workers

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/camera_worker/camera_oak.py

@@ -1,6 +1,7 @@
 import numpy as np
 import depthai as dai
 import time
+import threading
 
 from camera_worker.tcp_streamer import CameraTcpStreamer
 from health_worker.modulos.imu import IMUCamera
@@ -8,10 +9,11 @@ from shared.enums import StatusModulo, T_Code
 from shared.contexto_global_redis import ContextoGlobalRedis
 
 class CameraOak:
-    def __init__(self, mostrar_log, mx_id, modelo_ia_seg=None, modelo_ia_det=None, iniciar_imu=False):
+    def __init__(self, mostrar_log, mx_id, modelo_ia_seg=None, modelo_ia_det=None, iniciar_imu=False, perf_monitor=None):
         self.mostrar_log = mostrar_log
         self.modelo_ia_seg = modelo_ia_seg
         self.modelo_ia_det = modelo_ia_det
+        self.perf = perf_monitor
         self.stream = None
 
         self.dispositivo = T_Code.Vzo
@@ -27,12 +29,39 @@ class CameraOak:
         self.rodando = False
         self.iniciado = False
 
+        self._rgb_seq = None
+        self._depth_seq = None
+        self._rgb_device_ts = None
+        self._depth_device_ts = None
+
+        self._cache_lock = threading.Lock()
+        self._cache_thread = None
+
+        self._rgb_cache = None
+        self._rgb_cache_ts = 0.0
+        self._rgb_cache_resultado = {
+            "erro": "sem frame rgb em cache",
+            "duracao": 0,
+            "frame_valido": False
+        }
+
+        self._depth_cache = None
+        self._depth_cache_ts = 0.0
+        self._depth_cache_resultado = {
+            "erro": "sem frame depth em cache",
+            "duracao": 0,
+            "frame_valido": False
+        }
+
+        self._cache_rodando = False
+
+        self.parametros = {}
+
         disp_list = dai.Device.getAllAvailableDevices()
         disp_info = next((d for d in disp_list if d.getMxId() == mx_id), None)
 
         if not disp_info:
-            return
-            #raise RuntimeError(f"Dispositivo com mxid {mx_id} não encontrado")
+            raise RuntimeError(f"Dispositivo com mxid {mx_id} não encontrado")
 
         self.dev_info = disp_info
 
@@ -47,7 +76,7 @@ class CameraOak:
             iniciando=True,
             iniciado=False,
             rodando=False,
-            parametros={}
+            parametros=self.parametros
         )
 
         # Fase 1: detectar sensores sem pipeline
@@ -70,9 +99,10 @@ class CameraOak:
         except Exception as e:
             pass
             self.mostrar_log(f"Falha ao detectar sensores: {e}")
-
+        
         ContextoGlobalRedis.atualizar_ctx_dict(
             ContextoGlobalRedis.CamKey(self.mx_id),
+            timestamp=time.time(),
             modelo=self.modelo,
             dispositivo=self.dispositivo.value,
             tem_depth=self.tem_depth,
@@ -90,7 +120,7 @@ class CameraOak:
 
             if self.tem_imu and iniciar_imu:
                 self.q_imu = self.device.getOutputQueue(name="imu", maxSize=50, blocking=False)
-                self.imu = IMUCamera(self.q_imu, freq=100, angulo_inicial=26.3)
+                self.imu = IMUCamera(self.mx_id, self.q_imu, freq=100, angulo_inicial=26.3)
 
             if self.modelo_ia_seg is not None:
                 self.q_seg = self.device.getOutputQueue(name="seg", maxSize=1, blocking=False)
@@ -104,6 +134,8 @@ class CameraOak:
                     except:
                         pass
 
+            self._iniciar_cache_frames()
+
             if self.dispositivo == T_Code.Snr:
                 dadosSnr = ContextoGlobalRedis.get_operacao().get("Snr", {})
                 self.parametros = {
@@ -152,22 +184,392 @@ class CameraOak:
         except Exception as e:
             self.mostrar_log(f"Erro ao iniciar camera: {e}")
             pass
-
+        
         ContextoGlobalRedis.atualizar_ctx_dict(
             ContextoGlobalRedis.CamKey(self.mx_id),
+            timestamp=time.time(),
             parametros=self.parametros,
             iniciado=self.iniciado,
             iniciando=False,
             iniciado_em=time.time() if self.iniciado else None
         )
 
+    def parar(self):
+        self._cache_rodando = False
+        self.rodando = False
+        self.iniciado = False
+
+        try:
+            if self._cache_thread is not None and self._cache_thread.is_alive():
+                self._cache_thread.join(timeout=1.0)
+        except Exception:
+            pass
+
+        self._cache_thread = None
+
+        try:
+            if self.imu is not None:
+                self.imu.parar()
+        except Exception:
+            pass
+
+        self.imu = None
+
+        try:
+            if hasattr(self, "device") and self.device is not None:
+                self.device.close()
+        except Exception:
+            pass
+
+        self.device = None
+        self.pipeline = None
+
+        try:
+            self.q_video = None
+            self.q_depth = None
+            self.q_imu = None
+            self.q_seg = None
+            self.q_det = None
+            self.q_det_track = None
+        except Exception:
+            pass
+
+        with self._cache_lock:
+            self._rgb_cache = None
+            self._depth_cache = None
+
+            self._rgb_cache_ts = 0.0
+            self._depth_cache_ts = 0.0
+
+            self.timestamp_ultimo_frame_rgb = None
+            self.timestamp_ultimo_frame_depth = None
+            self.timestamp_ultima_deteccao = None
+            self.timestamp_ultima_segmentacao = None
+
+            self._rgb_cache_resultado = {
+                "erro": "pipeline parado",
+                "duracao": 0.0,
+                "frame_valido": False,
+            }
+
+            self._depth_cache_resultado = {
+                "erro": "pipeline parado",
+                "duracao": 0.0,
+                "frame_valido": False,
+            }
+
+        try:
+            self.stream = None
+        except Exception:
+            pass
+
+    def _is_erro_fatal_depthai(self, erro):
+        if erro is None:
+            return False
+
+        txt = str(erro)
+
+        sinais_fatais = [
+            "X_LINK_ERROR",
+            "Communication exception",
+            "Couldn't read data from stream",
+            "Device already closed",
+            "device has been closed",
+            "Nenhum dispositivo DepthAI",
+            "No available devices",
+            "não encontrado",
+            "not found",
+        ]
+
+        return any(s in txt for s in sinais_fatais)
+
+    def esta_utilizavel(self):
+        if not self.iniciado:
+            return False
+
+        if self.device is None:
+            return False
+
+        erro_rgb = (self._rgb_cache_resultado or {}).get("erro")
+        erro_depth = (self._depth_cache_resultado or {}).get("erro")
+
+        if self._is_erro_fatal_depthai(erro_rgb):
+            return False
+
+        if self._is_erro_fatal_depthai(erro_depth):
+            return False
+
+        try:
+            if hasattr(self.device, "isPipelineRunning"):
+                if not self.device.isPipelineRunning():
+                    return False
+        except Exception as e:
+            if self._is_erro_fatal_depthai(e):
+                return False
+
+        return True
+
+    def tem_frame_recente(self, timeout=5.0):
+        agora = time.time()
+
+        ts_rgb = self.timestamp_ultimo_frame_rgb or 0.0
+        ts_depth = self.timestamp_ultimo_frame_depth or 0.0
+
+        rgb_ok = (agora - ts_rgb) <= timeout
+        depth_ok = (agora - ts_depth) <= timeout if self.tem_depth else True
+
+        return rgb_ok or depth_ok
+
+    def _marcar_desconectada(self, motivo):
+        agora = time.time()
+
+        self.iniciado = False
+        self.rodando = False
+
+        self.ultima_saude = {
+            "timestamp": agora,
+            "conectado": False,
+            "status": StatusModulo.DESCONECTADO.value,
+            "saude": 0,
+            "motivos": [str(motivo)],
+            "saude_individual": [],
+        }
+
+        try:
+            from camera_worker.manager import definir_saude_camera
+
+            definir_saude_camera(
+                self.mx_id,
+                StatusModulo.DESCONECTADO,
+                0,
+                [str(motivo)],
+                False,
+                {
+                    "temperatura": 0.0,
+                    "memoria_usada": 0.0,
+                    "executando": False,
+                    "velocidade": "",
+                },
+                False,
+                agora,
+                self.dispositivo,
+                imu={
+                    "valido": False,
+                    "timestamp": 0.0,
+                    "roll": 0.0,
+                    "pitch": 0.0,
+                    "yaw": 0.0,
+                }
+            )
+        except Exception:
+            pass
+
+    def _falha_fatal_depthai(self, motivo):
+        self.mostrar_log(f"[CameraOak] Falha fatal DepthAI. Fechando pipeline: {motivo}")
+
+        self._marcar_desconectada(motivo)
+
+        try:
+            self.parar()
+        except Exception as e:
+            self.mostrar_log(f"[CameraOak] Erro ao parar após falha fatal: {e}")
+
+    def _iniciar_cache_frames(self):
+        if self._cache_rodando:
+            return
+
+        self._cache_rodando = True
+
+        def loop():
+            while self._cache_rodando:
+                try:
+                    teve_pkt = False
+                    # RGB
+                    if hasattr(self, "q_video") and self.q_video is not None:
+                        pkts = self.q_video.getAll()
+                        n_pkts = len(pkts)
+                        pkt = pkts[-1] if pkts else self.q_video.tryGet()
+
+                        if pkt is not None:
+                            teve_pkt = True
+                            t0 = time.time()
+                            frame = pkt.getCvFrame()
+                            dur = time.time() - t0
+
+                            resultado = {
+                                "erro": None,
+                                "duracao": dur,
+                                "frame_valido": frame is not None and frame.shape[0] > 0
+                            }
+
+                            if resultado["frame_valido"]:
+                                self.rodando = True
+
+                            pkt_ts_device = None
+                            try:
+                                pkt_ts_device = pkt.getTimestamp().total_seconds()
+                            except Exception:
+                                pass
+                            pkt_seq = None
+                            try:
+                                pkt_seq = pkt.getSequenceNum()
+                            except Exception:
+                                pass
+                            seq_anterior = self._rgb_seq
+                            seq_delta = None
+                            frames_descartados = 0
+                            if seq_anterior is not None and pkt_seq is not None:
+                                seq_delta = pkt_seq - seq_anterior
+                                frames_descartados = max(0, seq_delta - 1)
+
+                            if hasattr(self, "perf") and self.perf is not None:
+                                self.perf.tick(
+                                    "camera_rgb",
+                                    latencia_ms=dur * 1000.0,
+                                    frame_ts_host=self._rgb_cache_ts,
+                                    frame_ts_device=pkt_ts_device,
+                                    seq=pkt_seq,
+                                    seq_delta=seq_delta,
+                                    frames_descartados=frames_descartados,
+                                    n_pkts_getall=n_pkts,
+                                    valido=resultado["frame_valido"]
+                                )
+
+                            with self._cache_lock:
+                                self._rgb_cache = frame
+                                self._rgb_cache_ts = time.time()
+                                self._rgb_cache_resultado = resultado
+                                self.timestamp_ultimo_frame_rgb = self._rgb_cache_ts
+                                self._rgb_seq = pkt_seq
+                                self._rgb_device_ts = pkt_ts_device
+
+                    # DEPTH
+                    if self.tem_depth and hasattr(self, "q_depth") and self.q_depth is not None:
+                        pkts = self.q_depth.getAll()
+                        n_pkts = len(pkts)
+                        pkt = pkts[-1] if pkts else self.q_depth.tryGet()
+
+                        if pkt is not None:
+                            teve_pkt = True
+                            t0 = time.time()
+                            frame = pkt.getFrame()
+                            dur = time.time() - t0
+
+                            resultado = {
+                                "erro": None,
+                                "duracao": dur,
+                                "frame_valido": frame is not None and frame.shape[0] > 0
+                            }
+
+                            if resultado["frame_valido"]:
+                                self.rodando = True
+
+                            pkt_ts_device = None
+                            try:
+                                pkt_ts_device = pkt.getTimestamp().total_seconds()
+                            except Exception:
+                                pass
+                            pkt_seq = None
+                            try:
+                                pkt_seq = pkt.getSequenceNum()
+                            except Exception:
+                                pass
+                            seq_anterior = self._depth_seq
+                            seq_delta = None
+                            frames_descartados = 0
+                            if seq_anterior is not None and pkt_seq is not None:
+                                seq_delta = pkt_seq - seq_anterior
+                                frames_descartados = max(0, seq_delta - 1)
+
+                            if hasattr(self, "perf") and self.perf is not None:
+                                self.perf.tick(
+                                    "camera_depth",
+                                    latencia_ms=dur * 1000.0,
+                                    frame_ts_host=self._depth_cache_ts,
+                                    frame_ts_device=pkt_ts_device,
+                                    seq=pkt_seq,
+                                    seq_delta=seq_delta,
+                                    frames_descartados=frames_descartados,
+                                    n_pkts_getall=n_pkts,
+                                    valido=resultado["frame_valido"]
+                                )
+
+                            with self._cache_lock:
+                                self._depth_cache = frame
+                                self._depth_cache_ts = time.time()
+                                self._depth_cache_resultado = resultado
+                                self.timestamp_ultimo_frame_depth = self._depth_cache_ts
+                                self.ultimo_resultado_depth = resultado
+                                self._depth_seq = pkt_seq
+                                self._depth_device_ts = pkt_ts_device
+
+                    if not teve_pkt:
+                        time.sleep(0.001)
+
+                except Exception as e:
+                    erro = str(e)
+
+                    with self._cache_lock:
+                        self._rgb_cache_resultado = {
+                            "erro": erro,
+                            "duracao": 0,
+                            "frame_valido": False
+                        }
+
+                        self._depth_cache_resultado = {
+                            "erro": erro,
+                            "duracao": 0,
+                            "frame_valido": False
+                        }
+
+                    if self._is_erro_fatal_depthai(e):
+                        self.mostrar_log(f"[CameraOak] Erro fatal no cache loop: {e}")
+                        self._cache_rodando = False
+
+                        # Não chama parar() diretamente de dentro da própria thread,
+                        # porque parar() tenta dar join nela mesma.
+                        self._marcar_desconectada(e)
+
+                        try:
+                            if self.device is not None:
+                                self.device.close()
+                        except Exception:
+                            pass
+
+                        self.device = None
+                        self.pipeline = None
+                        self.iniciado = False
+                        self.rodando = False
+                        break
+
+        self._cache_thread = threading.Thread(target=loop, daemon=True)
+        self._cache_thread.start()
+
     def atualizar_saude(self):
         #self.mostrar_log(f"[{self.mx_id}] Atualizando saude {self.dispositivo.name}...")
 
         if self.imu is not None:
             self.imu.atualizar_saude()
 
-        conectado = ContextoGlobalRedis.get_cameras().get(self.mx_id) is not None
+        agora = time.time()
+
+        ts_rgb = self.timestamp_ultimo_frame_rgb or 0.0
+        ts_depth = self.timestamp_ultimo_frame_depth or 0.0
+
+        frame_rgb_recente = (agora - ts_rgb) <= 5.0
+        frame_depth_recente = (agora - ts_depth) <= 5.0 if self.tem_depth else True
+
+        camera_no_ctx = (ContextoGlobalRedis.get_cameras() or {}).get(self.mx_id) is not None
+
+        erro_cache = (self._rgb_cache_resultado or {}).get("erro")
+        erro_fatal = self._is_erro_fatal_depthai(erro_cache)
+
+        conectado = bool(
+            self.iniciado
+            and self.device is not None
+            and not erro_fatal
+            and (frame_rgb_recente or camera_no_ctx)
+        )
 
         saude = 50
         motivos = []
@@ -175,9 +577,20 @@ class CameraOak:
         performance = {
             "temperatura": 0,
             "memoria_usada": 0,
-            "executando": False, 
+            "executando": False,
             "velocidade": ""
         }
+
+        imu = self.imu.get_dados() if self.imu else {
+            "valido": False,
+            "timestamp": 0.0,
+            "roll": 0.0,
+            "pitch": 0.0,
+            "yaw": 0.0,
+        }
+
+        resultado = dict(self._rgb_cache_resultado or {})
+
         if conectado:
             try:
                 dev = self.device
@@ -223,26 +636,30 @@ class CameraOak:
                     motivos.append(f"USB lenta: {speed}")
                     saude -= 15
 
-                frame, resultado = self.requisitar_frame_rgb()
+                resultado = self._rgb_cache_resultado
             except Exception as e:
                 self.mostrar_log(f"Erro ao requisitar dados da camera para atualizar saude: {e}")
+
                 resultado = {
                     "erro": str(e),
                     "frame_valido": False,
                     "duracao": 0
                 }
-                if "Communication exception" in str(e) or "X_LINK_ERROR" in str(e):
+
+                if self._is_erro_fatal_depthai(e):
                     conectado = False
+                    self._falha_fatal_depthai(e)
 
         if not conectado:
             motivos.append("desconectado")
             saude = 0
-        elif resultado["erro"]:
-            motivos.append(resultado["erro"])
+        elif resultado.get("erro"):
+            motivos.append(resultado.get("erro"))
             saude = 0
-            if "Communication exception" in resultado["erro"] or "X_LINK_ERROR" in resultado["erro"]:
+            if self._is_erro_fatal_depthai(resultado.get("erro")):
                 conectado = False
-        elif not resultado["frame_valido"]:
+                self._falha_fatal_depthai(resultado.get("erro"))
+        elif not resultado.get("frame_valido", False):
             motivos.append("Frame inválido ou vazio")
             saude = 0
         else:
@@ -277,10 +694,23 @@ class CameraOak:
         timeout = 2.0
         ts_depth = self.timestamp_ultimo_frame_depth or 0
         ts_rgb = self.timestamp_ultimo_frame_rgb or 0
-        self.rodando = ((agora - ts_depth) <= timeout or (agora - ts_rgb) <= timeout)
+        self.rodando = bool(
+            conectado
+            and (
+                (agora - (self.timestamp_ultimo_frame_rgb or 0.0)) <= 2.0
+                or (
+                    self.tem_depth
+                    and (agora - (self.timestamp_ultimo_frame_depth or 0.0)) <= 2.0
+                )
+            )
+        )
 
         from camera_worker.manager import definir_saude_camera
-        definir_saude_camera(self.mx_id, status, saude, motivos, self.rodando, performance, conectado, agora, self.dispositivo)
+        definir_saude_camera(self.mx_id, status, saude, motivos, self.rodando, performance, conectado, agora, self.dispositivo, imu=imu)
+
+    def _definir_heartbeat(self):
+        from camera_worker.manager import definir_heartbeat_camera
+        definir_heartbeat_camera(self.mx_id)
 
     def enviar_frame_tcp(self, frame_bgr):
         self.stream.enviar_frame_tcp(frame_bgr)
@@ -341,30 +771,34 @@ class CameraOak:
             except Exception as e:
                 self.mostrar_log(f"[WARN] Falha ao montar pipeline imu: {e}")
 
-        script = pipeline.create(dai.node.Script)
+        script = None
         if self.modelo_ia_seg is not None or self.modelo_ia_det is not None:
             try:
+                script = pipeline.create(dai.node.Script)
+
                 N_seg = (self.modelo_ia_seg or {}).get("seg_every_n", 1)
                 N_det = (self.modelo_ia_det or {}).get("det_every_n", 1)
+
                 script.setProcessor(dai.ProcessorType.LEON_CSS)
                 script.setScript(f"""
-                i = 0
-                while True:
-                    f = node.io['in'].get()
-                    send_seg = {1 if self.modelo_ia_seg else 0} and (i % {N_seg} == 0)
-                    send_det = {1 if self.modelo_ia_det else 0} and (i % {N_det} == 0)
+                    i = 0
+                    while True:
+                        f = node.io['in'].get()
+                        send_seg = {1 if self.modelo_ia_seg else 0} and (i % {N_seg} == 0)
+                        send_det = {1 if self.modelo_ia_det else 0} and (i % {N_det} == 0)
 
-                    if send_seg and send_det:
-                        node.io['toSeg'].send(f)          # move o original
-                        node.io['toDet'].send(f)  # clone para a segunda rota
-                    elif send_seg:
-                        node.io['toSeg'].send(f)
-                    elif send_det:
-                        node.io['toDet'].send(f)
-                    # se nenhum for enviar, apenas descarta 'f'
-                    i += 1
-                """)
+                        if send_seg and send_det:
+                            node.io['toSeg'].send(f)
+                            node.io['toDet'].send(f)
+                        elif send_seg:
+                            node.io['toSeg'].send(f)
+                        elif send_det:
+                            node.io['toDet'].send(f)
+
+                        i += 1
+                    """)
                 cam.video.link(script.inputs['in'])
+
             except Exception as e:
                 self.mostrar_log(f"Erro ao criar script: {e}")
 
@@ -495,43 +929,31 @@ class CameraOak:
         #self.mostrar_log(f"[CALIB] {self.parametros}")
 
     def _latest_pkt(self, q):
-        # pega tudo que chegou e fica só com o último
         pkts = q.getAll()
         if pkts:
             return pkts[-1]
-        # fallback: tentativa não-bloqueante
-        pkt = q.tryGet()
-        if pkt is not None:
-            return pkt
-        # último recurso: uma única espera curta
-        return q.get()  # só se não tiver nada mesmo
+        return q.tryGet()
 
     def requisitar_frame_rgb(self):
-        #print("Requisitando frame")
-        try:
-            start = time.time()
-            pkt = self._latest_pkt(self.q_video)
-            frame = pkt.getCvFrame()
-            self.timestamp_ultimo_frame_rgb = pkt.getTimestamp().total_seconds()  # ou device ts
-            #frame = self.q_video.get().getCvFrame()
-            #self.timestamp_ultimo_frame_rgb = time.time()
-            dur = time.time() - start
+        with self._cache_lock:
+            resultado = dict(self._rgb_cache_resultado)
 
-            resultado = {
-                "erro": None,
-                "duracao": dur,
-                "frame_valido": frame is not None and frame.shape[0] > 0
-            }
-            
-            return frame, resultado
+            if self._is_erro_fatal_depthai(resultado.get("erro")):
+                erro = resultado.get("erro")
+            else:
+                erro = None
 
-        except Exception as e:
-            print(f"Erro ao requisitar frame: {e}")
-            return None, {
-                "erro": str(e),
-                "duracao": 0,
-                "frame_valido": False
-            }
+            if self._rgb_cache is None:
+                frame = None
+            else:
+                frame = self._rgb_cache.copy()
+
+        if erro:
+            self._falha_fatal_depthai(erro)
+            return None, resultado
+
+        self._definir_heartbeat()
+        return frame, resultado
 
     def requisitar_frame_depth(self):
         if not self.tem_depth:
@@ -540,27 +962,26 @@ class CameraOak:
                 "duracao": 0,
                 "frame_valido": False
             }
-        try:
-            start = time.time()
-            pkt = self._latest_pkt(self.q_depth)
-            frame = pkt.getFrame()
-            self.timestamp_ultimo_frame_depth = pkt.getTimestamp().total_seconds()
-            #frame = self.q_depth.get().getFrame()
-            #self.timestamp_ultimo_frame_depth = time.time()
-            dur = time.time() - start
 
-            return frame, {
-                "erro": None,
-                "duracao": dur,
-                "frame_valido": frame is not None and frame.shape[0] > 0
-            }
+        with self._cache_lock:
+            resultado = dict(self._depth_cache_resultado)
 
-        except Exception as e:
-            return None, {
-                "erro": str(e),
-                "duracao": 0,
-                "frame_valido": False
-            }
+            if self._is_erro_fatal_depthai(resultado.get("erro")):
+                erro = resultado.get("erro")
+            else:
+                erro = None
+
+            if self._depth_cache is None:
+                frame = None
+            else:
+                frame = self._depth_cache.copy()
+
+        if erro:
+            self._falha_fatal_depthai(erro)
+            return None, resultado
+
+        self._definir_heartbeat()
+        return frame, resultado
 
     def requisitar_segmentacao(self):
         if not hasattr(self, "q_seg"):
@@ -581,7 +1002,16 @@ class CameraOak:
             return pred_ids, {"erro": None, "duracao": dur, "frame_valido": True}
         except Exception as e:
             dur = time.time() - start
-            return None, {"erro": str(e), "duracao": dur, "frame_valido": False}
+            erro = str(e)
+
+            if self._is_erro_fatal_depthai(e):
+                self._falha_fatal_depthai(e)
+
+            return None, {
+                "erro": erro,
+                "duracao": dur,
+                "frame_valido": False
+            }
     
     def requisitar_deteccao(self, mapear_para_fullframe: bool = False):
         """
@@ -750,6 +1180,28 @@ class CameraOak:
 
         except Exception as e:
             dur = time.time() - start
-            return [], {"erro": str(e), "duracao": dur, "frame_valido": False}
+            erro = str(e)
 
+            if self._is_erro_fatal_depthai(e):
+                self._falha_fatal_depthai(e)
+
+            return [], {
+                "erro": erro,
+                "duracao": dur,
+                "frame_valido": False
+            }
+
+
+    def get_cache_stats(self):
+        with self._cache_lock:
+            return {
+                "rgb_ts_host": self._rgb_cache_ts,
+                "depth_ts_host": self._depth_cache_ts,
+                "rgb_ts_device": self._rgb_device_ts,
+                "depth_ts_device": self._depth_device_ts,
+                "rgb_seq": self._rgb_seq,
+                "depth_seq": self._depth_seq,
+                "rgb_resultado": dict(self._rgb_cache_resultado or {}),
+                "depth_resultado": dict(self._depth_cache_resultado or {}),
+            }
     

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/camera_worker/manager.py

@@ -10,61 +10,180 @@ class CameraManager:
         self._ultimo_scan = 0
 
     def atualizar_cameras(self):
-        if time.time() - self._ultimo_scan < 10:
+        agora = time.time()
+
+        if agora - self._ultimo_scan < 10:
             return
-        self._ultimo_scan = time.time()
-        
+
+        self._ultimo_scan = agora
+
+        TIMEOUT_CAMERA_ABERTA = 30     # pipeline aberto, worker precisa responder saúde
+        TIMEOUT_CAMERA_SUMIR = 60      # remove do mapa se passou disso
+
+        dispositivos_serializados = []
+
         try:
             dispositivos = dai.Device.getAllAvailableDevices()
-            dispositivos_serializados = [d.getMxId() for d in dispositivos]
+
+            for d in dispositivos:
+                device_id = None
+
+                try:
+                    device_id = d.getMxId()
+                except Exception:
+                    pass
+
+                if not device_id:
+                    try:
+                        device_id = d.getDeviceId()
+                    except Exception:
+                        pass
+
+                if not device_id:
+                    device_id = getattr(d, "deviceId", None)
+
+                if device_id:
+                    dispositivos_serializados.append(str(device_id))
+
         except Exception as e:
-            dispositivos_serializados = []
+            self.mostrar_log("[CameraManager] Erro ao listar dispositivos DepthAI:", e)
 
         try:
             with GalService() as cam:
-                if cam:
-                    info = cam.get_device_info()
-                    if info:
-                        dispositivos_serializados.append(info.get("serial"))
-        except Exception as e:
+                info = cam.get_device_info() if cam else None
+                serial = info.get("serial") if info else None
+
+                if serial:
+                    dispositivos_serializados.append(str(serial))
+
+        except Exception:
             pass
-        
-        cameras_mapeadas = ContextoGlobalRedis.get_cameras()
-        
+
+        dispositivos_serializados = list(set(dispositivos_serializados))
+
+        #self.mostrar_log("Dispositivos Serializados", dispositivos_serializados)
+
+        cameras_mapeadas = ContextoGlobalRedis.get_cameras() or {}
+
+        # 1) Atualiza câmeras vistas fisicamente no scan
         for mx_id in dispositivos_serializados:
             cam_existente = cameras_mapeadas.get(mx_id)
+
             if not cam_existente:
                 cameras_mapeadas[mx_id] = {
-                    "timestamp": self._ultimo_scan, 
-                    "mx_id": mx_id, 
+                    "timestamp": agora,
+                    "mx_id": mx_id,
+                    "presente_scan": True,
+                    "status_scan": "detectada",
                 }
             else:
-                cam_existente["timestamp"] = self._ultimo_scan
+                #cam_existente["timestamp"] = agora
+                cam_existente["presente_scan"] = True
+                cam_existente["status_scan"] = "detectada"
 
         ids_para_remover = []
-        for cam_id, val in cameras_mapeadas.items():
-            cam = ContextoGlobalRedis.get_camera(cam_id)
-            if cam is not None and cam.get("saude", {}).get("status", StatusModulo.DESCONECTADO.value) != StatusModulo.DESCONECTADO.value:
-                if cam.get("dispositivo", T_Code.Vzo.value) == T_Code.Snr.value:
-                    ContextoGlobalRedis.publicar_comando(CmdKey.VisualWorkerRx, { "cmd": VisualWorkerCommandType.AtualizarSaudeCamera.value } )
-                elif cam.get("dispositivo", T_Code.Vzo.value) == T_Code.Cam.value:
-                    ContextoGlobalRedis.publicar_comando(CmdKey.WeedWorkerRx, { "cmd": WeedWorkerCommandType.AtualizarSaudeCamera.value } )
-            elif cam is not None and val.get("timestamp", 0) < self._ultimo_scan:
+
+        # 2) Avalia câmeras já conhecidas
+        for cam_id, val in list(cameras_mapeadas.items()):
+            cam = ContextoGlobalRedis.get_camera(cam_id) or val
+
+            timestamp = cam.get("timestamp", val.get("timestamp", 0))
+            idade = agora - timestamp
+            #self.mostrar_log(f"mx_id: {cam_id}, ts: {timestamp}, idade: {idade}")
+
+            vista_no_scan = cam_id in dispositivos_serializados
+
+            dispositivo = cam.get("dispositivo", val.get("dispositivo"))
+
+            # Se não apareceu no scan, pode estar com pipeline aberto.
+            # Então pedimos saúde, mas só se ainda estiver dentro da janela tolerável.
+            if not vista_no_scan and idade <= TIMEOUT_CAMERA_ABERTA:
+                if dispositivo == T_Code.Snr.value:
+                    ContextoGlobalRedis.publicar_comando(
+                        CmdKey.VisualWorkerRx,
+                        {"cmd": VisualWorkerCommandType.AtualizarSaudeCamera.value}
+                    )
+
+                elif dispositivo == T_Code.Cam.value:
+                    ContextoGlobalRedis.publicar_comando(
+                        CmdKey.WeedWorkerRx,
+                        {"cmd": WeedWorkerCommandType.AtualizarSaudeCamera.value}
+                    )
+
+                val["presente_scan"] = False
+                val["status_scan"] = "provavelmente_em_uso"
+
+            # Se passou muito tempo sem scan e sem saúde, remove
+            elif not vista_no_scan and idade > TIMEOUT_CAMERA_SUMIR:
                 ids_para_remover.append(cam_id)
 
+            # Se apareceu no scan, mantém
+            elif vista_no_scan:
+                val["presente_scan"] = True
+                val["status_scan"] = "detectada"
+
         for cam_id in ids_para_remover:
+            self.mostrar_log(f"[CameraManager] Removendo câmera inativa: {cam_id}")
             cameras_mapeadas.pop(cam_id, None)
+
         ContextoGlobalRedis.set(CtxKey.DadosCameras, cameras_mapeadas)
-        
-        visual_worker_camera_id = ContextoGlobalRedis.get_equipamento().get("camera_caminho_id")
-        if visual_worker_camera_id is not None and visual_worker_camera_id in cameras_mapeadas:
-            ContextoGlobalRedis.publicar_comando(CmdKey.VisualWorkerRx, { "cmd": VisualWorkerCommandType.IniciarCameraManager.value, "params": visual_worker_camera_id } )
-        weed_worker_camera_id = ContextoGlobalRedis.get_equipamento().get("camera_solo_id")
-        if (weed_worker_camera_id is not None and weed_worker_camera_id in cameras_mapeadas):
-            ContextoGlobalRedis.publicar_comando(CmdKey.WeedWorkerRx, { "cmd": WeedWorkerCommandType.IniciarCameraManager.value, "params": weed_worker_camera_id } )
+
+        #self.mostrar_log("Cameras Mapeadas", cameras_mapeadas)
+
+        equipamento = ContextoGlobalRedis.get_equipamento() or {}
+
+        visual_worker_camera_id = equipamento.get("camera_caminho_id")
+        weed_worker_camera_id = equipamento.get("camera_solo_id")
+
+        # 3) Só tenta iniciar se a câmera estiver realmente válida
+        if self._camera_pode_iniciar(visual_worker_camera_id, cameras_mapeadas, agora):
+            ContextoGlobalRedis.publicar_comando(
+                CmdKey.VisualWorkerRx,
+                {
+                    "cmd": VisualWorkerCommandType.IniciarCameraManager.value,
+                    "params": visual_worker_camera_id
+                }
+            )
+
+        if self._camera_pode_iniciar(weed_worker_camera_id, cameras_mapeadas, agora):
+            ContextoGlobalRedis.publicar_comando(
+                CmdKey.WeedWorkerRx,
+                {
+                    "cmd": WeedWorkerCommandType.IniciarCameraManager.value,
+                    "params": weed_worker_camera_id
+                }
+            )
+
+    def _camera_pode_iniciar(self, camera_id, cameras_mapeadas, agora):
+        if not camera_id:
+            return False
+
+        cam = cameras_mapeadas.get(camera_id)
+        if not cam:
+            return False
+
+        timestamp = cam.get("timestamp", 0)
+        idade = agora - timestamp
+
+        presente_scan = cam.get("presente_scan", False)
+        status_scan = cam.get("status_scan")
+
+        # Pode iniciar se foi vista no scan agora.
+        if presente_scan:
+            return True
+
+        # Se não apareceu no scan porque já está em uso, NÃO manda iniciar de novo.
+        if status_scan == "provavelmente_em_uso":
+            return False
+
+        # Segurança extra: se está velha, não inicia.
+        if idade > 30:
+            return False
+
+        return True
 
     
-def definir_saude_camera(mx_id: str, status: StatusModulo, saude: int, motivos: list, rodando: bool, performance: dict, conectado: bool, ts=None, disp: T_Code = None):
+def definir_saude_camera(mx_id: str, status: StatusModulo, saude: int, motivos: list, rodando: bool, performance: dict, conectado: bool, ts=None, disp: T_Code = None, imu: dict = None):
     #print(f"Definindo saude da camera {mx_id}")
     _camera = ContextoGlobalRedis.get_camera(mx_id) or {}
     #print(_cameras)
@@ -78,9 +197,12 @@ def definir_saude_camera(mx_id: str, status: StatusModulo, saude: int, motivos:
     }
     #if not conectado:
     #    _cameras[mx_id] = {}
+    if rodando:
+        _camera["timestamp"] = time.time()
     _camera["saude"] = saude_geral
     _camera["rodando"] = rodando
     _camera["performance"] = performance
+    _camera["imu"] = imu
     if disp is not None:
         _camera["dispositivo"] = disp.value
     disp = T_Code(_camera.get("dispositivo", T_Code.Vzo.value))
@@ -90,4 +212,18 @@ def definir_saude_camera(mx_id: str, status: StatusModulo, saude: int, motivos:
             saude=saude_geral
         )
     ContextoGlobalRedis.set(ContextoGlobalRedis.CamKey(mx_id), _camera)
+
+def definir_imu_camera(mx_id: str, dados: dict, saude: dict):
+    ContextoGlobalRedis.atualizar_ctx_dict(
+        ContextoGlobalRedis.CamImuKey(mx_id),
+        timestamp=time.time(),
+        dados=dados,
+        saude=saude
+    )
     
+def definir_heartbeat_camera(mx_id: str):
+    ContextoGlobalRedis.atualizar_ctx_dict(
+        ContextoGlobalRedis.CamKey(mx_id),
+        timestamp=time.time()
+    )
+

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/camera_worker/oak_fcc3_core/benchmark.py

@@ -0,0 +1,620 @@
+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()

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/camera_worker/oak_fcc3_core/benchmark_preview.py

@@ -0,0 +1,645 @@
+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()

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/camera_worker/oak_fcc3_core/oak_fcc3_client.py

@@ -0,0 +1,672 @@
+import numpy as np
+import cv2
+import json
+import os
+
+from .oak_fcc3_service import OakFcc3Service
+from .raw_processor_core import RawProcessorCore
+from .raw_processor_preview import RawProcessorPreview
+from .radiometric_controller import RadiometricController
+
+
+class OakFcc3Client:
+    def __init__(
+        self,
+        width=640,
+        height=400,
+        bayer="BGGR",
+        fps=30,
+        frame_type="RAW_BRUTO",
+        output_dtype="uint8",
+        capture_mode="AUTO",
+        raw_policy="allow_single",
+        module_calibration_json=None,
+        sync_mode="best",
+        sync_tolerance_ms=25.0,
+        mx_id=None,
+        **kwargs,
+    ):
+        self.width = width
+        self.height = height
+        self.bayer = bayer
+        self.fps = fps
+        self.frame_type = frame_type
+        self.output_dtype = output_dtype
+        self.capture_mode = capture_mode
+        self.raw_policy = raw_policy
+        self.module_calibration_json = module_calibration_json
+        self.module_params = self._load_module_params(module_calibration_json)
+        self.fusion_config = self.module_params.get("fusion_config", {}) or {}
+
+        self.mx_id = str(mx_id) if mx_id else None
+
+        self.svc = OakFcc3Service(
+            timeout=10,
+            fps=fps,
+            width=width,
+            height=height,
+            frame_type=frame_type,
+            output_dtype=output_dtype,
+            capture_mode=capture_mode,
+            raw_policy=raw_policy,
+            sync_mode=sync_mode,
+            sync_tolerance_ms=sync_tolerance_ms,
+            mx_id=self.mx_id,
+            module_calibration_json=module_calibration_json,
+            **kwargs,
+        )
+
+        self.applied_camera_controls = {}
+        self.radiometric_controller = None
+        self.core = RawProcessorCore(
+            sensor_width=width,
+            sensor_height=height,
+            bayer_pattern=bayer,
+            calibration_json_path=module_calibration_json,
+        )
+        self.preview = RawProcessorPreview(
+            sensor_width=width,
+            sensor_height=height,
+            bayer_pattern=bayer,
+        )
+
+    def __enter__(self):
+        self.start()
+        return self
+
+    def __exit__(self, exc_type, exc, tb):
+        self.stop()
+
+    def _load_module_params(self, path):
+        if not path or not os.path.isfile(path):
+            return {}
+
+        with open(path, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    def apply_module_camera_settings(self):
+        camera_settings = self.module_params.get("camera_settings", {}) or {}
+
+        applied = {}
+
+        for role, settings in camera_settings.items():
+            if not isinstance(settings, dict):
+                continue
+
+            try:
+                resp = self.svc.apply_camera_controls(
+                    role=role,
+                    controls=settings,
+                )
+                applied[role] = resp
+
+            except Exception as e:
+                applied[role] = {
+                    "ok": False,
+                    "error": str(e),
+                    "requested": settings,
+                }
+
+        self.applied_camera_controls = applied
+        return applied
+
+    def enable_radiometric_controller(self):
+        self.radiometric_controller = RadiometricController(
+            client=self,
+            config_json_path=self.module_calibration_json,
+        )
+        
+        self.radiometric_controller.sync_from_camera_controls(self.applied_camera_controls)
+        self.radiometric_controller.sync_from_actual_camera_controls()
+        
+        return self.radiometric_controller
+
+    def update_radiometry(self, decoded, meta=None):
+        if self.radiometric_controller is None:
+            return None
+
+        return self.radiometric_controller.update(decoded, meta)
+
+    def get_current_camera_controls(self):
+        controls = {}
+
+        for role in ("rgb", "re", "nir"):
+            try:
+                controls[role] = self.svc.get_camera_controls(role=role)
+            except Exception as e:
+                controls[role] = {
+                    "ok": False,
+                    "role": role,
+                    "error": str(e),
+                }
+
+        return controls
+
+    def get_radiometric_last_result(self):
+        if self.radiometric_controller is None:
+            return None
+
+        return self.radiometric_controller.last_result
+
+    def start(self, print_debug=False):
+        self.svc.connect()
+
+        resp = self.svc.begin(
+            frame_type=self.frame_type,
+            output_dtype=self.output_dtype,
+            capture_mode=self.capture_mode,
+        )
+
+        try:
+            self.mx_id = self.svc.manager.mx_id
+        except Exception:
+            pass
+
+        applied = self.apply_module_camera_settings()
+
+        if print_debug:
+            print("[OAK CLIENT] START:", resp)
+            print("[OAK CLIENT] APPLIED CAMERA SETTINGS:", applied)
+
+        self.enable_radiometric_controller()
+
+        return resp
+
+    def stop(self):
+        try:
+            self.svc.stop()
+        finally:
+            self.svc.disconnect()
+
+    def get_status(self):
+        return self.svc.get_status()
+
+    def get_next_raw_frame(self, timeout=1.0):
+        return self.svc.capture_frame(timeout=timeout)
+
+    def get_next_frame(self, timeout=2.0):
+        frame, meta, _ = self.get_next_decoded(timeout=timeout)
+        return frame, meta
+
+    def get_next_decoded(self, timeout=2.0):
+        raw_frame, raw_meta = self.get_next_raw_frame(timeout=timeout)
+
+        frame_type = str(raw_meta.get("frame_type", self.frame_type)).upper()
+        meta = dict(raw_meta)
+
+        if frame_type == "RAW_BRUTO":
+            decoded = self.decode_stream_cameras(raw_frame, raw_meta)
+
+            self.update_radiometry(decoded, raw_meta)
+
+            frame = raw_frame
+            return frame, meta, decoded
+
+        elif frame_type == "RGB":
+            decoded = self.decode_stream_cameras(raw_frame, raw_meta)
+
+            self.update_radiometry(decoded, raw_meta)
+
+            frame = self.build_rgb_tensor(decoded)
+            meta["output_layout"] = "CHW"
+            meta["channels"] = ["R", "G", "B"]
+            meta["shape"] = list(frame.shape)
+            meta["dtype"] = str(frame.dtype)
+
+            return frame, meta, decoded
+
+        elif frame_type == "MULTISPEC":
+            decoded = self.decode_oak_aligned_multispec(raw_frame, raw_meta)
+
+            self.update_radiometry(decoded, raw_meta)
+
+            # Versão inicial segura:
+            # não chama core.fuse_multispec_cameras(), porque ali teria homografia de novo.
+            frame = self.build_multispec_tensor_from_oak_aligned(decoded, meta=raw_meta)
+
+            meta["output_layout"] = "CHW"
+            meta["channels"] = ["R", "G", "B", "RE", "NIR"]
+            meta["shape"] = list(frame.shape)
+            meta["dtype"] = str(frame.dtype)
+            meta["aligned_by_oak"] = True
+            meta["geometry_stage"] = "oak"
+
+            return frame, meta, decoded
+
+        elif frame_type == "PREVIEW":
+            decoded = self.decode_stream_cameras(raw_frame, raw_meta)
+            frame = raw_frame
+            return frame, meta, decoded
+
+        else:
+            raise RuntimeError(f"frame_type não suportado: {frame_type}")
+
+    def get_next_tensor_preview(self, timeout=2.0):
+        frame, meta, decoded = self.get_next_decoded(timeout=timeout)
+
+        frame_type = str(meta.get("frame_type", self.frame_type)).upper()
+
+        if frame_type == "RGB":
+            rgb_hwc = np.transpose(frame[:3], (1, 2, 0))
+            preview = self._rgb01_to_bgr(rgb_hwc)
+            return {"rgb_tensor": preview}, meta
+
+        if frame_type == "MULTISPEC":
+            rgb_hwc = np.transpose(frame[:3], (1, 2, 0))
+            re01 = frame[3]
+            nir01 = frame[4]
+
+            return {
+                "rgb_tensor": self._rgb01_to_bgr(rgb_hwc),
+                "re_tensor": self._gray01_to_bgr(re01),
+                "nir_tensor": self._gray01_to_bgr(nir01),
+            }, meta
+
+        else:
+            return self.build_visual_preview_from_raw(frame, meta), meta
+
+        raise RuntimeError(f"frame_type não suportado para preview: {frame_type}")
+
+    def get_next_preview(self, timeout=2.0):
+        raw_frame, raw_meta = self.get_next_raw_frame(timeout=timeout)
+
+        meta = dict(raw_meta)
+
+        previews = self.build_visual_preview_from_raw(raw_frame, meta)
+
+        return previews, meta
+
+    def get_last_patch_normalization_result(self):
+        try:
+            return self.core.last_patch_normalization_result
+        except Exception:
+            return None
+
+    def get_last_radiometric_normalization_result(self):
+        try:
+            return self.core.get_last_radiometric_normalization_result()
+        except Exception:
+            return None
+
+    def build_infer_tensor(self, frame, meta, channels_expected, target_size=None):
+        return self.core.build_infer_tensor_from_stream(
+            frame,
+            meta,
+            channels_expected=channels_expected,
+            target_size=target_size,
+        )
+
+    def build_infer_tensor_from_decoded(self, decoded, meta, channels_expected, target_size=None):
+        tensor = self.core.fuse_multispec_cameras(decoded, meta, channels_expected)
+        return self.core.resize_tensor_chw(tensor, target_size=target_size)
+
+    def decode_stream_cameras(self, frame, meta):
+        if str(meta.get("frame_type", self.frame_type)).upper() == "PREVIEW":
+            decoded = {}
+
+            camera_info = meta.get("camera_info", {}) or {}
+
+            for cam_id, img in frame.items():
+                info = camera_info.get(cam_id, {}) or {}
+                role = info.get("role", cam_id)
+                img01 = self._frame_to_float01(cam_id, img, role)
+
+                decoded[cam_id] = {
+                    "name": role.upper(),
+                    "role": role,
+                    "image": img01,
+                    "meta": {
+                        "cam_id": cam_id,
+                        "role": role,
+                        "socket": info.get("socket"),
+                        "sensor": info.get("sensor"),
+                        "timestamp": (meta.get("timestamps") or {}).get(cam_id),
+                        "shape": list(img.shape),
+                        "dtype": str(img.dtype),
+                    },
+                }
+
+            return decoded
+
+        return self.core.decode_stream_cameras(frame, meta)
+
+    def build_rgb_tensor(self, decoded):
+        cam_id, item = self._find_decoded_by_role(decoded, "rgb")
+
+        rgb01 = item["image"]
+
+        if rgb01.ndim != 3 or rgb01.shape[2] != 3:
+            raise RuntimeError(f"{cam_id} RGB inválida: shape={rgb01.shape}")
+
+        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):
+        tensor = self.build_infer_tensor_from_decoded(
+            decoded=decoded,
+            meta=meta,
+            channels_expected=5,
+        )
+        return np.ascontiguousarray(tensor.astype(np.float32, copy=False))
+    
+    def build_preview_from_raw_payload(self, frame, meta):
+        """
+        Gera preview priorizando a câmera com role='rgb'.
+        Retorna:
+            preview_bgr: imagem BGR uint8 para OpenCV
+            payload_float_preview: tensor CHW float32 [0..1]
+            preview_source_id: cam_id usado
+        """
+        decoded = self.decode_stream_cameras(frame, meta)
+
+        if not decoded:
+            raise RuntimeError("Nenhum frame decodificado disponível para preview.")
+
+        try:
+            cam_id, item = self._find_decoded_by_role(decoded, "rgb")
+            rgb01 = item["image"]
+
+            if rgb01.ndim != 3 or rgb01.shape[2] != 3:
+                raise RuntimeError(f"{cam_id} decodificada inválida para preview RGB: shape={rgb01.shape}")
+
+            preview_bgr = self._rgb01_to_bgr(rgb01)
+            payload_float = np.transpose(rgb01.astype(np.float32), (2, 0, 1))
+
+            return preview_bgr, np.ascontiguousarray(payload_float), cam_id
+
+        except RuntimeError:
+            pass
+
+        first_id = list(decoded.keys())[0]
+        img01 = decoded[first_id]["image"]
+
+        if img01.ndim == 2:
+            preview_bgr = self._gray01_to_bgr(img01)
+            payload_float = np.stack([img01, img01, img01], axis=0).astype(np.float32)
+
+        elif img01.ndim == 3 and img01.shape[2] == 3:
+            preview_bgr = self._rgb01_to_bgr(img01)
+            payload_float = np.transpose(img01.astype(np.float32), (2, 0, 1))
+
+        else:
+            raise RuntimeError(f"Frame decodificado inválido para preview: cam={first_id}, shape={img01.shape}")
+
+        return preview_bgr, np.ascontiguousarray(payload_float), first_id
+
+    def build_visual_preview_from_raw(self, frame, meta):
+        camera_info = meta.get("camera_info", {}) or {}
+        previews = {}
+
+        for cam_id, arr in frame.items():
+            info = camera_info.get(cam_id, {}) or {}
+            role = info.get("role", cam_id)
+            bit_depth = int(info.get("bit_depth", 10))
+
+            if arr.ndim == 3 and arr.shape[2] == 1:
+                arr = arr[:, :, 0]
+
+            if bit_depth == 10 and arr.ndim == 2:
+                raw16 = self.core.unpack_raw10_packed(
+                    arr,
+                    sensor_width=int(info.get("width", self.width)),
+                    sensor_height=int(info.get("height", self.height)),
+                )
+
+                if role == "rgb":
+                    previews[cam_id] = self.preview.raw16_to_preview_bgr(
+                        raw16,
+                        bit_depth=bit_depth,
+                        apply_wb=True,
+                        apply_contrast=True,
+                    )
+                else:
+                    vis8 = self.preview.raw16_to_vis8(
+                        raw16,
+                        bit_depth=bit_depth,
+                        gamma=2.2,
+                    )
+                    previews[cam_id] = cv2.cvtColor(vis8, cv2.COLOR_GRAY2BGR)
+
+            else:
+                decoded = self.decode_stream_cameras({cam_id: arr}, {"camera_info": {cam_id: info}})
+                img01 = decoded[cam_id]["image"]
+
+                if img01.ndim == 2:
+                    previews[cam_id] = self._gray01_to_bgr(img01)
+                else:
+                    previews[cam_id] = self._rgb01_to_bgr(img01)
+
+        return previews
+
+    def build_save_preview_from_cam_a(
+        self,
+        packed_raw_by_camera: dict | None,
+        meta_stream: dict,
+        sensor_width: int,
+        sensor_height: int,
+        bayer_pattern: str,
+    ) -> np.ndarray | None:
+        """
+        Gera o preview salvo no mesmo padrão do 'CAM_A reconstruido'.
+
+        Usa apenas CAM_A do RAW_BRUTO:
+        CAM_A packed RAW10
+            -> unpack_raw10_packed
+            -> RawProcessorPreview.raw16_to_preview_bgr
+
+        Retorna BGR uint8 pronto para cv2.imwrite.
+        """
+        if not packed_raw_by_camera or "CAM_A" not in packed_raw_by_camera:
+            return None
+
+        stream_meta = meta_stream or {}
+
+        camera_info = stream_meta.get("camera_info", {}) or {}
+        cam_meta = camera_info.get("CAM_A", {}) or {}
+
+        bit_depth = int(cam_meta.get("bit_depth", 10))
+
+        bayer = (
+            cam_meta.get("bayer_pattern")
+            or cam_meta.get("bayer")
+            or stream_meta.get("bayer_pattern")
+            or bayer_pattern
+            or "RGGB"
+        )
+        bayer = str(bayer).upper()
+
+        arr = packed_raw_by_camera["CAM_A"]
+
+        if arr is None:
+            return None
+
+        packed = arr
+        if packed.ndim == 3 and packed.shape[2] == 1:
+            packed = packed[:, :, 0]
+
+        if packed.ndim != 2:
+            return None
+
+        # Mantém a mesma lógica do validador:
+        # RAW10 packed => sensor_w = packed_w * 4 // 5
+        packed_h, packed_w = packed.shape[:2]
+
+        if bit_depth == 10:
+            real_w = int(cam_meta.get("width", sensor_width))
+            real_h = int(cam_meta.get("height", sensor_height))
+
+            # Fallback caso o meta não tenha width/height confiáveis
+            if real_w <= 0 or real_h <= 0:
+                real_w = int((packed_w * 4) // 5)
+                real_h = int(packed_h)
+        else:
+            real_w = int(cam_meta.get("width", sensor_width))
+            real_h = int(cam_meta.get("height", sensor_height))
+
+        core = RawProcessorCore(
+            sensor_width=real_w,
+            sensor_height=real_h,
+            bayer_pattern=bayer,
+        )
+
+        preview = RawProcessorPreview(
+            sensor_width=real_w,
+            sensor_height=real_h,
+            bayer_pattern=bayer,
+        )
+
+        raw16 = core.unpack_raw10_packed(
+            packed,
+            sensor_width=real_w,
+            sensor_height=real_h,
+        )
+
+        preview_bgr = preview.raw16_to_preview_bgr(
+            raw16,
+            bit_depth=bit_depth,
+        )
+
+        return preview_bgr
+
+    def _find_decoded_by_role(self, decoded, role):
+        role = str(role).lower()
+
+        for cam_id, item in decoded.items():
+            if str(item.get("role", "")).lower() == role:
+                return cam_id, item
+
+        raise RuntimeError(f"Nenhuma câmera com role={role} encontrada.")
+
+    def _frame_to_float01(self, cam_id, img, role):
+        if img is None:
+            return None
+
+        arr = img
+
+        if arr.dtype == np.uint8:
+            arr01 = arr.astype(np.float32) / 255.0
+        elif arr.dtype == np.uint16:
+            arr01 = arr.astype(np.float32) / 65535.0
+        else:
+            arr01 = arr.astype(np.float32)
+            if arr01.max() > 1.5:
+                arr01 = arr01 / 255.0
+
+        arr01 = np.clip(arr01, 0.0, 1.0)
+
+        if role == "rgb":
+            # DepthAI/OpenCV entrega BGR HWC. Calibradores esperam RGB HWC.
+            if arr01.ndim == 3 and arr01.shape[2] == 3:
+                arr01 = cv2.cvtColor((arr01 * 255).astype(np.uint8), cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
+            elif arr01.ndim == 2:
+                arr01 = np.stack([arr01, arr01, arr01], axis=2)
+
+            return arr01
+
+        # Espectrais devem virar mono HW.
+        if arr01.ndim == 3:
+            arr01 = cv2.cvtColor((arr01 * 255).astype(np.uint8), cv2.COLOR_BGR2GRAY).astype(np.float32) / 255.0
+
+        return arr01
+
+    @staticmethod
+    def _rgb01_to_bgr(rgb01):
+        rgb_u8 = np.clip(rgb01 * 255.0, 0, 255).astype(np.uint8)
+        return cv2.cvtColor(rgb_u8, cv2.COLOR_RGB2BGR)
+
+    @staticmethod
+    def _gray01_to_bgr(gray01):
+        g = np.clip(gray01 * 255.0, 0, 255).astype(np.uint8)
+        return cv2.cvtColor(g, cv2.COLOR_GRAY2BGR)
+
+
+
+    def decode_oak_aligned_multispec(self, frame, meta):
+        """
+        Decodifica frames já alinhados pela OAK.
+
+        Entrada esperada:
+            frame = {
+                "CAM_A": BGR uint8 HWC,
+                "CAM_B": GRAY uint8 HW,
+                "CAM_C": GRAY uint8 HW,
+            }
+
+        Saída:
+            decoded por role, em float32 0..1.
+        """
+        if not isinstance(frame, dict):
+            raise RuntimeError("MULTISPEC alinhado esperado como dict de câmeras.")
+
+        decoded = {}
+        camera_info = meta.get("camera_info", {}) or {}
+
+        for cam_id, img in frame.items():
+            info = camera_info.get(cam_id, {}) or {}
+            role = str(info.get("role", "")).lower()
+
+            if not role:
+                role = str(self.svc.manager.roles.get(cam_id, cam_id)).lower()
+
+            img01 = self._frame_to_float01(cam_id, img, role)
+
+            decoded[cam_id] = {
+                "name": role.upper(),
+                "role": role,
+                "image": img01,
+                "meta": {
+                    **info,
+                    "cam_id": cam_id,
+                    "role": role,
+                    "aligned_by_oak": True,
+                    "geometry_stage": "oak",
+                    "homography_applied": role in ("re", "nir"),
+                    "crop_resize_applied": True,
+                    "shape": list(img.shape),
+                    "dtype": str(img.dtype),
+                },
+            }
+
+        return decoded
+
+    def build_multispec_tensor_from_oak_aligned(self, decoded, meta=None):
+        """
+        Monta CHW [R,G,B,RE,NIR] sem reaplicar homografia.
+        """
+        _, rgb_item = self._find_decoded_by_role(decoded, "rgb")
+        _, re_item = self._find_decoded_by_role(decoded, "re")
+        _, nir_item = self._find_decoded_by_role(decoded, "nir")
+
+        rgb = rgb_item["image"].astype(np.float32, copy=False)
+        re = re_item["image"].astype(np.float32, copy=False)
+        nir = nir_item["image"].astype(np.float32, copy=False)
+
+        if rgb.ndim != 3 or rgb.shape[2] != 3:
+            raise RuntimeError(f"RGB alinhado inválido: shape={rgb.shape}")
+
+        h, w = rgb.shape[:2]
+
+        if re.ndim == 3:
+            re = re[:, :, 0]
+
+        if nir.ndim == 3:
+            nir = nir[:, :, 0]
+
+        if re.shape[:2] != (h, w):
+            re = cv2.resize(re, (w, h), interpolation=cv2.INTER_LINEAR)
+
+        if nir.shape[:2] != (h, w):
+            nir = cv2.resize(nir, (w, h), interpolation=cv2.INTER_LINEAR)
+
+        tensor = np.stack(
+            [
+                rgb[:, :, 0],  # R
+                rgb[:, :, 1],  # G
+                rgb[:, :, 2],  # B
+                re,
+                nir,
+            ],
+            axis=0,
+        ).astype(np.float32, copy=False)
+
+        return np.ascontiguousarray(tensor)

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/camera_worker/oak_fcc3_core/oak_fcc3_service.py

@@ -0,0 +1,230 @@
+import time
+from .oak_fcc3_manager import OakFcc3Manager
+
+
+class OakFcc3Service:
+    def __init__(self, timeout=10, **kwargs):
+        self.timeout = timeout
+        self.manager = OakFcc3Manager(**kwargs)
+        self.connected = False
+
+    def connect(self):
+        self.connected = True
+        return {"ok": True, "backend": "oak_fcc3", "connected": True}
+
+    def disconnect(self):
+        self.stop()
+        self.connected = False
+        return {"ok": True, "connected": False}
+
+    def ping(self):
+        return {
+            "ok": True,
+            "backend": "oak_fcc3",
+            "msg": "pong",
+            "ts": time.time(),
+        }
+
+    def get_status(self):
+        status = self.manager.get_status()
+        active_ids = [c["id"] for c in status.get("cameras", [])]
+        active_roles = {
+            c.get("role"): c.get("id")
+            for c in status.get("cameras", [])
+        }
+
+        status.update({
+            "ok": True,
+            "connected": self.connected,
+            "active_camera_ids": active_ids,
+            "active_roles": active_roles,
+            "camera_count_active": len(active_ids),
+        })
+
+        return status
+
+    def get_config(self):
+        return {
+            "mx_id": self.manager.mx_id,
+            "ok": True,
+            "fps": self.manager.fps,
+            "width": self.manager.width,
+            "height": self.manager.height,
+            "frame_type": self.manager.frame_type,
+            "output_dtype": self.manager.output_dtype,
+            "capture_mode": self.manager.capture_mode,
+            "raw_policy": self.manager.raw_policy,
+            "sync_mode": getattr(self.manager, "sync_mode", "best"),
+            "sync_tolerance_ms": self.manager.sync_tolerance_ms,
+        }
+
+    def set_fps(self, fps):
+        self._ensure_stopped_for_config()
+        self.manager.fps = int(fps)
+        return {"ok": True, "fps": self.manager.fps}
+
+    def set_resolution(self, width, height):
+        self._ensure_stopped_for_config()
+        self.manager.width = int(width)
+        self.manager.height = int(height)
+        self.manager.size = (self.manager.width, self.manager.height)
+        return {
+            "ok": True,
+            "width": self.manager.width,
+            "height": self.manager.height,
+        }
+
+    def set_capture_mode(self, mode):
+        self._ensure_stopped_for_config()
+        mode = str(mode).upper()
+        self.manager.capture_mode = self._validate_capture_mode(mode)
+        return {"ok": True, "capture_mode": self.manager.capture_mode}
+
+    def set_frame_type(self, frame_type):
+        self._ensure_stopped_for_config()
+        frame_type = str(frame_type).upper()
+        self.manager.frame_type = self._validate_frame_type(frame_type)
+        return {"ok": True, "frame_type": self.manager.frame_type}
+
+    def set_output_dtype(self, dtype):
+        self._ensure_stopped_for_config()
+        dtype = str(dtype).lower()
+        self.manager.output_dtype = self._validate_output_dtype(dtype)
+        return {"ok": True, "output_dtype": self.manager.output_dtype}
+
+    def begin(self, frame_type=None, output_dtype=None, capture_mode=None):
+        if not self.connected:
+            self.connect()
+
+        if self.manager.running:
+            return {
+                "ok": True,
+                "started": True,
+                "already_running": True,
+                "status": self.get_status(),
+            }
+
+        if frame_type is not None:
+            self.manager.frame_type = self._validate_frame_type(frame_type)
+
+        if output_dtype is not None:
+            self.manager.output_dtype = self._validate_output_dtype(output_dtype)
+
+        if capture_mode is not None:
+            self.manager.capture_mode = self._validate_capture_mode(capture_mode)
+
+        self.manager.start()
+
+        return {
+            "ok": True,
+            "started": True,
+            "already_running": False,
+            "status": self.get_status(),
+        }
+
+    def capture_frame(self, timeout=None):
+        if timeout is None:
+            timeout = self.timeout
+
+        frame, meta = self.manager.get_next_frame(timeout=timeout)
+
+        return frame, meta
+
+    def stop(self):
+        self.manager.stop()
+        return {"ok": True, "stopped": True}
+
+    def _ensure_stopped_for_config(self):
+        if self.manager.running:
+            raise RuntimeError(
+                "Configuração estrutural só pode ser alterada com o manager parado. "
+                "Chame stop() antes."
+            )
+    
+    def resolve_camera_id(self, cam_id=None, role=None):
+        if role is not None:
+            role = str(role).lower()
+
+            status = self.manager.get_status()
+            for cam in status.get("cameras", []):
+                if str(cam.get("role", "")).lower() == role:
+                    return cam["id"]
+
+            raise ValueError(f"Nenhuma câmera ativa encontrada para role={role}")
+
+        if cam_id is None:
+            raise ValueError("Informe cam_id ou role.")
+
+        return str(cam_id)
+        
+
+    def get_camera_controls(self, cam_id=None, role=None):
+        cam_id = self.resolve_camera_id(cam_id=cam_id, role=role)
+        ctrl = self.manager.get_camera_controls(cam_id)
+        ctrl["ok"] = True
+        ctrl["camera_id"] = cam_id
+        ctrl["role"] = role
+        return ctrl
+
+    def set_ae_enable(self, cam_id=None, role=None, enable=False):
+        cam_id = self.resolve_camera_id(cam_id=cam_id, role=role)
+        ctrl = self.manager.set_ae_enable(cam_id, bool(enable))
+        ctrl["ok"] = True
+        ctrl["camera_id"] = cam_id
+        ctrl["role"] = role
+        return ctrl
+
+    def set_awb_enable(self, cam_id=None, role=None, enable=False):
+        cam_id = self.resolve_camera_id(cam_id=cam_id, role=role)
+        ctrl = self.manager.set_awb_enable(cam_id, bool(enable))
+        ctrl["ok"] = True
+        ctrl["camera_id"] = cam_id
+        ctrl["role"] = role
+        return ctrl
+
+    def set_exposure_time(self, cam_id=None, role=None, exposure_time_us=None):
+        if exposure_time_us is None:
+            raise ValueError("exposure_time_us é obrigatório.")
+        cam_id = self.resolve_camera_id(cam_id=cam_id, role=role)
+        ctrl = self.manager.set_exposure_time(cam_id, int(exposure_time_us))
+        ctrl["ok"] = True
+        ctrl["camera_id"] = cam_id
+        ctrl["role"] = role
+        return ctrl
+
+    def set_analogue_gain(self, cam_id=None, role=None, analogue_gain=None):
+        if analogue_gain is None:
+            raise ValueError("analogue_gain é obrigatório.")
+        cam_id = self.resolve_camera_id(cam_id=cam_id, role=role)
+        ctrl = self.manager.set_analogue_gain(cam_id, float(analogue_gain))
+        ctrl["ok"] = True
+        ctrl["camera_id"] = cam_id
+        ctrl["role"] = role
+        return ctrl
+
+    def apply_camera_controls(self, cam_id=None, role=None, controls=None):
+        cam_id = self.resolve_camera_id(cam_id=cam_id, role=role)
+        ctrl = self.manager.apply_camera_controls(cam_id, controls or {})
+        ctrl["ok"] = True
+        ctrl["camera_id"] = cam_id
+        ctrl["role"] = role
+        return ctrl
+    
+
+    def _validate_frame_type(self, frame_type):
+        frame_type = str(frame_type).upper()
+        if frame_type not in ("RAW_BRUTO", "RGB", "MULTISPEC", "PREVIEW"):
+            raise ValueError(f"frame_type inválido: {frame_type}")
+        return frame_type
+
+    def _validate_output_dtype(self, dtype):
+        dtype = str(dtype).lower()
+        if dtype not in ("uint8", "uint16", "float32"):
+            raise ValueError(f"output_dtype inválido: {dtype}")
+        return dtype
+
+    def _validate_capture_mode(self, mode):
+        mode = str(mode).upper()
+        if mode not in ("AUTO", "SINGLE", "DOUBLE", "TRIPLE"):
+            raise ValueError(f"capture_mode inválido: {mode}")
+        return mode

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/camera_worker/oak_fcc3_core/raw_processor_preview.py

@@ -0,0 +1,126 @@
+import cv2
+import numpy as np
+from typing import Optional
+
+
+class RawProcessorPreview:
+    def __init__(self, sensor_width: int, sensor_height: int, bayer_pattern: str = "GBRG"):
+        self.sensor_width = sensor_width
+        self.sensor_height = sensor_height
+        self.bayer_pattern = bayer_pattern.upper()
+
+    def raw16_to_vis8(
+        self, raw16: np.ndarray, 
+        black_level: Optional[int] = None, 
+        white_level: Optional[int] = None, 
+        gamma: float = 2.2, 
+        bit_depth: int = 10
+    ) -> np.ndarray:
+        """
+        Conversão para visualização:
+        - auto-level
+        - gamma
+        """
+        max_val = float((1 << bit_depth) - 1)
+
+        raw = raw16.astype(np.float32)
+
+        if black_level is None:
+            black_level = float(raw.min())
+        if white_level is None:
+            white_level = float(raw.max())
+
+        if white_level <= black_level:
+            norm = raw / max_val
+        else:
+            norm = (raw - black_level) / (white_level - black_level)
+
+        norm = np.clip(norm, 0.0, 1.0)
+
+        if gamma is not None and gamma > 0:
+            norm = np.power(norm, 1.0 / gamma)
+
+        return (norm * 255.0).clip(0, 255).astype(np.uint8)
+
+    def _debayer_code(self):
+        mapping = {
+            "RGGB": cv2.COLOR_BayerRG2RGB_EA,
+            "BGGR": cv2.COLOR_BayerBG2RGB_EA,
+            "GRBG": cv2.COLOR_BayerGR2RGB_EA,
+            "GBRG": cv2.COLOR_BayerGB2RGB_EA,
+        } 
+
+        if self.bayer_pattern not in mapping:
+            raise ValueError(f"Padrão Bayer não suportado: {self.bayer_pattern}")
+
+        return mapping[self.bayer_pattern]
+
+    def apply_preview_white_balance(self, bgr: np.ndarray, strength: float = 1.0) -> np.ndarray:
+        """
+        Gray-world simples para deixar o preview mais agradável.
+        Não usar no raw de treino.
+        """
+        img = bgr.astype(np.float32)
+
+        mean_b = float(img[:, :, 0].mean())
+        mean_g = float(img[:, :, 1].mean())
+        mean_r = float(img[:, :, 2].mean())
+
+        mean_gray = (mean_b + mean_g + mean_r) / 3.0
+
+        eps = 1e-6
+        gain_b = mean_gray / max(mean_b, eps)
+        gain_g = mean_gray / max(mean_g, eps)
+        gain_r = mean_gray / max(mean_r, eps)
+
+        # strength=1 aplica total, strength=0 não aplica
+        gain_b = 1.0 + (gain_b - 1.0) * strength
+        gain_g = 1.0 + (gain_g - 1.0) * strength
+        gain_r = 1.0 + (gain_r - 1.0) * strength
+
+        img[:, :, 0] *= gain_b
+        img[:, :, 1] *= gain_g
+        img[:, :, 2] *= gain_r
+
+        return np.clip(img, 0, 255).astype(np.uint8)
+
+    def apply_preview_contrast(self, bgr: np.ndarray, alpha: float = 1.08, beta: float = 0.0) -> np.ndarray:
+        """
+        Ajuste leve de contraste/brilho para preview.
+        """
+        out = cv2.convertScaleAbs(bgr, alpha=alpha, beta=beta)
+        return out
+
+    def raw16_to_preview_bgr(
+        self,
+        raw16: np.ndarray,
+        gamma: float = 2.2,
+        wb_strength: float = 0.8,
+        apply_wb: bool = True,
+        apply_contrast: bool = True,
+        bit_depth: int = 10,
+    ) -> np.ndarray:
+        """
+        Pipeline de preview bonito:
+        1. auto-level + gamma no mosaico
+        2. demosaic
+        3. white balance simples
+        4. leve contraste final
+        """
+        vis8 = self.raw16_to_vis8(raw16, gamma=gamma, bit_depth=bit_depth)
+        bgr = cv2.cvtColor(vis8, self._debayer_code())
+
+        if apply_wb:
+            bgr = self.apply_preview_white_balance(bgr, strength=wb_strength)
+
+        if apply_contrast:
+            bgr = self.apply_preview_contrast(bgr, alpha=1.08, beta=0.0)
+
+        return bgr
+
+    def raw16_to_preview_jpg_bytes(self, raw16: np.ndarray, jpeg_quality: int = 95) -> bytes:
+        bgr = self.raw16_to_preview_bgr(raw16)
+        ok, enc = cv2.imencode(".jpg", bgr, [int(cv2.IMWRITE_JPEG_QUALITY), int(jpeg_quality)])
+        if not ok:
+            raise RuntimeError("Falha ao codificar preview JPG")
+        return enc.tobytes()

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/camera_worker/oak_fcc3_core/test.json

@@ -0,0 +1,104 @@
+{
+            "debug_visual": false,
+            "frames_consecutivos": 3,
+            "frames_histerese": 2,
+            "min_area_px": 400, 
+            "max_area_frac": 0.2,
+            "ia_roi_begin": 0.0,
+            "ia_roi_size": 1.0,
+            "ia_resolution": [1024,640],
+            "ia_channels": 5,
+            "ia_use_ndvi": false,
+            "erva_top_band_frac": 0.30,
+            "erva_frac_ema": 0.3,
+            "erva_thresh_vel_gain": 0.4,
+            "min_frac_erva_global_on": 0.0020,
+            "min_frac_erva_global_off": 0.0015,
+            "min_frac_erva_top_on": 0.0015,
+            "min_frac_erva_top_off": 0.0010,
+            "min_frac_erva_por_bico": 0.02,
+            "usar_morfologia": true,
+            "kernel_morf": 3,
+
+            "usar_radar_global_gate": true,
+            "max_frac_cana_por_bico": 0.009,
+            "ema_frac_bico": 0.35,
+            "on_frames_required": 3,
+            "off_frames_required": 2,
+            "cana_halo_px": 5,
+            "min_area_erva_px": 80,
+            "erva_thresh_vel_gain_local": 0.6,
+            "k_roi_shift_px_per_vnorm": 24.0,
+
+            "analise_fps": 20,
+            "tensor_fps": 20,
+
+            "fps": 20,
+            "module_params": "C:\\ZendionInc\\agrobot_base\\Python\\OAK\\datasets\\oak-fcc-3\\calibration\\module_params.json",
+
+            "qtd_bicos": 7,
+            "velocidade_robo": 0,
+            "ia_model_path": "C:\\AgroBaseModels\\Ervas\\model-3_1.pt",
+            "ia_labelmap_path": "C:\\AgroBaseModels\\Ervas\\model-3_1.txt",
+            "ia_norm_stats_path": "C:\\AgroBaseModels\\Ervas\\model-1_1.json",
+            "ia_backbone": "nvidia/mit-b1",
+
+            "faixa_atuacao_bicos": 0.7,
+            "area_atuacao_bicos": 0.1,
+            "min_frac_erva_por_bico_on": 0.02,
+            "min_frac_erva_por_bico_off": 0.01,
+
+            "tipo_camera_solo": "multispectral",
+            "channels": 5,
+            "input_channels": ["R", "G", "B", "RE", "NIR"],
+            "backbone": "nvidia/mit-b1",
+            "ckpt": "C:\\ZendionInc\\agrobot_base\\Python\\OAK\\datasets\\oak-fcc-3\\backup\\segformer_b1\\target_teached\\stacked_raw5\\best_score.pt",
+            "norm_stats_path": "C:\\ZendionInc\\agrobot_base\\Python\\OAK\\datasets\\oak-fcc-3\\backup\\segformer_b1\\target_teached\\stacked_raw5\\norm_stats.json",
+            "module_calibration_json": "C:\\ZendionInc\\agrobot_base\\Python\\OAK\\datasets\\oak-fcc-3\\calibration\\module_params.json",
+            "camera_width": 1280,
+            "camera_height": 800,
+            "camera_fps": 40,
+            "amp": true,
+            "fold_input_norm": true,
+            "runtime_mode": "target_direct",
+            "prediction_contract": "target_binary",
+            "output_mask_fullres": false,
+            "lowres_argmax": true,
+            "trust_input": true,
+            "channels_last": false,
+            "model_half": true,
+            "sync_for_timing": false,
+            "torch_compile": false,
+            "torch_compile_mode": "reduce-overhead",
+            "heads": {
+                "semantic": {
+                    "enabled": true,
+                    "type": "multiclass",
+                    "num_classes": 3,
+                    "classes": {"chao": 0, "cana": 1, "erva": 2},
+                    "ignore_index": 255
+                },
+                "vegetation": {
+                    "enabled": true,
+                    "type": "binary",
+                    "num_classes": 2,
+                    "classes": {"background": 0, "vegetation": 1},
+                    "ignore_index": 255
+                },
+                "cana": {
+                    "enabled": true,
+                    "type": "binary",
+                    "num_classes": 2,
+                    "classes": {"not_cana": 0, "cana": 1},
+                    "ignore_index": 255
+                },
+                "target": {
+                    "enabled": true,
+                    "type": "binary",
+                    "num_classes": 2,
+                    "classes": {"background": 0, "target": 1},
+                    "ignore_index": 255
+                }
+            }
+
+        }

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/camera_worker/segformer_runner.py

@@ -1,11 +1,34 @@
 import json
 import os
 import time
-from PIL import Image
 import cv2
 import numpy as np
 import torch
-from transformers import SegformerForSemanticSegmentation
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import SegformerConfig, SegformerForSemanticSegmentation
+
+
+class LabelHead(nn.Module):
+    def __init__(self, feat_ch: int, num_seg_classes: int, num_label_classes: int, hidden: int = 256, dropout: float = 0.2):
+        super().__init__()
+        in_ch = feat_ch + num_seg_classes
+        self.pool = nn.AdaptiveAvgPool2d((1, 1))
+        self.net = nn.Sequential(
+            nn.Linear(in_ch, hidden),
+            nn.ReLU(inplace=True),
+            nn.Dropout(dropout),
+            nn.Linear(hidden, num_label_classes),
+        )
+
+    def forward(self, feat: torch.Tensor, logits_seg: torch.Tensor) -> torch.Tensor:
+        if feat.shape[-2:] != logits_seg.shape[-2:]:
+            feat = F.interpolate(feat, size=logits_seg.shape[-2:], mode="bilinear", align_corners=False)
+
+        x = torch.cat([feat, logits_seg], dim=1)
+        x = self.pool(x).flatten(1)
+        return self.net(x)
+
 
 class SegformerNavRunner:
     IMAGENET_MEAN = [0.485, 0.456, 0.406]
@@ -15,16 +38,30 @@ class SegformerNavRunner:
         from shared.utils import carregar_labelmap_completo
 
         self.device = torch.device(device if torch.cuda.is_available() else "cpu")
-        self.cor_para_id, self.colormap_rgb, self.classes, self.ignore_rgb = carregar_labelmap_completo(seg_config["ia_labelmap_path"])
-        self.model = self.load_segformer_from_checkpoint(seg_config, self.device)
-        self.resolucao = tuple(seg_config["ia_resolution"])
+
+        self.use_amp = bool(seg_config.get("use_amp", True))
+        self.use_channels_last = bool(seg_config.get("use_channels_last", True))
+        self.use_compact_aux = bool(seg_config.get("use_compact_aux", True))
+        self.debug_timing = bool(seg_config.get("debug_timing", False))
+        self._last_aux_result = None
+        self._last_aux_ts = 0.0
+
+        self.cor_para_id, self.colormap_rgb, self.classes, self.ignore_rgb = carregar_labelmap_completo(
+            seg_config["ia_labelmap_path"]
+        )
+
+        self.resolucao = tuple(seg_config["ia_resolution"])  # [W,H]
         self.roi_inicio = seg_config["ia_roi_begin"]
         self.roi_tamanho = seg_config["ia_roi_size"]
         self.last_infer = None
 
-        # ==========================
-        # Normalização fixa (igual treino)
-        # ==========================
+        self.mode = seg_config.get("ia_mode", "dual_label")
+        self.model = None
+        self.aux_head = None
+        self.label_names = {}
+
+        self._load_dual_checkpoint(seg_config)
+
         norm_mean = self.IMAGENET_MEAN
         norm_std = self.IMAGENET_STD
 
@@ -35,161 +72,344 @@ class SegformerNavRunner:
 
             stats_channels = norm_stats.get("channels", [])
             stats_mean = norm_stats.get("mean", [])
-            stats_std  = norm_stats.get("std", [])
-
-            print(f"[NORM] usando stats fixos de: {norm_stats_path}")
-            print(f"[NORM] channels={stats_channels}")
-            print(f"[NORM] mean={stats_mean}")
-            print(f"[NORM] std ={stats_std}")
+            stats_std = norm_stats.get("std", [])
 
             idx_by_name = {name: i for i, name in enumerate(stats_channels)}
 
-            m_R  = stats_mean[idx_by_name["R"]]
-            m_G  = stats_mean[idx_by_name["G"]]
-            m_B  = stats_mean[idx_by_name["B"]]
-
-            s_R  = stats_std[idx_by_name["R"]]
-            s_G  = stats_std[idx_by_name["G"]]
-            s_B  = stats_std[idx_by_name["B"]]
-
-            norm_mean = [m_R, m_G, m_B]
-            norm_std  = [s_R, s_G, s_B]
+            if all(ch in idx_by_name for ch in ["R", "G", "B"]):
+                norm_mean = [
+                    stats_mean[idx_by_name["R"]],
+                    stats_mean[idx_by_name["G"]],
+                    stats_mean[idx_by_name["B"]],
+                ]
+                norm_std = [
+                    stats_std[idx_by_name["R"]],
+                    stats_std[idx_by_name["G"]],
+                    stats_std[idx_by_name["B"]],
+                ]
+                print(f"[NORM] usando stats fixos de: {norm_stats_path}")
+            else:
+                print("[NORM] norm_stats não contém R,G,B. Usando ImageNet.")
         else:
-            print(f"[NORM] norm_stats.json não encontrado em {norm_stats_path}. "
-                f"Usando normalização dinâmica por frame.")
+            print(f"[NORM] norm_stats não encontrado em {norm_stats_path}. Usando ImageNet.")
 
         self.set_norm_stats(norm_mean, norm_std)
 
     def set_norm_stats(self, mean, std):
-        mean = torch.tensor(mean, dtype=torch.float32).view(3, 1, 1)
-        std = torch.tensor(std, dtype=torch.float32).view(3, 1, 1).clamp_min(1e-6)
-        self._norm_mean = mean
-        self._norm_std = std
+        self._norm_mean = torch.tensor(mean, dtype=torch.float32, device=self.device).view(3, 1, 1)
+        self._norm_std = torch.tensor(std, dtype=torch.float32, device=self.device).view(3, 1, 1).clamp_min(1e-6)
 
-    def _extract_state_dict(self, ckpt):
-        """
-        Aceita:
-        - state_dict puro (dict de tensores)
-        - checkpoint com chaves comuns: state_dict / model_state_dict / model
-        """
-        if not isinstance(ckpt, dict):
-            return None
+    def normalize_img(self, img):
+        return (img - self._norm_mean) / self._norm_std
 
-        # caso já seja um state_dict puro
-        if any(isinstance(v, torch.Tensor) for v in ckpt.values()):
-            return ckpt
-
-        for k in ("state_dict", "model_state_dict", "model"):
-            if k in ckpt and isinstance(ckpt[k], dict):
-                return ckpt[k]
-
-        return None
-
-    def load_segformer_from_checkpoint(
-        self,
-        seg_config: dict,
-        device: torch.device,
-    ):
-        """
-        Carrega um SegFormer (B0, B1, B2, B3...) compatível com o treino:
-
-        - Cria o modelo via from_pretrained(backbone, num_labels=num_classes)
-        - Carrega o state_dict salvo pelo script de treino
-        """
-        pt_path = seg_config.get("ia_model_path")
-        backbone = seg_config.get("ia_backbone")
-        num_classes = len(self.classes)
-        ckpt = torch.load(pt_path, map_location="cpu", weights_only=True)
-        state_dict = self._extract_state_dict(ckpt)
-        if state_dict is None:
-            raise RuntimeError(f"Não consegui extrair state_dict de {pt_path}. keys={list(ckpt.keys())}")
-
-        # limpar prefixos comuns
-        cleaned = {}
-        for k, v in state_dict.items():
-            nk = k
-            if nk.startswith("model."):
-                nk = nk[len("model."):]
-            if nk.startswith("module."):
-                nk = nk[len("module."):]
-            cleaned[nk] = v
-
-        model = SegformerForSemanticSegmentation.from_pretrained(
+    def _build_base_model(self, backbone: str, num_classes: int):
+        config = SegformerConfig.from_pretrained(
             backbone,
-            num_labels=num_classes,
-            ignore_mismatched_sizes=True,
-            use_safetensors=True
+            local_files_only=True
         )
 
-        missing, unexpected = model.load_state_dict(cleaned, strict=False)
-        print(f"[load] missing={len(missing)} unexpected={len(unexpected)}")
-        if missing:
-            print("[load] missing sample:", missing[:10])
-        if unexpected:
-            print("[load] unexpected sample:", unexpected[:10])
+        config.num_labels = int(num_classes)
+        config.output_hidden_states = True
 
-        model.to(device).eval()
+        model = SegformerForSemanticSegmentation(config)
         return model
 
-    def normalize_img(self, img: torch.Tensor) -> torch.Tensor:
-        return (img - self._norm_mean.to(img.device)) / self._norm_std.to(img.device)
+    def _load_dual_checkpoint(self, seg_config):
+        pt_path = seg_config["ia_model_path"]
+        backbone = seg_config["ia_backbone"]
+        num_classes = len(self.classes)
+
+        ckpt = torch.load(pt_path, map_location="cpu", weights_only=False)
+        self.model = self._build_base_model(backbone, num_classes)
+        self.model.load_state_dict(ckpt["model"], strict=True)
+        self.model.to(self.device).eval()
+
+        if self.use_channels_last and self.device.type == "cuda":
+            self.model.to(memory_format=torch.channels_last)
+
+        torch.backends.cudnn.benchmark = True
+
+        extra = ckpt.get("extra", {}) or {}
+        label_names_raw = extra.get("label_name_by_id", {}) or {}
+        self.label_names = {int(k): str(v) for k, v in label_names_raw.items()} if label_names_raw else {}
+
+        if self.mode == "dual_label":
+            aux_sd = ckpt.get("aux_head")
+            if aux_sd is None:
+                raise RuntimeError("Checkpoint não possui aux_head. Este arquivo parece não ser dual_head_label.")
+
+            max_label_id = -1
+
+            for k, v in aux_sd.items():
+                if k.endswith("net.3.weight") or k.endswith("net.3.bias"):
+                    max_label_id = int(v.shape[0]) - 1
+                    break
+
+            if max_label_id < 0:
+                raise RuntimeError("Não consegui inferir número de classes da LabelHead.")
+
+            num_label_classes = max_label_id + 1
+
+            for i in range(num_label_classes):
+                self.label_names.setdefault(i, f"label_{i}")
+
+            feat_ch = int(self.model.config.hidden_sizes[-1])
+
+            self.aux_head = LabelHead(
+                feat_ch=feat_ch,
+                num_seg_classes=num_classes,
+                num_label_classes=num_label_classes,
+                hidden=256,
+                dropout=0.2,
+            )
+
+            self.aux_head.load_state_dict(aux_sd, strict=True)
+            self.aux_head.to(self.device).eval()
+            if self.use_channels_last and self.device.type == "cuda":
+                # Linear não usa channels_last, mas manter aqui não atrapalha.
+                pass
+
+            print(f"[DUAL] LabelHead carregada: classes={num_label_classes} names={self.label_names}")
+
+        else:
+            self.aux_head = None
+            print("[SEG] Modo single carregado.")
+
+        print(f"[MODEL] ckpt={pt_path}")
+        print(f"[MODEL] epoch={ckpt.get('epoch')} bests={ckpt.get('bests')}")
 
     def compute_roi_indices(self, H: int, zona_inicio: float, faixa_atuacao: float):
         y_inicio = int((1.0 - zona_inicio) * H)
         y_fim = int((1.0 - (zona_inicio + faixa_atuacao)) * H)
+
         y_fim = max(0, min(H, y_fim))
         y_inicio = max(0, min(H, y_inicio))
+
         if y_fim >= y_inicio:
             y_fim = max(0, y_inicio - 1)
+
         return y_fim, y_inicio
 
     def resize_keep_width(self, img: np.ndarray, new_w: int, min_h: int, interpolation: int) -> np.ndarray:
         h, w = img.shape[:2]
-        new_h = int(round(new_w * (h / w)))
+        new_h = int(round(new_w * (h / max(1, w))))
+
         if min_h is not None and new_h < min_h:
             new_h = min_h
+
         return cv2.resize(img, (new_w, new_h), interpolation=interpolation)
 
-    @torch.no_grad()
-    def segformer_predict_ids(self, img_tensor):
-        """
-        img_tensor: [1,3,H,W] float32 normalizado.
-        retorna: pred_ids [H,W] (numpy int)
-        """
-        out = self.model(pixel_values=img_tensor)
-        logits = out.logits  # [B, C, h, w] (pode ser menor que input)
-        # Upsample logits para o tamanho do input
-        logits = torch.nn.functional.interpolate(
-            logits,
-            size=img_tensor.shape[-2:],
-            mode="bilinear",
-            align_corners=False
+    def _preprocess_roi(self, roi_rgb: np.ndarray):
+        roi_resized = self.resize_keep_width(
+            roi_rgb,
+            self.resolucao[0],
+            self.resolucao[1],
+            cv2.INTER_AREA,
         )
-        pred = torch.argmax(logits, dim=1)  # [B,H,W]
-        return pred.squeeze(0).cpu().numpy().astype(np.uint8)
 
+        # Garante array contínuo para reduzir cópia torta no torch.from_numpy
+        roi_resized = np.ascontiguousarray(roi_resized)
 
+        img_tensor = torch.from_numpy(roi_resized).to(
+            device=self.device,
+            dtype=torch.float32,
+            non_blocking=True
+        )
 
+        # HWC -> NCHW
+        img_tensor = img_tensor.permute(2, 0, 1).unsqueeze(0)
+        img_tensor = img_tensor.div_(255.0)
+
+        img_tensor = self.normalize_img(img_tensor)
+
+        if self.use_channels_last and self.device.type == "cuda":
+            img_tensor = img_tensor.contiguous(memory_format=torch.channels_last)
+
+        return img_tensor, roi_resized
+
+    @torch.inference_mode()
     def infer_ids(self, frame_rgb):
-        if False:
-            img_rgb = np.array(Image.open(r"C:\ZendionInc\agrobot_base\t_cor\108_rgb.jpeg").convert("RGB"))
-            H, W = img_rgb.shape[:2]
-            y_fim, y_inicio = self.compute_roi_indices(H, self.roi_inicio, self.roi_tamanho)
-            roi = img_rgb[y_fim:y_inicio, 0:W]
-        else:
-            H, W = frame_rgb.shape[:2]
-            y_fim, y_inicio = self.compute_roi_indices(H, self.roi_inicio, self.roi_tamanho)
-            roi = frame_rgb[y_fim:y_inicio, 0:W]
+        if frame_rgb is None or not hasattr(frame_rgb, "shape") or frame_rgb.size == 0:
+            return None, None, None, None, None
+        t0 = time.perf_counter()
+        H, W = frame_rgb.shape[:2]
+        y_fim, y_inicio = self.compute_roi_indices(H, self.roi_inicio, self.roi_tamanho)
 
-        roi_resized = self.resize_keep_width(roi, self.resolucao[0], self.resolucao[1], cv2.INTER_AREA)
-        roi_norm = roi_resized.astype(np.float32) / 255.0
-        img_tensor = torch.from_numpy(roi_norm).permute(2, 0, 1).unsqueeze(0).to(self.device)
-        img_tensor = self.normalize_img(img_tensor).float()
+        roi_rgb = frame_rgb[y_fim:y_inicio, 0:W]
+        t_crop = time.perf_counter()
+        if roi_rgb is None or roi_rgb.size == 0:
+            return None, None, None, (y_fim, y_inicio), None
+        img_tensor, roi_resized = self._preprocess_roi(roi_rgb)
+        t_pre = time.perf_counter()
+
+        use_amp_now = self.use_amp and self.device.type == "cuda"
+
+        with torch.autocast(device_type="cuda", dtype=torch.float16, enabled=use_amp_now):
+            out = self.model(pixel_values=img_tensor, output_hidden_states=True)
+
+        t_model = time.perf_counter()
+
+        logits_seg = out.logits
+
+        t_arg0 = time.perf_counter()
+        pred_low = torch.argmax(logits_seg, dim=1)[0]
+        pred_low_np = pred_low.detach().cpu().numpy().astype(np.uint8)
+        pred_ids_np = cv2.resize(
+            pred_low_np,
+            (roi_resized.shape[1], roi_resized.shape[0]),
+            interpolation=cv2.INTER_NEAREST
+        )
+        t_arg1 = time.perf_counter()
+
+        aux_result = None
+
+        t_aux0 = time.perf_counter()
+        if self.mode == "dual_label" and self.aux_head is not None:
+            feat = out.hidden_states[-1]
+
+            if feat.shape[-2:] != logits_seg.shape[-2:]:
+                feat = F.interpolate(
+                    feat,
+                    size=logits_seg.shape[-2:],
+                    mode="bilinear",
+                    align_corners=False,
+                )
+
+            with torch.autocast(device_type="cuda", dtype=torch.float16, enabled=use_amp_now):
+                logits_label = self.aux_head(feat, logits_seg)
+
+            probs = torch.softmax(logits_label, dim=1)[0].detach().cpu().numpy()
+
+            label_id = int(np.argmax(probs))
+            label_conf = float(probs[label_id])
+            label_name = self.label_names.get(label_id, f"label_{label_id}")
+
+            if self.use_compact_aux:
+                aux_result = {
+                    "type": "label",
+                    "label_id": label_id,
+                    "label_name": label_name,
+                    "label_conf": label_conf,
+                }
+            else:
+                aux_result = {
+                    "type": "label",
+                    "label_id": label_id,
+                    "label_name": label_name,
+                    "label_conf": label_conf,
+                    "label_probs": probs.astype(float).tolist(),
+                    "label_names": self.label_names,
+                }
+            if aux_result is not None:
+                self._last_aux_result = aux_result
+                self._last_aux_ts = time.time()
+
+        t_aux1 = time.perf_counter()
 
-        pred_ids = self.segformer_predict_ids(img_tensor)  # (H,W)
         self.last_infer = time.time()
-        return pred_ids, self.last_infer, roi_resized, (y_fim, y_inicio)
+
+        t_end = time.perf_counter()
+
+        if self.debug_timing:
+            print(
+                f"RUNNER | crop={(t_crop-t0)*1000:.1f}ms | "
+                f"pre={(t_pre-t_crop)*1000:.1f}ms | "
+                f"model={(t_model-t_pre)*1000:.1f}ms | "
+                f"arg={(t_arg1-t_arg0)*1000:.1f}ms | "
+                f"aux={(t_aux1-t_aux0)*1000:.1f}ms | "
+                f"total={(t_end-t0)*1000:.1f}ms"
+            )
+
+        return pred_ids_np, self.last_infer, roi_resized, (y_fim, y_inicio), aux_result
     
+    @torch.inference_mode()
+    def infer_ids_seg_only(self, frame_rgb):
+        """
+        Inferência rápida apenas da cabeça de segmentação.
+
+        Diferenças para infer_ids():
+        - não pede hidden_states;
+        - não roda aux_head;
+        - reutiliza self._last_aux_result, se existir;
+        - mantém o mesmo formato de retorno:
+        pred_ids_np, ts, roi_resized, roi_info, aux_result
+        """
+        if frame_rgb is None or not hasattr(frame_rgb, "shape") or frame_rgb.size == 0:
+            return None, None, None, None, None
+
+        t0 = time.perf_counter()
+
+        H, W = frame_rgb.shape[:2]
+        y_fim, y_inicio = self.compute_roi_indices(
+            H,
+            self.roi_inicio,
+            self.roi_tamanho
+        )
+
+        roi_rgb = frame_rgb[y_fim:y_inicio, 0:W]
+        t_crop = time.perf_counter()
+
+        if roi_rgb is None or roi_rgb.size == 0:
+            return None, None, None, (y_fim, y_inicio), None
+
+        img_tensor, roi_resized = self._preprocess_roi(roi_rgb)
+        t_pre = time.perf_counter()
+
+        use_amp_now = getattr(self, "use_amp", True) and self.device.type == "cuda"
+
+        # Aqui está o ponto principal do teste:
+        # NÃO pede hidden_states, então o modelo só precisa entregar logits de segmentação.
+        with torch.autocast(
+            device_type="cuda",
+            dtype=torch.float16,
+            enabled=use_amp_now
+        ):
+            out = self.model(
+                pixel_values=img_tensor,
+                output_hidden_states=False
+            )
+
+        t_model = time.perf_counter()
+
+        logits_seg = out.logits
+
+        t_arg0 = time.perf_counter()
+
+        pred_low = torch.argmax(logits_seg, dim=1)[0]
+        pred_low_np = pred_low.detach().cpu().numpy().astype(np.uint8)
+
+        pred_ids_np = cv2.resize(
+            pred_low_np,
+            (roi_resized.shape[1], roi_resized.shape[0]),
+            interpolation=cv2.INTER_NEAREST
+        )
+
+        t_arg1 = time.perf_counter()
+
+        # Reaproveita o último status conhecido.
+        # Para o teste inicial, pode ser None mesmo.
+        aux_result = getattr(self, "_last_aux_result", None)
+
+        if aux_result is not None:
+            aux_result = dict(aux_result)
+            aux_result["stale"] = True
+            aux_result["age_ms"] = (
+                time.time() - getattr(self, "_last_aux_ts", time.time())
+            ) * 1000.0
+
+        self.last_infer = time.time()
+        t_end = time.perf_counter()
+
+        if getattr(self, "debug_timing", False):
+            print(
+                f"RUNNER_SEG_ONLY | "
+                f"crop={(t_crop - t0) * 1000:.1f}ms | "
+                f"pre={(t_pre - t_crop) * 1000:.1f}ms | "
+                f"model={(t_model - t_pre) * 1000:.1f}ms | "
+                f"arg={(t_arg1 - t_arg0) * 1000:.1f}ms | "
+                f"total={(t_end - t0) * 1000:.1f}ms"
+            )
+
+        return pred_ids_np, self.last_infer, roi_resized, (y_fim, y_inicio), aux_result
 
 
-    

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/health_worker/main.py

@@ -30,7 +30,7 @@ def main():
         T_Code.Sen: ModuloSensoriamento(),
         T_Code.Atu: ModuloAtuador(), 
         T_Code.Lra: ModuloLoRa(),
-        T_Code.Imu: IMUCamera(),
+        #T_Code.Imu: IMUCamera(),
         T_Code.Npc: ModuloPC(),
         T_Code.Lvx: ModuloLivox(),
         T_Code.Ipb: ModuloIPBribge(),

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/health_worker/modulos/imu.py

@@ -9,13 +9,18 @@ from shared.enums import StatusModulo, T_Code
 from health_worker.modulos.base import ModuloDiagnosticoBase
 
 class IMUCamera(ModuloDiagnosticoBase):
-    def __init__(self, queue=None, freq=100, angulo_inicial=26.3):
+    def __init__(self, mx_id, queue=None, freq=100, angulo_inicial=26.3):
+        self.mx_id = mx_id
         self.freq = freq
 
+        self.ultima_saude = None
+
         self.ativo = False
         self.last_packet_ts = 0.0
         self.last_publish_ts = 0.0
         self.ultimo_erro_ts = 0.0
+
+        self.last_data = None
         
         if queue is None:
             return
@@ -75,6 +80,13 @@ class IMUCamera(ModuloDiagnosticoBase):
             self.ativo = False
             self.mostrar_log("Task parada")
 
+        try:
+            th = getattr(self, "imu_thread", None)
+            if th is not None and th.is_alive():
+                th.join(timeout=1.0)
+        except Exception:
+            pass
+
     def _mediana_curta(self, hist, valor):
         hist.append(valor)
         return float(np.median(hist))
@@ -177,6 +189,7 @@ class IMUCamera(ModuloDiagnosticoBase):
         self.estabilidade_idx = round(self.clamp(estabilidade_idx, 0.0, 100.0), 1)
 
     def imu_task_loop(self):
+        from camera_worker.manager import definir_imu_camera
         t0 = time.perf_counter()
 
         while self.ativo:
@@ -308,26 +321,48 @@ class IMUCamera(ModuloDiagnosticoBase):
 
                         ts_imu = (time.time() * 1000)
 
-                        ContextoGlobalRedis.atualizar_ctx_dict(
-                            ContextoGlobalRedis.ModKey(T_Code.Imu),
-                            roll=round(-roll, 2),
-                            pitch=round(pitch, 2),
-                            yaw=round(yaw, 2),
-                            roll_seg=round(-self.roll_seg, 2),
-                            pitch_seg=round(self.pitch_seg, 2),
-                            vel_mps=round(velocidade_mps, 3),
-                            em_movimento=self.em_movimento,
-                            movimento_idx=round(self.movimento_idx, 1),
-                            rugosidade_idx=round(self.rugosidade_idx, 1),
-                            impacto_idx=round(self.impacto_idx, 1),
-                            estabilidade_idx=round(self.estabilidade_idx, 1),
-                            timestamp=ts_imu,
-                            last_packet_ts=self.last_packet_ts,
-                            last_publish_ts=self.last_publish_ts,
-                            ultimo_erro_ts=self.ultimo_erro_ts,
-                            latencia=latencia,
-                            frequencia=f_tick
-                        )
+                        #ContextoGlobalRedis.atualizar_ctx_dict(
+                        #    ContextoGlobalRedis.ModKey(T_Code.Imu),
+                        #    roll=round(-roll, 2),
+                        #    pitch=round(pitch, 2),
+                        #    yaw=round(yaw, 2),
+                        #    roll_seg=round(-self.roll_seg, 2),
+                        #    pitch_seg=round(self.pitch_seg, 2),
+                        #    vel_mps=round(velocidade_mps, 3),
+                        #    em_movimento=self.em_movimento,
+                        #    movimento_idx=round(self.movimento_idx, 1),
+                        #    rugosidade_idx=round(self.rugosidade_idx, 1),
+                        #    impacto_idx=round(self.impacto_idx, 1),
+                        #    estabilidade_idx=round(self.estabilidade_idx, 1),
+                        #    timestamp=ts_imu,
+                        #    last_packet_ts=self.last_packet_ts,
+                        #    last_publish_ts=self.last_publish_ts,
+                        #    ultimo_erro_ts=self.ultimo_erro_ts,
+                        #    latencia=latencia,
+                        #    frequencia=f_tick
+                        #)
+
+                        self.last_data = {
+                            "roll": round(-roll, 2),
+                            "pitch": round(pitch, 2),
+                            "yaw": round(yaw, 2),
+                            "roll_seg": round(-self.roll_seg, 2),
+                            "pitch_seg": round(self.pitch_seg, 2),
+                            "vel_mps": round(velocidade_mps, 3),
+                            "em_movimento": self.em_movimento,
+                            "movimento_idx": round(self.movimento_idx, 1),
+                            "rugosidade_idx": round(self.rugosidade_idx, 1),
+                            "impacto_idx": round(self.impacto_idx, 1),
+                            "estabilidade_idx": round(self.estabilidade_idx, 1),
+                            "timestamp": ts_imu,
+                            "last_packet_ts": self.last_packet_ts,
+                            "last_publish_ts": self.last_publish_ts,
+                            "ultimo_erro_ts": self.ultimo_erro_ts,
+                            "latencia": latencia,
+                            "frequencia": f_tick
+                        }
+
+                        definir_imu_camera(self.mx_id, self.last_data, self.ultima_saude)
 
             except Exception as e:
                 self.ultimo_erro_ts = time.time()
@@ -345,7 +380,7 @@ class IMUCamera(ModuloDiagnosticoBase):
                     delay_corrigido = max(0.0, (1.0 / self.freq) - latencia)
                     time.sleep(delay_corrigido)
 
-    def atualizar_saude(self):
+    def atualizar_saude_bkp(self):
         try:
             agora = time.time()
             modulo = ContextoGlobalRedis.get_modulo(T_Code.Imu) or {}
@@ -469,5 +504,156 @@ class IMUCamera(ModuloDiagnosticoBase):
         except Exception as e:
             self.mostrar_log(f"Erro ao atualizar saude: {e}")
 
+    def atualizar_saude(self):
+        try:
+            agora = time.time()
+
+            FREQ_BASE = float(self.freq)
+            FREQ_MIN = FREQ_BASE * 0.5
+            SAUDE_MIN_ALERTA = 80
+            SAUDE_MIN_FALHA = 45
+
+            data = self.get_dados()
+
+            last_packet_ts = float(data.get("last_packet_ts", self.last_packet_ts) or 0.0)
+            last_publish_ts = float(data.get("last_publish_ts", self.last_publish_ts) or 0.0)
+            ultimo_erro_ts = float(data.get("ultimo_erro_ts", self.ultimo_erro_ts) or 0.0)
+
+            freq_hz = float(data.get("frequencia", 0.0) or 0.0)
+            latencia = float(data.get("latencia", 0.0) or 0.0)
+            valido = bool(data.get("valido", False))
+
+            tempo_sem_packet = (agora - last_packet_ts) if last_packet_ts > 0 else 999.0
+            tempo_sem_atualizar = (agora - last_publish_ts) if last_publish_ts > 0 else 999.0
+            tempo_desde_erro = (agora - ultimo_erro_ts) if ultimo_erro_ts > 0 else 999.0
+
+            conectado = bool(self.ativo and tempo_sem_packet < 10.0)
+
+            saude = 100
+            motivos = []
+
+            if not self.ativo:
+                conectado = False
+                saude = 0
+                motivos.append("Thread da IMU parada")
+
+            elif not valido:
+                saude = 0
+                motivos.append("IMU ainda sem leitura válida")
+
+            elif not conectado:
+                saude = 0
+                motivos.append(f"Sem packets da IMU há {tempo_sem_packet:.2f}s")
+
+            else:
+                if tempo_sem_packet > 1.5:
+                    saude -= 45
+                    motivos.append(f"Sem packets há {tempo_sem_packet:.2f}s")
+                elif tempo_sem_packet > 0.7:
+                    saude -= 25
+                    motivos.append(f"Packets atrasados há {tempo_sem_packet:.2f}s")
+                elif tempo_sem_packet > 0.3:
+                    saude -= 10
+                    motivos.append(f"Leve atraso de packets: {tempo_sem_packet:.2f}s")
+
+                if tempo_sem_atualizar > 1.5:
+                    saude -= 30
+                    motivos.append(f"Sem atualizar dados há {tempo_sem_atualizar:.2f}s")
+                elif tempo_sem_atualizar > 0.8:
+                    saude -= 15
+                    motivos.append(f"Atualização atrasada há {tempo_sem_atualizar:.2f}s")
+                elif tempo_sem_atualizar > 0.3:
+                    saude -= 5
+                    motivos.append(f"Leve atraso na atualização: {tempo_sem_atualizar:.2f}s")
+
+                if freq_hz <= 0:
+                    saude -= 20
+                    motivos.append("Frequência zerada")
+                elif freq_hz <= FREQ_MIN:
+                    saude -= 35
+                    motivos.append(f"Frequência baixa: {freq_hz:.2f} Hz")
+                else:
+                    f = min(freq_hz, FREQ_BASE)
+                    erro = (FREQ_BASE - f) / FREQ_BASE
+                    p = min(int(erro * 100), 20)
+                    saude -= p
+                    if p > 0:
+                        motivos.append(f"Frequência abaixo do ideal: {freq_hz:.2f} Hz")
+
+                if latencia > 1.0:
+                    saude -= 25
+                    motivos.append(f"Latência muito alta: {latencia:.2f}s")
+                elif latencia > 0.5:
+                    saude -= 15
+                    motivos.append(f"Latência alta: {latencia:.2f}s")
+                elif latencia > 0.2:
+                    saude -= 5
+                    motivos.append(f"Latência moderada: {latencia:.2f}s")
+
+                if tempo_desde_erro < 2.0:
+                    saude -= 20
+                    motivos.append("Erro muito recente no loop")
+                elif tempo_desde_erro < 5.0:
+                    saude -= 10
+                    motivos.append("Erro recente no loop")
+
+            saude = max(0, min(100, saude))
+
+            status = StatusModulo.OPERANTE
+            if not conectado:
+                status = StatusModulo.DESCONECTADO
+            elif saude <= SAUDE_MIN_FALHA:
+                status = StatusModulo.FALHA
+            elif saude < SAUDE_MIN_ALERTA:
+                status = StatusModulo.ALERTA
+
+            self.ultima_saude = {
+                "conectado": conectado,
+                "status": status.value,
+                "saude": saude,
+                "motivos": motivos,
+                "saude_individual": [],
+            }
+
+            return self.ultima_saude
+
+        except Exception as e:
+            self.mostrar_log(f"Erro ao atualizar saude: {e}")
+            self.ultima_saude = {
+                "conectado": False,
+                "status": StatusModulo.FALHA.value,
+                "saude": 0,
+                "motivos": [str(e)],
+                "saude_individual": [],
+            }
+            return self.ultima_saude
+
+    def get_dados(self):
+        if self.last_data is None:
+            return {
+                "roll": 0.0,
+                "pitch": 0.0,
+                "yaw": 0.0,
+                "roll_seg": 0.0,
+                "pitch_seg": 0.0,
+                "vel_mps": 0.0,
+                "em_movimento": False,
+                "movimento_idx": 0.0,
+                "rugosidade_idx": 0.0,
+                "impacto_idx": 0.0,
+                "estabilidade_idx": 100.0,
+                "timestamp": 0.0,
+                "last_packet_ts": 0.0,
+                "last_publish_ts": 0.0,
+                "ultimo_erro_ts": 0.0,
+                "latencia": 0.0,
+                "frequencia": 0.0,
+                "valido": False
+            }
+
+        data = dict(self.last_data)
+        data["valido"] = True
+        return data
+
     def mostrar_log(self, mensagem):
         print(f"{time.time()} - [IMU][id={id(self)}] {mensagem}")

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/shared/contexto_global_redis.py

@@ -158,6 +158,11 @@ class ContextoGlobalRedis:
     def CamKey(cls, mx_id: str):
         """Lê a estrutura e decodifica de volta para objeto"""
         return CtxKey.DadosCameras.value + mx_id
+    
+    @classmethod
+    def CamImuKey(cls, mx_id: str):
+        """Lê a estrutura e decodifica de volta para objeto"""
+        return CtxKey.DadosCameras.value + mx_id + "_Imu"
 
     @classmethod
     def get_cameras(cls):
@@ -166,6 +171,10 @@ class ContextoGlobalRedis:
     @classmethod
     def get_camera(cls, mx_id: str):
         return cls.get(cls.CamKey(mx_id), None)
+    
+    @classmethod
+    def get_camera_imu(cls, mx_id: str):
+        return cls.get(cls.CamImuKey(mx_id), None)
 
     @classmethod
     def get_equipamento(cls):

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/shared/gpu_priority_controller.py

@@ -0,0 +1,544 @@
+import time
+from dataclasses import dataclass, field
+from typing import Any, Dict, Optional
+
+
+@dataclass
+class GpuModeConfig:
+    mode: str
+    visual_fps: Dict[str, float]
+
+
+class GpuPriorityController:
+    """
+    Controlador adaptativo de prioridade de GPU.
+
+    Primeira versão:
+    - Não usa lock real de GPU.
+    - Não centraliza inferência.
+    - Apenas reduz/aumenta FPS do visual_worker conforme saúde do weed_worker.
+
+    O objetivo é proteger o weed_worker, mantendo o visual_worker vivo,
+    mas descartável quando o sistema estiver pesado.
+    """
+
+    MODES = ["critical", "safe", "eco", "normal"]
+
+    def __init__(
+        self,
+        mostrar_log=None,
+        enabled: bool = True,
+        update_interval_s: float = 1.0,
+        log_interval_s: float = 3.0,
+        weed_targets: Optional[Dict[str, float]] = None,
+        visual_targets_normal: Optional[Dict[str, float]] = None,
+        mode_configs: Optional[Dict[str, Dict[str, float]]] = None,
+        bad_cycles_to_degrade: int = 3,
+        good_cycles_to_recover: int = 5,
+        min_data_age_s: float = 0.0,
+        max_data_age_s: float = 3.0,
+        log_periodic: bool = False,
+        log_warnings: bool = True,
+    ):
+        self.mostrar_log = mostrar_log
+        self.enabled = bool(enabled)
+
+        self.update_interval_s = float(update_interval_s)
+        self.log_interval_s = float(log_interval_s)
+
+        self.weed_targets = weed_targets or {
+            "tensor": 18.0,
+            "inferencia": 15.0,
+            "deteccao": 15.0,
+        }
+
+        self.visual_targets_normal = visual_targets_normal or {
+            "segmentacao": 8.0,
+            "grid": 6.0,
+            "deteccao": 5.0,
+            "publicacao": 15.0,
+            "analise": 8.0,
+        }
+
+        # Modos do visual. Ajustamos principalmente segmentação e grid.
+        self.mode_configs = mode_configs or {
+            "normal": {
+                "segmentacao": self.visual_targets_normal.get("segmentacao", 8.0),
+                "grid": self.visual_targets_normal.get("grid", 6.0),
+                "deteccao": self.visual_targets_normal.get("deteccao", 5.0),
+                "publicacao": self.visual_targets_normal.get("publicacao", 15.0),
+                "analise": self.visual_targets_normal.get("analise", 8.0),
+            },
+            "eco": {
+                "segmentacao": 4.0,
+                "grid": 4.0,
+                "deteccao": 5.0,
+                "publicacao": 10.0,
+                "analise": 6.0,
+            },
+            "safe": {
+                "segmentacao": 2.0,
+                "grid": 2.0,
+                "deteccao": 3.0,
+                "publicacao": 5.0,
+                "analise": 4.0,
+            },
+            "critical": {
+                "segmentacao": 0.5,
+                "grid": 1.0,
+                "deteccao": 2.0,
+                "publicacao": 5.0,
+                "analise": 2.0,
+            },
+        }
+
+        self.bad_cycles_to_degrade = int(bad_cycles_to_degrade)
+        self.good_cycles_to_recover = int(good_cycles_to_recover)
+
+        self.min_data_age_s = float(min_data_age_s)
+        self.max_data_age_s = float(max_data_age_s)
+
+        self._mode = "normal"
+        self._desired_mode = "normal"
+
+        self._bad_cycles = 0
+        self._good_cycles = 0
+
+        self._last_update = 0.0
+        self._last_log = 0.0
+
+        self._last_weed_perf = {}
+        self._last_health = {
+            "health": 1.0,
+            "reason": "init",
+            "fps": {},
+            "ratios": {},
+            "gpu_ms": 0.0,
+            "data_age_s": None,
+            "data_ok": False,
+        }
+
+        self.log_periodic = bool(log_periodic)
+        self.log_warnings = bool(log_warnings)
+        self._last_warning_reason = None
+
+    # ==========================================================
+    # API pública
+    # ==========================================================
+
+    def update_from_redis(self):
+        """
+        Lê Redis e atualiza modo.
+        Seguro para chamar várias vezes; respeita update_interval_s.
+        """
+        now = time.time()
+
+        if not self.enabled:
+            self._mode = "normal"
+            return self._last_health
+
+        if now - self._last_update < self.update_interval_s:
+            return self._last_health
+
+        self._last_update = now
+
+        weed_ctx = self._read_weed_context()
+        health = self._evaluate_weed_health(weed_ctx)
+
+        self._last_weed_perf = weed_ctx or {}
+        self._last_health = health
+
+        desired = self._choose_desired_mode(health)
+        self._apply_hysteresis(desired)
+
+        if self.log_periodic and (now - self._last_log >= self.log_interval_s):
+            self._last_log = now
+            self._log_status()
+
+        if self.log_warnings:
+            self._log_warning_if_needed()
+
+        return self._last_health
+
+    def get_mode(self) -> str:
+        return self._mode
+
+    def get_health(self) -> Dict[str, Any]:
+        return dict(self._last_health)
+
+    def get_fps(self, task_name: str, fallback: Optional[float] = None) -> float:
+        """
+        Retorna FPS atual para uma tarefa do visual_worker.
+
+        task_name esperado:
+        - segmentacao
+        - grid
+        - deteccao
+        - publicacao
+        - analise
+        """
+        if not self.enabled:
+            if fallback is not None:
+                return float(fallback)
+            return float(self.visual_targets_normal.get(task_name, 1.0))
+
+        cfg = self.mode_configs.get(self._mode, self.mode_configs["normal"])
+        fps = cfg.get(task_name)
+
+        if fps is None:
+            fps = fallback if fallback is not None else self.visual_targets_normal.get(task_name, 1.0)
+
+        try:
+            return max(0.0, float(fps))
+        except Exception:
+            return 1.0
+
+    def allow(self, task_name: str) -> bool:
+        """
+        Por enquanto é simples:
+        - se FPS da tarefa <= 0, bloqueia.
+        - caso contrário, permite.
+        """
+        return self.get_fps(task_name, fallback=1.0) > 0.01
+
+    def sleep_for_task(self, task_name: str, t0_wall: float, fallback_fps: float):
+        """
+        Sleep adaptativo para usar no finally dos loops.
+        """
+        fps = self.get_fps(task_name, fallback=fallback_fps)
+
+        if fps <= 0.01:
+            time.sleep(0.25)
+            return
+
+        elapsed = time.time() - t0_wall
+        period = 1.0 / max(fps, 0.01)
+        time.sleep(max(0.0, period - elapsed))
+
+    # ==========================================================
+    # Leitura Redis
+    # ==========================================================
+
+    def _read_weed_context(self) -> Dict[str, Any]:
+        """
+        Leitura robusta do contexto do weed_worker.
+
+        Ajuste o nome da chave se no seu CtxKey estiver diferente.
+        """
+        try:
+            from shared.contexto_global_redis import ContextoGlobalRedis, CtxKey
+
+            # Tenta nomes prováveis sem quebrar se algum não existir.
+            possible_attrs = [
+                "DadosWeedWorker",
+                "DadosErvasWorker",
+                "DadosWeed",
+                "WeedWorker",
+            ]
+
+            for attr in possible_attrs:
+                if hasattr(CtxKey, attr):
+                    key = getattr(CtxKey, attr)
+                    ctx = ContextoGlobalRedis.get(key, {})
+                    if isinstance(ctx, dict) and ctx:
+                        return ctx
+
+            # Fallback opcional caso seu Redis aceite string.
+            try:
+                ctx = ContextoGlobalRedis.get("DadosWeedWorker", {})
+                if isinstance(ctx, dict):
+                    return ctx
+            except Exception:
+                pass
+
+        except Exception as e:
+            self._log(f"[GPU_CTRL] erro lendo contexto weed: {e}")
+
+        return {}
+
+    # ==========================================================
+    # Avaliação de saúde
+    # ==========================================================
+
+    def _evaluate_weed_health(self, ctx: Dict[str, Any]) -> Dict[str, Any]:
+        now = time.time()
+
+        if not isinstance(ctx, dict) or not ctx:
+            return {
+                "health": 0.0,
+                "reason": "sem_ctx_weed",
+                "fps": {},
+                "ratios": {},
+                "gpu_ms": 0.0,
+                "data_age_s": None,
+                "data_ok": False,
+            }
+
+        perf = self._extract_perf_root(ctx)
+
+        ts = self._extract_timestamp(ctx, perf)
+        data_age_s = None
+
+        if ts:
+            data_age_s = max(0.0, now - float(ts))
+
+        data_ok = True
+        if data_age_s is not None:
+            data_ok = self.min_data_age_s <= data_age_s <= self.max_data_age_s
+
+        fps_tensor = self._extract_loop_fps(perf, ["tensor", "tensores"])
+        fps_inf = self._extract_loop_fps(perf, ["inferencia", "inf", "infer"])
+        fps_det = self._extract_loop_fps(perf, ["deteccao", "det", "detector"])
+
+        gpu_ms = self._extract_gpu_ms(perf)
+
+        fps = {
+            "tensor": fps_tensor,
+            "inferencia": fps_inf,
+            "deteccao": fps_det,
+        }
+
+        ratios = {}
+        for name, target in self.weed_targets.items():
+            real = fps.get(name, 0.0)
+            target = max(float(target), 0.01)
+            ratios[name] = max(0.0, min(1.5, float(real) / target))
+
+        # O gargalo manda. Se um deles despencou, saúde despenca.
+        valid_ratios = [v for v in ratios.values() if v is not None]
+        health = min(valid_ratios) if valid_ratios else 0.0
+
+        reason = "ok"
+
+        if not data_ok:
+            health = min(health, 0.40)
+            reason = "ctx_weed_desatualizado"
+
+        if fps_inf <= 0.1:
+            health = 0.0
+            reason = "weed_sem_inferencia"
+
+        # Latência GPU como alarme extra.
+        # Não derruba direto para zero, mas limita a saúde.
+        if gpu_ms >= 120:
+            health = min(health, 0.35)
+            reason = "gpu_ms_muito_alto"
+        elif gpu_ms >= 95:
+            health = min(health, 0.60)
+            reason = "gpu_ms_alto"
+        elif gpu_ms >= 80:
+            health = min(health, 0.80)
+            reason = "gpu_ms_moderado"
+
+        return {
+            "health": float(health),
+            "reason": reason,
+            "fps": fps,
+            "ratios": ratios,
+            "gpu_ms": float(gpu_ms or 0.0),
+            "data_age_s": data_age_s,
+            "data_ok": bool(data_ok),
+        }
+
+    def _extract_perf_root(self, ctx: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Aceita formatos variados:
+        - ctx["performance_weed"]
+        - ctx["performance"]
+        - ctx direto com loops
+        """
+        for key in ["performance_weed", "performance", "perf", "performance_visual"]:
+            val = ctx.get(key)
+            if isinstance(val, dict):
+                return val
+
+        return ctx
+
+    def _extract_timestamp(self, ctx: Dict[str, Any], perf: Dict[str, Any]) -> Optional[float]:
+        for root in [perf, ctx]:
+            for key in ["ts", "timestamp", "ts_analise", "ultima_chamada"]:
+                val = root.get(key)
+                if isinstance(val, (int, float)) and val > 0:
+                    return float(val)
+        return None
+
+    def _extract_loop_fps(self, perf: Dict[str, Any], aliases) -> float:
+        loops = perf.get("loops", {})
+
+        if isinstance(loops, dict):
+            for alias in aliases:
+                loop = loops.get(alias)
+                if isinstance(loop, dict):
+                    fps = loop.get("fps_real", loop.get("fps", None))
+                    if fps is not None:
+                        try:
+                            return float(fps)
+                        except Exception:
+                            pass
+
+        # Fallbacks planos
+        for alias in aliases:
+            for key in [
+                f"fps_{alias}",
+                f"{alias}_fps",
+                alias,
+            ]:
+                val = perf.get(key)
+                if isinstance(val, (int, float)):
+                    return float(val)
+
+        return 0.0
+
+    def _extract_gpu_ms(self, perf: Dict[str, Any]) -> float:
+        loops = perf.get("loops", {})
+
+        candidates = []
+
+        if isinstance(loops, dict):
+            for name in ["inferencia", "inf", "infer"]:
+                loop = loops.get(name)
+                if not isinstance(loop, dict):
+                    continue
+
+                metrics = loop.get("metrics_ms", {})
+                if isinstance(metrics, dict):
+                    for metric_name in ["gpu_ms", "infer_gpu_ms", "infer_ms"]:
+                        metric = metrics.get(metric_name)
+                        if isinstance(metric, dict):
+                            val = metric.get("med", metric.get("avg", metric.get("last")))
+                            if isinstance(val, (int, float)):
+                                candidates.append(float(val))
+                        elif isinstance(metric, (int, float)):
+                            candidates.append(float(metric))
+
+        for key in ["gpu_ms", "infer_gpu_ms", "weed_gpu_ms"]:
+            val = perf.get(key)
+            if isinstance(val, (int, float)):
+                candidates.append(float(val))
+
+        return candidates[0] if candidates else 0.0
+
+    # ==========================================================
+    # Decisão de modo
+    # ==========================================================
+
+    def _choose_desired_mode(self, health: Dict[str, Any]) -> str:
+        h = float(health.get("health", 0.0))
+
+        if h >= 0.90:
+            return "normal"
+        if h >= 0.75:
+            return "eco"
+        if h >= 0.45:
+            return "safe"
+        return "critical"
+
+    def _mode_index(self, mode: str) -> int:
+        try:
+            return self.MODES.index(mode)
+        except ValueError:
+            return self.MODES.index("normal")
+
+    def _apply_hysteresis(self, desired: str):
+        current_i = self._mode_index(self._mode)
+        desired_i = self._mode_index(desired)
+
+        self._desired_mode = desired
+
+        # Quanto maior o índice, mais leve/restritivo? Aqui:
+        # critical=0, safe=1, eco=2, normal=3.
+        if desired_i < current_i:
+            # Piorar modo.
+            self._bad_cycles += 1
+            self._good_cycles = 0
+
+            if self._bad_cycles >= self.bad_cycles_to_degrade:
+                old = self._mode
+                self._mode = desired
+                self._bad_cycles = 0
+                self._log(f"[GPU_CTRL] modo visual: {old} -> {self._mode}")
+
+        elif desired_i > current_i:
+            # Melhorar modo.
+            self._good_cycles += 1
+            self._bad_cycles = 0
+
+            if self._good_cycles >= self.good_cycles_to_recover:
+                old = self._mode
+                self._mode = desired
+                self._good_cycles = 0
+                self._log(f"[GPU_CTRL] modo visual: {old} -> {self._mode}")
+
+        else:
+            self._bad_cycles = 0
+            self._good_cycles = 0
+
+    # ==========================================================
+    # Logs
+    # ==========================================================
+
+    def _log_status(self):
+        h = self._last_health
+        fps = h.get("fps", {})
+        ratios = h.get("ratios", {})
+
+        self._log(
+            "[GPU_CTRL] "
+            f"mode={self._mode} desired={self._desired_mode} "
+            f"health={h.get('health', 0.0):.2f} reason={h.get('reason')} "
+            f"weed_fps tensor={fps.get('tensor', 0.0):.1f} "
+            f"inf={fps.get('inferencia', 0.0):.1f} "
+            f"det={fps.get('deteccao', 0.0):.1f} "
+            f"ratios tensor={ratios.get('tensor', 0.0):.2f} "
+            f"inf={ratios.get('inferencia', 0.0):.2f} "
+            f"det={ratios.get('deteccao', 0.0):.2f} "
+            f"gpu={h.get('gpu_ms', 0.0):.1f}ms "
+            f"visual_fps seg={self.get_fps('segmentacao'):.1f} "
+            f"grid={self.get_fps('grid'):.1f} "
+            f"det={self.get_fps('deteccao'):.1f}"
+        )
+
+    def _log(self, msg: str):
+        if self.mostrar_log:
+            try:
+                self.mostrar_log(msg)
+            except Exception:
+                pass
+
+    def _log_warning_if_needed(self):
+        h = self._last_health
+        reason = h.get("reason", "ok")
+        health = float(h.get("health", 1.0) or 0.0)
+
+        # Só avisa quando é algo realmente relevante.
+        warning_reason = None
+
+        if reason not in ["ok", "init"]:
+            warning_reason = reason
+        elif health < 0.45:
+            warning_reason = "weed_health_critical"
+        elif health < 0.75:
+            warning_reason = "weed_health_low"
+
+        if warning_reason is None:
+            self._last_warning_reason = None
+            return
+
+        # Evita repetir o mesmo aviso em todo ciclo.
+        if warning_reason == self._last_warning_reason:
+            return
+
+        self._last_warning_reason = warning_reason
+
+        fps = h.get("fps", {})
+        self._log(
+            "[GPU_CTRL][WARN] "
+            f"{warning_reason} | "
+            f"mode={self._mode} desired={self._desired_mode} "
+            f"health={health:.2f} reason={reason} "
+            f"weed_fps tensor={fps.get('tensor', 0.0):.1f} "
+            f"inf={fps.get('inferencia', 0.0):.1f} "
+            f"det={fps.get('deteccao', 0.0):.1f} "
+            f"gpu={h.get('gpu_ms', 0.0):.1f}ms"
+        )
+    

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/shared/perf_monitor.py

@@ -0,0 +1,159 @@
+import time
+import threading
+from collections import defaultdict, deque
+import numpy as np
+
+
+class VisualPerfMonitor:
+    def __init__(self, janela=120):
+        self.janela = janela
+        self.lock = threading.RLock()
+        self.eventos = defaultdict(lambda: deque(maxlen=janela))
+        self.contadores = defaultdict(int)
+        self.ultimo_log = 0.0
+
+    def tick(self, nome, **dados):
+        agora = time.time()
+        item = {
+            "ts": agora,
+            **dados
+        }
+
+        with self.lock:
+            self.eventos[nome].append(item)
+            self.contadores[f"{nome}_count"] += 1
+
+    def inc(self, nome, n=1):
+        with self.lock:
+            self.contadores[nome] += n
+
+    def _stats_lista(self, valores):
+        valores = [v for v in valores if v is not None and np.isfinite(v)]
+        if not valores:
+            return {
+                "min": 0.0,
+                "med": 0.0,
+                "p95": 0.0,
+                "max": 0.0
+            }
+
+        arr = np.asarray(valores, dtype=np.float32)
+        return {
+            "min": float(np.min(arr)),
+            "med": float(np.mean(arr)),
+            "p95": float(np.percentile(arr, 95)),
+            "max": float(np.max(arr))
+        }
+
+    def resumo_loop(self, nome):
+        with self.lock:
+            evs = list(self.eventos.get(nome, []))
+
+        if len(evs) < 2:
+            return {
+                "fps_real": 0.0,
+                "periodo_ms": {},
+                "latencia_ms": {},
+                "idade_ms": {},
+                "metrics_ms": {},
+                "n": len(evs),
+            }
+
+        tss = [e["ts"] for e in evs]
+        dts = np.diff(tss)
+
+        fps_real = 1.0 / max(float(np.mean(dts)), 1e-6)
+
+        latencias = [e.get("latencia_ms") for e in evs]
+        idades = [e.get("idade_frame_ms") for e in evs]
+
+        metrics_ms = {}
+
+        ignorar = {
+            "ts",
+            "frame_ts",
+            "depth_ts",
+            "seg_ts",
+            "det_ts",
+            "seq",
+            "seq_delta",
+            "frames_descartados",
+            "n_pkts_getall",
+            "valido",
+            "n_dets",
+        }
+
+        for k in evs[-1].keys():
+            if k in ignorar:
+                continue
+
+            if not (
+                k.endswith("_ms")
+                or k in ["latencia_ms", "idade_frame_ms"]
+            ):
+                continue
+
+            vals = []
+            for e in evs:
+                v = e.get(k)
+                if isinstance(v, (int, float)) and np.isfinite(v):
+                    vals.append(float(v))
+
+            if vals:
+                metrics_ms[k] = self._stats_lista(vals)
+
+        ultimo = evs[-1] if evs else {}
+
+        return {
+            "fps_real": float(fps_real),
+            "periodo_ms": self._stats_lista([dt * 1000.0 for dt in dts]),
+            "latencia_ms": self._stats_lista(latencias),
+            "idade_ms": self._stats_lista(idades),
+            "metrics_ms": metrics_ms,
+            "last": ultimo,
+            "n": len(evs),
+        }
+
+    def resumo(self):
+        nomes = [
+            "camera_rgb",
+            "camera_depth",
+            "tensor",
+            "inferencia",
+            "segmentacao",
+            "deteccao",
+            "grid",
+            "stream",
+            "publicacao",
+        ]
+
+        saida = {
+            "timestamp": time.time(),
+            "loops": {},
+            "contadores": dict(self.contadores)
+        }
+
+        for nome in nomes:
+            saida["loops"][nome] = self.resumo_loop(nome)
+
+        # Sincronismo RGB/depth
+        with self.lock:
+            rgb = list(self.eventos.get("camera_rgb", []))
+            depth = list(self.eventos.get("camera_depth", []))
+
+        if rgb and depth:
+            rgb_ts = rgb[-1].get("frame_ts_host", rgb[-1].get("ts"))
+            depth_ts = depth[-1].get("frame_ts_host", depth[-1].get("ts"))
+            saida["sync"] = {
+                "rgb_depth_dt_ms": float(abs(rgb_ts - depth_ts) * 1000.0),
+                "rgb_age_ms": float((time.time() - rgb_ts) * 1000.0),
+                "depth_age_ms": float((time.time() - depth_ts) * 1000.0),
+            }
+        else:
+            saida["sync"] = {
+                "rgb_depth_dt_ms": None,
+                "rgb_age_ms": None,
+                "depth_age_ms": None,
+            }
+
+        return saida

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/visual_worker/config.py

@@ -40,50 +40,80 @@ _CONFIG_LOCK = threading.Lock()
 def load_seg_config(force_reload=False):
     global _CONFIG_CACHE, _CONFIG_MTIME
     with _CONFIG_LOCK:
-        #try:
-        #    mtime = os.path.getmtime(_CONFIG_PATH)
-        #    if force_reload or _CONFIG_CACHE is None or mtime != _CONFIG_MTIME:
-        #        with open(_CONFIG_PATH, "r", encoding="utf-8") as f:
-        #            _CONFIG_CACHE = json.load(f)
-        #            _CONFIG_MTIME = mtime
-        #except Exception as e:
-        #    mostrar_log(f"Erro ao ler config: {e}")
-        #    if _CONFIG_CACHE is None:
-        #        # Valores default se der ruim no primeiro load
-        #        _CONFIG_CACHE = {
-        #            "debug_visual": True,
-        #            "frames_consecutivos": 3,
-        #            "frames_histerese": 2,
-        #            "min_area_px": 400, 
-        #            "max_area_frac": 0.2,
-        #            "area_atuacao_bicos": 0.1,
-        #            "ia_roi_begin": 0.0,
-        #            "ia_roi_size": 1.0,
-        #            "ia_resolution": [512,288],
-        #            "erva_top_band_frac": 0.30,
-        #            "erva_frac_ema": 0.3,
-        #            "erva_thresh_vel_gain": 0.4,
-        #            "min_frac_erva_global_on": 0.0020,
-        #            "min_frac_erva_global_off": 0.0015,
-        #            "min_frac_erva_top_on": 0.0015,
-        #            "min_frac_erva_top_off": 0.0010,
-        #            "min_frac_erva_por_bico": 0.02,
-        #            "usar_morfologia": True,
-        #            "kernel_morf": 3
-        #        }
         _CONFIG_CACHE = {
             "debug_visual": False,
+            "debug_perf": False,
             "ia_roi_begin": 0.0,
             "ia_roi_size": 1.0,
+
+            "analise_fps": 8.0,
+            "grid_fps": 5.0,
+            "inferencia_fps": 8.0,
+            "deteccao_fps": 5.0,
+            "publicacao_fps": 15.0,
+            "gpu_priority": {
+                "enabled": True,
+                "update_interval_s": 1.0,
+                "log_interval_s": 3.0,
+                "log_periodic": False,
+                "log_warnings": True,
+
+                "bad_cycles_to_degrade": 3,
+                "good_cycles_to_recover": 5,
+                "max_data_age_s": 3.0,
+
+                "mode_configs": {
+                    "normal": {
+                        "segmentacao": 8.0,
+                        "grid": 6.0,
+                        "deteccao": 5.0,
+                        "publicacao": 15.0,
+                        "analise": 8.0
+                    },
+                    "eco": {
+                        "segmentacao": 4.0,
+                        "grid": 4.0,
+                        "deteccao": 5.0,
+                        "publicacao": 10.0,
+                        "analise": 6.0
+                    },
+                    "safe": {
+                        "segmentacao": 2.0,
+                        "grid": 2.0,
+                        "deteccao": 3.0,
+                        "publicacao": 5.0,
+                        "analise": 4.0
+                    },
+                    "critical": {
+                        "segmentacao": 0.5,
+                        "grid": 1.0,
+                        "deteccao": 2.0,
+                        "publicacao": 5.0,
+                        "analise": 2.0
+                    }
+                }
+            },
+
             "ia_resolution": [1024,576],
             "seg_every_n": 1,
-            "det_every_n": 3,
+            "det_every_n": 1,
+            "use_amp": True,
+            "use_channels_last": True,
+            "use_compact_aux": True,
+            "debug_timing": False,
+            "runtime_fast": True,
+            "gerar_mask_color": False,
+            "gerar_debug_status": False,
+            "usar_connected_components": True,
+            "usar_corridor_grid": True
         }
-        _CONFIG_CACHE["ia_model_path"] = ContextoGlobalRedis.get_equipamento().get("path_ia_model_ruas_seg")
-        _CONFIG_CACHE["ia_labelmap_path"] = ContextoGlobalRedis.get_equipamento().get("path_ia_labelmap_ruas_seg")
-        _CONFIG_CACHE["ia_norm_stats_path"] = ContextoGlobalRedis.get_equipamento().get("path_ia_norm_stats_ruas_seg")
-        _CONFIG_CACHE["ia_backbone"] = ContextoGlobalRedis.get_equipamento().get("ia_backbone_ruas_seg")
-
+        _equipamento = ContextoGlobalRedis.get_equipamento()
+        _CONFIG_CACHE["ia_mode"] = _equipamento.get("ia_mode_ruas")
+        _CONFIG_CACHE["ia_backbone"] = _equipamento.get("ia_backbone_ruas_seg")
+        _CONFIG_CACHE["ia_model_path"] = _equipamento.get("path_ia_model_ruas_seg")
+        _CONFIG_CACHE["ia_labelmap_path"] = _equipamento.get("path_ia_labelmap_ruas_seg")
+        _CONFIG_CACHE["ia_norm_stats_path"] = _equipamento.get("path_ia_norm_stats_ruas_seg")
+        
     return _CONFIG_CACHE
 
 def reload_seg_config():
@@ -97,7 +127,7 @@ def load_det_config():
         "ia_roi_size": 1.0,
         "ia_resolution": [300,300],
         "seg_every_n": 1,
-        "det_every_n": 3,
+        "det_every_n": 1,
         "ia_conf": 0.5,
         "classes": [
             "background",

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/visual_worker/processamento/segmentacao_semantica.py

@@ -16,6 +16,12 @@ class SegmentacaoManager:
     def __init__(self, color_map, classes):
         from visual_worker.config import load_seg_config
         config = load_seg_config()
+        self.runtime_fast = bool(config.get("runtime_fast", True))
+        self.gerar_mask_color = bool(config.get("gerar_mask_color", False))
+        self.gerar_debug_status = bool(config.get("gerar_debug_status", False))
+        self.usar_connected_components = bool(config.get("usar_connected_components", True))
+        self.usar_corridor_grid = bool(config.get("usar_corridor_grid", True))
+
         resolucao = config.get("ia_resolution")
         self.color_map = color_map
         self.classes = classes
@@ -49,40 +55,65 @@ class SegmentacaoManager:
         self._status_hist = deque(maxlen=self.max_len)
         self._status_final_hist = deque(maxlen=2)
 
+        self.debug_timing = bool(config.get("debug_timing_segmentacao", False))
 
-    def segmentar(self, predictions):
+
+    def segmentar(self, predictions, aux_result=None):
         try:
-            # 🔸 Constrói a máscara colorida e outras saídas com base na predictions já pronta
-            resultado = self._segmentar_predictions(predictions)
+            t0 = time.perf_counter()
+
+            resultado = self._segmentar_predictions(
+                predictions,
+                gerar_mask_color=self.gerar_mask_color
+            )
+            t_mask = time.perf_counter()
+
             if resultado is None:
                 print("[Erro] Segmentação vazia ou falhou")
-                return None
+                return None, self.log
 
             if predictions is None:
                 print("[Erro] Máscara de classes não encontrada no resultado")
-                return None
-            
-            self.dados_visuais = self._analisar_corredor_visual(predictions)
+                return None, self.log
+
+            self.dados_visuais = self._analisar_corredor_visual(
+                predictions,
+                aux_result=aux_result
+            )
+            t_ana = time.perf_counter()
+
             resultado["dados_visuais"] = self.dados_visuais
 
+            if getattr(self, "debug_timing", False):
+                print(
+                    f"SEGMENTACAO_MANAGER | "
+                    f"mask={(t_mask-t0)*1000:.1f}ms | "
+                    f"analise={(t_ana-t_mask)*1000:.1f}ms | "
+                    f"total={(t_ana-t0)*1000:.1f}ms"
+                )
+
             return resultado, self.log
 
         except Exception as e:
             self.log = f"❌ Erro na segmentação: {e}"
             return None, self.log
 
-    def _segmentar_predictions(self, predictions):
+    def _segmentar_predictions(self, predictions, gerar_mask_color=None):
         try:
-            self.pred_rgb[:] = self.lut[predictions]
+            if gerar_mask_color is None:
+                gerar_mask_color = self.gerar_mask_color
 
-            mask_color = self.pred_rgb
-            frame_color = encode_image_base64(mask_color)
+            mask_color = None
+
+            if gerar_mask_color:
+                self.pred_rgb[:] = self.lut[predictions]
+                mask_color = self.pred_rgb
 
             return {
                 "timestamp": time.time(),
                 "frame": {
                     "timestamp": time.time(),
-                    "frame": frame_color
+                    "frame": None
                 },
                 "mask_color": mask_color,
                 "classes": predictions
@@ -92,12 +123,15 @@ class SegmentacaoManager:
             print(f"Erro ao processar predictions: {e}")
             return None
 
-    def _analisar_corredor_visual(self, predictions, grid_rows_y_px=None, near_is_bottom=True):
+    def _analisar_corredor_visual(self, predictions, aux_result=None, grid_rows_y_px=None, near_is_bottom=True):
         # --- inputs/base ---
         self.predictions = predictions
         H, W = self.predictions.shape
         cx_img = W // 2
 
+        status_modelo_now, status_final_fast, status_before_fast, debug_fast = self._resolver_status_modelo_rapido(aux_result)
+        status_fast_ok = status_final_fast is not None
+
         mask_corredor = self._extrair_corredor_principal(self.predictions).astype(bool)
 
         # --- score de corredor por grid (robusto) ---
@@ -143,24 +177,9 @@ class SegmentacaoManager:
         if len(pts) >= 2:
             ys_fit = np.array([p[1] for p in pts], dtype=np.float32)
             xs_fit = np.array([p[0] for p in pts], dtype=np.float32)
-            n = len(ys_fit)
-
-            # pesos: linhas "mais perto" pesam mais
-            if near_is_bottom:
-                wts = np.linspace(2.0, 1.0, n, dtype=np.float32)
-            else:
-                wts = np.linspace(1.0, 2.0, n, dtype=np.float32)
-
-            Wm = np.diag(wts)
-            Y = ys_fit.reshape(-1, 1)
-            X = np.hstack([Y, np.ones_like(Y)])
-            XtW = X.T @ Wm
-            beta = np.linalg.pinv(XtW @ X) @ (XtW @ xs_fit)
-            a = float(beta[0])
-
-            ang_rad = np.arctan(a)
-            # unwrap simples
-            ang_rad = (ang_rad + np.pi) % (2*np.pi) - np.pi
+            a = self._fit_linha_ponderada(ys_fit, xs_fit, near_is_bottom=near_is_bottom)
+            if a is not None:
+                ang_rad = np.arctan(a)
             # EMA em graus
             deg = float(np.degrees(ang_rad))
             if getattr(self, "_ema_ang", None) is None:
@@ -189,8 +208,16 @@ class SegmentacaoManager:
             self._ema_lat = (1 - self._ema_alpha_lat) * self._ema_lat + self._ema_alpha_lat * float(erro_lateral_pct)
         lat_out = round(float(self._ema_lat), 3)
 
-        mask_nav = (self.predictions == ClassesSegmentacao.NAVEGAVEL.value)
-        status_now, status_final, status_before, debug = self.classificar_status_corredor(mask_nav)
+        if status_fast_ok:
+            status_final = status_final_fast
+            status_before = status_before_fast
+            debug = debug_fast
+        else:
+            mask_nav = (self.predictions == ClassesSegmentacao.NAVEGAVEL.value)
+            status_now, status_final, status_before, debug = self.resolver_status_corredor(
+                mask_nav=mask_nav,
+                aux_result=aux_result
+            )
 
         return {
             "timestamp": time.time(),
@@ -199,12 +226,82 @@ class SegmentacaoManager:
             "erro_angular": ang_out,
             "erro_lateral_pct": lat_out,
             "status_corredor": status_final.value,
+            "status_corredor_nome": status_final.name,
             "status_corredor_anterior": status_before.value,
+            "status_corredor_anterior_nome": status_before.name,
             "status_corredor_debug": debug,
             "centros_corredor": centros,
             "larguras_px": larguras,
             "confianca": round(float(score), 3),
         }
+    
+    def resolver_status_corredor(self, mask_nav: np.ndarray, aux_result=None):
+        if aux_result and aux_result.get("type") == "label":
+            conf = float(aux_result.get("label_conf", 0.0))
+            label_id = int(aux_result.get("label_id", StatusCarroMapa.Indefinido.value))
+
+            if conf >= 0.70:
+                status_now = StatusCarroMapa(label_id)
+                debug = {
+                    "origem_status": "modelo",
+                    "status_modelo": label_id,
+                    "status_modelo_conf": conf,
+                    "heuristica_executada": False,
+                }
+
+                self._status_hist.append((status_now, self._now()))
+                status_final = self._maioria_ultimos()
+                self._status_final_hist.append(status_final)
+                status_before = self._status_final_hist[0] if len(self._status_final_hist) > 1 else status_final
+
+                return status_now, status_final, status_before, debug
+        
+        status_heur, _, _, debug_heur = self.classificar_status_corredor(mask_nav)
+
+        status_modelo = None
+        label_conf = 0.0
+        label_probs = None
+        label_name = None
+
+        if aux_result and aux_result.get("type") == "label":
+            try:
+                label_id = int(aux_result.get("label_id"))
+                label_conf = float(aux_result.get("label_conf", 0.0))
+                label_probs = aux_result.get("label_probs")
+                label_name = aux_result.get("label_name")
+                status_modelo = StatusCarroMapa(label_id)
+            except Exception:
+                status_modelo = None
+
+        if status_modelo is not None and label_conf >= 0.70:
+            status_now = status_modelo
+            origem = "modelo"
+        elif status_modelo is not None and label_conf >= 0.45:
+            status_now = status_modelo
+            origem = "modelo_baixa_conf"
+        else:
+            status_now = status_heur
+            origem = "heuristica"
+
+        self._status_hist.append((status_now, self._now()))
+        status_final = self._maioria_ultimos()
+
+        self._status_final_hist.append(status_final)
+        status_before = self._status_final_hist[0] if len(self._status_final_hist) > 1 else status_final
+
+        debug = {
+            "origem_status": origem,
+            "status_modelo": status_modelo.value if status_modelo is not None else None,
+            "status_modelo_nome": status_modelo.name if status_modelo is not None else None,
+            "status_modelo_label_name": label_name,
+            "status_modelo_conf": round(label_conf, 4),
+            "status_modelo_probs": label_probs,
+            "status_heuristico": status_heur.value,
+            "status_heuristico_nome": status_heur.name,
+            "heuristica_debug": debug_heur,
+        }
+
+        return status_now, status_final, status_before, debug
 
     def _extrair_corredor_principal(self, mask_nav):
         H, W = mask_nav.shape
@@ -267,15 +364,22 @@ class SegmentacaoManager:
         row_h = H // rows
         col_w = W // cols
 
-        # fração de NAO NAVEGAVEL por célula
+        mask_nao = (mask_classes == ClassesSegmentacao.NAONAVEGAVEL.value).astype(np.uint8)
+        ii = cv2.integral(mask_nao)  # shape (H+1, W+1)
+
         frac = np.zeros((rows, cols), np.float32)
+
         for i in range(rows):
-            y0, y1 = i*row_h, H if i==rows-1 else (i+1)*row_h
+            y0 = i * row_h
+            y1 = H if i == rows - 1 else (i + 1) * row_h
+
             for j in range(cols):
-                x0, x1 = j*col_w, W if j==cols-1 else (j+1)*col_w
-                cell = mask_classes[y0:y1, x0:x1]
-                if cell.size:
-                    frac[i, j] = np.mean(cell == ClassesSegmentacao.NAONAVEGAVEL.value)
+                x0 = j * col_w
+                x1 = W if j == cols - 1 else (j + 1) * col_w
+
+                area = max(1, (y1 - y0) * (x1 - x0))
+                s = self._sum_integral(ii, y0, y1, x0, x1)
+                frac[i, j] = s / float(area)
 
         # limiares por faixa (modelo em “A”: mais chão embaixo, mais cana no topo)
         near_lado_min, near_canal_max = 0.05, 0.70
@@ -767,3 +871,63 @@ class SegmentacaoManager:
             return overlay
         except Exception as e:
             print(f"Erro ao gerar display_segmentation_debug: {e}")
+
+
+    def _resolver_status_modelo_rapido(self, aux_result):
+        if not aux_result or aux_result.get("type") != "label":
+            return None, None, None, None
+
+        conf = float(aux_result.get("label_conf", 0.0))
+        label_id = int(aux_result.get("label_id", StatusCarroMapa.Indefinido.value))
+
+        if conf < 0.70:
+            return None, None, None, None
+
+        status_now = StatusCarroMapa(label_id)
+
+        self._status_hist.append((status_now, self._now()))
+        status_final = self._maioria_ultimos()
+        self._status_final_hist.append(status_final)
+        status_before = self._status_final_hist[0] if len(self._status_final_hist) > 1 else status_final
+
+        debug = None
+        if self.gerar_debug_status:
+            debug = {
+                "origem_status": "modelo_fast",
+                "status_modelo": label_id,
+                "status_modelo_conf": conf,
+                "heuristica_executada": False,
+            }
+
+        return status_now, status_final, status_before, debug
+    
+    def _fit_linha_ponderada(self, ys_fit, xs_fit, near_is_bottom=True):
+        n = len(ys_fit)
+        if n < 2:
+            return None
+
+        if near_is_bottom:
+            w = np.linspace(2.0, 1.0, n, dtype=np.float32)
+        else:
+            w = np.linspace(1.0, 2.0, n, dtype=np.float32)
+
+        y = ys_fit.astype(np.float32)
+        x = xs_fit.astype(np.float32)
+
+        sw = np.sum(w)
+        y_mean = np.sum(w * y) / max(sw, 1e-6)
+        x_mean = np.sum(w * x) / max(sw, 1e-6)
+
+        dy = y - y_mean
+        dx = x - x_mean
+
+        denom = np.sum(w * dy * dy)
+        if abs(denom) < 1e-6:
+            return 0.0
+
+        a = np.sum(w * dy * dx) / denom
+        return float(a)
+
+    def _sum_integral(self, ii, y0, y1, x0, x1):
+        return ii[y1, x1] - ii[y0, x1] - ii[y1, x0] + ii[y0, x0]
+

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/weed_worker/config.py

@@ -42,47 +42,44 @@ _CONFIG_LOCK = threading.Lock()
 def load_seg_config(force_reload=False):
     global _CONFIG_CACHE, _CONFIG_MTIME
     with _CONFIG_LOCK:
-        #try:
-        #    mtime = os.path.getmtime(_CONFIG_PATH)
-        #    if force_reload or _CONFIG_CACHE is None or mtime != _CONFIG_MTIME:
-        #        with open(_CONFIG_PATH, "r", encoding="utf-8") as f:
-        #            _CONFIG_CACHE = json.load(f)
-        #            _CONFIG_MTIME = mtime
-        #except Exception as e:
-        #    mostrar_log(f"Erro ao ler config: {e}")
-        #    if _CONFIG_CACHE is None:
-        #        # Valores default se der ruim no primeiro load
-        #        _CONFIG_CACHE = {
-        #            "debug_visual": True,
-        #            "frames_consecutivos": 3,
-        #            "frames_histerese": 2,
-        #            "min_area_px": 400, 
-        #            "max_area_frac": 0.2,
-        #            "ia_roi_begin": 0.0,
-        #            "ia_roi_size": 1.0,
-        #            "ia_resolution": [512,288],
-        #            "erva_top_band_frac": 0.30,
-        #            "erva_frac_ema": 0.3,
-        #            "erva_thresh_vel_gain": 0.4,
-        #            "min_frac_erva_global_on": 0.0020,
-        #            "min_frac_erva_global_off": 0.0015,
-        #            "min_frac_erva_top_on": 0.0015,
-        #            "min_frac_erva_top_off": 0.0010,
-        #            "min_frac_erva_por_bico": 0.02,
-        #            "usar_morfologia": True,
-        #            "kernel_morf": 3
-        #        }
         _CONFIG_CACHE = {
             "debug_visual": False,
+            "debug_perf": False,
             "frames_consecutivos": 3,
             "frames_histerese": 2,
             "min_area_px": 400, 
             "max_area_frac": 0.2,
             "ia_roi_begin": 0.0,
             "ia_roi_size": 1.0,
-            "ia_resolution": [672,544],
+
+            "analise_fps": 15.0,
+            "inferencia_fps": 15.0,
+            "deteccao_fps": 15.0,
+            "tensor_fps": 18.0,
+            "publicacao_fps": 15.0,
+
+            "tipo_camera_solo": "multispectral",
+            "camera_width": 1280,
+            "camera_height": 800,
+            "fps": 40,
+            "ia_resolution": [1024,640],
             "ia_channels": 5,
-            "ia_use_ndvi": True,
+            "ia_input_channels": ["R", "G", "B", "RE", "NIR"],
+            "ia_use_ndvi": False,
+            "amp": True,
+            "fold_input_norm": True,
+            "runtime_mode": "target_direct",
+            "prediction_contract": "target_binary",
+            "output_mask_fullres": False,
+            "lowres_argmax": True,
+            "trust_input": True,
+            "channels_last": False,
+            "model_half": True,
+            "sync_for_timing": False,
+            "torch_compile": False,
+            "torch_compile_mode": "reduce-overhead",
+            "return_full_fast": False,
+
             "erva_top_band_frac": 0.30,
             "erva_frac_ema": 0.3,
             "erva_thresh_vel_gain": 0.4,
@@ -102,7 +99,38 @@ def load_seg_config(force_reload=False):
             "cana_halo_px": 5,
             "min_area_erva_px": 80,
             "erva_thresh_vel_gain_local": 0.6,
-            "k_roi_shift_px_per_vnorm": 24.0
+            "k_roi_shift_px_per_vnorm": 24.0,
+
+            "heads": {
+                "semantic": {
+                    "enabled": True,
+                    "type": "multiclass",
+                    "num_classes": 3,
+                    "classes": {"chao": 0, "cana": 1, "erva": 2},
+                    "ignore_index": 255
+                },
+                "vegetation": {
+                    "enabled": True,
+                    "type": "binary",
+                    "num_classes": 2,
+                    "classes": {"background": 0, "vegetation": 1},
+                    "ignore_index": 255
+                },
+                "cana": {
+                    "enabled": True,
+                    "type": "binary",
+                    "num_classes": 2,
+                    "classes": {"not_cana": 0, "cana": 1},
+                    "ignore_index": 255
+                },
+                "target": {
+                    "enabled": True,
+                    "type": "binary",
+                    "num_classes": 2,
+                    "classes": {"background": 0, "target": 1},
+                    "ignore_index": 255
+                },
+            }
         }
         dadosAtu = ContextoGlobalRedis.get_operacao().get("Atu", {})
         contexto = ContextoGlobalRedis.get_contexto()
@@ -113,12 +141,39 @@ def load_seg_config(force_reload=False):
         _CONFIG_CACHE["ia_model_path"] = ContextoGlobalRedis.get_equipamento().get("path_ia_model_ervas")
         _CONFIG_CACHE["ia_labelmap_path"] = ContextoGlobalRedis.get_equipamento().get("path_ia_labelmap_ervas")
         _CONFIG_CACHE["ia_norm_stats_path"] = ContextoGlobalRedis.get_equipamento().get("path_ia_norm_stats_ervas")
+        _CONFIG_CACHE["ia_module_params_path"] = ContextoGlobalRedis.get_equipamento().get("path_ia_module_params_ervas")
         _CONFIG_CACHE["ia_backbone"] = ContextoGlobalRedis.get_equipamento().get("ia_backbone_ervas")
-        _CONFIG_CACHE["faixa_atuacao_bicos"] = dadosAtu.get("percent_vertical_deteccao", 0.7)
 
+        _CONFIG_CACHE["faixa_atuacao_bicos"] = dadosAtu.get("percent_vertical_deteccao", 0.7)
         _CONFIG_CACHE["area_atuacao_bicos"] = dadosAtu.get("height_area_deteccao", 0.1)
         _CONFIG_CACHE["min_frac_erva_por_bico_on"] = dadosAtu.get("pct_erva_bico_on", 0.02)
         _CONFIG_CACHE["min_frac_erva_por_bico_off"] = dadosAtu.get("pct_erva_bico_off", 0.01)
+
+        # ============================================================
+        # Compatibilidade MultiSpecSegformerService
+        # ============================================================
+        input_channels = _CONFIG_CACHE.get("ia_input_channels", ["R", "G", "B", "RE", "NIR"])
+        if isinstance(input_channels, str):
+            input_channels = [c.strip().upper() for c in input_channels.split(",") if c.strip()]
+        else:
+            input_channels = [str(c).upper() for c in input_channels]
+
+        _CONFIG_CACHE["input_channels"] = input_channels
+        _CONFIG_CACHE["channels"] = int(_CONFIG_CACHE.get("ia_channels") or len(input_channels))
+
+        if _CONFIG_CACHE["channels"] != len(input_channels):
+            mostrar_log(
+                f"[WARN] ia_channels={_CONFIG_CACHE['channels']} diferente de "
+                f"len(input_channels)={len(input_channels)}. Usando len(input_channels)."
+            )
+            _CONFIG_CACHE["channels"] = len(input_channels)
+
+        _CONFIG_CACHE["backbone"] = _CONFIG_CACHE.get("ia_backbone") or "nvidia/mit-b1"
+        _CONFIG_CACHE["ckpt"] = _CONFIG_CACHE.get("ia_model_path")
+        _CONFIG_CACHE["norm_stats_path"] = _CONFIG_CACHE.get("ia_norm_stats_path")
+        _CONFIG_CACHE["module_calibration_json"] = (_CONFIG_CACHE.get("ia_module_params_path"))
+        _CONFIG_CACHE["camera_fps"] = int(_CONFIG_CACHE.get("fps"))
+
     return _CONFIG_CACHE
 
 def reload_seg_config():

AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/weed_worker/weed_detector.py

@@ -16,15 +16,41 @@ class WeedDetector:
         resolucao = config.get("ia_resolution")
         self.color_map = color_map
         self.classes = classes
+        self.runtime_mode = str(config.get("runtime_mode", "semantic")).lower()
+        self.prediction_contract = str(config.get("prediction_contract", "") or "").lower()
+        if not self.prediction_contract:
+            if self.runtime_mode in ("target_direct", "direct_target", "target_head", "target", "spray", "operational"):
+                self.prediction_contract = "target_binary"
+            else:
+                self.prediction_contract = "semantic"
+        if self.prediction_contract in ("target_binary", "binary_target", "target"):
+            self.classes = {"background": 0, "target": 1}
+            self.color_map = [
+                (30, 30, 30),      # background
+                (255, 70, 30),     # target
+            ]
+        else:
+            self.classes = classes
+            self.color_map = color_map
         self.resolucao = (resolucao[0], resolucao[1])
-        self.color_lut = np.array(self.color_map, np.uint8)
+        # LUT completa 0..255 em BGR, segura para OpenCV e ignore_id.
         self.lut = np.zeros((256, 3), dtype=np.uint8)
-        for i, color in enumerate(color_map):
-            #self.lut[i] = color
-            self.lut[i] = (color[2], color[1], color[0])  # converte pra (B, G, R)
+
+        for i, color in enumerate(self.color_map):
+            if i >= 256:
+                break
+
+            # color_map vem em RGB. OpenCV/debug/base64 atual usa BGR.
+            r, g, b = int(color[0]), int(color[1]), int(color[2])
+            self.lut[i] = (b, g, r)
+
         IGNORE_ID = 255
         self.lut[IGNORE_ID] = (255, 255, 255)
 
+        # Mantém compatibilidade com métodos antigos de debug.
+        # Antes era array curto; agora fica LUT completa também.
+        self.color_lut = self.lut
+
         self._reiniciar_deteccoes()
         self.use_mock = False
         self.img_mock = "C:\\ZendionInc\\agrobot_base\\AgroBase\\AgroBase\\bin\\x64\\Debug\\Operacoes\\25_07_2025_14_39_14\\Cam0\\85_rgb.jpeg"
@@ -52,11 +78,39 @@ class WeedDetector:
         self.ervas_registradas_bico = [set() for _ in range(qtd_bicos)]
         self.pred_rgb = np.empty((self.resolucao[1], self.resolucao[0], 3), dtype=np.uint8)
 
-        # LUTs
+        # LUTs de interpretação da máscara de entrada.
+        # semantic:
+        #   0=chao, 1=cana, 2=erva
+        # target_binary:
+        #   0=background, 1=target/alvo pulverizável
         self._is_weed = np.zeros(256, dtype=bool)
-        self._is_weed[int(ClassesSegmentacao.ERVA.value)] = True
         self._is_cane = np.zeros(256, dtype=bool)
-        self._is_cane[int(ClassesSegmentacao.CANA.value)] = True
+
+        runtime_mode = str(config.get("runtime_mode", getattr(self, "runtime_mode", "semantic"))).lower()
+        contract = str(config.get("prediction_contract", getattr(self, "prediction_contract", "")) or "").lower()
+
+        if not contract:
+            if runtime_mode in ("target_direct", "direct_target", "target_head", "target", "spray", "operational"):
+                contract = "target_binary"
+            else:
+                contract = "semantic"
+
+        self.prediction_contract = contract
+
+        if contract in ("target_binary", "binary_target", "target"):
+            # A máscara já é o alvo final: 1 = pulverizável.
+            self._is_weed[1] = True
+
+            # Não há classe cana nessa saída.
+            # O veto por cana fica naturalmente inativo porque mask_cana será tudo False.
+            self._is_cane[:] = False
+
+        elif contract in ("semantic", "semantic_3class"):
+            self._is_weed[int(ClassesSegmentacao.ERVA.value)] = True
+            self._is_cane[int(ClassesSegmentacao.CANA.value)] = True
+
+        else:
+            raise RuntimeError(f"prediction_contract inválido: {contract}")
 
         self._inv_total = 1.0 / (self.resolucao[1] * self.resolucao[0])
 
@@ -427,7 +481,7 @@ class WeedDetector:
         except Exception as e:
             print(f"Erro ao mostrar debug: {e}")
 
-    def _mostrar_debug_bicos_overlay(self, overlay_bgr, detections, atuacao_bicos, config=None, show: bool = True):
+    def _mostrar_debug_bicos_overlay(self, overlay_bgr, detections, atuacao_bicos, config=None, show: bool = True, metricas_perf=None):
         """
         Versão otimizada do debug: recebe o overlay BGR já montado
         (RGB + segmentação) e só desenha faixa, bicos, bboxes e HUD.
@@ -545,6 +599,12 @@ class WeedDetector:
                 (0, 255, 0),
                 2
             )
+            
+            metricas_perf = metricas_perf or {}
+            fps_infer = float(metricas_perf.get("fps_infer") or 0.0)
+            infer_ms = float(metricas_perf.get("infer_ms") or 0.0)
+            infer_gpu_ms = float(metricas_perf.get("infer_gpu_ms") or 0.0)
+            fps_loop = float(metricas_perf.get("fps_loop") or 0.0)
             cv2.putText(
                 self._dbg_img,
                 f"Dbg FPS: {dbg_fps:.1f}",
@@ -554,13 +614,34 @@ class WeedDetector:
                 (0, 255, 0),
                 2
             )
+            cv2.putText(
+                self._dbg_img,
+                f"Infer FPS: {fps_infer:.1f} | infer: {infer_ms:.1f}ms",
+                (10, 90),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.75,
+                (0, 255, 255),
+                2
+            )
+            cv2.putText(
+                self._dbg_img,
+                f"Loop FPS: {fps_loop:.1f} | GPU: {infer_gpu_ms:.1f}ms",
+                (10, 120),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.75,
+                (0, 255, 255),
+                2
+            )
 
             if (show):
                 cv2.imshow("Debug Weed Worker", self._dbg_img)
                 cv2.waitKey(1)
 
+            return self._dbg_img
+
         except Exception as e:
             print(f"Erro ao mostrar debug (overlay): {e}")
+            return None
 
     def _fps_update(self, last_ts_attr: str, ema_attr: str, alpha: float = 0.2):
         """Atualiza e retorna FPS (EMA) baseado no timestamp anterior salvo em self"""