps[ch]
+
+        before = grid_uniformity(signal)
+        after = grid_uniformity(corrected)
+
+        before_cv = float(before["grid_cv"])
+        after_cv = float(after["grid_cv"])
+
+        if before_cv > 1e-9:
+            improvement = 1.0 - (after_cv / before_cv)
+        else:
+            improvement = 0.0
+
+        local_after = local_defect_stats(
+            corrected,
+            outlier_thr=th["local_dev_p99_warning"],
+        )
+
+        gain_report = evaluate_gain_channel(gain_maps[ch], th)
+
+        status = gain_report["status"]
+        reasons = list(gain_report["reasons"])
+
+        if after_cv > th["corrected_grid_cv_bad"]:
+            status = merge_status(status, "bad")
+            reasons.append(f"corrected_grid_cv_bad:{after_cv:.4f}")
+        elif after_cv > th["corrected_grid_cv_warning"]:
+            status = merge_status(status, "warning")
+            reasons.append(f"corrected_grid_cv_warn:{after_cv:.4f}")
+
+        if before_cv >= th["min_before_grid_cv_for_improvement"]:
+            if improvement < th["worsening_bad"]:
+                status = merge_status(status, "bad")
+                reasons.append(f"flat_worsened:{improvement:.3f}")
+            elif improvement < th["improvement_bad"]:
+                status = merge_status(status, "bad")
+                reasons.append(f"improvement_bad:{improvement:.3f}")
+            elif improvement < th["improvement_warning"]:
+                status = merge_status(status, "warning")
+                reasons.append(f"improvement_warn:{improvement:.3f}")
+
+        if local_after.get("valid"):
+            dev = float(local_after["local_dev_p99"])
+            out_pct = float(local_after["local_outlier_pct"])
+
+            if dev > th["local_dev_p99_bad"]:
+                status = merge_status(status, "bad")
+                reasons.append(f"local_dev_bad:{dev:.3f}")
+            elif dev > th["local_dev_p99_warning"]:
+                status = merge_status(status, "warning")
+                reasons.append(f"local_dev_warn:{dev:.3f}")
+
+            if out_pct > th["local_outlier_pct_bad"]:
+                status = merge_status(status, "bad")
+                reasons.append(f"local_outlier_bad:{out_pct:.2f}%")
+            elif out_pct > th["local_outlier_pct_warning"]:
+                status = merge_status(status, "warning")
+                reasons.append(f"local_outlier_warn:{out_pct:.2f}%")
+
+        result["channels"][ch] = {
+            "status": status,
+            "reasons": reasons,
+            "before_uniformity": before,
+            "after_uniformity": after,
+            "grid_cv_before": before_cv,
+            "grid_cv_after": after_cv,
+            "improvement": float(improvement),
+            "local_after": local_after,
+            "gain": gain_report,
+        }
+
+        result["status"] = merge_status(result["status"], status)
+        result["reasons"].extend([f"{ch}:{x}" for x in reasons])
+
+    return result
+
+
+# ============================================================
+# Previews
+# ============================================================
+
+def gray_bgr(arr: np.ndarray) -> np.ndarray:
+    return cv2.cvtColor(normalize_display(arr), cv2.COLOR_GRAY2BGR)
+
+
+def save_previews(
+    out_dir: Path,
+    gain_maps: dict,
+    white_signal_a: dict,
+    white_b: dict,
+    dark: Optional[dict],
+):
+    ensure_dir(out_dir)
+
+    for ch in CHANNELS:
+        gain = gain_maps[ch]
+
+        wb = white_b[ch].astype(np.float32)
+        d = dark[ch].astype(np.float32) if dark is not None else np.zeros_like(wb)
+        validation_signal = np.maximum(wb - d, 1e-8)
+        corrected = validation_signal * gain
+
+        cv2.imwrite(str(out_dir / f"{ch}_gain.png"), gray_bgr(gain))
+        cv2.imwrite(str(out_dir / f"{ch}_white_A_signal.png"), gray_bgr(white_signal_a[ch]))
+        cv2.imwrite(str(out_dir / f"{ch}_white_B_before.png"), gray_bgr(validation_signal))
+        cv2.imwrite(str(out_dir / f"{ch}_white_B_after.png"), gray_bgr(corrected))
+
+        # Comparativo lado a lado.
+        before = gray_bgr(validation_signal)
+        after = gray_bgr(corrected)
+
+        h = min(before.shape[0], after.shape[0])
+        w = min(before.shape[1], after.shape[1])
+
+        before = cv2.resize(before, (w, h))
+        after = cv2.resize(after, (w, h))
+
+        overlay_hud(before, [f"{ch} BEFORE"], x=12, y=26, font_scale=0.65, line_step=24)
+        overlay_hud(after, [f"{ch} AFTER"], x=12, y=26, font_scale=0.65, line_step=24)
+
+        side = np.hstack([before, after])
+        cv2.imwrite(str(out_dir / f"{ch}_validation_compare.png"), side)
+
+
+# ============================================================
+# Persistência / promoção
+# ============================================================
+
+def suggested_flatfield_config(active_npz: str) -> dict:
+    return {
+        "flatfield_config": {
+            "enabled": True,
+            "npz_file": str(active_npz).replace("\\", "/"),
+            "apply_before_fusion": True,
+            "apply_after_decode": True,
+            "apply_space": "native_camera_space",
+            "map_type": "gain",
+            "channels": list(CHANNELS),
+            "channel_maps": {
+                ch: f"gain_{ch}"
+                for ch in CHANNELS
+            },
+            "subtract_dark": False,
+            "clip_output": True,
+        }
+    }
+
+
+def save_candidate_npz(
+    path: Path,
+    gain_maps: dict,
+    flat_norm: dict,
+    white_a: dict,
+    white_b: dict,
+    dark: Optional[dict],
+    raw_white_a: dict,
+    raw_white_b: dict,
+    raw_dark: Optional[dict],
+):
+    payload = {}
+
+    for ch in CHANNELS:
+        payload[f"gain_{ch}"] = gain_maps[ch].astype(np.float32)
+        payload[f"flat_norm_{ch}"] = flat_norm[ch].astype(np.float32)
+
+        # Novo nome correto.
+        payload[f"white_reference_{ch}"] = white_a[ch].astype(np.float32)
+        payload[f"validation_white_{ch}"] = white_b[ch].astype(np.float32)
+
+        # Alias legado para ferramentas existentes.
+        payload[f"white_median_{ch}"] = white_a[ch].astype(np.float32)
+
+        if dark is not None:
+            payload[f"dark_reference_{ch}"] = dark[ch].astype(np.float32)
+            payload[f"dark_median_{ch}"] = dark[ch].astype(np.float32)
+
+    # Auditoria RAW nativa por câmera.
+    for role in ROLES:
+        payload[f"raw_white_A_{role}"] = raw_white_a[role].astype(np.float32)
+        payload[f"raw_white_B_{role}"] = raw_white_b[role].astype(np.float32)
+
+        if raw_dark is not None:
+            payload[f"raw_dark_{role}"] = raw_dark[role].astype(np.float32)
+
+    ensure_dir(path.parent)
+    tmp = path.with_suffix(path.suffix + ".tmp.npz")
+    np.savez_compressed(tmp, **payload)
+    os.replace(tmp, path)
+
+
+def atomic_promote(
+    candidate_npz: Path,
+    candidate_report: Path,
+    active_npz: Path,
+    active_json: Path,
+):
+    ensure_dir(active_npz.parent)
+    ensure_dir(active_json.parent)
+
+    # Backup da calibração ativa anterior.
+    stamp = session_id()
+
+    if active_npz.exists():
+        backup = active_npz.with_name(active_npz.stem + f".backup_{stamp}" + active_npz.suffix)
+        shutil.copy2(active_npz, backup)
+
+    if active_json.exists():
+        backup = active_json.with_name(active_json.stem + f".backup_{stamp}" + active_json.suffix)
+        shutil.copy2(active_json, backup)
+
+    tmp_npz = active_npz.with_suffix(active_npz.suffix + ".tmp")
+    tmp_json = active_json.with_suffix(active_json.suffix + ".tmp")
+
+    shutil.copy2(candidate_npz, tmp_npz)
+    shutil.copy2(candidate_report, tmp_json)
+
+    # Só troca depois que ambos foram copiados.
+    os.replace(tmp_npz, active_npz)
+    os.replace(tmp_json, active_json)
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Calibrador de flat-field/dark-frame de produção para "
+            "OAK-FFC-3P: OV9782 ou AR0234 + 2x OV9282."
+        ),
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    # Hardware
+    parser.add_argument("--mx-id", default=None)
+    parser.add_argument("--fps", type=float, default=20.0)
+    parser.add_argument(
+        "--anti-banding",
+        default="OFF",
+        choices=["OFF", "50", "60", "50HZ", "60HZ"],
+    )
+    parser.add_argument(
+        "--rgb-bayer",
+        default="auto",
+        choices=["auto", "RGGB", "BGGR", "GRBG", "GBRG"],
+    )
+    parser.add_argument(
+        "--module-params",
+        default="calibration/module_params.json",
+        help=(
+            "Lê SOMENTE rgb_processing.mode e demosaic_algorithm para manter "
+            "o mesmo domínio de decode da produção. O JSON completo NÃO é "
+            "carregado no RawProcessorCore durante a calibração, evitando que "
+            "flat/radiometric/enhancement antigos contaminem o novo flat."
+        ),
+    )
+
+    # Captura
+    parser.add_argument("--frames", type=int, default=60)
+    parser.add_argument("--validation-frames", type=int, default=30)
+    parser.add_argument("--dark-frames", type=int, default=60)
+    parser.add_argument("--discard-frames", type=int, default=12)
+    parser.add_argument("--lock-settle-frames", type=int, default=8)
+
+    # Overrides manuais opcionais
+    for role in ROLES:
+        parser.add_argument(f"--{role}-exp-us", type=int, default=None)
+        parser.add_argument(f"--{role}-iso", type=int, default=None)
+
+    # Processamento
+    parser.add_argument(
+        "--sigma-frac",
+        type=float,
+        default=0.025,
+        help="Sigma do shading gaussiano como fração da menor dimensão.",
+    )
+    parser.add_argument("--min-gain", type=float, default=0.25)
+    parser.add_argument("--max-gain", type=float, default=4.0)
+    parser.add_argument("--epsilon", type=float, default=1e-6)
+
+    # Política
+    parser.add_argument(
+        "--skip-dark",
+        action="store_true",
+        help=(
+            "Permite gerar candidate sem dark. Por padrão candidate sem dark "
+            "NÃO é promovido, a menos que --allow-no-dark-promote seja usado."
+        ),
+    )
+    parser.add_argument("--allow-no-dark-promote", action="store_true")
+    parser.add_argument("--allow-warning-preflight", action="store_true")
+    parser.add_argument(
+        "--force-preflight",
+        action="store_true",
+        help="Override explícito de engenharia. Fica registrado no report.",
+    )
+    parser.add_argument(
+        "--promote-warning",
+        action="store_true",
+        help="Permite promover acceptance WARNING. Nunca promove BAD.",
+    )
+    parser.add_argument("--no-promote", action="store_true")
+
+    # Output
+    parser.add_argument(
+        "--candidate-root",
+        default="calibration/flatfield_candidates",
+    )
+    parser.add_argument(
+        "--active-npz",
+        default="calibration/flatfield_maps_v1.npz",
+    )
+    parser.add_argument(
+        "--active-json",
+        default="calibration/flatfield_maps_v1.json",
+    )
+
+    # UI
+    parser.add_argument("--panel-width", type=int, default=640)
+    parser.add_argument("--panel-height", type=int, default=400)
+
+    parser.add_argument("--notes", default="")
+
+    args = parser.parse_args()
+
+    if args.frames < 10 or args.validation_frames < 10 or args.dark_frames < 10:
+        raise ValueError("Produção: use pelo menos 10 frames por estágio.")
+
+    if not (0.001 <= args.sigma_frac <= 0.20):
+        raise ValueError("--sigma-frac fora da faixa segura [0.001, 0.20].")
+
+    thresholds = dict(DEFAULT_THRESHOLDS)
+
+    sid = session_id()
+    candidate_dir = Path(args.candidate_root) / sid
+    preview_dir = candidate_dir / "previews"
+    candidate_npz = candidate_dir / "flatfield_maps.npz"
+    candidate_report_path = candidate_dir / "report.json"
+
+    ensure_dir(candidate_dir)
+    ensure_dir(preview_dir)
+
+    module_params = load_json(args.module_params)
+
+    # --------------------------------------------------------
+    # Descoberta
+    # --------------------------------------------------------
+
+    dev_info = select_device_info(args.mx_id)
+    mx_id = device_id_from_info(dev_info)
+
+    features, usb_speed = discover_features(dev_info)
+
+    specs = validate_and_build_specs(
+        features=features,
+        rgb_bayer_arg=args.rgb_bayer,
+        module_params=module_params,
+    )
+
+    rgb_spec = specs["rgb"]
+
+    print("=" * 82)
+    print("FLAT-FIELD CALIBRATION - PRODUCTION")
+    print(f"DepthAI : {getattr(dai, '__version__', 'unknown')}")
+    print(f"MX ID   : {mx_id}")
+    print(f"USB     : {usb_speed}")
+    print("-" * 82)
+
+    for role in ROLES:
+        s = specs[role]
+        extra = f" | Bayer={s.bayer_pattern}" if s.bayer_pattern else ""
+        print(
+            f"{s.socket_name} -> {role.upper():3s} | {s.sensor_name:8s} | "
+            f"{s.width}x{s.height} | {s.resolution_enum}{extra}"
+        )
+
+    print("=" * 82)
+
+    # --------------------------------------------------------
+    # Pipeline
+    # --------------------------------------------------------
+
+    pipeline = build_pipeline(
+        specs,
+        fps=args.fps,
+        anti_banding=args.anti_banding,
+    )
+
+    device = dai.Device(pipeline, dev_info)
+
+    cv2.namedWindow(
+        "Flat Field Calibration - Production",
+        cv2.WINDOW_NORMAL,
+    )
+
+    report = {
+        "schema": SCHEMA,
+        "session_id": sid,
+        "created_at": now_str(),
+        "status": "running",
+        "promoted": False,
+        "depthai_version": getattr(dai, "__version__", "unknown"),
+        "device": {
+            "mx_id": mx_id,
+            "usb_speed": usb_speed,
+        },
+        "hardware_inventory": features,
+        "resolved_setup": {
+            role: asdict(spec)
+            for role, spec in specs.items()
+        },
+        "module_params_path": args.module_params,
+        "rgb_processing": None,
+        "capture": {
+            "fps": args.fps,
+            "anti_banding": args.anti_banding,
+            "frames": args.frames,
+            "dark_frames": args.dark_frames,
+            "validation_frames": args.validation_frames,
+            "discard_frames": args.discard_frames,
+        },
+        "policy": {
+            "skip_dark": bool(args.skip_dark),
+            "allow_no_dark_promote": bool(args.allow_no_dark_promote),
+            "allow_warning_preflight": bool(args.allow_warning_preflight),
+            "force_preflight": bool(args.force_preflight),
+            "promote_warning": bool(args.promote_warning),
+            "no_promote": bool(args.no_promote),
+        },
+        "thresholds": thresholds,
+        "notes": args.notes,
+        "stages": {},
+        "outputs": {
+            "candidate_dir": str(candidate_dir),
+            "candidate_npz": str(candidate_npz),
+            "candidate_report": str(candidate_report_path),
+            "active_npz": args.active_npz,
+            "active_json": args.active_json,
+        },
+        "suggested_module_params_patch": suggested_flatfield_config(args.active_npz),
+    }
+
+    try:
+        ctx = AcquisitionContext(
+            device=device,
+            specs=specs,
+            thresholds=thresholds,
+            panel_width=args.panel_width,
+            panel_height=args.panel_height,
+            rgb_bayer=rgb_spec.bayer_pattern,
+        )
+
+        # ----------------------------------------------------
+        # Core de processamento da própria aplicação
+        # ----------------------------------------------------
+
+        core = create_processing_core(specs, args.module_params)
+
+        report["rgb_processing"] = {
+            "mode": str(core.rgb_processing_config.get("mode")),
+            "demosaic_algorithm": str(core.rgb_processing_config.get("demosaic_algorithm")),
+            "enhancement_forced_off": True,
+            "flatfield_forced_off": True,
+            "radiometric_normalization_forced_off": True,
+        }
+
+        print(
+            "[CORE] rgb_processing.mode="
+            f"{report['rgb_processing']['mode']}"
+        )
+
+        # ----------------------------------------------------
+        # WHITE PREFLIGHT + lock
+        # ----------------------------------------------------
+
+        preflight_report, locked = wait_white_preflight(
+            ctx,
+            args,
+            thresholds,
+        )
+
+        report["white_preflight"] = preflight_report
+        report["locked_controls"] = {
+            role: asdict(ctrl)
+            for role, ctrl in locked.items()
+        }
+
+        # ----------------------------------------------------
+        # WHITE A
+        # ----------------------------------------------------
+
+        wait_stage_prompt(
+            ctx,
+            title="ETAPA 1/3 - WHITE A",
+            instructions=[
+                "Mantenha o MESMO alvo do preflight.",
+                "Não mova módulo, lente ou iluminação.",
+                "Esta captura CONSTRÓI o flat.",
+                "EXP/ISO já estão travados.",
+            ],
+        )
+
+        raw_white_a, white_a_capture_report = capture_stage(
+            ctx=ctx,
+            stage_name="white_A",
+            frames=args.frames,
+            discard_frames=args.discard_frames,
+            locked=locked,
+            thresholds=thresholds,
+        )
+
+        report["stages"]["white_A"] = white_a_capture_report
+
+        # ----------------------------------------------------
+        # DARK
+        # ----------------------------------------------------
+
+        raw_dark = None
+
+        if not args.skip_dark:
+            wait_stage_prompt(
+                ctx,
+                title="ETAPA 2/3 - DARK",
+                instructions=[
+                    "TAMPE COMPLETAMENTE as três lentes.",
+                    "Não use fundo preto distante.",
+                    "Não altere iluminação nem controles.",
+                    "EXP/ISO permanecem idênticos ao WHITE.",
+                ],
+            )
+
+            raw_dark, dark_capture_report = capture_stage(
+                ctx=ctx,
+                stage_name="dark",
+                frames=args.dark_frames,
+                discard_frames=args.discard_frames,
+                locked=locked,
+                thresholds=thresholds,
+            )
+
+            report["stages"]["dark"] = dark_capture_report
+        else:
+            report["stages"]["dark"] = {
+                "skipped": True,
+            }
+
+        # ----------------------------------------------------
+        # WHITE B / acceptance independente
+        # ----------------------------------------------------
+
+        wait_stage_prompt(
+            ctx,
+            title="ETAPA 3/3 - WHITE B / VALIDATION",
+            instructions=[
+                "DESTAMPE as lentes.",
+                "Volte exatamente ao mesmo alvo WHITE.",
+                "Esta captura NÃO participa do cálculo do flat.",
+                "Ela mede se a correção funciona de verdade.",
+            ],
+        )
+
+        raw_white_b, white_b_capture_report = capture_stage(
+            ctx=ctx,
+            stage_name="white_B_validation",
+            frames=args.validation_frames,
+            discard_frames=args.discard_frames,
+            locked=locked,
+            thresholds=thresholds,
+        )
+
+        report["stages"]["white_B_validation"] = white_b_capture_report
+
+        # ----------------------------------------------------
+        # Decode das referências pelo RawProcessorCore
+        # ----------------------------------------------------
+
+        processing_screen = np.zeros((720, 1280, 3), dtype=np.uint8)
+        overlay_hud(
+            processing_screen,
+            [
+                "PROCESSANDO FLAT-FIELD...",
+                "",
+                f"RGB sensor: {rgb_spec.sensor_name}",
+                f"RGB Bayer : {rgb_spec.bayer_pattern}",
+                f"RGB mode  : {report['rgb_processing']['mode']}",
+                "",
+                "Decodificando referências pelo RawProcessorCore.",
+                "Gerando shading field, gain maps e acceptance test.",
+            ],
+            x=40,
+            y=80,
+            font_scale=0.72,
+            line_step=31,
+        )
+        cv2.imshow("Flat Field Calibration - Production", processing_screen)
+        cv2.waitKey(1)
+
+        white_a = decode_reference(core, specs, raw_white_a)
+        white_b = decode_reference(core, specs, raw_white_b)
+        dark = decode_reference(core, specs, raw_dark) if raw_dark is not None else None
+
+        # Garante shapes invariantes por canal.
+        for ch in CHANNELS:
+            if white_a[ch].shape != white_b[ch].shape:
+                raise RuntimeError(
+                    f"{ch}: WHITE_A shape {white_a[ch].shape} != "
+                    f"WHITE_B shape {white_b[ch].shape}"
+                )
+
+            if dark is not None and white_a[ch].shape != dark[ch].shape:
+                raise RuntimeError(
+                    f"{ch}: WHITE_A shape {white_a[ch].shape} != "
+                    f"DARK shape {dark[ch].shape}"
+                )
+
+        report["decoded_channel_shapes"] = {
+            ch: list(white_a[ch].shape)
+            for ch in CHANNELS
+        }
+
+        # ----------------------------------------------------
+        # Dark QA
+        # ----------------------------------------------------
+
+        dark_quality = None
+
+        if dark is not None:
+            dark_quality = evaluate_dark_channels(dark, thresholds)
+            report["dark_quality"] = dark_quality
+
+        # ----------------------------------------------------
+        # Gain
+        # ----------------------------------------------------
+
+        built = build_gain_maps(
+            white_a=white_a,
+            dark=dark,
+            epsilon=args.epsilon,
+            sigma_frac=args.sigma_frac,
+            min_gain=args.min_gain,
+            max_gain=args.max_gain,
+        )
+
+        gain_maps = built["gain_maps"]
+        flat_norm = built["flat_norm"]
+        white_signal_a = built["white_signal"]
+
+        report["gain_processing"] = {
+            "sigma_frac": args.sigma_frac,
+            "min_gain": args.min_gain,
+            "max_gain": args.max_gain,
+            "epsilon": args.epsilon,
+            "channels": built["processing_meta"],
+        }
+
+        # ----------------------------------------------------
+        # Acceptance
+        # ----------------------------------------------------
+
+        acceptance = acceptance_test(
+            gain_maps=gain_maps,
+            white_b=white_b,
+            dark=dark,
+            th=thresholds,
+        )
+
+        report["acceptance"] = acceptance
+
+        # Dark BAD também torna calibração BAD.
+        final_status = acceptance["status"]
+
+        if dark_quality is not None:
+            final_status = merge_status(final_status, dark_quality["status"])
+
+        if args.skip_dark and not args.allow_no_dark_promote:
+            final_status = merge_status(final_status, "warning")
+            report.setdefault("reasons", []).append(
+                "dark_skipped:promotion_blocked_without_allow_no_dark_promote"
+            )
+
+        report["status"] = final_status
+        report["finished_at"] = now_str()
+
+        # ----------------------------------------------------
+        # Candidate
+        # ----------------------------------------------------
+
+        save_candidate_npz(
+            path=candidate_npz,
+            gain_maps=gain_maps,
+            flat_norm=flat_norm,
+            white_a=white_a,
+            white_b=white_b,
+            dark=dark,
+            raw_white_a=raw_white_a,
+            raw_white_b=raw_white_b,
+            raw_dark=raw_dark,
+        )
+
+        save_previews(
+            out_dir=preview_dir,
+            gain_maps=gain_maps,
+            white_signal_a=white_signal_a,
+            white_b=white_b,
+            dark=dark,
+        )
+
+        report["candidate_sha256"] = sha256_file(candidate_npz)
+
+        # Decide promoção ANTES de salvar report final.
+        promote_allowed = False
+
+        if final_status == "good":
+            promote_allowed = True
+
+        elif final_status == "warning" and args.promote_warning:
+            promote_allowed = True
+
+        if args.skip_dark and not args.allow_no_dark_promote:
+            promote_allowed = False
+
+        if args.no_promote:
+            promote_allowed = False
+
+        report["promotion_decision"] = {
+            "allowed_by_quality": bool(promote_allowed),
+            "final_status": final_status,
+            "no_promote": bool(args.no_promote),
+        }
+
+        save_json(candidate_report_path, report)
+
+        # Marca candidate.
+        marker = candidate_dir / (
+            "PASS.txt" if promote_allowed else "FAIL.txt"
+        )
+
+        marker.write_text(
+            (
+                f"status={final_status}\n"
+                f"promote_allowed={promote_allowed}\n"
+                f"created_at={report['created_at']}\n"
+                f"finished_at={report['finished_at']}\n"
+            ),
+            encoding="utf-8",
+        )
+
+        # ----------------------------------------------------
+        # Promoção atômica
+        # ----------------------------------------------------
+
+        if promote_allowed:
+            atomic_promote(
+                candidate_npz=candidate_npz,
+                candidate_report=candidate_report_path,
+                active_npz=Path(args.active_npz),
+                active_json=Path(args.active_json),
+            )
+
+            report["promoted"] = True
+            report["promoted_at"] = now_str()
+
+            # Atualiza report candidate e active JSON com estado promovido.
+            save_json(candidate_report_path, report)
+            save_json(args.active_json, report)
+
+            print("")
+            print("=" * 82)
+            print("[PASS] FLAT-FIELD APROVADO E PROMOVIDO")
+            print(f"[ACTIVE] NPZ : {args.active_npz}")
+            print(f"[ACTIVE] JSON: {args.active_json}")
+            print(f"[CANDIDATE] {candidate_dir}")
+            print("=" * 82)
+
+        else:
+            print("")
+            print("=" * 82)
+            print("[FAIL/WARN] CANDIDATE NÃO FOI PROMOVIDO")
+            print(f"[STATUS] {final_status.upper()}")
+            print(f"[CANDIDATE] {candidate_dir}")
+            print("[SAFE] Calibração ativa anterior foi preservada.")
+            print("=" * 82)
+
+        # ----------------------------------------------------
+        # Resumo visual final
+        # ----------------------------------------------------
+
+        final_panel = np.zeros((720, 1280, 3), dtype=np.uint8)
+
+        lines = [
+            "FLAT-FIELD - RESULTADO FINAL",
+            "",
+            f"STATUS: {final_status.upper()}",
+            f"PROMOVIDO: {'SIM' if report.get('promoted') else 'NAO'}",
+            "",
+            f"RGB: {rgb_spec.sensor_name} {rgb_spec.width}x{rgb_spec.height}",
+            f"Bayer: {rgb_spec.bayer_pattern}",
+            f"RGB processing: {report['rgb_processing']['mode']}",
+            "",
+        ]
+
+        for ch in CHANNELS:
+            a = acceptance["channels"][ch]
+            lines.append(
+                f"{ch}: {a['status'].upper()} | "
+                f"gridCV {a['grid_cv_before']:.4f}->{a['grid_cv_after']:.4f} | "
+                f"melhora={a['improvement']*100:.1f}%"
+            )
+
+        lines.extend([
+            "",
+            f"Candidate: {candidate_dir}",
+            "",
+            "Qualquer tecla fecha.",
+        ])
+
+        overlay_hud(
+            final_panel,
+            lines,
+            x=34,
+            y=58,
+            font_scale=0.62,
+            line_step=27,
+        )
+
+        cv2.imshow("Flat Field Calibration - Production", final_panel)
+        cv2.waitKey(0)
+
+    except KeyboardInterrupt as exc:
+        report["status"] = "cancelled"
+        report["finished_at"] = now_str()
+        report["error"] = str(exc)
+
+        try:
+            save_json(candidate_report_path, report)
+        except Exception:
+            pass
+
+        print(f"[CANCELADO] {exc}")
+
+    except Exception as exc:
+        report["status"] = "error"
+        report["finished_at"] = now_str()
+        report["error"] = f"{type(exc).__name__}: {exc}"
+
+        try:
+            save_json(candidate_report_path, report)
+        except Exception:
+            pass
+
+        print("")
+        print("=" * 82)
+        print("[ERRO] CALIBRAÇÃO ABORTADA - NADA FOI PROMOVIDO")
+        print(f"{type(exc).__name__}: {exc}")
+        print(f"Report parcial: {candidate_report_path}")
+        print("=" * 82)
+
+        raise
+
+    finally:
+        try:
+            device.close()
+        except Exception:
+            pass
+
+        cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/_4_radiometric_config_tool.py b/Python/OAK/datasets/oak-fcc-3/utils/_4_radiometric_config_tool.py
new file mode 100644
index 000000000..380fe5d46
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/utils/_4_radiometric_config_tool.py
@@ -0,0 +1,5357 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+radiometric_calibration_production.py
+=====================================
+
+Calibrador FINAL de produção para a NORMALIZAÇÃO RADIOMÉTRICA do módulo
+multiespectral OAK-FFC-3P.
+
+Topologia oficial
+-----------------
+    CAM_A = RGB = OV9782  1280x800
+                 OU
+                 AR0234   1920x1200
+
+    CAM_B = RE  = OV9282  1280x800
+    CAM_C = NIR = OV9282  1280x800
+
+O que esta ferramenta calibra
+-----------------------------
+Esta ferramenta calibra o bloco que o runtime chama de:
+
+    radiometric_normalization
+
+Contrato atual:
+
+    method        = oak_ae_frame_controls_v1
+    factor_model  = exposure_time_us_x_iso
+    iso_base      = 100
+    scale         = reference_factor / actual_factor
+
+onde:
+
+    actual_factor =
+        exposure_time_us * (sensitivity_iso / iso_base)
+
+Ela NÃO configura automaticamente o controlador dinâmico de exposição
+`radiometric_config` e NÃO habilita `patch_normalization`.
+
+Esses dois blocos são políticas diferentes:
+    - radiometric_config:
+        controlador ativo de exposição em campo
+    - patch_normalization:
+        normalização pós-fusão por cartões/patches
+
+Por segurança de produto, ambos permanecem recomendados como DISABLED até
+uma etapa específica de validação de campo decidir o contrário.
+
+Por que fazer uma calibração real?
+----------------------------------
+Trocar OV9782 -> AR0234 muda sensibilidade, resposta e faixa operacional.
+Copiar reference_controls antigos pode deslocar a escala dos canais que entra
+no modelo e alterar relações espectrais.
+
+Esta ferramenta mede, por sensor:
+    1. estabilidade temporal;
+    2. resposta RAW vs exposição;
+    3. linearidade da hipótese exposição x ISO;
+    4. intercepto/offset efetivo;
+    5. erro da normalização ao longo de um sweep;
+    6. ponto de referência para um alvo radiométrico definido;
+    7. validação independente no ponto calculado.
+
+Domínio de medição
+------------------
+A medição é feita no RAW nativo da câmera, antes de:
+    - demosaic;
+    - enhancement;
+    - homografia;
+    - flat gain;
+    - resize;
+    - patch normalization.
+
+Isso casa com o propósito do bloco runtime:
+    apply_stage = after_dark_before_flat_gain
+
+IMPORTANTE:
+-----------
+A referência absoluta depende do contrato físico da bancada.
+
+Para repetir calibrações entre módulos, mantenha fixos:
+    - painel difuso/cinza usado;
+    - distância;
+    - geometria;
+    - fonte de luz;
+    - temperatura/tempo de aquecimento;
+    - intensidade da fonte.
+
+Se a bancada mudar, a referência radiométrica muda junto.
+
+Uso recomendado
+---------------
+1. Coloque um painel cinza/difuso uniforme preenchendo o FOV.
+2. Use iluminação de bancada estável e reproduzível.
+3. Rode:
+
+    python radiometric_calibration_production.py ^
+        --rig-id RIG_MS_01 ^
+        --target-id GRAY_PANEL_01 ^
+        --illumination-id LED_CAL_01
+
+4. Espere o preflight ficar GOOD.
+5. ENTER inicia a calibração automática.
+6. Não mexa mais em câmera, painel ou luz.
+7. O script executa o sweep, regressão e validação.
+8. PASS promove o artefato.
+
+Saídas
+------
+calibration/radiometry_candidates/<session_id>/
+    report.json
+    sweep.csv
+    PASS.txt / FAIL.txt / CANCELLED.txt
+    response_rgb.png
+    response_re.png
+    response_nir.png
+    validation_rgb.png
+    validation_re.png
+    validation_nir.png
+
+Se PASS:
+    calibration/radiometry_calibration_v5.json
+
+O JSON ativo contém:
+    module_params_fragment.radiometric_normalization
+
+O assembler final continua responsável por montar module_params.json.
+
+Política fail-closed
+--------------------
+- BAD nunca promove.
+- WARNING só promove com --promote-warning.
+- FAIL/CANCEL preserva o ativo anterior.
+- sensor/resolução diferentes do produto abortam.
+- controles manuais divergentes abortam.
+- sweep insuficiente aborta.
+- o script nunca reativa silenciosamente o controlador de campo.
+
+Teclas
+------
+PRE-FLIGHT:
+    ENTER -> iniciar
+    Q/ESC -> cancelar
+
+RESULTADO:
+    qualquer tecla -> fechar
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import hashlib
+import json
+import math
+import os
+import shutil
+import time
+from collections import deque
+from dataclasses import dataclass, asdict
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, Optional, Tuple, List, Any
+
+import cv2
+import depthai as dai
+import numpy as np
+
+
+# ============================================================
+# Contrato do produto
+# ============================================================
+
+ROLES = ("rgb", "re", "nir")
+
+PRODUCT_TOPOLOGY = {
+    "rgb": {
+        "socket": "CAM_A",
+        "allowed_sensors": ("OV9782", "AR0234"),
+    },
+    "re": {
+        "socket": "CAM_B",
+        "allowed_sensors": ("OV9282",),
+    },
+    "nir": {
+        "socket": "CAM_C",
+        "allowed_sensors": ("OV9282",),
+    },
+}
+
+RGB_SENSOR_MODES = {
+    "OV9782": {
+        "resolution_enum": "THE_800_P",
+        "width": 1280,
+        "height": 800,
+    },
+    "AR0234": {
+        "resolution_enum": "THE_1200_P",
+        "width": 1920,
+        "height": 1200,
+    },
+}
+
+MONO_SENSOR_MODES = {
+    "OV9282": {
+        "resolution_enum": "THE_800_P",
+        "width": 1280,
+        "height": 800,
+    },
+}
+
+SCHEMA = "multispec_radiometric_calibration_v5"
+CALIBRATION_DOMAIN = "native_sensor_raw_linear"
+NORMALIZATION_METHOD = "oak_ae_frame_controls_v1"
+FACTOR_MODEL = "exposure_time_us_x_iso"
+ISO_BASE = 100.0
+
+
+# ============================================================
+# QA de produção
+# ============================================================
+
+DEFAULT_QA = {
+    # Preflight em painel cinza
+    "preflight_p50_min": 0.12,
+    "preflight_p50_max": 0.78,
+    "preflight_sat_warning_pct": 0.10,
+    "preflight_sat_bad_pct": 0.50,
+    "preflight_dark_warning_pct": 2.0,
+    "preflight_dark_bad_pct": 8.0,
+    "preflight_temporal_cv_warning": 0.010,
+    "preflight_temporal_cv_bad": 0.025,
+
+    # Sweep
+    "min_sweep_levels": 6,
+    "min_valid_levels": 5,
+    "valid_p50_min": 0.035,
+    "valid_p50_max": 0.88,
+    "valid_sat_max_pct": 0.20,
+    "level_temporal_cv_warning": 0.010,
+    "level_temporal_cv_bad": 0.025,
+
+    # Regressão
+    "r2_warning": 0.995,
+    "r2_bad": 0.985,
+    "intercept_fraction_warning": 0.035,
+    "intercept_fraction_bad": 0.080,
+
+    # Validação do modelo de normalização
+    "norm_median_rel_error_warning": 0.030,
+    "norm_median_rel_error_bad": 0.060,
+    "norm_p95_rel_error_warning": 0.050,
+    "norm_p95_rel_error_bad": 0.090,
+
+    # Validação independente no reference point
+    "validation_target_rel_error_warning": 0.050,
+    "validation_target_rel_error_bad": 0.100,
+    "validation_temporal_cv_warning": 0.010,
+    "validation_temporal_cv_bad": 0.025,
+    "validation_sat_warning_pct": 0.10,
+    "validation_sat_bad_pct": 0.50,
+
+    # Diferença entre target real escolhido e faixa observada
+    "reference_extrapolation_margin": 0.03,
+
+    # Controles
+    "exposure_rel_tolerance": 0.012,
+    "exposure_abs_tolerance_us": 25,
+    "iso_tolerance": 5,
+
+    # Sync visual, não é requisito rígido da regressão
+    "sync_warning_ms": 40.0,
+}
+
+
+DEFAULT_SWEEP_FACTORS = (
+    0.30,
+    0.42,
+    0.55,
+    0.70,
+    0.85,
+    1.00,
+    1.18,
+    1.38,
+    1.58,
+)
+
+
+# ============================================================
+# Helpers
+# ============================================================
+
+def now_str() -> str:
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+def session_stamp() -> str:
+    return datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+
+
+def ensure_dir(path: str | Path):
+    Path(path).mkdir(parents=True, exist_ok=True)
+
+
+def save_json_atomic(path: str | Path, data: dict):
+    p = Path(path)
+    ensure_dir(p.parent)
+
+    tmp = p.with_suffix(p.suffix + ".tmp")
+
+    with tmp.open("w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+        f.flush()
+        os.fsync(f.fileno())
+
+    os.replace(tmp, p)
+
+
+def load_json(path: str | Path) -> dict:
+    p = Path(path)
+
+    if not p.is_file():
+        return {}
+
+    with p.open("r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def sha256_file(path: str | Path) -> str:
+    h = hashlib.sha256()
+
+    with open(path, "rb") as f:
+        while True:
+            chunk = f.read(1024 * 1024)
+            if not chunk:
+                break
+            h.update(chunk)
+
+    return h.hexdigest()
+
+
+def safe_int(v, default=None):
+    try:
+        if v is None:
+            return default
+        return int(v)
+    except Exception:
+        return default
+
+
+def safe_float(v, default=None):
+    try:
+        if v is None:
+            return default
+        return float(v)
+    except Exception:
+        return default
+
+
+def clamp(v, lo, hi):
+    return max(lo, min(hi, v))
+
+
+def status_rank(s: str) -> int:
+    return {
+        "good": 0,
+        "warning": 1,
+        "bad": 2,
+    }.get(str(s).lower(), 2)
+
+
+def merge_status(a: str, b: str) -> str:
+    return a if status_rank(a) >= status_rank(b) else b
+
+
+def overlay_hud(
+    img,
+    lines,
+    x=12,
+    y=24,
+    font_scale=0.50,
+    line_step=20,
+    color=(255, 255, 255),
+):
+    yy = int(y)
+    h = img.shape[0]
+
+    for line in lines:
+        if yy > h - 6:
+            break
+
+        text = str(line)
+
+        cv2.putText(
+            img,
+            text,
+            (int(x), yy),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            font_scale,
+            (0, 0, 0),
+            3,
+            cv2.LINE_AA,
+        )
+
+        cv2.putText(
+            img,
+            text,
+            (int(x), yy),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            font_scale,
+            color,
+            1,
+            cv2.LINE_AA,
+        )
+
+        yy += int(line_step)
+
+
+def socket_name(socket) -> str:
+    name = getattr(socket, "name", None)
+
+    if name:
+        return str(name)
+
+    text = str(socket)
+
+    for candidate in (
+        "CAM_A",
+        "CAM_B",
+        "CAM_C",
+        "CAM_D",
+    ):
+        if candidate in text:
+            return candidate
+
+    return text
+
+
+def get_socket(name: str):
+    mapping = {
+        "CAM_A": dai.CameraBoardSocket.CAM_A,
+        "CAM_B": dai.CameraBoardSocket.CAM_B,
+        "CAM_C": dai.CameraBoardSocket.CAM_C,
+    }
+
+    key = str(name).upper()
+
+    if key not in mapping:
+        raise ValueError(
+            f"Socket inválido: {name}"
+        )
+
+    return mapping[key]
+
+
+def enum_if_exists(enum_cls, name: str):
+    return getattr(
+        enum_cls,
+        name,
+        None,
+    )
+
+
+def supported_type_strings(feature):
+    return [
+        str(x).upper()
+        for x in (
+            getattr(feature, "supportedTypes", [])
+            or []
+        )
+    ]
+
+
+def feature_is_color(feature) -> bool:
+    types = supported_type_strings(feature)
+    sensor = str(
+        getattr(feature, "sensorName", "")
+        or ""
+    ).upper()
+
+    if any("COLOR" in x for x in types):
+        return True
+
+    if any("MONO" in x for x in types):
+        return False
+
+    return sensor in {
+        "OV9782",
+        "AR0234",
+    }
+
+
+def feature_is_mono(feature) -> bool:
+    types = supported_type_strings(feature)
+
+    if any("MONO" in x for x in types):
+        return True
+
+    if any("COLOR" in x for x in types):
+        return False
+
+    return not feature_is_color(feature)
+
+
+def resize_panel(
+    img,
+    width,
+    height,
+):
+    return cv2.resize(
+        img,
+        (
+            int(width),
+            int(height),
+        ),
+        interpolation=cv2.INTER_AREA,
+    )
+
+
+def robust_median(arr: np.ndarray) -> float:
+    f = np.asarray(
+        arr,
+        dtype=np.float32,
+    )
+
+    f = f[
+        np.isfinite(f)
+    ]
+
+    if f.size == 0:
+        return 0.0
+
+    return float(
+        np.median(f)
+    )
+
+
+def temporal_cv(values) -> float:
+    arr = np.asarray(
+        list(values),
+        dtype=np.float64,
+    )
+
+    if arr.size < 3:
+        return float("inf")
+
+    mean = float(
+        np.mean(arr)
+    )
+
+    if abs(mean) < 1e-12:
+        return float("inf")
+
+    return float(
+        np.std(arr)
+        / abs(mean)
+    )
+
+
+def central_roi(
+    img01: np.ndarray,
+    frac: float,
+):
+    h, w = img01.shape[:2]
+
+    frac = clamp(
+        float(frac),
+        0.05,
+        1.0,
+    )
+
+    rw = max(
+        8,
+        int(round(w * frac)),
+    )
+
+    rh = max(
+        8,
+        int(round(h * frac)),
+    )
+
+    x0 = (w - rw) // 2
+    y0 = (h - rh) // 2
+
+    return img01[
+        y0:y0 + rh,
+        x0:x0 + rw,
+    ], (
+        x0,
+        y0,
+        x0 + rw,
+        y0 + rh,
+    )
+
+
+def image_stats(
+    img01: np.ndarray,
+    roi_frac: float,
+):
+    roi, rect = central_roi(
+        img01,
+        roi_frac,
+    )
+
+    a = np.asarray(
+        roi,
+        dtype=np.float32,
+    )
+
+    f = a[
+        np.isfinite(a)
+    ]
+
+    if f.size == 0:
+        return {
+            "valid": False,
+            "roi_rect": list(rect),
+        }
+
+    return {
+        "valid": True,
+        "roi_rect": list(rect),
+        "pixels": int(f.size),
+        "mean": float(
+            np.mean(f)
+        ),
+        "std": float(
+            np.std(f)
+        ),
+        "p01": float(
+            np.percentile(f, 1)
+        ),
+        "p05": float(
+            np.percentile(f, 5)
+        ),
+        "p50": float(
+            np.percentile(f, 50)
+        ),
+        "p95": float(
+            np.percentile(f, 95)
+        ),
+        "p99": float(
+            np.percentile(f, 99)
+        ),
+        "sat_pct": float(
+            (f >= 0.995).mean()
+            * 100.0
+        ),
+        "dark_pct": float(
+            (f <= 0.010).mean()
+            * 100.0
+        ),
+    }
+
+
+def gray_preview(
+    img01,
+    roi_frac,
+):
+    vis = np.clip(
+        img01 * 255.0,
+        0,
+        255,
+    ).astype(np.uint8)
+
+    bgr = cv2.cvtColor(
+        vis,
+        cv2.COLOR_GRAY2BGR,
+    )
+
+    _, rect = central_roi(
+        img01,
+        roi_frac,
+    )
+
+    x0, y0, x1, y1 = rect
+
+    cv2.rectangle(
+        bgr,
+        (x0, y0),
+        (x1, y1),
+        (0, 255, 255),
+        2,
+    )
+
+    return bgr
+
+
+# ============================================================
+# RAW decode
+# ============================================================
+
+def unpack_raw10(
+    data,
+    width: int,
+    height: int,
+    stride: Optional[int] = None,
+) -> np.ndarray:
+    width = int(width)
+    height = int(height)
+
+    if width % 4 != 0:
+        raise ValueError(
+            f"RAW10 width não múltiplo de 4: {width}"
+        )
+
+    arr = np.asarray(
+        data,
+        dtype=np.uint8,
+    ).reshape(-1)
+
+    row_bytes = (
+        width // 4
+    ) * 5
+
+    if stride is None or int(stride) < row_bytes:
+        stride = row_bytes
+
+    stride = int(stride)
+    expected = stride * height
+
+    if arr.size < expected:
+        raise RuntimeError(
+            f"RAW10 curto: {arr.size} < {expected}"
+        )
+
+    rows = arr[
+        :expected
+    ].reshape(
+        height,
+        stride,
+    )
+
+    payload = rows[
+        :,
+        :row_bytes,
+    ]
+
+    g = payload.reshape(
+        height,
+        width // 4,
+        5,
+    )
+
+    out = np.empty(
+        (
+            height,
+            width // 4,
+            4,
+        ),
+        dtype=np.uint16,
+    )
+
+    out[:, :, 0] = (
+        g[:, :, 0].astype(np.uint16) << 2
+    ) | (
+        g[:, :, 4] & 0x03
+    )
+
+    out[:, :, 1] = (
+        g[:, :, 1].astype(np.uint16) << 2
+    ) | (
+        (g[:, :, 4] >> 2)
+        & 0x03
+    )
+
+    out[:, :, 2] = (
+        g[:, :, 2].astype(np.uint16) << 2
+    ) | (
+        (g[:, :, 4] >> 4)
+        & 0x03
+    )
+
+    out[:, :, 3] = (
+        g[:, :, 3].astype(np.uint16) << 2
+    ) | (
+        (g[:, :, 4] >> 6)
+        & 0x03
+    )
+
+    return np.ascontiguousarray(
+        out.reshape(
+            height,
+            width,
+        )
+    )
+
+
+def unpack_raw8(
+    data,
+    width: int,
+    height: int,
+    stride: Optional[int] = None,
+) -> np.ndarray:
+    arr = np.asarray(
+        data,
+        dtype=np.uint8,
+    ).reshape(-1)
+
+    width = int(width)
+    height = int(height)
+
+    if stride is None or int(stride) < width:
+        stride = width
+
+    stride = int(stride)
+    expected = stride * height
+
+    if arr.size < expected:
+        raise RuntimeError(
+            f"RAW8 curto: {arr.size} < {expected}"
+        )
+
+    rows = arr[
+        :expected
+    ].reshape(
+        height,
+        stride,
+    )
+
+    return np.ascontiguousarray(
+        rows[:, :width]
+    )
+
+
+def decode_raw_packet(
+    packet,
+    spec,
+):
+    width = int(
+        packet.getWidth()
+    )
+
+    height = int(
+        packet.getHeight()
+    )
+
+    if (
+        width,
+        height,
+    ) != (
+        spec.width,
+        spec.height,
+    ):
+        raise RuntimeError(
+            f"{spec.role}: frame "
+            f"{width}x{height} != "
+            f"{spec.width}x{spec.height}"
+        )
+
+    stride = None
+
+    try:
+        stride = int(
+            packet.getStride()
+        )
+    except Exception:
+        pass
+
+    raw_type = str(
+        packet.getType()
+    ).upper()
+
+    if "RAW10" in raw_type:
+        raw = unpack_raw10(
+            packet.getData(),
+            width,
+            height,
+            stride,
+        )
+
+        img01 = (
+            raw.astype(np.float32)
+            / 1023.0
+        )
+
+        bit_depth = 10
+
+    elif (
+        "RAW8" in raw_type
+        or "GRAY8" in raw_type
+    ):
+        raw = unpack_raw8(
+            packet.getData(),
+            width,
+            height,
+            stride,
+        )
+
+        img01 = (
+            raw.astype(np.float32)
+            / 255.0
+        )
+
+        bit_depth = 8
+
+    else:
+        # Último fallback para MonoCamera.out-like payload,
+        # caso a API não nomeie RAW8 claramente.
+        data = np.asarray(
+            packet.getData(),
+            dtype=np.uint8,
+        ).reshape(-1)
+
+        if data.size >= width * height:
+            raw = unpack_raw8(
+                data,
+                width,
+                height,
+                stride,
+            )
+
+            img01 = (
+                raw.astype(np.float32)
+                / 255.0
+            )
+
+            bit_depth = 8
+        else:
+            raise RuntimeError(
+                f"{spec.role}: tipo RAW não suportado: "
+                f"{raw_type}"
+            )
+
+    return {
+        "image01": np.clip(
+            img01,
+            0.0,
+            1.0,
+        ),
+        "raw_type": raw_type,
+        "bit_depth": bit_depth,
+        "stride": stride,
+    }
+
+
+# ============================================================
+# Hardware discovery
+# ============================================================
+
+@dataclass
+class CameraSpec:
+    role: str
+    socket_name: str
+    sensor_name: str
+    width: int
+    height: int
+    resolution_name: str
+    stream_name: str
+    control_name: str
+    is_color: bool
+
+
+def discover_cameras(
+    mx_id: Optional[str],
+):
+    device_info = (
+        dai.DeviceInfo(mx_id)
+        if mx_id
+        else None
+    )
+
+    ctx = (
+        dai.Device(device_info)
+        if device_info is not None
+        else dai.Device()
+    )
+
+    with ctx as device:
+        actual_mx = None
+
+        for method_name in (
+            "getMxId",
+            "getDeviceId",
+        ):
+            fn = getattr(
+                device,
+                method_name,
+                None,
+            )
+
+            if callable(fn):
+                try:
+                    value = fn()
+
+                    if value:
+                        actual_mx = str(
+                            value
+                        )
+                        break
+
+                except Exception:
+                    pass
+
+        usb_speed = None
+
+        try:
+            usb_speed = str(
+                device.getUsbSpeed()
+            )
+        except Exception:
+            pass
+
+        rows = []
+
+        for feature in (
+            device
+            .getConnectedCameraFeatures()
+        ):
+            rows.append({
+                "socket_name": socket_name(
+                    feature.socket
+                ),
+                "sensor_name": str(
+                    feature.sensorName
+                    or ""
+                ).upper(),
+                "width": int(
+                    getattr(
+                        feature,
+                        "width",
+                        0,
+                    ) or 0
+                ),
+                "height": int(
+                    getattr(
+                        feature,
+                        "height",
+                        0,
+                    ) or 0
+                ),
+                "supported_types": (
+                    supported_type_strings(
+                        feature
+                    )
+                ),
+                "is_color": (
+                    feature_is_color(
+                        feature
+                    )
+                ),
+                "is_mono": (
+                    feature_is_mono(
+                        feature
+                    )
+                ),
+            })
+
+    return rows, actual_mx, usb_speed
+
+
+def validate_product_topology(
+    camera_rows,
+):
+    by_socket = {
+        row["socket_name"]: row
+        for row in camera_rows
+    }
+
+    errors = []
+
+    for role in ROLES:
+        contract = (
+            PRODUCT_TOPOLOGY[role]
+        )
+
+        socket = contract[
+            "socket"
+        ]
+
+        row = by_socket.get(
+            socket
+        )
+
+        if row is None:
+            errors.append(
+                f"{role}: {socket} ausente"
+            )
+            continue
+
+        sensor = row[
+            "sensor_name"
+        ]
+
+        if sensor not in contract[
+            "allowed_sensors"
+        ]:
+            errors.append(
+                f"{role}: {socket} "
+                f"sensor={sensor}, "
+                f"esperado="
+                f"{contract['allowed_sensors']}"
+            )
+
+        if (
+            role == "rgb"
+            and not row["is_color"]
+        ):
+            errors.append(
+                f"RGB {socket}/{sensor} "
+                "não anunciado COLOR"
+            )
+
+        if (
+            role in ("re", "nir")
+            and not row["is_mono"]
+        ):
+            errors.append(
+                f"{role.upper()} "
+                f"{socket}/{sensor} "
+                "não anunciado MONO"
+            )
+
+    if errors:
+        raise RuntimeError(
+            "Topologia inválida:\n  - "
+            + "\n  - ".join(errors)
+        )
+
+    return {
+        "valid": True,
+        "errors": [],
+    }
+
+
+def build_specs(
+    camera_rows,
+):
+    by_socket = {
+        row["socket_name"]: row
+        for row in camera_rows
+    }
+
+    specs = {}
+
+    for role in ROLES:
+        socket = (
+            PRODUCT_TOPOLOGY[role]
+            ["socket"]
+        )
+
+        row = by_socket[
+            socket
+        ]
+
+        sensor = row[
+            "sensor_name"
+        ]
+
+        cfg = (
+            RGB_SENSOR_MODES[sensor]
+            if role == "rgb"
+            else MONO_SENSOR_MODES[sensor]
+        )
+
+        if (
+            row["width"]
+            and row["height"]
+            and (
+                row["width"],
+                row["height"],
+            )
+            != (
+                cfg["width"],
+                cfg["height"],
+            )
+        ):
+            raise RuntimeError(
+                f"{socket}/{sensor}: "
+                f"anunciado "
+                f"{row['width']}x{row['height']}, "
+                f"esperado "
+                f"{cfg['width']}x{cfg['height']}"
+            )
+
+        specs[role] = CameraSpec(
+            role=role,
+            socket_name=socket,
+            sensor_name=sensor,
+            width=int(
+                cfg["width"]
+            ),
+            height=int(
+                cfg["height"]
+            ),
+            resolution_name=(
+                cfg["resolution_enum"]
+            ),
+            stream_name=(
+                f"rad_raw_{role}"
+            ),
+            control_name=(
+                f"rad_ctrl_{role}"
+            ),
+            is_color=(
+                role == "rgb"
+            ),
+        )
+
+    return specs
+
+
+def hardware_signature(
+    specs,
+):
+    return {
+        role: {
+            "socket": specs[
+                role
+            ].socket_name,
+            "sensor": specs[
+                role
+            ].sensor_name,
+            "size": [
+                specs[role].width,
+                specs[role].height,
+            ],
+        }
+        for role in ROLES
+    }
+
+
+# ============================================================
+# Pipeline
+# ============================================================
+
+def build_pipeline(
+    specs,
+    args,
+):
+    pipeline = dai.Pipeline()
+
+    for role in ROLES:
+        spec = specs[role]
+        socket = get_socket(
+            spec.socket_name
+        )
+
+        if spec.is_color:
+            cam = (
+                pipeline
+                .createColorCamera()
+            )
+
+            cam.setBoardSocket(
+                socket
+            )
+
+            enum_value = enum_if_exists(
+                dai.ColorCameraProperties
+                .SensorResolution,
+                spec.resolution_name,
+            )
+
+            if enum_value is None:
+                raise RuntimeError(
+                    "DepthAI sem "
+                    f"{spec.resolution_name}"
+                )
+
+            cam.setResolution(
+                enum_value
+            )
+
+            cam.setFps(
+                float(args.fps)
+            )
+
+            cam.setInterleaved(
+                False
+            )
+
+        else:
+            cam = (
+                pipeline
+                .createMonoCamera()
+            )
+
+            cam.setBoardSocket(
+                socket
+            )
+
+            enum_value = enum_if_exists(
+                dai.MonoCameraProperties
+                .SensorResolution,
+                spec.resolution_name,
+            )
+
+            if enum_value is None:
+                raise RuntimeError(
+                    "DepthAI sem "
+                    f"{spec.resolution_name}"
+                )
+
+            cam.setResolution(
+                enum_value
+            )
+
+            cam.setFps(
+                float(args.fps)
+            )
+
+        try:
+            cam.initialControl.setAutoExposureEnable()
+        except Exception:
+            pass
+
+        if not hasattr(
+            cam,
+            "raw",
+        ):
+            raise RuntimeError(
+                f"{spec.socket_name}/"
+                f"{spec.sensor_name} "
+                "não expõe saída raw."
+            )
+
+        xout = (
+            pipeline
+            .createXLinkOut()
+        )
+
+        xout.setStreamName(
+            spec.stream_name
+        )
+
+        cam.raw.link(
+            xout.input
+        )
+
+        xin = (
+            pipeline
+            .createXLinkIn()
+        )
+
+        xin.setStreamName(
+            spec.control_name
+        )
+
+        if not hasattr(
+            cam,
+            "inputControl",
+        ):
+            raise RuntimeError(
+                f"{spec.socket_name} "
+                "sem inputControl"
+            )
+
+        xin.out.link(
+            cam.inputControl
+        )
+
+    return pipeline
+
+
+# ============================================================
+# Controles
+# ============================================================
+
+@dataclass
+class LockedControl:
+    role: str
+    exposure_time_us: int
+    sensitivity_iso: int
+    source: str
+
+
+def packet_controls(
+    packet,
+):
+    exposure_us = None
+    iso = None
+    sequence_num = None
+    timestamp_s = None
+
+    try:
+        exp = (
+            packet
+            .getExposureTime()
+        )
+
+        if hasattr(
+            exp,
+            "total_seconds",
+        ):
+            exposure_us = int(
+                round(
+                    exp.total_seconds()
+                    * 1_000_000
+                )
+            )
+        else:
+            exposure_us = int(exp)
+
+    except Exception:
+        pass
+
+    try:
+        iso = int(
+            packet.getSensitivity()
+        )
+    except Exception:
+        pass
+
+    try:
+        sequence_num = int(
+            packet.getSequenceNum()
+        )
+    except Exception:
+        pass
+
+    for method_name in (
+        "getTimestampDevice",
+        "getTimestamp",
+    ):
+        fn = getattr(
+            packet,
+            method_name,
+            None,
+        )
+
+        if callable(fn):
+            try:
+                ts = fn()
+
+                if hasattr(
+                    ts,
+                    "total_seconds",
+                ):
+                    timestamp_s = float(
+                        ts.total_seconds()
+                    )
+                else:
+                    timestamp_s = float(
+                        ts
+                    )
+
+                break
+
+            except Exception:
+                pass
+
+    return {
+        "exposure_time_us": (
+            exposure_us
+        ),
+        "sensitivity_iso": iso,
+        "sequence_num": (
+            sequence_num
+        ),
+        "timestamp_s": (
+            timestamp_s
+        ),
+    }
+
+
+def controls_match(
+    actual,
+    target: LockedControl,
+    qa,
+):
+    exp = safe_int(
+        actual.get(
+            "exposure_time_us"
+        ),
+        None,
+    )
+
+    iso = safe_int(
+        actual.get(
+            "sensitivity_iso"
+        ),
+        None,
+    )
+
+    if exp is None or iso is None:
+        return False
+
+    exp_tol = max(
+        int(
+            qa[
+                "exposure_abs_tolerance_us"
+            ]
+        ),
+        int(
+            round(
+                target.exposure_time_us
+                * qa[
+                    "exposure_rel_tolerance"
+                ]
+            )
+        ),
+    )
+
+    return (
+        abs(
+            exp
+            - target.exposure_time_us
+        )
+        <= exp_tol
+        and abs(
+            iso
+            - target.sensitivity_iso
+        )
+        <= int(
+            qa[
+                "iso_tolerance"
+            ]
+        )
+    )
+
+
+def send_manual_control(
+    queue,
+    lock: LockedControl,
+):
+    ctrl = dai.CameraControl()
+
+    ctrl.setManualExposure(
+        int(
+            lock.exposure_time_us
+        ),
+        int(
+            lock.sensitivity_iso
+        ),
+    )
+
+    queue.send(
+        ctrl
+    )
+
+
+# ============================================================
+# Runtime
+# ============================================================
+
+class RadiometricRuntime:
+    def __init__(
+        self,
+        device,
+        specs,
+        args,
+        qa,
+    ):
+        self.device = device
+        self.specs = specs
+        self.args = args
+        self.qa = qa
+
+        self.out_q = {
+            role: (
+                device
+                .getOutputQueue(
+                    name=spec.stream_name,
+                    maxSize=3,
+                    blocking=False,
+                )
+            )
+            for role, spec in (
+                specs.items()
+            )
+        }
+
+        self.ctrl_q = {
+            role: (
+                device
+                .getInputQueue(
+                    spec.control_name
+                )
+            )
+            for role, spec in (
+                specs.items()
+            )
+        }
+
+        self.images = {
+            role: None
+            for role in ROLES
+        }
+
+        self.controls = {
+            role: {}
+            for role in ROLES
+        }
+
+        self.raw_meta = {
+            role: {}
+            for role in ROLES
+        }
+
+        self.last_seq = {
+            role: None
+            for role in ROLES
+        }
+
+        self.fps = {
+            role: 0.0
+            for role in ROLES
+        }
+
+        self._fps_count = {
+            role: 0
+            for role in ROLES
+        }
+
+        self._fps_t0 = {
+            role: time.time()
+            for role in ROLES
+        }
+
+        self.temporal_p50 = {
+            role: deque(
+                maxlen=20
+            )
+            for role in ROLES
+        }
+
+    def poll(self):
+        fresh = set()
+
+        for role in ROLES:
+            packet = (
+                self.out_q[role]
+                .tryGet()
+            )
+
+            if packet is None:
+                continue
+
+            ctrl = packet_controls(
+                packet
+            )
+
+            seq = ctrl.get(
+                "sequence_num"
+            )
+
+            if (
+                seq is not None
+                and seq
+                == self.last_seq[role]
+            ):
+                continue
+
+            decoded = decode_raw_packet(
+                packet,
+                self.specs[role],
+            )
+
+            self.images[role] = (
+                decoded["image01"]
+            )
+
+            self.controls[role] = (
+                ctrl
+            )
+
+            self.raw_meta[role] = {
+                "raw_type": decoded[
+                    "raw_type"
+                ],
+                "bit_depth": decoded[
+                    "bit_depth"
+                ],
+                "stride": decoded[
+                    "stride"
+                ],
+            }
+
+            self.last_seq[role] = seq
+
+            st = image_stats(
+                self.images[role],
+                self.args.roi_frac,
+            )
+
+            if st.get("valid"):
+                self.temporal_p50[
+                    role
+                ].append(
+                    st["p50"]
+                )
+
+            fresh.add(role)
+
+            self._fps_count[
+                role
+            ] += 1
+
+            dt = (
+                time.time()
+                - self._fps_t0[role]
+            )
+
+            if dt >= 1.0:
+                self.fps[role] = (
+                    self._fps_count[
+                        role
+                    ]
+                    / dt
+                )
+
+                self._fps_count[
+                    role
+                ] = 0
+
+                self._fps_t0[
+                    role
+                ] = time.time()
+
+        return fresh
+
+    def wait_all(
+        self,
+        timeout=8.0,
+    ):
+        t0 = time.time()
+
+        while (
+            time.time() - t0
+            < timeout
+        ):
+            self.poll()
+
+            if all(
+                self.images[
+                    role
+                ] is not None
+                for role in ROLES
+            ):
+                return
+
+            time.sleep(
+                0.003
+            )
+
+        missing = [
+            role
+            for role in ROLES
+            if self.images[
+                role
+            ] is None
+        ]
+
+        raise TimeoutError(
+            f"Sem frames: {missing}"
+        )
+
+    def send_controls(
+        self,
+        controls_by_role,
+    ):
+        for role in ROLES:
+            send_manual_control(
+                self.ctrl_q[role],
+                controls_by_role[
+                    role
+                ],
+            )
+
+    def verify_controls(
+        self,
+        controls_by_role,
+        settle_frames=5,
+        timeout=10.0,
+    ):
+        matched = {
+            role: 0
+            for role in ROLES
+        }
+
+        seen_seq = dict(
+            self.last_seq
+        )
+
+        t0 = time.time()
+
+        while (
+            time.time() - t0
+            < timeout
+        ):
+            fresh = self.poll()
+
+            for role in fresh:
+                seq = (
+                    self.last_seq[
+                        role
+                    ]
+                )
+
+                if (
+                    seq
+                    == seen_seq.get(
+                        role
+                    )
+                ):
+                    continue
+
+                seen_seq[
+                    role
+                ] = seq
+
+                if controls_match(
+                    self.controls[
+                        role
+                    ],
+                    controls_by_role[
+                        role
+                    ],
+                    self.qa,
+                ):
+                    matched[
+                        role
+                    ] += 1
+                else:
+                    matched[
+                        role
+                    ] = 0
+
+            if all(
+                matched[role]
+                >= int(
+                    settle_frames
+                )
+                for role in ROLES
+            ):
+                return
+
+            time.sleep(
+                0.002
+            )
+
+        raise RuntimeError(
+            "Controles manuais "
+            "não estabilizaram: "
+            f"{matched}"
+        )
+
+    def capture_level(
+        self,
+        frames_count,
+        controls_by_role,
+    ):
+        values = {
+            role: []
+            for role in ROLES
+        }
+
+        p95_values = {
+            role: []
+            for role in ROLES
+        }
+
+        sat_values = {
+            role: []
+            for role in ROLES
+        }
+
+        dark_values = {
+            role: []
+            for role in ROLES
+        }
+
+        latest_stats = {
+            role: None
+            for role in ROLES
+        }
+
+        seen = dict(
+            self.last_seq
+        )
+
+        t0 = time.time()
+
+        while any(
+            len(
+                values[role]
+            ) < int(
+                frames_count
+            )
+            for role in ROLES
+        ):
+            fresh = self.poll()
+
+            for role in fresh:
+                if (
+                    len(values[role])
+                    >= int(
+                        frames_count
+                    )
+                ):
+                    continue
+
+                seq = self.last_seq[
+                    role
+                ]
+
+                if seq == seen.get(
+                    role
+                ):
+                    continue
+
+                seen[role] = seq
+
+                if not controls_match(
+                    self.controls[
+                        role
+                    ],
+                    controls_by_role[
+                        role
+                    ],
+                    self.qa,
+                ):
+                    raise RuntimeError(
+                        f"{role}: "
+                        "controle mudou durante "
+                        "captura"
+                    )
+
+                st = image_stats(
+                    self.images[
+                        role
+                    ],
+                    self.args.roi_frac,
+                )
+
+                if not st.get(
+                    "valid"
+                ):
+                    continue
+
+                values[role].append(
+                    float(
+                        st["p50"]
+                    )
+                )
+
+                p95_values[
+                    role
+                ].append(
+                    float(
+                        st["p95"]
+                    )
+                )
+
+                sat_values[
+                    role
+                ].append(
+                    float(
+                        st["sat_pct"]
+                    )
+                )
+
+                dark_values[
+                    role
+                ].append(
+                    float(
+                        st["dark_pct"]
+                    )
+                )
+
+                latest_stats[
+                    role
+                ] = st
+
+            if (
+                time.time() - t0
+                > 12.0
+            ):
+                raise TimeoutError(
+                    "Timeout capturando "
+                    "nível do sweep."
+                )
+
+            time.sleep(
+                0.001
+            )
+
+        out = {}
+
+        for role in ROLES:
+            arr = np.asarray(
+                values[role],
+                dtype=np.float64,
+            )
+
+            out[role] = {
+                "frames": int(
+                    arr.size
+                ),
+                "p50": float(
+                    np.median(
+                        arr
+                    )
+                ),
+                "p50_mean": float(
+                    np.mean(
+                        arr
+                    )
+                ),
+                "p50_std": float(
+                    np.std(
+                        arr
+                    )
+                ),
+                "p50_temporal_cv": float(
+                    np.std(arr)
+                    / max(
+                        abs(
+                            np.mean(
+                                arr
+                            )
+                        ),
+                        1e-12,
+                    )
+                ),
+                "p95": float(
+                    np.median(
+                        np.asarray(
+                            p95_values[
+                                role
+                            ]
+                        )
+                    )
+                ),
+                "sat_pct": float(
+                    np.median(
+                        np.asarray(
+                            sat_values[
+                                role
+                            ]
+                        )
+                    )
+                ),
+                "dark_pct": float(
+                    np.median(
+                        np.asarray(
+                            dark_values[
+                                role
+                            ]
+                        )
+                    )
+                ),
+                "last_stats": (
+                    latest_stats[
+                        role
+                    ]
+                ),
+            }
+
+        return out
+
+
+# ============================================================
+# Preflight
+# ============================================================
+
+def preflight_report(
+    runtime,
+    qa,
+):
+    result = {
+        "status": "good",
+        "roles": {},
+        "reasons": [],
+    }
+
+    for role in ROLES:
+        img = runtime.images[
+            role
+        ]
+
+        if img is None:
+            result[
+                "status"
+            ] = "bad"
+
+            result[
+                "roles"
+            ][role] = {
+                "status": "bad",
+                "reasons": [
+                    "sem_frame"
+                ],
+            }
+
+            continue
+
+        st = image_stats(
+            img,
+            runtime.args.roi_frac,
+        )
+
+        cv = temporal_cv(
+            runtime.temporal_p50[
+                role
+            ]
+        )
+
+        status = "good"
+        reasons = []
+
+        p50 = st[
+            "p50"
+        ]
+
+        if (
+            p50
+            < qa[
+                "preflight_p50_min"
+            ]
+        ):
+            status = "bad"
+            reasons.append(
+                f"p50_low:{p50:.3f}"
+            )
+
+        elif (
+            p50
+            > qa[
+                "preflight_p50_max"
+            ]
+        ):
+            status = "bad"
+            reasons.append(
+                f"p50_high:{p50:.3f}"
+            )
+
+        if (
+            st["sat_pct"]
+            > qa[
+                "preflight_sat_bad_pct"
+            ]
+        ):
+            status = merge_status(
+                status,
+                "bad",
+            )
+
+            reasons.append(
+                "sat_bad:"
+                f"{st['sat_pct']:.3f}%"
+            )
+
+        elif (
+            st["sat_pct"]
+            > qa[
+                "preflight_sat_warning_pct"
+            ]
+        ):
+            status = merge_status(
+                status,
+                "warning",
+            )
+
+            reasons.append(
+                "sat_warn:"
+                f"{st['sat_pct']:.3f}%"
+            )
+
+        if (
+            st["dark_pct"]
+            > qa[
+                "preflight_dark_bad_pct"
+            ]
+        ):
+            status = merge_status(
+                status,
+                "bad",
+            )
+
+            reasons.append(
+                "dark_bad:"
+                f"{st['dark_pct']:.3f}%"
+            )
+
+        elif (
+            st["dark_pct"]
+            > qa[
+                "preflight_dark_warning_pct"
+            ]
+        ):
+            status = merge_status(
+                status,
+                "warning",
+            )
+
+            reasons.append(
+                "dark_warn:"
+                f"{st['dark_pct']:.3f}%"
+            )
+
+        if math.isfinite(cv):
+            if (
+                cv
+                > qa[
+                    "preflight_temporal_cv_bad"
+                ]
+            ):
+                status = merge_status(
+                    status,
+                    "bad",
+                )
+
+                reasons.append(
+                    f"temporal_cv_bad:{cv:.4f}"
+                )
+
+            elif (
+                cv
+                > qa[
+                    "preflight_temporal_cv_warning"
+                ]
+            ):
+                status = merge_status(
+                    status,
+                    "warning",
+                )
+
+                reasons.append(
+                    f"temporal_cv_warn:{cv:.4f}"
+                )
+
+        result[
+            "roles"
+        ][role] = {
+            "status": status,
+            "stats": st,
+            "temporal_cv": (
+                None
+                if not math.isfinite(
+                    cv
+                )
+                else float(cv)
+            ),
+            "controls": dict(
+                runtime.controls[
+                    role
+                ]
+            ),
+            "raw_meta": dict(
+                runtime.raw_meta[
+                    role
+                ]
+            ),
+            "reasons": reasons,
+        }
+
+        result[
+            "status"
+        ] = merge_status(
+            result[
+                "status"
+            ],
+            status,
+        )
+
+        result[
+            "reasons"
+        ].extend(
+            [
+                f"{role}:{x}"
+                for x in reasons
+            ]
+        )
+
+    return result
+
+
+def choose_base_equivalent_exposure(
+    runtime,
+    calibration_iso,
+):
+    """
+    Converte o ponto encontrado pelo AE para exposição equivalente no ISO
+    de calibração:
+
+        factor = exp * ISO/100
+        exp_cal = factor / (ISO_cal/100)
+    """
+    out = {}
+
+    for role in ROLES:
+        ctrl = runtime.controls[
+            role
+        ]
+
+        exp = safe_float(
+            ctrl.get(
+                "exposure_time_us"
+            ),
+            None,
+        )
+
+        iso = safe_float(
+            ctrl.get(
+                "sensitivity_iso"
+            ),
+            None,
+        )
+
+        if (
+            exp is None
+            or iso is None
+            or exp <= 0
+            or iso <= 0
+        ):
+            raise RuntimeError(
+                f"Sem EXP/ISO válido "
+                f"para {role}"
+            )
+
+        factor = (
+            exp
+            * (
+                iso
+                / ISO_BASE
+            )
+        )
+
+        cal_gain = (
+            float(
+                calibration_iso
+            )
+            / ISO_BASE
+        )
+
+        exp_equiv = (
+            factor
+            / cal_gain
+        )
+
+        out[role] = {
+            "ae_exposure_time_us": (
+                float(exp)
+            ),
+            "ae_sensitivity_iso": (
+                float(iso)
+            ),
+            "ae_factor": (
+                float(factor)
+            ),
+            "calibration_iso": int(
+                calibration_iso
+            ),
+            "equivalent_exposure_us": (
+                float(
+                    exp_equiv
+                )
+            ),
+        }
+
+    return out
+
+
+# ============================================================
+# Sweep
+# ============================================================
+
+def parse_sweep_factors(
+    text: str,
+):
+    if not text:
+        return list(
+            DEFAULT_SWEEP_FACTORS
+        )
+
+    vals = []
+
+    for token in (
+        str(text)
+        .replace(";", ",")
+        .split(",")
+    ):
+        token = token.strip()
+
+        if not token:
+            continue
+
+        v = float(token)
+
+        if v <= 0:
+            raise ValueError(
+                "Sweep factor deve ser > 0"
+            )
+
+        vals.append(
+            v
+        )
+
+    vals = sorted(
+        set(
+            round(v, 6)
+            for v in vals
+        )
+    )
+
+    return vals
+
+
+def build_sweep_plan(
+    base_equiv,
+    factors,
+    args,
+):
+    """
+    Cada nível possui exposição própria por role, mas o mesmo fator relativo
+    ao ponto de trabalho encontrado pelo AE.
+    """
+    levels = []
+
+    max_frame_exp = int(
+        min(
+            float(
+                args.max_exposure_us
+            ),
+            (
+                1_000_000.0
+                / max(
+                    float(
+                        args.fps
+                    ),
+                    1.0,
+                )
+            )
+            * float(
+                args.frame_period_fraction
+            ),
+        )
+    )
+
+    min_exp = int(
+        args.min_exposure_us
+    )
+
+    for factor in factors:
+        controls = {}
+
+        for role in ROLES:
+            base = float(
+                base_equiv[
+                    role
+                ][
+                    "equivalent_exposure_us"
+                ]
+            )
+
+            exp = int(
+                round(
+                    base
+                    * float(
+                        factor
+                    )
+                )
+            )
+
+            exp = int(
+                clamp(
+                    exp,
+                    min_exp,
+                    max_frame_exp,
+                )
+            )
+
+            controls[role] = LockedControl(
+                role=role,
+                exposure_time_us=exp,
+                sensitivity_iso=int(
+                    args.calibration_iso
+                ),
+                source=(
+                    "radiometric_sweep"
+                ),
+            )
+
+        signature = tuple(
+            controls[
+                role
+            ].exposure_time_us
+            for role in ROLES
+        )
+
+        if (
+            levels
+            and tuple(
+                levels[-1][
+                    "controls"
+                ][role]
+                .exposure_time_us
+                for role in ROLES
+            )
+            == signature
+        ):
+            continue
+
+        levels.append({
+            "factor": float(
+                factor
+            ),
+            "controls": controls,
+        })
+
+    return levels, {
+        "min_exposure_us": min_exp,
+        "max_exposure_us": max_frame_exp,
+    }
+
+
+def show_progress_board(
+    runtime,
+    title,
+    lines,
+    args,
+):
+    pw = int(
+        args.panel_width
+    )
+
+    ph = int(
+        args.panel_height
+    )
+
+    panels = {}
+
+    for role in ROLES:
+        img = runtime.images[
+            role
+        ]
+
+        if img is None:
+            panel = np.zeros(
+                (
+                    ph,
+                    pw,
+                    3,
+                ),
+                dtype=np.uint8,
+            )
+        else:
+            view = gray_preview(
+                img,
+                args.roi_frac,
+            )
+
+            panel = resize_panel(
+                view,
+                pw,
+                ph,
+            )
+
+        st = (
+            image_stats(
+                img,
+                args.roi_frac,
+            )
+            if img is not None
+            else {}
+        )
+
+        ctrl = runtime.controls[
+            role
+        ]
+
+        overlay_hud(
+            panel,
+            [
+                f"{role.upper()} | "
+                f"{runtime.specs[role].sensor_name}",
+                f"RAW "
+                f"{runtime.specs[role].width}x"
+                f"{runtime.specs[role].height}",
+                f"p50={st.get('p50', 0):.3f} "
+                f"p95={st.get('p95', 0):.3f}",
+                f"sat={st.get('sat_pct', 0):.3f}%",
+                f"EXP={ctrl.get('exposure_time_us')}us "
+                f"ISO={ctrl.get('sensitivity_iso')}",
+            ],
+            x=8,
+            y=18,
+            font_scale=0.39,
+            line_step=17,
+        )
+
+        panels[
+            role
+        ] = panel
+
+    data = np.zeros(
+        (
+            ph,
+            pw,
+            3,
+        ),
+        dtype=np.uint8,
+    )
+
+    overlay_hud(
+        data,
+        [
+            title,
+            "",
+            *lines,
+        ],
+        x=14,
+        y=25,
+        font_scale=0.43,
+        line_step=19,
+    )
+
+    return np.vstack([
+        np.hstack([
+            panels["rgb"],
+            panels["re"],
+        ]),
+        np.hstack([
+            panels["nir"],
+            data,
+        ]),
+    ])
+
+
+# ============================================================
+# Regression / calibration math
+# ============================================================
+
+def linear_fit(
+    x,
+    y,
+):
+    x = np.asarray(
+        x,
+        dtype=np.float64,
+    )
+
+    y = np.asarray(
+        y,
+        dtype=np.float64,
+    )
+
+    if x.size < 2:
+        raise ValueError(
+            "Poucos pontos para regressão"
+        )
+
+    A = np.vstack([
+        x,
+        np.ones_like(x),
+    ]).T
+
+    slope, intercept = (
+        np.linalg.lstsq(
+            A,
+            y,
+            rcond=None,
+        )[0]
+    )
+
+    pred = (
+        slope * x
+        + intercept
+    )
+
+    resid = y - pred
+
+    ss_res = float(
+        np.sum(
+            resid ** 2
+        )
+    )
+
+    ss_tot = float(
+        np.sum(
+            (
+                y
+                - np.mean(y)
+            ) ** 2
+        )
+    )
+
+    r2 = (
+        1.0
+        if ss_tot <= 1e-18
+        else 1.0
+        - ss_res
+        / ss_tot
+    )
+
+    return {
+        "slope": float(
+            slope
+        ),
+        "intercept": float(
+            intercept
+        ),
+        "r2": float(
+            r2
+        ),
+        "prediction": pred,
+        "residual": resid,
+    }
+
+
+def describe(
+    values,
+):
+    arr = np.asarray(
+        list(values),
+        dtype=np.float64,
+    )
+
+    if arr.size == 0:
+        return {
+            "count": 0,
+        }
+
+    return {
+        "count": int(
+            arr.size
+        ),
+        "min": float(
+            np.min(arr)
+        ),
+        "p05": float(
+            np.percentile(
+                arr,
+                5,
+            )
+        ),
+        "p50": float(
+            np.percentile(
+                arr,
+                50,
+            )
+        ),
+        "p95": float(
+            np.percentile(
+                arr,
+                95,
+            )
+        ),
+        "max": float(
+            np.max(arr)
+        ),
+        "mean": float(
+            np.mean(arr)
+        ),
+        "std": float(
+            np.std(arr)
+        ),
+    }
+
+
+def analyze_role_sweep(
+    sweep_rows,
+    role,
+    target_p50,
+    args,
+    qa,
+):
+    all_points = []
+
+    for row in sweep_rows:
+        r = row[
+            "roles"
+        ][role]
+
+        ctrl = row[
+            "controls"
+        ][role]
+
+        exposure = float(
+            ctrl[
+                "exposure_time_us"
+            ]
+        )
+
+        iso = float(
+            ctrl[
+                "sensitivity_iso"
+            ]
+        )
+
+        actual_factor = (
+            exposure
+            * (
+                iso
+                / ISO_BASE
+            )
+        )
+
+        p50 = float(
+            r["p50"]
+        )
+
+        point = {
+            "level_index": int(
+                row[
+                    "level_index"
+                ]
+            ),
+            "sweep_factor": float(
+                row[
+                    "sweep_factor"
+                ]
+            ),
+            "exposure_time_us": exposure,
+            "sensitivity_iso": iso,
+            "actual_factor": actual_factor,
+            "p50": p50,
+            "p95": float(
+                r["p95"]
+            ),
+            "sat_pct": float(
+                r["sat_pct"]
+            ),
+            "dark_pct": float(
+                r["dark_pct"]
+            ),
+            "temporal_cv": float(
+                r[
+                    "p50_temporal_cv"
+                ]
+            ),
+        }
+
+        valid = (
+            p50
+            >= qa[
+                "valid_p50_min"
+            ]
+            and p50
+            <= qa[
+                "valid_p50_max"
+            ]
+            and point[
+                "sat_pct"
+            ]
+            <= qa[
+                "valid_sat_max_pct"
+            ]
+            and point[
+                "temporal_cv"
+            ]
+            <= qa[
+                "level_temporal_cv_bad"
+            ]
+        )
+
+        point["valid"] = bool(
+            valid
+        )
+
+        all_points.append(
+            point
+        )
+
+    valid_points = [
+        p
+        for p in all_points
+        if p["valid"]
+    ]
+
+    result = {
+        "status": "good",
+        "reasons": [],
+        "role": role,
+        "target_p50": float(
+            target_p50
+        ),
+        "all_points": all_points,
+        "valid_points": valid_points,
+    }
+
+    if (
+        len(valid_points)
+        < qa[
+            "min_valid_levels"
+        ]
+    ):
+        result[
+            "status"
+        ] = "bad"
+
+        result[
+            "reasons"
+        ].append(
+            "valid_levels_bad:"
+            f"{len(valid_points)}"
+        )
+
+        return result
+
+    x = np.asarray(
+        [
+            p[
+                "actual_factor"
+            ]
+            for p in valid_points
+        ],
+        dtype=np.float64,
+    )
+
+    y = np.asarray(
+        [
+            p["p50"]
+            for p in valid_points
+        ],
+        dtype=np.float64,
+    )
+
+    fit = linear_fit(
+        x,
+        y,
+    )
+
+    slope = float(
+        fit["slope"]
+    )
+
+    intercept = float(
+        fit["intercept"]
+    )
+
+    if slope <= 0:
+        result[
+            "status"
+        ] = "bad"
+
+        result[
+            "reasons"
+        ].append(
+            f"negative_slope:{slope}"
+        )
+
+        return result
+
+    reference_factor = (
+        float(target_p50)
+        - intercept
+    ) / slope
+
+    x_min = float(
+        np.min(x)
+    )
+
+    x_max = float(
+        np.max(x)
+    )
+
+    extrap_margin = float(
+        qa[
+            "reference_extrapolation_margin"
+        ]
+    )
+
+    if (
+        reference_factor
+        < x_min
+        * (
+            1.0
+            - extrap_margin
+        )
+        or reference_factor
+        > x_max
+        * (
+            1.0
+            + extrap_margin
+        )
+    ):
+        result[
+            "status"
+        ] = "bad"
+
+        result[
+            "reasons"
+        ].append(
+            "reference_outside_measured_range:"
+            f"{reference_factor:.1f}"
+        )
+
+    reference_factor = float(
+        clamp(
+            reference_factor,
+            x_min,
+            x_max,
+        )
+    )
+
+    reference_iso = int(
+        args.calibration_iso
+    )
+
+    reference_exposure_us = (
+        reference_factor
+        / (
+            reference_iso
+            / ISO_BASE
+        )
+    )
+
+    # Testa exatamente o modelo utilizado no runtime:
+    # normalized = measured * reference_factor / actual_factor
+    normalized_values = (
+        y
+        * reference_factor
+        / x
+    )
+
+    ref_response = (
+        slope
+        * reference_factor
+        + intercept
+    )
+
+    rel_errors = np.abs(
+        normalized_values
+        - ref_response
+    ) / max(
+        abs(
+            ref_response
+        ),
+        1e-9,
+    )
+
+    intercept_fraction = (
+        abs(
+            intercept
+        )
+        / max(
+            abs(
+                ref_response
+            ),
+            1e-9,
+        )
+    )
+
+    status = result[
+        "status"
+    ]
+
+    reasons = list(
+        result[
+            "reasons"
+        ]
+    )
+
+    r2 = float(
+        fit["r2"]
+    )
+
+    if r2 < qa[
+        "r2_bad"
+    ]:
+        status = merge_status(
+            status,
+            "bad",
+        )
+
+        reasons.append(
+            f"r2_bad:{r2:.6f}"
+        )
+
+    elif r2 < qa[
+        "r2_warning"
+    ]:
+        status = merge_status(
+            status,
+            "warning",
+        )
+
+        reasons.append(
+            f"r2_warn:{r2:.6f}"
+        )
+
+    if (
+        intercept_fraction
+        > qa[
+            "intercept_fraction_bad"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "bad",
+        )
+
+        reasons.append(
+            "intercept_fraction_bad:"
+            f"{intercept_fraction:.4f}"
+        )
+
+    elif (
+        intercept_fraction
+        > qa[
+            "intercept_fraction_warning"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "warning",
+        )
+
+        reasons.append(
+            "intercept_fraction_warn:"
+            f"{intercept_fraction:.4f}"
+        )
+
+    rel_med = float(
+        np.median(
+            rel_errors
+        )
+    )
+
+    rel_p95 = float(
+        np.percentile(
+            rel_errors,
+            95,
+        )
+    )
+
+    if (
+        rel_med
+        > qa[
+            "norm_median_rel_error_bad"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "bad",
+        )
+
+        reasons.append(
+            "norm_med_rel_bad:"
+            f"{rel_med:.4f}"
+        )
+
+    elif (
+        rel_med
+        > qa[
+            "norm_median_rel_error_warning"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "warning",
+        )
+
+        reasons.append(
+            "norm_med_rel_warn:"
+            f"{rel_med:.4f}"
+        )
+
+    if (
+        rel_p95
+        > qa[
+            "norm_p95_rel_error_bad"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "bad",
+        )
+
+        reasons.append(
+            "norm_p95_rel_bad:"
+            f"{rel_p95:.4f}"
+        )
+
+    elif (
+        rel_p95
+        > qa[
+            "norm_p95_rel_error_warning"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "warning",
+        )
+
+        reasons.append(
+            "norm_p95_rel_warn:"
+            f"{rel_p95:.4f}"
+        )
+
+    measured_scales = (
+        reference_factor
+        / x
+    )
+
+    result.update({
+        "status": status,
+        "reasons": reasons,
+        "fit": {
+            "slope_per_factor": slope,
+            "intercept": intercept,
+            "r2": r2,
+            "intercept_fraction_of_reference_response": (
+                float(
+                    intercept_fraction
+                )
+            ),
+            "reference_response_predicted": float(
+                ref_response
+            ),
+        },
+        "reference": {
+            "reference_factor": float(
+                reference_factor
+            ),
+            "exposure_time_us": int(
+                round(
+                    reference_exposure_us
+                )
+            ),
+            "sensitivity_iso": int(
+                reference_iso
+            ),
+            "target_p50": float(
+                target_p50
+            ),
+        },
+        "normalization_model_validation": {
+            "normalized_values": [
+                float(x)
+                for x in normalized_values
+            ],
+            "relative_errors": [
+                float(x)
+                for x in rel_errors
+            ],
+            "median_relative_error": (
+                rel_med
+            ),
+            "p95_relative_error": (
+                rel_p95
+            ),
+            "max_relative_error": float(
+                np.max(
+                    rel_errors
+                )
+            ),
+        },
+        "validated_scale_range": {
+            "min": float(
+                np.min(
+                    measured_scales
+                )
+            ),
+            "max": float(
+                np.max(
+                    measured_scales
+                )
+            ),
+        },
+        "observed_factor_range": {
+            "min": x_min,
+            "max": x_max,
+        },
+    })
+
+    return result
+
+
+# ============================================================
+# Validation at reference
+# ============================================================
+
+def evaluate_reference_validation(
+    role_analysis,
+    validation_stats,
+    qa,
+):
+    role = role_analysis[
+        "role"
+    ]
+
+    target = float(
+        role_analysis[
+            "target_p50"
+        ]
+    )
+
+    st = validation_stats[
+        role
+    ]
+
+    measured = float(
+        st["p50"]
+    )
+
+    target_rel_error = (
+        abs(
+            measured
+            - target
+        )
+        / max(
+            abs(
+                target
+            ),
+            1e-9,
+        )
+    )
+
+    temporal = float(
+        st[
+            "p50_temporal_cv"
+        ]
+    )
+
+    sat = float(
+        st[
+            "sat_pct"
+        ]
+    )
+
+    status = "good"
+    reasons = []
+
+    if (
+        target_rel_error
+        > qa[
+            "validation_target_rel_error_bad"
+        ]
+    ):
+        status = "bad"
+
+        reasons.append(
+            "target_rel_bad:"
+            f"{target_rel_error:.4f}"
+        )
+
+    elif (
+        target_rel_error
+        > qa[
+            "validation_target_rel_error_warning"
+        ]
+    ):
+        status = "warning"
+
+        reasons.append(
+            "target_rel_warn:"
+            f"{target_rel_error:.4f}"
+        )
+
+    if (
+        temporal
+        > qa[
+            "validation_temporal_cv_bad"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "bad",
+        )
+
+        reasons.append(
+            "temporal_cv_bad:"
+            f"{temporal:.4f}"
+        )
+
+    elif (
+        temporal
+        > qa[
+            "validation_temporal_cv_warning"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "warning",
+        )
+
+        reasons.append(
+            "temporal_cv_warn:"
+            f"{temporal:.4f}"
+        )
+
+    if (
+        sat
+        > qa[
+            "validation_sat_bad_pct"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "bad",
+        )
+
+        reasons.append(
+            f"sat_bad:{sat:.4f}%"
+        )
+
+    elif (
+        sat
+        > qa[
+            "validation_sat_warning_pct"
+        ]
+    ):
+        status = merge_status(
+            status,
+            "warning",
+        )
+
+        reasons.append(
+            f"sat_warn:{sat:.4f}%"
+        )
+
+    return {
+        "status": status,
+        "reasons": reasons,
+        "target_p50": target,
+        "measured_p50": measured,
+        "target_relative_error": (
+            float(
+                target_rel_error
+            )
+        ),
+        "temporal_cv": temporal,
+        "sat_pct": sat,
+        "dark_pct": float(
+            st[
+                "dark_pct"
+            ]
+        ),
+        "p95": float(
+            st["p95"]
+        ),
+    }
+
+
+# ============================================================
+# Plots / CSV
+# ============================================================
+
+def save_sweep_csv(
+    path,
+    sweep_rows,
+):
+    ensure_dir(
+        Path(path).parent
+    )
+
+    fields = [
+        "level_index",
+        "sweep_factor",
+        "role",
+        "exposure_time_us",
+        "sensitivity_iso",
+        "p50",
+        "p95",
+        "sat_pct",
+        "dark_pct",
+        "p50_temporal_cv",
+    ]
+
+    with open(
+        path,
+        "w",
+        newline="",
+        encoding="utf-8",
+    ) as f:
+        writer = csv.DictWriter(
+            f,
+            fieldnames=fields,
+        )
+
+        writer.writeheader()
+
+        for row in sweep_rows:
+            for role in ROLES:
+                ctrl = row[
+                    "controls"
+                ][role]
+
+                st = row[
+                    "roles"
+                ][role]
+
+                writer.writerow({
+                    "level_index": (
+                        row[
+                            "level_index"
+                        ]
+                    ),
+                    "sweep_factor": (
+                        row[
+                            "sweep_factor"
+                        ]
+                    ),
+                    "role": role,
+                    "exposure_time_us": (
+                        ctrl[
+                            "exposure_time_us"
+                        ]
+                    ),
+                    "sensitivity_iso": (
+                        ctrl[
+                            "sensitivity_iso"
+                        ]
+                    ),
+                    "p50": st[
+                        "p50"
+                    ],
+                    "p95": st[
+                        "p95"
+                    ],
+                    "sat_pct": st[
+                        "sat_pct"
+                    ],
+                    "dark_pct": st[
+                        "dark_pct"
+                    ],
+                    "p50_temporal_cv": (
+                        st[
+                            "p50_temporal_cv"
+                        ]
+                    ),
+                })
+
+
+def save_response_plot(
+    path,
+    analysis,
+    width=1000,
+    height=650,
+):
+    canvas = np.zeros(
+        (
+            height,
+            width,
+            3,
+        ),
+        dtype=np.uint8,
+    )
+
+    canvas[:] = 25
+
+    margin_l = 90
+    margin_r = 40
+    margin_t = 70
+    margin_b = 90
+
+    x0 = margin_l
+    y0 = height - margin_b
+    x1 = width - margin_r
+    y1 = margin_t
+
+    cv2.rectangle(
+        canvas,
+        (x0, y1),
+        (x1, y0),
+        (180, 180, 180),
+        1,
+    )
+
+    valid = analysis.get(
+        "valid_points",
+        [],
+    )
+
+    if not valid or "fit" not in analysis:
+        overlay_hud(
+            canvas,
+            [
+                f"{analysis.get('role', '').upper()}",
+                "Sem modelo válido",
+            ],
+            x=40,
+            y=45,
+            font_scale=0.7,
+            line_step=28,
+        )
+
+        cv2.imwrite(
+            str(path),
+            canvas,
+        )
+
+        return
+
+    xs = np.asarray(
+        [
+            p[
+                "actual_factor"
+            ]
+            for p in valid
+        ],
+        dtype=np.float64,
+    )
+
+    ys = np.asarray(
+        [
+            p["p50"]
+            for p in valid
+        ],
+        dtype=np.float64,
+    )
+
+    xmin = float(
+        np.min(xs)
+    )
+
+    xmax = float(
+        np.max(xs)
+    )
+
+    ymin = 0.0
+
+    ymax = max(
+        0.05,
+        float(
+            np.max(ys)
+        )
+        * 1.10,
+    )
+
+    def px(x):
+        return int(
+            x0
+            + (
+                float(x)
+                - xmin
+            )
+            / max(
+                xmax - xmin,
+                1e-9,
+            )
+            * (
+                x1 - x0
+            )
+        )
+
+    def py(y):
+        return int(
+            y0
+            - (
+                float(y)
+                - ymin
+            )
+            / max(
+                ymax - ymin,
+                1e-9,
+            )
+            * (
+                y0 - y1
+            )
+        )
+
+    slope = analysis[
+        "fit"
+    ][
+        "slope_per_factor"
+    ]
+
+    intercept = analysis[
+        "fit"
+    ][
+        "intercept"
+    ]
+
+    line_x = np.linspace(
+        xmin,
+        xmax,
+        120,
+    )
+
+    prev = None
+
+    for xv in line_x:
+        yv = (
+            slope
+            * xv
+            + intercept
+        )
+
+        pt = (
+            px(xv),
+            py(yv),
+        )
+
+        if prev is not None:
+            cv2.line(
+                canvas,
+                prev,
+                pt,
+                (180, 180, 180),
+                2,
+                cv2.LINE_AA,
+            )
+
+        prev = pt
+
+    for p in analysis[
+        "all_points"
+    ]:
+        color = (
+            (0, 220, 0)
+            if p["valid"]
+            else (0, 0, 220)
+        )
+
+        cv2.circle(
+            canvas,
+            (
+                px(
+                    p[
+                        "actual_factor"
+                    ]
+                ),
+                py(
+                    p["p50"]
+                ),
+            ),
+            6,
+            color,
+            -1,
+        )
+
+    ref = analysis[
+        "reference"
+    ]
+
+    ref_x = float(
+        ref[
+            "reference_factor"
+        ]
+    )
+
+    cv2.line(
+        canvas,
+        (
+            px(ref_x),
+            y1,
+        ),
+        (
+            px(ref_x),
+            y0,
+        ),
+        (0, 255, 255),
+        2,
+    )
+
+    overlay_hud(
+        canvas,
+        [
+            f"{analysis['role'].upper()} RADIOMETRIC RESPONSE",
+            f"status={analysis['status'].upper()}",
+            f"R2={analysis['fit']['r2']:.6f}",
+            f"ref={ref['exposure_time_us']}us ISO={ref['sensitivity_iso']}",
+            f"norm med err="
+            f"{analysis['normalization_model_validation']['median_relative_error']*100:.2f}%",
+            f"norm p95 err="
+            f"{analysis['normalization_model_validation']['p95_relative_error']*100:.2f}%",
+        ],
+        x=30,
+        y=28,
+        font_scale=0.48,
+        line_step=21,
+    )
+
+    cv2.putText(
+        canvas,
+        "Exposure x ISO factor",
+        (
+            width // 2 - 90,
+            height - 30,
+        ),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.55,
+        (220, 220, 220),
+        1,
+        cv2.LINE_AA,
+    )
+
+    cv2.putText(
+        canvas,
+        "RAW p50",
+        (
+            18,
+            height // 2,
+        ),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.55,
+        (220, 220, 220),
+        1,
+        cv2.LINE_AA,
+    )
+
+    cv2.imwrite(
+        str(path),
+        canvas,
+    )
+
+
+# ============================================================
+# Runtime fragment
+# ============================================================
+
+def build_normalization_fragment(
+    role_analyses,
+    args,
+):
+    reference_controls = {}
+
+    scale_limits = {}
+
+    for role in ROLES:
+        ref = role_analyses[
+            role
+        ][
+            "reference"
+        ]
+
+        reference_controls[
+            role
+        ] = {
+            "exposure_time_us": int(
+                ref[
+                    "exposure_time_us"
+                ]
+            ),
+            "sensitivity_iso": int(
+                ref[
+                    "sensitivity_iso"
+                ]
+            ),
+        }
+
+        scale_limits[
+            role
+        ] = {
+            "min": float(
+                args.runtime_scale_min
+            ),
+            "max": float(
+                args.runtime_scale_max
+            ),
+        }
+
+    scale_limits[
+        "default"
+    ] = {
+        "min": float(
+            args.runtime_scale_min
+        ),
+        "max": float(
+            args.runtime_scale_max
+        ),
+    }
+
+    return {
+        "radiometric_normalization": {
+            "enabled": True,
+            "method": NORMALIZATION_METHOD,
+            "apply_stage": (
+                "after_dark_before_flat_gain"
+            ),
+            "control_source": (
+                "stream_meta.frame_controls"
+            ),
+            "role_mapping_source": (
+                "camera_info"
+            ),
+            "factor_model": FACTOR_MODEL,
+            "iso_base": ISO_BASE,
+            "reference_mode": "fixed",
+            "reference_controls": (
+                reference_controls
+            ),
+            "scale_limits": (
+                scale_limits
+            ),
+            "missing_controls_policy": (
+                "skip"
+            ),
+            "invalid_controls_policy": (
+                "skip"
+            ),
+            "clip_output": False,
+            "save_debug": True,
+        }
+    }
+
+
+# ============================================================
+# Active artifact
+# ============================================================
+
+def promote_active(
+    active_path: Path,
+    report,
+):
+    ensure_dir(
+        active_path.parent
+    )
+
+    if active_path.exists():
+        old = load_json(
+            active_path
+        )
+
+        old_sig = old.get(
+            "hardware_signature"
+        )
+
+        new_sig = report.get(
+            "hardware_signature"
+        )
+
+        # Diferente de homografia, aqui trocar sensor RGB é justamente
+        # um motivo legítimo para recalibrar. Portanto não bloqueamos,
+        # apenas mantemos backup.
+        backup = active_path.with_name(
+            active_path.stem
+            + f".backup_{session_stamp()}"
+            + active_path.suffix
+        )
+
+        shutil.copy2(
+            active_path,
+            backup,
+        )
+
+    save_json_atomic(
+        active_path,
+        report,
+    )
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Calibração radiométrica de produção para "
+            "OV9782/AR0234 + 2x OV9282."
+        ),
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    # Hardware
+    parser.add_argument(
+        "--mx-id",
+        default=None,
+    )
+
+    parser.add_argument(
+        "--fps",
+        type=float,
+        default=20.0,
+    )
+
+    # Bancada
+    parser.add_argument(
+        "--rig-id",
+        default="",
+        help="ID da bancada radiométrica.",
+    )
+
+    parser.add_argument(
+        "--target-id",
+        default="",
+        help="ID/lote do painel cinza/difuso.",
+    )
+
+    parser.add_argument(
+        "--illumination-id",
+        default="",
+        help="ID/configuração da iluminação controlada.",
+    )
+
+    parser.add_argument(
+        "--target-distance-mm",
+        type=float,
+        default=None,
+    )
+
+    parser.add_argument(
+        "--warmup-s",
+        type=float,
+        default=3.0,
+    )
+
+    # ROI
+    parser.add_argument(
+        "--roi-frac",
+        type=float,
+        default=0.50,
+        help=(
+            "Fração central usada para medição, "
+            "evitando bordas/vignette."
+        ),
+    )
+
+    # Sweep
+    parser.add_argument(
+        "--calibration-iso",
+        type=int,
+        default=100,
+    )
+
+    parser.add_argument(
+        "--sweep-factors",
+        default=",".join(
+            str(x)
+            for x in DEFAULT_SWEEP_FACTORS
+        ),
+    )
+
+    parser.add_argument(
+        "--frames-per-level",
+        type=int,
+        default=14,
+    )
+
+    parser.add_argument(
+        "--validation-frames",
+        type=int,
+        default=24,
+    )
+
+    parser.add_argument(
+        "--settle-frames",
+        type=int,
+        default=5,
+    )
+
+    parser.add_argument(
+        "--min-exposure-us",
+        type=int,
+        default=100,
+    )
+
+    parser.add_argument(
+        "--max-exposure-us",
+        type=int,
+        default=30000,
+    )
+
+    parser.add_argument(
+        "--frame-period-fraction",
+        type=float,
+        default=0.85,
+        help=(
+            "Limite de exposição como fração do período "
+            "de frame."
+        ),
+    )
+
+    # Target canônico por role
+    parser.add_argument(
+        "--target-p50-rgb",
+        type=float,
+        default=0.45,
+    )
+
+    parser.add_argument(
+        "--target-p50-re",
+        type=float,
+        default=0.45,
+    )
+
+    parser.add_argument(
+        "--target-p50-nir",
+        type=float,
+        default=0.45,
+    )
+
+    # Runtime scale policy, mantemos contrato atual amplo.
+    parser.add_argument(
+        "--runtime-scale-min",
+        type=float,
+        default=0.05,
+    )
+
+    parser.add_argument(
+        "--runtime-scale-max",
+        type=float,
+        default=3.0,
+    )
+
+    # Política
+    parser.add_argument(
+        "--promote-warning",
+        action="store_true",
+    )
+
+    # UI
+    parser.add_argument(
+        "--panel-width",
+        type=int,
+        default=640,
+    )
+
+    parser.add_argument(
+        "--panel-height",
+        type=int,
+        default=400,
+    )
+
+    # Output
+    parser.add_argument(
+        "--candidate-root",
+        default=(
+            "calibration/"
+            "radiometry_candidates"
+        ),
+    )
+
+    parser.add_argument(
+        "--active-json",
+        default=(
+            "calibration/"
+            "radiometry_calibration_v5.json"
+        ),
+    )
+
+    # Trace
+    parser.add_argument(
+        "--module-id",
+        default="",
+    )
+
+    parser.add_argument(
+        "--operator",
+        default="",
+    )
+
+    parser.add_argument(
+        "--notes",
+        default="",
+    )
+
+    args = parser.parse_args()
+
+    if not (
+        0.10
+        <= args.roi_frac
+        <= 0.95
+    ):
+        raise ValueError(
+            "--roi-frac deve estar "
+            "entre 0.10 e 0.95"
+        )
+
+    if (
+        args.calibration_iso
+        < 50
+    ):
+        raise ValueError(
+            "--calibration-iso inválido"
+        )
+
+    if (
+        args.runtime_scale_min
+        <= 0
+        or args.runtime_scale_max
+        <= args.runtime_scale_min
+    ):
+        raise ValueError(
+            "Runtime scale limits inválidos"
+        )
+
+    qa = dict(
+        DEFAULT_QA
+    )
+
+    targets = {
+        "rgb": float(
+            args.target_p50_rgb
+        ),
+        "re": float(
+            args.target_p50_re
+        ),
+        "nir": float(
+            args.target_p50_nir
+        ),
+    }
+
+    for role, value in targets.items():
+        if not (
+            0.10
+            <= value
+            <= 0.80
+        ):
+            raise ValueError(
+                f"target-p50-{role} "
+                f"fora de [0.10,0.80]"
+            )
+
+    factors = parse_sweep_factors(
+        args.sweep_factors
+    )
+
+    if (
+        len(factors)
+        < qa[
+            "min_sweep_levels"
+        ]
+    ):
+        raise ValueError(
+            "Sweep com poucos fatores"
+        )
+
+    sid = session_stamp()
+
+    candidate_dir = (
+        Path(
+            args.candidate_root
+        )
+        / sid
+    )
+
+    candidate_report_path = (
+        candidate_dir
+        / "report.json"
+    )
+
+    sweep_csv_path = (
+        candidate_dir
+        / "sweep.csv"
+    )
+
+    ensure_dir(
+        candidate_dir
+    )
+
+    camera_rows = []
+    specs = {}
+    report = None
+    sweep_rows = []
+    role_analyses = {}
+    validation_stats = None
+
+    window_name = (
+        "Radiometric Calibration - Production"
+    )
+
+    cv2.namedWindow(
+        window_name,
+        cv2.WINDOW_NORMAL,
+    )
+
+    try:
+        (
+            camera_rows,
+            actual_mx,
+            usb_speed,
+        ) = discover_cameras(
+            args.mx_id
+        )
+
+        topology = (
+            validate_product_topology(
+                camera_rows
+            )
+        )
+
+        specs = build_specs(
+            camera_rows
+        )
+
+        print("=" * 88)
+        print(
+            "RADIOMETRIC CALIBRATION - PRODUCTION"
+        )
+        print(
+            f"MX ID : {actual_mx}"
+        )
+        print(
+            f"USB   : {usb_speed}"
+        )
+        print("-" * 88)
+
+        for role in ROLES:
+            s = specs[
+                role
+            ]
+
+            print(
+                f"{s.socket_name} -> "
+                f"{role.upper():3s} | "
+                f"{s.sensor_name:8s} | "
+                f"{s.width}x{s.height}"
+            )
+
+        print("=" * 88)
+
+        pipeline = build_pipeline(
+            specs,
+            args,
+        )
+
+        device_info = (
+            dai.DeviceInfo(
+                actual_mx
+            )
+            if actual_mx
+            else None
+        )
+
+        device_ctx = (
+            dai.Device(
+                pipeline,
+                device_info,
+            )
+            if device_info is not None
+            else dai.Device(
+                pipeline
+            )
+        )
+
+        report = {
+            "schema": SCHEMA,
+            "session_id": sid,
+            "created_at": now_str(),
+            "status": "running",
+            "promoted": False,
+            "depthai_version": getattr(
+                dai,
+                "__version__",
+                "unknown",
+            ),
+            "opencv_version": (
+                cv2.__version__
+            ),
+            "device_mx_id": (
+                actual_mx
+            ),
+            "usb_speed": usb_speed,
+            "topology": topology,
+            "camera_inventory": (
+                camera_rows
+            ),
+            "hardware_signature": (
+                hardware_signature(
+                    specs
+                )
+            ),
+            "calibration_domain": (
+                CALIBRATION_DOMAIN
+            ),
+            "normalization_contract": {
+                "method": (
+                    NORMALIZATION_METHOD
+                ),
+                "factor_model": (
+                    FACTOR_MODEL
+                ),
+                "iso_base": (
+                    ISO_BASE
+                ),
+                "apply_stage": (
+                    "after_dark_before_flat_gain"
+                ),
+            },
+            "bench_contract": {
+                "rig_id": (
+                    args.rig_id
+                    or None
+                ),
+                "target_id": (
+                    args.target_id
+                    or None
+                ),
+                "illumination_id": (
+                    args.illumination_id
+                    or None
+                ),
+                "target_distance_mm": (
+                    args.target_distance_mm
+                ),
+                "roi_frac": float(
+                    args.roi_frac
+                ),
+                "target_p50_by_role": (
+                    targets
+                ),
+                "operator_instruction": (
+                    "Painel difuso uniforme, "
+                    "geometria e iluminação "
+                    "reproduzíveis entre módulos."
+                ),
+            },
+            "sweep_config": {
+                "calibration_iso": int(
+                    args.calibration_iso
+                ),
+                "factors": (
+                    factors
+                ),
+                "frames_per_level": int(
+                    args.frames_per_level
+                ),
+                "validation_frames": int(
+                    args.validation_frames
+                ),
+                "min_exposure_us": int(
+                    args.min_exposure_us
+                ),
+                "max_exposure_us": int(
+                    args.max_exposure_us
+                ),
+                "frame_period_fraction": float(
+                    args.frame_period_fraction
+                ),
+            },
+            "qa_thresholds": qa,
+            "traceability": {
+                "module_id": (
+                    args.module_id
+                    or None
+                ),
+                "operator": (
+                    args.operator
+                    or None
+                ),
+            },
+            "notes": (
+                args.notes
+                or ""
+            ),
+            "preflight": None,
+            "base_equivalent_exposure": None,
+            "sweep_plan": None,
+            "sweep": [],
+            "analysis": {},
+            "reference_validation": {},
+            "module_params_fragment": None,
+            "runtime_policy_recommendation": {
+                "radiometric_config": {
+                    "enabled": False,
+                    "reason": (
+                        "Field AE controller is a "
+                        "separate policy and is not "
+                        "factory-calibrated here."
+                    ),
+                },
+                "patch_normalization": {
+                    "enabled": False,
+                    "reason": (
+                        "Patch normalization is a "
+                        "separate post-fusion policy."
+                    ),
+                },
+            },
+            "candidate_dir": str(
+                candidate_dir
+            ),
+            "active_json": (
+                args.active_json
+            ),
+        }
+
+        with device_ctx as device:
+            runtime = (
+                RadiometricRuntime(
+                    device,
+                    specs,
+                    args,
+                    qa,
+                )
+            )
+
+            runtime.wait_all()
+
+            # Warm-up
+            warmup_end = (
+                time.time()
+                + max(
+                    0.0,
+                    float(
+                        args.warmup_s
+                    ),
+                )
+            )
+
+            while (
+                time.time()
+                < warmup_end
+            ):
+                runtime.poll()
+
+                board = (
+                    show_progress_board(
+                        runtime,
+                        "WARM-UP",
+                        [
+                            "Painel cinza/difuso "
+                            "preenchendo o FOV.",
+                            "Não altere luz, câmera "
+                            "ou distância.",
+                            "",
+                            f"restante="
+                            f"{max(0.0, warmup_end-time.time()):.1f}s",
+                        ],
+                        args,
+                    )
+                )
+
+                cv2.imshow(
+                    window_name,
+                    board,
+                )
+
+                k = (
+                    cv2.waitKey(1)
+                    & 0xFF
+                )
+
+                if k in (
+                    ord("q"),
+                    ord("Q"),
+                    27,
+                ):
+                    raise KeyboardInterrupt(
+                        "Cancelado no warm-up."
+                    )
+
+            # Preflight
+            print("")
+            print(
+                "[PRE-FLIGHT] "
+                "Aguarde GOOD e pressione ENTER."
+            )
+
+            last_preflight = None
+
+            while True:
+                runtime.poll()
+
+                last_preflight = (
+                    preflight_report(
+                        runtime,
+                        qa,
+                    )
+                )
+
+                lines = [
+                    "Painel uniforme e iluminação estável.",
+                    "",
+                    f"GLOBAL="
+                    f"{last_preflight['status'].upper()}",
+                    "",
+                    "ENTER = iniciar sweep",
+                    "Q/ESC = cancelar",
+                ]
+
+                for role in ROLES:
+                    item = (
+                        last_preflight[
+                            "roles"
+                        ][role]
+                    )
+
+                    st = item.get(
+                        "stats",
+                        {},
+                    )
+
+                    lines.append(
+                        f"{role.upper()}: "
+                        f"{item['status'].upper()} "
+                        f"p50={st.get('p50',0):.3f} "
+                        f"cv="
+                        f"{(item.get('temporal_cv') or 0)*100:.2f}%"
+                    )
+
+                board = (
+                    show_progress_board(
+                        runtime,
+                        "PRE-FLIGHT",
+                        lines,
+                        args,
+                    )
+                )
+
+                cv2.imshow(
+                    window_name,
+                    board,
+                )
+
+                k = (
+                    cv2.waitKey(1)
+                    & 0xFF
+                )
+
+                if k in (
+                    ord("q"),
+                    ord("Q"),
+                    27,
+                ):
+                    raise KeyboardInterrupt(
+                        "Cancelado no preflight."
+                    )
+
+                if k in (
+                    13,
+                    10,
+                ):
+                    if (
+                        last_preflight[
+                            "status"
+                        ]
+                        != "good"
+                    ):
+                        print(
+                            "[BLOCK] Preflight "
+                            "ainda não está GOOD."
+                        )
+                        continue
+
+                    break
+
+                time.sleep(
+                    0.002
+                )
+
+            report[
+                "preflight"
+            ] = last_preflight
+
+            base_equiv = (
+                choose_base_equivalent_exposure(
+                    runtime,
+                    args.calibration_iso,
+                )
+            )
+
+            report[
+                "base_equivalent_exposure"
+            ] = base_equiv
+
+            plan, plan_limits = (
+                build_sweep_plan(
+                    base_equiv,
+                    factors,
+                    args,
+                )
+            )
+
+            if (
+                len(plan)
+                < qa[
+                    "min_sweep_levels"
+                ]
+            ):
+                raise RuntimeError(
+                    "Sweep ficou com poucos "
+                    "níveis únicos após clamps. "
+                    "Ajuste iluminação/fps."
+                )
+
+            report[
+                "sweep_plan"
+            ] = {
+                "levels": [
+                    {
+                        "factor": (
+                            level[
+                                "factor"
+                            ]
+                        ),
+                        "controls": {
+                            role: asdict(
+                                level[
+                                    "controls"
+                                ][role]
+                            )
+                            for role in ROLES
+                        },
+                    }
+                    for level in plan
+                ],
+                "limits": (
+                    plan_limits
+                ),
+            }
+
+            save_json_atomic(
+                candidate_report_path,
+                report,
+            )
+
+            # Sweep automático
+            print("")
+            print(
+                f"[SWEEP] "
+                f"{len(plan)} níveis."
+            )
+
+            for idx, level in enumerate(
+                plan,
+                start=1,
+            ):
+                controls = level[
+                    "controls"
+                ]
+
+                runtime.send_controls(
+                    controls
+                )
+
+                runtime.verify_controls(
+                    controls,
+                    settle_frames=(
+                        args.settle_frames
+                    ),
+                )
+
+                stats = (
+                    runtime.capture_level(
+                        args.frames_per_level,
+                        controls,
+                    )
+                )
+
+                row = {
+                    "level_index": idx,
+                    "sweep_factor": (
+                        level[
+                            "factor"
+                        ]
+                    ),
+                    "captured_at": (
+                        now_str()
+                    ),
+                    "controls": {
+                        role: {
+                            "exposure_time_us": (
+                                controls[
+                                    role
+                                ].exposure_time_us
+                            ),
+                            "sensitivity_iso": (
+                                controls[
+                                    role
+                                ].sensitivity_iso
+                            ),
+                        }
+                        for role in ROLES
+                    },
+                    "roles": stats,
+                }
+
+                sweep_rows.append(
+                    row
+                )
+
+                report[
+                    "sweep"
+                ] = sweep_rows
+
+                save_json_atomic(
+                    candidate_report_path,
+                    report,
+                )
+
+                save_sweep_csv(
+                    sweep_csv_path,
+                    sweep_rows,
+                )
+
+                lines = [
+                    f"nível {idx}/{len(plan)}",
+                    f"factor={level['factor']:.3f}",
+                    "",
+                ]
+
+                for role in ROLES:
+                    lines.append(
+                        f"{role.upper()}: "
+                        f"exp="
+                        f"{controls[role].exposure_time_us}us "
+                        f"p50="
+                        f"{stats[role]['p50']:.3f} "
+                        f"sat="
+                        f"{stats[role]['sat_pct']:.3f}%"
+                    )
+
+                board = (
+                    show_progress_board(
+                        runtime,
+                        "SWEEP RADIOMÉTRICO",
+                        lines,
+                        args,
+                    )
+                )
+
+                cv2.imshow(
+                    window_name,
+                    board,
+                )
+
+                cv2.waitKey(1)
+
+                print(
+                    "[LEVEL] "
+                    + " | ".join(
+                        [
+                            f"{role.upper()} "
+                            f"{stats[role]['p50']:.3f}"
+                            for role in ROLES
+                        ]
+                    )
+                )
+
+            # Análise
+            overall_status = "good"
+            overall_reasons = []
+
+            for role in ROLES:
+                analysis = (
+                    analyze_role_sweep(
+                        sweep_rows,
+                        role,
+                        targets[role],
+                        args,
+                        qa,
+                    )
+                )
+
+                role_analyses[
+                    role
+                ] = analysis
+
+                overall_status = (
+                    merge_status(
+                        overall_status,
+                        analysis[
+                            "status"
+                        ],
+                    )
+                )
+
+                overall_reasons.extend(
+                    [
+                        f"{role}:{x}"
+                        for x in analysis.get(
+                            "reasons",
+                            [],
+                        )
+                    ]
+                )
+
+                save_response_plot(
+                    candidate_dir
+                    / (
+                        f"response_"
+                        f"{role}.png"
+                    ),
+                    analysis,
+                )
+
+            report[
+                "analysis"
+            ] = role_analyses
+
+            # Não tenta reference validation se algum papel não conseguiu
+            # sequer gerar referência.
+            if any(
+                "reference"
+                not in role_analyses[
+                    role
+                ]
+                for role in ROLES
+            ):
+                report[
+                    "status"
+                ] = "bad"
+
+                report[
+                    "finished_at"
+                ] = now_str()
+
+                report[
+                    "evaluation_reasons"
+                ] = (
+                    overall_reasons
+                )
+
+                save_json_atomic(
+                    candidate_report_path,
+                    report,
+                )
+
+                raise RuntimeError(
+                    "Não foi possível gerar "
+                    "reference_controls para "
+                    "todas as câmeras."
+                )
+
+            # Reference validation
+            reference_controls = {
+                role: LockedControl(
+                    role=role,
+                    exposure_time_us=int(
+                        role_analyses[
+                            role
+                        ][
+                            "reference"
+                        ][
+                            "exposure_time_us"
+                        ]
+                    ),
+                    sensitivity_iso=int(
+                        role_analyses[
+                            role
+                        ][
+                            "reference"
+                        ][
+                            "sensitivity_iso"
+                        ]
+                    ),
+                    source=(
+                        "radiometric_reference_validation"
+                    ),
+                )
+                for role in ROLES
+            }
+
+            runtime.send_controls(
+                reference_controls
+            )
+
+            runtime.verify_controls(
+                reference_controls,
+                settle_frames=(
+                    args.settle_frames
+                ),
+            )
+
+            validation_stats = (
+                runtime.capture_level(
+                    args.validation_frames,
+                    reference_controls,
+                )
+            )
+
+            validation_report = {}
+
+            for role in ROLES:
+                vr = (
+                    evaluate_reference_validation(
+                        role_analyses[
+                            role
+                        ],
+                        validation_stats,
+                        qa,
+                    )
+                )
+
+                validation_report[
+                    role
+                ] = vr
+
+                overall_status = (
+                    merge_status(
+                        overall_status,
+                        vr[
+                            "status"
+                        ],
+                    )
+                )
+
+                overall_reasons.extend(
+                    [
+                        f"{role}:validation:{x}"
+                        for x in vr[
+                            "reasons"
+                        ]
+                    ]
+                )
+
+                # Salva frame de evidência no ponto de referência.
+                if runtime.images[
+                    role
+                ] is not None:
+                    vis = gray_preview(
+                        runtime.images[
+                            role
+                        ],
+                        args.roi_frac,
+                    )
+
+                    overlay_hud(
+                        vis,
+                        [
+                            f"{role.upper()} REFERENCE VALIDATION",
+                            f"p50="
+                            f"{vr['measured_p50']:.4f}",
+                            f"target="
+                            f"{vr['target_p50']:.4f}",
+                            f"status="
+                            f"{vr['status'].upper()}",
+                            f"EXP="
+                            f"{reference_controls[role].exposure_time_us}us "
+                            f"ISO="
+                            f"{reference_controls[role].sensitivity_iso}",
+                        ],
+                        x=12,
+                        y=25,
+                        font_scale=0.55,
+                        line_step=23,
+                    )
+
+                    cv2.imwrite(
+                        str(
+                            candidate_dir
+                            / (
+                                f"validation_"
+                                f"{role}.png"
+                            )
+                        ),
+                        vis,
+                    )
+
+            report[
+                "reference_validation"
+            ] = validation_report
+
+            fragment = (
+                build_normalization_fragment(
+                    role_analyses,
+                    args,
+                )
+            )
+
+            report[
+                "module_params_fragment"
+            ] = fragment
+
+            report[
+                "evaluation_reasons"
+            ] = overall_reasons
+
+            report[
+                "status"
+            ] = overall_status
+
+            report[
+                "finished_at"
+            ] = now_str()
+
+            promote = (
+                overall_status
+                == "good"
+                or (
+                    overall_status
+                    == "warning"
+                    and args.promote_warning
+                )
+            )
+
+            report[
+                "promoted"
+            ] = bool(
+                promote
+            )
+
+            report[
+                "promotion_policy"
+            ] = {
+                "promote_warning": bool(
+                    args.promote_warning
+                ),
+                "allowed": bool(
+                    promote
+                ),
+            }
+
+            save_json_atomic(
+                candidate_report_path,
+                report,
+            )
+
+            marker_name = (
+                "PASS.txt"
+                if promote
+                else "FAIL.txt"
+            )
+
+            (
+                candidate_dir
+                / marker_name
+            ).write_text(
+                (
+                    f"status={overall_status}\n"
+                    f"promoted={promote}\n"
+                    f"session={sid}\n"
+                ),
+                encoding="utf-8",
+            )
+
+            if promote:
+                report[
+                    "promoted_at"
+                ] = now_str()
+
+                promote_active(
+                    Path(
+                        args.active_json
+                    ),
+                    report,
+                )
+
+                report[
+                    "active_json_sha256"
+                ] = sha256_file(
+                    args.active_json
+                )
+
+                save_json_atomic(
+                    candidate_report_path,
+                    report,
+                )
+
+            # Final UI
+            final = np.zeros(
+                (
+                    760,
+                    1320,
+                    3,
+                ),
+                dtype=np.uint8,
+            )
+
+            lines = [
+                "RADIOMETRIC CALIBRATION - RESULT",
+                "",
+                f"STATUS={overall_status.upper()}",
+                f"PROMOTED={'YES' if promote else 'NO'}",
+                "",
+            ]
+
+            for role in ROLES:
+                a = role_analyses[
+                    role
+                ]
+
+                v = validation_report[
+                    role
+                ]
+
+                if "fit" in a:
+                    lines.append(
+                        f"{role.upper()}: "
+                        f"ref="
+                        f"{a['reference']['exposure_time_us']}us "
+                        f"ISO{a['reference']['sensitivity_iso']} | "
+                        f"R2={a['fit']['r2']:.6f} | "
+                        f"norm p95="
+                        f"{a['normalization_model_validation']['p95_relative_error']*100:.2f}% | "
+                        f"val p50="
+                        f"{v['measured_p50']:.3f}"
+                    )
+                else:
+                    lines.append(
+                        f"{role.upper()}: INVALID"
+                    )
+
+            lines.extend([
+                "",
+                "Field radiometric controller: remains DISABLED",
+                "Patch normalization: remains DISABLED",
+                "",
+                f"Candidate: {candidate_dir}",
+                (
+                    f"Active: {args.active_json}"
+                    if promote
+                    else "Active calibration preserved"
+                ),
+                "",
+                "Any key closes.",
+            ])
+
+            overlay_hud(
+                final,
+                lines,
+                x=42,
+                y=62,
+                font_scale=0.62,
+                line_step=30,
+            )
+
+            cv2.imshow(
+                window_name,
+                final,
+            )
+
+            cv2.waitKey(0)
+
+            if promote:
+                print("")
+                print("=" * 88)
+                print(
+                    "[PASS] RADIOMETRIC "
+                    "CALIBRATION PROMOTED"
+                )
+                print(
+                    f"[ACTIVE] {args.active_json}"
+                )
+                print(
+                    f"[CANDIDATE] {candidate_dir}"
+                )
+                print("=" * 88)
+
+            else:
+                print("")
+                print("=" * 88)
+                print(
+                    "[NOT PROMOTED] "
+                    f"status={overall_status.upper()}"
+                )
+                print(
+                    "[SAFE] Active calibration "
+                    "preserved."
+                )
+                print(
+                    f"[CANDIDATE] {candidate_dir}"
+                )
+                print("=" * 88)
+
+    except KeyboardInterrupt as exc:
+        if report is None:
+            report = {
+                "schema": SCHEMA,
+                "session_id": sid,
+                "created_at": now_str(),
+            }
+
+        report[
+            "status"
+        ] = "cancelled"
+
+        report[
+            "finished_at"
+        ] = now_str()
+
+        report[
+            "promoted"
+        ] = False
+
+        report[
+            "error"
+        ] = str(exc)
+
+        report[
+            "sweep"
+        ] = sweep_rows
+
+        try:
+            save_json_atomic(
+                candidate_report_path,
+                report,
+            )
+
+            (
+                candidate_dir
+                / "CANCELLED.txt"
+            ).write_text(
+                f"reason={exc}\n",
+                encoding="utf-8",
+            )
+
+        except Exception:
+            pass
+
+        print(
+            f"[CANCELADO] {exc}"
+        )
+
+        print(
+            "[SAFE] Calibração ativa "
+            "preservada."
+        )
+
+    except Exception as exc:
+        if report is None:
+            report = {
+                "schema": SCHEMA,
+                "session_id": sid,
+                "created_at": now_str(),
+            }
+
+        # Preserva BAD já calculado, caso exista.
+        if report.get(
+            "status"
+        ) == "running":
+            report[
+                "status"
+            ] = "error"
+
+        report[
+            "finished_at"
+        ] = now_str()
+
+        report[
+            "promoted"
+        ] = False
+
+        report[
+            "error"
+        ] = (
+            f"{type(exc).__name__}: "
+            f"{exc}"
+        )
+
+        report[
+            "sweep"
+        ] = sweep_rows
+
+        try:
+            save_json_atomic(
+                candidate_report_path,
+                report,
+            )
+
+            (
+                candidate_dir
+                / "FAIL.txt"
+            ).write_text(
+                (
+                    f"error="
+                    f"{type(exc).__name__}: "
+                    f"{exc}\n"
+                ),
+                encoding="utf-8",
+            )
+
+        except Exception:
+            pass
+
+        print("")
+        print("=" * 88)
+        print(
+            "[ERRO] RADIOMETRIC "
+            "CALIBRATION ABORTED"
+        )
+        print(
+            f"{type(exc).__name__}: {exc}"
+        )
+        print(
+            "[SAFE] Active calibration "
+            "preserved."
+        )
+        print("=" * 88)
+
+        raise
+
+    finally:
+        cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/_5_camera_startup_profile.py b/Python/OAK/datasets/oak-fcc-3/utils/_5_camera_startup_profile.py
new file mode 100644
index 000000000..1dbfbfa52
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/utils/_5_camera_startup_profile.py
@@ -0,0 +1,761 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+camera_startup_profile_production.py
+====================================
+
+Gerador/auditor FINAL de produção para o bloco `camera_settings`
+do módulo multiespectral.
+
+IMPORTANTE
+----------
+Isto NÃO é uma calibração óptica/radiométrica nova.
+
+O script antigo "Sensor Calibration Tool" misturava:
+    - camera_settings;
+    - ajustes manuais AE/AWB/exposure/gain;
+    - ROIs de cana/erva/solo;
+    - guidance heurístico;
+    - rgb_calibration manual;
+    - snapshots/offline samples.
+
+Na arquitetura final de produto essas responsabilidades já foram separadas.
+
+Esta ferramenta preserva SOMENTE o que ainda é necessário ao runtime:
+    camera_settings
+
+e força explicitamente:
+    rgb_calibration.enabled = false
+    gains = 1,1,1
+
+para impedir que ganhos RGB históricos/experimentais vazem para o tensor.
+
+Dependência
+-----------
+A ferramenta lê o artefato homologado da etapa radiométrica:
+
+    calibration/radiometry_calibration_v5.json
+
+e reaproveita:
+    radiometric_normalization.reference_controls
+
+como valores de fallback do startup.
+
+Política normal de produto
+--------------------------
+    RGB:
+        AE  = ON
+        AWB = ON
+        exposure_time_us = referência radiométrica (fallback)
+        analogue_gain    = ISO_ref / 100
+        colour_gains     = [1.0, 1.0]
+
+    RE:
+        AE  = ON
+        AWB = OFF
+        exposure_time_us = referência radiométrica (fallback)
+        analogue_gain    = ISO_ref / 100
+
+    NIR:
+        AE  = ON
+        AWB = OFF
+        exposure_time_us = referência radiométrica (fallback)
+        analogue_gain    = ISO_ref / 100
+
+Por que manter os valores manuais se AE está ON?
+-------------------------------------------------
+No manager atual, com AE ligado, exposure/gain do JSON não são forçados
+fisicamente. Eles ficam armazenados como estado/fallback para uma eventual
+transição futura para manual.
+
+Assim:
+    - o campo continua adaptando exposição automaticamente;
+    - a normalização radiométrica usa os controles reais de cada frame;
+    - se AE for desabilitado em algum fluxo futuro, o fallback já é coerente
+      com a calibração radiométrica do módulo.
+
+Saída
+-----
+    calibration/camera_startup_profile_v3.json
+
+Contém:
+    module_params_fragment.camera_settings
+    module_params_fragment.rgb_calibration
+
+O assembler final do module_params continua sendo a autoridade de merge.
+
+Fail-closed
+-----------
+- exige CAM_A = OV9782 ou AR0234;
+- exige CAM_B/C = OV9282;
+- exige artefato radiométrico promovido;
+- exige hardware_signature compatível;
+- exige reference_controls válidos para rgb/re/nir;
+- não aceita ISO inválido;
+- não ativa rgb_calibration;
+- preserva backup do ativo anterior.
+
+Uso
+---
+    python camera_startup_profile_production.py
+
+Com rastreabilidade:
+    python camera_startup_profile_production.py ^
+        --module-id MOD_MS_001 ^
+        --operator Diego
+
+Somente auditoria, sem salvar:
+    python camera_startup_profile_production.py --check-only
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+from dataclasses import dataclass, asdict
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, Optional, List
+
+import depthai as dai
+
+
+SCHEMA = "multispec_camera_startup_profile_v3"
+ISO_BASE = 100.0
+
+ROLES = ("rgb", "re", "nir")
+
+PRODUCT_TOPOLOGY = {
+    "rgb": {
+        "socket": "CAM_A",
+        "allowed_sensors": ("OV9782", "AR0234"),
+    },
+    "re": {
+        "socket": "CAM_B",
+        "allowed_sensors": ("OV9282",),
+    },
+    "nir": {
+        "socket": "CAM_C",
+        "allowed_sensors": ("OV9282",),
+    },
+}
+
+SENSOR_NATIVE_SIZE = {
+    "OV9782": (1280, 800),
+    "AR0234": (1920, 1200),
+    "OV9282": (1280, 800),
+}
+
+
+def now_str() -> str:
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+def stamp() -> str:
+    return datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+
+
+def ensure_dir(path: str | Path):
+    Path(path).mkdir(parents=True, exist_ok=True)
+
+
+def load_json(path: str | Path) -> dict:
+    p = Path(path)
+    if not p.is_file():
+        raise FileNotFoundError(f"Arquivo não encontrado: {p}")
+
+    with p.open("r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def save_json_atomic(path: str | Path, data: dict):
+    p = Path(path)
+    ensure_dir(p.parent)
+
+    tmp = p.with_suffix(p.suffix + ".tmp")
+
+    with tmp.open("w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+        f.flush()
+        os.fsync(f.fileno())
+
+    os.replace(tmp, p)
+
+
+def socket_name(socket) -> str:
+    name = getattr(socket, "name", None)
+    if name:
+        return str(name)
+
+    text = str(socket)
+    for candidate in ("CAM_A", "CAM_B", "CAM_C", "CAM_D"):
+        if candidate in text:
+            return candidate
+
+    return text
+
+
+def supported_types(feature) -> List[str]:
+    return [
+        str(v).upper()
+        for v in (getattr(feature, "supportedTypes", []) or [])
+    ]
+
+
+def feature_is_color(feature) -> bool:
+    types = supported_types(feature)
+    sensor = str(getattr(feature, "sensorName", "") or "").upper()
+
+    if any("COLOR" in x for x in types):
+        return True
+    if any("MONO" in x for x in types):
+        return False
+
+    return sensor in {"OV9782", "AR0234"}
+
+
+def feature_is_mono(feature) -> bool:
+    types = supported_types(feature)
+
+    if any("MONO" in x for x in types):
+        return True
+    if any("COLOR" in x for x in types):
+        return False
+
+    return not feature_is_color(feature)
+
+
+@dataclass
+class CameraInfo:
+    role: str
+    socket: str
+    sensor: str
+    width: int
+    height: int
+    is_color: bool
+    is_mono: bool
+
+
+def discover_hardware(mx_id: Optional[str]):
+    info = dai.DeviceInfo(mx_id) if mx_id else None
+    ctx = dai.Device(info) if info is not None else dai.Device()
+
+    with ctx as device:
+        actual_mx = None
+
+        for method_name in ("getMxId", "getDeviceId"):
+            fn = getattr(device, method_name, None)
+            if callable(fn):
+                try:
+                    value = fn()
+                    if value:
+                        actual_mx = str(value)
+                        break
+                except Exception:
+                    pass
+
+        usb_speed = None
+        try:
+            usb_speed = str(device.getUsbSpeed())
+        except Exception:
+            pass
+
+        rows = []
+
+        for f in device.getConnectedCameraFeatures():
+            rows.append({
+                "socket": socket_name(f.socket),
+                "sensor": str(getattr(f, "sensorName", "") or "").upper(),
+                "width": int(getattr(f, "width", 0) or 0),
+                "height": int(getattr(f, "height", 0) or 0),
+                "is_color": feature_is_color(f),
+                "is_mono": feature_is_mono(f),
+                "supported_types": supported_types(f),
+            })
+
+    return rows, actual_mx, usb_speed
+
+
+def validate_and_resolve_hardware(rows: list[dict]) -> Dict[str, CameraInfo]:
+    by_socket = {row["socket"]: row for row in rows}
+    resolved = {}
+    errors = []
+
+    for role in ROLES:
+        contract = PRODUCT_TOPOLOGY[role]
+        sock = contract["socket"]
+
+        row = by_socket.get(sock)
+        if row is None:
+            errors.append(f"{role.upper()}: {sock} ausente")
+            continue
+
+        sensor = row["sensor"]
+
+        if sensor not in contract["allowed_sensors"]:
+            errors.append(
+                f"{role.upper()}: {sock} sensor={sensor}, "
+                f"permitidos={contract['allowed_sensors']}"
+            )
+            continue
+
+        expected_w, expected_h = SENSOR_NATIVE_SIZE[sensor]
+
+        if row["width"] and row["height"]:
+            if (row["width"], row["height"]) != (expected_w, expected_h):
+                errors.append(
+                    f"{role.upper()}: {sensor} anunciou "
+                    f"{row['width']}x{row['height']}, esperado "
+                    f"{expected_w}x{expected_h}"
+                )
+
+        if role == "rgb" and not row["is_color"]:
+            errors.append(f"RGB: {sock}/{sensor} não anunciado COLOR")
+
+        if role in ("re", "nir") and not row["is_mono"]:
+            errors.append(f"{role.upper()}: {sock}/{sensor} não anunciado MONO")
+
+        resolved[role] = CameraInfo(
+            role=role,
+            socket=sock,
+            sensor=sensor,
+            width=expected_w,
+            height=expected_h,
+            is_color=bool(row["is_color"]),
+            is_mono=bool(row["is_mono"]),
+        )
+
+    if errors:
+        raise RuntimeError(
+            "Topologia de produto inválida:\n  - "
+            + "\n  - ".join(errors)
+        )
+
+    return resolved
+
+
+def hardware_signature_from_resolved(resolved: Dict[str, CameraInfo]) -> dict:
+    return {
+        role: {
+            "socket": resolved[role].socket,
+            "sensor": resolved[role].sensor,
+            "size": [resolved[role].width, resolved[role].height],
+        }
+        for role in ROLES
+    }
+
+
+def normalize_signature(sig: dict) -> dict:
+    """
+    Aceita assinatura no formato dos calibradores de produção.
+    Retorna apenas role/socket/sensor/size para comparação robusta.
+    """
+    out = {}
+
+    for role in ROLES:
+        item = (sig or {}).get(role, {}) or {}
+
+        socket = (
+            item.get("socket")
+            or item.get("socket_name")
+        )
+
+        sensor = (
+            item.get("sensor")
+            or item.get("sensor_name")
+        )
+
+        size = item.get("size")
+
+        if size is None:
+            width = item.get("width")
+            height = item.get("height")
+            if width is not None and height is not None:
+                size = [int(width), int(height)]
+
+        if socket is None or sensor is None or size is None:
+            raise RuntimeError(
+                f"hardware_signature incompleta para {role}: {item}"
+            )
+
+        out[role] = {
+            "socket": str(socket),
+            "sensor": str(sensor).upper(),
+            "size": [int(size[0]), int(size[1])],
+        }
+
+    return out
+
+
+def extract_radiometric_fragment(data: dict) -> dict:
+    """
+    O artefato radiométrico de produção guarda:
+      module_params_fragment.radiometric_normalization
+    """
+    fragment = data.get("module_params_fragment", {}) or {}
+    rn = fragment.get("radiometric_normalization")
+
+    if not isinstance(rn, dict):
+        raise RuntimeError(
+            "Artefato radiométrico sem "
+            "module_params_fragment.radiometric_normalization"
+        )
+
+    if not rn.get("enabled", False):
+        raise RuntimeError(
+            "radiometric_normalization do artefato não está enabled=true"
+        )
+
+    return rn
+
+
+def validate_radiometry_artifact(
+    data: dict,
+    current_signature: dict,
+):
+    schema = str(data.get("schema", ""))
+
+    if schema != "multispec_radiometric_calibration_v5":
+        raise RuntimeError(
+            f"Schema radiométrico inesperado: {schema!r}. "
+            "Esperado 'multispec_radiometric_calibration_v5'."
+        )
+
+    status = str(data.get("status", "")).lower()
+
+    if status not in ("good", "warning"):
+        raise RuntimeError(
+            f"Radiometria não homologada para consumo: status={status!r}"
+        )
+
+    if not bool(data.get("promoted", False)):
+        raise RuntimeError(
+            "Artefato radiométrico não foi promovido."
+        )
+
+    artifact_sig = normalize_signature(
+        data.get("hardware_signature", {})
+    )
+
+    if artifact_sig != current_signature:
+        raise RuntimeError(
+            "Hardware conectado não corresponde ao hardware da radiometria.\n"
+            f"Atual : {current_signature}\n"
+            f"Rad   : {artifact_sig}"
+        )
+
+    rn = extract_radiometric_fragment(data)
+
+    refs = rn.get("reference_controls", {}) or {}
+
+    for role in ROLES:
+        ref = refs.get(role)
+
+        if not isinstance(ref, dict):
+            raise RuntimeError(
+                f"reference_controls ausente para {role}"
+            )
+
+        exp = ref.get("exposure_time_us")
+        iso = ref.get("sensitivity_iso")
+
+        if exp is None or float(exp) <= 0:
+            raise RuntimeError(
+                f"reference exposure inválida para {role}: {exp}"
+            )
+
+        if iso is None or float(iso) < 50:
+            raise RuntimeError(
+                f"reference ISO inválido para {role}: {iso}"
+            )
+
+    return rn
+
+
+def iso_to_analogue_gain(iso: float) -> float:
+    """
+    O manager atual representa analogue_gain aproximadamente como ISO/100.
+    """
+    gain = float(iso) / ISO_BASE
+    return max(1.0, gain)
+
+
+def build_camera_settings(reference_controls: dict) -> dict:
+    settings = {}
+
+    for role in ROLES:
+        ref = reference_controls[role]
+
+        exp = int(round(float(ref["exposure_time_us"])))
+        iso = float(ref["sensitivity_iso"])
+        gain = iso_to_analogue_gain(iso)
+
+        if role == "rgb":
+            settings[role] = {
+                "ae_enable": True,
+                "awb_enable": True,
+                "exposure_time_us": exp,
+                "analogue_gain": gain,
+                "colour_gains": [1.0, 1.0],
+            }
+        else:
+            settings[role] = {
+                "ae_enable": True,
+                "awb_enable": False,
+                "exposure_time_us": exp,
+                "analogue_gain": gain,
+                "colour_gains": None,
+            }
+
+    return settings
+
+
+def build_rgb_calibration_policy() -> dict:
+    """
+    Produto final mantém correção multiplicativa RGB desligada.
+    Se um dia ela for homologada, deve existir um calibrador separado e
+    dataset/runtime precisam usar o mesmo contrato.
+    """
+    return {
+        "enabled": False,
+        "gains": {
+            "R": 1.0,
+            "G": 1.0,
+            "B": 1.0,
+        },
+        "policy": "disabled_product_default",
+        "reason": (
+            "No validated RGB tensor-gain calibration is currently active. "
+            "Historical manual gains are intentionally discarded."
+        ),
+    }
+
+
+def compare_existing_module_params(
+    module_params_path: Optional[str],
+    generated_fragment: dict,
+) -> dict:
+    if not module_params_path:
+        return {
+            "available": False,
+            "path": None,
+        }
+
+    p = Path(module_params_path)
+
+    if not p.is_file():
+        return {
+            "available": False,
+            "path": str(p),
+        }
+
+    current = load_json(p)
+
+    current_camera = current.get("camera_settings")
+    current_rgb = current.get("rgb_calibration")
+
+    generated_camera = generated_fragment["camera_settings"]
+    generated_rgb = generated_fragment["rgb_calibration"]
+
+    return {
+        "available": True,
+        "path": str(p),
+        "schema": current.get("schema"),
+        "camera_settings_equal": current_camera == generated_camera,
+        "rgb_calibration_equal": current_rgb == generated_rgb,
+        "current_camera_settings": current_camera,
+        "generated_camera_settings": generated_camera,
+        "current_rgb_calibration": current_rgb,
+        "generated_rgb_calibration": generated_rgb,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Gerador/auditor de produção para camera_settings e "
+            "política rgb_calibration do módulo multiespectral."
+        ),
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument(
+        "--mx-id",
+        default=None,
+        help="MX ID da OAK. Vazio usa o primeiro dispositivo disponível.",
+    )
+
+    parser.add_argument(
+        "--radiometry-json",
+        default="calibration/radiometry_calibration_v5.json",
+        help="Artefato radiométrico homologado.",
+    )
+
+    parser.add_argument(
+        "--out-json",
+        default="calibration/camera_startup_profile_v3.json",
+        help="Artefato ativo desta etapa.",
+    )
+
+    parser.add_argument(
+        "--compare-module-params",
+        default="calibration/module_params.json",
+        help="Opcional: audita diferenças contra module_params existente.",
+    )
+
+    parser.add_argument(
+        "--module-id",
+        default="",
+    )
+
+    parser.add_argument(
+        "--operator",
+        default="",
+    )
+
+    parser.add_argument(
+        "--notes",
+        default="",
+    )
+
+    parser.add_argument(
+        "--check-only",
+        action="store_true",
+        help="Valida e imprime o fragmento, mas não salva.",
+    )
+
+    args = parser.parse_args()
+
+    rows, actual_mx, usb_speed = discover_hardware(args.mx_id)
+    resolved = validate_and_resolve_hardware(rows)
+
+    current_signature = hardware_signature_from_resolved(resolved)
+
+    radiometry = load_json(args.radiometry_json)
+
+    rn = validate_radiometry_artifact(
+        radiometry,
+        current_signature,
+    )
+
+    reference_controls = rn["reference_controls"]
+
+    camera_settings = build_camera_settings(reference_controls)
+    rgb_calibration = build_rgb_calibration_policy()
+
+    module_params_fragment = {
+        "camera_settings": camera_settings,
+        "rgb_calibration": {
+            "enabled": False,
+            "gains": {
+                "R": 1.0,
+                "G": 1.0,
+                "B": 1.0,
+            },
+        },
+    }
+
+    audit = compare_existing_module_params(
+        args.compare_module_params,
+        module_params_fragment,
+    )
+
+    payload = {
+        "schema": SCHEMA,
+        "created_at": now_str(),
+        "status": "good",
+        "promoted": not args.check_only,
+        "runtime_effect": (
+            "startup_camera_policy_and_explicit_rgb_gain_disable"
+        ),
+        "device_mx_id": actual_mx,
+        "usb_speed": usb_speed,
+        "hardware_inventory": rows,
+        "hardware_signature": current_signature,
+        "source_radiometry": {
+            "path": str(args.radiometry_json),
+            "schema": radiometry.get("schema"),
+            "session_id": radiometry.get("session_id"),
+            "status": radiometry.get("status"),
+            "promoted": radiometry.get("promoted"),
+            "reference_controls": reference_controls,
+        },
+        "startup_policy": {
+            "mode": "ae_with_calibrated_manual_fallback",
+            "rgb_awb": "auto_for_isp_preview_only",
+            "spectral_awb": "off_not_applicable",
+            "manual_fallback_source": (
+                "radiometric_normalization.reference_controls"
+            ),
+            "note": (
+                "With AE enabled, manager stores exposure/gain as fallback "
+                "and does not force them manually at startup."
+            ),
+        },
+        "rgb_calibration_policy": rgb_calibration,
+        "module_params_fragment": module_params_fragment,
+        "existing_module_params_audit": audit,
+        "traceability": {
+            "module_id": args.module_id or None,
+            "operator": args.operator or None,
+        },
+        "notes": args.notes or "",
+    }
+
+    print("=" * 82)
+    print("CAMERA STARTUP PROFILE - PRODUCTION")
+    print(f"MX ID : {actual_mx}")
+    print(f"USB   : {usb_speed}")
+    print("-" * 82)
+
+    for role in ROLES:
+        c = resolved[role]
+        s = camera_settings[role]
+
+        print(
+            f"{c.socket} -> {role.upper():3s} | "
+            f"{c.sensor:8s} | {c.width}x{c.height} | "
+            f"AE={s['ae_enable']} AWB={s['awb_enable']} | "
+            f"fallback={s['exposure_time_us']}us gain={s['analogue_gain']:.3f}"
+        )
+
+    print("-" * 82)
+    print("rgb_calibration: DISABLED | gains = 1.0 / 1.0 / 1.0")
+
+    if audit.get("available"):
+        print(
+            "module_params audit: "
+            f"camera_settings_equal={audit['camera_settings_equal']} | "
+            f"rgb_calibration_equal={audit['rgb_calibration_equal']}"
+        )
+    else:
+        print("module_params audit: arquivo não disponível")
+
+    if args.check_only:
+        print("[CHECK-ONLY] Nenhum arquivo alterado.")
+        print("=" * 82)
+        return
+
+    out = Path(args.out_json)
+    ensure_dir(out.parent)
+
+    if out.exists():
+        backup = out.with_name(
+            out.stem
+            + f".backup_{stamp()}"
+            + out.suffix
+        )
+        shutil.copy2(out, backup)
+
+    save_json_atomic(out, payload)
+
+    print(f"[PASS] Artefato salvo: {out}")
+    print("[ASSEMBLER] Consumir module_params_fragment deste JSON.")
+    print("=" * 82)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/_6_homography_calibration_tool.py b/Python/OAK/datasets/oak-fcc-3/utils/_6_homography_calibration_tool.py
new file mode 100644
index 000000000..064fde68f
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/utils/_6_homography_calibration_tool.py
@@ -0,0 +1,4904 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+homography_calibration_production.py
+====================================
+
+Calibrador FINAL de produção para alinhamento geométrico RGB / RE / NIR
+do módulo OAK-FFC-3P usando ChArUco.
+
+Topologia oficial
+-----------------
+    CAM_A = RGB = OV9782  1280x800
+                 OU
+                 AR0234   1920x1200
+
+    CAM_B = RE  = OV9282  1280x800
+    CAM_C = NIR = OV9282  1280x800
+
+Princípio geométrico
+--------------------
+A homografia é estimada DIRETAMENTE entre os espaços nativos:
+
+    H_RE_to_RGB  : coordenadas RE  1280x800 -> coordenadas RGB nativas
+    H_NIR_to_RGB : coordenadas NIR 1280x800 -> coordenadas RGB nativas
+
+Portanto:
+    - OV9782: destino RGB = 1280x800
+    - AR0234: destino RGB = 1920x1200
+
+NÃO redimensionamos RE/NIR para o tamanho da RGB antes de detectar pontos.
+Isso seria geometricamente incorreto para uma calibração de produto.
+
+Domínio da calibração
+---------------------
+As imagens usadas aqui são:
+    RGB -> saída ISP full-resolution
+    RE  -> saída MONO full-resolution
+    NIR -> saída MONO full-resolution
+
+Nenhum undistort/remap externo é aplicado nesta ferramenta.
+
+O JSON registra explicitamente:
+    coordinate_space = "native_stream_no_external_undistort"
+
+Assim o assembler final do module_params sabe em qual espaço a matriz nasceu.
+Se uma etapa posterior introduzir remap/undistort antes da homografia, o
+assembler/runtime deve respeitar/compor os espaços corretamente.
+
+Por que ChArUco?
+----------------
+O ChArUco fornece IDs comuns subpixel entre as três câmeras e reduz o erro
+humano da seleção manual. Esta versão também:
+    - captura triplets frescos;
+    - valida coerência temporal;
+    - rejeita amostras repetidas;
+    - exige distribuição espacial pelo FOV;
+    - rejeita amostras incompatíveis com o mesmo plano físico;
+    - faz RANSAC;
+    - faz validação por AMOSTRA (leave-one-sample-out);
+    - calcula overlap útil;
+    - só promove perfil que passa no acceptance test.
+
+Perfis de altura/plano
+----------------------
+Uma única homografia é válida para UM plano físico.
+
+Exemplos do projeto:
+    baixa -> plano mais distante / chão
+    media -> plano médio
+    alta  -> plano mais próximo
+
+Execute uma sessão por perfil, mantendo o ChArUco NO MESMO PLANO durante
+todas as amostras daquela sessão.
+
+Exemplos:
+---------
+    python homography_calibration_production.py ^
+        --profile-name media ^
+        --depth-cm 66
+
+    python homography_calibration_production.py ^
+        --profile-name baixa ^
+        --depth-cm 120
+
+    python homography_calibration_production.py ^
+        --profile-name alta ^
+        --depth-cm 36
+
+Fluxo
+-----
+1. PRE-FLIGHT:
+   - ChArUco visível nas três câmeras
+   - AE estabiliza
+   - ENTER congela exposição/ISO
+
+2. CAPTURA:
+   - mova o ChArUco lateralmente DENTRO DO MESMO PLANO
+   - mantenha o tabuleiro coplanar ao plano calibrado
+   - G captura uma amostra tripla nova
+   - a ferramenta rejeita detecção ruim / repetida / dessincronizada
+
+3. Quando houver amostras suficientes e boa cobertura:
+   - A calcula e executa acceptance
+
+4. PASS:
+   - candidate auditável é salvo
+   - perfil é promovido para homography_calibration_v4.json
+   - outros perfis previamente aprovados são preservados
+
+5. FAIL/CANCEL:
+   - candidate é preservado
+   - calibração ativa anterior NÃO é alterada
+
+Teclas
+------
+    G       captura nova amostra ChArUco
+    A       calcula / tenta aprovar perfil
+    2       overlay RGB + RE
+    3       overlay RGB + NIR
+    TAB     alterna RE/NIR
+    V       limpa todas as amostras da sessão
+    S       salva snapshot da tela
+    Q/ESC   cancela
+
+Dependências
+------------
+    pip install depthai opencv-contrib-python numpy
+
+Artefato consumível pelo assembler
+----------------------------------
+O JSON ativo contém um module_params_fragment com fusion_config/homography_profiles.
+O assembler final continua sendo a autoridade que monta module_params.
+
+Schema:
+    multispec_homography_calibration_v4
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import os
+import shutil
+import time
+from collections import deque
+from dataclasses import dataclass, asdict
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, Optional, Tuple, List, Any
+
+import cv2
+import depthai as dai
+import numpy as np
+
+
+# ============================================================
+# Contrato do produto
+# ============================================================
+
+ROLES = ("rgb", "re", "nir")
+SPEC_ROLES = ("re", "nir")
+
+PRODUCT_TOPOLOGY = {
+    "rgb": {
+        "socket": "CAM_A",
+        "allowed_sensors": ("OV9782", "AR0234"),
+    },
+    "re": {
+        "socket": "CAM_B",
+        "allowed_sensors": ("OV9282",),
+    },
+    "nir": {
+        "socket": "CAM_C",
+        "allowed_sensors": ("OV9282",),
+    },
+}
+
+RGB_SENSOR_MODES = {
+    "OV9782": {
+        "resolution_enum": "THE_800_P",
+        "width": 1280,
+        "height": 800,
+    },
+    "AR0234": {
+        "resolution_enum": "THE_1200_P",
+        "width": 1920,
+        "height": 1200,
+    },
+}
+
+MONO_SENSOR_MODES = {
+    "OV9282": {
+        "resolution_enum": "THE_800_P",
+        "width": 1280,
+        "height": 800,
+    },
+}
+
+SCHEMA = "multispec_homography_calibration_v4"
+COORDINATE_SPACE = "native_stream_no_external_undistort"
+
+
+# ============================================================
+# QA defaults
+# ============================================================
+
+DEFAULT_QA = {
+    # Captura
+    "sync_tolerance_ms": 35.0,
+    "min_markers_each": 4,
+    "min_charuco_corners_each": 8,
+    "min_common_corners_each_pair": 8,
+
+    # Dataset
+    "min_samples": 8,
+    "recommended_samples": 12,
+    "min_total_pairs": 70,
+    "min_unique_charuco_ids": 18,
+
+    # Repetição de pose
+    "duplicate_center_distance_norm": 0.025,
+    "duplicate_scale_delta": 0.045,
+
+    # RANSAC
+    "ransac_reproj_threshold_px": 3.0,
+    "ransac_confidence": 0.999,
+    "ransac_max_iters": 10000,
+
+    # Rejeição de amostra planar incompatível
+    "sample_reject_median_error_px": 4.0,
+    "max_sample_rejection_rounds": 2,
+
+    # Acceptance in-sample final
+    "inlier_ratio_warning": 0.95,
+    "inlier_ratio_bad": 0.90,
+
+    "fit_median_error_warning_px": 1.20,
+    "fit_median_error_bad_px": 2.00,
+
+    "fit_p95_error_warning_px": 2.50,
+    "fit_p95_error_bad_px": 4.00,
+
+    # Leave-one-sample-out
+    "cv_median_error_warning_px": 1.60,
+    "cv_median_error_bad_px": 2.60,
+
+    "cv_p95_error_warning_px": 3.20,
+    "cv_p95_error_bad_px": 4.80,
+
+    "cv_max_sample_median_warning_px": 2.60,
+    "cv_max_sample_median_bad_px": 4.00,
+
+    # Cobertura no destino RGB
+    "coverage_span_x_warning": 0.60,
+    "coverage_span_x_bad": 0.48,
+
+    "coverage_span_y_warning": 0.55,
+    "coverage_span_y_bad": 0.43,
+
+    "coverage_hull_warning": 0.24,
+    "coverage_hull_bad": 0.15,
+
+    # Overlap útil das imagens
+    "overlap_warning": 0.80,
+    "overlap_bad": 0.70,
+
+    # Sanidade matricial
+    "condition_number_warning": 1.0e5,
+    "condition_number_bad": 1.0e7,
+
+    # Exposição manual
+    "exposure_rel_tolerance": 0.01,
+    "exposure_abs_tolerance_us": 20,
+    "iso_tolerance": 5,
+}
+
+
+# ============================================================
+# Helpers
+# ============================================================
+
+def now_str() -> str:
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+def session_stamp() -> str:
+    return datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+
+
+def ensure_dir(path: str | Path):
+    Path(path).mkdir(parents=True, exist_ok=True)
+
+
+def save_json_atomic(path: str | Path, data: dict):
+    p = Path(path)
+    ensure_dir(p.parent)
+
+    tmp = p.with_suffix(p.suffix + ".tmp")
+
+    with tmp.open("w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+        f.flush()
+        os.fsync(f.fileno())
+
+    os.replace(tmp, p)
+
+
+def load_json(path: str | Path) -> dict:
+    p = Path(path)
+    if not p.is_file():
+        return {}
+
+    with p.open("r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def sha256_file(path: str | Path) -> str:
+    h = hashlib.sha256()
+
+    with open(path, "rb") as f:
+        while True:
+            chunk = f.read(1024 * 1024)
+            if not chunk:
+                break
+            h.update(chunk)
+
+    return h.hexdigest()
+
+
+def safe_int(v, default=None):
+    try:
+        if v is None:
+            return default
+        return int(v)
+    except Exception:
+        return default
+
+
+def safe_float(v, default=None):
+    try:
+        if v is None:
+            return default
+        return float(v)
+    except Exception:
+        return default
+
+
+def status_rank(s: str) -> int:
+    return {
+        "good": 0,
+        "warning": 1,
+        "bad": 2,
+    }.get(str(s).lower(), 2)
+
+
+def merge_status(a: str, b: str) -> str:
+    return a if status_rank(a) >= status_rank(b) else b
+
+
+def overlay_hud(
+    img,
+    lines,
+    x=12,
+    y=24,
+    font_scale=0.50,
+    line_step=20,
+    color=(255, 255, 255),
+):
+    yy = int(y)
+    h = img.shape[0]
+
+    for line in lines:
+        if yy > h - 6:
+            break
+
+        text = str(line)
+
+        cv2.putText(
+            img,
+            text,
+            (int(x), yy),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            font_scale,
+            (0, 0, 0),
+            3,
+            cv2.LINE_AA,
+        )
+
+        cv2.putText(
+            img,
+            text,
+            (int(x), yy),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            font_scale,
+            color,
+            1,
+            cv2.LINE_AA,
+        )
+
+        yy += int(line_step)
+
+
+def socket_name(socket) -> str:
+    name = getattr(socket, "name", None)
+    if name:
+        return str(name)
+
+    text = str(socket)
+
+    for candidate in ("CAM_A", "CAM_B", "CAM_C", "CAM_D"):
+        if candidate in text:
+            return candidate
+
+    return text
+
+
+def get_socket(name: str):
+    mapping = {
+        "CAM_A": dai.CameraBoardSocket.CAM_A,
+        "CAM_B": dai.CameraBoardSocket.CAM_B,
+        "CAM_C": dai.CameraBoardSocket.CAM_C,
+    }
+
+    key = str(name).upper()
+
+    if key not in mapping:
+        raise ValueError(f"Socket inválido: {name}")
+
+    return mapping[key]
+
+
+def enum_if_exists(enum_cls, name: str):
+    return getattr(enum_cls, name, None)
+
+
+def supported_type_strings(feature) -> List[str]:
+    return [
+        str(v).upper()
+        for v in (getattr(feature, "supportedTypes", []) or [])
+    ]
+
+
+def feature_is_color(feature) -> bool:
+    types = supported_type_strings(feature)
+    sensor = str(getattr(feature, "sensorName", "") or "").upper()
+
+    if any("COLOR" in x for x in types):
+        return True
+
+    if any("MONO" in x for x in types):
+        return False
+
+    return sensor in {"OV9782", "AR0234"}
+
+
+def feature_is_mono(feature) -> bool:
+    types = supported_type_strings(feature)
+
+    if any("MONO" in x for x in types):
+        return True
+
+    if any("COLOR" in x for x in types):
+        return False
+
+    return not feature_is_color(feature)
+
+
+def resize_panel(img: np.ndarray, width: int, height: int) -> np.ndarray:
+    return cv2.resize(
+        img,
+        (int(width), int(height)),
+        interpolation=cv2.INTER_AREA,
+    )
+
+
+def gray_bgr(gray_u8: np.ndarray) -> np.ndarray:
+    if gray_u8.ndim == 3:
+        return gray_u8
+
+    return cv2.cvtColor(
+        gray_u8,
+        cv2.COLOR_GRAY2BGR,
+    )
+
+
+def colorize_spec(gray_u8: np.ndarray, role: str) -> np.ndarray:
+    if gray_u8.ndim == 3:
+        gray_u8 = cv2.cvtColor(
+            gray_u8,
+            cv2.COLOR_BGR2GRAY,
+        )
+
+    z = np.zeros_like(gray_u8)
+
+    if role == "re":
+        return np.dstack([z, z, gray_u8])
+
+    if role == "nir":
+        return np.dstack([gray_u8, gray_u8, z])
+
+    return gray_bgr(gray_u8)
+
+
+def normalize_gray_u8(img) -> np.ndarray:
+    if img is None:
+        raise ValueError("Imagem None")
+
+    if img.ndim == 3:
+        gray = cv2.cvtColor(
+            img,
+            cv2.COLOR_BGR2GRAY,
+        )
+    else:
+        gray = img
+
+    if gray.dtype == np.uint8:
+        return gray
+
+    arr = gray.astype(np.float32)
+    finite = arr[np.isfinite(arr)]
+
+    if finite.size == 0:
+        return np.zeros(arr.shape[:2], dtype=np.uint8)
+
+    lo = float(np.percentile(finite, 1.0))
+    hi = float(np.percentile(finite, 99.5))
+
+    if hi <= lo:
+        hi = lo + 1e-6
+
+    out = (arr - lo) / (hi - lo)
+
+    return np.clip(out * 255.0, 0, 255).astype(np.uint8)
+
+
+# ============================================================
+# Hardware
+# ============================================================
+
+@dataclass
+class CameraSpec:
+    role: str
+    socket_name: str
+    sensor_name: str
+    width: int
+    height: int
+    resolution_name: str
+    stream_name: str
+    control_name: str
+    is_color: bool
+    source: str
+
+
+def discover_cameras(mx_id: Optional[str]):
+    device_info = dai.DeviceInfo(mx_id) if mx_id else None
+
+    ctx = (
+        dai.Device(device_info)
+        if device_info is not None
+        else dai.Device()
+    )
+
+    with ctx as device:
+        actual_mx = None
+
+        for method_name in ("getMxId", "getDeviceId"):
+            fn = getattr(device, method_name, None)
+
+            if callable(fn):
+                try:
+                    value = fn()
+
+                    if value:
+                        actual_mx = str(value)
+                        break
+
+                except Exception:
+                    pass
+
+        usb_speed = None
+
+        try:
+            usb_speed = str(device.getUsbSpeed())
+        except Exception:
+            pass
+
+        rows = []
+
+        for feature in device.getConnectedCameraFeatures():
+            rows.append({
+                "socket_name": socket_name(feature.socket),
+                "sensor_name": str(feature.sensorName or "").upper(),
+                "width": int(getattr(feature, "width", 0) or 0),
+                "height": int(getattr(feature, "height", 0) or 0),
+                "supported_types": supported_type_strings(feature),
+                "is_color": feature_is_color(feature),
+                "is_mono": feature_is_mono(feature),
+                "has_autofocus_ic": int(
+                    getattr(feature, "hasAutofocusIC", 0) or 0
+                ),
+            })
+
+    return rows, actual_mx, usb_speed
+
+
+def validate_product_topology(camera_rows: list[dict]):
+    by_socket = {
+        row["socket_name"]: row
+        for row in camera_rows
+    }
+
+    errors = []
+
+    for role in ROLES:
+        contract = PRODUCT_TOPOLOGY[role]
+        socket = contract["socket"]
+
+        row = by_socket.get(socket)
+
+        if row is None:
+            errors.append(
+                f"{role.upper()}: {socket} ausente"
+            )
+            continue
+
+        sensor = row["sensor_name"]
+
+        if sensor not in contract["allowed_sensors"]:
+            errors.append(
+                f"{role.upper()}: {socket} sensor={sensor}, "
+                f"esperado={contract['allowed_sensors']}"
+            )
+
+        if role == "rgb" and not row["is_color"]:
+            errors.append(
+                f"RGB {socket}/{sensor} não anunciado COLOR"
+            )
+
+        if role in SPEC_ROLES and not row["is_mono"]:
+            errors.append(
+                f"{role.upper()} {socket}/{sensor} não anunciado MONO"
+            )
+
+    if errors:
+        raise RuntimeError(
+            "Topologia do produto inválida:\n  - "
+            + "\n  - ".join(errors)
+        )
+
+    return {
+        "valid": True,
+        "errors": [],
+    }
+
+
+def build_specs(camera_rows: list[dict]) -> Dict[str, CameraSpec]:
+    by_socket = {
+        row["socket_name"]: row
+        for row in camera_rows
+    }
+
+    specs = {}
+
+    for role in ROLES:
+        socket = PRODUCT_TOPOLOGY[role]["socket"]
+        row = by_socket[socket]
+        sensor = row["sensor_name"]
+
+        if role == "rgb":
+            cfg = RGB_SENSOR_MODES[sensor]
+        else:
+            cfg = MONO_SENSOR_MODES[sensor]
+
+        if row["width"] and row["height"]:
+            if (
+                row["width"],
+                row["height"],
+            ) != (
+                cfg["width"],
+                cfg["height"],
+            ):
+                raise RuntimeError(
+                    f"{socket}/{sensor}: dimensão anunciada "
+                    f"{row['width']}x{row['height']} != "
+                    f"{cfg['width']}x{cfg['height']}"
+                )
+
+        specs[role] = CameraSpec(
+            role=role,
+            socket_name=socket,
+            sensor_name=sensor,
+            width=int(cfg["width"]),
+            height=int(cfg["height"]),
+            resolution_name=cfg["resolution_enum"],
+            stream_name=f"hcal_{role}",
+            control_name=f"hcal_ctrl_{role}",
+            is_color=(role == "rgb"),
+            source="ISP" if role == "rgb" else "MONO_OUT",
+        )
+
+    return specs
+
+
+def build_pipeline(specs: Dict[str, CameraSpec], args):
+    pipeline = dai.Pipeline()
+
+    isp_settings = {
+        "sharpness": None,
+        "luma_denoise": None,
+        "chroma_denoise": None,
+        "errors": [],
+    }
+
+    for role in ROLES:
+        spec = specs[role]
+        socket = get_socket(spec.socket_name)
+
+        if spec.is_color:
+            cam = pipeline.createColorCamera()
+            cam.setBoardSocket(socket)
+
+            enum_value = enum_if_exists(
+                dai.ColorCameraProperties.SensorResolution,
+                spec.resolution_name,
+            )
+
+            if enum_value is None:
+                raise RuntimeError(
+                    f"DepthAI não expõe ColorCamera {spec.resolution_name}"
+                )
+
+            cam.setResolution(enum_value)
+            cam.setFps(float(args.fps))
+            cam.setInterleaved(False)
+
+            try:
+                cam.setColorOrder(
+                    dai.ColorCameraProperties.ColorOrder.BGR
+                )
+            except Exception:
+                pass
+
+            try:
+                cam.setIspScale(1, 1)
+            except Exception:
+                pass
+
+            try:
+                cam.initialControl.setAutoExposureEnable()
+            except Exception:
+                pass
+
+            try:
+                cam.initialControl.setAutoWhiteBalanceLock(False)
+            except Exception:
+                pass
+
+            for key, method_name, value in (
+                ("sharpness", "setSharpness", int(args.isp_sharpness)),
+                ("luma_denoise", "setLumaDenoise", int(args.isp_luma_denoise)),
+                ("chroma_denoise", "setChromaDenoise", int(args.isp_chroma_denoise)),
+            ):
+                fn = getattr(cam.initialControl, method_name, None)
+
+                if not callable(fn):
+                    isp_settings["errors"].append(
+                        f"{method_name}:unsupported"
+                    )
+                    continue
+
+                try:
+                    fn(value)
+                    isp_settings[key] = value
+                except Exception as exc:
+                    isp_settings["errors"].append(
+                        f"{method_name}:{exc}"
+                    )
+
+            output = cam.isp
+
+        else:
+            cam = pipeline.createMonoCamera()
+            cam.setBoardSocket(socket)
+
+            enum_value = enum_if_exists(
+                dai.MonoCameraProperties.SensorResolution,
+                spec.resolution_name,
+            )
+
+            if enum_value is None:
+                raise RuntimeError(
+                    f"DepthAI não expõe MonoCamera {spec.resolution_name}"
+                )
+
+            cam.setResolution(enum_value)
+            cam.setFps(float(args.fps))
+
+            try:
+                cam.initialControl.setAutoExposureEnable()
+            except Exception:
+                pass
+
+            output = cam.out
+
+        xout = pipeline.createXLinkOut()
+        xout.setStreamName(spec.stream_name)
+        output.link(xout.input)
+
+        xin = pipeline.createXLinkIn()
+        xin.setStreamName(spec.control_name)
+
+        if not hasattr(cam, "inputControl"):
+            raise RuntimeError(
+                f"{spec.socket_name}/{spec.sensor_name} sem inputControl"
+            )
+
+        xin.out.link(cam.inputControl)
+
+    return pipeline, isp_settings
+
+
+# ============================================================
+# Controles de exposição
+# ============================================================
+
+@dataclass
+class LockedControl:
+    role: str
+    exposure_time_us: int
+    sensitivity_iso: int
+    source: str
+
+
+def packet_controls(packet) -> dict:
+    exposure_us = None
+    iso = None
+    color_temperature = None
+    sequence_num = None
+    timestamp_s = None
+
+    try:
+        exp = packet.getExposureTime()
+
+        if hasattr(exp, "total_seconds"):
+            exposure_us = int(
+                round(exp.total_seconds() * 1_000_000)
+            )
+        else:
+            exposure_us = int(exp)
+
+    except Exception:
+        pass
+
+    try:
+        iso = int(packet.getSensitivity())
+    except Exception:
+        pass
+
+    try:
+        color_temperature = int(
+            packet.getColorTemperature()
+        )
+    except Exception:
+        pass
+
+    try:
+        sequence_num = int(
+            packet.getSequenceNum()
+        )
+    except Exception:
+        pass
+
+    for method_name in (
+        "getTimestampDevice",
+        "getTimestamp",
+    ):
+        fn = getattr(packet, method_name, None)
+
+        if callable(fn):
+            try:
+                ts = fn()
+
+                if hasattr(ts, "total_seconds"):
+                    timestamp_s = float(
+                        ts.total_seconds()
+                    )
+                else:
+                    timestamp_s = float(ts)
+
+                break
+            except Exception:
+                pass
+
+    return {
+        "exposure_time_us": exposure_us,
+        "sensitivity_iso": iso,
+        "color_temperature_k": color_temperature,
+        "sequence_num": sequence_num,
+        "timestamp_s": timestamp_s,
+    }
+
+
+def controls_match(
+    actual: dict,
+    target: LockedControl,
+    qa: dict,
+) -> bool:
+    exp = safe_int(
+        actual.get("exposure_time_us"),
+        None,
+    )
+
+    iso = safe_int(
+        actual.get("sensitivity_iso"),
+        None,
+    )
+
+    if exp is None or iso is None:
+        return False
+
+    exp_tol = max(
+        int(qa["exposure_abs_tolerance_us"]),
+        int(
+            round(
+                target.exposure_time_us
+                * qa["exposure_rel_tolerance"]
+            )
+        ),
+    )
+
+    return (
+        abs(exp - target.exposure_time_us)
+        <= exp_tol
+        and abs(iso - target.sensitivity_iso)
+        <= int(qa["iso_tolerance"])
+    )
+
+
+def send_manual_control(
+    queue,
+    lock: LockedControl,
+    rgb=False,
+):
+    ctrl = dai.CameraControl()
+
+    ctrl.setManualExposure(
+        int(lock.exposure_time_us),
+        int(lock.sensitivity_iso),
+    )
+
+    if rgb:
+        try:
+            ctrl.setAutoWhiteBalanceLock(True)
+        except Exception:
+            pass
+
+    queue.send(ctrl)
+
+
+# ============================================================
+# Runtime de aquisição
+# ============================================================
+
+class AcquisitionRuntime:
+    def __init__(
+        self,
+        device,
+        specs: Dict[str, CameraSpec],
+        args,
+        qa,
+    ):
+        self.device = device
+        self.specs = specs
+        self.args = args
+        self.qa = qa
+
+        self.output_queues = {
+            role: device.getOutputQueue(
+                name=spec.stream_name,
+                maxSize=3,
+                blocking=False,
+            )
+            for role, spec in specs.items()
+        }
+
+        self.control_queues = {
+            role: device.getInputQueue(
+                spec.control_name
+            )
+            for role, spec in specs.items()
+        }
+
+        self.frames = {
+            role: None
+            for role in ROLES
+        }
+
+        self.controls = {
+            role: {}
+            for role in ROLES
+        }
+
+        self.last_seq = {
+            role: None
+            for role in ROLES
+        }
+
+        self.frame_counter = {
+            role: 0
+            for role in ROLES
+        }
+
+        self.fps = {
+            role: 0.0
+            for role in ROLES
+        }
+
+        self._fps_counter = {
+            role: 0
+            for role in ROLES
+        }
+
+        self._fps_t0 = {
+            role: time.time()
+            for role in ROLES
+        }
+
+    def poll(self) -> set[str]:
+        fresh = set()
+
+        for role in ROLES:
+            packet = self.output_queues[role].tryGet()
+
+            if packet is None:
+                continue
+
+            ctrl = packet_controls(packet)
+            seq = ctrl.get("sequence_num")
+
+            if (
+                seq is not None
+                and seq == self.last_seq[role]
+            ):
+                continue
+
+            img = packet.getCvFrame()
+
+            if img is None:
+                continue
+
+            if img.ndim == 2:
+                img = np.ascontiguousarray(img)
+            else:
+                img = np.ascontiguousarray(img)
+
+            expected = (
+                self.specs[role].height,
+                self.specs[role].width,
+            )
+
+            if img.shape[:2] != expected:
+                raise RuntimeError(
+                    f"{role.upper()}: frame {img.shape[:2]} != "
+                    f"esperado {expected}"
+                )
+
+            self.frames[role] = img
+            self.controls[role] = ctrl
+            self.last_seq[role] = seq
+            self.frame_counter[role] += 1
+
+            fresh.add(role)
+
+            self._fps_counter[role] += 1
+
+            dt = time.time() - self._fps_t0[role]
+
+            if dt >= 1.0:
+                self.fps[role] = (
+                    self._fps_counter[role]
+                    / dt
+                )
+
+                self._fps_counter[role] = 0
+                self._fps_t0[role] = time.time()
+
+        return fresh
+
+    def wait_all(self, timeout=8.0):
+        t0 = time.time()
+
+        while time.time() - t0 < timeout:
+            self.poll()
+
+            if all(
+                self.frames[r] is not None
+                for r in ROLES
+            ):
+                return
+
+            time.sleep(0.003)
+
+        missing = [
+            r
+            for r in ROLES
+            if self.frames[r] is None
+        ]
+
+        raise TimeoutError(
+            f"Timeout aguardando câmeras: {missing}"
+        )
+
+    def send_manual(self, locked):
+        for role in ROLES:
+            send_manual_control(
+                self.control_queues[role],
+                locked[role],
+                rgb=(role == "rgb"),
+            )
+
+    def verify_manual(
+        self,
+        locked,
+        settle_frames=8,
+        timeout=10.0,
+    ):
+        matched = {
+            r: 0
+            for r in ROLES
+        }
+
+        seen = dict(self.last_seq)
+        t0 = time.time()
+
+        while time.time() - t0 < timeout:
+            fresh = self.poll()
+
+            for role in fresh:
+                seq = self.last_seq[role]
+
+                if seq == seen.get(role):
+                    continue
+
+                seen[role] = seq
+
+                if controls_match(
+                    self.controls[role],
+                    locked[role],
+                    self.qa,
+                ):
+                    matched[role] += 1
+                else:
+                    matched[role] = 0
+
+            if all(
+                matched[r] >= int(settle_frames)
+                for r in ROLES
+            ):
+                return
+
+            time.sleep(0.002)
+
+        raise RuntimeError(
+            "Não foi possível confirmar exposição manual: "
+            f"{matched}"
+        )
+
+    def fresh_triplet(
+        self,
+        timeout=3.0,
+    ):
+        """
+        Aguarda um frame NOVO de cada role após o pedido.
+        Retorna snapshots e metadados.
+        """
+        start_seq = dict(self.last_seq)
+
+        captured = {}
+        captured_controls = {}
+
+        t0 = time.time()
+
+        while time.time() - t0 < timeout:
+            fresh = self.poll()
+
+            for role in fresh:
+                seq = self.last_seq[role]
+
+                if (
+                    role not in captured
+                    and seq != start_seq.get(role)
+                ):
+                    captured[role] = (
+                        self.frames[role].copy()
+                    )
+                    captured_controls[role] = dict(
+                        self.controls[role]
+                    )
+
+            if len(captured) == 3:
+                break
+
+            time.sleep(0.001)
+
+        if len(captured) != 3:
+            missing = [
+                r
+                for r in ROLES
+                if r not in captured
+            ]
+
+            raise TimeoutError(
+                f"Triplet incompleto. Faltando: {missing}"
+            )
+
+        timestamps = [
+            captured_controls[r].get("timestamp_s")
+            for r in ROLES
+        ]
+
+        timestamps = [
+            float(x)
+            for x in timestamps
+            if x is not None
+        ]
+
+        if len(timestamps) == 3:
+            sync_dt_ms = (
+                max(timestamps) - min(timestamps)
+            ) * 1000.0
+        else:
+            sync_dt_ms = None
+
+        return {
+            "frames": captured,
+            "controls": captured_controls,
+            "sync_dt_ms": sync_dt_ms,
+        }
+
+
+# ============================================================
+# ChArUco compatibility layer
+# ============================================================
+
+def require_aruco():
+    if not hasattr(cv2, "aruco"):
+        raise RuntimeError(
+            "cv2.aruco indisponível. "
+            "Instale opencv-contrib-python."
+        )
+
+    if not (
+        hasattr(cv2.aruco, "CharucoBoard")
+        or hasattr(cv2.aruco, "CharucoBoard_create")
+    ):
+        raise RuntimeError(
+            "OpenCV sem CharucoBoard. "
+            "Use opencv-contrib-python."
+        )
+
+
+def get_aruco_dictionary(name: str):
+    require_aruco()
+
+    if not hasattr(cv2.aruco, name):
+        available = sorted(
+            x
+            for x in dir(cv2.aruco)
+            if x.startswith("DICT_")
+        )
+
+        raise ValueError(
+            f"Dicionário ArUco inválido: {name}. "
+            f"Disponíveis: {available}"
+        )
+
+    dict_id = getattr(cv2.aruco, name)
+
+    if hasattr(
+        cv2.aruco,
+        "getPredefinedDictionary",
+    ):
+        return cv2.aruco.getPredefinedDictionary(
+            dict_id
+        )
+
+    return cv2.aruco.Dictionary_get(
+        dict_id
+    )
+
+
+def create_charuco_board(
+    squares_x,
+    squares_y,
+    square_length,
+    marker_length,
+    dictionary,
+):
+    require_aruco()
+
+    if hasattr(cv2.aruco, "CharucoBoard"):
+        try:
+            return cv2.aruco.CharucoBoard(
+                (
+                    int(squares_x),
+                    int(squares_y),
+                ),
+                float(square_length),
+                float(marker_length),
+                dictionary,
+            )
+        except TypeError:
+            pass
+
+    if hasattr(
+        cv2.aruco,
+        "CharucoBoard_create",
+    ):
+        return cv2.aruco.CharucoBoard_create(
+            int(squares_x),
+            int(squares_y),
+            float(square_length),
+            float(marker_length),
+            dictionary,
+        )
+
+    raise RuntimeError(
+        "API CharucoBoard incompatível."
+    )
+
+
+def create_detector_params():
+    if hasattr(
+        cv2.aruco,
+        "DetectorParameters",
+    ):
+        params = cv2.aruco.DetectorParameters()
+    else:
+        params = cv2.aruco.DetectorParameters_create()
+
+    # Conservador, prioriza robustez.
+    for name, value in (
+        ("cornerRefinementMethod", getattr(cv2.aruco, "CORNER_REFINE_SUBPIX", 1)),
+        ("cornerRefinementWinSize", 5),
+        ("cornerRefinementMaxIterations", 40),
+        ("cornerRefinementMinAccuracy", 0.01),
+    ):
+        try:
+            setattr(params, name, value)
+        except Exception:
+            pass
+
+    return params
+
+
+def prepare_charuco_gray(
+    img,
+    equalize=False,
+    invert=False,
+):
+    gray = normalize_gray_u8(img)
+
+    if invert:
+        gray = 255 - gray
+
+    if equalize:
+        try:
+            clahe = cv2.createCLAHE(
+                clipLimit=2.0,
+                tileGridSize=(8, 8),
+            )
+            gray = clahe.apply(gray)
+        except Exception:
+            gray = cv2.equalizeHist(gray)
+
+    return gray
+
+
+def detect_markers_compat(
+    gray,
+    dictionary,
+    params,
+):
+    if hasattr(cv2.aruco, "ArucoDetector"):
+        detector = cv2.aruco.ArucoDetector(
+            dictionary,
+            params,
+        )
+
+        return detector.detectMarkers(
+            gray
+        )
+
+    return cv2.aruco.detectMarkers(
+        gray,
+        dictionary,
+        parameters=params,
+    )
+
+
+def interpolate_charuco_compat(
+    gray,
+    board,
+    marker_corners,
+    marker_ids,
+):
+    if marker_ids is None or len(marker_ids) == 0:
+        return None, None
+
+    # API moderna.
+    # IMPORTANTE: em OpenCV recente os argumentos posicionais 2/3 são
+    # charucoCorners/charucoIds de saída, não markerCorners/markerIds.
+    # Portanto usamos nomes explícitos para não trocar a semântica.
+    if hasattr(cv2.aruco, "CharucoDetector"):
+        try:
+            detector = cv2.aruco.CharucoDetector(
+                board
+            )
+
+            result = detector.detectBoard(
+                image=gray,
+                markerCorners=marker_corners,
+                markerIds=marker_ids,
+            )
+
+            if (
+                isinstance(result, tuple)
+                and len(result) >= 2
+            ):
+                return result[0], result[1]
+
+        except Exception:
+            # Fallback: deixa o próprio CharucoDetector detectar os markers.
+            try:
+                result = detector.detectBoard(
+                    gray
+                )
+
+                if (
+                    isinstance(result, tuple)
+                    and len(result) >= 2
+                ):
+                    return result[0], result[1]
+
+            except Exception:
+                pass
+
+    # API legacy estável
+    if hasattr(
+        cv2.aruco,
+        "interpolateCornersCharuco",
+    ):
+        ret, corners, ids = (
+            cv2.aruco.interpolateCornersCharuco(
+                markerCorners=marker_corners,
+                markerIds=marker_ids,
+                image=gray,
+                board=board,
+            )
+        )
+
+        if ret is None or ret <= 0:
+            return None, None
+
+        return corners, ids
+
+    raise RuntimeError(
+        "Nenhuma API ChArUco compatível encontrada."
+    )
+
+
+def refine_charuco_subpix(
+    gray,
+    corners,
+):
+    if corners is None or len(corners) == 0:
+        return corners
+
+    pts = np.asarray(
+        corners,
+        dtype=np.float32,
+    ).reshape(-1, 1, 2)
+
+    try:
+        cv2.cornerSubPix(
+            gray,
+            pts,
+            (4, 4),
+            (-1, -1),
+            (
+                cv2.TERM_CRITERIA_EPS
+                + cv2.TERM_CRITERIA_MAX_ITER,
+                40,
+                0.01,
+            ),
+        )
+    except Exception:
+        pass
+
+    return pts
+
+
+def detect_charuco_variant(
+    img,
+    board,
+    dictionary,
+    params,
+    equalize,
+    invert,
+):
+    gray = prepare_charuco_gray(
+        img,
+        equalize=equalize,
+        invert=invert,
+    )
+
+    marker_corners, marker_ids, rejected = (
+        detect_markers_compat(
+            gray,
+            dictionary,
+            params,
+        )
+    )
+
+    marker_count = (
+        0
+        if marker_ids is None
+        else int(len(marker_ids))
+    )
+
+    if marker_count == 0:
+        return {
+            "points": {},
+            "markers": 0,
+            "corners": 0,
+            "equalize": equalize,
+            "invert": invert,
+            "gray": gray,
+        }
+
+    charuco_corners, charuco_ids = (
+        interpolate_charuco_compat(
+            gray,
+            board,
+            marker_corners,
+            marker_ids,
+        )
+    )
+
+    if (
+        charuco_corners is None
+        or charuco_ids is None
+    ):
+        return {
+            "points": {},
+            "markers": marker_count,
+            "corners": 0,
+            "equalize": equalize,
+            "invert": invert,
+            "gray": gray,
+        }
+
+    charuco_corners = (
+        refine_charuco_subpix(
+            gray,
+            charuco_corners,
+        )
+    )
+
+    ids_flat = np.asarray(
+        charuco_ids
+    ).reshape(-1)
+
+    pts = np.asarray(
+        charuco_corners
+    ).reshape(-1, 2)
+
+    point_by_id = {
+        int(cid): (
+            float(pt[0]),
+            float(pt[1]),
+        )
+        for cid, pt in zip(
+            ids_flat,
+            pts,
+        )
+    }
+
+    return {
+        "points": point_by_id,
+        "markers": marker_count,
+        "corners": len(point_by_id),
+        "equalize": bool(equalize),
+        "invert": bool(invert),
+        "gray": gray,
+    }
+
+
+def detect_charuco_best(
+    img,
+    board,
+    dictionary,
+    params,
+    allow_invert=True,
+):
+    """
+    Tenta variantes de contraste e escolhe a que produz mais cantos.
+    As coordenadas continuam no frame nativo original.
+    """
+    variants = [
+        (False, False),
+        (True, False),
+    ]
+
+    if allow_invert:
+        variants += [
+            (False, True),
+            (True, True),
+        ]
+
+    results = [
+        detect_charuco_variant(
+            img,
+            board,
+            dictionary,
+            params,
+            equalize=eq,
+            invert=inv,
+        )
+        for eq, inv in variants
+    ]
+
+    results.sort(
+        key=lambda x: (
+            x["corners"],
+            x["markers"],
+        ),
+        reverse=True,
+    )
+
+    return results[0]
+
+
+# ============================================================
+# Sample
+# ============================================================
+
+@dataclass
+class PoseSignature:
+    center_x_norm: float
+    center_y_norm: float
+    bbox_area_norm: float
+
+
+def pose_signature(
+    rgb_points: dict,
+    rgb_shape_hw,
+) -> PoseSignature:
+    if not rgb_points:
+        return PoseSignature(
+            0.0,
+            0.0,
+            0.0,
+        )
+
+    pts = np.array(
+        list(rgb_points.values()),
+        dtype=np.float32,
+    )
+
+    h, w = rgb_shape_hw
+
+    x0 = float(np.min(pts[:, 0]))
+    x1 = float(np.max(pts[:, 0]))
+    y0 = float(np.min(pts[:, 1]))
+    y1 = float(np.max(pts[:, 1]))
+
+    cx = 0.5 * (x0 + x1)
+    cy = 0.5 * (y0 + y1)
+
+    area = (
+        max(0.0, x1 - x0)
+        * max(0.0, y1 - y0)
+    )
+
+    return PoseSignature(
+        center_x_norm=cx / max(float(w), 1.0),
+        center_y_norm=cy / max(float(h), 1.0),
+        bbox_area_norm=area / max(
+            float(w * h),
+            1.0,
+        ),
+    )
+
+
+def is_duplicate_pose(
+    signature: PoseSignature,
+    prior_signatures: list[PoseSignature],
+    qa: dict,
+):
+    for prior in prior_signatures:
+        dc = math.hypot(
+            signature.center_x_norm
+            - prior.center_x_norm,
+            signature.center_y_norm
+            - prior.center_y_norm,
+        )
+
+        da = abs(
+            signature.bbox_area_norm
+            - prior.bbox_area_norm
+        )
+
+        if (
+            dc
+            < qa["duplicate_center_distance_norm"]
+            and da
+            < qa["duplicate_scale_delta"]
+        ):
+            return True, {
+                "center_distance_norm": dc,
+                "area_delta_norm": da,
+            }
+
+    return False, None
+
+
+def common_pairs(
+    rgb_points: dict,
+    spec_points: dict,
+):
+    ids = sorted(
+        set(rgb_points.keys())
+        & set(spec_points.keys())
+    )
+
+    src = [
+        spec_points[cid]
+        for cid in ids
+    ]
+
+    dst = [
+        rgb_points[cid]
+        for cid in ids
+    ]
+
+    return ids, src, dst
+
+
+# ============================================================
+# Homography math
+# ============================================================
+
+def find_homography_ransac(
+    src_pts,
+    dst_pts,
+    qa,
+):
+    if (
+        len(src_pts) < 4
+        or len(dst_pts) < 4
+        or len(src_pts) != len(dst_pts)
+    ):
+        return None, None
+
+    src = np.asarray(
+        src_pts,
+        dtype=np.float32,
+    )
+
+    dst = np.asarray(
+        dst_pts,
+        dtype=np.float32,
+    )
+
+    H, mask = cv2.findHomography(
+        src,
+        dst,
+        method=cv2.RANSAC,
+        ransacReprojThreshold=float(
+            qa["ransac_reproj_threshold_px"]
+        ),
+        maxIters=int(
+            qa["ransac_max_iters"]
+        ),
+        confidence=float(
+            qa["ransac_confidence"]
+        ),
+    )
+
+    if H is None:
+        return None, mask
+
+    H = np.asarray(
+        H,
+        dtype=np.float64,
+    )
+
+    if abs(H[2, 2]) > 1e-12:
+        H = H / H[2, 2]
+
+    return H, mask
+
+
+def project_points(
+    H,
+    src_pts,
+):
+    src = np.asarray(
+        src_pts,
+        dtype=np.float32,
+    ).reshape(-1, 1, 2)
+
+    out = cv2.perspectiveTransform(
+        src,
+        np.asarray(H, dtype=np.float64),
+    )
+
+    return out.reshape(-1, 2)
+
+
+def reprojection_errors(
+    H,
+    src_pts,
+    dst_pts,
+):
+    projected = project_points(
+        H,
+        src_pts,
+    )
+
+    dst = np.asarray(
+        dst_pts,
+        dtype=np.float32,
+    )
+
+    return np.linalg.norm(
+        projected - dst,
+        axis=1,
+    )
+
+
+def error_stats(errors):
+    e = np.asarray(
+        errors,
+        dtype=np.float64,
+    )
+
+    if e.size == 0:
+        return {
+            "count": 0,
+            "mean_px": None,
+            "median_px": None,
+            "p95_px": None,
+            "max_px": None,
+        }
+
+    return {
+        "count": int(e.size),
+        "mean_px": float(np.mean(e)),
+        "median_px": float(np.median(e)),
+        "p95_px": float(
+            np.percentile(e, 95)
+        ),
+        "max_px": float(np.max(e)),
+    }
+
+
+def coverage_stats(
+    dst_pts,
+    image_size_wh,
+):
+    w, h = image_size_wh
+
+    pts = np.asarray(
+        dst_pts,
+        dtype=np.float32,
+    )
+
+    if len(pts) < 3:
+        return {
+            "span_x": 0.0,
+            "span_y": 0.0,
+            "hull_area_fraction": 0.0,
+        }
+
+    x0 = float(np.min(pts[:, 0]))
+    x1 = float(np.max(pts[:, 0]))
+    y0 = float(np.min(pts[:, 1]))
+    y1 = float(np.max(pts[:, 1]))
+
+    hull = cv2.convexHull(
+        pts.reshape(-1, 1, 2)
+    )
+
+    hull_area = float(
+        cv2.contourArea(hull)
+    )
+
+    return {
+        "span_x": (
+            (x1 - x0)
+            / max(float(w), 1.0)
+        ),
+        "span_y": (
+            (y1 - y0)
+            / max(float(h), 1.0)
+        ),
+        "hull_area_fraction": (
+            hull_area
+            / max(float(w * h), 1.0)
+        ),
+        "bbox": [
+            x0,
+            y0,
+            x1,
+            y1,
+        ],
+    }
+
+
+def overlap_fraction(
+    H,
+    source_size_wh,
+    destination_size_wh,
+):
+    sw, sh = source_size_wh
+    dw, dh = destination_size_wh
+
+    mask = np.ones(
+        (int(sh), int(sw)),
+        dtype=np.uint8,
+    )
+
+    warped = cv2.warpPerspective(
+        mask,
+        np.asarray(H, dtype=np.float64),
+        (int(dw), int(dh)),
+        flags=cv2.INTER_NEAREST,
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=0,
+    )
+
+    return float(
+        (warped > 0).mean()
+    )
+
+
+def homography_condition(H):
+    Hn = np.asarray(
+        H,
+        dtype=np.float64,
+    )
+
+    if abs(Hn[2, 2]) > 1e-12:
+        Hn = Hn / Hn[2, 2]
+
+    try:
+        return float(
+            np.linalg.cond(Hn)
+        )
+    except Exception:
+        return float("inf")
+
+
+# ============================================================
+# Dataset assembly / sample rejection
+# ============================================================
+
+def role_pairs_from_samples(
+    samples,
+    role,
+    sample_ids_filter=None,
+):
+    src = []
+    dst = []
+    sample_ids = []
+    charuco_ids = []
+
+    allowed = (
+        None
+        if sample_ids_filter is None
+        else set(sample_ids_filter)
+    )
+
+    for sample in samples:
+        sid = int(sample["sample_id"])
+
+        if (
+            allowed is not None
+            and sid not in allowed
+        ):
+            continue
+
+        pair = sample["pairs"][role]
+
+        for cid, spt, dpt in zip(
+            pair["charuco_ids"],
+            pair["src_points"],
+            pair["dst_points"],
+        ):
+            src.append(spt)
+            dst.append(dpt)
+            sample_ids.append(sid)
+            charuco_ids.append(int(cid))
+
+    return {
+        "src": src,
+        "dst": dst,
+        "sample_ids": sample_ids,
+        "charuco_ids": charuco_ids,
+    }
+
+
+def per_sample_residuals(
+    H,
+    pairs,
+):
+    errors = reprojection_errors(
+        H,
+        pairs["src"],
+        pairs["dst"],
+    )
+
+    by_sample = {}
+
+    for err, sid in zip(
+        errors,
+        pairs["sample_ids"],
+    ):
+        by_sample.setdefault(
+            int(sid),
+            [],
+        ).append(float(err))
+
+    result = {}
+
+    for sid, values in by_sample.items():
+        st = error_stats(values)
+
+        result[int(sid)] = st
+
+    return result
+
+
+def reject_incompatible_samples(
+    samples,
+    role,
+    qa,
+):
+    retained = [
+        int(s["sample_id"])
+        for s in samples
+    ]
+
+    rejected = []
+
+    rounds = []
+
+    for round_idx in range(
+        int(qa["max_sample_rejection_rounds"]) + 1
+    ):
+        pairs = role_pairs_from_samples(
+            samples,
+            role,
+            sample_ids_filter=retained,
+        )
+
+        if len(pairs["src"]) < 4:
+            break
+
+        H, mask = find_homography_ransac(
+            pairs["src"],
+            pairs["dst"],
+            qa,
+        )
+
+        if H is None:
+            break
+
+        residual = per_sample_residuals(
+            H,
+            pairs,
+        )
+
+        bad = [
+            sid
+            for sid, st in residual.items()
+            if (
+                st["median_px"] is not None
+                and st["median_px"]
+                > qa["sample_reject_median_error_px"]
+            )
+        ]
+
+        rounds.append({
+            "round": round_idx,
+            "retained": list(retained),
+            "bad_candidates": list(bad),
+            "per_sample": residual,
+        })
+
+        if not bad:
+            break
+
+        # Remove primeiro o pior, mantendo mínimo de amostras.
+        worst = max(
+            bad,
+            key=lambda sid: residual[sid]["median_px"],
+        )
+
+        if (
+            len(retained) - 1
+            < int(qa["min_samples"])
+        ):
+            break
+
+        retained.remove(worst)
+        rejected.append(worst)
+
+    return {
+        "retained_sample_ids": retained,
+        "rejected_sample_ids": rejected,
+        "rounds": rounds,
+    }
+
+
+# ============================================================
+# Leave-one-sample-out validation
+# ============================================================
+
+def leave_one_sample_out_cv(
+    samples,
+    role,
+    retained_sample_ids,
+    qa,
+):
+    retained = list(
+        sorted(set(retained_sample_ids))
+    )
+
+    sample_reports = []
+    all_errors = []
+
+    if len(retained) < 5:
+        return {
+            "valid": False,
+            "reason": "menos_de_5_amostras",
+            "samples": [],
+            "overall": error_stats([]),
+        }
+
+    for held_out in retained:
+        train_ids = [
+            sid
+            for sid in retained
+            if sid != held_out
+        ]
+
+        train = role_pairs_from_samples(
+            samples,
+            role,
+            train_ids,
+        )
+
+        test = role_pairs_from_samples(
+            samples,
+            role,
+            [held_out],
+        )
+
+        if (
+            len(train["src"]) < 4
+            or len(test["src"]) == 0
+        ):
+            continue
+
+        H, _ = find_homography_ransac(
+            train["src"],
+            train["dst"],
+            qa,
+        )
+
+        if H is None:
+            continue
+
+        errors = reprojection_errors(
+            H,
+            test["src"],
+            test["dst"],
+        )
+
+        all_errors.extend(
+            [float(x) for x in errors]
+        )
+
+        sample_reports.append({
+            "held_out_sample_id": int(held_out),
+            "train_samples": len(train_ids),
+            "test_points": len(test["src"]),
+            "error": error_stats(errors),
+        })
+
+    return {
+        "valid": len(sample_reports) == len(retained),
+        "samples": sample_reports,
+        "overall": error_stats(all_errors),
+    }
+
+
+# ============================================================
+# Role model
+# ============================================================
+
+def build_role_model(
+    samples,
+    role,
+    specs,
+    qa,
+):
+    rejection = reject_incompatible_samples(
+        samples,
+        role,
+        qa,
+    )
+
+    retained = rejection[
+        "retained_sample_ids"
+    ]
+
+    pairs = role_pairs_from_samples(
+        samples,
+        role,
+        retained,
+    )
+
+    H, inlier_mask = find_homography_ransac(
+        pairs["src"],
+        pairs["dst"],
+        qa,
+    )
+
+    if H is None:
+        return {
+            "status": "bad",
+            "reasons": [
+                "findHomography_failed"
+            ],
+            "role": role,
+            "rejection": rejection,
+        }
+
+    errors = reprojection_errors(
+        H,
+        pairs["src"],
+        pairs["dst"],
+    )
+
+    if inlier_mask is None:
+        inlier = np.ones(
+            len(errors),
+            dtype=bool,
+        )
+    else:
+        inlier = (
+            np.asarray(inlier_mask)
+            .reshape(-1)
+            .astype(bool)
+        )
+
+    inlier_errors = (
+        errors[inlier]
+        if np.any(inlier)
+        else errors
+    )
+
+    fit = error_stats(
+        inlier_errors
+    )
+
+    inlier_ratio = float(
+        np.sum(inlier)
+        / max(len(inlier), 1)
+    )
+
+    unique_ids = sorted(
+        set(pairs["charuco_ids"])
+    )
+
+    dst_size = (
+        specs["rgb"].width,
+        specs["rgb"].height,
+    )
+
+    src_size = (
+        specs[role].width,
+        specs[role].height,
+    )
+
+    coverage = coverage_stats(
+        pairs["dst"],
+        dst_size,
+    )
+
+    overlap = overlap_fraction(
+        H,
+        src_size,
+        dst_size,
+    )
+
+    condition = homography_condition(H)
+
+    cv_report = leave_one_sample_out_cv(
+        samples,
+        role,
+        retained,
+        qa,
+    )
+
+    cv_overall = cv_report["overall"]
+
+    cv_sample_medians = [
+        float(x["error"]["median_px"])
+        for x in cv_report["samples"]
+        if x["error"]["median_px"] is not None
+    ]
+
+    cv_max_sample_median = (
+        max(cv_sample_medians)
+        if cv_sample_medians
+        else None
+    )
+
+    # Inverse validation no conjunto final
+    try:
+        H_inv = np.linalg.inv(H)
+
+        reverse_errors = reprojection_errors(
+            H_inv,
+            pairs["dst"],
+            pairs["src"],
+        )
+
+        reverse_fit = error_stats(
+            reverse_errors
+        )
+
+    except Exception:
+        H_inv = None
+        reverse_fit = error_stats([])
+
+    # QA
+    status = "good"
+    reasons = []
+
+    def check_low(
+        value,
+        warning,
+        bad,
+        name,
+    ):
+        nonlocal status
+
+        if value < bad:
+            status = merge_status(status, "bad")
+            reasons.append(
+                f"{name}_bad:{value:.4f}"
+            )
+
+        elif value < warning:
+            status = merge_status(status, "warning")
+            reasons.append(
+                f"{name}_warning:{value:.4f}"
+            )
+
+    def check_high(
+        value,
+        warning,
+        bad,
+        name,
+    ):
+        nonlocal status
+
+        if value > bad:
+            status = merge_status(status, "bad")
+            reasons.append(
+                f"{name}_bad:{value:.4f}"
+            )
+
+        elif value > warning:
+            status = merge_status(status, "warning")
+            reasons.append(
+                f"{name}_warning:{value:.4f}"
+            )
+
+    if len(retained) < qa["min_samples"]:
+        status = "bad"
+        reasons.append(
+            f"samples_bad:{len(retained)}"
+        )
+
+    if len(pairs["src"]) < qa["min_total_pairs"]:
+        status = "bad"
+        reasons.append(
+            f"pairs_bad:{len(pairs['src'])}"
+        )
+
+    if len(unique_ids) < qa["min_unique_charuco_ids"]:
+        status = "bad"
+        reasons.append(
+            f"unique_ids_bad:{len(unique_ids)}"
+        )
+
+    check_low(
+        inlier_ratio,
+        qa["inlier_ratio_warning"],
+        qa["inlier_ratio_bad"],
+        "inlier_ratio",
+    )
+
+    if fit["median_px"] is not None:
+        check_high(
+            fit["median_px"],
+            qa["fit_median_error_warning_px"],
+            qa["fit_median_error_bad_px"],
+            "fit_median_px",
+        )
+
+    if fit["p95_px"] is not None:
+        check_high(
+            fit["p95_px"],
+            qa["fit_p95_error_warning_px"],
+            qa["fit_p95_error_bad_px"],
+            "fit_p95_px",
+        )
+
+    if not cv_report["valid"]:
+        status = "bad"
+        reasons.append(
+            "cross_validation_invalid"
+        )
+
+    if cv_overall["median_px"] is not None:
+        check_high(
+            cv_overall["median_px"],
+            qa["cv_median_error_warning_px"],
+            qa["cv_median_error_bad_px"],
+            "cv_median_px",
+        )
+
+    if cv_overall["p95_px"] is not None:
+        check_high(
+            cv_overall["p95_px"],
+            qa["cv_p95_error_warning_px"],
+            qa["cv_p95_error_bad_px"],
+            "cv_p95_px",
+        )
+
+    if cv_max_sample_median is not None:
+        check_high(
+            cv_max_sample_median,
+            qa["cv_max_sample_median_warning_px"],
+            qa["cv_max_sample_median_bad_px"],
+            "cv_max_sample_median_px",
+        )
+
+    check_low(
+        coverage["span_x"],
+        qa["coverage_span_x_warning"],
+        qa["coverage_span_x_bad"],
+        "coverage_span_x",
+    )
+
+    check_low(
+        coverage["span_y"],
+        qa["coverage_span_y_warning"],
+        qa["coverage_span_y_bad"],
+        "coverage_span_y",
+    )
+
+    check_low(
+        coverage["hull_area_fraction"],
+        qa["coverage_hull_warning"],
+        qa["coverage_hull_bad"],
+        "coverage_hull",
+    )
+
+    check_low(
+        overlap,
+        qa["overlap_warning"],
+        qa["overlap_bad"],
+        "overlap",
+    )
+
+    check_high(
+        condition,
+        qa["condition_number_warning"],
+        qa["condition_number_bad"],
+        "condition_number",
+    )
+
+    return {
+        "status": status,
+        "reasons": reasons,
+        "role": role,
+        "homography": H.tolist(),
+        "homography_inverse": (
+            H_inv.tolist()
+            if H_inv is not None
+            else None
+        ),
+        "source_size": [
+            int(src_size[0]),
+            int(src_size[1]),
+        ],
+        "destination_size": [
+            int(dst_size[0]),
+            int(dst_size[1]),
+        ],
+        "retained_sample_ids": retained,
+        "rejected_sample_ids": rejection[
+            "rejected_sample_ids"
+        ],
+        "sample_rejection": rejection,
+        "points": int(len(pairs["src"])),
+        "unique_charuco_ids": len(unique_ids),
+        "unique_charuco_id_list": unique_ids,
+        "inliers": int(np.sum(inlier)),
+        "outliers": int(
+            len(inlier) - np.sum(inlier)
+        ),
+        "inlier_ratio": inlier_ratio,
+        "fit_error": fit,
+        "reverse_fit_error_source_px": reverse_fit,
+        "cross_validation": cv_report,
+        "cv_max_sample_median_px": cv_max_sample_median,
+        "coverage": coverage,
+        "overlap_fraction": overlap,
+        "condition_number": condition,
+    }
+
+
+# ============================================================
+# Profile QA
+# ============================================================
+
+def evaluate_profile(
+    samples,
+    specs,
+    qa,
+):
+    models = {
+        role: build_role_model(
+            samples,
+            role,
+            specs,
+            qa,
+        )
+        for role in SPEC_ROLES
+    }
+
+    status = "good"
+    reasons = []
+
+    for role in SPEC_ROLES:
+        model = models[role]
+
+        status = merge_status(
+            status,
+            model["status"],
+        )
+
+        reasons.extend(
+            [
+                f"{role}:{x}"
+                for x in model.get(
+                    "reasons",
+                    [],
+                )
+            ]
+        )
+
+    retained_common = sorted(
+        set(
+            models["re"].get(
+                "retained_sample_ids",
+                [],
+            )
+        )
+        & set(
+            models["nir"].get(
+                "retained_sample_ids",
+                [],
+            )
+        )
+    )
+
+    return {
+        "status": status,
+        "reasons": reasons,
+        "models": models,
+        "common_retained_sample_ids": retained_common,
+        "common_frames_used": len(
+            retained_common
+        ),
+    }
+
+
+# ============================================================
+# Detection / sample capture
+# ============================================================
+
+def detect_triplet(
+    triplet,
+    board,
+    dictionary,
+    detector_params,
+    qa,
+    allow_invert=True,
+):
+    detections = {}
+
+    for role in ROLES:
+        detections[role] = detect_charuco_best(
+            triplet["frames"][role],
+            board,
+            dictionary,
+            detector_params,
+            allow_invert=allow_invert,
+        )
+
+    reasons = []
+
+    for role in ROLES:
+        det = detections[role]
+
+        if det["markers"] < qa["min_markers_each"]:
+            reasons.append(
+                f"{role}:markers={det['markers']}"
+            )
+
+        if det["corners"] < qa["min_charuco_corners_each"]:
+            reasons.append(
+                f"{role}:corners={det['corners']}"
+            )
+
+    pairs = {}
+
+    rgb_points = detections["rgb"]["points"]
+
+    for role in SPEC_ROLES:
+        ids, src, dst = common_pairs(
+            rgb_points,
+            detections[role]["points"],
+        )
+
+        pairs[role] = {
+            "charuco_ids": ids,
+            "src_points": [
+                [float(x), float(y)]
+                for x, y in src
+            ],
+            "dst_points": [
+                [float(x), float(y)]
+                for x, y in dst
+            ],
+        }
+
+        if len(ids) < qa["min_common_corners_each_pair"]:
+            reasons.append(
+                f"{role}:common={len(ids)}"
+            )
+
+    sync_dt_ms = triplet["sync_dt_ms"]
+
+    if (
+        sync_dt_ms is not None
+        and sync_dt_ms > qa["sync_tolerance_ms"]
+    ):
+        reasons.append(
+            f"sync_dt_ms={sync_dt_ms:.2f}"
+        )
+
+    signature = pose_signature(
+        rgb_points,
+        triplet["frames"]["rgb"].shape[:2],
+    )
+
+    return {
+        "valid": len(reasons) == 0,
+        "reasons": reasons,
+        "detections": detections,
+        "pairs": pairs,
+        "pose_signature": asdict(signature),
+        "sync_dt_ms": sync_dt_ms,
+    }
+
+
+# ============================================================
+# Preflight
+# ============================================================
+
+def sample_ae_controls(
+    runtime: AcquisitionRuntime,
+    args,
+):
+    locked = {}
+
+    for role in ROLES:
+        exp_override = getattr(
+            args,
+            f"{role}_exp_us",
+        )
+
+        iso_override = getattr(
+            args,
+            f"{role}_iso",
+        )
+
+        if (
+            exp_override is not None
+            or iso_override is not None
+        ):
+            if (
+                exp_override is None
+                or iso_override is None
+            ):
+                raise ValueError(
+                    f"Override {role}: informe EXP e ISO."
+                )
+
+            locked[role] = LockedControl(
+                role=role,
+                exposure_time_us=int(
+                    exp_override
+                ),
+                sensitivity_iso=int(
+                    iso_override
+                ),
+                source="cli_manual",
+            )
+
+            continue
+
+        exp_values = []
+        iso_values = []
+
+        t0 = time.time()
+
+        while time.time() - t0 < 0.8:
+            fresh = runtime.poll()
+
+            if role not in fresh:
+                time.sleep(0.002)
+                continue
+
+            ctrl = runtime.controls[role]
+
+            exp = safe_int(
+                ctrl.get(
+                    "exposure_time_us"
+                ),
+                None,
+            )
+
+            iso = safe_int(
+                ctrl.get(
+                    "sensitivity_iso"
+                ),
+                None,
+            )
+
+            if exp is not None and exp > 0:
+                exp_values.append(exp)
+
+            if iso is not None and iso > 0:
+                iso_values.append(iso)
+
+        if not exp_values or not iso_values:
+            raise RuntimeError(
+                f"Não consegui ler EXP/ISO de {role.upper()}."
+            )
+
+        locked[role] = LockedControl(
+            role=role,
+            exposure_time_us=int(
+                round(
+                    np.median(exp_values)
+                )
+            ),
+            sensitivity_iso=int(
+                round(
+                    np.median(iso_values)
+                )
+            ),
+            source="ae_snapshot",
+        )
+
+    return locked
+
+
+def preflight_detection(
+    runtime,
+    board,
+    dictionary,
+    detector_params,
+    qa,
+    allow_invert,
+):
+    frames = runtime.frames
+
+    result = {
+        "status": "good",
+        "roles": {},
+        "reasons": [],
+    }
+
+    for role in ROLES:
+        if frames[role] is None:
+            result["status"] = "bad"
+            result["roles"][role] = {
+                "status": "bad",
+                "markers": 0,
+                "corners": 0,
+                "reasons": ["sem_frame"],
+            }
+            continue
+
+        det = detect_charuco_best(
+            frames[role],
+            board,
+            dictionary,
+            detector_params,
+            allow_invert=allow_invert,
+        )
+
+        role_status = "good"
+        reasons = []
+
+        if det["markers"] < qa["min_markers_each"]:
+            role_status = "bad"
+            reasons.append(
+                f"markers={det['markers']}"
+            )
+
+        if det["corners"] < qa["min_charuco_corners_each"]:
+            role_status = "bad"
+            reasons.append(
+                f"corners={det['corners']}"
+            )
+
+        result["roles"][role] = {
+            "status": role_status,
+            "markers": det["markers"],
+            "corners": det["corners"],
+            "equalize": det["equalize"],
+            "invert": det["invert"],
+            "reasons": reasons,
+        }
+
+        result["status"] = merge_status(
+            result["status"],
+            role_status,
+        )
+
+        result["reasons"].extend(
+            [
+                f"{role}:{x}"
+                for x in reasons
+            ]
+        )
+
+    return result
+
+
+# ============================================================
+# Visualization
+# ============================================================
+
+def draw_detected_points(
+    panel,
+    point_by_id,
+    source_shape_hw,
+    color,
+    max_labels=60,
+):
+    if not point_by_id:
+        return
+
+    ph, pw = panel.shape[:2]
+    sh, sw = source_shape_hw
+
+    for idx, (cid, pt) in enumerate(
+        sorted(point_by_id.items())
+    ):
+        x = int(
+            pt[0]
+            * pw
+            / max(sw, 1)
+        )
+
+        y = int(
+            pt[1]
+            * ph
+            / max(sh, 1)
+        )
+
+        cv2.circle(
+            panel,
+            (x, y),
+            3,
+            color,
+            -1,
+        )
+
+        if idx < max_labels:
+            cv2.putText(
+                panel,
+                str(cid),
+                (x + 4, y - 4),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.34,
+                color,
+                1,
+                cv2.LINE_AA,
+            )
+
+
+def provisional_h(
+    samples,
+    role,
+    qa,
+):
+    if not samples:
+        return None
+
+    pairs = role_pairs_from_samples(
+        samples,
+        role,
+    )
+
+    if len(pairs["src"]) < 4:
+        return None
+
+    H, _ = find_homography_ransac(
+        pairs["src"],
+        pairs["dst"],
+        qa,
+    )
+
+    return H
+
+
+def build_overlay(
+    rgb,
+    spec,
+    role,
+    H,
+    alpha=0.45,
+):
+    if rgb is None:
+        return None
+
+    if rgb.ndim == 2:
+        base = cv2.cvtColor(
+            rgb,
+            cv2.COLOR_GRAY2BGR,
+        )
+    else:
+        base = rgb.copy()
+
+    if spec is None or H is None:
+        return base
+
+    dw = base.shape[1]
+    dh = base.shape[0]
+
+    if spec.ndim == 3:
+        spec_gray = cv2.cvtColor(
+            spec,
+            cv2.COLOR_BGR2GRAY,
+        )
+    else:
+        spec_gray = spec
+
+    warped = cv2.warpPerspective(
+        spec_gray,
+        np.asarray(H, dtype=np.float64),
+        (dw, dh),
+        flags=cv2.INTER_LINEAR,
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=0,
+    )
+
+    colored = colorize_spec(
+        warped,
+        role,
+    )
+
+    return cv2.addWeighted(
+        base,
+        1.0 - alpha,
+        colored,
+        alpha,
+        0.0,
+    )
+
+
+def save_sample_preview(
+    path,
+    frames,
+    detection,
+    panel_width=480,
+    panel_height=300,
+):
+    panels = []
+
+    colors = {
+        "rgb": (0, 255, 0),
+        "re": (0, 255, 255),
+        "nir": (255, 255, 0),
+    }
+
+    for role in ROLES:
+        img = frames[role]
+
+        if img.ndim == 2:
+            view = cv2.cvtColor(
+                img,
+                cv2.COLOR_GRAY2BGR,
+            )
+        else:
+            view = img.copy()
+
+        panel = resize_panel(
+            view,
+            panel_width,
+            panel_height,
+        )
+
+        draw_detected_points(
+            panel,
+            detection["detections"][role]["points"],
+            img.shape[:2],
+            colors[role],
+        )
+
+        overlay_hud(
+            panel,
+            [
+                role.upper(),
+                f"markers={detection['detections'][role]['markers']}",
+                f"corners={detection['detections'][role]['corners']}",
+            ],
+            x=8,
+            y=18,
+            font_scale=0.38,
+            line_step=16,
+        )
+
+        panels.append(panel)
+
+    board = np.hstack(panels)
+
+    cv2.imwrite(
+        str(path),
+        board,
+    )
+
+
+def build_ui_board(
+    runtime,
+    specs,
+    samples,
+    selected_role,
+    last_detection,
+    profile_eval,
+    args,
+    qa,
+    message="",
+):
+    pw = int(args.panel_width)
+    ph = int(args.panel_height)
+
+    H_preview = provisional_h(
+        samples,
+        selected_role,
+        qa,
+    )
+
+    overlay_native = build_overlay(
+        runtime.frames["rgb"],
+        runtime.frames[selected_role],
+        selected_role,
+        H_preview,
+        alpha=args.overlay_alpha,
+    )
+
+    panels = {}
+
+    for role in ROLES:
+        img = runtime.frames[role]
+
+        if img is None:
+            panel = np.zeros(
+                (ph, pw, 3),
+                dtype=np.uint8,
+            )
+
+            overlay_hud(
+                panel,
+                [
+                    role.upper(),
+                    "sem frame",
+                ],
+            )
+
+            panels[role] = panel
+            continue
+
+        if img.ndim == 2:
+            if role in SPEC_ROLES:
+                view = colorize_spec(
+                    img,
+                    role,
+                )
+            else:
+                view = cv2.cvtColor(
+                    img,
+                    cv2.COLOR_GRAY2BGR,
+                )
+        else:
+            view = img.copy()
+
+        panel = resize_panel(
+            view,
+            pw,
+            ph,
+        )
+
+        if (
+            last_detection is not None
+            and role
+            in last_detection["detections"]
+        ):
+            colors = {
+                "rgb": (0, 255, 0),
+                "re": (0, 255, 255),
+                "nir": (255, 255, 0),
+            }
+
+            draw_detected_points(
+                panel,
+                last_detection[
+                    "detections"
+                ][role]["points"],
+                img.shape[:2],
+                colors[role],
+            )
+
+        overlay_hud(
+            panel,
+            [
+                f"{role.upper()} | {specs[role].socket_name} | {specs[role].sensor_name}",
+                f"{img.shape[1]}x{img.shape[0]}",
+                f"EXP={runtime.controls[role].get('exposure_time_us')}us "
+                f"ISO={runtime.controls[role].get('sensitivity_iso')}",
+                f"FPS={runtime.fps[role]:.1f}",
+            ],
+            x=8,
+            y=18,
+            font_scale=0.38,
+            line_step=16,
+        )
+
+        panels[role] = panel
+
+    if overlay_native is None:
+        overlay_panel = np.zeros(
+            (ph, pw, 3),
+            dtype=np.uint8,
+        )
+    else:
+        overlay_panel = resize_panel(
+            overlay_native,
+            pw,
+            ph,
+        )
+
+    overlay_hud(
+        overlay_panel,
+        [
+            f"OVERLAY RGB + {selected_role.upper()}",
+            (
+                "H provisional"
+                if H_preview is not None
+                else "H ainda indisponivel"
+            ),
+        ],
+        x=8,
+        y=18,
+        font_scale=0.40,
+        line_step=18,
+    )
+
+    data = np.zeros(
+        (ph, pw, 3),
+        dtype=np.uint8,
+    )
+
+    signatures = [
+        PoseSignature(
+            **sample["pose_signature"]
+        )
+        for sample in samples
+    ]
+
+    rgb_pts_all = []
+
+    for sample in samples:
+        for role in SPEC_ROLES:
+            rgb_pts_all.extend(
+                sample["pairs"][role][
+                    "dst_points"
+                ]
+            )
+
+    coverage = coverage_stats(
+        rgb_pts_all,
+        (
+            specs["rgb"].width,
+            specs["rgb"].height,
+        ),
+    ) if rgb_pts_all else {
+        "span_x": 0.0,
+        "span_y": 0.0,
+        "hull_area_fraction": 0.0,
+    }
+
+    lines = [
+        "HOMOGRAPHY CALIBRATION - PRODUCTION",
+        f"profile={args.profile_name} | depth={args.depth_cm} cm",
+        f"selected overlay={selected_role.upper()}",
+        "",
+        f"samples={len(samples)} / min={qa['min_samples']} rec={qa['recommended_samples']}",
+        f"coverage X={coverage['span_x']*100:.1f}%",
+        f"coverage Y={coverage['span_y']*100:.1f}%",
+        f"coverage hull={coverage['hull_area_fraction']*100:.1f}%",
+        "",
+        "G captura sample | A avalia",
+        "2/3 overlay RE/NIR | TAB alterna",
+        "V limpa sessão | S snapshot",
+        "Q/ESC cancela",
+    ]
+
+    if last_detection is not None:
+        lines.extend([
+            "",
+            f"last sync={last_detection.get('sync_dt_ms')} ms",
+            f"last RGB corners={last_detection['detections']['rgb']['corners']}",
+            f"last RE common={len(last_detection['pairs']['re']['charuco_ids'])}",
+            f"last NIR common={len(last_detection['pairs']['nir']['charuco_ids'])}",
+        ])
+
+    if profile_eval is not None:
+        lines.extend([
+            "",
+            f"LAST EVAL={profile_eval['status'].upper()}",
+        ])
+
+        for role in SPEC_ROLES:
+            model = profile_eval["models"][role]
+
+            if "fit_error" in model:
+                lines.append(
+                    f"{role.upper()}: fit med="
+                    f"{model['fit_error']['median_px']:.2f}px "
+                    f"CV med="
+                    f"{model['cross_validation']['overall']['median_px']:.2f}px "
+                    f"overlap={model['overlap_fraction']*100:.1f}%"
+                )
+
+    overlay_hud(
+        data,
+        lines,
+        x=12,
+        y=20,
+        font_scale=0.40,
+        line_step=17,
+    )
+
+    board = np.vstack([
+        np.hstack([
+            overlay_panel,
+            panels["rgb"],
+        ]),
+        np.hstack([
+            panels["re"],
+            panels["nir"],
+        ]),
+    ])
+
+    # Data mini-panel sobre o overlay inferior direito por composição?
+    # Usamos uma janela maior com data à direita para não esconder imagens.
+    top = np.hstack([
+        overlay_panel,
+        panels["rgb"],
+        data,
+    ])
+
+    bottom = np.hstack([
+        panels["re"],
+        panels["nir"],
+        np.zeros_like(data),
+    ])
+
+    board = np.vstack([
+        top,
+        bottom,
+    ])
+
+    if message:
+        cv2.putText(
+            board,
+            message,
+            (
+                16,
+                board.shape[0] - 14,
+            ),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.55,
+            (0, 255, 0),
+            2,
+            cv2.LINE_AA,
+        )
+
+    return board
+
+
+# ============================================================
+# Active artifact / module fragment
+# ============================================================
+
+def hardware_signature(specs):
+    return {
+        role: {
+            "socket": specs[role].socket_name,
+            "sensor": specs[role].sensor_name,
+            "size": [
+                specs[role].width,
+                specs[role].height,
+            ],
+        }
+        for role in ROLES
+    }
+
+
+def profile_to_module_fragment(
+    profile_name,
+    profile_payload,
+    baseline_mm,
+):
+    return {
+        "fusion_config": {
+            "alignment_mode": "homography",
+            "baseline_mm": float(
+                baseline_mm
+            ),
+            "reference_camera": "rgb",
+            "homography_profile": profile_name,
+            "homography_profile_by_role": {
+                "re": profile_name,
+                "nir": profile_name,
+            },
+            "homography_profiles": {
+                profile_name: {
+                    "description": profile_payload[
+                        "description"
+                    ],
+                    "depth": profile_payload[
+                        "depth_cm"
+                    ],
+                    "depth_unit": "cm",
+                    "homography_source": (
+                        "charuco_native_multi_sample_group_cv"
+                    ),
+                    "coordinate_space": COORDINATE_SPACE,
+                    "reference_size": profile_payload[
+                        "coordinate_spaces"
+                    ]["rgb"]["size"],
+                    "source_size_by_role": {
+                        "re": profile_payload[
+                            "coordinate_spaces"
+                        ]["re"]["size"],
+                        "nir": profile_payload[
+                            "coordinate_spaces"
+                        ]["nir"]["size"],
+                    },
+                    # Compatibilidade semântica com assembler antigo:
+                    # agora significa TAMANHO DA REFERÊNCIA RGB.
+                    "homography_calibration_size": profile_payload[
+                        "coordinate_spaces"
+                    ]["rgb"]["size"],
+                    "homography_stats": profile_payload[
+                        "homography_stats"
+                    ],
+                    "homographies": {
+                        "re_to_rgb": profile_payload[
+                            "homographies"
+                        ]["re_to_rgb"],
+                        "nir_to_rgb": profile_payload[
+                            "homographies"
+                        ]["nir_to_rgb"],
+                    },
+                }
+            },
+        }
+    }
+
+
+def merge_all_profiles_fragment(
+    active_data,
+):
+    profiles = active_data.get(
+        "profiles",
+        {},
+    )
+
+    recommended = active_data.get(
+        "recommended_profile"
+    )
+
+    fusion_profiles = {}
+
+    for name, profile in profiles.items():
+        one = profile_to_module_fragment(
+            name,
+            profile,
+            active_data["baseline_mm"],
+        )
+
+        fusion_profiles[name] = (
+            one["fusion_config"][
+                "homography_profiles"
+            ][name]
+        )
+
+    return {
+        "fusion_config": {
+            "alignment_mode": "homography",
+            "baseline_mm": float(
+                active_data["baseline_mm"]
+            ),
+            "reference_camera": "rgb",
+            "homography_profile": recommended,
+            "homography_profile_by_role": {
+                "re": recommended,
+                "nir": recommended,
+            },
+            "homography_profiles": fusion_profiles,
+        }
+    }
+
+
+def create_or_update_active(
+    active_path: Path,
+    candidate_report: dict,
+    profile_payload: dict,
+    args,
+    specs,
+):
+    active = load_json(
+        active_path
+    )
+
+    signature = hardware_signature(
+        specs
+    )
+
+    if active:
+        if active.get("schema") != SCHEMA:
+            raise RuntimeError(
+                f"Arquivo ativo existente usa schema "
+                f"{active.get('schema')!r}, esperado {SCHEMA!r}. "
+                "Use outro --active-json ou arquive o antigo."
+            )
+
+        if (
+            active.get(
+                "hardware_signature"
+            )
+            != signature
+        ):
+            raise RuntimeError(
+                "Homography ativa pertence a outro conjunto de sensores/resoluções. "
+                "Não é seguro misturar perfis antigos com o hardware atual."
+            )
+
+        if (
+            active.get("coordinate_space")
+            != COORDINATE_SPACE
+        ):
+            raise RuntimeError(
+                "Coordinate space do ativo é incompatível."
+            )
+
+    else:
+        active = {
+            "schema": SCHEMA,
+            "created_at": now_str(),
+            "hardware_signature": signature,
+            "coordinate_space": COORDINATE_SPACE,
+            "reference_camera": "rgb",
+            "baseline_mm": float(
+                args.baseline_mm
+            ),
+            "profiles": {},
+            "recommended_profile": None,
+            "module_params_fragment": {},
+        }
+
+    profile_name = args.profile_name
+
+    active["updated_at"] = now_str()
+    active["profiles"][profile_name] = (
+        profile_payload
+    )
+
+    if (
+        active["recommended_profile"] is None
+        or args.set_recommended
+    ):
+        active["recommended_profile"] = (
+            profile_name
+        )
+
+    active["module_params_fragment"] = (
+        merge_all_profiles_fragment(
+            active
+        )
+    )
+
+    active["last_candidate_session"] = (
+        candidate_report["session_id"]
+    )
+
+    ensure_dir(
+        active_path.parent
+    )
+
+    if active_path.exists():
+        backup = active_path.with_name(
+            active_path.stem
+            + f".backup_{session_stamp()}"
+            + active_path.suffix
+        )
+
+        shutil.copy2(
+            active_path,
+            backup,
+        )
+
+    save_json_atomic(
+        active_path,
+        active,
+    )
+
+    return active
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Homography calibration de produção por ChArUco "
+            "para OAK-FFC-3P OV9782/AR0234 + 2x OV9282."
+        ),
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    # Perfil
+    parser.add_argument(
+        "--profile-name",
+        default="media",
+    )
+
+    parser.add_argument(
+        "--depth-cm",
+        type=float,
+        default=66.0,
+    )
+
+    parser.add_argument(
+        "--profile-description",
+        default="Plano de calibração ChArUco.",
+    )
+
+    parser.add_argument(
+        "--baseline-mm",
+        type=float,
+        default=75.0,
+    )
+
+    parser.add_argument(
+        "--set-recommended",
+        action="store_true",
+        help=(
+            "Define este perfil como recomendado no JSON ativo."
+        ),
+    )
+
+    # Hardware
+    parser.add_argument(
+        "--mx-id",
+        default=None,
+    )
+
+    parser.add_argument(
+        "--fps",
+        type=float,
+        default=20.0,
+    )
+
+    parser.add_argument(
+        "--isp-sharpness",
+        type=int,
+        default=0,
+    )
+
+    parser.add_argument(
+        "--isp-luma-denoise",
+        type=int,
+        default=0,
+    )
+
+    parser.add_argument(
+        "--isp-chroma-denoise",
+        type=int,
+        default=0,
+    )
+
+    # Controles manuais opcionais
+    for role in ROLES:
+        parser.add_argument(
+            f"--{role}-exp-us",
+            type=int,
+            default=None,
+        )
+
+        parser.add_argument(
+            f"--{role}-iso",
+            type=int,
+            default=None,
+        )
+
+    parser.add_argument(
+        "--lock-settle-frames",
+        type=int,
+        default=8,
+    )
+
+    # ChArUco
+    parser.add_argument(
+        "--charuco-dictionary",
+        default="DICT_4X4_50",
+    )
+
+    parser.add_argument(
+        "--charuco-squares-x",
+        type=int,
+        default=13,
+    )
+
+    parser.add_argument(
+        "--charuco-squares-y",
+        type=int,
+        default=7,
+    )
+
+    parser.add_argument(
+        "--charuco-square-length",
+        type=float,
+        default=0.031,
+    )
+
+    parser.add_argument(
+        "--charuco-marker-length",
+        type=float,
+        default=0.023,
+    )
+
+    parser.add_argument(
+        "--no-auto-invert",
+        action="store_true",
+        help=(
+            "Não tenta versões invertidas na detecção RE/NIR/RGB."
+        ),
+    )
+
+    # Política
+    parser.add_argument(
+        "--promote-warning",
+        action="store_true",
+        help=(
+            "Permite promover perfil WARNING. BAD nunca promove."
+        ),
+    )
+
+    # UI
+    parser.add_argument(
+        "--panel-width",
+        type=int,
+        default=560,
+    )
+
+    parser.add_argument(
+        "--panel-height",
+        type=int,
+        default=350,
+    )
+
+    parser.add_argument(
+        "--overlay-alpha",
+        type=float,
+        default=0.45,
+    )
+
+    # Output
+    parser.add_argument(
+        "--candidate-root",
+        default="calibration/homography_candidates",
+    )
+
+    parser.add_argument(
+        "--active-json",
+        default="calibration/homography_calibration_v4.json",
+    )
+
+    # Rastreabilidade
+    parser.add_argument(
+        "--module-id",
+        default="",
+    )
+
+    parser.add_argument(
+        "--operator",
+        default="",
+    )
+
+    parser.add_argument(
+        "--notes",
+        default="",
+    )
+
+    args = parser.parse_args()
+
+    qa = dict(DEFAULT_QA)
+
+    sid = session_stamp()
+    candidate_dir = (
+        Path(args.candidate_root)
+        / sid
+    )
+
+    sample_dir = (
+        candidate_dir
+        / "samples"
+    )
+
+    snapshot_dir = (
+        candidate_dir
+        / "snapshots"
+    )
+
+    candidate_report_path = (
+        candidate_dir
+        / "report.json"
+    )
+
+    ensure_dir(sample_dir)
+    ensure_dir(snapshot_dir)
+
+    require_aruco()
+
+    dictionary = get_aruco_dictionary(
+        args.charuco_dictionary
+    )
+
+    board = create_charuco_board(
+        args.charuco_squares_x,
+        args.charuco_squares_y,
+        args.charuco_square_length,
+        args.charuco_marker_length,
+        dictionary,
+    )
+
+    detector_params = create_detector_params()
+
+    camera_rows = []
+    specs = {}
+    report = None
+    samples = []
+    last_detection = None
+    profile_eval = None
+    actual_mx = None
+    usb_speed = None
+
+    window_name = (
+        "Homography Calibration - Production"
+    )
+
+    cv2.namedWindow(
+        window_name,
+        cv2.WINDOW_NORMAL,
+    )
+
+    try:
+        # ----------------------------------------------------
+        # Hardware discovery
+        # ----------------------------------------------------
+
+        (
+            camera_rows,
+            actual_mx,
+            usb_speed,
+        ) = discover_cameras(
+            args.mx_id
+        )
+
+        topology = validate_product_topology(
+            camera_rows
+        )
+
+        specs = build_specs(
+            camera_rows
+        )
+
+        print("=" * 88)
+        print("HOMOGRAPHY CALIBRATION - PRODUCTION")
+        print(f"Profile : {args.profile_name}")
+        print(f"Depth   : {args.depth_cm} cm")
+        print(f"MX ID   : {actual_mx}")
+        print(f"USB     : {usb_speed}")
+        print("-" * 88)
+
+        for role in ROLES:
+            spec = specs[role]
+
+            print(
+                f"{spec.socket_name} -> "
+                f"{role.upper():3s} | "
+                f"{spec.sensor_name:8s} | "
+                f"{spec.width}x{spec.height} | "
+                f"{spec.source}"
+            )
+
+        print("-" * 88)
+        print(
+            f"H_RE : 1280x800 -> "
+            f"{specs['rgb'].width}x{specs['rgb'].height}"
+        )
+        print(
+            f"H_NIR: 1280x800 -> "
+            f"{specs['rgb'].width}x{specs['rgb'].height}"
+        )
+        print("=" * 88)
+
+        pipeline, isp_settings = (
+            build_pipeline(
+                specs,
+                args,
+            )
+        )
+
+        device_info = (
+            dai.DeviceInfo(actual_mx)
+            if actual_mx
+            else None
+        )
+
+        device_ctx = (
+            dai.Device(
+                pipeline,
+                device_info,
+            )
+            if device_info is not None
+            else dai.Device(pipeline)
+        )
+
+        report = {
+            "schema": SCHEMA,
+            "session_id": sid,
+            "created_at": now_str(),
+            "status": "running",
+            "promoted": False,
+            "depthai_version": getattr(
+                dai,
+                "__version__",
+                "unknown",
+            ),
+            "opencv_version": cv2.__version__,
+            "device_mx_id": actual_mx,
+            "usb_speed": usb_speed,
+            "topology": topology,
+            "camera_inventory": camera_rows,
+            "hardware_signature": hardware_signature(
+                specs
+            ),
+            "coordinate_space": COORDINATE_SPACE,
+            "coordinate_spaces": {
+                role: {
+                    "role": role,
+                    "sensor": specs[role].sensor_name,
+                    "size": [
+                        specs[role].width,
+                        specs[role].height,
+                    ],
+                    "stream_source": specs[role].source,
+                    "external_undistort_applied": False,
+                }
+                for role in ROLES
+            },
+            "profile": {
+                "name": args.profile_name,
+                "depth_cm": float(
+                    args.depth_cm
+                ),
+                "description": args.profile_description,
+                "same_physical_plane_required": True,
+            },
+            "baseline_mm": float(
+                args.baseline_mm
+            ),
+            "charuco": {
+                "dictionary": args.charuco_dictionary,
+                "squares_x": int(
+                    args.charuco_squares_x
+                ),
+                "squares_y": int(
+                    args.charuco_squares_y
+                ),
+                "square_length": float(
+                    args.charuco_square_length
+                ),
+                "marker_length": float(
+                    args.charuco_marker_length
+                ),
+                "auto_invert": not args.no_auto_invert,
+            },
+            "isp_measurement_settings": isp_settings,
+            "qa_thresholds": qa,
+            "traceability": {
+                "module_id": args.module_id or None,
+                "operator": args.operator or None,
+            },
+            "notes": args.notes or "",
+            "locked_controls": None,
+            "preflight": None,
+            "samples": [],
+            "evaluation": None,
+            "module_params_fragment": None,
+            "candidate_dir": str(
+                candidate_dir
+            ),
+            "active_json": args.active_json,
+        }
+
+        with device_ctx as device:
+            runtime = AcquisitionRuntime(
+                device,
+                specs,
+                args,
+                qa,
+            )
+
+            runtime.wait_all()
+
+            # ------------------------------------------------
+            # Preflight
+            # ------------------------------------------------
+
+            print("")
+            print("[PRE-FLIGHT]")
+            print(
+                "Mostre o ChArUco às três câmeras no plano que será calibrado."
+            )
+            print(
+                "ENTER congela exposição quando as três detecções estiverem GOOD."
+            )
+
+            while True:
+                runtime.poll()
+
+                preflight = preflight_detection(
+                    runtime,
+                    board,
+                    dictionary,
+                    detector_params,
+                    qa,
+                    allow_invert=(
+                        not args.no_auto_invert
+                    ),
+                )
+
+                panels = []
+
+                for role in ROLES:
+                    img = runtime.frames[role]
+
+                    if img.ndim == 2:
+                        view = cv2.cvtColor(
+                            img,
+                            cv2.COLOR_GRAY2BGR,
+                        )
+                    else:
+                        view = img.copy()
+
+                    panel = resize_panel(
+                        view,
+                        args.panel_width,
+                        args.panel_height,
+                    )
+
+                    p = preflight["roles"][role]
+
+                    overlay_hud(
+                        panel,
+                        [
+                            f"{role.upper()} | {specs[role].sensor_name}",
+                            f"PREFLIGHT={p['status'].upper()}",
+                            f"markers={p['markers']} corners={p['corners']}",
+                            f"EXP={runtime.controls[role].get('exposure_time_us')}us "
+                            f"ISO={runtime.controls[role].get('sensitivity_iso')}",
+                        ],
+                        x=10,
+                        y=22,
+                        font_scale=0.42,
+                        line_step=18,
+                    )
+
+                    panels.append(panel)
+
+                data = np.zeros(
+                    (
+                        args.panel_height,
+                        args.panel_width,
+                        3,
+                    ),
+                    dtype=np.uint8,
+                )
+
+                overlay_hud(
+                    data,
+                    [
+                        "HOMOGRAPHY PRE-FLIGHT",
+                        "",
+                        f"GLOBAL={preflight['status'].upper()}",
+                        "",
+                        "Mesmo plano físico durante toda a sessão.",
+                        "Mova lateralmente, não mude altura/inclinação.",
+                        "",
+                        "ENTER = lock EXP/ISO",
+                        "Q/ESC = cancelar",
+                        "",
+                        *[
+                            f"! {x}"
+                            for x in preflight[
+                                "reasons"
+                            ][:8]
+                        ],
+                    ],
+                    x=14,
+                    y=28,
+                    font_scale=0.44,
+                    line_step=20,
+                )
+
+                pre_board = np.vstack([
+                    np.hstack([
+                        panels[0],
+                        panels[1],
+                    ]),
+                    np.hstack([
+                        panels[2],
+                        data,
+                    ]),
+                ])
+
+                cv2.imshow(
+                    window_name,
+                    pre_board,
+                )
+
+                k = cv2.waitKey(1) & 0xFF
+
+                if k in (
+                    ord("q"),
+                    ord("Q"),
+                    27,
+                ):
+                    raise KeyboardInterrupt(
+                        "Cancelado no preflight."
+                    )
+
+                if k in (13, 10):
+                    if preflight["status"] != "good":
+                        print(
+                            "[BLOCK] Preflight ainda não está GOOD."
+                        )
+                        continue
+
+                    locked = sample_ae_controls(
+                        runtime,
+                        args,
+                    )
+
+                    runtime.send_manual(
+                        locked
+                    )
+
+                    runtime.verify_manual(
+                        locked,
+                        settle_frames=args.lock_settle_frames,
+                    )
+
+                    report["preflight"] = (
+                        preflight
+                    )
+
+                    report["locked_controls"] = {
+                        role: asdict(
+                            locked[role]
+                        )
+                        for role in ROLES
+                    }
+
+                    break
+
+                time.sleep(0.002)
+
+            # ------------------------------------------------
+            # Capture loop
+            # ------------------------------------------------
+
+            selected_role = "re"
+            last_message = ""
+            last_message_t = 0.0
+
+            print("")
+            print("[CAPTURE]")
+            print(
+                "G = captura. Mova o chart pelo MESMO plano entre samples."
+            )
+            print(
+                f"Meta: >= {qa['recommended_samples']} samples, boa cobertura do FOV."
+            )
+
+            while True:
+                runtime.poll()
+
+                msg = (
+                    last_message
+                    if (
+                        last_message
+                        and time.time()
+                        - last_message_t
+                        < 4.0
+                    )
+                    else ""
+                )
+
+                board_ui = build_ui_board(
+                    runtime,
+                    specs,
+                    samples,
+                    selected_role,
+                    last_detection,
+                    profile_eval,
+                    args,
+                    qa,
+                    message=msg,
+                )
+
+                cv2.imshow(
+                    window_name,
+                    board_ui,
+                )
+
+                k = cv2.waitKey(1) & 0xFF
+
+                if k in (
+                    ord("q"),
+                    ord("Q"),
+                    27,
+                ):
+                    raise KeyboardInterrupt(
+                        "Sessão cancelada."
+                    )
+
+                elif k == ord("2"):
+                    selected_role = "re"
+                    last_message = (
+                        "Overlay: RGB + RE"
+                    )
+                    last_message_t = time.time()
+
+                elif k == ord("3"):
+                    selected_role = "nir"
+                    last_message = (
+                        "Overlay: RGB + NIR"
+                    )
+                    last_message_t = time.time()
+
+                elif k == 9:
+                    selected_role = (
+                        "nir"
+                        if selected_role == "re"
+                        else "re"
+                    )
+                    last_message = (
+                        f"Overlay: RGB + {selected_role.upper()}"
+                    )
+                    last_message_t = time.time()
+
+                elif k in (
+                    ord("v"),
+                    ord("V"),
+                ):
+                    samples.clear()
+                    report["samples"] = []
+                    last_detection = None
+                    profile_eval = None
+
+                    last_message = (
+                        "Todas as amostras foram limpas."
+                    )
+                    last_message_t = time.time()
+
+                    save_json_atomic(
+                        candidate_report_path,
+                        report,
+                    )
+
+                elif k in (
+                    ord("s"),
+                    ord("S"),
+                ):
+                    snap_path = (
+                        snapshot_dir
+                        / (
+                            "board_"
+                            + session_stamp()
+                            + ".png"
+                        )
+                    )
+
+                    cv2.imwrite(
+                        str(snap_path),
+                        board_ui,
+                    )
+
+                    last_message = (
+                        f"Snapshot salvo: {snap_path.name}"
+                    )
+                    last_message_t = time.time()
+
+                elif k in (
+                    ord("g"),
+                    ord("G"),
+                ):
+                    try:
+                        triplet = (
+                            runtime.fresh_triplet(
+                                timeout=3.0
+                            )
+                        )
+
+                        detection = (
+                            detect_triplet(
+                                triplet,
+                                board,
+                                dictionary,
+                                detector_params,
+                                qa,
+                                allow_invert=(
+                                    not args.no_auto_invert
+                                ),
+                            )
+                        )
+
+                        last_detection = (
+                            detection
+                        )
+
+                        if not detection["valid"]:
+                            last_message = (
+                                "REJEITADO: "
+                                + " | ".join(
+                                    detection[
+                                        "reasons"
+                                    ]
+                                )
+                            )
+                            last_message_t = (
+                                time.time()
+                            )
+                            continue
+
+                        signature = PoseSignature(
+                            **detection[
+                                "pose_signature"
+                            ]
+                        )
+
+                        prior = [
+                            PoseSignature(
+                                **x[
+                                    "pose_signature"
+                                ]
+                            )
+                            for x in samples
+                        ]
+
+                        duplicate, duplicate_info = (
+                            is_duplicate_pose(
+                                signature,
+                                prior,
+                                qa,
+                            )
+                        )
+
+                        if duplicate:
+                            last_message = (
+                                "REJEITADO: pose muito parecida; "
+                                "mova o ChArUco para outra região."
+                            )
+                            last_message_t = (
+                                time.time()
+                            )
+                            continue
+
+                        sample_id = (
+                            len(samples) + 1
+                        )
+
+                        sample = {
+                            "sample_id": sample_id,
+                            "captured_at": now_str(),
+                            "sync_dt_ms": detection[
+                                "sync_dt_ms"
+                            ],
+                            "pose_signature": detection[
+                                "pose_signature"
+                            ],
+                            "frame_controls": triplet[
+                                "controls"
+                            ],
+                            "detections": {
+                                role: {
+                                    "markers": detection[
+                                        "detections"
+                                    ][role][
+                                        "markers"
+                                    ],
+                                    "corners": detection[
+                                        "detections"
+                                    ][role][
+                                        "corners"
+                                    ],
+                                    "equalize": detection[
+                                        "detections"
+                                    ][role][
+                                        "equalize"
+                                    ],
+                                    "invert": detection[
+                                        "detections"
+                                    ][role][
+                                        "invert"
+                                    ],
+                                }
+                                for role in ROLES
+                            },
+                            "pairs": detection[
+                                "pairs"
+                            ],
+                        }
+
+                        samples.append(
+                            sample
+                        )
+
+                        preview_path = (
+                            sample_dir
+                            / f"sample_{sample_id:03d}.png"
+                        )
+
+                        save_sample_preview(
+                            preview_path,
+                            triplet["frames"],
+                            detection,
+                        )
+
+                        sample[
+                            "preview_png"
+                        ] = str(preview_path)
+
+                        report["samples"] = (
+                            samples
+                        )
+
+                        save_json_atomic(
+                            candidate_report_path,
+                            report,
+                        )
+
+                        last_message = (
+                            f"ACCEPTED sample #{sample_id:03d} | "
+                            f"RE={len(sample['pairs']['re']['charuco_ids'])} "
+                            f"NIR={len(sample['pairs']['nir']['charuco_ids'])} "
+                            f"sync={sample['sync_dt_ms']}"
+                        )
+                        last_message_t = (
+                            time.time()
+                        )
+
+                        print(
+                            "[SAMPLE] "
+                            + last_message
+                        )
+
+                    except Exception as exc:
+                        last_message = (
+                            f"Captura rejeitada: {exc}"
+                        )
+                        last_message_t = (
+                            time.time()
+                        )
+
+                elif k in (
+                    ord("a"),
+                    ord("A"),
+                ):
+                    if (
+                        len(samples)
+                        < qa["min_samples"]
+                    ):
+                        last_message = (
+                            f"Precisa >= {qa['min_samples']} samples. "
+                            f"Atual={len(samples)}"
+                        )
+                        last_message_t = (
+                            time.time()
+                        )
+                        continue
+
+                    profile_eval = (
+                        evaluate_profile(
+                            samples,
+                            specs,
+                            qa,
+                        )
+                    )
+
+                    report["evaluation"] = (
+                        profile_eval
+                    )
+
+                    save_json_atomic(
+                        candidate_report_path,
+                        report,
+                    )
+
+                    if (
+                        profile_eval["status"]
+                        == "bad"
+                    ):
+                        last_message = (
+                            "PROFILE BAD: "
+                            + " | ".join(
+                                profile_eval[
+                                    "reasons"
+                                ][:4]
+                            )
+                        )
+                        last_message_t = (
+                            time.time()
+                        )
+
+                        print(
+                            "[EVAL BAD] "
+                            + last_message
+                        )
+
+                        continue
+
+                    if (
+                        profile_eval["status"]
+                        == "warning"
+                        and not args.promote_warning
+                    ):
+                        last_message = (
+                            "PROFILE WARNING. "
+                            "Não promove sem --promote-warning."
+                        )
+                        last_message_t = (
+                            time.time()
+                        )
+
+                        print(
+                            "[EVAL WARNING] "
+                            + " | ".join(
+                                profile_eval[
+                                    "reasons"
+                                ]
+                            )
+                        )
+
+                        continue
+
+                    # ----------------------------------------
+                    # PASS / profile payload
+                    # ----------------------------------------
+
+                    re_model = (
+                        profile_eval[
+                            "models"
+                        ]["re"]
+                    )
+
+                    nir_model = (
+                        profile_eval[
+                            "models"
+                        ]["nir"]
+                    )
+
+                    homography_stats = {
+                        "re_total_points": re_model[
+                            "points"
+                        ],
+                        "re_inliers": re_model[
+                            "inliers"
+                        ],
+                        "re_inlier_pct": (
+                            re_model[
+                                "inlier_ratio"
+                            ]
+                            * 100.0
+                        ),
+                        "nir_total_points": nir_model[
+                            "points"
+                        ],
+                        "nir_inliers": nir_model[
+                            "inliers"
+                        ],
+                        "nir_inlier_pct": (
+                            nir_model[
+                                "inlier_ratio"
+                            ]
+                            * 100.0
+                        ),
+                        "re_frames_used": len(
+                            re_model[
+                                "retained_sample_ids"
+                            ]
+                        ),
+                        "nir_frames_used": len(
+                            nir_model[
+                                "retained_sample_ids"
+                            ]
+                        ),
+                        "common_frames_used": (
+                            profile_eval[
+                                "common_frames_used"
+                            ]
+                        ),
+                        "re_fit_median_error_px": (
+                            re_model[
+                                "fit_error"
+                            ]["median_px"]
+                        ),
+                        "nir_fit_median_error_px": (
+                            nir_model[
+                                "fit_error"
+                            ]["median_px"]
+                        ),
+                        "re_cv_median_error_px": (
+                            re_model[
+                                "cross_validation"
+                            ]["overall"][
+                                "median_px"
+                            ]
+                        ),
+                        "nir_cv_median_error_px": (
+                            nir_model[
+                                "cross_validation"
+                            ]["overall"][
+                                "median_px"
+                            ]
+                        ),
+                        "re_overlap_pct": (
+                            re_model[
+                                "overlap_fraction"
+                            ]
+                            * 100.0
+                        ),
+                        "nir_overlap_pct": (
+                            nir_model[
+                                "overlap_fraction"
+                            ]
+                            * 100.0
+                        ),
+                        "overlap_common_pct": (
+                            min(
+                                re_model[
+                                    "overlap_fraction"
+                                ],
+                                nir_model[
+                                    "overlap_fraction"
+                                ],
+                            )
+                            * 100.0
+                        ),
+                    }
+
+                    profile_payload = {
+                        "profile_name": args.profile_name,
+                        "created_at": now_str(),
+                        "description": args.profile_description,
+                        "depth_cm": float(
+                            args.depth_cm
+                        ),
+                        "same_physical_plane_required": True,
+                        "coordinate_space": COORDINATE_SPACE,
+                        "coordinate_spaces": {
+                            role: {
+                                "size": [
+                                    specs[role].width,
+                                    specs[role].height,
+                                ],
+                                "sensor": specs[role].sensor_name,
+                                "stream_source": specs[role].source,
+                                "external_undistort_applied": False,
+                            }
+                            for role in ROLES
+                        },
+                        "homography_source": (
+                            "charuco_native_multi_sample_group_cv"
+                        ),
+                        "homography_stats": homography_stats,
+                        "homographies": {
+                            "re_to_rgb": re_model[
+                                "homography"
+                            ],
+                            "nir_to_rgb": nir_model[
+                                "homography"
+                            ],
+                        },
+                        "quality": profile_eval,
+                        "charuco": report[
+                            "charuco"
+                        ],
+                        "locked_controls": report[
+                            "locked_controls"
+                        ],
+                        "source_session_id": sid,
+                    }
+
+                    report[
+                        "profile_payload"
+                    ] = profile_payload
+
+                    report[
+                        "module_params_fragment"
+                    ] = (
+                        profile_to_module_fragment(
+                            args.profile_name,
+                            profile_payload,
+                            args.baseline_mm,
+                        )
+                    )
+
+                    report["status"] = (
+                        "pass"
+                        if profile_eval[
+                            "status"
+                        ] == "good"
+                        else "warning_promoted"
+                    )
+
+                    report["finished_at"] = (
+                        now_str()
+                    )
+
+                    report["promoted"] = True
+
+                    save_json_atomic(
+                        candidate_report_path,
+                        report,
+                    )
+
+                    active = (
+                        create_or_update_active(
+                            Path(
+                                args.active_json
+                            ),
+                            report,
+                            profile_payload,
+                            args,
+                            specs,
+                        )
+                    )
+
+                    report[
+                        "active_json_sha256"
+                    ] = sha256_file(
+                        args.active_json
+                    )
+
+                    save_json_atomic(
+                        candidate_report_path,
+                        report,
+                    )
+
+                    marker = (
+                        candidate_dir
+                        / (
+                            "PASS.txt"
+                            if profile_eval[
+                                "status"
+                            ] == "good"
+                            else "PASS_WARNING.txt"
+                        )
+                    )
+
+                    marker.write_text(
+                        (
+                            f"status={report['status']}\n"
+                            f"profile={args.profile_name}\n"
+                            f"depth_cm={args.depth_cm}\n"
+                            f"active_json={args.active_json}\n"
+                        ),
+                        encoding="utf-8",
+                    )
+
+                    print("")
+                    print("=" * 88)
+                    print(
+                        "[PASS] HOMOGRAPHY PROFILE PROMOVIDO"
+                    )
+                    print(
+                        f"[PROFILE] {args.profile_name} @ {args.depth_cm} cm"
+                    )
+                    print(
+                        f"[ACTIVE] {args.active_json}"
+                    )
+                    print(
+                        f"[CANDIDATE] {candidate_dir}"
+                    )
+                    print("=" * 88)
+
+                    final = np.zeros(
+                        (720, 1280, 3),
+                        dtype=np.uint8,
+                    )
+
+                    final_lines = [
+                        "HOMOGRAPHY CALIBRATION - PASS",
+                        "",
+                        f"profile={args.profile_name}",
+                        f"depth={args.depth_cm} cm",
+                        "",
+                        f"RE fit med={re_model['fit_error']['median_px']:.2f}px",
+                        f"RE CV med={re_model['cross_validation']['overall']['median_px']:.2f}px",
+                        f"RE overlap={re_model['overlap_fraction']*100:.1f}%",
+                        "",
+                        f"NIR fit med={nir_model['fit_error']['median_px']:.2f}px",
+                        f"NIR CV med={nir_model['cross_validation']['overall']['median_px']:.2f}px",
+                        f"NIR overlap={nir_model['overlap_fraction']*100:.1f}%",
+                        "",
+                        f"Active: {args.active_json}",
+                        "",
+                        "Qualquer tecla fecha.",
+                    ]
+
+                    overlay_hud(
+                        final,
+                        final_lines,
+                        x=46,
+                        y=62,
+                        font_scale=0.68,
+                        line_step=31,
+                    )
+
+                    cv2.imshow(
+                        window_name,
+                        final,
+                    )
+
+                    cv2.waitKey(0)
+
+                    break
+
+                time.sleep(0.001)
+
+    except KeyboardInterrupt as exc:
+        if report is None:
+            report = {
+                "schema": SCHEMA,
+                "session_id": sid,
+                "created_at": now_str(),
+                "coordinate_space": COORDINATE_SPACE,
+            }
+
+        report["status"] = "cancelled"
+        report["finished_at"] = now_str()
+        report["promoted"] = False
+        report["error"] = str(exc)
+        report["samples"] = samples
+
+        try:
+            save_json_atomic(
+                candidate_report_path,
+                report,
+            )
+
+            (
+                candidate_dir
+                / "CANCELLED.txt"
+            ).write_text(
+                f"reason={exc}\n",
+                encoding="utf-8",
+            )
+
+        except Exception:
+            pass
+
+        print(f"[CANCELADO] {exc}")
+        print(
+            "[SAFE] Homography ativa anterior preservada."
+        )
+
+    except Exception as exc:
+        if report is None:
+            report = {
+                "schema": SCHEMA,
+                "session_id": sid,
+                "created_at": now_str(),
+                "coordinate_space": COORDINATE_SPACE,
+            }
+
+        report["status"] = "error"
+        report["finished_at"] = now_str()
+        report["promoted"] = False
+        report["error"] = (
+            f"{type(exc).__name__}: {exc}"
+        )
+        report["samples"] = samples
+
+        try:
+            save_json_atomic(
+                candidate_report_path,
+                report,
+            )
+
+            (
+                candidate_dir
+                / "FAIL.txt"
+            ).write_text(
+                (
+                    f"error={type(exc).__name__}: {exc}\n"
+                ),
+                encoding="utf-8",
+            )
+
+        except Exception:
+            pass
+
+        print("")
+        print("=" * 88)
+        print(
+            "[ERRO] HOMOGRAPHY CALIBRATION ABORTADA"
+        )
+        print(
+            f"{type(exc).__name__}: {exc}"
+        )
+        print(
+            "[SAFE] Homography ativa anterior preservada."
+        )
+        print("=" * 88)
+
+        raise
+
+    finally:
+        cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/_8_build_module_params.py b/Python/OAK/datasets/oak-fcc-3/utils/_8_build_module_params.py
new file mode 100644
index 000000000..036f7d53f
--- /dev/null
+++ b/Python/OAK/datasets/oak-fcc-3/utils/_8_build_module_params.py
@@ -0,0 +1,2869 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+module_params_assembler_production.py
+=====================================
+
+Assembler FINAL de produção do module_params do módulo multiespectral.
+
+Este script NÃO calibra nada.
+Ele somente:
+  1. carrega os artefatos HOMOLOGADOS das etapas anteriores;
+  2. valida schema/status/promoção;
+  3. cruza hardware/MX ID;
+  4. cruza domínio geométrico Intrinsics <-> Homography;
+  5. cruza Startup Profile <-> Radiometry;
+  6. valida o NPZ do Flat-Field;
+  7. preserva apenas políticas runtime explicitamente permitidas;
+  8. monta o module_params final de forma fail-closed;
+  9. faz validação estrutural final;
+ 10. salva atomicamente + backup + relatório de assembly.
+
+Linha oficial de entrada
+------------------------
+  Focus:
+    calibration/focus_qc_active.json
+
+  Flat-Field:
+    calibration/flatfield_maps_v1.json
+    calibration/flatfield_maps_v1.npz
+
+  Radiometry:
+    calibration/radiometry_calibration_v5.json
+
+  Camera Startup:
+    calibration/camera_startup_profile_v3.json
+
+  Intrinsics:
+    calibration/intrinsics_calibration_v1.json
+
+  Homography:
+    calibration/homography_calibration_v4.json
+
+  Runtime base opcional:
+    calibration/module_params.json
+
+O runtime base NÃO é fonte de calibração.
+Ele só pode fornecer políticas não calibradas, por exemplo:
+  - onnx_model_path
+  - frame_type / capture mode / raw policy
+  - rgb_processing
+  - alguns knobs de fusion runtime:
+      use_remap_cache
+      use_remap_for_rgb
+      use_remap_for_spec
+      crop_valid_common
+      resize_after_crop
+      target_size
+
+Todo bloco calibrado é substituído pelos artefatos homologados.
+
+Política final
+--------------
+  radiometric_config.enabled = false
+  patch_normalization.enabled = false
+  rgb_calibration.enabled = false / unity
+
+Essas políticas foram deliberadamente separadas da calibração de fábrica.
+
+Compatibilidade
+---------------
+O schema final continua:
+    multispec_module_params_v3
+
+para evitar quebrar consumers existentes.
+
+Entretanto o arquivo passa a conter também:
+    sensor_size_by_role
+    camera_hardware
+    intrinsics_config
+    calibration_provenance
+    assembly_metadata
+
+Root sensor_width/sensor_height:
+    representam a câmera RGB de referência.
+
+Para AR0234:
+    sensor_width  = 1920
+    sensor_height = 1200
+
+RE/NIR permanecem explicitamente registrados em:
+    sensor_size_by_role
+
+Bayer
+-----
+O bayer_pattern root é obtido do Flat-Field de produção, que já resolveu
+o Bayer da câmera RGB durante a calibração.
+
+Fail-closed
+-----------
+O assembler ABORTA se:
+  - algum artefato obrigatório não existe;
+  - schema não é o esperado;
+  - artefato não foi promovido;
+  - Focus não é homologação normal;
+  - hardware signatures divergem;
+  - MX IDs divergem;
+  - Startup veio de outra Radiometry;
+  - reference_controls divergem;
+  - Homography e Intrinsics nasceram em espaços geométricos incompatíveis;
+  - runtime_undistort está ligado com Homography no espaço errado;
+  - Flat-Field NPZ não existe;
+  - Flat-Field NPZ não contém os gains obrigatórios;
+  - perfil de Homography selecionado não existe;
+  - matriz H ou K/D é estruturalmente inválida.
+
+Uso normal
+----------
+  python module_params_assembler_production.py
+
+Selecionando perfil de homografia:
+  python module_params_assembler_production.py ^
+      --homography-profile media
+
+Somente auditoria:
+  python module_params_assembler_production.py --check-only
+
+Saídas
+------
+  calibration/module_params.json
+  calibration/module_params_assembly_report.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import shutil
+import zipfile
+from copy import deepcopy
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Optional
+
+
+# ============================================================
+# Contratos
+# ============================================================
+
+FINAL_SCHEMA = "multispec_module_params_v3"
+ASSEMBLER_SCHEMA = "multispec_module_params_assembly_v1"
+
+EXPECTED_SCHEMAS = {
+    "focus": "multispec_focus_qc_v3",
+    "flatfield": "multispec_flatfield_production_v2",
+    "radiometry": "multispec_radiometric_calibration_v5",
+    "startup": "multispec_camera_startup_profile_v3",
+    "intrinsics": "multispec_intrinsics_calibration_v1",
+    "homography": "multispec_homography_calibration_v4",
+}
+
+ROLES = ("rgb", "re", "nir")
+CHANNELS = ("R", "G", "B", "RE", "NIR")
+
+EXPECTED_TOPOLOGY = {
+    "rgb": {
+        "socket": "CAM_A",
+        "allowed_sensors": ("OV9782", "AR0234"),
+    },
+    "re": {
+        "socket": "CAM_B",
+        "allowed_sensors": ("OV9282",),
+    },
+    "nir": {
+        "socket": "CAM_C",
+        "allowed_sensors": ("OV9282",),
+    },
+}
+
+EXPECTED_NATIVE_SIZES = {
+    "OV9782": [1280, 800],
+    "AR0234": [1920, 1200],
+    "OV9282": [1280, 800],
+}
+
+CALIBRATION_SPACE_NATIVE = "native_stream_no_external_undistort"
+
+
+# ============================================================
+# Helpers
+# ============================================================
+
+def now_str() -> str:
+    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+def stamp() -> str:
+    return datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+
+
+def ensure_dir(path: str | Path):
+    Path(path).mkdir(parents=True, exist_ok=True)
+
+
+def load_json(path: str | Path, *, required: bool = True) -> dict:
+    p = Path(path)
+
+    if not p.is_file():
+        if required:
+            raise FileNotFoundError(
+                f"Arquivo obrigatório não encontrado: {p}"
+            )
+
+        return {}
+
+    with p.open("r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    if not isinstance(data, dict):
+        raise RuntimeError(
+            f"JSON root deve ser object/dict: {p}"
+        )
+
+    return data
+
+
+def save_json_atomic(path: str | Path, data: dict):
+    p = Path(path)
+    ensure_dir(p.parent)
+
+    tmp = p.with_suffix(
+        p.suffix + ".tmp"
+    )
+
+    with tmp.open(
+        "w",
+        encoding="utf-8",
+    ) as f:
+        json.dump(
+            data,
+            f,
+            ensure_ascii=False,
+            indent=2,
+        )
+        f.write("\n")
+        f.flush()
+        os.fsync(f.fileno())
+
+    os.replace(
+        tmp,
+        p,
+    )
+
+
+def sha256_file(path: str | Path) -> str:
+    h = hashlib.sha256()
+
+    with open(
+        path,
+        "rb",
+    ) as f:
+        while True:
+            chunk = f.read(
+                1024 * 1024
+            )
+
+            if not chunk:
+                break
+
+            h.update(
+                chunk
+            )
+
+    return h.hexdigest()
+
+
+def norm_path(path: str | Path) -> str:
+    return str(
+        path
+    ).replace(
+        "\\",
+        "/",
+    )
+
+
+def deep_merge(
+    base: dict,
+    update: dict,
+) -> dict:
+    """
+    Merge simples para POLÍTICAS runtime.
+    Calibrações não devem depender disso.
+    """
+    out = deepcopy(
+        base
+        if isinstance(base, dict)
+        else {}
+    )
+
+    if not isinstance(
+        update,
+        dict,
+    ):
+        return out
+
+    for key, value in update.items():
+        if (
+            isinstance(value, dict)
+            and isinstance(
+                out.get(key),
+                dict,
+            )
+        ):
+            out[key] = deep_merge(
+                out[key],
+                value,
+            )
+        else:
+            out[key] = deepcopy(
+                value
+            )
+
+    return out
+
+
+def require_schema(
+    name: str,
+    data: dict,
+):
+    expected = EXPECTED_SCHEMAS[
+        name
+    ]
+
+    actual = data.get(
+        "schema"
+    )
+
+    if actual != expected:
+        raise RuntimeError(
+            f"{name}: schema={actual!r}, "
+            f"esperado={expected!r}"
+        )
+
+
+def require_promoted(
+    name: str,
+    data: dict,
+    *,
+    allowed_statuses: tuple[str, ...],
+):
+    if not bool(
+        data.get(
+            "promoted",
+            False,
+        )
+    ):
+        raise RuntimeError(
+            f"{name}: artefato não promovido."
+        )
+
+    status = str(
+        data.get(
+            "status",
+            ""
+        )
+    ).lower()
+
+    if (
+        status
+        not in allowed_statuses
+    ):
+        raise RuntimeError(
+            f"{name}: status={status!r}, "
+            f"aceitos={allowed_statuses}"
+        )
+
+
+def is_matrix_3x3(value) -> bool:
+    if not (
+        isinstance(
+            value,
+            list,
+        )
+        and len(value) == 3
+    ):
+        return False
+
+    return all(
+        isinstance(
+            row,
+            list,
+        )
+        and len(row) == 3
+        for row in value
+    )
+
+
+def is_positive_int(v) -> bool:
+    try:
+        return int(v) > 0
+    except Exception:
+        return False
+
+
+def artifact_meta(
+    path: str | Path,
+    data: dict,
+) -> dict:
+    p = Path(path)
+
+    return {
+        "path": norm_path(p),
+        "sha256": sha256_file(
+            p
+        ),
+        "schema": data.get(
+            "schema"
+        ),
+        "status": data.get(
+            "status"
+        ),
+        "promoted": data.get(
+            "promoted"
+        ),
+        "session_id": data.get(
+            "session_id"
+        ),
+        "created_at": data.get(
+            "created_at"
+        ),
+        "finished_at": data.get(
+            "finished_at"
+        ),
+    }
+
+
+# ============================================================
+# Hardware signature normalization
+# ============================================================
+
+def normalize_role_item(
+    item: dict,
+) -> dict:
+    if not isinstance(
+        item,
+        dict,
+    ):
+        raise RuntimeError(
+            f"Hardware role inválido: {item!r}"
+        )
+
+    socket = (
+        item.get("socket")
+        or item.get(
+            "socket_name"
+        )
+    )
+
+    sensor = (
+        item.get("sensor")
+        or item.get(
+            "sensor_name"
+        )
+    )
+
+    size = item.get(
+        "size"
+    )
+
+    if size is None:
+        width = item.get(
+            "width"
+        )
+
+        height = item.get(
+            "height"
+        )
+
+        if (
+            width is not None
+            and height is not None
+        ):
+            size = [
+                int(width),
+                int(height),
+            ]
+
+    if (
+        socket is None
+        or sensor is None
+        or size is None
+        or len(size) != 2
+    ):
+        raise RuntimeError(
+            f"Hardware role incompleto: {item}"
+        )
+
+    return {
+        "socket": str(
+            socket
+        ),
+        "sensor": str(
+            sensor
+        ).upper(),
+        "size": [
+            int(
+                size[0]
+            ),
+            int(
+                size[1]
+            ),
+        ],
+    }
+
+
+def normalize_signature(
+    signature: dict,
+) -> dict:
+    if not isinstance(
+        signature,
+        dict,
+    ):
+        raise RuntimeError(
+            "hardware_signature inválida."
+        )
+
+    out = {}
+
+    for role in ROLES:
+        item = signature.get(
+            role
+        )
+
+        if item is None:
+            raise RuntimeError(
+                f"hardware_signature sem role {role}"
+            )
+
+        out[
+            role
+        ] = normalize_role_item(
+            item
+        )
+
+    return out
+
+
+def signature_from_resolved_setup(
+    resolved_setup: dict,
+) -> dict:
+    return normalize_signature(
+        resolved_setup
+    )
+
+
+def signature_from_focus(
+    data: dict,
+) -> dict:
+    return signature_from_resolved_setup(
+        data.get(
+            "resolved_setup",
+            {},
+        )
+    )
+
+
+def signature_from_flatfield(
+    data: dict,
+) -> dict:
+    return signature_from_resolved_setup(
+        data.get(
+            "resolved_setup",
+            {},
+        )
+    )
+
+
+def signature_from_direct(
+    data: dict,
+) -> dict:
+    return normalize_signature(
+        data.get(
+            "hardware_signature",
+            {},
+        )
+    )
+
+
+def validate_product_signature(
+    signature: dict,
+):
+    for role in ROLES:
+        item = signature[
+            role
+        ]
+
+        contract = EXPECTED_TOPOLOGY[
+            role
+        ]
+
+        if (
+            item["socket"]
+            != contract["socket"]
+        ):
+            raise RuntimeError(
+                f"{role}: socket={item['socket']}, "
+                f"esperado={contract['socket']}"
+            )
+
+        if (
+            item["sensor"]
+            not in contract[
+                "allowed_sensors"
+            ]
+        ):
+            raise RuntimeError(
+                f"{role}: sensor={item['sensor']}, "
+                f"permitidos={contract['allowed_sensors']}"
+            )
+
+        expected_size = (
+            EXPECTED_NATIVE_SIZES[
+                item["sensor"]
+            ]
+        )
+
+        if (
+            item["size"]
+            != expected_size
+        ):
+            raise RuntimeError(
+                f"{role}/{item['sensor']}: "
+                f"size={item['size']}, "
+                f"esperado={expected_size}"
+            )
+
+
+def compare_signatures(
+    named_signatures: dict[str, dict],
+) -> dict:
+    names = list(
+        named_signatures.keys()
+    )
+
+    if not names:
+        raise RuntimeError(
+            "Nenhuma assinatura de hardware."
+        )
+
+    reference_name = names[
+        0
+    ]
+
+    reference = named_signatures[
+        reference_name
+    ]
+
+    mismatches = []
+
+    for name in names[
+        1:
+    ]:
+        if (
+            named_signatures[
+                name
+            ]
+            != reference
+        ):
+            mismatches.append({
+                "artifact": name,
+                "signature": named_signatures[
+                    name
+                ],
+            })
+
+    if mismatches:
+        raise RuntimeError(
+            "Artefatos pertencem a hardwares diferentes.\n"
+            f"Referência {reference_name}: {reference}\n"
+            f"Divergências: {mismatches}"
+        )
+
+    validate_product_signature(
+        reference
+    )
+
+    return reference
+
+
+# ============================================================
+# MX ID / module ID cross-check
+# ============================================================
+
+def extract_mx_ids(
+    artifacts: dict[str, dict],
+) -> dict:
+    out = {}
+
+    for name, data in artifacts.items():
+        candidates = [
+            data.get(
+                "device_mx_id"
+            ),
+            (
+                data.get(
+                    "device",
+                    {}
+                )
+                or {}
+            ).get(
+                "mx_id"
+            ),
+        ]
+
+        value = next(
+            (
+                str(x)
+                for x in candidates
+                if x
+            ),
+            None,
+        )
+
+        if value:
+            out[
+                name
+            ] = value
+
+    return out
+
+
+def validate_single_value(
+    values: dict[str, str],
+    label: str,
+) -> Optional[str]:
+    unique = sorted(
+        set(
+            values.values()
+        )
+    )
+
+    if len(
+        unique
+    ) > 1:
+        raise RuntimeError(
+            f"{label} divergente entre artefatos: {values}"
+        )
+
+    return (
+        unique[0]
+        if unique
+        else None
+    )
+
+
+def extract_module_ids(
+    artifacts: dict[str, dict],
+) -> dict:
+    out = {}
+
+    for name, data in artifacts.items():
+        trace = (
+            data.get(
+                "traceability",
+                {}
+            )
+            or {}
+        )
+
+        value = trace.get(
+            "module_id"
+        )
+
+        if value:
+            out[
+                name
+            ] = str(
+                value
+            )
+
+    return out
+
+
+# ============================================================
+# Artefact validators
+# ============================================================
+
+def validate_focus(
+    data: dict,
+):
+    require_schema(
+        "focus",
+        data,
+    )
+
+    require_promoted(
+        "focus",
+        data,
+        allowed_statuses=(
+            "pass",
+        ),
+    )
+
+    if not bool(
+        data.get(
+            "normal_production_homologation",
+            False,
+        )
+    ):
+        raise RuntimeError(
+            "Focus não é homologação normal de produção."
+        )
+
+    if data.get(
+        "runtime_effect"
+    ) != "none":
+        raise RuntimeError(
+            "Focus inesperadamente declara efeito de runtime."
+        )
+
+
+def validate_flatfield(
+    data: dict,
+    flatfield_json_path: str | Path,
+    flatfield_npz_override: Optional[str],
+) -> tuple[dict, Path]:
+    require_schema(
+        "flatfield",
+        data,
+    )
+
+    require_promoted(
+        "flatfield",
+        data,
+        allowed_statuses=(
+            "good",
+            "warning",
+        ),
+    )
+
+    patch = data.get(
+        "suggested_module_params_patch"
+    )
+
+    if not isinstance(
+        patch,
+        dict,
+    ):
+        raise RuntimeError(
+            "Flat-Field sem suggested_module_params_patch."
+        )
+
+    cfg = patch.get(
+        "flatfield_config"
+    )
+
+    if not isinstance(
+        cfg,
+        dict,
+    ):
+        raise RuntimeError(
+            "Flat-Field sem suggested_module_params_patch.flatfield_config."
+        )
+
+    if not bool(
+        cfg.get(
+            "enabled",
+            False,
+        )
+    ):
+        raise RuntimeError(
+            "Flat-Field homologado, mas patch está disabled."
+        )
+
+    outputs = (
+        data.get(
+            "outputs",
+            {}
+        )
+        or {}
+    )
+
+    npz_path = (
+        flatfield_npz_override
+        or outputs.get(
+            "active_npz"
+        )
+        or cfg.get(
+            "npz_file"
+        )
+    )
+
+    if not npz_path:
+        raise RuntimeError(
+            "Flat-Field não informa active NPZ."
+        )
+
+    npz = Path(
+        npz_path
+    )
+
+    if not npz.is_file():
+        # Se caminho foi gravado relativo ao projeto, tenta relativo ao JSON.
+        candidate = (
+            Path(
+                flatfield_json_path
+            )
+            .parent
+            / npz_path
+        )
+
+        if candidate.is_file():
+            npz = candidate
+        else:
+            raise FileNotFoundError(
+                f"Flat-Field NPZ não encontrado: {npz_path}"
+            )
+
+    required_keys = {
+        f"gain_{ch}"
+        for ch in CHANNELS
+    }
+
+    with zipfile.ZipFile(
+        npz,
+        "r",
+    ) as zf:
+        names = set(
+            zf.namelist()
+        )
+
+    # NPZ guarda <key>.npy
+    expected_members = {
+        f"{key}.npy"
+        for key in required_keys
+    }
+
+    missing = sorted(
+        expected_members - names
+    )
+
+    if missing:
+        raise RuntimeError(
+            f"Flat-Field NPZ sem gains obrigatórios: {missing}"
+        )
+
+    runtime_cfg = deepcopy(
+        cfg
+    )
+
+    # Canonicaliza channel_maps para o contrato real do RawProcessorCore.
+    # O calibrador Flat-Field pode emitir:
+    #     "R": "gain_R"
+    # enquanto o runtime atual consome:
+    #     "R": {
+    #         "gain_key": "gain_R",
+    #         "dark_median_key": "dark_median_R",
+    #     }
+    raw_channel_maps = (
+        runtime_cfg.get(
+            "channel_maps",
+            {},
+        )
+        or {}
+    )
+
+    normalized_channel_maps = {}
+
+    for ch in CHANNELS:
+        item = raw_channel_maps.get(
+            ch
+        )
+
+        if isinstance(
+            item,
+            str,
+        ):
+            normalized_channel_maps[
+                ch
+            ] = {
+                "gain_key": item,
+                "dark_median_key": (
+                    f"dark_median_{ch}"
+                ),
+            }
+
+        elif isinstance(
+            item,
+            dict,
+        ):
+            entry = deepcopy(
+                item
+            )
+
+            entry.setdefault(
+                "gain_key",
+                f"gain_{ch}",
+            )
+
+            entry.setdefault(
+                "dark_median_key",
+                f"dark_median_{ch}",
+            )
+
+            normalized_channel_maps[
+                ch
+            ] = entry
+
+        else:
+            normalized_channel_maps[
+                ch
+            ] = {
+                "gain_key": (
+                    f"gain_{ch}"
+                ),
+                "dark_median_key": (
+                    f"dark_median_{ch}"
+                ),
+            }
+
+    runtime_cfg[
+        "channel_maps"
+    ] = normalized_channel_maps
+
+    runtime_cfg[
+        "npz_file"
+    ] = norm_path(
+        npz
+    )
+
+    runtime_cfg[
+        "json_file"
+    ] = norm_path(
+        flatfield_json_path
+    )
+
+    runtime_cfg[
+        "schema"
+    ] = data.get(
+        "schema"
+    )
+
+    runtime_cfg[
+        "created_at"
+    ] = data.get(
+        "created_at"
+    )
+
+    # Política final já decidida: não ativar dark model simples.
+    runtime_cfg[
+        "subtract_dark"
+    ] = False
+
+    return runtime_cfg, npz
+
+
+def validate_radiometry(
+    data: dict,
+) -> dict:
+    require_schema(
+        "radiometry",
+        data,
+    )
+
+    require_promoted(
+        "radiometry",
+        data,
+        allowed_statuses=(
+            "good",
+            "warning",
+        ),
+    )
+
+    frag = (
+        data.get(
+            "module_params_fragment",
+            {}
+        )
+        or {}
+    )
+
+    cfg = frag.get(
+        "radiometric_normalization"
+    )
+
+    if not isinstance(
+        cfg,
+        dict,
+    ):
+        raise RuntimeError(
+            "Radiometry sem module_params_fragment.radiometric_normalization."
+        )
+
+    if not bool(
+        cfg.get(
+            "enabled",
+            False,
+        )
+    ):
+        raise RuntimeError(
+            "Radiometric normalization homologada, mas disabled."
+        )
+
+    if (
+        cfg.get(
+            "method"
+        )
+        != "oak_ae_frame_controls_v1"
+    ):
+        raise RuntimeError(
+            f"Método radiométrico inesperado: {cfg.get('method')}"
+        )
+
+    if (
+        cfg.get(
+            "factor_model"
+        )
+        != "exposure_time_us_x_iso"
+    ):
+        raise RuntimeError(
+            f"factor_model inesperado: {cfg.get('factor_model')}"
+        )
+
+    refs = (
+        cfg.get(
+            "reference_controls",
+            {}
+        )
+        or {}
+    )
+
+    for role in ROLES:
+        ref = refs.get(
+            role
+        )
+
+        if not isinstance(
+            ref,
+            dict,
+        ):
+            raise RuntimeError(
+                f"Radiometry sem reference_controls.{role}"
+            )
+
+        if not is_positive_int(
+            ref.get(
+                "exposure_time_us"
+            )
+        ):
+            raise RuntimeError(
+                f"Radiometry exposure inválida em {role}"
+            )
+
+        if not is_positive_int(
+            ref.get(
+                "sensitivity_iso"
+            )
+        ):
+            raise RuntimeError(
+                f"Radiometry ISO inválido em {role}"
+            )
+
+    return deepcopy(
+        cfg
+    )
+
+
+def validate_startup(
+    data: dict,
+    radiometry: dict,
+) -> tuple[dict, dict]:
+    require_schema(
+        "startup",
+        data,
+    )
+
+    require_promoted(
+        "startup",
+        data,
+        allowed_statuses=(
+            "good",
+        ),
+    )
+
+    frag = (
+        data.get(
+            "module_params_fragment",
+            {}
+        )
+        or {}
+    )
+
+    camera_settings = frag.get(
+        "camera_settings"
+    )
+
+    rgb_calibration = frag.get(
+        "rgb_calibration"
+    )
+
+    if not isinstance(
+        camera_settings,
+        dict,
+    ):
+        raise RuntimeError(
+            "Startup sem camera_settings."
+        )
+
+    if not isinstance(
+        rgb_calibration,
+        dict,
+    ):
+        raise RuntimeError(
+            "Startup sem rgb_calibration."
+        )
+
+    if bool(
+        rgb_calibration.get(
+            "enabled",
+            False,
+        )
+    ):
+        raise RuntimeError(
+            "Startup tentou habilitar rgb_calibration."
+        )
+
+    gains = (
+        rgb_calibration.get(
+            "gains",
+            {}
+        )
+        or {}
+    )
+
+    for ch in (
+        "R",
+        "G",
+        "B",
+    ):
+        if float(
+            gains.get(
+                ch,
+                0.0,
+            )
+        ) != 1.0:
+            raise RuntimeError(
+                f"Startup rgb gain {ch} não é unity."
+            )
+
+    for role in ROLES:
+        cfg = camera_settings.get(
+            role
+        )
+
+        if not isinstance(
+            cfg,
+            dict,
+        ):
+            raise RuntimeError(
+                f"Startup sem camera_settings.{role}"
+            )
+
+        if not bool(
+            cfg.get(
+                "ae_enable",
+                False,
+            )
+        ):
+            raise RuntimeError(
+                f"Startup produto exige AE=true em {role}."
+            )
+
+    source_rad = (
+        data.get(
+            "source_radiometry",
+            {}
+        )
+        or {}
+    )
+
+    rad_session = radiometry.get(
+        "session_id"
+    )
+
+    source_session = source_rad.get(
+        "session_id"
+    )
+
+    if (
+        rad_session
+        and source_session
+        and rad_session
+        != source_session
+    ):
+        raise RuntimeError(
+            "Startup Profile foi gerado a partir de outra Radiometry: "
+            f"startup={source_session}, atual={rad_session}"
+        )
+
+    rad_refs = (
+        (
+            radiometry.get(
+                "module_params_fragment",
+                {}
+            )
+            or {}
+        )
+        .get(
+            "radiometric_normalization",
+            {}
+        )
+        .get(
+            "reference_controls",
+            {}
+        )
+    )
+
+    startup_refs = source_rad.get(
+        "reference_controls",
+        {}
+    )
+
+    if (
+        startup_refs
+        and startup_refs != rad_refs
+    ):
+        raise RuntimeError(
+            "Startup reference_controls divergem da Radiometry ativa."
+        )
+
+    return (
+        deepcopy(
+            camera_settings
+        ),
+        deepcopy(
+            rgb_calibration
+        ),
+    )
+
+
+def validate_intrinsics(
+    data: dict,
+) -> dict:
+    require_schema(
+        "intrinsics",
+        data,
+    )
+
+    require_promoted(
+        "intrinsics",
+        data,
+        allowed_statuses=(
+            "pass",
+            "warning_promoted",
+        ),
+    )
+
+    frag = (
+        data.get(
+            "module_params_fragment",
+            {}
+        )
+        or {}
+    )
+
+    cfg = frag.get(
+        "intrinsics_config"
+    )
+
+    if not isinstance(
+        cfg,
+        dict,
+    ):
+        raise RuntimeError(
+            "Intrinsics sem module_params_fragment.intrinsics_config."
+        )
+
+    if not bool(
+        cfg.get(
+            "enabled",
+            False,
+        )
+    ):
+        raise RuntimeError(
+            "Intrinsics homologado, mas intrinsics_config disabled."
+        )
+
+    cameras = (
+        cfg.get(
+            "cameras",
+            {}
+        )
+        or {}
+    )
+
+    for role in ROLES:
+        cam = cameras.get(
+            role
+        )
+
+        if not isinstance(
+            cam,
+            dict,
+        ):
+            raise RuntimeError(
+                f"Intrinsics sem câmera {role}."
+            )
+
+        if not is_matrix_3x3(
+            cam.get(
+                "camera_matrix"
+            )
+        ):
+            raise RuntimeError(
+                f"Intrinsics K inválida em {role}."
+            )
+
+        D = cam.get(
+            "dist_coeffs"
+        )
+
+        if not (
+            isinstance(
+                D,
+                list,
+            )
+            and len(D) >= 4
+        ):
+            raise RuntimeError(
+                f"Intrinsics dist_coeffs inválida em {role}."
+            )
+
+        size = cam.get(
+            "image_size"
+        )
+
+        if not (
+            isinstance(
+                size,
+                list,
+            )
+            and len(size) == 2
+            and is_positive_int(
+                size[0]
+            )
+            and is_positive_int(
+                size[1]
+            )
+        ):
+            raise RuntimeError(
+                f"Intrinsics image_size inválida em {role}."
+            )
+
+    return deepcopy(
+        cfg
+    )
+
+
+def validate_homography(
+    data: dict,
+    profile_override: Optional[str],
+) -> dict:
+    require_schema(
+        "homography",
+        data,
+    )
+
+    # O active v4 é um container de perfis e não carrega promoted=true.
+    profiles = (
+        data.get(
+            "profiles",
+            {}
+        )
+        or {}
+    )
+
+    if not profiles:
+        raise RuntimeError(
+            "Homography ativa não possui perfis."
+        )
+
+    profile = (
+        profile_override
+        or data.get(
+            "recommended_profile"
+        )
+    )
+
+    if not profile:
+        raise RuntimeError(
+            "Homography sem recommended_profile. "
+            "Use --homography-profile."
+        )
+
+    if profile not in profiles:
+        raise RuntimeError(
+            f"Perfil de homografia {profile!r} não existe. "
+            f"Disponíveis={sorted(profiles)}"
+        )
+
+    # Garante que todos os perfis guardados são produtos de um acceptance válido.
+    for name, payload in profiles.items():
+        quality = (
+            payload.get(
+                "quality",
+                {}
+            )
+            or {}
+        )
+
+        status = str(
+            quality.get(
+                "status",
+                ""
+            )
+        ).lower()
+
+        if status not in (
+            "good",
+            "warning",
+        ):
+            raise RuntimeError(
+                f"Homography profile {name}: quality.status={status!r}"
+            )
+
+        homographies = (
+            payload.get(
+                "homographies",
+                {}
+            )
+            or {}
+        )
+
+        for key in (
+            "re_to_rgb",
+            "nir_to_rgb",
+        ):
+            if not is_matrix_3x3(
+                homographies.get(
+                    key
+                )
+            ):
+                raise RuntimeError(
+                    f"Homography profile {name}: matriz {key} inválida."
+                )
+
+    fragment = (
+        data.get(
+            "module_params_fragment",
+            {}
+        )
+        or {}
+    )
+
+    cfg = fragment.get(
+        "fusion_config"
+    )
+
+    if not isinstance(
+        cfg,
+        dict,
+    ):
+        raise RuntimeError(
+            "Homography active sem module_params_fragment.fusion_config."
+        )
+
+    out = deepcopy(
+        cfg
+    )
+
+    out[
+        "homography_profile"
+    ] = profile
+
+    out[
+        "homography_profile_by_role"
+    ] = {
+        "re": profile,
+        "nir": profile,
+    }
+
+    selected = (
+        out.get(
+            "homography_profiles",
+            {}
+        )
+        or {}
+    ).get(
+        profile
+    )
+
+    if not isinstance(
+        selected,
+        dict,
+    ):
+        raise RuntimeError(
+            "Fragmento fusion_config não contém o perfil selecionado."
+        )
+
+    out[
+        "homography_calibration_size"
+    ] = deepcopy(
+        selected.get(
+            "reference_size"
+        )
+        or selected.get(
+            "homography_calibration_size"
+        )
+    )
+
+    return out
+
+
+# ============================================================
+# Cross-artifact geometry checks
+# ============================================================
+
+def validate_geometry_contract(
+    intrinsics_cfg: dict,
+    homography_data: dict,
+    fusion_cfg: dict,
+):
+    intr_space = intrinsics_cfg.get(
+        "calibration_space"
+    )
+
+    hom_space = homography_data.get(
+        "coordinate_space"
+    )
+
+    runtime_und = (
+        intrinsics_cfg.get(
+            "runtime_undistort",
+            {}
+        )
+        or {}
+    )
+
+    und_enabled = bool(
+        runtime_und.get(
+            "enabled",
+            False,
+        )
+    )
+
+    if not und_enabled:
+        if (
+            intr_space != CALIBRATION_SPACE_NATIVE
+            or hom_space != CALIBRATION_SPACE_NATIVE
+        ):
+            raise RuntimeError(
+                "Domínio geométrico inconsistente: "
+                f"intrinsics={intr_space!r}, homography={hom_space!r}, "
+                "runtime_undistort=false."
+            )
+
+    else:
+        if (
+            hom_space
+            == CALIBRATION_SPACE_NATIVE
+        ):
+            raise RuntimeError(
+                "runtime_undistort=true, mas Homography foi calibrada "
+                "no espaço nativo distorcido. Recalibre a Homography."
+            )
+
+    selected_profile = fusion_cfg[
+        "homography_profile"
+    ]
+
+    profile = (
+        fusion_cfg[
+            "homography_profiles"
+        ][
+            selected_profile
+        ]
+    )
+
+    ref_size = (
+        profile.get(
+            "reference_size"
+        )
+        or profile.get(
+            "homography_calibration_size"
+        )
+    )
+
+    src_sizes = (
+        profile.get(
+            "source_size_by_role",
+            {}
+        )
+        or {}
+    )
+
+    cams = intrinsics_cfg[
+        "cameras"
+    ]
+
+    if (
+        ref_size
+        != cams[
+            "rgb"
+        ][
+            "image_size"
+        ]
+    ):
+        raise RuntimeError(
+            "Homography RGB reference_size diverge de Intrinsics RGB: "
+            f"{ref_size} != {cams['rgb']['image_size']}"
+        )
+
+    for role in (
+        "re",
+        "nir",
+    ):
+        source_size = src_sizes.get(
+            role
+        )
+
+        if (
+            source_size is not None
+            and source_size
+            != cams[
+                role
+            ][
+                "image_size"
+            ]
+        ):
+            raise RuntimeError(
+                f"Homography source_size {role} diverge de Intrinsics: "
+                f"{source_size} != {cams[role]['image_size']}"
+            )
+
+
+# ============================================================
+# Runtime base, whitelist only
+# ============================================================
+
+def runtime_defaults() -> dict:
+    return {
+        "onnx_model_path": None,
+        "frame_type": "RAW_BRUTO",
+        "capture_mode_requested": "AUTO",
+        "capture_mode_effective": "AUTO",
+        "raw_policy": "allow_single",
+        "rgb_processing": {
+            "mode": "bayer_planes",
+        },
+        "fusion_runtime": {
+            "use_remap_cache": True,
+            "use_remap_for_rgb": False,
+            "use_remap_for_spec": True,
+            "crop_valid_common": True,
+            "resize_after_crop": True,
+            "target_size": None,
+        },
+    }
+
+
+def read_runtime_policy(
+    base: dict,
+) -> dict:
+    """
+    Extrai SOMENTE campos que não são calibração.
+    """
+    defaults = runtime_defaults()
+
+    if not isinstance(
+        base,
+        dict,
+    ):
+        return defaults
+
+    out = deepcopy(
+        defaults
+    )
+
+    for key in (
+        "onnx_model_path",
+        "frame_type",
+        "capture_mode_requested",
+        "capture_mode_effective",
+        "raw_policy",
+    ):
+        if key in base:
+            out[
+                key
+            ] = deepcopy(
+                base[
+                    key
+                ]
+            )
+
+    if isinstance(
+        base.get(
+            "rgb_processing"
+        ),
+        dict,
+    ):
+        out[
+            "rgb_processing"
+        ] = deepcopy(
+            base[
+                "rgb_processing"
+            ]
+        )
+
+    old_fusion = (
+        base.get(
+            "fusion_config",
+            {}
+        )
+        or {}
+    )
+
+    for key in (
+        "use_remap_cache",
+        "use_remap_for_rgb",
+        "use_remap_for_spec",
+        "crop_valid_common",
+        "resize_after_crop",
+        "target_size",
+    ):
+        if key in old_fusion:
+            out[
+                "fusion_runtime"
+            ][
+                key
+            ] = deepcopy(
+                old_fusion[
+                    key
+                ]
+            )
+
+    return out
+
+
+# ============================================================
+# Bayer from Flat-Field
+# ============================================================
+
+def resolve_bayer_from_flatfield(
+    flatfield: dict,
+) -> str:
+    setup = (
+        flatfield.get(
+            "resolved_setup",
+            {}
+        )
+        or {}
+    )
+
+    rgb = (
+        setup.get(
+            "rgb",
+            {}
+        )
+        or {}
+    )
+
+    bayer = rgb.get(
+        "bayer_pattern"
+    )
+
+    if not bayer:
+        raise RuntimeError(
+            "Flat-Field homologado não registra RGB bayer_pattern. "
+            "Rode novamente o Flat-Field de produção."
+        )
+
+    bayer = str(
+        bayer
+    ).upper()
+
+    if bayer not in (
+        "RGGB",
+        "BGGR",
+        "GRBG",
+        "GBRG",
+    ):
+        raise RuntimeError(
+            f"Bayer inválido no Flat-Field: {bayer!r}"
+        )
+
+    return bayer
+
+
+# ============================================================
+# Final build
+# ============================================================
+
+def disabled_radiometric_controller() -> dict:
+    return {
+        "enabled": False,
+        "policy": "disabled_product_default",
+        "reason": (
+            "Field radiometric controller is not part of factory calibration. "
+            "Per-frame exposure variation is handled by "
+            "radiometric_normalization."
+        ),
+    }
+
+
+def disabled_patch_normalization() -> dict:
+    return {
+        "enabled": False,
+        "policy": "disabled_product_default",
+        "reason": (
+            "Reference-patch post-fusion normalization is not active "
+            "in the homologated product pipeline."
+        ),
+    }
+
+
+def build_final_module_params(
+    runtime_policy: dict,
+    hardware_signature: dict,
+    bayer_pattern: str,
+    camera_settings: dict,
+    rgb_calibration: dict,
+    radiometric_normalization: dict,
+    flatfield_config: dict,
+    intrinsics_config: dict,
+    fusion_config: dict,
+    provenance: dict,
+    assembly_report_path: str,
+) -> dict:
+    rgb_hw = hardware_signature[
+        "rgb"
+    ]
+
+    fusion = deepcopy(
+        fusion_config
+    )
+
+    for key, value in runtime_policy[
+        "fusion_runtime"
+    ].items():
+        fusion[
+            key
+        ] = deepcopy(
+            value
+        )
+
+    result = {
+        "schema": FINAL_SCHEMA,
+        "saved_at": now_str(),
+
+        # Runtime/model policy
+        "onnx_model_path": runtime_policy.get(
+            "onnx_model_path"
+        ),
+        "frame_type": runtime_policy[
+            "frame_type"
+        ],
+        "capture_mode_requested": runtime_policy[
+            "capture_mode_requested"
+        ],
+        "capture_mode_effective": runtime_policy[
+            "capture_mode_effective"
+        ],
+        "raw_policy": runtime_policy[
+            "raw_policy"
+        ],
+
+        # Compatibilidade root: RGB reference camera
+        "sensor_width": int(
+            rgb_hw[
+                "size"
+            ][0]
+        ),
+        "sensor_height": int(
+            rgb_hw[
+                "size"
+            ][1]
+        ),
+        "bayer_pattern": bayer_pattern,
+
+        # Novo contrato explícito de geometria física
+        "sensor_size_by_role": {
+            role: deepcopy(
+                hardware_signature[
+                    role
+                ][
+                    "size"
+                ]
+            )
+            for role in ROLES
+        },
+        "camera_hardware": deepcopy(
+            hardware_signature
+        ),
+
+        "rgb_processing": deepcopy(
+            runtime_policy[
+                "rgb_processing"
+            ]
+        ),
+
+        # Calibrações/políticas homologadas
+        "camera_settings": deepcopy(
+            camera_settings
+        ),
+        "fusion_config": fusion,
+        "intrinsics_config": deepcopy(
+            intrinsics_config
+        ),
+        "radiometric_config": (
+            disabled_radiometric_controller()
+        ),
+        "radiometric_normalization": deepcopy(
+            radiometric_normalization
+        ),
+        "patch_normalization": (
+            disabled_patch_normalization()
+        ),
+        "rgb_calibration": deepcopy(
+            rgb_calibration
+        ),
+        "flatfield_config": deepcopy(
+            flatfield_config
+        ),
+
+        # Auditoria. Consumers antigos podem ignorar.
+        "calibration_provenance": deepcopy(
+            provenance
+        ),
+        "assembly_metadata": {
+            "schema": ASSEMBLER_SCHEMA,
+            "assembled_at": now_str(),
+            "assembly_report": norm_path(
+                assembly_report_path
+            ),
+            "calibration_line": [
+                "focus",
+                "intrinsics",
+                "flatfield",
+                "radiometry",
+                "camera_startup",
+                "homography",
+                "module_params_assembler",
+            ],
+            "runtime_undistort_enabled": bool(
+                (
+                    intrinsics_config.get(
+                        "runtime_undistort",
+                        {}
+                    )
+                    or {}
+                ).get(
+                    "enabled",
+                    False,
+                )
+            ),
+        },
+    }
+
+    # Não escreve null desnecessário para ONNX se base não tinha.
+    if (
+        result[
+            "onnx_model_path"
+        ]
+        is None
+    ):
+        result.pop(
+            "onnx_model_path",
+            None,
+        )
+
+    return result
+
+
+# ============================================================
+# Final structural validation
+# ============================================================
+
+def validate_final_module_params(
+    mp: dict,
+):
+    required_root = (
+        "schema",
+        "sensor_width",
+        "sensor_height",
+        "bayer_pattern",
+        "sensor_size_by_role",
+        "camera_hardware",
+        "rgb_processing",
+        "camera_settings",
+        "fusion_config",
+        "intrinsics_config",
+        "radiometric_config",
+        "radiometric_normalization",
+        "patch_normalization",
+        "rgb_calibration",
+        "flatfield_config",
+    )
+
+    missing = [
+        key
+        for key in required_root
+        if key not in mp
+    ]
+
+    if missing:
+        raise RuntimeError(
+            f"module_params final sem chaves: {missing}"
+        )
+
+    if (
+        mp[
+            "schema"
+        ]
+        != FINAL_SCHEMA
+    ):
+        raise RuntimeError(
+            "Schema final inválido."
+        )
+
+    if not is_positive_int(
+        mp[
+            "sensor_width"
+        ]
+    ) or not is_positive_int(
+        mp[
+            "sensor_height"
+        ]
+    ):
+        raise RuntimeError(
+            "sensor_width/height inválidos."
+        )
+
+    for role in ROLES:
+        if role not in mp[
+            "camera_settings"
+        ]:
+            raise RuntimeError(
+                f"camera_settings sem {role}"
+            )
+
+        if role not in mp[
+            "sensor_size_by_role"
+        ]:
+            raise RuntimeError(
+                f"sensor_size_by_role sem {role}"
+            )
+
+    fusion = mp[
+        "fusion_config"
+    ]
+
+    if (
+        fusion.get(
+            "alignment_mode"
+        )
+        != "homography"
+    ):
+        raise RuntimeError(
+            "fusion_config.alignment_mode deve ser homography."
+        )
+
+    selected = fusion.get(
+        "homography_profile"
+    )
+
+    profiles = (
+        fusion.get(
+            "homography_profiles",
+            {}
+        )
+        or {}
+    )
+
+    if (
+        not selected
+        or selected not in profiles
+    ):
+        raise RuntimeError(
+            "Perfil de homografia final inválido."
+        )
+
+    if bool(
+        mp[
+            "radiometric_config"
+        ].get(
+            "enabled",
+            True,
+        )
+    ):
+        raise RuntimeError(
+            "radiometric_config deve permanecer disabled."
+        )
+
+    if bool(
+        mp[
+            "patch_normalization"
+        ].get(
+            "enabled",
+            True,
+        )
+    ):
+        raise RuntimeError(
+            "patch_normalization deve permanecer disabled."
+        )
+
+    if bool(
+        mp[
+            "rgb_calibration"
+        ].get(
+            "enabled",
+            True,
+        )
+    ):
+        raise RuntimeError(
+            "rgb_calibration deve permanecer disabled."
+        )
+
+    if not bool(
+        mp[
+            "radiometric_normalization"
+        ].get(
+            "enabled",
+            False,
+        )
+    ):
+        raise RuntimeError(
+            "radiometric_normalization deve estar enabled."
+        )
+
+    if not bool(
+        mp[
+            "flatfield_config"
+        ].get(
+            "enabled",
+            False,
+        )
+    ):
+        raise RuntimeError(
+            "flatfield_config deve estar enabled."
+        )
+
+    flat_maps = (
+        mp[
+            "flatfield_config"
+        ].get(
+            "channel_maps",
+            {},
+        )
+        or {}
+    )
+
+    for ch in CHANNELS:
+        ch_cfg = flat_maps.get(
+            ch
+        )
+
+        if not isinstance(
+            ch_cfg,
+            dict,
+        ):
+            raise RuntimeError(
+                f"flatfield_config.channel_maps.{ch} deve ser dict."
+            )
+
+        if not ch_cfg.get(
+            "gain_key"
+        ):
+            raise RuntimeError(
+                f"flatfield_config.channel_maps.{ch}.gain_key ausente."
+            )
+
+    if not bool(
+        mp[
+            "intrinsics_config"
+        ].get(
+            "enabled",
+            False,
+        )
+    ):
+        raise RuntimeError(
+            "intrinsics_config deve estar enabled."
+        )
+
+    # Root deve refletir RGB.
+    rgb_size = mp[
+        "sensor_size_by_role"
+    ][
+        "rgb"
+    ]
+
+    if [
+        mp[
+            "sensor_width"
+        ],
+        mp[
+            "sensor_height"
+        ],
+    ] != rgb_size:
+        raise RuntimeError(
+            "sensor_width/height root não correspondem ao RGB."
+        )
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Assembler fail-closed do module_params final "
+            "para o módulo multiespectral."
+        ),
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument(
+        "--focus-json",
+        default="calibration/focus_qc_active.json",
+    )
+
+    parser.add_argument(
+        "--flatfield-json",
+        default="calibration/flatfield_maps_v1.json",
+    )
+
+    parser.add_argument(
+        "--flatfield-npz",
+        default=None,
+        help=(
+            "Override opcional do NPZ. Normalmente é obtido "
+            "do próprio flatfield JSON."
+        ),
+    )
+
+    parser.add_argument(
+        "--radiometry-json",
+        default="calibration/radiometry_calibration_v5.json",
+    )
+
+    parser.add_argument(
+        "--startup-json",
+        default="calibration/camera_startup_profile_v3.json",
+    )
+
+    parser.add_argument(
+        "--intrinsics-json",
+        default="calibration/intrinsics_calibration_v1.json",
+    )
+
+    parser.add_argument(
+        "--homography-json",
+        default="calibration/homography_calibration_v4.json",
+    )
+
+    parser.add_argument(
+        "--homography-profile",
+        default=None,
+        help=(
+            "Override do perfil ativo. Vazio usa recommended_profile "
+            "da calibração de homografia."
+        ),
+    )
+
+    parser.add_argument(
+        "--runtime-base-json",
+        default="calibration/module_params.json",
+        help=(
+            "Fonte SOMENTE de políticas runtime não calibradas. "
+            "Se inexistente, usa defaults seguros."
+        ),
+    )
+
+    parser.add_argument(
+        "--out",
+        default="calibration/module_params.json",
+    )
+
+    parser.add_argument(
+        "--report",
+        default="calibration/module_params_assembly_report.json",
+    )
+
+    parser.add_argument(
+        "--check-only",
+        action="store_true",
+        help="Audita tudo sem escrever module_params.",
+    )
+
+    args = parser.parse_args()
+
+    # --------------------------------------------------------
+    # Load all official artifacts
+    # --------------------------------------------------------
+
+    focus = load_json(
+        args.focus_json
+    )
+
+    flatfield = load_json(
+        args.flatfield_json
+    )
+
+    radiometry = load_json(
+        args.radiometry_json
+    )
+
+    startup = load_json(
+        args.startup_json
+    )
+
+    intrinsics = load_json(
+        args.intrinsics_json
+    )
+
+    homography = load_json(
+        args.homography_json
+    )
+
+    artifacts = {
+        "focus": focus,
+        "flatfield": flatfield,
+        "radiometry": radiometry,
+        "startup": startup,
+        "intrinsics": intrinsics,
+        "homography": homography,
+    }
+
+    # --------------------------------------------------------
+    # Individual validation
+    # --------------------------------------------------------
+
+    validate_focus(
+        focus
+    )
+
+    flatfield_cfg, flatfield_npz = (
+        validate_flatfield(
+            flatfield,
+            args.flatfield_json,
+            args.flatfield_npz,
+        )
+    )
+
+    rad_norm = validate_radiometry(
+        radiometry
+    )
+
+    camera_settings, rgb_calibration = (
+        validate_startup(
+            startup,
+            radiometry,
+        )
+    )
+
+    intrinsics_cfg = (
+        validate_intrinsics(
+            intrinsics
+        )
+    )
+
+    fusion_cfg = (
+        validate_homography(
+            homography,
+            args.homography_profile,
+        )
+    )
+
+    # --------------------------------------------------------
+    # Hardware consistency
+    # --------------------------------------------------------
+
+    signatures = {
+        "focus": signature_from_focus(
+            focus
+        ),
+        "flatfield": signature_from_flatfield(
+            flatfield
+        ),
+        "radiometry": signature_from_direct(
+            radiometry
+        ),
+        "startup": signature_from_direct(
+            startup
+        ),
+        "intrinsics": signature_from_direct(
+            intrinsics
+        ),
+        "homography": normalize_signature(
+            homography.get(
+                "hardware_signature",
+                {},
+            )
+        ),
+    }
+
+    common_signature = (
+        compare_signatures(
+            signatures
+        )
+    )
+
+    mx_ids = extract_mx_ids(
+        artifacts
+    )
+
+    common_mx = validate_single_value(
+        mx_ids,
+        "MX ID",
+    )
+
+    module_ids = extract_module_ids(
+        artifacts
+    )
+
+    common_module_id = (
+        validate_single_value(
+            module_ids,
+            "module_id",
+        )
+    )
+
+    # --------------------------------------------------------
+    # Cross-contract checks
+    # --------------------------------------------------------
+
+    validate_geometry_contract(
+        intrinsics_cfg,
+        homography,
+        fusion_cfg,
+    )
+
+    # Intrinsics dimensions must exactly match common hardware.
+    for role in ROLES:
+        intr_size = (
+            intrinsics_cfg[
+                "cameras"
+            ][role][
+                "image_size"
+            ]
+        )
+
+        if (
+            intr_size
+            != common_signature[
+                role
+            ][
+                "size"
+            ]
+        ):
+            raise RuntimeError(
+                f"Intrinsics {role} size={intr_size} "
+                f"!= hardware={common_signature[role]['size']}"
+            )
+
+    # Startup fallback must match calibrated reference controls.
+    refs = rad_norm[
+        "reference_controls"
+    ]
+
+    for role in ROLES:
+        startup_role = camera_settings[
+            role
+        ]
+
+        if int(
+            startup_role[
+                "exposure_time_us"
+            ]
+        ) != int(
+            refs[
+                role
+            ][
+                "exposure_time_us"
+            ]
+        ):
+            raise RuntimeError(
+                f"Startup fallback EXP de {role} diverge da Radiometry."
+            )
+
+    # --------------------------------------------------------
+    # Runtime policy whitelist
+    # --------------------------------------------------------
+
+    runtime_base = load_json(
+        args.runtime_base_json,
+        required=False,
+    )
+
+    runtime_policy = (
+        read_runtime_policy(
+            runtime_base
+        )
+    )
+
+    # Bayer comes from production Flat-Field hardware resolution.
+    bayer = resolve_bayer_from_flatfield(
+        flatfield
+    )
+
+    # --------------------------------------------------------
+    # Provenance
+    # --------------------------------------------------------
+
+    provenance = {
+        "hardware_signature": deepcopy(
+            common_signature
+        ),
+        "device_mx_id": common_mx,
+        "module_id": common_module_id,
+        "artifacts": {
+            "focus": artifact_meta(
+                args.focus_json,
+                focus,
+            ),
+            "flatfield": {
+                **artifact_meta(
+                    args.flatfield_json,
+                    flatfield,
+                ),
+                "npz_path": norm_path(
+                    flatfield_npz
+                ),
+                "npz_sha256": sha256_file(
+                    flatfield_npz
+                ),
+            },
+            "radiometry": artifact_meta(
+                args.radiometry_json,
+                radiometry,
+            ),
+            "startup": artifact_meta(
+                args.startup_json,
+                startup,
+            ),
+            "intrinsics": artifact_meta(
+                args.intrinsics_json,
+                intrinsics,
+            ),
+            "homography": {
+                **artifact_meta(
+                    args.homography_json,
+                    homography,
+                ),
+                "selected_profile": (
+                    fusion_cfg[
+                        "homography_profile"
+                    ]
+                ),
+            },
+        },
+    }
+
+    # --------------------------------------------------------
+    # Assemble
+    # --------------------------------------------------------
+
+    module_params = build_final_module_params(
+        runtime_policy=runtime_policy,
+        hardware_signature=common_signature,
+        bayer_pattern=bayer,
+        camera_settings=camera_settings,
+        rgb_calibration=rgb_calibration,
+        radiometric_normalization=rad_norm,
+        flatfield_config=flatfield_cfg,
+        intrinsics_config=intrinsics_cfg,
+        fusion_config=fusion_cfg,
+        provenance=provenance,
+        assembly_report_path=args.report,
+    )
+
+    validate_final_module_params(
+        module_params
+    )
+
+    report = {
+        "schema": ASSEMBLER_SCHEMA,
+        "created_at": now_str(),
+        "status": "pass",
+        "check_only": bool(
+            args.check_only
+        ),
+        "output": norm_path(
+            args.out
+        ),
+        "runtime_base_json": norm_path(
+            args.runtime_base_json
+        ),
+        "runtime_base_found": bool(
+            runtime_base
+        ),
+        "hardware_signature": (
+            common_signature
+        ),
+        "device_mx_id": (
+            common_mx
+        ),
+        "module_id": (
+            common_module_id
+        ),
+        "bayer_pattern": bayer,
+        "homography_profile": (
+            fusion_cfg[
+                "homography_profile"
+            ]
+        ),
+        "geometric_contract": {
+            "intrinsics_space": (
+                intrinsics_cfg.get(
+                    "calibration_space"
+                )
+            ),
+            "homography_space": (
+                homography.get(
+                    "coordinate_space"
+                )
+            ),
+            "runtime_undistort_enabled": bool(
+                (
+                    intrinsics_cfg.get(
+                        "runtime_undistort",
+                        {}
+                    )
+                    or {}
+                ).get(
+                    "enabled",
+                    False,
+                )
+            ),
+        },
+        "runtime_policy": runtime_policy,
+        "calibration_provenance": provenance,
+        "final_checks": {
+            "focus_gate": "pass",
+            "flatfield": "pass",
+            "radiometry": "pass",
+            "startup": "pass",
+            "intrinsics": "pass",
+            "homography": "pass",
+            "hardware_consistency": "pass",
+            "geometry_consistency": "pass",
+            "startup_radiometry_consistency": "pass",
+            "flatfield_npz_integrity": "pass",
+            "module_params_structure": "pass",
+        },
+    }
+
+    # --------------------------------------------------------
+    # Print audit before write
+    # --------------------------------------------------------
+
+    print("=" * 92)
+    print("MODULE PARAMS ASSEMBLER - PRODUCTION")
+    print(f"MX ID       : {common_mx}")
+    print(f"Module ID   : {common_module_id}")
+    print(
+        "Hardware    : "
+        f"RGB={common_signature['rgb']['sensor']} "
+        f"{common_signature['rgb']['size'][0]}x{common_signature['rgb']['size'][1]} | "
+        f"RE={common_signature['re']['sensor']} "
+        f"{common_signature['re']['size'][0]}x{common_signature['re']['size'][1]} | "
+        f"NIR={common_signature['nir']['sensor']} "
+        f"{common_signature['nir']['size'][0]}x{common_signature['nir']['size'][1]}"
+    )
+    print(f"Bayer       : {bayer}")
+    print(
+        f"RGB process : "
+        f"{runtime_policy['rgb_processing'].get('mode')}"
+    )
+    print(
+        f"Homography  : "
+        f"{fusion_cfg['homography_profile']}"
+    )
+    print(
+        f"Undistort   : "
+        f"{intrinsics_cfg['runtime_undistort'].get('enabled', False)}"
+    )
+    print(
+        "Policies     : "
+        "radiometric_config=OFF | patch_normalization=OFF | rgb_calibration=OFF"
+    )
+    print(
+        "Active       : "
+        "radiometric_normalization=ON | flatfield=ON | intrinsics=ON | homography=ON"
+    )
+    print("-" * 92)
+
+    for name in (
+        "focus",
+        "flatfield",
+        "radiometry",
+        "startup",
+        "intrinsics",
+        "homography",
+    ):
+        meta = provenance[
+            "artifacts"
+        ][name]
+
+        print(
+            f"[OK] {name:11s} "
+            f"schema={meta.get('schema')} "
+            f"sha256={meta.get('sha256', '')[:12]}..."
+        )
+
+    if args.check_only:
+        print("-" * 92)
+        print("[CHECK-ONLY] Tudo consistente. Nenhum arquivo foi alterado.")
+        print("=" * 92)
+        return
+
+    # --------------------------------------------------------
+    # Atomic write + backup
+    # --------------------------------------------------------
+
+    out = Path(
+        args.out
+    )
+
+    ensure_dir(
+        out.parent
+    )
+
+    if out.exists():
+        backup = out.with_name(
+            out.stem
+            + ".backup_"
+            + stamp()
+            + out.suffix
+        )
+
+        shutil.copy2(
+            out,
+            backup,
+        )
+
+        report[
+            "backup_created"
+        ] = norm_path(
+            backup
+        )
+
+    save_json_atomic(
+        out,
+        module_params,
+    )
+
+    report[
+        "output_sha256"
+    ] = sha256_file(
+        out
+    )
+
+    save_json_atomic(
+        args.report,
+        report,
+    )
+
+    print("-" * 92)
+    print(f"[PASS] module_params : {out}")
+    print(f"[PASS] assembly report: {args.report}")
+    print(f"[SHA256] {report['output_sha256']}")
+    print("=" * 92)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/build_module_params.py b/Python/OAK/datasets/oak-fcc-3/utils/build_module_params.py
deleted file mode 100644
index 798f1b23f..000000000
--- a/Python/OAK/datasets/oak-fcc-3/utils/build_module_params.py
+++ /dev/null
@@ -1,654 +0,0 @@
-import json
-import argparse
-import os
-from copy import deepcopy
-from datetime import datetime
-
-
-# ============================================================
-# Helpers
-# ============================================================
-
-
-def now_str():
-    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-
-def load_json(path, required=True):
-    if not path or not os.path.isfile(path):
-        if required:
-            raise FileNotFoundError(f"Arquivo não encontrado: {path}")
-        return {}
-
-    with open(path, "r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-def save_json(path, data):
-    out_dir = os.path.dirname(os.path.abspath(path))
-    if out_dir:
-        os.makedirs(out_dir, exist_ok=True)
-
-    with open(path, "w", encoding="utf-8") as f:
-        json.dump(data, f, ensure_ascii=False, indent=2)
-        f.write("\n")
-
-
-def rel_or_abs(path):
-    """
-    Mantém o caminho como veio, mas normaliza separadores.
-    Isso evita quebrar projetos Windows/Linux e deixa o module_params legível.
-    """
-    if path is None:
-        return None
-    return str(path).replace("\\", "/")
-
-
-def deep_merge(base, update, *, skip_none=True):
-    """
-    Merge recursivo seguro.
-
-    - dict + dict: combina recursivamente.
-    - listas/escalares: valor novo substitui o antigo.
-    - None: por padrão NÃO apaga valor antigo, para evitar perder calibração quando
-      um arquivo fonte não conhece determinada chave.
-    """
-    if not isinstance(base, dict):
-        base = {}
-    out = deepcopy(base)
-
-    if not isinstance(update, dict):
-        return out
-
-    for key, value in update.items():
-        if value is None and skip_none:
-            continue
-
-        if isinstance(value, dict) and isinstance(out.get(key), dict):
-            out[key] = deep_merge(out[key], value, skip_none=skip_none)
-        else:
-            out[key] = deepcopy(value)
-
-    return out
-
-
-def first_dict(*values):
-    for value in values:
-        if isinstance(value, dict):
-            return value
-    return None
-
-
-# ============================================================
-# Defaults coerentes com RawProcessorCore + module_params atual
-# ============================================================
-
-
-DEFAULT_RGB_PROCESSING = {
-    "mode": "bayer_planes",
-}
-
-
-DEFAULT_PATCH_NORMALIZATION = {
-    "enabled": True,
-    "apply_when_metering_mode": "reference_patches",
-    "apply_stage": "after_fusion",
-    "method": "gray_scale_with_white_guard",
-    "space": "multispec_tensor",
-    "targets_by_patch_channel": {
-        "black": {
-            "R": 0.06,
-            "G": 0.06,
-            "B": 0.06,
-            "RE": 0.06,
-            "NIR": 0.06,
-        },
-        "gray": {
-            "R": 0.34,
-            "G": 0.34,
-            "B": 0.34,
-            "RE": 0.24,
-            "NIR": 0.30,
-        },
-        "white": {
-            "R": 0.78,
-            "G": 0.78,
-            "B": 0.78,
-            "RE": 0.78,
-            "NIR": 0.78,
-        },
-    },
-    "white_guard_max": 0.92,
-    "white_guard_max_by_channel": {
-        "R": 0.92,
-        "G": 0.92,
-        "B": 0.92,
-        "RE": 0.88,
-        "NIR": 0.88,
-    },
-    "scale_min": 0.35,
-    "scale_max": 2.5,
-    "clip_output": True,
-    "require_valid_gray": True,
-    "use_black_for_offset": False,
-    "save_patch_stats": True,
-    "rgb_saturation_guard_enabled": True,
-    "rgb_saturation_guard_mode": "fade_strength",
-    "rgb_saturation_soft_start": 0.88,
-    "rgb_saturation_hard": 0.97,
-    "rgb_saturation_threshold": 0.97,
-}
-
-
-DEFAULT_FLATFIELD_RUNTIME = {
-    "strength": 0.35,
-    "strength_by_channel": {
-        "R": 0.9,
-        "G": 0.9,
-        "B": 0.9,
-        "RE": 0.25,
-        "NIR": 0.25,
-    },
-    "gain_min_runtime": 0.75,
-    "gain_max_runtime": 1.35,
-    "runtime_smooth_ksize": 81,
-    "saturation_guard_enabled": True,
-    "saturation_guard_mode": "fade_strength",
-    "saturation_guard_threshold": 0.97,
-    "saturation_guard_soft_start": 0.88,
-    "saturation_guard_hard": 0.97,
-}
-
-
-DEFAULT_RADIOMETRIC_NORMALIZATION = {
-    "enabled": False,
-    "method": "exposure_gain_reference",
-    "apply_stage": "after_dark_before_flat_gain",
-    "reference_controls": {
-        "rgb": {"exposure_time_us": 3000, "analogue_gain": 1.0},
-        "re": {"exposure_time_us": 7000, "analogue_gain": 1.0},
-        "nir": {"exposure_time_us": 7000, "analogue_gain": 1.0},
-    },
-    "clip_output": True,
-}
-
-
-DEFAULT_RADIOMETRIC_CONFIG = {
-    "enabled": True,
-    "interval_s": 0.25,
-    "verbose": True,
-    "metering_mode": "reference_patches",
-    "spectral_control_mode": "shared",
-    "control_metric": "p50",
-    "target_value": 0.5,
-    "deadband": 0.055,
-    "p95_limit": 0.975,
-    "saturation_limit_pct": 5.0,
-    "alpha": 0.18,
-    "exp_step_gain": 0.55,
-    "prefer_exposure": True,
-    "exp_min_us": 100,
-    "exp_max_us": 80000,
-    "gain_min": 1.0,
-    "gain_max": 4.0,
-    "exp_apply_threshold_us": 15,
-    "gain_apply_threshold": 0.05,
-    "apply_same_spectral_to_both": True,
-    "spectral_roles": ["re", "nir"],
-    "dark_limit_pct": 35.0,
-    "control_strategy": "ratio",
-    "ratio_alpha": 0.42,
-    "ratio_min": 0.72,
-    "ratio_max": 1.38,
-    "reduce_fast_factor": 0.8,
-    "factor_min": 0.62,
-    "factor_max": 1.42,
-    "gain_return_enabled": True,
-    "gain_reduce_on_saturation": True,
-    "gain_increase_required_cycles": 3,
-    "gain_decrease_required_cycles": 1,
-    "gain_step_up": 0.3,
-    "gain_step_down": 0.5,
-    "gain_hard_reset_on_saturation": False,
-    "exp_high_ratio_for_gain": 0.95,
-    "exp_low_ratio_for_gain_return": 0.75,
-    "role_limits": {
-        "rgb": {"exp_min_us": 100, "exp_max_us": 80000, "gain_min": 1.0, "gain_max": 2.0},
-        "re": {"exp_min_us": 100, "exp_max_us": 3500, "gain_min": 1.0, "gain_max": 2.0},
-        "nir": {"exp_min_us": 100, "exp_max_us": 3500, "gain_min": 1.0, "gain_max": 2.0},
-    },
-    "ready_required_cycles": 3,
-    "patch_control_mode": "gray_primary",
-    "patch_require_order": True,
-    "patch_min_separation": 0.08,
-    "patch_white_sat_limit_pct": 5.0,
-    "patch_white_p95_limit": 0.985,
-    "patch_black_dark_limit_pct": 80.0,
-    "patch_black_max_p50": 0.2,
-    "patch_gray_min_p50": 0.08,
-    "patch_gray_max_p50": 0.85,
-    "patch_roi_contract": "multi_roi_by_role_v1",
-    "patch_roi_reduce_method": "median_valid_rois",
-    "patch_roi_outlier_reject": True,
-    "patch_roi_max_p50_delta": 0.12,
-    "global_saturation_guard_enabled": True,
-    "global_guard_roi_pct": {"x0": 0.05, "y0": 0.05, "x1": 0.95, "y1": 0.76},
-    "global_guard_sat_threshold": 0.985,
-    "global_guard_near_sat_threshold": 0.94,
-    "global_guard_sat_pct_soft": 0.50,
-    "global_guard_sat_pct_hard": 1.5,
-    "global_guard_sat_pct_extreme": 5.0,
-    "global_guard_blob_pct_soft": 0.20,
-    "global_guard_blob_pct_hard": 0.80,
-    "global_guard_blob_pct_extreme": 2.2,
-    "global_guard_min_blob_px": 48,
-    "global_guard_downsample_max_side": 320,
-    "global_guard_reduce_factor_soft": 0.96,
-    "global_guard_reduce_factor_hard": 0.82,
-    "global_guard_reduce_factor_extreme": 0.62,
-    "sun_guard_enabled": True,
-    "sun_guard_p99_threshold": 0.96,
-    "sun_guard_near_sat_pct_threshold": 2.0,
-    "sun_guard_freeze_increase_cycles": 1,
-    "sun_guard_allow_decrease": True,
-    "guard_force_apply_enabled": True,
-    "guard_force_apply_soft": False,
-    "guard_force_apply_hard": True,
-    "guard_force_apply_extreme": True,
-    "guard_force_apply_on_patch_saturation": True,
-    "guard_freeze_cycles_soft": 1,
-    "guard_freeze_cycles_hard": 2,
-    "guard_freeze_cycles_extreme": 3,
-    "guard_reapply_min_exp_on_emergency": True,
-    "guard_min_exp_margin_us": 80,
-    "patch_two_roi_soften_risk": True,
-    "patch_two_roi_white_risk_percentile": 75,
-    "patch_two_roi_other_risk_percentile": 50,
-    "patch_white_single_roi_saturation_reject": True,
-    "patch_white_roi_reject_sat_pct": 5.0,
-    "patch_white_roi_reject_p95": 0.995,
-}
-
-
-def default_module_template():
-    return {
-        "schema": "multispec_module_params_v3",
-        "saved_at": now_str(),
-        "frame_type": "RAW_BRUTO",
-        "capture_mode_requested": "AUTO",
-        "capture_mode_effective": "AUTO",
-        "raw_policy": "allow_single",
-        "sensor_width": 1280,
-        "sensor_height": 800,
-        "bayer_pattern": "BGGR",
-        "rgb_processing": deepcopy(DEFAULT_RGB_PROCESSING),
-        "camera_settings": {},
-        "fusion_config": {
-            "alignment_mode": "manual_affine",
-            "baseline_mm": 75.0,
-            "reference_camera": "rgb",
-            "manual_offsets": {
-                "re": {"dx": 0, "dy": 0, "theta_deg": 0.0},
-                "nir": {"dx": 0, "dy": 0, "theta_deg": 0.0},
-            },
-            "homographies": {
-                "re_to_rgb": None,
-                "nir_to_rgb": None,
-            },
-            "crop_valid_common": True,
-            "resize_after_crop": True,
-            "target_size": None,
-        },
-        "radiometric_config": deepcopy(DEFAULT_RADIOMETRIC_CONFIG),
-        "radiometric_normalization": deepcopy(DEFAULT_RADIOMETRIC_NORMALIZATION),
-        "patch_normalization": deepcopy(DEFAULT_PATCH_NORMALIZATION),
-        "rgb_calibration": {
-            "enabled": False,
-            "gains": {"R": 1.0, "G": 1.0, "B": 1.0},
-        },
-        "flatfield_config": deep_merge(
-            {
-                "enabled": False,
-                "reason": "flatfield não informado ou arquivo inexistente",
-                "subtract_dark": True,
-                "apply_before_fusion": True,
-                "apply_after_decode": True,
-                "apply_space": "native_camera_space",
-                "map_type": "gain",
-                "channels": ["R", "G", "B", "RE", "NIR"],
-                "channel_maps": {},
-                "clip_output": True,
-            },
-            DEFAULT_FLATFIELD_RUNTIME,
-        ),
-    }
-
-
-# ============================================================
-# Builders / normalizers
-# ============================================================
-
-
-def build_flatfield_config(flatfield_json_path, flatfield_data, previous_flatfield_config=None):
-    """
-    Espera o JSON gerado pelo flatfield_calibration_tool_v2.py.
-
-    Importante: usa previous_flatfield_config como base para preservar knobs runtime
-    que não existem no arquivo de calibração do flat-field, como strength,
-    gain_min_runtime, runtime_smooth_ksize e saturation_guard_*.
-    """
-    base = deep_merge(
-        deep_merge({}, previous_flatfield_config or {}),
-        DEFAULT_FLATFIELD_RUNTIME,
-    )
-
-    if not isinstance(flatfield_data, dict):
-        return deep_merge(base, {
-            "enabled": False,
-            "reason": "flatfield_json ausente ou inválido",
-        })
-
-    outputs = flatfield_data.get("outputs", {}) or {}
-    maps = flatfield_data.get("maps", {}) or {}
-
-    npz_path = outputs.get("npz")
-    if not npz_path:
-        base_name, _ = os.path.splitext(flatfield_json_path)
-        npz_path = base_name + ".npz"
-
-    channels = flatfield_data.get("channels") or base.get("channels") or ["R", "G", "B", "RE", "NIR"]
-
-    channel_maps = {}
-    previous_channel_maps = base.get("channel_maps", {}) or {}
-    for ch in channels:
-        m = maps.get(ch, {}) or {}
-        prev = previous_channel_maps.get(ch, {}) or {}
-        channel_maps[ch] = deep_merge(prev, {
-            "gain_key": m.get("gain_key", f"gain_{ch}"),
-            "flat_norm_key": m.get("flat_norm_key", f"flat_norm_{ch}"),
-            "white_median_key": m.get("white_median_key", f"white_median_{ch}"),
-            "dark_median_key": m.get("dark_median_key", f"dark_median_{ch}"),
-            "shape": m.get("shape"),
-            "gain_min": m.get("gain_min"),
-            "gain_max": m.get("gain_max"),
-            "gain_mean": m.get("gain_mean"),
-            "gain_std": m.get("gain_std"),
-        })
-
-    generated = {
-        "enabled": True,
-        "subtract_dark": True,
-        "schema": flatfield_data.get("schema", "multispec_flatfield_v1"),
-        "created_at": flatfield_data.get("created_at"),
-        "json_file": rel_or_abs(flatfield_json_path),
-        "npz_file": rel_or_abs(npz_path),
-        "apply_before_fusion": True,
-        "apply_after_decode": True,
-        "apply_space": "native_camera_space",
-        "map_type": "gain",
-        "formula": "channel_corrected = max(channel_linear - dark, 0) * gain_map",
-        "channels": channels,
-        "channel_maps": channel_maps,
-        "exp_gain_correct_during_flat_capture": bool(flatfield_data.get("exp_gain_correct", False)),
-        "smooth_ksize": flatfield_data.get("smooth_ksize"),
-        "min_gain": flatfield_data.get("min_gain"),
-        "max_gain": flatfield_data.get("max_gain"),
-        "notes": flatfield_data.get("notes", ""),
-    }
-
-    return deep_merge(base, generated)
-
-
-def pick_radiometric_config(radiometric_data: dict, selected_profile: str | None = None):
-    if not isinstance(radiometric_data, dict):
-        return None
-
-    root_cfg = radiometric_data.get("radiometric_config")
-    if isinstance(root_cfg, dict):
-        return root_cfg
-
-    active_profile = radiometric_data.get("active_profile")
-    if active_profile in ("global_scene_mode", "three_reference_patches_mode"):
-        cfg = radiometric_data.get(active_profile, {}).get("radiometric_config")
-        if isinstance(cfg, dict):
-            return cfg
-
-    if selected_profile:
-        cfg = radiometric_data.get(selected_profile, {}).get("radiometric_config")
-        if isinstance(cfg, dict):
-            return cfg
-
-    return None
-
-
-def normalize_patch_normalization_contract(base_patch_config, incoming_patch_config=None):
-    """
-    Garante o contrato atual do RawProcessorCore.
-
-    - Sempre tem targets_by_patch_channel.
-    - Preserva white_guard_max_by_channel.
-    - Preserva rgb_saturation_guard_*.
-    - Remove a chave legada targets, porque ela não é usada pelo core atual.
-    """
-    cfg = deep_merge(DEFAULT_PATCH_NORMALIZATION, base_patch_config or {})
-    cfg = deep_merge(cfg, incoming_patch_config or {})
-
-    legacy_targets = cfg.pop("targets", None)
-    if isinstance(legacy_targets, dict) and "targets_by_patch_channel" not in cfg:
-        # Fallback conservador. Na prática, com DEFAULT_PATCH_NORMALIZATION acima,
-        # normalmente não entra aqui. Mantido só para arquivos muito antigos.
-        t = deepcopy(DEFAULT_PATCH_NORMALIZATION["targets_by_patch_channel"])
-        for patch_type in ("black", "gray", "white"):
-            if patch_type in legacy_targets:
-                scalar = legacy_targets.get(patch_type)
-                try:
-                    scalar = float(scalar)
-                    for ch in ("R", "G", "B", "RE", "NIR"):
-                        t[patch_type][ch] = scalar
-                except Exception:
-                    pass
-        cfg["targets_by_patch_channel"] = t
-
-    cfg = deep_merge(DEFAULT_PATCH_NORMALIZATION, cfg)
-    return cfg
-
-
-def normalize_radiometric_config(base_rad_config, incoming_rad_config=None):
-    cfg = deep_merge(DEFAULT_RADIOMETRIC_CONFIG, base_rad_config or {})
-    cfg = deep_merge(cfg, incoming_rad_config or {})
-    return cfg
-
-
-def normalize_radiometric_normalization(base_config, incoming_config=None):
-    cfg = deep_merge(DEFAULT_RADIOMETRIC_NORMALIZATION, base_config or {})
-    cfg = deep_merge(cfg, incoming_config or {})
-    cfg["enabled"] = bool(cfg.get("enabled", False))
-    return cfg
-
-
-def build_fusion_config(fusion_data, previous_fusion_config=None):
-    base = previous_fusion_config or {}
-    generated = {
-        "alignment_mode": fusion_data.get("alignment_mode"),
-        "baseline_mm": fusion_data.get("baseline_mm"),
-        "reference_camera": fusion_data.get("reference_camera"),
-        "manual_offsets": fusion_data.get("manual_offsets"),
-        "homographies": fusion_data.get("homographies"),
-        "crop_valid_common": fusion_data.get("crop_valid_common"),
-        "resize_after_crop": fusion_data.get("resize_after_crop"),
-        "target_size": fusion_data.get("target_size"),
-    }
-    cfg = deep_merge(default_module_template()["fusion_config"], base)
-    cfg = deep_merge(cfg, generated)
-    return cfg
-
-
-def load_base_module(args):
-    """
-    Carrega defaults do module_params atual.
-
-    Prioridade:
-      1. --base_module_json, se informado.
-      2. --out, se já existir.
-      3. template interno coerente com o contrato atual.
-    """
-    candidates = []
-    if args.base_module_json:
-        candidates.append(args.base_module_json)
-    if args.out:
-        candidates.append(args.out)
-
-    for path in candidates:
-        if path and os.path.isfile(path):
-            print(f"[INFO] Usando module_params base: {path}")
-            return deep_merge(default_module_template(), load_json(path, required=True))
-
-    print("[WARN] Nenhum module_params base encontrado. Usando defaults internos.")
-    return default_module_template()
-
-
-# ============================================================
-# Main
-# ============================================================
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Monta o module_params.json preservando o contrato atual do RawProcessorCore.",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-    )
-    parser.add_argument("--camera_json", default="calibration/sensor_calibration.json")
-    parser.add_argument("--fusion_json", default="calibration/manual_offsets.json")
-    parser.add_argument("--radiometric_json", default="calibration/radiometric_config.json")
-    parser.add_argument(
-        "--radiometric_profile",
-        default="global_scene_mode",
-        choices=["global_scene_mode", "three_reference_patches_mode"],
-    )
-    parser.add_argument("--flatfield_json", default="calibration/flatfield_maps_v1.json")
-    parser.add_argument("--disable_flatfield", action="store_true")
-    parser.add_argument("--base_module_json", default=None, help="module_params atual usado como defaults antes de sobrescrever")
-    parser.add_argument("--out", default="calibration/module_params.json")
-    args = parser.parse_args()
-
-    module_base = load_base_module(args)
-
-    cam_data = load_json(args.camera_json, required=True)
-    fusion_data = load_json(args.fusion_json, required=True)
-    radiometric_data = load_json(args.radiometric_json, required=False) if args.radiometric_json else {}
-
-    # =========================
-    # MODULE PARAMS FINAL
-    # =========================
-    module_params = deepcopy(module_base)
-    module_params["schema"] = "multispec_module_params_v3"
-    module_params["saved_at"] = now_str()
-
-    # =========================
-    # ROOT / CAMERA
-    # =========================
-    root_updates = {
-        "frame_type": cam_data.get("frame_type", fusion_data.get("frame_type")),
-        "capture_mode_requested": cam_data.get("capture_mode_requested"),
-        "capture_mode_effective": cam_data.get("capture_mode_effective"),
-        "raw_policy": cam_data.get("raw_policy"),
-        "sensor_width": cam_data.get("sensor_width", fusion_data.get("sensor_width")),
-        "sensor_height": cam_data.get("sensor_height", fusion_data.get("sensor_height")),
-        "bayer_pattern": cam_data.get("bayer_pattern", fusion_data.get("bayer_pattern")),
-    }
-    module_params = deep_merge(module_params, root_updates)
-
-    module_params["rgb_processing"] = deep_merge(
-        deep_merge(DEFAULT_RGB_PROCESSING, module_base.get("rgb_processing", {})),
-        cam_data.get("rgb_processing") if isinstance(cam_data.get("rgb_processing"), dict) else {},
-    )
-
-    camera_settings = cam_data.get("camera_settings")
-    if not isinstance(camera_settings, dict):
-        camera_settings = module_base.get("camera_settings")
-    if not isinstance(camera_settings, dict):
-        raise RuntimeError("camera_json sem camera_settings válido e sem fallback no module_params base")
-    module_params["camera_settings"] = camera_settings
-
-    module_params["rgb_calibration"] = deep_merge(
-        module_base.get("rgb_calibration", {}),
-        cam_data.get("rgb_calibration") if isinstance(cam_data.get("rgb_calibration"), dict) else {},
-    )
-
-    # =========================
-    # FUSION
-    # =========================
-    module_params["fusion_config"] = build_fusion_config(
-        fusion_data,
-        previous_fusion_config=module_base.get("fusion_config", {}),
-    )
-
-    # =========================
-    # RADIOMETRIC
-    # =========================
-    incoming_rad = pick_radiometric_config(radiometric_data, selected_profile=args.radiometric_profile)
-    if not isinstance(incoming_rad, dict):
-        incoming_rad = cam_data.get("radiometric_config") if isinstance(cam_data.get("radiometric_config"), dict) else {}
-
-    module_params["radiometric_config"] = normalize_radiometric_config(
-        module_base.get("radiometric_config", {}),
-        incoming_rad,
-    )
-
-    incoming_rad_norm = first_dict(
-        radiometric_data.get("radiometric_normalization") if isinstance(radiometric_data, dict) else None,
-        cam_data.get("radiometric_normalization") if isinstance(cam_data, dict) else None,
-    ) or {}
-    module_params["radiometric_normalization"] = normalize_radiometric_normalization(
-        module_base.get("radiometric_normalization", {}),
-        incoming_rad_norm,
-    )
-
-    incoming_patch_norm = first_dict(
-        radiometric_data.get("patch_normalization") if isinstance(radiometric_data, dict) else None,
-        cam_data.get("patch_normalization") if isinstance(cam_data, dict) else None,
-    ) or {}
-    module_params["patch_normalization"] = normalize_patch_normalization_contract(
-        module_base.get("patch_normalization", {}),
-        incoming_patch_norm,
-    )
-
-    # =========================
-    # FLATFIELD
-    # =========================
-    previous_flatfield = module_base.get("flatfield_config", {}) or {}
-    if args.disable_flatfield:
-        module_params["flatfield_config"] = deep_merge(previous_flatfield, {
-            "enabled": False,
-            "reason": "desabilitado via --disable_flatfield",
-        })
-    elif args.flatfield_json and os.path.isfile(args.flatfield_json):
-        flatfield_data = load_json(args.flatfield_json, required=True)
-        module_params["flatfield_config"] = build_flatfield_config(
-            args.flatfield_json,
-            flatfield_data,
-            previous_flatfield_config=previous_flatfield,
-        )
-    else:
-        # Não achou novo flatfield: preserva o anterior se já existia.
-        module_params["flatfield_config"] = deep_merge(previous_flatfield, DEFAULT_FLATFIELD_RUNTIME)
-        if not module_params["flatfield_config"].get("enabled", False):
-            module_params["flatfield_config"]["reason"] = "flatfield não informado ou arquivo inexistente"
-
-    # =========================
-    # Save
-    # =========================
-    save_json(args.out, module_params)
-
-    print(f"[OK] module_params gerado em: {args.out}")
-    print("[OK] contrato preservado: rgb_processing, patch_normalization, radiometric_config e knobs runtime do flatfield")
-
-    flat_cfg = module_params.get("flatfield_config", {}) or {}
-    if flat_cfg.get("enabled"):
-        print(f"[OK] flatfield habilitado: {flat_cfg.get('npz_file')}")
-    else:
-        print(f"[WARN] flatfield desabilitado: {flat_cfg.get('reason')}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/flatfield_calibration_tool_quality.py b/Python/OAK/datasets/oak-fcc-3/utils/flatfield_calibration_tool_quality.py
deleted file mode 100644
index 0452a96c8..000000000
--- a/Python/OAK/datasets/oak-fcc-3/utils/flatfield_calibration_tool_quality.py
+++ /dev/null
@@ -1,1345 +0,0 @@
-import os
-import json
-import time
-import argparse
-from datetime import datetime
-from pathlib import Path
-
-import cv2
-import numpy as np
-
-from core.oak_fcc3_client import OakFcc3Client as MultiSpectralClient
-
-
-# ============================================================
-# Helpers gerais
-# ============================================================
-
-def now_str() -> str:
-    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-
-def ensure_dir(path: str):
-    os.makedirs(path, exist_ok=True)
-
-
-def safe_float(v, default=None):
-    try:
-        if v is None:
-            return default
-        return float(v)
-    except Exception:
-        return default
-
-
-def safe_int(v, default=None):
-    try:
-        if v is None:
-            return default
-        return int(v)
-    except Exception:
-        return default
-
-
-def overlay_hud(
-    img_bgr,
-    lines,
-    x=12,
-    y=24,
-    font_scale=0.58,
-    line_step=22,
-    color=(255, 255, 255),
-    shadow=(0, 0, 0),
-):
-    yy = int(y)
-    for s in lines:
-        cv2.putText(img_bgr, str(s), (int(x), yy), cv2.FONT_HERSHEY_SIMPLEX,
-                    font_scale, shadow, 3, cv2.LINE_AA)
-        cv2.putText(img_bgr, str(s), (int(x), yy), cv2.FONT_HERSHEY_SIMPLEX,
-                    font_scale, color, 1, cv2.LINE_AA)
-        yy += int(line_step)
-
-
-def to_bgr_u8_from_rgb01(rgb01: np.ndarray) -> np.ndarray:
-    rgb_u8 = np.clip(rgb01 * 255.0, 0, 255).astype(np.uint8)
-    return cv2.cvtColor(rgb_u8, cv2.COLOR_RGB2BGR)
-
-
-def gray_to_bgr_u8(gray01: np.ndarray) -> np.ndarray:
-    g = np.clip(gray01 * 255.0, 0, 255).astype(np.uint8)
-    return cv2.cvtColor(g, cv2.COLOR_GRAY2BGR)
-
-
-def resize_if_needed(img: np.ndarray, target_hw: tuple[int, int]) -> np.ndarray:
-    if img is None:
-        return None
-    th, tw = target_hw
-    if img.shape[:2] == (th, tw):
-        return img
-    return cv2.resize(img, (tw, th), interpolation=cv2.INTER_LINEAR)
-
-
-def get_decoded_by_role(decoded: dict, role: str):
-    role = str(role).lower()
-    for cam_id, item in (decoded or {}).items():
-        if str(item.get("role", "")).lower() == role:
-            return cam_id, item
-    return None, None
-
-
-def get_image_by_role(decoded: dict, role: str):
-    cam_id, item = get_decoded_by_role(decoded, role)
-    if item is None:
-        return cam_id, None
-    return cam_id, item.get("image")
-
-
-def validate_module_ready(status: dict, raw_policy: str):
-    if not status.get("ok", True):
-        raise RuntimeError(f"Status invalido retornado pelo modulo: {status}")
-
-    active_roles = status.get("active_roles", {}) or {}
-    active_count = int(status.get("camera_count_active", 0))
-
-    if raw_policy == "require_triple":
-        missing = [role for role in ("rgb", "nir", "re") if role not in active_roles]
-        if missing:
-            raise RuntimeError(
-                "RAW_BRUTO com require_triple exige rgb/nir/re ativas. "
-                f"Faltando: {missing}. Ativas: {active_roles}"
-            )
-    elif active_count < 1:
-        raise RuntimeError("RAW_BRUTO requer ao menos uma câmera ativa.")
-
-
-def compute_image_stats(img01: np.ndarray) -> dict:
-    if img01 is None:
-        return {
-            "valid": False,
-            "mean": 0.0,
-            "std": 0.0,
-            "p01": 0.0,
-            "p05": 0.0,
-            "p50": 0.0,
-            "p95": 0.0,
-            "p99": 0.0,
-            "sat_pct": 0.0,
-            "dark_pct": 0.0,
-        }
-
-    arr = img01.astype(np.float32).reshape(-1)
-    return {
-        "valid": True,
-        "mean": float(arr.mean()),
-        "std": float(arr.std()),
-        "p01": float(np.percentile(arr, 1)),
-        "p05": float(np.percentile(arr, 5)),
-        "p50": float(np.percentile(arr, 50)),
-        "p95": float(np.percentile(arr, 95)),
-        "p99": float(np.percentile(arr, 99)),
-        "sat_pct": float((arr >= 0.985).mean() * 100.0),
-        "dark_pct": float((arr <= 0.015).mean() * 100.0),
-    }
-
-
-def robust_center(x: np.ndarray, low_pct=10.0, high_pct=90.0) -> float:
-    arr = x[np.isfinite(x)].astype(np.float32).reshape(-1)
-    if arr.size == 0:
-        return 1.0
-
-    lo = np.percentile(arr, low_pct)
-    hi = np.percentile(arr, high_pct)
-    core = arr[(arr >= lo) & (arr <= hi)]
-    if core.size == 0:
-        core = arr
-
-    v = float(np.median(core))
-    if not np.isfinite(v) or v <= 1e-8:
-        return 1.0
-    return v
-
-
-def normalize_for_display(img: np.ndarray) -> np.ndarray:
-    if img is None:
-        return None
-
-    arr = img.astype(np.float32)
-    finite = arr[np.isfinite(arr)]
-    if finite.size == 0:
-        return np.zeros_like(arr, dtype=np.float32)
-
-    lo = np.percentile(finite, 1)
-    hi = np.percentile(finite, 99)
-    if hi <= lo:
-        hi = lo + 1e-6
-
-    out = (arr - lo) / (hi - lo)
-    return np.clip(out, 0.0, 1.0)
-
-
-def smooth_map_gain(gain: np.ndarray, ksize: int) -> np.ndarray:
-    if ksize is None or ksize <= 1:
-        return gain.astype(np.float32)
-
-    if ksize % 2 == 0:
-        ksize += 1
-
-    return cv2.GaussianBlur(
-        gain.astype(np.float32),
-        (ksize, ksize),
-        sigmaX=0,
-        sigmaY=0,
-        borderType=cv2.BORDER_REFLECT,
-    )
-
-
-def clip_gain_map(gain: np.ndarray, min_gain: float, max_gain: float) -> np.ndarray:
-    return np.clip(gain.astype(np.float32), float(min_gain), float(max_gain)).astype(np.float32)
-
-
-# ============================================================
-# Qualidade de calibração flat-field
-# ============================================================
-
-QUALITY_THRESHOLDS = {
-    # Frame WHITE antes/durante captura
-    "white_sat_warning_pct": 0.10,
-    "white_sat_bad_pct": 0.50,
-    "white_dark_warning_pct": 1.00,
-    "white_dark_bad_pct": 5.00,
-    "white_uniformity_warning": 0.20,
-    "white_uniformity_bad": 0.35,
-    "white_side_ratio_low_warning": 0.80,
-    "white_side_ratio_low_bad": 0.65,
-    "white_side_ratio_high_warning": 1.25,
-    "white_side_ratio_high_bad": 1.45,
-
-    # Mapa final de ganho
-    "gain_p99_warning": 1.60,
-    "gain_p99_bad": 1.85,
-    "gain_max_warning": 1.80,
-    "gain_max_bad": 2.10,
-    "gain_side_ratio_warning": 1.30,
-    "gain_side_ratio_bad": 1.55,
-    "gain_center_low_warning": 0.80,
-    "gain_center_low_bad": 0.65,
-
-    # Defeitos locais (sujeira/manchas) após remover tendência suave
-    "white_local_defect_dev_warning": 0.12,
-    "white_local_defect_dev_bad": 0.20,
-    "white_local_outlier_pct_warning": 1.00,
-    "white_local_outlier_pct_bad": 3.00,
-    "gain_local_defect_dev_warning": 0.10,
-    "gain_local_defect_dev_bad": 0.18,
-    "gain_local_outlier_pct_warning": 1.00,
-    "gain_local_outlier_pct_bad": 3.00,
-}
-
-
-def _status_rank(status: str) -> int:
-    return {"good": 0, "warning": 1, "bad": 2}.get(str(status).lower(), 0)
-
-
-def _merge_status(a: str, b: str) -> str:
-    return a if _status_rank(a) >= _status_rank(b) else b
-
-
-def _append_reason(reasons: list[str], condition: bool, status_ref: str, reason: str):
-    if condition:
-        reasons.append(reason)
-        return status_ref
-    return "good"
-
-
-def _safe_ratio(num: float, den: float, default: float = 1.0) -> float:
-    try:
-        num = float(num)
-        den = float(den)
-        if not np.isfinite(num) or not np.isfinite(den) or abs(den) <= 1e-9:
-            return default
-        return num / den
-    except Exception:
-        return default
-
-
-def _crop_margin(arr: np.ndarray, margin_frac: float = 0.08) -> np.ndarray:
-    h, w = arr.shape[:2]
-    mx = int(w * margin_frac)
-    my = int(h * margin_frac)
-    x0, x1 = mx, max(mx + 1, w - mx)
-    y0, y1 = my, max(my + 1, h - my)
-    return arr[y0:y1, x0:x1]
-
-
-def region_profile_stats(img01: np.ndarray) -> dict:
-    """
-    Mede homogeneidade espacial em 3x3 e também em faixas esquerda/centro/direita.
-    Usa p50 para ser robusto contra textura fina/ruído.
-    """
-    if img01 is None:
-        return {"valid": False}
-
-    arr = img01.astype(np.float32)
-    if arr.ndim == 3:
-        arr = (0.299 * arr[:, :, 0] + 0.587 * arr[:, :, 1] + 0.114 * arr[:, :, 2]).astype(np.float32)
-
-    arr = _crop_margin(arr, margin_frac=0.04)
-    h, w = arr.shape[:2]
-    if h < 9 or w < 9:
-        return {"valid": False}
-
-    cells = []
-    grid = []
-    for gy in range(3):
-        row = []
-        y0 = int(round(gy * h / 3.0))
-        y1 = int(round((gy + 1) * h / 3.0))
-        for gx in range(3):
-            x0 = int(round(gx * w / 3.0))
-            x1 = int(round((gx + 1) * w / 3.0))
-            cell = arr[y0:y1, x0:x1].reshape(-1)
-            cell = cell[np.isfinite(cell)]
-            p50 = float(np.percentile(cell, 50)) if cell.size else 0.0
-            row.append(p50)
-            cells.append(p50)
-        grid.append(row)
-
-    cells_np = np.array(cells, dtype=np.float32)
-    center = float(grid[1][1])
-    if center <= 1e-9 or not np.isfinite(center):
-        center = float(np.median(cells_np)) if cells_np.size else 1.0
-
-    left = float(np.median([grid[0][0], grid[1][0], grid[2][0]]))
-    mid = float(np.median([grid[0][1], grid[1][1], grid[2][1]]))
-    right = float(np.median([grid[0][2], grid[1][2], grid[2][2]]))
-    top = float(np.median(grid[0]))
-    bottom = float(np.median(grid[2]))
-
-    return {
-        "valid": True,
-        "grid_p50": grid,
-        "center_p50": center,
-        "left_p50": left,
-        "mid_p50": mid,
-        "right_p50": right,
-        "top_p50": top,
-        "bottom_p50": bottom,
-        "min_region_p50": float(cells_np.min()),
-        "max_region_p50": float(cells_np.max()),
-        "median_region_p50": float(np.median(cells_np)),
-        "uniformity_delta": _safe_ratio(float(cells_np.max() - cells_np.min()), center, default=0.0),
-        "left_center_ratio": _safe_ratio(left, center),
-        "right_center_ratio": _safe_ratio(right, center),
-        "top_center_ratio": _safe_ratio(top, center),
-        "bottom_center_ratio": _safe_ratio(bottom, center),
-    }
-
-
-
-
-def local_defect_stats(img01: np.ndarray, blur_frac: float = 0.18, margin_frac: float = 0.04, outlier_thr: float = 0.10) -> dict:
-    """
-    Detecta defeitos locais após remover tendência suave (vinheta/gradiente global).
-    Útil para sujeira na lente, manchas, poeira, gotas e sombras localizadas.
-    """
-    if img01 is None:
-        return {"valid": False}
-
-    arr = img01.astype(np.float32)
-    if arr.ndim == 3:
-        arr = (0.299 * arr[:, :, 0] + 0.587 * arr[:, :, 1] + 0.114 * arr[:, :, 2]).astype(np.float32)
-
-    arr = _crop_margin(arr, margin_frac=margin_frac)
-    h, w = arr.shape[:2]
-    if h < 32 or w < 32:
-        return {"valid": False}
-
-    k = int(round(min(h, w) * blur_frac))
-    k = max(31, k)
-    if k % 2 == 0:
-        k += 1
-
-    smooth = cv2.GaussianBlur(arr, (k, k), sigmaX=0, sigmaY=0, borderType=cv2.BORDER_REFLECT)
-    ratio = arr / np.maximum(smooth, 1e-6)
-    ratio = np.clip(ratio, 0.0, 4.0)
-    resid = ratio - 1.0
-    abs_resid = np.abs(resid)
-
-    finite = abs_resid[np.isfinite(abs_resid)]
-    if finite.size == 0:
-        return {"valid": False}
-
-    p95 = float(np.percentile(finite, 95))
-    p99 = float(np.percentile(finite, 99))
-    max_dev = float(np.max(finite))
-    outlier_pct = float((finite >= float(outlier_thr)).mean() * 100.0)
-
-    # Percentis direcionais para diagnosticar manchas escuras/claras.
-    ratio_flat = ratio[np.isfinite(ratio)].reshape(-1)
-    r_p01 = float(np.percentile(ratio_flat, 1)) if ratio_flat.size else 1.0
-    r_p99 = float(np.percentile(ratio_flat, 99)) if ratio_flat.size else 1.0
-
-    return {
-        "valid": True,
-        "blur_ksize": int(k),
-        "ratio_p01": r_p01,
-        "ratio_p99": r_p99,
-        "local_dev_p95": p95,
-        "local_dev_p99": p99,
-        "local_dev_max": max_dev,
-        "local_outlier_pct": outlier_pct,
-        "outlier_threshold": float(outlier_thr),
-    }
-
-def evaluate_white_frame_quality(channels: dict, thresholds: dict | None = None) -> dict:
-    """Avalia se a cena WHITE/flat está homogênea antes de capturar."""
-    th = dict(QUALITY_THRESHOLDS)
-    if thresholds:
-        th.update(thresholds)
-
-    result = {
-        "status": "good",
-        "reasons": [],
-        "channels": {},
-        "thresholds": th,
-    }
-
-    for ch, img in (channels or {}).items():
-        st = compute_image_stats(img)
-        rp = region_profile_stats(img)
-        ld = local_defect_stats(img, outlier_thr=th.get("white_local_defect_dev_warning", 0.10))
-        status = "good"
-        reasons = []
-
-        sat = float(st.get("sat_pct", 0.0))
-        dark = float(st.get("dark_pct", 0.0))
-        uniformity = float(rp.get("uniformity_delta", 0.0)) if rp.get("valid") else 0.0
-        lcr = float(rp.get("left_center_ratio", 1.0)) if rp.get("valid") else 1.0
-        rcr = float(rp.get("right_center_ratio", 1.0)) if rp.get("valid") else 1.0
-        tcr = float(rp.get("top_center_ratio", 1.0)) if rp.get("valid") else 1.0
-        bcr = float(rp.get("bottom_center_ratio", 1.0)) if rp.get("valid") else 1.0
-        ld_dev = float(ld.get("local_dev_p99", 0.0)) if ld.get("valid") else 0.0
-        ld_out = float(ld.get("local_outlier_pct", 0.0)) if ld.get("valid") else 0.0
-
-        if sat > th["white_sat_bad_pct"]:
-            status = _merge_status(status, "bad"); reasons.append(f"sat_pct_bad:{sat:.2f}%")
-        elif sat > th["white_sat_warning_pct"]:
-            status = _merge_status(status, "warning"); reasons.append(f"sat_pct_warning:{sat:.2f}%")
-
-        if dark > th["white_dark_bad_pct"]:
-            status = _merge_status(status, "bad"); reasons.append(f"dark_pct_bad:{dark:.2f}%")
-        elif dark > th["white_dark_warning_pct"]:
-            status = _merge_status(status, "warning"); reasons.append(f"dark_pct_warning:{dark:.2f}%")
-
-        if uniformity > th["white_uniformity_bad"]:
-            status = _merge_status(status, "bad"); reasons.append(f"uniformity_bad:{uniformity:.2f}")
-        elif uniformity > th["white_uniformity_warning"]:
-            status = _merge_status(status, "warning"); reasons.append(f"uniformity_warning:{uniformity:.2f}")
-
-        for name, val in (("left", lcr), ("right", rcr), ("top", tcr), ("bottom", bcr)):
-            if val < th["white_side_ratio_low_bad"] or val > th["white_side_ratio_high_bad"]:
-                status = _merge_status(status, "bad"); reasons.append(f"{name}_center_ratio_bad:{val:.2f}")
-            elif val < th["white_side_ratio_low_warning"] or val > th["white_side_ratio_high_warning"]:
-                status = _merge_status(status, "warning"); reasons.append(f"{name}_center_ratio_warning:{val:.2f}")
-
-        if ld.get("valid"):
-            if ld_dev > th["white_local_defect_dev_bad"]:
-                status = _merge_status(status, "bad"); reasons.append(f"local_defect_dev_bad:{ld_dev:.2f}")
-            elif ld_dev > th["white_local_defect_dev_warning"]:
-                status = _merge_status(status, "warning"); reasons.append(f"local_defect_dev_warning:{ld_dev:.2f}")
-
-            if ld_out > th["white_local_outlier_pct_bad"]:
-                status = _merge_status(status, "bad"); reasons.append(f"local_outlier_pct_bad:{ld_out:.2f}%")
-            elif ld_out > th["white_local_outlier_pct_warning"]:
-                status = _merge_status(status, "warning"); reasons.append(f"local_outlier_pct_warning:{ld_out:.2f}%")
-
-        result["channels"][ch] = {
-            "status": status,
-            "reasons": reasons,
-            "stats": st,
-            "region_profile": rp,
-            "local_defect": ld,
-        }
-        result["status"] = _merge_status(result["status"], status)
-        result["reasons"].extend([f"{ch}:{r}" for r in reasons[:3]])
-
-    result["summary"] = {
-        "channel_count": len(result["channels"]),
-        "bad_count": sum(1 for v in result["channels"].values() if v.get("status") == "bad"),
-        "warning_count": sum(1 for v in result["channels"].values() if v.get("status") == "warning"),
-    }
-    return result
-
-
-def evaluate_gain_maps_quality(gain_maps: dict, corrected_white: dict | None = None, thresholds: dict | None = None) -> dict:
-    """Avalia se os mapas de ganho finais estão agressivos demais ou assimétricos."""
-    th = dict(QUALITY_THRESHOLDS)
-    if thresholds:
-        th.update(thresholds)
-
-    result = {
-        "status": "good",
-        "reasons": [],
-        "channels": {},
-        "thresholds": th,
-    }
-
-    for ch, gain in (gain_maps or {}).items():
-        g = gain.astype(np.float32)
-        finite = g[np.isfinite(g)]
-        if finite.size == 0:
-            ch_status = "bad"
-            reasons = ["gain_map_invalid"]
-            stats = {}
-            rp = {"valid": False}
-            ld = {"valid": False}
-        else:
-            stats = {
-                "min": float(np.min(finite)),
-                "p01": float(np.percentile(finite, 1)),
-                "p05": float(np.percentile(finite, 5)),
-                "p50": float(np.percentile(finite, 50)),
-                "p95": float(np.percentile(finite, 95)),
-                "p99": float(np.percentile(finite, 99)),
-                "max": float(np.max(finite)),
-                "mean": float(np.mean(finite)),
-                "std": float(np.std(finite)),
-            }
-            rp = region_profile_stats(g)
-            ld = local_defect_stats(g, outlier_thr=th.get("gain_local_defect_dev_warning", 0.08))
-            ch_status = "good"
-            reasons = []
-
-            if stats["p99"] > th["gain_p99_bad"]:
-                ch_status = _merge_status(ch_status, "bad"); reasons.append(f"gain_p99_bad:{stats['p99']:.2f}")
-            elif stats["p99"] > th["gain_p99_warning"]:
-                ch_status = _merge_status(ch_status, "warning"); reasons.append(f"gain_p99_warning:{stats['p99']:.2f}")
-
-            if stats["max"] > th["gain_max_bad"]:
-                ch_status = _merge_status(ch_status, "bad"); reasons.append(f"gain_max_bad:{stats['max']:.2f}")
-            elif stats["max"] > th["gain_max_warning"]:
-                ch_status = _merge_status(ch_status, "warning"); reasons.append(f"gain_max_warning:{stats['max']:.2f}")
-
-            if rp.get("valid"):
-                center = float(rp.get("center_p50", 1.0))
-                rcr = float(rp.get("right_center_ratio", 1.0))
-                lcr = float(rp.get("left_center_ratio", 1.0))
-                center_gain = center
-
-                for name, ratio in (("left", lcr), ("right", rcr)):
-                    inv = max(ratio, 1.0 / max(ratio, 1e-6))
-                    if inv > th["gain_side_ratio_bad"]:
-                        ch_status = _merge_status(ch_status, "bad"); reasons.append(f"gain_{name}_center_ratio_bad:{ratio:.2f}")
-                    elif inv > th["gain_side_ratio_warning"]:
-                        ch_status = _merge_status(ch_status, "warning"); reasons.append(f"gain_{name}_center_ratio_warning:{ratio:.2f}")
-
-                if center_gain < th["gain_center_low_bad"]:
-                    ch_status = _merge_status(ch_status, "bad"); reasons.append(f"gain_center_low_bad:{center_gain:.2f}")
-                elif center_gain < th["gain_center_low_warning"]:
-                    ch_status = _merge_status(ch_status, "warning"); reasons.append(f"gain_center_low_warning:{center_gain:.2f}")
-
-            if ld.get("valid"):
-                ld_dev = float(ld.get("local_dev_p99", 0.0))
-                ld_out = float(ld.get("local_outlier_pct", 0.0))
-                if ld_dev > th["gain_local_defect_dev_bad"]:
-                    ch_status = _merge_status(ch_status, "bad"); reasons.append(f"gain_local_defect_dev_bad:{ld_dev:.2f}")
-                elif ld_dev > th["gain_local_defect_dev_warning"]:
-                    ch_status = _merge_status(ch_status, "warning"); reasons.append(f"gain_local_defect_dev_warning:{ld_dev:.2f}")
-
-                if ld_out > th["gain_local_outlier_pct_bad"]:
-                    ch_status = _merge_status(ch_status, "bad"); reasons.append(f"gain_local_outlier_pct_bad:{ld_out:.2f}%")
-                elif ld_out > th["gain_local_outlier_pct_warning"]:
-                    ch_status = _merge_status(ch_status, "warning"); reasons.append(f"gain_local_outlier_pct_warning:{ld_out:.2f}%")
-
-        white_quality = None
-        if corrected_white is not None and ch in corrected_white:
-            white_quality = evaluate_white_frame_quality({ch: corrected_white[ch]}, thresholds=th).get("channels", {}).get(ch)
-
-        result["channels"][ch] = {
-            "status": ch_status,
-            "reasons": reasons,
-            "gain_stats": stats,
-            "gain_region_profile": rp,
-            "local_defect": ld,
-            "white_signal_quality": white_quality,
-        }
-        result["status"] = _merge_status(result["status"], ch_status)
-        result["reasons"].extend([f"{ch}:{r}" for r in reasons[:4]])
-
-    result["summary"] = {
-        "channel_count": len(result["channels"]),
-        "bad_count": sum(1 for v in result["channels"].values() if v.get("status") == "bad"),
-        "warning_count": sum(1 for v in result["channels"].values() if v.get("status") == "warning"),
-    }
-    return result
-
-
-def format_quality_lines(report: dict | None, title: str = "QUALITY", max_channels: int = 5, max_reasons: int = 5) -> list[str]:
-    if not report:
-        return [f"{title}: sem dados"]
-
-    status = str(report.get("status", "unknown")).upper()
-    lines = [f"{title}: {status}"]
-
-    channels = report.get("channels", {}) or {}
-    for ch in list(channels.keys())[:max_channels]:
-        item = channels.get(ch, {}) or {}
-        st = str(item.get("status", "unknown")).upper()
-        rp = item.get("region_profile") or item.get("gain_region_profile") or {}
-        stats = item.get("stats") or item.get("gain_stats") or {}
-        ld = item.get("local_defect") or {}
-        ld_txt = ""
-        if isinstance(ld, dict) and ld.get("valid"):
-            ld_txt = f" LD={ld.get('local_dev_p99', 0):.2f}/{ld.get('local_outlier_pct', 0):.1f}%"
-        if rp.get("valid"):
-            lines.append(
-                f"{ch}: {st} | p50={stats.get('p50', stats.get('p50', 0)):.3f} "
-                f"unif={rp.get('uniformity_delta', 0):.2f} "
-                f"L/C={rp.get('left_center_ratio', 1):.2f} "
-                f"R/C={rp.get('right_center_ratio', 1):.2f}{ld_txt}"
-            )
-        else:
-            lines.append(f"{ch}: {st}{ld_txt}")
-
-    reasons = report.get("reasons", []) or []
-    for r in reasons[:max_reasons]:
-        lines.append(f"! {r}")
-
-    if len(reasons) > max_reasons:
-        lines.append(f"! +{len(reasons) - max_reasons} avisos")
-
-    return lines
-
-
-# ============================================================
-# Controle de câmera e extração de canais
-# ============================================================
-
-def get_controls_for_role(cam: MultiSpectralClient, role: str) -> dict:
-    try:
-        ctrl = cam.svc.get_camera_controls(role=role) or {}
-        return {
-            "ok": True,
-            "role": role,
-            "ae_enable": bool(ctrl.get("ae_enable", False)),
-            "awb_enable": bool(ctrl.get("awb_enable", False)),
-            "exposure_time_us": safe_int(ctrl.get("exposure_time_us"), None),
-            "analogue_gain": safe_float(ctrl.get("analogue_gain"), None),
-            "colour_gains": ctrl.get("colour_gains", None),
-            "raw": ctrl,
-        }
-    except Exception as e:
-        return {
-            "ok": False,
-            "role": role,
-            "error": str(e),
-            "ae_enable": None,
-            "awb_enable": None,
-            "exposure_time_us": None,
-            "analogue_gain": None,
-            "colour_gains": None,
-        }
-
-
-def get_all_controls(cam: MultiSpectralClient) -> dict:
-    return {role: get_controls_for_role(cam, role) for role in ("rgb", "re", "nir")}
-
-
-def exposure_gain_factor(ctrl: dict, fallback_exp=1.0, fallback_gain=1.0) -> float:
-    exp = safe_float(ctrl.get("exposure_time_us"), fallback_exp)
-    gain = safe_float(ctrl.get("analogue_gain"), fallback_gain)
-
-    if exp is None or exp <= 0:
-        exp = fallback_exp
-    if gain is None or gain <= 0:
-        gain = fallback_gain
-
-    return float(exp * gain)
-
-
-def extract_channels_from_decoded(decoded: dict) -> dict:
-    """
-    Retorna canais em float32 0..1 no espaço nativo de cada câmera:
-      R/G/B vêm do debayer da role rgb.
-      RE vem da role re.
-      NIR vem da role nir.
-    """
-    _, rgb01 = get_image_by_role(decoded, "rgb")
-    _, re01 = get_image_by_role(decoded, "re")
-    _, nir01 = get_image_by_role(decoded, "nir")
-
-    def assert_not_raw10_packed_image(role, img, expected_w=1280):
-        if img is None:
-            return
-
-        if img.ndim == 2 and img.shape[1] == int(expected_w * 10 / 8):
-            raise RuntimeError(
-                f"{role.upper()} parece RAW10_PACKED interpretado como imagem: "
-                f"shape={img.shape}. Esperado decodificado com largura {expected_w}."
-            )
-        
-    assert_not_raw10_packed_image("re", re01, expected_w=1280)
-    assert_not_raw10_packed_image("nir", nir01, expected_w=1280)
-
-    out = {}
-
-    if rgb01 is not None:
-        rgb01 = rgb01.astype(np.float32)
-        if rgb01.ndim == 3 and rgb01.shape[2] >= 3:
-            out["R"] = rgb01[:, :, 0].copy()
-            out["G"] = rgb01[:, :, 1].copy()
-            out["B"] = rgb01[:, :, 2].copy()
-
-    if re01 is not None:
-        out["RE"] = re01.astype(np.float32).copy()
-
-    if nir01 is not None:
-        out["NIR"] = nir01.astype(np.float32).copy()
-
-    return out
-
-
-def channel_to_role(ch: str) -> str:
-    ch = ch.upper()
-    if ch in ("R", "G", "B"):
-        return "rgb"
-    if ch == "RE":
-        return "re"
-    if ch == "NIR":
-        return "nir"
-    raise ValueError(f"Canal desconhecido: {ch}")
-
-
-def apply_exp_gain_correction(channels: dict, controls: dict, enabled: bool) -> dict:
-    if not enabled:
-        return {k: v.astype(np.float32).copy() for k, v in channels.items()}
-
-    corrected = {}
-    for ch, img in channels.items():
-        role = channel_to_role(ch)
-        ctrl = controls.get(role, {}) or {}
-        factor = exposure_gain_factor(ctrl, fallback_exp=1.0, fallback_gain=1.0)
-        corrected[ch] = (img.astype(np.float32) / max(factor, 1e-6)).astype(np.float32)
-
-    return corrected
-
-
-# ============================================================
-# UI
-# ============================================================
-
-def build_board(decoded, controls, state_lines, progress_lines, preview_scale=1.0, quality_lines=None):
-    rgb_id, rgb01 = get_image_by_role(decoded, "rgb")
-    re_id, re01 = get_image_by_role(decoded, "re")
-    nir_id, nir01 = get_image_by_role(decoded, "nir")
-
-    if rgb01 is not None:
-        rgb_panel = to_bgr_u8_from_rgb01(rgb01)
-        base_h, base_w = rgb01.shape[:2]
-    else:
-        base_h, base_w = 800, 1280
-        rgb_panel = np.zeros((base_h, base_w, 3), dtype=np.uint8)
-        overlay_hud(rgb_panel, ["RGB", "sem frame"])
-
-    re01 = resize_if_needed(re01, (base_h, base_w)) if re01 is not None else None
-    nir01 = resize_if_needed(nir01, (base_h, base_w)) if nir01 is not None else None
-
-    re_panel = gray_to_bgr_u8(re01) if re01 is not None else np.zeros_like(rgb_panel)
-    nir_panel = gray_to_bgr_u8(nir01) if nir01 is not None else np.zeros_like(rgb_panel)
-
-    rgb_stats = compute_image_stats(rgb01[:, :, 1] if rgb01 is not None and rgb01.ndim == 3 else None)
-    re_stats = compute_image_stats(re01)
-    nir_stats = compute_image_stats(nir01)
-
-    overlay_hud(rgb_panel, [
-        f"RGB ({rgb_id})",
-        f"p50={rgb_stats['p50']:.3f} p95={rgb_stats['p95']:.3f} sat={rgb_stats['sat_pct']:.2f}%",
-        f"EXP={controls.get('rgb', {}).get('exposure_time_us')} GAIN={controls.get('rgb', {}).get('analogue_gain')}",
-    ])
-
-    overlay_hud(re_panel, [
-        f"RE ({re_id})",
-        f"p50={re_stats['p50']:.3f} p95={re_stats['p95']:.3f} sat={re_stats['sat_pct']:.2f}%",
-        f"EXP={controls.get('re', {}).get('exposure_time_us')} GAIN={controls.get('re', {}).get('analogue_gain')}",
-    ])
-
-    overlay_hud(nir_panel, [
-        f"NIR ({nir_id})",
-        f"p50={nir_stats['p50']:.3f} p95={nir_stats['p95']:.3f} sat={nir_stats['sat_pct']:.2f}%",
-        f"EXP={controls.get('nir', {}).get('exposure_time_us')} GAIN={controls.get('nir', {}).get('analogue_gain')}",
-    ])
-
-    data_panel = np.zeros_like(rgb_panel)
-
-    lines = []
-    lines.extend(state_lines)
-    lines.append("")
-    lines.extend(progress_lines)
-    if quality_lines:
-        lines.append("")
-        lines.extend(quality_lines)
-    lines.append("")
-    lines.extend([
-        "ENTER = iniciar etapa atual",
-        "S = pular etapa dark/preto",
-        "Q / ESC = sair sem salvar",
-        "",
-        "Dica: branco/preto devem preencher todo o campo de visao.",
-        "Para dark-frame perfeito, tampe as lentes em vez de usar fundo preto.",
-    ])
-
-    overlay_hud(data_panel, lines, x=18, y=34, font_scale=0.58, line_step=24)
-
-    top = np.hstack([rgb_panel, re_panel])
-    bottom = np.hstack([nir_panel, data_panel])
-    board = np.vstack([top, bottom])
-
-    if preview_scale != 1.0:
-        board = cv2.resize(
-            board,
-            (int(board.shape[1] * preview_scale), int(board.shape[0] * preview_scale)),
-            interpolation=cv2.INTER_NEAREST,
-        )
-
-    return board
-
-
-# ============================================================
-# Captura e processamento
-# ============================================================
-
-def capture_stage(
-    cam: MultiSpectralClient,
-    stage_name: str,
-    frames_count: int,
-    discard_frames: int,
-    exp_gain_correct: bool,
-    preview_scale: float,
-    window_name: str,
-):
-    """
-    Captura frames decodificados e retorna:
-      channel_stack: dict canal -> list[np.ndarray]
-      controls_log: lista dos controles reais lidos por frame
-      meta_log: lista de metadados básicos por frame
-    """
-    channel_stack = {}
-    controls_log = []
-    meta_log = []
-
-    total_target = int(frames_count)
-    captured = 0
-    seen_frame_ids = set()
-    last_decoded = {}
-    last_controls = {}
-
-    while captured < total_target:
-        frame, meta, decoded = cam.get_next_decoded(timeout=2.0)
-        if meta is None or frame is None:
-            continue
-
-        frame_id = meta.get("frame_id")
-        if frame_id in seen_frame_ids:
-            continue
-        seen_frame_ids.add(frame_id)
-
-        last_decoded = decoded
-        last_controls = get_all_controls(cam)
-
-        if len(seen_frame_ids) <= discard_frames:
-            progress_lines = [
-                f"Etapa: {stage_name}",
-                f"Descartando frames iniciais: {len(seen_frame_ids)}/{discard_frames}",
-                "Aguardando estabilizacao de exposicao/stream...",
-            ]
-            board = build_board(
-                decoded=last_decoded,
-                controls=last_controls,
-                state_lines=[f"CALIBRACAO FLAT-FIELD - {stage_name.upper()}"],
-                progress_lines=progress_lines,
-                preview_scale=preview_scale,
-            )
-            cv2.imshow(window_name, board)
-            cv2.waitKey(1)
-            continue
-
-        channels = extract_channels_from_decoded(decoded)
-        channels = apply_exp_gain_correction(channels, last_controls, enabled=exp_gain_correct)
-
-        for ch, img in channels.items():
-            channel_stack.setdefault(ch, []).append(img.astype(np.float32).copy())
-
-        meta_log.append({
-            "frame_id": frame_id,
-            "sync_ok": meta.get("sync_ok"),
-            "sync_dt_ms": meta.get("sync_dt_ms"),
-            "timestamps": meta.get("timestamps"),
-        })
-        controls_log.append(last_controls)
-
-        captured += 1
-
-        progress_lines = [
-            f"Etapa: {stage_name}",
-            f"Capturando: {captured}/{total_target}",
-            f"exp_gain_correction={'ON' if exp_gain_correct else 'OFF'}",
-        ]
-
-        board = build_board(
-            decoded=last_decoded,
-            controls=last_controls,
-            state_lines=[f"CALIBRACAO FLAT-FIELD - {stage_name.upper()}"],
-            progress_lines=progress_lines,
-            preview_scale=preview_scale,
-        )
-
-        # Barra de progresso simples.
-        h, w = board.shape[:2]
-        pct = captured / max(total_target, 1)
-        cv2.rectangle(board, (30, h - 38), (w - 30, h - 18), (80, 80, 80), -1)
-        cv2.rectangle(board, (30, h - 38), (30 + int((w - 60) * pct), h - 18), (0, 220, 0), -1)
-
-        cv2.imshow(window_name, board)
-
-        k = cv2.waitKey(1) & 0xFF
-        if k in (ord("q"), ord("Q"), 27):
-            raise KeyboardInterrupt("Captura cancelada pelo usuario.")
-
-    return channel_stack, controls_log, meta_log
-
-
-def median_stack(channel_stack: dict) -> dict:
-    med = {}
-    for ch, frames in channel_stack.items():
-        if not frames:
-            continue
-        arr = np.stack(frames, axis=0).astype(np.float32)
-        med[ch] = np.median(arr, axis=0).astype(np.float32)
-    return med
-
-
-def build_gain_maps(
-    white_med: dict,
-    dark_med: dict | None,
-    epsilon: float,
-    smooth_ksize: int,
-    min_gain: float,
-    max_gain: float,
-):
-    gain_maps = {}
-    flat_norm_maps = {}
-    corrected_white = {}
-    dark_used = {}
-
-    for ch, white in white_med.items():
-        white = white.astype(np.float32)
-
-        if dark_med is not None and ch in dark_med:
-            dark = resize_if_needed(dark_med[ch].astype(np.float32), white.shape[:2])
-        else:
-            dark = np.zeros_like(white, dtype=np.float32)
-
-        signal = white - dark
-        signal = np.maximum(signal, float(epsilon)).astype(np.float32)
-
-        center = robust_center(signal, low_pct=10.0, high_pct=90.0)
-        flat_norm = signal / max(center, epsilon)
-
-        gain = center / np.maximum(signal, epsilon)
-        gain = smooth_map_gain(gain, smooth_ksize)
-        gain = clip_gain_map(gain, min_gain=min_gain, max_gain=max_gain)
-
-        gain_maps[ch] = gain.astype(np.float32)
-        flat_norm_maps[ch] = flat_norm.astype(np.float32)
-        corrected_white[ch] = signal.astype(np.float32)
-        dark_used[ch] = dark.astype(np.float32)
-
-    return gain_maps, flat_norm_maps, corrected_white, dark_used
-
-
-def save_preview_maps(out_dir: str, gain_maps: dict, corrected_white: dict):
-    ensure_dir(out_dir)
-
-    for ch, gain in gain_maps.items():
-        gain_vis = normalize_for_display(gain)
-        cv2.imwrite(os.path.join(out_dir, f"gain_{ch}.png"), gray_to_bgr_u8(gain_vis))
-
-    for ch, white in corrected_white.items():
-        white_vis = normalize_for_display(white)
-        cv2.imwrite(os.path.join(out_dir, f"white_signal_{ch}.png"), gray_to_bgr_u8(white_vis))
-
-
-def show_final_preview(window_name: str, gain_maps: dict, preview_scale: float, quality_report: dict | None = None):
-    order = ["R", "G", "B", "RE", "NIR"]
-    panels = []
-
-    # RGB e mono podem sair com tamanhos diferentes dependendo do decode/preview.
-    # Para o painel final, padronizamos tudo para o maior H/W encontrado.
-    shapes = [gain_maps[ch].shape[:2] for ch in order if ch in gain_maps]
-    if not shapes:
-        return
-
-    target_h = max(s[0] for s in shapes)
-    target_w = max(s[1] for s in shapes)
-
-    def fit_panel(img_bgr: np.ndarray) -> np.ndarray:
-        if img_bgr.shape[:2] == (target_h, target_w):
-            return img_bgr
-        return cv2.resize(img_bgr, (target_w, target_h), interpolation=cv2.INTER_NEAREST)
-
-    for ch in order:
-        if ch not in gain_maps:
-            panel = np.zeros((target_h, target_w, 3), dtype=np.uint8)
-            overlay_hud(panel, [ch, "sem mapa"])
-        else:
-            vis = normalize_for_display(gain_maps[ch])
-            panel = gray_to_bgr_u8(vis)
-            panel = fit_panel(panel)
-            g = gain_maps[ch]
-            overlay_hud(panel, [
-                f"GAIN MAP {ch}",
-                f"shape={list(g.shape)}",
-                f"min={float(np.min(g)):.3f} max={float(np.max(g)):.3f}",
-                f"mean={float(np.mean(g)):.3f} std={float(np.std(g)):.3f}",
-            ])
-        panels.append(panel)
-
-    blank = np.zeros((target_h, target_w, 3), dtype=np.uint8)
-    final_lines = [
-        "Flat-field salvo com sucesso.",
-        "ENTER/qualquer tecla = fechar",
-        "",
-    ]
-    final_lines.extend(format_quality_lines(quality_report, title="FINAL QUALITY", max_channels=5, max_reasons=8))
-    final_lines.extend([
-        "",
-        "Use estes mapas antes da fusao geometrica.",
-        "",
-        "Obs: RGB e mono podem ter shapes diferentes;",
-        "isso e normal se o decode gerar resolucoes distintas.",
-    ])
-    overlay_hud(blank, final_lines, x=18, y=36)
-
-    top = np.hstack([panels[0], panels[1], panels[2]])
-    bottom = np.hstack([panels[3], panels[4], blank])
-    board = np.vstack([top, bottom])
-
-    if preview_scale != 1.0:
-        board = cv2.resize(
-            board,
-            (int(board.shape[1] * preview_scale), int(board.shape[0] * preview_scale)),
-            interpolation=cv2.INTER_NEAREST,
-        )
-
-    cv2.imshow(window_name, board)
-    cv2.waitKey(0)
-
-
-def wait_for_enter_or_skip(
-    cam: MultiSpectralClient,
-    window_name: str,
-    title: str,
-    instruction_lines: list[str],
-    preview_scale: float,
-    allow_skip=False,
-    quality_check_white=False,
-    require_good_quality=False,
-):
-    while True:
-        frame, meta, decoded = cam.get_next_decoded(timeout=2.0)
-        if meta is None or frame is None:
-            continue
-
-        controls = get_all_controls(cam)
-
-        progress_lines = list(instruction_lines)
-        quality_report = None
-        quality_lines = None
-        if quality_check_white:
-            try:
-                channels_now = extract_channels_from_decoded(decoded)
-                quality_report = evaluate_white_frame_quality(channels_now)
-                quality_lines = format_quality_lines(quality_report, title="WHITE PREFLIGHT")
-                if require_good_quality and quality_report.get("status") != "good":
-                    progress_lines.append("")
-                    progress_lines.append("ENTER bloqueado: ajuste fundo/luz ate WHITE PREFLIGHT=GOOD.")
-                else:
-                    progress_lines.append("")
-                    progress_lines.append("ENTER inicia. Use o status abaixo para decidir se esta seguro.")
-            except Exception as e:
-                quality_report = {"status": "bad", "reasons": [f"preflight_error:{e}"], "channels": {}}
-                quality_lines = format_quality_lines(quality_report, title="WHITE PREFLIGHT")
-
-        print(f"QUALITY_LINES: {quality_lines}")
-
-        if allow_skip:
-            progress_lines.append("")
-            progress_lines.append("S = pular esta etapa")
-
-        board = build_board(
-            decoded=decoded,
-            controls=controls,
-            state_lines=[title],
-            progress_lines=progress_lines,
-            preview_scale=preview_scale,
-            quality_lines=quality_lines,
-        )
-        cv2.imshow(window_name, board)
-
-        k = cv2.waitKey(1) & 0xFF
-        if k in (13, 10):
-            if quality_check_white and require_good_quality and quality_report and quality_report.get("status") != "good":
-                continue
-            return "start"
-        if allow_skip and k in (ord("s"), ord("S")):
-            return "skip"
-        if k in (ord("q"), ord("Q"), 27):
-            raise KeyboardInterrupt("Cancelado pelo usuario.")
-
-
-# ============================================================
-# Main
-# ============================================================
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Calibrador automatico de flat-field/dark-frame para o módulo RGB/RE/NIR OAK-FCC-3.",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-    )
-
-    parser.add_argument("--fps", type=int, default=20)
-    parser.add_argument("--width", type=int, default=1280)
-    parser.add_argument("--height", type=int, default=800)
-    parser.add_argument("--bayer", default="BGGR", choices=["GBRG", "GRBG", "RGGB", "BGGR"])
-    parser.add_argument("--capture_mode", default="AUTO", choices=["AUTO", "SINGLE", "DOUBLE", "TRIPLE"])
-    parser.add_argument("--raw_policy", default="require_triple", choices=["allow_single", "require_triple"])
-    parser.add_argument("--module_calibration_json", default="calibration/module_params.json")
-
-    parser.add_argument("--frames", type=int, default=60, help="Frames úteis capturados por etapa.")
-    parser.add_argument("--discard_frames", type=int, default=15, help="Frames descartados antes de cada etapa.")
-    parser.add_argument("--preview_scale", type=float, default=0.65)
-
-    parser.add_argument("--out_npz", default="calibration/flatfield_maps_v1.npz")
-    parser.add_argument("--out_json", default="calibration/flatfield_maps_v1.json")
-    parser.add_argument("--preview_dir", default="calibration/flatfield_previews")
-
-    parser.add_argument("--smooth_ksize", type=int, default=31, help="Kernel gaussiano para suavizar mapa de ganho. Use 1 para desligar.")
-    parser.add_argument("--min_gain", type=float, default=0.25)
-    parser.add_argument("--max_gain", type=float, default=4.0)
-    parser.add_argument("--epsilon", type=float, default=1e-8)
-
-    parser.add_argument("--exp_gain_correct", action="store_true",
-                        help="Divide frames por exposure_time_us*analogue_gain antes de calcular o flat.")
-    parser.add_argument("--skip_dark", action="store_true",
-                        help="Pula etapa dark/preto. O mapa será calculado sem subtração dark.")
-    parser.add_argument("--notes", default="")
-    parser.add_argument("--require_good_preflight", action="store_true",
-                        help="Bloqueia ENTER na etapa WHITE se o preflight estiver BAD/WARNING.")
-
-    args = parser.parse_args()
-
-    ensure_dir(os.path.dirname(args.out_npz) or ".")
-    ensure_dir(os.path.dirname(args.out_json) or ".")
-    ensure_dir(args.preview_dir)
-
-    window_name = "Flat Field Calibration Tool"
-    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
-
-    session_meta = {
-        "schema": "multispec_flatfield_v1",
-        "created_at": now_str(),
-        "sensor_width": args.width,
-        "sensor_height": args.height,
-        "bayer_pattern": args.bayer,
-        "fps": args.fps,
-        "capture_mode_requested": args.capture_mode,
-        "raw_policy": args.raw_policy,
-        "module_calibration_json": args.module_calibration_json,
-        "frames_per_stage": args.frames,
-        "discard_frames": args.discard_frames,
-        "exp_gain_correct": bool(args.exp_gain_correct),
-        "smooth_ksize": args.smooth_ksize,
-        "min_gain": args.min_gain,
-        "max_gain": args.max_gain,
-        "epsilon": args.epsilon,
-        "channels": ["R", "G", "B", "RE", "NIR"],
-        "notes": args.notes,
-        "require_good_preflight": bool(args.require_good_preflight),
-        "stages": {},
-        "outputs": {
-            "npz": args.out_npz,
-            "json": args.out_json,
-            "preview_dir": args.preview_dir,
-        },
-    }
-
-    try:
-        with MultiSpectralClient(
-            width=args.width,
-            height=args.height,
-            bayer=args.bayer,
-            fps=args.fps,
-            frame_type="RAW_BRUTO",
-            output_dtype="uint8",
-            capture_mode=args.capture_mode,
-            raw_policy=args.raw_policy,
-            module_calibration_json=args.module_calibration_json or None
-        ) as cam:
-
-            validate_module_ready(cam.get_status(), args.raw_policy)
-
-            wait_for_enter_or_skip(
-                cam=cam,
-                window_name=window_name,
-                title="ETAPA 1/2 - WHITE / FLAT FIELD",
-                instruction_lines=[
-                    "Posicione o modulo na altura real de operacao.",
-                    "Aponte para uma superficie branca/cinza fosca, uniforme e sem textura.",
-                    "Evite reflexos, sombras laterais e saturacao.",
-                    "A superficie deve preencher todo o campo de visao.",
-                    "Pressione ENTER para comecar a captura WHITE.",
-                ],
-                preview_scale=args.preview_scale,
-                allow_skip=False,
-                quality_check_white=True,
-                require_good_quality=args.require_good_preflight,
-            )
-
-            white_stack, white_controls, white_meta = capture_stage(
-                cam=cam,
-                stage_name="white",
-                frames_count=args.frames,
-                discard_frames=args.discard_frames,
-                exp_gain_correct=args.exp_gain_correct,
-                preview_scale=args.preview_scale,
-                window_name=window_name,
-            )
-
-            session_meta["stages"]["white"] = {
-                "controls_log": white_controls,
-                "meta_log": white_meta,
-                "captured_channels": {ch: len(v) for ch, v in white_stack.items()},
-            }
-
-            dark_stack = None
-            dark_controls = []
-            dark_meta = []
-
-            if not args.skip_dark:
-                action = wait_for_enter_or_skip(
-                    cam=cam,
-                    window_name=window_name,
-                    title="ETAPA 2/2 - DARK / PRETO",
-                    instruction_lines=[
-                        "Agora faca a captura dark/preto.",
-                        "Melhor opcao: tampe as lentes completamente.",
-                        "Alternativa: use fundo preto fosco preenchendo todo o frame.",
-                        "Mantenha exposicao/ganho iguais aos da etapa anterior, se possivel.",
-                        "Pressione ENTER para capturar DARK/PRETO.",
-                    ],
-                    preview_scale=args.preview_scale,
-                    allow_skip=True,
-                )
-
-                if action == "start":
-                    dark_stack, dark_controls, dark_meta = capture_stage(
-                        cam=cam,
-                        stage_name="dark",
-                        frames_count=args.frames,
-                        discard_frames=args.discard_frames,
-                        exp_gain_correct=args.exp_gain_correct,
-                        preview_scale=args.preview_scale,
-                        window_name=window_name,
-                    )
-
-                    session_meta["stages"]["dark"] = {
-                        "controls_log": dark_controls,
-                        "meta_log": dark_meta,
-                        "captured_channels": {ch: len(v) for ch, v in dark_stack.items()},
-                    }
-                else:
-                    session_meta["stages"]["dark"] = {"skipped": True}
-            else:
-                session_meta["stages"]["dark"] = {"skipped": True}
-
-            # Processamento robusto.
-            processing_panel = np.zeros((720, 1280, 3), dtype=np.uint8)
-            overlay_hud(processing_panel, [
-                "Processando calibracao flat-field...",
-                "Calculando medianas robustas por canal.",
-                "Gerando mapas de ganho e previews.",
-            ], x=40, y=80, font_scale=0.8, line_step=34)
-            cv2.imshow(window_name, processing_panel)
-            cv2.waitKey(1)
-
-            white_med = median_stack(white_stack)
-            dark_med = median_stack(dark_stack) if dark_stack is not None else None
-
-            gain_maps, flat_norm_maps, corrected_white, dark_used = build_gain_maps(
-                white_med=white_med,
-                dark_med=dark_med,
-                epsilon=args.epsilon,
-                smooth_ksize=args.smooth_ksize,
-                min_gain=args.min_gain,
-                max_gain=args.max_gain,
-            )
-
-            white_median_quality = evaluate_white_frame_quality(white_med)
-            final_quality_report = evaluate_gain_maps_quality(gain_maps, corrected_white=corrected_white)
-            session_meta["white_median_quality"] = white_median_quality
-            session_meta["quality_report"] = final_quality_report
-
-            # Salva .npz.
-            save_payload = {}
-            for ch, arr in gain_maps.items():
-                save_payload[f"gain_{ch}"] = arr.astype(np.float32)
-            for ch, arr in flat_norm_maps.items():
-                save_payload[f"flat_norm_{ch}"] = arr.astype(np.float32)
-            for ch, arr in white_med.items():
-                save_payload[f"white_median_{ch}"] = arr.astype(np.float32)
-            if dark_med is not None:
-                for ch, arr in dark_med.items():
-                    save_payload[f"dark_median_{ch}"] = arr.astype(np.float32)
-
-            np.savez_compressed(args.out_npz, **save_payload)
-
-            # Estatísticas finais.
-            session_meta["maps"] = {}
-            for ch, gain in gain_maps.items():
-                session_meta["maps"][ch] = {
-                    "shape": list(gain.shape),
-                    "gain_key": f"gain_{ch}",
-                    "flat_norm_key": f"flat_norm_{ch}",
-                    "white_median_key": f"white_median_{ch}",
-                    "dark_median_key": f"dark_median_{ch}" if dark_med is not None and ch in dark_med else None,
-                    "gain_min": float(np.min(gain)),
-                    "gain_max": float(np.max(gain)),
-                    "gain_mean": float(np.mean(gain)),
-                    "gain_std": float(np.std(gain)),
-                    "white_signal_stats": compute_image_stats(normalize_for_display(corrected_white[ch])),
-                    "quality": (final_quality_report.get("channels", {}).get(ch, {}) if isinstance(final_quality_report, dict) else {}),
-                }
-
-            save_preview_maps(args.preview_dir, gain_maps, corrected_white)
-
-            with open(args.out_json, "w", encoding="utf-8") as f:
-                json.dump(session_meta, f, ensure_ascii=False, indent=2)
-
-            show_final_preview(window_name, gain_maps, args.preview_scale, quality_report=final_quality_report)
-
-            print("")
-            print("[OK] Flat-field gerado com sucesso.")
-            print(f"[OK] NPZ : {args.out_npz}")
-            print(f"[OK] JSON: {args.out_json}")
-            print(f"[OK] PNGs: {args.preview_dir}")
-            print(f"[QUALITY] status={final_quality_report.get('status')} reasons={final_quality_report.get('reasons', [])[:6]}")
-
-    except KeyboardInterrupt as e:
-        print(f"[CANCELADO] {e}")
-
-    finally:
-        cv2.destroyAllWindows()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/focus_calibration_tool.py b/Python/OAK/datasets/oak-fcc-3/utils/focus_calibration_tool.py
deleted file mode 100644
index 4a3a564a0..000000000
--- a/Python/OAK/datasets/oak-fcc-3/utils/focus_calibration_tool.py
+++ /dev/null
@@ -1,1673 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-"""
-Focus Calibration Tool - OAK-FFC-3P - Sensor Aware
-====================================================
-
-Objetivo
---------
-Ferramenta standalone para:
-  - detectar automaticamente os sensores conectados;
-  - suportar CAM_A com OV9782 (1280x800) OU AR0234 (1920x1200);
-  - visualizar RGB + RE + NIR simultaneamente;
-  - medir foco manual por Laplacian / Tenengrad / Brenner;
-  - permitir ROI por câmera;
-  - salvar melhores scores e snapshots em JSON/PNG.
-
-Importante
-----------
-Este script NÃO usa o OakFcc3Client e NÃO depende do pipeline RAW_BRUTO.
-Para foco óptico ele usa:
-  - saída ISP da câmera colorida (RGB);
-  - saída nativa das câmeras mono (RE/NIR).
-
-Assim, Bayer pattern e normalização do dataset não interferem no teste de foco.
-
-Configuração padrão esperada:
-  CAM_A = RGB  -> OV9782 ou AR0234
-  CAM_B = RE   -> OV9282
-  CAM_C = NIR  -> OV9282
-
-Exemplos
---------
-# Detecta e abre tudo que estiver disponível:
-python focus_calibration_sensor_aware.py
-
-# Primeiro teste somente da RGB nova:
-python focus_calibration_sensor_aware.py --mode rgb
-
-# Exige as três câmeras:
-python focus_calibration_sensor_aware.py --mode triple
-
-# Se RE e NIR estiverem fisicamente invertidas:
-python focus_calibration_sensor_aware.py --re-socket CAM_C --nir-socket CAM_B
-
-Teclas
-------
-1 = RGB
-2 = RE
-3 = NIR
-M = troca métrica
-D = informa sentido atual da lente
-E = equalização ON/OFF
-L = trava/destrava ROI
-C = centraliza ROI
-R = reseta score da câmera/métrica ativa
-S = salva snapshot PNG + registro
-SPACE = salva JSON completo
-Q / ESC = sair
-"""
-
-import os
-import json
-import time
-import argparse
-from dataclasses import dataclass, asdict
-from datetime import datetime
-from collections import deque
-from typing import Dict, Optional, Tuple, List
-
-import cv2
-import numpy as np
-import depthai as dai
-
-
-# ============================================================
-# Helpers gerais
-# ============================================================
-
-METHODS = ("laplacian", "tenengrad", "brenner")
-ROLES = ("rgb", "re", "nir")
-
-
-def now_str() -> str:
-    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-
-def now_file_str() -> str:
-    return datetime.now().strftime("%Y%m%d_%H%M%S")
-
-
-def ensure_dir(path: str):
-    if path:
-        os.makedirs(path, exist_ok=True)
-
-
-def overlay_hud(
-    img_bgr,
-    lines,
-    x=12,
-    y=24,
-    font_scale=0.58,
-    line_step=22,
-    color=(255, 255, 255),
-    shadow=True,
-):
-    yy = y
-    h, _ = img_bgr.shape[:2]
-
-    for s in lines:
-        if yy > h - 8:
-            break
-
-        text = str(s)
-
-        if shadow:
-            cv2.putText(
-                img_bgr,
-                text,
-                (x, yy),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                font_scale,
-                (0, 0, 0),
-                3,
-                cv2.LINE_AA,
-            )
-
-        cv2.putText(
-            img_bgr,
-            text,
-            (x, yy),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            font_scale,
-            color,
-            1,
-            cv2.LINE_AA,
-        )
-
-        yy += line_step
-
-
-def build_empty_panel(shape_hw: Tuple[int, int], title: str, text="sem frame disponivel"):
-    h, w = shape_hw
-    img = np.zeros((h, w, 3), dtype=np.uint8)
-    overlay_hud(
-        img,
-        [title, text],
-        x=18,
-        y=44,
-        font_scale=0.75,
-        line_step=32,
-    )
-    return img
-
-
-def socket_name(socket) -> str:
-    name = getattr(socket, "name", None)
-    if name:
-        return str(name)
-
-    s = str(socket)
-    for candidate in ("CAM_A", "CAM_B", "CAM_C", "CAM_D"):
-        if candidate in s:
-            return candidate
-
-    return s
-
-
-def get_socket(name: str):
-    name = str(name).upper().strip()
-    mapping = {
-        "CAM_A": dai.CameraBoardSocket.CAM_A,
-        "CAM_B": dai.CameraBoardSocket.CAM_B,
-        "CAM_C": dai.CameraBoardSocket.CAM_C,
-    }
-
-    if hasattr(dai.CameraBoardSocket, "CAM_D"):
-        mapping["CAM_D"] = dai.CameraBoardSocket.CAM_D
-
-    if name not in mapping:
-        raise ValueError(f"Socket inválido: {name}. Opções: {sorted(mapping)}")
-
-    return mapping[name]
-
-
-def supported_type_strings(feature) -> List[str]:
-    values = getattr(feature, "supportedTypes", []) or []
-    return [str(v).upper() for v in values]
-
-
-def feature_is_color(feature) -> bool:
-    types = supported_type_strings(feature)
-    sensor = str(getattr(feature, "sensorName", "") or "").upper()
-
-    if any("COLOR" in t for t in types):
-        return True
-
-    if any("MONO" in t for t in types):
-        return False
-
-    # Fallback conhecido do nosso módulo.
-    return sensor in {"OV9782", "AR0234"}
-
-
-def feature_is_mono(feature) -> bool:
-    types = supported_type_strings(feature)
-
-    if any("MONO" in t for t in types):
-        return True
-
-    if any("COLOR" in t for t in types):
-        return False
-
-    return not feature_is_color(feature)
-
-
-def bgr_to_gray01(img_bgr: np.ndarray) -> Optional[np.ndarray]:
-    if img_bgr is None:
-        return None
-
-    if img_bgr.ndim == 2:
-        gray = img_bgr
-    elif img_bgr.ndim == 3 and img_bgr.shape[2] == 1:
-        gray = img_bgr[:, :, 0]
-    else:
-        gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
-
-    gray = gray.astype(np.float32) / 255.0
-    return np.clip(gray, 0.0, 1.0)
-
-
-def resize_panel(img: np.ndarray, target_hw: Tuple[int, int]) -> np.ndarray:
-    th, tw = target_hw
-    return cv2.resize(img, (tw, th), interpolation=cv2.INTER_AREA)
-
-
-def colorize_mono_for_view(img_bgr: np.ndarray, role: str) -> np.ndarray:
-    if img_bgr is None:
-        return None
-
-    if img_bgr.ndim == 3:
-        gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
-    else:
-        gray = img_bgr
-
-    z = np.zeros_like(gray)
-
-    role = str(role).lower()
-
-    # Apenas visual. As métricas são calculadas no frame original.
-    if role == "re":
-        # Vermelho no BGR.
-        return np.dstack([z, z, gray])
-
-    if role == "nir":
-        # Ciano no BGR.
-        return np.dstack([gray, gray, z])
-
-    return cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
-
-
-def default_roi_for_shape(shape_hw, frac=0.42):
-    h, w = shape_hw
-    rw = max(8, int(w * frac))
-    rh = max(8, int(h * frac))
-    x0 = (w - rw) // 2
-    y0 = (h - rh) // 2
-    return (x0, y0, x0 + rw, y0 + rh)
-
-
-def sanitize_roi(rect, shape_hw):
-    if rect is None:
-        return None
-
-    h, w = shape_hw
-    x0, y0, x1, y1 = rect
-
-    x0, x1 = sorted((int(x0), int(x1)))
-    y0, y1 = sorted((int(y0), int(y1)))
-
-    x0 = max(0, min(w - 1, x0))
-    x1 = max(1, min(w, x1))
-    y0 = max(0, min(h - 1, y0))
-    y1 = max(1, min(h, y1))
-
-    if x1 - x0 < 4 or y1 - y0 < 4:
-        return None
-
-    return (x0, y0, x1, y1)
-
-
-def crop_rect(img: np.ndarray, rect):
-    if img is None or rect is None:
-        return None
-
-    rect = sanitize_roi(rect, img.shape[:2])
-    if rect is None:
-        return None
-
-    x0, y0, x1, y1 = rect
-    return img[y0:y1, x0:x1]
-
-
-# ============================================================
-# Descoberta / resolução sensor-aware
-# ============================================================
-
-@dataclass
-class SensorSpec:
-    role: str
-    socket_name: str
-    sensor_name: str
-    feature_width: int
-    feature_height: int
-    is_color: bool
-    configured_width: int
-    configured_height: int
-    resolution_name: str
-    stream_name: str
-    source: str
-
-
-def discover_cameras(mx_id: Optional[str]):
-    device_info = dai.DeviceInfo(mx_id) if mx_id else None
-
-    if device_info is not None:
-        ctx = dai.Device(device_info)
-    else:
-        ctx = dai.Device()
-
-    with ctx as device:
-        features = list(device.getConnectedCameraFeatures())
-
-        actual_mx = None
-        for attr in ("getMxId", "getDeviceId"):
-            if hasattr(device, attr):
-                try:
-                    actual_mx = str(getattr(device, attr)())
-                    if actual_mx:
-                        break
-                except Exception:
-                    pass
-
-        usb_speed = None
-        try:
-            usb_speed = str(device.getUsbSpeed())
-        except Exception:
-            pass
-
-        rows = []
-        for f in features:
-            rows.append({
-                "socket_obj": f.socket,
-                "socket_name": socket_name(f.socket),
-                "sensor_name": str(getattr(f, "sensorName", "") or ""),
-                "width": int(getattr(f, "width", 0) or 0),
-                "height": int(getattr(f, "height", 0) or 0),
-                "supported_types": supported_type_strings(f),
-                "is_color": feature_is_color(f),
-                "is_mono": feature_is_mono(f),
-                "has_autofocus_ic": int(getattr(f, "hasAutofocusIC", 0) or 0),
-            })
-
-    return rows, actual_mx, usb_speed
-
-
-def enum_if_exists(enum_cls, name: str):
-    return getattr(enum_cls, name, None)
-
-
-def pick_color_resolution(sensor_name: str, width: int, height: int):
-    """
-    Retorna:
-      (enum_resolution, resolution_name, configured_width, configured_height)
-    """
-    sensor = str(sensor_name or "").upper()
-    enum_cls = dai.ColorCameraProperties.SensorResolution
-
-    if "AR0234" in sensor:
-        enum_value = enum_if_exists(enum_cls, "THE_1200_P")
-        if enum_value is None:
-            raise RuntimeError(
-                "Seu depthai não possui ColorCameraProperties.SensorResolution.THE_1200_P. "
-                "Atualize a biblioteca DepthAI antes de testar a AR0234."
-            )
-        return enum_value, "THE_1200_P", 1920, 1200
-
-    if "OV9782" in sensor:
-        enum_value = enum_if_exists(enum_cls, "THE_800_P")
-        if enum_value is None:
-            raise RuntimeError("DepthAI sem THE_800_P para ColorCamera.")
-        return enum_value, "THE_800_P", 1280, 800
-
-    # Fallback por resolução anunciada pelo próprio sensor.
-    candidates = [
-        ((1920, 1200), "THE_1200_P"),
-        ((1280, 800), "THE_800_P"),
-        ((1920, 1080), "THE_1080_P"),
-        ((1280, 720), "THE_720_P"),
-        ((3840, 2160), "THE_4_K"),
-    ]
-
-    for (w, h), enum_name in candidates:
-        if (width, height) == (w, h):
-            enum_value = enum_if_exists(enum_cls, enum_name)
-            if enum_value is not None:
-                return enum_value, enum_name, w, h
-
-    raise RuntimeError(
-        f"Sensor colorido não mapeado: {sensor_name} ({width}x{height}). "
-        "Adicione o modo em pick_color_resolution()."
-    )
-
-
-def pick_mono_resolution(sensor_name: str, width: int, height: int):
-    sensor = str(sensor_name or "").upper()
-    enum_cls = dai.MonoCameraProperties.SensorResolution
-
-    if "OV9282" in sensor or (width, height) == (1280, 800):
-        enum_value = enum_if_exists(enum_cls, "THE_800_P")
-        if enum_value is None:
-            raise RuntimeError("DepthAI sem THE_800_P para MonoCamera.")
-        return enum_value, "THE_800_P", 1280, 800
-
-    candidates = [
-        ((1280, 800), "THE_800_P"),
-        ((1280, 720), "THE_720_P"),
-        ((640, 480), "THE_480_P"),
-        ((640, 400), "THE_400_P"),
-    ]
-
-    for (w, h), enum_name in candidates:
-        if (width, height) == (w, h):
-            enum_value = enum_if_exists(enum_cls, enum_name)
-            if enum_value is not None:
-                return enum_value, enum_name, w, h
-
-    raise RuntimeError(
-        f"Sensor mono não mapeado: {sensor_name} ({width}x{height}). "
-        "Adicione o modo em pick_mono_resolution()."
-    )
-
-
-def build_specs(camera_rows, args) -> Dict[str, SensorSpec]:
-    by_socket = {row["socket_name"]: row for row in camera_rows}
-
-    role_socket = {
-        "rgb": args.rgb_socket.upper(),
-        "re": args.re_socket.upper(),
-        "nir": args.nir_socket.upper(),
-    }
-
-    if args.mode == "rgb":
-        requested_roles = ["rgb"]
-    else:
-        requested_roles = ["rgb", "re", "nir"]
-
-    specs = {}
-
-    for role in requested_roles:
-        sock_name = role_socket[role]
-        row = by_socket.get(sock_name)
-
-        if row is None:
-            if args.mode == "triple":
-                raise RuntimeError(
-                    f"Modo triple exige {role.upper()} em {sock_name}, "
-                    f"mas esse socket não foi detectado."
-                )
-            continue
-
-        if role == "rgb":
-            if not row["is_color"]:
-                raise RuntimeError(
-                    f"{sock_name} foi escolhido como RGB, mas o sensor detectado "
-                    f"({row['sensor_name']}) não foi anunciado como COLOR."
-                )
-
-            _, res_name, cw, ch = pick_color_resolution(
-                row["sensor_name"], row["width"], row["height"]
-            )
-
-            specs[role] = SensorSpec(
-                role=role,
-                socket_name=sock_name,
-                sensor_name=row["sensor_name"],
-                feature_width=row["width"],
-                feature_height=row["height"],
-                is_color=True,
-                configured_width=cw,
-                configured_height=ch,
-                resolution_name=res_name,
-                stream_name="focus_rgb",
-                source="ISP",
-            )
-
-        else:
-            if not row["is_mono"]:
-                raise RuntimeError(
-                    f"{sock_name} foi escolhido como {role.upper()}, mas o sensor detectado "
-                    f"({row['sensor_name']}) não foi anunciado como MONO."
-                )
-
-            _, res_name, cw, ch = pick_mono_resolution(
-                row["sensor_name"], row["width"], row["height"]
-            )
-
-            specs[role] = SensorSpec(
-                role=role,
-                socket_name=sock_name,
-                sensor_name=row["sensor_name"],
-                feature_width=row["width"],
-                feature_height=row["height"],
-                is_color=False,
-                configured_width=cw,
-                configured_height=ch,
-                resolution_name=res_name,
-                stream_name=f"focus_{role}",
-                source="MONO_OUT",
-            )
-
-    if "rgb" not in specs:
-        raise RuntimeError(
-            f"RGB não encontrada em {args.rgb_socket}. "
-            "Confira os flats e/ou use --rgb-socket."
-        )
-
-    if args.mode == "triple":
-        missing = [r for r in ROLES if r not in specs]
-        if missing:
-            raise RuntimeError(f"Modo triple: faltando roles {missing}")
-
-    return specs
-
-
-def build_pipeline(specs: Dict[str, SensorSpec], fps: float):
-    pipeline = dai.Pipeline()
-
-    for role, spec in specs.items():
-        socket = get_socket(spec.socket_name)
-
-        if spec.is_color:
-            cam = pipeline.createColorCamera()
-            cam.setBoardSocket(socket)
-
-            enum_value, _, _, _ = pick_color_resolution(
-                spec.sensor_name,
-                spec.feature_width,
-                spec.feature_height,
-            )
-
-            cam.setResolution(enum_value)
-            cam.setFps(float(fps))
-            cam.setInterleaved(False)
-
-            try:
-                cam.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
-            except Exception:
-                pass
-
-            xout = pipeline.createXLinkOut()
-            xout.setStreamName(spec.stream_name)
-
-            # ISP mantém a resolução útil do sensor e getCvFrame() entrega
-            # uma imagem pronta para inspeção, sem depender do nosso Bayer.
-            cam.isp.link(xout.input)
-
-        else:
-            cam = pipeline.createMonoCamera()
-            cam.setBoardSocket(socket)
-
-            enum_value, _, _, _ = pick_mono_resolution(
-                spec.sensor_name,
-                spec.feature_width,
-                spec.feature_height,
-            )
-
-            cam.setResolution(enum_value)
-            cam.setFps(float(fps))
-
-            xout = pipeline.createXLinkOut()
-            xout.setStreamName(spec.stream_name)
-            cam.out.link(xout.input)
-
-    return pipeline
-
-
-# ============================================================
-# Métricas de foco
-# ============================================================
-
-def preprocess_focus_gray(gray01: np.ndarray, equalize=False):
-    g = np.clip(gray01 * 255.0, 0, 255).astype(np.uint8)
-
-    if equalize:
-        g = cv2.equalizeHist(g)
-
-    return g
-
-
-def focus_laplacian_var(gray_u8: np.ndarray) -> float:
-    lap = cv2.Laplacian(gray_u8, cv2.CV_64F, ksize=3)
-    return float(lap.var())
-
-
-def focus_tenengrad(gray_u8: np.ndarray) -> float:
-    sx = cv2.Sobel(gray_u8, cv2.CV_64F, 1, 0, ksize=3)
-    sy = cv2.Sobel(gray_u8, cv2.CV_64F, 0, 1, ksize=3)
-    return float(np.mean(sx * sx + sy * sy))
-
-
-def focus_brenner(gray_u8: np.ndarray) -> float:
-    arr = gray_u8.astype(np.float32)
-
-    if arr.shape[1] < 3:
-        return 0.0
-
-    diff = arr[:, 2:] - arr[:, :-2]
-    return float(np.mean(diff * diff))
-
-
-def compute_focus_metrics(img_bgr: np.ndarray, roi_rect, equalize=False):
-    gray01 = bgr_to_gray01(img_bgr)
-    roi = crop_rect(gray01, roi_rect)
-
-    if roi is None or roi.size < 64:
-        return {
-            "valid": False,
-            "laplacian": 0.0,
-            "tenengrad": 0.0,
-            "brenner": 0.0,
-            "mean": 0.0,
-            "std": 0.0,
-            "p95": 0.0,
-            "pct_saturated": 0.0,
-            "pct_dark": 0.0,
-            "pixels": 0,
-        }
-
-    gray_u8 = preprocess_focus_gray(roi, equalize=equalize)
-    arr = roi.astype(np.float32).reshape(-1)
-
-    return {
-        "valid": True,
-        "laplacian": focus_laplacian_var(gray_u8),
-        "tenengrad": focus_tenengrad(gray_u8),
-        "brenner": focus_brenner(gray_u8),
-        "mean": float(arr.mean()),
-        "std": float(arr.std()),
-        "p95": float(np.percentile(arr, 95)),
-        "pct_saturated": float((arr >= 0.98).mean() * 100.0),
-        "pct_dark": float((arr <= 0.02).mean() * 100.0),
-        "pixels": int(arr.size),
-    }
-
-
-def metric_value(metrics: dict, method: str) -> float:
-    return float(metrics.get(method, 0.0) or 0.0)
-
-
-def smooth_from_history(history, window: int) -> float:
-    if not history:
-        return 0.0
-
-    vals = [float(x["score"]) for x in list(history)[-max(1, window):]]
-    return float(np.mean(vals))
-
-
-def analyze_trend(history, best_score, direction_name: str, drop_warn_pct=3.0):
-    if len(history) < 6:
-        return {
-            "status": "coletando",
-            "instruction": "gire devagar e observe o grafico",
-            "delta": 0.0,
-            "pct_of_best": 0.0,
-        }
-
-    recent = [float(x["smooth"]) for x in list(history)[-5:]]
-
-    if len(history) >= 12:
-        old = [float(x["smooth"]) for x in list(history)[-12:-7]]
-    else:
-        old = [float(x["smooth"]) for x in list(history)[:5]]
-
-    recent_mean = float(np.mean(recent))
-    old_mean = float(np.mean(old))
-    delta = recent_mean - old_mean
-
-    pct_of_best = 0.0 if best_score <= 0 else (recent_mean / best_score) * 100.0
-    drop_from_best = 100.0 - pct_of_best
-
-    if best_score > 0 and drop_from_best >= drop_warn_pct:
-        return {
-            "status": "passou_do_pico",
-            "instruction": f"volte um pouco: contrario de {direction_name}",
-            "delta": delta,
-            "pct_of_best": pct_of_best,
-        }
-
-    eps = max(best_score * 0.002, 1e-6)
-
-    if delta > eps:
-        return {
-            "status": "melhorando",
-            "instruction": f"continue {direction_name}",
-            "delta": delta,
-            "pct_of_best": pct_of_best,
-        }
-
-    if delta < -eps:
-        return {
-            "status": "piorando",
-            "instruction": f"inverta: contrario de {direction_name}",
-            "delta": delta,
-            "pct_of_best": pct_of_best,
-        }
-
-    return {
-        "status": "estavel",
-        "instruction": "ajuste fino ou trave a lente",
-        "delta": delta,
-        "pct_of_best": pct_of_best,
-    }
-
-
-# ============================================================
-# Desenho
-# ============================================================
-
-def draw_roi(panel, roi_src, src_shape_hw, active=False):
-    if roi_src is None:
-        return
-
-    ph, pw = panel.shape[:2]
-    sh, sw = src_shape_hw
-
-    x0, y0, x1, y1 = roi_src
-
-    px0 = int(x0 * pw / max(1, sw))
-    px1 = int(x1 * pw / max(1, sw))
-    py0 = int(y0 * ph / max(1, sh))
-    py1 = int(y1 * ph / max(1, sh))
-
-    color = (0, 255, 255) if active else (0, 180, 255)
-
-    cv2.rectangle(panel, (px0, py0), (px1, py1), color, 2)
-    cv2.putText(
-        panel,
-        "FOCUS ROI",
-        (px0 + 6, max(20, py0 - 8)),
-        cv2.FONT_HERSHEY_SIMPLEX,
-        0.55,
-        color,
-        2,
-        cv2.LINE_AA,
-    )
-
-
-def draw_crosshair(panel):
-    h, w = panel.shape[:2]
-
-    cv2.line(
-        panel,
-        (w // 2 - 18, h // 2),
-        (w // 2 + 18, h // 2),
-        (255, 255, 255),
-        1,
-        cv2.LINE_AA,
-    )
-
-    cv2.line(
-        panel,
-        (w // 2, h // 2 - 18),
-        (w // 2, h // 2 + 18),
-        (255, 255, 255),
-        1,
-        cv2.LINE_AA,
-    )
-
-
-def draw_panel_title(panel, title, selected=False):
-    color = (0, 255, 255) if selected else (255, 255, 255)
-    overlay_hud(
-        panel,
-        [title],
-        x=12,
-        y=24,
-        font_scale=0.60,
-        line_step=24,
-        color=color,
-    )
-
-
-def draw_score_bar(panel, pct, x, y, w, h, label):
-    pct = float(max(0.0, min(100.0, pct)))
-
-    cv2.rectangle(panel, (x, y), (x + w, y + h), (80, 80, 80), 1)
-
-    fill_w = int((pct / 100.0) * w)
-    cv2.rectangle(panel, (x, y), (x + fill_w, y + h), (230, 230, 230), -1)
-
-    cv2.rectangle(panel, (x, y), (x + w, y + h), (180, 180, 180), 1)
-
-    cv2.putText(
-        panel,
-        f"{label}: {pct:5.1f}%",
-        (x, y - 8),
-        cv2.FONT_HERSHEY_SIMPLEX,
-        0.50,
-        (255, 255, 255),
-        1,
-        cv2.LINE_AA,
-    )
-
-
-def draw_history_graph(panel, history, x, y, w, h, best_score):
-    cv2.rectangle(panel, (x, y), (x + w, y + h), (35, 35, 35), -1)
-    cv2.rectangle(panel, (x, y), (x + w, y + h), (120, 120, 120), 1)
-
-    if len(history) < 2:
-        cv2.putText(
-            panel,
-            "grafico aguardando historico...",
-            (x + 10, y + h // 2),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.50,
-            (180, 180, 180),
-            1,
-            cv2.LINE_AA,
-        )
-        return
-
-    vals = np.array([float(item["smooth"]) for item in history], dtype=np.float32)
-    vals = vals[-max(2, w):]
-
-    max_val = max(float(np.max(vals)), float(best_score), 1e-6)
-    min_val = min(float(np.min(vals)), max_val * 0.90)
-    span = max(max_val - min_val, 1e-6)
-
-    pts = []
-
-    for i, value in enumerate(vals):
-        px = x + int((i / max(1, len(vals) - 1)) * (w - 1))
-        py = y + h - 1 - int(((float(value) - min_val) / span) * (h - 1))
-        pts.append((px, py))
-
-    for p0, p1 in zip(pts[:-1], pts[1:]):
-        cv2.line(panel, p0, p1, (255, 255, 255), 2, cv2.LINE_AA)
-
-    if best_score > 0:
-        by = y + h - 1 - int(((best_score - min_val) / span) * (h - 1))
-        by = max(y, min(y + h - 1, by))
-
-        cv2.line(panel, (x, by), (x + w, by), (0, 255, 255), 1, cv2.LINE_AA)
-
-        cv2.putText(
-            panel,
-            "best",
-            (x + 6, max(y + 16, by - 4)),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.43,
-            (0, 255, 255),
-            1,
-            cv2.LINE_AA,
-        )
-
-
-def make_data_panel(
-    shape_hw,
-    spec: SensorSpec,
-    selected_role,
-    method,
-    metrics,
-    score,
-    smooth,
-    best,
-    trend,
-    fps_by_role,
-    fps_view,
-    direction_name,
-    history,
-    roi_locked,
-    equalize,
-):
-    h, w = shape_hw
-    panel = np.zeros((h, w, 3), dtype=np.uint8)
-
-    best_score = float(best.get("smooth", 0.0) or 0.0)
-    score_pct = 0.0 if best_score <= 0 else (smooth / best_score) * 100.0
-
-    lines = [
-        "FOCUS CALIBRATION - SENSOR AWARE",
-        f"ativa={selected_role.upper()} | metodo={method}",
-        f"sensor={spec.sensor_name} | socket={spec.socket_name}",
-        f"modo={spec.resolution_name} | {spec.configured_width}x{spec.configured_height}",
-        f"fonte={spec.source}",
-        f"score={score:.1f} | smooth={smooth:.1f}",
-        f"best={best_score:.1f} | atual/best={score_pct:.1f}%",
-        f"status={trend.get('status', 'coletando')}",
-        f"acao={trend.get('instruction', 'gire devagar')}",
-        f"sentido={direction_name}",
-        f"fps RGB/RE/NIR={fps_by_role.get('rgb', 0):.1f}/{fps_by_role.get('re', 0):.1f}/{fps_by_role.get('nir', 0):.1f}",
-        f"fps_view={fps_view:.1f}",
-        f"ROI={'travada' if roi_locked else 'editavel'} | equalize={'ON' if equalize else 'OFF'}",
-    ]
-
-    if metrics and metrics.get("valid"):
-        lines.extend([
-            "-",
-            f"mean={metrics['mean']:.3f} std={metrics['std']:.3f} p95={metrics['p95']:.3f}",
-            f"sat={metrics['pct_saturated']:.2f}% dark={metrics['pct_dark']:.2f}%",
-        ])
-
-    overlay_hud(
-        panel,
-        lines,
-        x=14,
-        y=26,
-        font_scale=0.48,
-        line_step=20,
-    )
-
-    bar_y = min(h - 165, 315)
-    bar_y = max(255, bar_y)
-
-    draw_score_bar(
-        panel,
-        min(100.0, score_pct),
-        18,
-        bar_y,
-        max(80, w - 36),
-        22,
-        "nitidez relativa",
-    )
-
-    graph_y = bar_y + 46
-    graph_h = max(65, h - graph_y - 62)
-
-    draw_history_graph(
-        panel,
-        history,
-        18,
-        graph_y,
-        max(100, w - 36),
-        graph_h,
-        best_score,
-    )
-
-    help_lines = [
-        "1 RGB | 2 RE | 3 NIR | M metrica | D sentido",
-        "mouse ROI | C centraliza | L trava | E equalize | R reset",
-        "S snapshot PNG | SPACE salva JSON | Q sai",
-    ]
-
-    overlay_hud(
-        panel,
-        help_lines,
-        x=14,
-        y=h - 48,
-        font_scale=0.40,
-        line_step=16,
-    )
-
-    return panel
-
-
-# ============================================================
-# Estado / persistência
-# ============================================================
-
-def empty_best():
-    return {
-        "score": 0.0,
-        "smooth": 0.0,
-        "metrics": None,
-        "timestamp": None,
-        "roi": None,
-    }
-
-
-def json_safe_best(best):
-    return {
-        "score": float(best.get("score", 0.0) or 0.0),
-        "smooth": float(best.get("smooth", 0.0) or 0.0),
-        "metrics": best.get("metrics"),
-        "timestamp": best.get("timestamp"),
-        "roi": best.get("roi"),
-    }
-
-
-def build_result_payload(
-    args,
-    camera_rows,
-    specs,
-    best_by_role_method,
-    snapshots,
-    actual_mx,
-    usb_speed,
-):
-    results = {}
-
-    for role in ROLES:
-        results[role] = {}
-        for method in METHODS:
-            results[role][method] = json_safe_best(best_by_role_method[role][method])
-
-    return {
-        "schema": "oak_ffc_focus_calibration_sensor_aware_v2",
-        "saved_at": now_str(),
-        "depthai_version": getattr(dai, "__version__", "unknown"),
-        "device_mx_id": actual_mx,
-        "usb_speed": usb_speed,
-        "mode": args.mode,
-        "fps_requested": args.fps,
-        "role_socket_map": {
-            "rgb": args.rgb_socket,
-            "re": args.re_socket,
-            "nir": args.nir_socket,
-        },
-        "camera_inventory": [
-            {k: v for k, v in row.items() if k != "socket_obj"}
-            for row in camera_rows
-        ],
-        "active_specs": {
-            role: asdict(spec)
-            for role, spec in specs.items()
-        },
-        "results_by_role_and_method": results,
-        "snapshots": snapshots,
-        "notes": args.notes or "",
-    }
-
-
-def save_json(path, payload):
-    ensure_dir(os.path.dirname(path) or ".")
-
-    with open(path, "w", encoding="utf-8") as f:
-        json.dump(payload, f, ensure_ascii=False, indent=2)
-
-
-# ============================================================
-# Main
-# ============================================================
-
-def main():
-    parser = argparse.ArgumentParser(
-        description=(
-            "Focus Calibration Tool standalone para OAK-FFC-3P, "
-            "sensor-aware para OV9782 e AR0234."
-        ),
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-    )
-
-    parser.add_argument(
-        "--mode",
-        default="auto",
-        choices=["auto", "rgb", "triple"],
-        help="auto abre o que existir; rgb abre só CAM_A/RGB; triple exige RGB+RE+NIR",
-    )
-
-    parser.add_argument("--fps", type=float, default=20.0)
-    parser.add_argument("--mx-id", default=None)
-
-    parser.add_argument("--rgb-socket", default="CAM_A")
-    parser.add_argument("--re-socket", default="CAM_B")
-    parser.add_argument("--nir-socket", default="CAM_C")
-
-    parser.add_argument(
-        "--panel-width",
-        type=int,
-        default=640,
-        help="Largura visual de cada quadrante. Métrica continua na resolução nativa.",
-    )
-    parser.add_argument(
-        "--panel-height",
-        type=int,
-        default=400,
-        help="Altura visual de cada quadrante.",
-    )
-
-    parser.add_argument(
-        "--method",
-        default="laplacian",
-        choices=list(METHODS),
-    )
-    parser.add_argument("--history", type=int, default=260)
-    parser.add_argument("--smooth-window", type=int, default=5)
-    parser.add_argument("--drop-warn-pct", type=float, default=3.0)
-    parser.add_argument("--equalize", action="store_true")
-
-    parser.add_argument(
-        "--out-json",
-        default="calibration/focus_calibration_sensor_aware.json",
-    )
-    parser.add_argument(
-        "--snapshot-dir",
-        default="calibration/focus_snapshots",
-    )
-    parser.add_argument("--notes", default="")
-
-    args = parser.parse_args()
-
-    # --------------------------------------------------------
-    # Descoberta
-    # --------------------------------------------------------
-
-    camera_rows, actual_mx, usb_speed = discover_cameras(args.mx_id)
-
-    print("=" * 74)
-    print("FOCUS CALIBRATION TOOL - SENSOR AWARE")
-    print(f"DepthAI      : {getattr(dai, '__version__', 'unknown')}")
-    print(f"MX ID        : {actual_mx}")
-    print(f"USB          : {usb_speed}")
-    print("-" * 74)
-
-    for row in camera_rows:
-        print(
-            f"{row['socket_name']:5s} | "
-            f"{row['sensor_name']:12s} | "
-            f"{row['width']}x{row['height']} | "
-            f"types={row['supported_types']} | "
-            f"AF_IC={row['has_autofocus_ic']}"
-        )
-
-    specs = build_specs(camera_rows, args)
-
-    print("-" * 74)
-
-    for role, spec in specs.items():
-        print(
-            f"[PIPE] {role.upper():3s} <- {spec.socket_name} "
-            f"{spec.sensor_name} | {spec.resolution_name} "
-            f"{spec.configured_width}x{spec.configured_height} | {spec.source}"
-        )
-
-    print("=" * 74)
-
-    # --------------------------------------------------------
-    # Pipeline
-    # --------------------------------------------------------
-
-    pipeline = build_pipeline(specs, args.fps)
-
-    device_info = dai.DeviceInfo(actual_mx) if actual_mx else None
-
-    if device_info is not None:
-        device_ctx = dai.Device(pipeline, device_info)
-    else:
-        device_ctx = dai.Device(pipeline)
-
-    # --------------------------------------------------------
-    # Estado da UI
-    # --------------------------------------------------------
-
-    selected_role = "rgb"
-    method = args.method
-    equalize = bool(args.equalize)
-
-    direction_idx = 0
-    direction_names = ["rosqueando", "desrosqueando"]
-
-    frames: Dict[str, Optional[np.ndarray]] = {role: None for role in ROLES}
-    roi_rects = {role: None for role in ROLES}
-
-    history_by_role_method = {
-        role: {
-            m: deque(maxlen=args.history)
-            for m in METHODS
-        }
-        for role in ROLES
-    }
-
-    best_by_role_method = {
-        role: {
-            m: empty_best()
-            for m in METHODS
-        }
-        for role in ROLES
-    }
-
-    snapshots = []
-
-    fps_by_role = {role: 0.0 for role in ROLES}
-    fps_count = {role: 0 for role in ROLES}
-    fps_t0 = {role: time.time() for role in ROLES}
-
-    fps_view = 0.0
-    view_count = 0
-    view_t0 = time.time()
-
-    roi_locked = False
-    dragging_roi = False
-    drag_start_src = None
-
-    panel_rects = {
-        "rgb": None,
-        "re": None,
-        "nir": None,
-        "data": None,
-    }
-
-    last_board = None
-    last_msg = ""
-    last_msg_t = 0.0
-
-    window_name = "Focus Calibration Tool - Sensor Aware"
-    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
-
-    def set_msg(text):
-        nonlocal last_msg, last_msg_t
-        last_msg = str(text)
-        last_msg_t = time.time()
-
-    def inside(rect, px, py):
-        if rect is None:
-            return False
-
-        x0, y0, x1, y1 = rect
-        return x0 <= px < x1 and y0 <= py < y1
-
-    def display_to_source(role, px, py):
-        rect = panel_rects.get(role)
-        img = frames.get(role)
-
-        if rect is None or img is None:
-            return None
-
-        x0, y0, x1, y1 = rect
-        pw = max(1, x1 - x0)
-        ph = max(1, y1 - y0)
-
-        sh, sw = img.shape[:2]
-
-        lx = max(0, min(pw - 1, int(px - x0)))
-        ly = max(0, min(ph - 1, int(py - y0)))
-
-        sx = int(lx * sw / pw)
-        sy = int(ly * sh / ph)
-
-        sx = max(0, min(sw - 1, sx))
-        sy = max(0, min(sh - 1, sy))
-
-        return sx, sy
-
-    def on_mouse(event, x, y, flags, param):
-        nonlocal dragging_roi, drag_start_src
-
-        if roi_locked:
-            return
-
-        rect = panel_rects.get(selected_role)
-
-        if rect is None or not inside(rect, x, y):
-            return
-
-        src_pt = display_to_source(selected_role, x, y)
-
-        if src_pt is None:
-            return
-
-        if event == cv2.EVENT_LBUTTONDOWN:
-            dragging_roi = True
-            drag_start_src = src_pt
-
-            sx, sy = src_pt
-            roi_rects[selected_role] = (sx, sy, sx + 1, sy + 1)
-
-        elif event == cv2.EVENT_MOUSEMOVE and dragging_roi and drag_start_src:
-            x0, y0 = drag_start_src
-            x1, y1 = src_pt
-            roi_rects[selected_role] = (x0, y0, x1, y1)
-
-        elif event == cv2.EVENT_LBUTTONUP and dragging_roi and drag_start_src:
-            x0, y0 = drag_start_src
-            x1, y1 = src_pt
-
-            rect_src = sanitize_roi(
-                (x0, y0, x1, y1),
-                frames[selected_role].shape[:2],
-            )
-
-            if rect_src is not None:
-                roi_rects[selected_role] = rect_src
-                set_msg(f"ROI atualizada: {selected_role.upper()}")
-
-            dragging_roi = False
-            drag_start_src = None
-
-    cv2.setMouseCallback(window_name, on_mouse)
-
-    ensure_dir(args.snapshot_dir)
-
-    # --------------------------------------------------------
-    # Execução
-    # --------------------------------------------------------
-
-    try:
-        with device_ctx as device:
-            queues = {
-                role: device.getOutputQueue(
-                    name=spec.stream_name,
-                    maxSize=2,
-                    blocking=False,
-                )
-                for role, spec in specs.items()
-            }
-
-            print("[OK] Pipeline iniciado. Ajuste a lente devagar.")
-            print("[OK] Q/ESC sai | 1/2/3 troca câmera | S salva snapshot")
-
-            while True:
-                # ------------------------------------------------
-                # Coleta independente: foco não precisa sync rígido.
-                # ------------------------------------------------
-                for role, queue in queues.items():
-                    packet = queue.tryGet()
-
-                    if packet is None:
-                        continue
-
-                    try:
-                        img = packet.getCvFrame()
-                    except Exception as exc:
-                        set_msg(f"Falha getCvFrame {role}: {exc}")
-                        continue
-
-                    if img is None:
-                        continue
-
-                    if img.ndim == 2:
-                        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
-                    frames[role] = img
-
-                    if roi_rects[role] is None:
-                        roi_rects[role] = default_roi_for_shape(img.shape[:2], frac=0.42)
-
-                    fps_count[role] += 1
-                    dt = time.time() - fps_t0[role]
-
-                    if dt >= 1.0:
-                        fps_by_role[role] = fps_count[role] / dt
-                        fps_count[role] = 0
-                        fps_t0[role] = time.time()
-
-                # ------------------------------------------------
-                # Se ativa não existe, cai para a primeira disponível.
-                # ------------------------------------------------
-                if selected_role not in specs or frames.get(selected_role) is None:
-                    available = [
-                        r for r in ROLES
-                        if r in specs and frames.get(r) is not None
-                    ]
-
-                    if available:
-                        selected_role = available[0]
-
-                active_img = frames.get(selected_role)
-
-                # ------------------------------------------------
-                # Métrica
-                # ------------------------------------------------
-                metrics = None
-                score = 0.0
-                smooth = 0.0
-                trend = {
-                    "status": "aguardando",
-                    "instruction": "aguardando frame",
-                    "pct_of_best": 0.0,
-                }
-
-                history = history_by_role_method[selected_role][method]
-                best = best_by_role_method[selected_role][method]
-
-                if active_img is not None:
-                    if roi_rects[selected_role] is None:
-                        roi_rects[selected_role] = default_roi_for_shape(
-                            active_img.shape[:2],
-                            frac=0.42,
-                        )
-
-                    metrics = compute_focus_metrics(
-                        active_img,
-                        roi_rects[selected_role],
-                        equalize=equalize,
-                    )
-
-                    if metrics.get("valid"):
-                        score = metric_value(metrics, method)
-
-                        history.append({
-                            "t": time.time(),
-                            "score": score,
-                            "smooth": score,
-                        })
-
-                        smooth = smooth_from_history(
-                            history,
-                            args.smooth_window,
-                        )
-
-                        history[-1]["smooth"] = smooth
-
-                        if smooth > float(best.get("smooth", 0.0) or 0.0):
-                            best.update({
-                                "score": float(score),
-                                "smooth": float(smooth),
-                                "metrics": metrics,
-                                "timestamp": now_str(),
-                                "roi": list(map(int, roi_rects[selected_role])),
-                            })
-
-                        trend = analyze_trend(
-                            history,
-                            float(best.get("smooth", 0.0) or 0.0),
-                            direction_names[direction_idx],
-                            drop_warn_pct=args.drop_warn_pct,
-                        )
-
-                # ------------------------------------------------
-                # Painéis
-                # ------------------------------------------------
-                ph = int(args.panel_height)
-                pw = int(args.panel_width)
-                target_hw = (ph, pw)
-
-                panels = {}
-
-                for role in ROLES:
-                    img = frames.get(role)
-                    spec = specs.get(role)
-
-                    if img is None:
-                        if spec is None:
-                            panels[role] = build_empty_panel(
-                                target_hw,
-                                role.upper(),
-                                "camera nao ativa neste modo",
-                            )
-                        else:
-                            panels[role] = build_empty_panel(
-                                target_hw,
-                                role.upper(),
-                                f"aguardando {spec.sensor_name}",
-                            )
-                        continue
-
-                    view = img.copy()
-
-                    if role in ("re", "nir"):
-                        view = colorize_mono_for_view(view, role)
-
-                    panel = resize_panel(view, target_hw)
-
-                    roi = roi_rects.get(role)
-                    if roi is not None:
-                        draw_roi(
-                            panel,
-                            roi,
-                            img.shape[:2],
-                            active=(role == selected_role),
-                        )
-
-                    draw_crosshair(panel)
-
-                    spec = specs[role]
-                    title = (
-                        f"{role.upper()} | {spec.socket_name} | "
-                        f"{spec.sensor_name} | {img.shape[1]}x{img.shape[0]}"
-                    )
-
-                    draw_panel_title(
-                        panel,
-                        title,
-                        selected=(role == selected_role),
-                    )
-
-                    panels[role] = panel
-
-                active_spec = specs.get(selected_role)
-
-                if active_spec is None:
-                    # Fallback apenas defensivo.
-                    active_spec = next(iter(specs.values()))
-
-                data_panel = make_data_panel(
-                    target_hw,
-                    active_spec,
-                    selected_role,
-                    method,
-                    metrics or {},
-                    score,
-                    smooth,
-                    best,
-                    trend,
-                    fps_by_role,
-                    fps_view,
-                    direction_names[direction_idx],
-                    history,
-                    roi_locked,
-                    equalize,
-                )
-
-                # Layout:
-                # RGB | RE
-                # NIR | DATA
-                panel_rects["rgb"] = (0, 0, pw, ph)
-                panel_rects["re"] = (pw, 0, pw * 2, ph)
-                panel_rects["nir"] = (0, ph, pw, ph * 2)
-                panel_rects["data"] = (pw, ph, pw * 2, ph * 2)
-
-                top = np.hstack([panels["rgb"], panels["re"]])
-                bottom = np.hstack([panels["nir"], data_panel])
-                board = np.vstack([top, bottom])
-
-                if last_msg and (time.time() - last_msg_t) < 2.5:
-                    cv2.putText(
-                        board,
-                        last_msg,
-                        (16, board.shape[0] - 14),
-                        cv2.FONT_HERSHEY_SIMPLEX,
-                        0.58,
-                        (0, 255, 0),
-                        2,
-                        cv2.LINE_AA,
-                    )
-
-                last_board = board.copy()
-
-                cv2.imshow(window_name, board)
-
-                # FPS da UI
-                view_count += 1
-                dt_view = time.time() - view_t0
-
-                if dt_view >= 1.0:
-                    fps_view = view_count / dt_view
-                    view_count = 0
-                    view_t0 = time.time()
-
-                # ------------------------------------------------
-                # Teclas
-                # ------------------------------------------------
-                k = cv2.waitKey(1) & 0xFF
-
-                if k in (ord("q"), ord("Q"), 27):
-                    break
-
-                elif k == ord("1"):
-                    if "rgb" in specs:
-                        selected_role = "rgb"
-                        set_msg("Selecionada: RGB")
-
-                elif k == ord("2"):
-                    if "re" in specs:
-                        selected_role = "re"
-                        set_msg("Selecionada: RE")
-                    else:
-                        set_msg("RE nao ativa neste modo")
-
-                elif k == ord("3"):
-                    if "nir" in specs:
-                        selected_role = "nir"
-                        set_msg("Selecionada: NIR")
-                    else:
-                        set_msg("NIR nao ativa neste modo")
-
-                elif k in (ord("m"), ord("M")):
-                    method = METHODS[(METHODS.index(method) + 1) % len(METHODS)]
-                    set_msg(f"Metrica -> {method}")
-
-                elif k in (ord("d"), ord("D")):
-                    direction_idx = 1 - direction_idx
-                    set_msg(f"Sentido -> {direction_names[direction_idx]}")
-
-                elif k in (ord("e"), ord("E")):
-                    equalize = not equalize
-                    set_msg(f"Equalize -> {'ON' if equalize else 'OFF'}")
-
-                elif k in (ord("l"), ord("L")):
-                    roi_locked = not roi_locked
-                    set_msg(f"ROI -> {'travada' if roi_locked else 'editavel'}")
-
-                elif k in (ord("c"), ord("C")):
-                    img = frames.get(selected_role)
-
-                    if img is not None:
-                        roi_rects[selected_role] = default_roi_for_shape(
-                            img.shape[:2],
-                            frac=0.42,
-                        )
-                        set_msg(f"ROI centralizada: {selected_role.upper()}")
-
-                elif k in (ord("r"), ord("R")):
-                    history_by_role_method[selected_role][method].clear()
-                    best_by_role_method[selected_role][method] = empty_best()
-
-                    set_msg(
-                        f"Reset: {selected_role.upper()} / {method}"
-                    )
-
-                elif k in (ord("s"), ord("S")):
-                    stamp = now_file_str()
-
-                    png_path = os.path.join(
-                        args.snapshot_dir,
-                        f"focus_{selected_role}_{method}_{stamp}.png",
-                    )
-
-                    if last_board is not None:
-                        cv2.imwrite(png_path, last_board)
-
-                    snap = {
-                        "timestamp": now_str(),
-                        "role": selected_role,
-                        "method": method,
-                        "sensor": asdict(specs[selected_role]),
-                        "roi": (
-                            list(map(int, roi_rects[selected_role]))
-                            if roi_rects[selected_role] is not None
-                            else None
-                        ),
-                        "current_score": float(score),
-                        "current_smooth": float(smooth),
-                        "best": json_safe_best(
-                            best_by_role_method[selected_role][method]
-                        ),
-                        "direction_name": direction_names[direction_idx],
-                        "equalize": bool(equalize),
-                        "png_path": png_path,
-                    }
-
-                    snapshots.append(snap)
-                    set_msg(f"Snapshot -> {png_path}")
-
-                elif k == 32:
-                    payload = build_result_payload(
-                        args,
-                        camera_rows,
-                        specs,
-                        best_by_role_method,
-                        snapshots,
-                        actual_mx,
-                        usb_speed,
-                    )
-
-                    save_json(args.out_json, payload)
-                    set_msg(f"JSON salvo -> {args.out_json}")
-
-    finally:
-        cv2.destroyAllWindows()
-
-    # Salva automaticamente no fechamento também.
-    try:
-        payload = build_result_payload(
-            args,
-            camera_rows,
-            specs,
-            best_by_role_method,
-            snapshots,
-            actual_mx,
-            usb_speed,
-        )
-        save_json(args.out_json, payload)
-        print(f"[OK] Resultado final salvo em: {args.out_json}")
-    except Exception as exc:
-        print(f"[WARN] Não foi possível salvar resultado final: {exc}")
-
-    print("Fim da calibração de foco.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/Python/OAK/datasets/oak-fcc-3/utils/radiometric_config_tool.py b/Python/OAK/datasets/oak-fcc-3/utils/radiometric_config_tool.py
deleted file mode 100644
index 25e88dc64..000000000
--- a/Python/OAK/datasets/oak-fcc-3/utils/radiometric_config_tool.py
+++ /dev/null
@@ -1,1617 +0,0 @@
-import os
-import json
-import time
-import argparse
-from datetime import datetime
-
-import cv2
-import numpy as np
-
-from core.oak_fcc3_client import OakFcc3Client as MultiSpectralClient
-
-
-# ============================================================
-# Helpers gerais
-# ============================================================
-
-def now_str() -> str:
-    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-
-def ensure_dir(path: str):
-    if path:
-        os.makedirs(path, exist_ok=True)
-
-
-def clamp(v, lo, hi):
-    return max(lo, min(hi, v))
-
-
-def overlay_hud(
-    img_bgr,
-    lines,
-    x=12,
-    y=24,
-    font_scale=0.58,
-    line_step=22,
-    color=(255, 255, 255),
-    shadow=(0, 0, 0),
-):
-    yy = int(y)
-    for s in lines:
-        cv2.putText(img_bgr, str(s), (int(x), yy), cv2.FONT_HERSHEY_SIMPLEX,
-                    font_scale, shadow, 3, cv2.LINE_AA)
-        cv2.putText(img_bgr, str(s), (int(x), yy), cv2.FONT_HERSHEY_SIMPLEX,
-                    font_scale, color, 1, cv2.LINE_AA)
-        yy += int(line_step)
-
-
-def to_bgr_u8_from_rgb01(rgb01: np.ndarray) -> np.ndarray:
-    rgb_u8 = np.clip(rgb01 * 255.0, 0, 255).astype(np.uint8)
-    return cv2.cvtColor(rgb_u8, cv2.COLOR_RGB2BGR)
-
-
-def gray_to_bgr_u8(gray01: np.ndarray) -> np.ndarray:
-    g = np.clip(gray01 * 255.0, 0, 255).astype(np.uint8)
-    return cv2.cvtColor(g, cv2.COLOR_GRAY2BGR)
-
-
-def resize_if_needed(img: np.ndarray, target_hw: tuple[int, int]) -> np.ndarray:
-    if img is None:
-        return None
-    th, tw = target_hw
-    if img.shape[:2] == (th, tw):
-        return img
-    return cv2.resize(img, (tw, th), interpolation=cv2.INTER_LINEAR)
-
-
-def compute_stats(img01: np.ndarray, roi_px=None) -> dict:
-    if img01 is None:
-        return {
-            "valid": False,
-            "pixels": 0,
-            "mean": 0.0,
-            "std": 0.0,
-            "p05": 0.0,
-            "p50": 0.0,
-            "p95": 0.0,
-            "sat_pct": 0.0,
-            "dark_pct": 0.0,
-        }
-
-    if img01.ndim == 3:
-        arr = (
-            0.299 * img01[:, :, 0] +
-            0.587 * img01[:, :, 1] +
-            0.114 * img01[:, :, 2]
-        ).astype(np.float32)
-    else:
-        arr = img01.astype(np.float32)
-
-    if roi_px is not None:
-        x0, y0, x1, y1 = roi_px
-        x0, x1 = sorted((int(x0), int(x1)))
-        y0, y1 = sorted((int(y0), int(y1)))
-        x0 = clamp(x0, 0, arr.shape[1] - 1)
-        x1 = clamp(x1, x0 + 1, arr.shape[1])
-        y0 = clamp(y0, 0, arr.shape[0] - 1)
-        y1 = clamp(y1, y0 + 1, arr.shape[0])
-        arr = arr[y0:y1, x0:x1]
-
-    flat = arr.reshape(-1).astype(np.float32)
-    if flat.size == 0:
-        return {
-            "valid": False,
-            "pixels": 0,
-            "mean": 0.0,
-            "std": 0.0,
-            "p05": 0.0,
-            "p50": 0.0,
-            "p95": 0.0,
-            "sat_pct": 0.0,
-            "dark_pct": 0.0,
-        }
-
-    return {
-        "valid": True,
-        "pixels": int(flat.size),
-        "mean": float(flat.mean()),
-        "std": float(flat.std()),
-        "p05": float(np.percentile(flat, 5)),
-        "p50": float(np.percentile(flat, 50)),
-        "p95": float(np.percentile(flat, 95)),
-        "sat_pct": float((flat >= 0.98).mean() * 100.0),
-        "dark_pct": float((flat <= 0.02).mean() * 100.0),
-    }
-
-
-def pct_to_px(roi_pct: dict, w: int, h: int):
-    x0 = int(float(roi_pct.get("x0", 0.0)) * w)
-    y0 = int(float(roi_pct.get("y0", 0.0)) * h)
-    x1 = int(float(roi_pct.get("x1", 1.0)) * w)
-    y1 = int(float(roi_pct.get("y1", 1.0)) * h)
-
-    x0 = clamp(x0, 0, w - 1)
-    x1 = clamp(x1, x0 + 1, w)
-    y0 = clamp(y0, 0, h - 1)
-    y1 = clamp(y1, y0 + 1, h)
-
-    return x0, y0, x1, y1
-
-
-def px_to_pct(rect, w: int, h: int):
-    x0, y0, x1, y1 = rect
-    x0, x1 = sorted((int(x0), int(x1)))
-    y0, y1 = sorted((int(y0), int(y1)))
-
-    x0 = clamp(x0, 0, w - 1)
-    x1 = clamp(x1, x0 + 1, w)
-    y0 = clamp(y0, 0, h - 1)
-    y1 = clamp(y1, y0 + 1, h)
-
-    return {
-        "x0": round(x0 / float(w), 6),
-        "y0": round(y0 / float(h), 6),
-        "x1": round(x1 / float(w), 6),
-        "y1": round(y1 / float(h), 6),
-    }
-
-
-def get_decoded_by_role(decoded: dict, role: str):
-    role = str(role).lower()
-    for cam_id, item in (decoded or {}).items():
-        if str(item.get("role", "")).lower() == role:
-            return cam_id, item
-    return None, None
-
-
-def get_image_by_role(decoded: dict, role: str):
-    cam_id, item = get_decoded_by_role(decoded, role)
-    if item is None:
-        return cam_id, None
-    return cam_id, item.get("image")
-
-
-def get_visual_preview_by_role(visual_previews: dict, meta: dict, role: str):
-    """
-    Busca uma imagem visual BGR dentro do retorno de cam.build_visual_preview_from_raw(),
-    usando camera_info para descobrir o role rgb/re/nir.
-
-    Retorna: cam_id, img_bgr
-    """
-    if not visual_previews:
-        return None, None
-
-    camera_info = (meta or {}).get("camera_info", {}) or {}
-    role = str(role).lower()
-
-    for cam_id, img in visual_previews.items():
-        cam_role = str(camera_info.get(cam_id, {}).get("role", "")).lower()
-        if cam_role == role:
-            return cam_id, img
-
-    return None, None
-
-
-def validate_module_ready(status: dict, raw_policy: str):
-    if not status.get("ok", True):
-        raise RuntimeError(f"Status inválido retornado pelo modulo: {status}")
-
-    active_roles = status.get("active_roles", {}) or {}
-    active_count = int(status.get("camera_count_active", 0))
-
-    if raw_policy == "require_triple":
-        missing = [role for role in ("rgb", "nir", "re") if role not in active_roles]
-        if missing:
-            raise RuntimeError(
-                f"RAW_BRUTO com require_triple exige rgb/nir/re ativas. "
-                f"Faltando: {missing}. Ativas: {active_roles}"
-            )
-    elif active_count < 1:
-        raise RuntimeError("RAW_BRUTO requer ao menos uma câmera ativa.")
-
-
-# ============================================================
-# Config radiométrico
-# ============================================================
-
-# Defaults espelhados do module_params.json atual.
-# Este bloco é a "semente boa" do AE Rad: patches + Global Saturation Guard + Sun Guard.
-DEFAULT_RADIOMETRIC_CONFIG = {'enabled': True,
- 'interval_s': 0.25,
- 'verbose': True,
- 'metering_mode': 'reference_patches',
- 'spectral_control_mode': 'shared',
- 'control_metric': 'p50',
- 'target_value': 0.5,
- 'deadband': 0.035,
- 'p95_limit': 0.94,
- 'saturation_limit_pct': 0.5,
- 'alpha': 0.18,
- 'exp_step_gain': 0.55,
- 'prefer_exposure': True,
- 'exp_min_us': 100,
- 'exp_max_us': 80000,
- 'gain_min': 1.0,
- 'gain_max': 4.0,
- 'reference_patches': [{'name': 'black_reference',
-                        'type': 'black',
-                        'roles': ['rgb', 're', 'nir'],
-                        'roi_pct': {'x0': 0.365625, 'y0': 0.8225, 'x1': 0.432812, 'y1': 0.995},
-                        'target_value': 0.08,
-                        'weight': 0.7,
-                        'roi_pct_by_role': {'rgb': {'x0': 0.365625, 'y0': 0.8225, 'x1': 0.432812, 'y1': 0.995},
-                                            're': {'x0': 0.395313, 'y0': 0.745, 'x1': 0.4625, 'y1': 0.9225},
-                                            'nir': {'x0': 0.353125, 'y0': 0.78, 'x1': 0.420312, 'y1': 0.9525}},
-                        'roi_list_by_role': {'rgb': [{'name': 'rgb_legacy_01',
-                                                      'enabled': True,
-                                                      'roi_pct': {'x0': 0.365625,
-                                                                  'y0': 0.8225,
-                                                                  'x1': 0.432812,
-                                                                  'y1': 0.995},
-                                                      'created_at': '2026-05-07 14:36:25',
-                                                      'updated_at': '2026-05-08 09:19:46'}],
-                                             're': [{'name': 're_legacy_01',
-                                                     'enabled': True,
-                                                     'roi_pct': {'x0': 0.395313,
-                                                                 'y0': 0.745,
-                                                                 'x1': 0.4625,
-                                                                 'y1': 0.9225},
-                                                     'created_at': '2026-05-07 14:36:25',
-                                                     'updated_at': '2026-05-08 09:20:14'}],
-                                             'nir': [{'name': 'nir_legacy_01',
-                                                      'enabled': True,
-                                                      'roi_pct': {'x0': 0.353125,
-                                                                  'y0': 0.78,
-                                                                  'x1': 0.420312,
-                                                                  'y1': 0.9525},
-                                                      'created_at': '2026-05-07 14:36:25',
-                                                      'updated_at': '2026-05-08 09:20:47'}]}},
-                       {'name': 'gray_reference',
-                        'type': 'gray',
-                        'roles': ['rgb', 're', 'nir'],
-                        'roi_pct': {'x0': 0.29375, 'y0': 0.825, 'x1': 0.3625, 'y1': 0.995},
-                        'target_value': 0.35,
-                        'target_value_by_role': {'rgb': 0.34, 're': 0.24, 'nir': 0.3},
-                        'weight': 1.0,
-                        'roi_pct_by_role': {'rgb': {'x0': 0.29375, 'y0': 0.825, 'x1': 0.3625, 'y1': 0.995},
-                                            're': {'x0': 0.325, 'y0': 0.75, 'x1': 0.389062, 'y1': 0.915},
-                                            'nir': {'x0': 0.284375, 'y0': 0.79, 'x1': 0.35, 'y1': 0.9525}},
-                        'roi_list_by_role': {'rgb': [{'name': 'rgb_legacy_01',
-                                                      'enabled': True,
-                                                      'roi_pct': {'x0': 0.29375,
-                                                                  'y0': 0.825,
-                                                                  'x1': 0.3625,
-                                                                  'y1': 0.995},
-                                                      'created_at': '2026-05-07 14:36:25',
-                                                      'updated_at': '2026-05-08 09:19:29'}],
-                                             're': [{'name': 're_legacy_01',
-                                                     'enabled': True,
-                                                     'roi_pct': {'x0': 0.325, 'y0': 0.75, 'x1': 0.389062, 'y1': 0.915},
-                                                     'created_at': '2026-05-07 14:36:25',
-                                                     'updated_at': '2026-05-08 09:20:22'}],
-                                             'nir': [{'name': 'nir_legacy_01',
-                                                      'enabled': True,
-                                                      'roi_pct': {'x0': 0.284375, 'y0': 0.79, 'x1': 0.35, 'y1': 0.9525},
-                                                      'created_at': '2026-05-07 14:36:25',
-                                                      'updated_at': '2026-05-08 09:20:53'}]}},
-                       {'name': 'white_reference',
-                        'type': 'white',
-                        'roles': ['rgb', 're', 'nir'],
-                        'roi_pct': {'x0': 0.220312, 'y0': 0.8225, 'x1': 0.2875, 'y1': 0.995},
-                        'target_value': 0.82,
-                        'weight': 0.8,
-                        'roi_pct_by_role': {'rgb': {'x0': 0.220312, 'y0': 0.8225, 'x1': 0.2875, 'y1': 0.995},
-                                            're': {'x0': 0.25, 'y0': 0.7525, 'x1': 0.315625, 'y1': 0.9225},
-                                            'nir': {'x0': 0.214062, 'y0': 0.785, 'x1': 0.282813, 'y1': 0.9525}},
-                        'roi_list_by_role': {'rgb': [{'name': 'rgb_legacy_01',
-                                                      'enabled': True,
-                                                      'roi_pct': {'x0': 0.220312,
-                                                                  'y0': 0.8225,
-                                                                  'x1': 0.2875,
-                                                                  'y1': 0.995},
-                                                      'created_at': '2026-05-07 14:36:25',
-                                                      'updated_at': '2026-05-08 09:19:04'}],
-                                             're': [{'name': 're_legacy_01',
-                                                     'enabled': True,
-                                                     'roi_pct': {'x0': 0.25,
-                                                                 'y0': 0.7525,
-                                                                 'x1': 0.315625,
-                                                                 'y1': 0.9225},
-                                                     'created_at': '2026-05-07 14:36:25',
-                                                     'updated_at': '2026-05-08 09:20:27'}],
-                                             'nir': [{'name': 'nir_legacy_01',
-                                                      'enabled': True,
-                                                      'roi_pct': {'x0': 0.214062,
-                                                                  'y0': 0.785,
-                                                                  'x1': 0.282813,
-                                                                  'y1': 0.9525},
-                                                      'created_at': '2026-05-07 14:36:25',
-                                                      'updated_at': '2026-05-08 09:21:02'}]}}],
- 'exp_apply_threshold_us': 80,
- 'gain_apply_threshold': 0.05,
- 'apply_same_spectral_to_both': True,
- 'spectral_roles': ['re', 'nir'],
- 'dark_limit_pct': 35.0,
- 'control_strategy': 'ratio',
- 'ratio_alpha': 0.35,
- 'ratio_min': 0.65,
- 'ratio_max': 1.35,
- 'reduce_fast_factor': 0.8,
- 'factor_min': 0.55,
- 'factor_max': 1.28,
- 'gain_return_enabled': True,
- 'gain_reduce_on_saturation': True,
- 'gain_increase_required_cycles': 5,
- 'gain_decrease_required_cycles': 2,
- 'gain_step_up': 0.2,
- 'gain_step_down': 0.5,
- 'gain_hard_reset_on_saturation': False,
- 'exp_high_ratio_for_gain': 0.95,
- 'exp_low_ratio_for_gain_return': 0.75,
- 'role_limits': {'rgb': {'exp_min_us': 100, 'exp_max_us': 80000, 'gain_min': 1.0, 'gain_max': 2.0},
-                 're': {'exp_min_us': 100, 'exp_max_us': 2500, 'gain_min': 1.0, 'gain_max': 2.0},
-                 'nir': {'exp_min_us': 100, 'exp_max_us': 3000, 'gain_min': 1.0, 'gain_max': 2.0}},
- 'ready_required_cycles': 3,
- 'patch_control_mode': 'gray_primary',
- 'patch_require_order': True,
- 'patch_min_separation': 0.08,
- 'patch_white_sat_limit_pct': 0.5,
- 'patch_white_p95_limit': 0.94,
- 'patch_black_dark_limit_pct': 80.0,
- 'patch_black_max_p50': 0.2,
- 'patch_gray_min_p50': 0.08,
- 'patch_gray_max_p50': 0.85,
- 'patch_roi_contract': 'multi_roi_by_role_v1',
- 'patch_roi_reduce_method': 'median_valid_rois',
- 'patch_roi_outlier_reject': True,
- 'patch_roi_max_p50_delta': 0.12,
- 'global_saturation_guard_enabled': True,
- 'global_guard_roi_pct': {'x0': 0.05, 'y0': 0.05, 'x1': 0.95, 'y1': 0.95},
- 'global_guard_sat_threshold': 0.985,
- 'global_guard_near_sat_threshold': 0.94,
- 'global_guard_sat_pct_soft': 0.05,
- 'global_guard_sat_pct_hard': 0.2,
- 'global_guard_sat_pct_extreme': 0.8,
- 'global_guard_blob_pct_soft': 0.015,
- 'global_guard_blob_pct_hard': 0.08,
- 'global_guard_blob_pct_extreme': 0.25,
- 'global_guard_min_blob_px': 48,
- 'global_guard_downsample_max_side': 320,
- 'global_guard_reduce_factor_soft': 0.82,
- 'global_guard_reduce_factor_hard': 0.6,
- 'global_guard_reduce_factor_extreme': 0.35,
- 'sun_guard_enabled': True,
- 'sun_guard_p99_threshold': 0.9,
- 'sun_guard_near_sat_pct_threshold': 0.8,
- 'sun_guard_freeze_increase_cycles': 2,
- 'sun_guard_allow_decrease': True,
- 'guard_force_apply_enabled': True,
- 'guard_force_apply_soft': True,
- 'guard_force_apply_hard': True,
- 'guard_force_apply_extreme': True,
- 'guard_force_apply_on_patch_saturation': True,
- 'guard_freeze_cycles_soft': 3,
- 'guard_freeze_cycles_hard': 5,
- 'guard_freeze_cycles_extreme': 8,
- 'guard_reapply_min_exp_on_emergency': True,
- 'guard_min_exp_margin_us': 80}
-
-DEFAULT_PATCH_NORMALIZATION = {'enabled': True,
- 'apply_when_metering_mode': 'reference_patches',
- 'apply_stage': 'after_fusion',
- 'method': 'gray_scale_with_white_guard',
- 'space': 'multispec_tensor',
- 'targets': {'black': 0.06, 'gray': 0.4, 'white': 0.78},
- 'white_guard_max': 0.92,
- 'scale_min': 0.35,
- 'scale_max': 2.5,
- 'clip_output': True,
- 'require_valid_gray': True,
- 'use_black_for_offset': False,
- 'save_patch_stats': True}
-
-PROFILE_SCHEMA = "multispec_radiometric_config_profiles_v4"
-MODULE_PARAMS_SCHEMA = "multispec_module_params_v3"
-
-
-def deep_clone(obj):
-    return json.loads(json.dumps(obj))
-
-
-def is_module_params_contract(data: dict) -> bool:
-    """Detecta o contrato completo do module_params.json, para não salvar wrappers do tool nele."""
-    if not isinstance(data, dict):
-        return False
-    schema = str(data.get("schema", ""))
-    if schema == MODULE_PARAMS_SCHEMA:
-        return True
-    module_keys = ("camera_settings", "fusion_config", "rgb_calibration", "flatfield_config")
-    return "radiometric_config" in data and any(k in data for k in module_keys)
-
-
-def sanitize_radiometric_config(cfg: dict) -> dict:
-    """Garante que o radiometric_config salvo siga o contrato runtime atual."""
-    out = deep_clone(DEFAULT_RADIOMETRIC_CONFIG)
-    if isinstance(cfg, dict):
-        # Preserva valores/ROIs escolhidos no tool, mas injeta qualquer chave nova faltante.
-        for k, v in cfg.items():
-            out[k] = v
-
-    out.setdefault("reference_patches", deep_clone(DEFAULT_RADIOMETRIC_CONFIG.get("reference_patches", [])))
-
-    # Garante contrato multi_roi_by_role_v1 em todos os patches.
-    out["patch_roi_contract"] = "multi_roi_by_role_v1"
-    for patch in out.get("reference_patches", []) or []:
-        if not isinstance(patch, dict):
-            continue
-        patch.setdefault("roles", ROLES[:] if "ROLES" in globals() else ["rgb", "re", "nir"])
-        patch.setdefault("roi_pct", {})
-        patch.setdefault("roi_pct_by_role", {"rgb": {}, "re": {}, "nir": {}})
-        patch.setdefault("roi_list_by_role", {"rgb": [], "re": [], "nir": []})
-        ensure_patch_roi_lists_by_role(patch)
-
-    # Garante guardas parrudas mesmo em arquivos antigos.
-    for k, v in DEFAULT_RADIOMETRIC_CONFIG.items():
-        if k.startswith("global_guard_") or k.startswith("sun_guard_") or k.startswith("guard_"):
-            out.setdefault(k, v)
-
-    return out
-
-
-def base_ae_contract():
-    cfg = deep_clone(DEFAULT_RADIOMETRIC_CONFIG)
-    # Removemos somente campos específicos de patches quando usado como base global.
-    cfg.pop("reference_patches", None)
-    cfg.pop("patch_control_mode", None)
-    cfg.pop("patch_require_order", None)
-    cfg.pop("patch_min_separation", None)
-    cfg.pop("patch_white_sat_limit_pct", None)
-    cfg.pop("patch_white_p95_limit", None)
-    cfg.pop("patch_black_dark_limit_pct", None)
-    cfg.pop("patch_black_max_p50", None)
-    cfg.pop("patch_gray_min_p50", None)
-    cfg.pop("patch_gray_max_p50", None)
-    cfg.pop("patch_roi_contract", None)
-    cfg.pop("patch_roi_reduce_method", None)
-    cfg.pop("patch_roi_outlier_reject", None)
-    cfg.pop("patch_roi_max_p50_delta", None)
-    return cfg
-
-
-def default_profile_global():
-    cfg = base_ae_contract()
-    base = {"x0": 0.08, "y0": 0.08, "x1": 0.92, "y1": 0.92}
-    guard_base = deep_clone(DEFAULT_RADIOMETRIC_CONFIG.get("global_guard_roi_pct", {"x0": 0.05, "y0": 0.05, "x1": 0.95, "y1": 0.95}))
-
-    cfg.update({
-        "metering_mode": "global",
-        "spectral_control_mode": DEFAULT_RADIOMETRIC_CONFIG.get("spectral_control_mode", "shared"),
-        "global_roi_pct": base,
-        "global_roi_pct_by_role": {"rgb": dict(base), "re": dict(base), "nir": dict(base)},
-        "global_guard_roi_pct": guard_base,
-    })
-    return {"radiometric_config": cfg}
-
-
-def make_default_patch(patch_type: str, target: float, weight: float):
-    # Preferimos copiar o patch correspondente do module_params.json atual.
-    for p in DEFAULT_RADIOMETRIC_CONFIG.get("reference_patches", []) or []:
-        if str(p.get("type", "")).lower() == str(patch_type).lower():
-            patch = deep_clone(p)
-            patch.setdefault("target_value", target)
-            patch.setdefault("weight", weight)
-            patch.setdefault("roles", ROLES[:] if "ROLES" in globals() else ["rgb", "re", "nir"])
-            patch.setdefault("roi_pct", {})
-            patch.setdefault("roi_pct_by_role", {"rgb": {}, "re": {}, "nir": {}})
-            patch.setdefault("roi_list_by_role", {"rgb": [], "re": [], "nir": []})
-            return patch
-
-    return {
-        "name": f"{patch_type}_reference",
-        "type": patch_type,
-        "roles": ROLES[:] if "ROLES" in globals() else ["rgb", "re", "nir"],
-        "target_value": target,
-        "weight": weight,
-        "roi_pct": {},
-        "roi_pct_by_role": {"rgb": {}, "re": {}, "nir": {}},
-        "roi_list_by_role": {"rgb": [], "re": [], "nir": []},
-    }
-
-
-def default_profile_patches():
-    cfg = sanitize_radiometric_config(DEFAULT_RADIOMETRIC_CONFIG)
-    cfg["metering_mode"] = "reference_patches"
-    cfg["spectral_control_mode"] = DEFAULT_RADIOMETRIC_CONFIG.get("spectral_control_mode", "shared")
-    return {"radiometric_config": cfg}
-
-def get_active_profile_name(data: dict) -> str:
-    name = str(data.get("active_profile", "global_scene_mode"))
-    if name not in ("global_scene_mode", "three_reference_patches_mode"):
-        return "global_scene_mode"
-    return name
-
-
-def set_active_profile_name(data: dict, profile_name: str):
-    if profile_name not in ("global_scene_mode", "three_reference_patches_mode"):
-        profile_name = "global_scene_mode"
-    data["active_profile"] = profile_name
-
-
-def get_active_radiometric_config(data: dict) -> dict:
-    profile_name = get_active_profile_name(data)
-    profile = data.get(profile_name, {}) or {}
-    cfg = profile.get("radiometric_config", {}) or {}
-    return json.loads(json.dumps(cfg))
-
-
-def update_root_radiometric_config(data: dict):
-    data["radiometric_config"] = get_active_radiometric_config(data)
-
-
-def load_or_default_config(path: str):
-    """
-    Carrega tanto:
-      1) calibration/module_params.json completo, contrato multispec_module_params_v3;
-      2) arquivo isolado do tool com perfis.
-
-    Em ambos os casos, o root radiometric_config é mantido no mesmo contrato do runtime.
-    """
-    if path and os.path.isfile(path):
-        with open(path, "r", encoding="utf-8") as f:
-            data = json.load(f)
-    else:
-        data = {}
-
-    module_contract = is_module_params_contract(data)
-    root_cfg = data.get("radiometric_config") if isinstance(data.get("radiometric_config"), dict) else None
-
-    if module_contract:
-        # Não troca o schema do module_params. Apenas cria perfis internos para a UI.
-        data.setdefault("schema", MODULE_PARAMS_SCHEMA)
-    else:
-        data.setdefault("schema", PROFILE_SCHEMA)
-
-    data.setdefault("saved_at", now_str())
-
-    # Se já existe um radiometric_config na raiz, ele é a fonte da verdade.
-    if root_cfg:
-        root_cfg = sanitize_radiometric_config(root_cfg)
-        active = "three_reference_patches_mode" if str(root_cfg.get("metering_mode", "")).lower() == "reference_patches" else "global_scene_mode"
-        data["active_profile"] = active
-        data.setdefault("global_scene_mode", default_profile_global())
-        data.setdefault("three_reference_patches_mode", default_profile_patches())
-        data[active]["radiometric_config"] = root_cfg
-    else:
-        data.setdefault("active_profile", "global_scene_mode")
-        data.setdefault("global_scene_mode", default_profile_global())
-        data.setdefault("three_reference_patches_mode", default_profile_patches())
-
-    data.setdefault("patch_normalization", deep_clone(DEFAULT_PATCH_NORMALIZATION))
-
-    # Migração: injeta chaves novas nos dois perfis sem sobrescrever ROIs existentes.
-    for profile_name, default_fn in (
-        ("global_scene_mode", default_profile_global),
-        ("three_reference_patches_mode", default_profile_patches),
-    ):
-        default_profile = default_fn()
-        data.setdefault(profile_name, default_profile)
-        data[profile_name].setdefault("radiometric_config", {})
-
-        default_cfg = default_profile["radiometric_config"]
-        cfg = data[profile_name]["radiometric_config"]
-
-        for k, v in default_cfg.items():
-            cfg.setdefault(k, deep_clone(v))
-
-        if profile_name == "three_reference_patches_mode":
-            data[profile_name]["radiometric_config"] = sanitize_radiometric_config(cfg)
-
-    update_root_radiometric_config(data)
-    return data
-
-def save_config(path: str, data: dict):
-    ensure_dir(os.path.dirname(path) or ".")
-    module_contract = is_module_params_contract(data)
-
-    # Atualiza radiometric_config root a partir do perfil ativo, usando o contrato runtime atual.
-    update_root_radiometric_config(data)
-    data["radiometric_config"] = sanitize_radiometric_config(data.get("radiometric_config", {}))
-    data["patch_normalization"] = data.get("patch_normalization") or deep_clone(DEFAULT_PATCH_NORMALIZATION)
-    data["saved_at"] = now_str()
-
-    if module_contract:
-        # Salva limpo no contrato multispec_module_params_v3, sem wrappers internos da UI.
-        out = dict(data)
-        out["schema"] = MODULE_PARAMS_SCHEMA
-        out.pop("active_profile", None)
-        out.pop("global_scene_mode", None)
-        out.pop("three_reference_patches_mode", None)
-    else:
-        out = dict(data)
-        out["schema"] = PROFILE_SCHEMA
-        out["active_profile"] = get_active_profile_name(data)
-        update_root_radiometric_config(out)
-        out["radiometric_config"] = sanitize_radiometric_config(out.get("radiometric_config", {}))
-
-    with open(path, "w", encoding="utf-8") as f:
-        json.dump(out, f, ensure_ascii=False, indent=2)
-
-
-
-ROLES = ["rgb", "re", "nir"]
-
-
-def normalize_role(role: str) -> str:
-    role = str(role or "rgb").lower()
-    return role if role in ROLES else "rgb"
-
-
-def default_roi():
-    return {"x0": 0.08, "y0": 0.08, "x1": 0.92, "y1": 0.92}
-
-
-def clone_roi(roi: dict) -> dict:
-    roi = roi or {}
-    return {
-        "x0": float(roi.get("x0", 0.08)),
-        "y0": float(roi.get("y0", 0.08)),
-        "x1": float(roi.get("x1", 0.92)),
-        "y1": float(roi.get("y1", 0.92)),
-    }
-
-
-def make_roi_by_role(base_roi=None):
-    base = clone_roi(base_roi or default_roi())
-    return {role: dict(base) for role in ROLES}
-
-
-def ensure_global_roi_by_role(data: dict):
-    data.setdefault("global_scene_mode", default_profile_global())
-    cfg = data["global_scene_mode"].setdefault(
-        "radiometric_config",
-        default_profile_global()["radiometric_config"],
-    )
-
-    legacy = cfg.get("global_roi_pct", default_roi())
-    by_role = cfg.setdefault("global_roi_pct_by_role", make_roi_by_role(legacy))
-
-    for role in ROLES:
-        if role not in by_role or not by_role[role]:
-            by_role[role] = clone_roi(legacy)
-
-    return by_role
-
-
-def get_global_roi_for_role(data: dict, role: str):
-    role = normalize_role(role)
-    by_role = ensure_global_roi_by_role(data)
-    return by_role.get(role, clone_roi(default_roi()))
-
-
-def set_global_roi_for_role(data: dict, role: str, roi_pct: dict):
-    role = normalize_role(role)
-    by_role = ensure_global_roi_by_role(data)
-    by_role[role] = roi_pct
-
-    # Compatibilidade: mantém uma ROI antiga preenchida.
-    # Uso: média/legado/visual antigo. O controller novo deverá usar by_role.
-    data["global_scene_mode"]["radiometric_config"]["global_roi_pct"] = by_role.get("rgb", roi_pct)
-
-
-def get_patches(data: dict):
-    return (
-        data.get("three_reference_patches_mode", {})
-        .get("radiometric_config", {})
-        .get("reference_patches", [])
-    )
-
-
-def make_roi_entry(roi_pct: dict, name: str | None = None, enabled: bool = True) -> dict:
-    return {
-        "name": name or "roi_01",
-        "enabled": bool(enabled),
-        "roi_pct": clone_roi(roi_pct),
-        "created_at": now_str(),
-        "updated_at": now_str(),
-    }
-
-
-def normalize_roi_entry(entry, idx: int) -> dict | None:
-    """Aceita formatos antigos e novos, devolvendo sempre um item padrão."""
-    if not entry:
-        return None
-
-    if isinstance(entry, dict) and "roi_pct" in entry:
-        roi = entry.get("roi_pct") or {}
-        if not roi:
-            return None
-        out = dict(entry)
-        out["name"] = str(out.get("name") or f"roi_{idx + 1:02d}")
-        out["enabled"] = bool(out.get("enabled", True))
-        out["roi_pct"] = clone_roi(roi)
-        out.setdefault("created_at", now_str())
-        out["updated_at"] = str(out.get("updated_at") or now_str())
-        return out
-
-    if isinstance(entry, dict) and all(k in entry for k in ("x0", "y0", "x1", "y1")):
-        return make_roi_entry(entry, name=f"roi_{idx + 1:02d}", enabled=True)
-
-    return None
-
-
-def sync_patch_legacy_roi_fields(patch: dict):
-    """Mantém roi_pct e roi_pct_by_role compatíveis com scripts antigos."""
-    roi_lists = patch.setdefault("roi_list_by_role", {})
-    by_role = patch.setdefault("roi_pct_by_role", {})
-
-    for role in ROLES:
-        entries = roi_lists.setdefault(role, [])
-        first_active = next((e.get("roi_pct") for e in entries if e.get("enabled", True) and e.get("roi_pct")), {})
-        by_role[role] = clone_roi(first_active) if first_active else {}
-
-    patch["roi_pct"] = by_role.get("rgb", {}) or {}
-
-
-def ensure_patch_roi_lists_by_role(patch: dict):
-    """
-    Migra o formato antigo:
-      roi_pct_by_role[role] = {x0,y0,x1,y1}
-    para o formato novo:
-      roi_list_by_role[role] = [{name, enabled, roi_pct, ...}, ...]
-
-    Também aceita, por tolerância, caso alguém já tenha salvo uma lista dentro de roi_pct_by_role.
-    """
-    legacy_global = patch.get("roi_pct", {}) or {}
-    legacy_by_role = patch.get("roi_pct_by_role", {}) or {}
-    roi_lists = patch.setdefault("roi_list_by_role", {})
-
-    for role in ROLES:
-        raw_list = roi_lists.get(role, [])
-
-        # Caso raro: formato novo foi salvo diretamente em roi_pct_by_role.
-        if not raw_list and isinstance(legacy_by_role.get(role), list):
-            raw_list = legacy_by_role.get(role) or []
-
-        normalized = []
-        if isinstance(raw_list, list):
-            for idx, item in enumerate(raw_list):
-                entry = normalize_roi_entry(item, idx)
-                if entry is not None:
-                    normalized.append(entry)
-        elif isinstance(raw_list, dict) and raw_list:
-            entry = normalize_roi_entry(raw_list, 0)
-            if entry is not None:
-                normalized.append(entry)
-
-        # Migração do formato antigo, se ainda não houver lista.
-        if not normalized:
-            old_roi = legacy_by_role.get(role, {}) if isinstance(legacy_by_role, dict) else {}
-            if not old_roi and legacy_global:
-                old_roi = legacy_global
-            if isinstance(old_roi, dict) and old_roi:
-                normalized.append(make_roi_entry(old_roi, name=f"{role}_legacy_01", enabled=True))
-
-        # Garante nomes estáveis e únicos.
-        seen = set()
-        for idx, entry in enumerate(normalized):
-            name = str(entry.get("name") or f"roi_{idx + 1:02d}")
-            if name in seen:
-                name = f"{name}_{idx + 1:02d}"
-            seen.add(name)
-            entry["name"] = name
-
-        roi_lists[role] = normalized
-
-    sync_patch_legacy_roi_fields(patch)
-    return roi_lists
-
-
-# Alias antigo mantido para não quebrar chamadas existentes.
-def ensure_patch_roi_by_role(patch: dict):
-    ensure_patch_roi_lists_by_role(patch)
-    return patch.setdefault("roi_pct_by_role", {})
-
-
-def get_patch_by_type(data: dict, patch_type: str):
-    patch_type = str(patch_type).lower()
-    for p in get_patches(data):
-        if str(p.get("type", "")).lower() == patch_type:
-            ensure_patch_roi_lists_by_role(p)
-            return p
-    return None
-
-
-def ensure_patch_exists(data: dict, patch_type: str):
-    data.setdefault("three_reference_patches_mode", default_profile_patches())
-    cfg = data["three_reference_patches_mode"].setdefault(
-        "radiometric_config",
-        default_profile_patches()["radiometric_config"],
-    )
-
-    patches = cfg.setdefault(
-        "reference_patches",
-        default_profile_patches()["radiometric_config"]["reference_patches"],
-    )
-
-    patch_type = str(patch_type).lower()
-    target = {"black": 0.06, "gray": 0.40, "white": 0.78}.get(patch_type, 0.40)
-    weight = {"black": 0.25, "gray": 1.0, "white": 0.7}.get(patch_type, 1.0)
-
-    for p in patches:
-        if str(p.get("type", "")).lower() == patch_type:
-            ensure_patch_roi_lists_by_role(p)
-            return p
-
-    patch = make_default_patch(patch_type, target, weight)
-    patches.append(patch)
-    ensure_patch_roi_lists_by_role(patch)
-    return patch
-
-
-def get_patch_roi_entries_for_role(data: dict, patch_type: str, role: str, enabled_only: bool = False):
-    role = normalize_role(role)
-    patch = get_patch_by_type(data, patch_type)
-    if not patch:
-        return []
-    roi_lists = ensure_patch_roi_lists_by_role(patch)
-    entries = list(roi_lists.get(role, []) or [])
-    if enabled_only:
-        entries = [e for e in entries if e.get("enabled", True) and e.get("roi_pct")]
-    return entries
-
-
-def get_patch_roi_for_role(data: dict, patch_type: str, role: str):
-    """Compatibilidade: retorna a primeira ROI ativa da lista."""
-    entries = get_patch_roi_entries_for_role(data, patch_type, role, enabled_only=True)
-    if entries:
-        return entries[0].get("roi_pct", {}) or {}
-
-    patch = get_patch_by_type(data, patch_type)
-    if not patch:
-        return {}
-    return patch.get("roi_pct_by_role", {}).get(normalize_role(role), {}) or patch.get("roi_pct", {}) or {}
-
-
-def get_patch_roi_entry(data: dict, patch_type: str, role: str, index: int):
-    entries = get_patch_roi_entries_for_role(data, patch_type, role, enabled_only=False)
-    if not entries:
-        return None, -1
-    index = clamp(int(index), 0, len(entries) - 1)
-    return entries[index], index
-
-
-def set_patch_roi_for_role(data: dict, patch_type: str, role: str, roi_pct: dict, index: int | None = None, append: bool = False):
-    patch = ensure_patch_exists(data, patch_type)
-    role = normalize_role(role)
-    roi_lists = ensure_patch_roi_lists_by_role(patch)
-    entries = roi_lists.setdefault(role, [])
-
-    if append or index is None or index >= len(entries) or index < 0:
-        entry = make_roi_entry(
-            roi_pct,
-            name=f"{patch_type}_{role}_{len(entries) + 1:02d}",
-            enabled=True,
-        )
-        entries.append(entry)
-        saved_index = len(entries) - 1
-    else:
-        saved_index = int(index)
-        old = entries[saved_index]
-        old["roi_pct"] = clone_roi(roi_pct)
-        old["enabled"] = bool(old.get("enabled", True))
-        old["updated_at"] = now_str()
-
-    sync_patch_legacy_roi_fields(patch)
-    return saved_index
-
-
-def delete_patch_roi_for_role(data: dict, patch_type: str, role: str, index: int):
-    patch = get_patch_by_type(data, patch_type)
-    if not patch:
-        return False, 0
-    role = normalize_role(role)
-    roi_lists = ensure_patch_roi_lists_by_role(patch)
-    entries = roi_lists.setdefault(role, [])
-    if not entries:
-        return False, 0
-    index = clamp(int(index), 0, len(entries) - 1)
-    entries.pop(index)
-    sync_patch_legacy_roi_fields(patch)
-    return True, len(entries)
-
-
-def toggle_patch_roi_enabled_for_role(data: dict, patch_type: str, role: str, index: int):
-    entry, idx = get_patch_roi_entry(data, patch_type, role, index)
-    if entry is None:
-        return False, False
-    entry["enabled"] = not bool(entry.get("enabled", True))
-    entry["updated_at"] = now_str()
-    patch = get_patch_by_type(data, patch_type)
-    if patch:
-        sync_patch_legacy_roi_fields(patch)
-    return True, bool(entry["enabled"])
-
-
-def add_empty_patch_roi_slot(data: dict, patch_type: str, role: str):
-    # Usa uma ROI pequena central como placeholder, para o usuário arrastar por cima depois.
-    return set_patch_roi_for_role(
-        data,
-        patch_type,
-        role,
-        {"x0": 0.45, "y0": 0.45, "x1": 0.55, "y1": 0.55},
-        append=True,
-    )
-
-def set_shared_mode(data: dict, shared: bool):
-    for profile in ("global_scene_mode", "three_reference_patches_mode"):
-        data.setdefault(profile, default_profile_global() if profile == "global_scene_mode" else default_profile_patches())
-        cfg = data[profile].setdefault("radiometric_config", {})
-        cfg["spectral_control_mode"] = "shared" if shared else "independent"
-        cfg["apply_same_spectral_to_both"] = bool(shared)
-
-    update_root_radiometric_config(data)
-
-
-# ============================================================
-# UI
-# ============================================================
-
-PATCH_COLORS = {
-    "global": (0, 255, 255),
-    "black": (80, 80, 80),
-    "gray": (180, 180, 180),
-    "white": (255, 255, 255),
-}
-
-PATCH_ORDER = ["black", "gray", "white"]
-
-
-def draw_roi_on_panel(panel, roi_pct, label, color, thickness=2):
-    if roi_pct is None:
-        return
-    h, w = panel.shape[:2]
-    x0, y0, x1, y1 = pct_to_px(roi_pct, w, h)
-
-    cv2.rectangle(panel, (x0, y0), (x1, y1), color, thickness)
-    cv2.putText(panel, label, (x0 + 5, max(20, y0 - 6)), cv2.FONT_HERSHEY_SIMPLEX,
-                0.55, (0, 0, 0), 3, cv2.LINE_AA)
-    cv2.putText(panel, label, (x0 + 5, max(20, y0 - 6)), cv2.FONT_HERSHEY_SIMPLEX,
-                0.55, color, 1, cv2.LINE_AA)
-
-
-def draw_all_rois(panel, data, selected_target, mode, panel_role, edit_role, selected_roi_index=0):
-    panel_role = normalize_role(panel_role)
-    edit_role = normalize_role(edit_role)
-
-    is_edit_panel = panel_role == edit_role
-
-    if mode == "global":
-        roi = get_global_roi_for_role(data, panel_role)
-        label = f"GLOBAL/{panel_role.upper()}"
-        thickness = 3 if is_edit_panel else 2
-        draw_roi_on_panel(panel, roi, label, PATCH_COLORS["global"], thickness)
-
-    else:
-        for p in get_patches(data):
-            typ = str(p.get("type", "")).lower()
-            color = PATCH_COLORS.get(typ, (0, 255, 255))
-            entries = get_patch_roi_entries_for_role(data, typ, panel_role, enabled_only=False)
-
-            for idx, entry in enumerate(entries):
-                roi = entry.get("roi_pct", {})
-                if not roi:
-                    continue
-
-                enabled = bool(entry.get("enabled", True))
-                selected = typ == selected_target and is_edit_panel and idx == selected_roi_index
-                thickness = 3 if selected else 1 if not enabled else 2
-
-                label = f"{typ.upper()}/{panel_role.upper()}#{idx + 1}"
-                if not enabled:
-                    label += " OFF"
-
-                draw_roi_on_panel(panel, roi, label, color, thickness)
-
-
-def build_board(
-    decoded,
-    data,
-    mode,
-    selected_target,
-    edit_role,
-    selected_roi_index,
-    drag_rect_local,
-    drag_role,
-    panel_rects,
-    preview_scale=1.0,
-    visual_previews=None,
-    meta=None,
-    beauty_preview=True,
-):
-    rgb_id, rgb01 = get_image_by_role(decoded, "rgb")
-    re_id, re01 = get_image_by_role(decoded, "re")
-    nir_id, nir01 = get_image_by_role(decoded, "nir")
-
-    # ------------------------------------------------------------
-    # Tamanho base SEMPRE vem do decoded, porque ROI/stats usam dado real.
-    # O preview visual é só para desenhar bonito.
-    # ------------------------------------------------------------
-    if rgb01 is not None:
-        base_h, base_w = rgb01.shape[:2]
-    elif re01 is not None:
-        base_h, base_w = re01.shape[:2]
-    elif nir01 is not None:
-        base_h, base_w = nir01.shape[:2]
-    else:
-        base_h, base_w = 800, 1280
-
-    # ------------------------------------------------------------
-    # Preview bonito, igual ao capture.
-    # ------------------------------------------------------------
-    rgb_vis_id, rgb_vis = get_visual_preview_by_role(visual_previews, meta, "rgb")
-    re_vis_id, re_vis = get_visual_preview_by_role(visual_previews, meta, "re")
-    nir_vis_id, nir_vis = get_visual_preview_by_role(visual_previews, meta, "nir")
-
-    if beauty_preview and rgb_vis is not None:
-        rgb_panel = rgb_vis.copy()
-        if rgb_panel.shape[:2] != (base_h, base_w):
-            rgb_panel = cv2.resize(rgb_panel, (base_w, base_h), interpolation=cv2.INTER_LINEAR)
-        rgb_id = rgb_vis_id
-    else:
-        if rgb01 is not None:
-            rgb_panel = to_bgr_u8_from_rgb01(rgb01)
-        else:
-            rgb_panel = np.zeros((base_h, base_w, 3), dtype=np.uint8)
-            overlay_hud(rgb_panel, ["RGB", "sem frame"])
-
-    if beauty_preview and re_vis is not None:
-        re_panel = re_vis.copy()
-        if re_panel.shape[:2] != (base_h, base_w):
-            re_panel = cv2.resize(re_panel, (base_w, base_h), interpolation=cv2.INTER_LINEAR)
-        re_id = re_vis_id
-    else:
-        re01_show = resize_if_needed(re01, (base_h, base_w)) if re01 is not None else None
-        re_panel = gray_to_bgr_u8(re01_show) if re01_show is not None else np.zeros_like(rgb_panel)
-
-    if beauty_preview and nir_vis is not None:
-        nir_panel = nir_vis.copy()
-        if nir_panel.shape[:2] != (base_h, base_w):
-            nir_panel = cv2.resize(nir_panel, (base_w, base_h), interpolation=cv2.INTER_LINEAR)
-        nir_id = nir_vis_id
-    else:
-        nir01_show = resize_if_needed(nir01, (base_h, base_w)) if nir01 is not None else None
-        nir_panel = gray_to_bgr_u8(nir01_show) if nir01_show is not None else np.zeros_like(rgb_panel)
-
-    draw_all_rois(rgb_panel, data, selected_target, mode, "rgb", edit_role, selected_roi_index)
-    draw_all_rois(re_panel, data, selected_target, mode, "re", edit_role, selected_roi_index)
-    draw_all_rois(nir_panel, data, selected_target, mode, "nir", edit_role, selected_roi_index)
-
-    if drag_rect_local is not None:
-        x0, y0, x1, y1 = drag_rect_local
-        color = PATCH_COLORS["global"] if mode == "global" else PATCH_COLORS.get(selected_target, (0, 255, 255))
-
-        if drag_role == "rgb":
-            cv2.rectangle(rgb_panel, (x0, y0), (x1, y1), color, 1)
-        elif drag_role == "re":
-            cv2.rectangle(re_panel, (x0, y0), (x1, y1), color, 1)
-        elif drag_role == "nir":
-            cv2.rectangle(nir_panel, (x0, y0), (x1, y1), color, 1)
-
-    overlay_hud(rgb_panel, [f"RGB ({rgb_id})"], y=24)
-    overlay_hud(re_panel, [f"RE ({re_id})"], y=24)
-    overlay_hud(nir_panel, [f"NIR ({nir_id})"], y=24)
-
-    ph = max(rgb_panel.shape[0], re_panel.shape[0], nir_panel.shape[0])
-    pw = max(rgb_panel.shape[1], re_panel.shape[1], nir_panel.shape[1])
-
-    def fit_panel(img):
-        if img.shape[:2] != (ph, pw):
-            return cv2.resize(img, (pw, ph), interpolation=cv2.INTER_NEAREST)
-        return img
-
-    rgb_panel = fit_panel(rgb_panel)
-    re_panel = fit_panel(re_panel)
-    nir_panel = fit_panel(nir_panel)
-
-    data_panel = np.zeros((ph, pw, 3), dtype=np.uint8)
-
-    panel_rects["rgb"] = (0, 0, pw, ph)
-    panel_rects["re"] = (pw, 0, pw * 2, ph)
-    panel_rects["nir"] = (0, ph, pw, ph * 2)
-    panel_rects["data"] = (pw, ph, pw * 2, ph * 2)
-
-    top = np.hstack([rgb_panel, re_panel])
-    bottom = np.hstack([nir_panel, data_panel])
-    board = np.vstack([top, bottom])
-
-    x0, y0, x1, y1 = panel_rects["data"]
-    lines = build_data_lines(decoded, data, mode, selected_target, edit_role, selected_roi_index, base_w, base_h)
-    overlay_hud(board, lines, x=x0 + 16, y=y0 + 28, font_scale=0.50, line_step=20)
-
-    if preview_scale != 1.0:
-        board = cv2.resize(
-            board,
-            (int(board.shape[1] * preview_scale), int(board.shape[0] * preview_scale)),
-            interpolation=cv2.INTER_NEAREST,
-        )
-
-    return board
-
-
-def build_data_lines(decoded, data, mode, selected_target, edit_role, selected_roi_index, base_w, base_h):
-    edit_role = normalize_role(edit_role)
-    active_profile = get_active_profile_name(data)
-    active_cfg = get_active_radiometric_config(data)
-
-    lines = [
-        "RADIOMETRIC CONFIG TOOL",
-        f"modo={mode.upper()} | camera={edit_role.upper()} | active={active_profile}",
-        f"spectral={active_cfg.get('spectral_control_mode')} | strategy={active_cfg.get('control_strategy')}",
-        f"roi_contract={active_cfg.get('patch_roi_contract', 'legacy_single_roi')}",
-        "",
-        "Arraste no painel da camera editada para definir/atualizar ROI.",
-        "PATCHES agora suportam N ROIs por cor e por camera.",
-        "",
-    ]
-
-    if mode == "global":
-        lines.append("GLOBAL ROI por camera:")
-        for role in ROLES:
-            roi_pct = get_global_roi_for_role(data, role)
-            marker = "*" if role == edit_role else " "
-            lines.append(f"{marker} {role.upper()}: roi={roi_pct}")
-
-        lines.append("")
-        lines.append("Stats GLOBAL:")
-        lines.extend(stats_lines_for_mode(data, decoded, mode="global", patch_type=None))
-
-    else:
-        entries_edit = get_patch_roi_entries_for_role(data, selected_target, edit_role, enabled_only=False)
-        n_edit = len(entries_edit)
-        selected_roi_index = clamp(selected_roi_index, 0, max(0, n_edit - 1)) if n_edit else 0
-
-        lines.append(f"PATCH selecionado: {selected_target.upper()}")
-        lines.append(f"ROI selecionada {edit_role.upper()}: #{selected_roi_index + 1 if n_edit else 0}/{n_edit}")
-        lines.append("Contagem de ROIs por camera:")
-        for role in ROLES:
-            entries = get_patch_roi_entries_for_role(data, selected_target, role, enabled_only=False)
-            enabled = sum(1 for e in entries if e.get("enabled", True))
-            marker = "*" if role == edit_role else " "
-            lines.append(f"{marker} {role.upper()}: {enabled}/{len(entries)} ativas")
-
-        if n_edit:
-            entry = entries_edit[selected_roi_index]
-            lines.append(f"ROI atual: {entry.get('name')} | enabled={entry.get('enabled', True)}")
-            lines.append(f"rect={entry.get('roi_pct')}")
-        else:
-            lines.append("ROI atual: nenhuma. Arraste para criar a primeira.")
-
-        sel_patch = get_patch_by_type(data, selected_target)
-        if sel_patch:
-            lines.append(
-                f"target={float(sel_patch.get('target_value', 0.0)):.2f} "
-                f"weight={float(sel_patch.get('weight', 1.0)):.2f}"
-            )
-
-        lines.append("")
-        lines.append(f"Stats robustas {selected_target.upper()}:")
-        lines.extend(stats_lines_for_mode(data, decoded, mode="patches", patch_type=selected_target))
-
-    lines.extend([
-        "",
-        "M = GLOBAL/PATCHES | C = camera | V = preview bonito/bruto",
-        "1/2/3 = BLACK/GRAY/WHITE | S = shared/independent",
-        "N = nova ROI | [ ] = troca ROI | D = apaga ROI | T = liga/desliga ROI",
-        "P ou SPACE = salva JSON | R = defaults | Q/Esc = sai",
-    ])
-
-    return lines
-
-
-def aggregate_roi_stats(stats_list: list[dict]) -> dict:
-    valid = [s for s in stats_list if s.get("valid")]
-    if not valid:
-        return {"valid": False, "count": 0}
-
-    p50 = np.array([s["p50"] for s in valid], dtype=np.float32)
-    p95 = np.array([s["p95"] for s in valid], dtype=np.float32)
-    sat = np.array([s["sat_pct"] for s in valid], dtype=np.float32)
-    dark = np.array([s["dark_pct"] for s in valid], dtype=np.float32)
-    std = np.array([s["std"] for s in valid], dtype=np.float32)
-
-    return {
-        "valid": True,
-        "count": len(valid),
-        "p50": float(np.median(p50)),
-        "p95": float(np.median(p95)),
-        "sat_pct": float(np.median(sat)),
-        "dark_pct": float(np.median(dark)),
-        "std": float(np.median(std)),
-        "p50_spread": float(p50.max() - p50.min()) if len(p50) > 1 else 0.0,
-        "p50_min": float(p50.min()),
-        "p50_max": float(p50.max()),
-    }
-
-
-def stats_lines_for_mode(data, decoded, mode: str, patch_type: str | None = None):
-    lines = []
-
-    for role in ROLES:
-        _, img = get_image_by_role(decoded, role)
-        if img is None:
-            lines.append(f"{role.upper()}: sem frame")
-            continue
-
-        h, w = img.shape[:2]
-
-        if mode == "global":
-            roi_pct = get_global_roi_for_role(data, role)
-            if not roi_pct:
-                lines.append(f"{role.upper()}: sem ROI")
-                continue
-            roi = pct_to_px(roi_pct, w, h)
-            st = compute_stats(img, roi)
-            lines.append(
-                f"{role.upper()}: p50={st['p50']:.3f} p95={st['p95']:.3f} "
-                f"sat={st['sat_pct']:.2f}% dark={st['dark_pct']:.1f}%"
-            )
-            continue
-
-        entries = get_patch_roi_entries_for_role(data, patch_type, role, enabled_only=True)
-        if not entries:
-            lines.append(f"{role.upper()}: sem ROI ativa")
-            continue
-
-        stats = []
-        p50_each = []
-        for entry in entries:
-            roi_pct = entry.get("roi_pct", {})
-            if not roi_pct:
-                continue
-            roi = pct_to_px(roi_pct, w, h)
-            st = compute_stats(img, roi)
-            stats.append(st)
-            if st.get("valid"):
-                p50_each.append(st["p50"])
-
-        ag = aggregate_roi_stats(stats)
-        if not ag.get("valid"):
-            lines.append(f"{role.upper()}: ROIs invalidas")
-            continue
-
-        mini = ",".join(f"{v:.2f}" for v in p50_each[:4])
-        if len(p50_each) > 4:
-            mini += ",..."
-
-        lines.append(
-            f"{role.upper()}: n={ag['count']} p50_med={ag['p50']:.3f} "
-            f"spread={ag['p50_spread']:.3f} sat_med={ag['sat_pct']:.2f}%"
-        )
-        lines.append(f"   p50_each=[{mini}]")
-
-    return lines
-
-def rect_inside(rect, x, y):
-    if rect is None:
-        return False
-    x0, y0, x1, y1 = rect
-    return x0 <= x < x1 and y0 <= y < y1
-
-
-def local_from_rect(rect, x, y):
-    x0, y0, _, _ = rect
-    return int(x - x0), int(y - y0)
-
-
-# ============================================================
-# Main
-# ============================================================
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Ferramenta visual para parametrizar o radiometric_config global ou por 3 patches.",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-    )
-
-    parser.add_argument("--fps", type=int, default=20)
-    parser.add_argument("--width", type=int, default=1280)
-    parser.add_argument("--height", type=int, default=800)
-    parser.add_argument("--bayer", default="RGGB", choices=["GBRG", "GRBG", "RGGB", "BGGR"])
-    parser.add_argument("--capture_mode", default="AUTO", choices=["AUTO", "SINGLE", "DOUBLE", "TRIPLE"])
-    parser.add_argument("--raw_policy", default="allow_single", choices=["allow_single", "require_triple"])
-    parser.add_argument("--module_calibration_json", default="calibration/module_params.json")
-    parser.add_argument("--out_json", default="calibration/radiometric_config.json")
-    parser.add_argument("--load_json", default="")
-    parser.add_argument("--preview_scale", type=float, default=0.75)
-
-    args = parser.parse_args()
-
-    config_path = args.load_json or args.out_json
-    data = load_or_default_config(config_path)
-
-    mode = "global"
-    selected_target = "gray"
-    selected_roi_index = 0
-    edit_role = "rgb"
-    drag_role = None
-    beauty_preview = True
-    visual_previews_last = {}
-    raw_meta_last = {}
-
-    panel_rects = {"rgb": None, "re": None, "nir": None, "data": None}
-    dragging = False
-    drag_start = None
-    drag_rect_local = None
-
-    last_msg = ""
-    last_msg_t = 0.0
-    last_frame_id = -1
-    decoded_last = {}
-
-    window_name = "Radiometric Config Tool"
-
-    def on_mouse(event, x, y, flags, param):
-        nonlocal dragging, drag_start, drag_rect_local, last_msg, last_msg_t, data, drag_role, selected_roi_index
-
-        # Coordenadas vêm depois do preview_scale. Reescala para board real.
-        if args.preview_scale != 1.0:
-            x = int(x / args.preview_scale)
-            y = int(y / args.preview_scale)
-
-        edit_rect = panel_rects.get(edit_role)
-        if not rect_inside(edit_rect, x, y):
-            return
-
-        lx, ly = local_from_rect(edit_rect, x, y)
-
-        if event == cv2.EVENT_LBUTTONDOWN:
-            dragging = True
-            drag_role = edit_role
-            drag_start = (lx, ly)
-            drag_rect_local = (lx, ly, lx + 1, ly + 1)
-
-        elif event == cv2.EVENT_MOUSEMOVE and dragging:
-            sx, sy = drag_start
-            drag_rect_local = (sx, sy, lx, ly)
-
-        elif event == cv2.EVENT_LBUTTONUP and dragging:
-            dragging = False
-            sx, sy = drag_start
-            rect = (sx, sy, lx, ly)
-            drag_rect_local = None
-
-            # Descobre tamanho local do painel da camera editada.
-            edit_rect = panel_rects.get(drag_role or edit_role)
-            if edit_rect is None:
-                return
-
-            _, _, x1, y1 = edit_rect
-            x0r, y0r, _, _ = edit_rect
-            w = x1 - x0r
-            h = y1 - y0r
-
-            roi_pct = px_to_pct(rect, w, h)
-
-            role_to_save = normalize_role(drag_role or edit_role)
-
-            if mode == "global":
-                set_global_roi_for_role(data, role_to_save, roi_pct)
-                last_msg = f"GLOBAL ROI {role_to_save.upper()} atualizada: {roi_pct}"
-            else:
-                selected_roi_index = set_patch_roi_for_role(
-                    data, selected_target, role_to_save, roi_pct, index=selected_roi_index, append=False
-                )
-                last_msg = (
-                    f"{selected_target.upper()} ROI {role_to_save.upper()} "
-                    f"#{selected_roi_index + 1} atualizada: {roi_pct}"
-                )
-
-            drag_role = None
-            last_msg_t = time.time()
-
-    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
-    cv2.setMouseCallback(window_name, on_mouse)
-
-    try:
-        with MultiSpectralClient(
-            width=args.width,
-            height=args.height,
-            bayer=args.bayer,
-            fps=args.fps,
-            frame_type="RAW_BRUTO",
-            output_dtype="uint8",
-            capture_mode=args.capture_mode,
-            raw_policy=args.raw_policy,
-            module_calibration_json=args.module_calibration_json
-        ) as cam:
-
-            validate_module_ready(cam.get_status(), args.raw_policy)
-
-            while True:
-                raw_frame, raw_meta, decoded = cam.get_next_decoded(timeout=2.0)
-                visual_previews = {}
-
-                try:
-                    if isinstance(raw_frame, dict):
-                        visual_previews = cam.build_visual_preview_from_raw(raw_frame, raw_meta)
-                except Exception as e:
-                    visual_previews = {}
-                    print(f"[WARN] Falha ao gerar beauty preview: {e}")
-
-                if raw_meta is not None and raw_meta.get("frame_id") != last_frame_id:
-                    last_frame_id = raw_meta.get("frame_id")
-                    decoded_last = decoded
-                    visual_previews_last = visual_previews
-                    raw_meta_last = raw_meta
-
-                if decoded_last:
-                    board = build_board(
-                        decoded=decoded_last,
-                        data=data,
-                        mode=mode,
-                        selected_target=selected_target,
-                        edit_role=edit_role,
-                        selected_roi_index=selected_roi_index,
-                        drag_rect_local=drag_rect_local,
-                        drag_role=drag_role,
-                        panel_rects=panel_rects,
-                        preview_scale=args.preview_scale,
-                        visual_previews=visual_previews_last,
-                        meta=raw_meta_last,
-                        beauty_preview=beauty_preview,
-                    )
-
-                    if last_msg and (time.time() - last_msg_t) < 2.5:
-                        cv2.putText(board, last_msg, (18, board.shape[0] - 20),
-                                    cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 255, 0), 2, cv2.LINE_AA)
-
-                    cv2.imshow(window_name, board)
-                else:
-                    blank = np.zeros((720, 1280, 3), dtype=np.uint8)
-                    overlay_hud(blank, ["Aguardando frames..."], x=40, y=80, font_scale=1.0)
-                    cv2.imshow(window_name, blank)
-
-                k = cv2.waitKey(1) & 0xFF
-
-                if k in (ord("q"), ord("Q"), 27):
-                    break
-
-                elif k in (ord("m"), ord("M")):
-                    mode = "patches" if mode == "global" else "global"
-
-                    if mode == "global":
-                        set_active_profile_name(data, "global_scene_mode")
-                    else:
-                        set_active_profile_name(data, "three_reference_patches_mode")
-
-                    update_root_radiometric_config(data)
-
-                    selected_roi_index = 0
-                    last_msg = f"Modo -> {mode} | active_profile={data['active_profile']}"
-                    last_msg_t = time.time()
-
-                elif k == ord("1"):
-                    mode = "patches"
-                    selected_target = "black"
-                    selected_roi_index = 0
-                    set_active_profile_name(data, "three_reference_patches_mode")
-                    update_root_radiometric_config(data)
-                    last_msg = "Selecionado: BLACK"
-                    last_msg_t = time.time()
-
-                elif k == ord("2"):
-                    mode = "patches"
-                    selected_target = "gray"
-                    selected_roi_index = 0
-                    set_active_profile_name(data, "three_reference_patches_mode")
-                    update_root_radiometric_config(data)
-                    last_msg = "Selecionado: GRAY"
-                    last_msg_t = time.time()
-
-                elif k == ord("3"):
-                    mode = "patches"
-                    selected_target = "white"
-                    selected_roi_index = 0
-                    set_active_profile_name(data, "three_reference_patches_mode")
-                    update_root_radiometric_config(data)
-                    last_msg = "Selecionado: WHITE"
-                    last_msg_t = time.time()
-
-                elif k in (ord("s"), ord("S")):
-                    cfg = data.get("global_scene_mode", {}).get("radiometric_config", {})
-                    curr = str(cfg.get("spectral_control_mode", "shared")).lower()
-                    set_shared_mode(data, shared=(curr != "shared"))
-                    new_mode = (
-                        data.get("global_scene_mode", {})
-                        .get("radiometric_config", {})
-                        .get("spectral_control_mode", "shared")
-                    )
-                    last_msg = f"spectral_control_mode -> {new_mode}"
-                    last_msg_t = time.time()
-
-                elif k in (ord("r"), ord("R")):
-                    # Restaura somente a parte radiométrica, preservando o restante do module_params quando existir.
-                    module_contract = is_module_params_contract(data)
-                    preserved = dict(data) if module_contract else {}
-
-                    if module_contract:
-                        preserved["radiometric_config"] = sanitize_radiometric_config(DEFAULT_RADIOMETRIC_CONFIG)
-                        preserved["patch_normalization"] = deep_clone(DEFAULT_PATCH_NORMALIZATION)
-                        preserved["active_profile"] = "three_reference_patches_mode"
-                        preserved["global_scene_mode"] = default_profile_global()
-                        preserved["three_reference_patches_mode"] = default_profile_patches()
-                        data = preserved
-                    else:
-                        data = {
-                            "schema": PROFILE_SCHEMA,
-                            "saved_at": now_str(),
-                            "active_profile": "three_reference_patches_mode",
-                            "global_scene_mode": default_profile_global(),
-                            "three_reference_patches_mode": default_profile_patches(),
-                            "patch_normalization": deep_clone(DEFAULT_PATCH_NORMALIZATION),
-                        }
-
-                    update_root_radiometric_config(data)
-                    last_msg = "Defaults restaurados"
-                    last_msg_t = time.time()
-
-                elif k in (ord("p"), ord("P"), 32):
-                    save_config(args.out_json, data)
-                    last_msg = f"Salvo em: {args.out_json}"
-                    last_msg_t = time.time()
-                    print(f"[OK] radiometric config salvo em: {args.out_json}")
-
-                elif k in (ord("c"), ord("C")):
-                    idx = ROLES.index(edit_role) if edit_role in ROLES else 0
-                    edit_role = ROLES[(idx + 1) % len(ROLES)]
-                    selected_roi_index = 0
-                    last_msg = f"Camera editada -> {edit_role.upper()}"
-                    last_msg_t = time.time()
-
-                elif mode == "patches" and k in (ord("n"), ord("N")):
-                    selected_roi_index = add_empty_patch_roi_slot(data, selected_target, edit_role)
-                    last_msg = f"Nova ROI {selected_target.upper()}/{edit_role.upper()} #{selected_roi_index + 1}. Arraste para posicionar."
-                    last_msg_t = time.time()
-
-                elif mode == "patches" and k in (ord("["), ord(",")):
-                    entries = get_patch_roi_entries_for_role(data, selected_target, edit_role, enabled_only=False)
-                    if entries:
-                        selected_roi_index = (selected_roi_index - 1) % len(entries)
-                        last_msg = f"ROI selecionada -> #{selected_roi_index + 1}/{len(entries)}"
-                    else:
-                        last_msg = "Nenhuma ROI para selecionar"
-                    last_msg_t = time.time()
-
-                elif mode == "patches" and k in (ord("]"), ord(".")):
-                    entries = get_patch_roi_entries_for_role(data, selected_target, edit_role, enabled_only=False)
-                    if entries:
-                        selected_roi_index = (selected_roi_index + 1) % len(entries)
-                        last_msg = f"ROI selecionada -> #{selected_roi_index + 1}/{len(entries)}"
-                    else:
-                        last_msg = "Nenhuma ROI para selecionar"
-                    last_msg_t = time.time()
-
-                elif mode == "patches" and k in (ord("d"), ord("D")):
-                    ok, n_left = delete_patch_roi_for_role(data, selected_target, edit_role, selected_roi_index)
-                    selected_roi_index = clamp(selected_roi_index, 0, max(0, n_left - 1))
-                    last_msg = f"ROI apagada. Restam {n_left}." if ok else "Nenhuma ROI para apagar"
-                    last_msg_t = time.time()
-
-                elif mode == "patches" and k in (ord("t"), ord("T")):
-                    ok, enabled = toggle_patch_roi_enabled_for_role(data, selected_target, edit_role, selected_roi_index)
-                    last_msg = f"ROI #{selected_roi_index + 1} enabled={enabled}" if ok else "Nenhuma ROI para alternar"
-                    last_msg_t = time.time()
-
-                elif k in (ord("v"), ord("V")):
-                    beauty_preview = not beauty_preview
-                    last_msg = f"Beauty Preview -> {beauty_preview}"
-                    last_msg_t = time.time()
-
-    finally:
-        cv2.destroyAllWindows()
-        print("Fim da parametrizacao radiometrica.")
-
-
-if __name__ == "__main__":
-    main()