679 lines
29 KiB
Python
679 lines
29 KiB
Python
import cv2
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import numpy as np
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import time
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import threading
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from scipy.optimize import linear_sum_assignment
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from scipy.spatial.distance import cosine
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from ultralytics import YOLO
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import onnxruntime as ort
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import os
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# ──────────────────────────────────────────────────────────────────────────────
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# CONFIGURACIÓN DEL SISTEMA
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# ──────────────────────────────────────────────────────────────────────────────
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USUARIO, PASSWORD, IP_DVR = "admin", "TCA200503", "192.168.1.65"
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SECUENCIA = [1, 7, 5, 8, 3, 6]
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os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;tcp"
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URLS = [f"rtsp://{USUARIO}:{PASSWORD}@{IP_DVR}:554/Streaming/Channels/{i}02" for i in SECUENCIA]
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ONNX_MODEL_PATH = "osnet_x0_25_msmt17.onnx"
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VECINOS = {
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"1": ["7"], "7": ["1", "5"], "5": ["7", "8"],
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"8": ["5", "3"], "3": ["8", "6"], "6": ["3"]
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}
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# ─── PARÁMETROS TÉCNICOS
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ASPECT_RATIO_MIN = 0.5
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ASPECT_RATIO_MAX = 4.0
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AREA_MIN_CALIDAD = 1200
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FRAMES_CALIDAD = 2
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TIEMPO_MAX_AUSENCIA = 800.0
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# ─── TIEMPOS DE VIDA DE TRACKERS
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TIEMPO_PACIENCIA_BORDE_CON_ID = 5.0 # Segundos antes de matar un ID conocido en borde
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TIEMPO_PACIENCIA_BORDE_SIN_ID = 1.0 # Segundos antes de matar un candidato en borde
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TIEMPO_PACIENCIA_INTERIOR = 8.0 # Vida para ID conocido en el interior
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TIEMPO_PACIENCIA_INTERIOR_NUEVO = 1.5 # Vida para candidato sin ID en interior
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# ─── RE-ADQUISICIÓN RÁPIDA (persona que sale y entra por la misma puerta)
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RADIO_REENTRADA = 80 # Píxeles: si reaparece a menos de esta distancia, re-adquirir
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TIEMPO_REENTRADA = 15.0 # Segundos máximos para considerar una reentrada
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# ─── UMBRALES DE RE-ID
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# ⚡ UMBRAL_REID_MISMA_CAM ajustado a 0.65 para recuperar estabilidad en sombras y giros
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UMBRAL_REID_MISMA_CAM = 0.65
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UMBRAL_REID_VECINO = 0.62
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UMBRAL_REID_NO_VECINO = 0.80
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MARGEN_MINIMO_REID = 0.07
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MAX_FIRMAS_MEMORIA = 15
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# ─── TIEMPO ENTRE TURNOS ACTIVOS (para el anti-fantasma dinámico)
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TIEMPO_TURNO_ROTATIVO = len(SECUENCIA) * 0.035
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# ─── COLORES Y FUENTE
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C_CANDIDATO = (150, 150, 150)
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C_LOCAL = (0, 255, 0)
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C_GLOBAL = (0, 165, 255)
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C_GRUPO = (0, 0, 255)
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C_APRENDIZAJE = (255, 255, 0)
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FUENTE = cv2.FONT_HERSHEY_SIMPLEX
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# ──────────────────────────────────────────────────────────────────────────────
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# INICIALIZACIÓN DEL MOTOR DEEP LEARNING (ONNX)
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# ──────────────────────────────────────────────────────────────────────────────
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print("Cargando motor de Re-Identificación (OSNet ONNX en CPU)...")
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try:
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ort_session = ort.InferenceSession(ONNX_MODEL_PATH, providers=['CPUExecutionProvider'])
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input_name = ort_session.get_inputs()[0].name
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print("OSNet cargado.")
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except Exception as e:
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print(f"ERROR FATAL: No se pudo cargar {ONNX_MODEL_PATH}. {e}")
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exit()
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MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 3, 1, 1)
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STD = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 3, 1, 1)
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# ──────────────────────────────────────────────────────────────────────────────
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# 1. MOTOR HÍBRIDO DE FIRMA
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# ──────────────────────────────────────────────────────────────────────────────
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def analizar_calidad(box):
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x1, y1, x2, y2 = box
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w, h = x2 - x1, y2 - y1
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if w <= 0 or h <= 0:
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return False
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return (ASPECT_RATIO_MIN < (h / w) < ASPECT_RATIO_MAX) and ((w * h) > AREA_MIN_CALIDAD)
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def preprocess_onnx(roi):
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img = cv2.resize(roi, (128, 256))
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = img.transpose(2, 0, 1).astype(np.float32) / 255.0
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img = np.expand_dims(img, axis=0)
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img = (img - MEAN) / STD
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return img
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def extraer_color_zonas(roi):
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h_roi = roi.shape[0]
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t1 = int(h_roi * 0.15)
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t2 = int(h_roi * 0.55)
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zonas = [roi[:t1, :], roi[t1:t2, :], roi[t2:, :]]
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def hist_zona(z):
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if z.size == 0:
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return np.zeros(16 * 8)
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hsv = cv2.cvtColor(z, cv2.COLOR_BGR2HSV)
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hist = cv2.calcHist([hsv], [0, 1], None, [16, 8], [0, 180, 0, 256])
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cv2.normalize(hist, hist)
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return hist.flatten()
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return np.concatenate([hist_zona(z) for z in zonas])
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def extraer_firma_hibrida(frame, box):
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try:
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x1, y1, x2, y2 = map(int, box)
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fh, fw = frame.shape[:2]
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x1_c = max(0, x1); y1_c = max(0, y1)
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x2_c = min(fw, x2); y2_c = min(fh, y2)
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roi = frame[y1_c:y2_c, x1_c:x2_c]
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if roi.size == 0 or roi.shape[0] < 20 or roi.shape[1] < 10:
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return None
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calidad_area = (x2_c - x1_c) * (y2_c - y1_c)
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# ⚡ ROI CRUDA: Pasamos la imagen tal cual, sin alterar su contraste artificialmente
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blob = preprocess_onnx(roi)
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blob_16 = np.zeros((16, 3, 256, 128), dtype=np.float32)
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blob_16[0] = blob[0]
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deep_feat = ort_session.run(None, {input_name: blob_16})[0][0].flatten()
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norma = np.linalg.norm(deep_feat)
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if norma > 0:
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deep_feat = deep_feat / norma
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color_feat = extraer_color_zonas(roi)
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return {'deep': deep_feat, 'color': color_feat, 'calidad': calidad_area}
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except Exception:
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return None
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def similitud_hibrida(f1, f2, cross_cam=False):
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if f1 is None or f2 is None:
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return 0.0
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sim_deep = max(0.0, 1.0 - cosine(f1['deep'], f2['deep']))
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if f1['color'].shape == f2['color'].shape and f1['color'].size > 1:
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L = len(f1['color']) // 3
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sim_head = max(0.0, float(cv2.compareHist(
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f1['color'][:L].astype(np.float32),
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f2['color'][:L].astype(np.float32), cv2.HISTCMP_CORREL)))
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sim_torso = max(0.0, float(cv2.compareHist(
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f1['color'][L:2*L].astype(np.float32),
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f2['color'][L:2*L].astype(np.float32), cv2.HISTCMP_CORREL)))
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sim_legs = max(0.0, float(cv2.compareHist(
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f1['color'][2*L:].astype(np.float32),
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f2['color'][2*L:].astype(np.float32), cv2.HISTCMP_CORREL)))
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sim_color = (0.10 * sim_head) + (0.60 * sim_torso) + (0.30 * sim_legs)
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else:
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sim_color = 0.0
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if cross_cam:
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return (sim_deep * 0.75) + (sim_color * 0.25)
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else:
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return (sim_deep * 0.85) + (sim_color * 0.15)
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# ──────────────────────────────────────────────────────────────────────────────
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# 2. KALMAN TRACKER
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# ──────────────────────────────────────────────────────────────────────────────
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class KalmanTrack:
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_count = 0
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def __init__(self, box, now):
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self.kf = cv2.KalmanFilter(7, 4)
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self.kf.measurementMatrix = np.array([
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[1,0,0,0,0,0,0], [0,1,0,0,0,0,0],
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[0,0,1,0,0,0,0], [0,0,0,1,0,0,0]], np.float32)
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self.kf.transitionMatrix = np.eye(7, dtype=np.float32)
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self.kf.transitionMatrix[0,4] = 1
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self.kf.transitionMatrix[1,5] = 1
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self.kf.transitionMatrix[2,6] = 1
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self.kf.processNoiseCov *= 0.03
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self.kf.statePost = np.zeros((7, 1), np.float32)
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self.kf.statePost[:4] = self._convert_bbox_to_z(box)
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self.local_id = KalmanTrack._count
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KalmanTrack._count += 1
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self.gid = None
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self.origen_global = False
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self.aprendiendo = False
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self.box = list(box)
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self.ts_creacion = now
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self.ts_ultima_deteccion = now
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self.time_since_update = 0
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self.en_grupo = False
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self.frames_buena_calidad = 0
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self.listo_para_id = False
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self.area_referencia = 0.0
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self.ultimo_aprendizaje = 0.0
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def _convert_bbox_to_z(self, bbox):
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w = bbox[2] - bbox[0]; h = bbox[3] - bbox[1]
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x = bbox[0] + w / 2.; y = bbox[1] + h / 2.
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return np.array([[x],[y],[w*h],[w/float(h+1e-6)]]).astype(np.float32)
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def _convert_x_to_bbox(self, x):
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cx = float(x[0].item()); cy = float(x[1].item())
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s = float(x[2].item()); r = float(x[3].item())
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w = np.sqrt(s * r); h = s / (w + 1e-6)
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return [cx-w/2., cy-h/2., cx+w/2., cy+h/2.]
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def predict(self, turno_activo=True):
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if (self.kf.statePost[6] + self.kf.statePost[2]) <= 0:
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self.kf.statePost[6] *= 0.0
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self.kf.predict()
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if turno_activo:
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self.time_since_update += 1
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self.aprendiendo = False
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self.box = self._convert_x_to_bbox(self.kf.statePre)
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return self.box
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def update(self, box, en_grupo, now):
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self.ts_ultima_deteccion = now
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self.time_since_update = 0
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self.box = list(box)
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self.en_grupo = en_grupo
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self.kf.correct(self._convert_bbox_to_z(box))
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if analizar_calidad(box) and not en_grupo:
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self.frames_buena_calidad += 1
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if self.frames_buena_calidad >= FRAMES_CALIDAD:
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self.listo_para_id = True
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elif self.gid is None:
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self.frames_buena_calidad = max(0, self.frames_buena_calidad - 1)
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# ──────────────────────────────────────────────────────────────────────────────
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# 3. MEMORIA GLOBAL
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# ──────────────────────────────────────────────────────────────────────────────
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class GlobalMemory:
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def __init__(self):
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self.db = {}
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self.next_gid = 100
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self.lock = threading.Lock()
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def _es_transito_posible(self, data, cam_destino, now):
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ultima_cam = str(data['last_cam'])
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cam_destino = str(cam_destino)
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dt = now - data['ts']
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if ultima_cam == cam_destino:
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return True
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vecinos = VECINOS.get(ultima_cam, [])
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if cam_destino in vecinos:
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return dt >= -0.5
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return dt >= 4.0
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def _sim_robusta(self, firma_nueva, firmas_guardadas, cross_cam=False):
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if not firmas_guardadas:
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return 0.0
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sims = sorted(
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[similitud_hibrida(firma_nueva, f, cross_cam=cross_cam)
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for f in firmas_guardadas],
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reverse=True
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)
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if len(sims) == 1:
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return sims[0]
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elif len(sims) <= 3:
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return (sims[0] * 0.60) + (sims[1] * 0.40)
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else:
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return (sims[0] * 0.50) + (sims[1] * 0.30) + (sims[2] * 0.20)
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def identificar_candidato(self, firma_hibrida, cam_id, now, active_gids):
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with self.lock:
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candidatos = []
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vecinos = VECINOS.get(str(cam_id), [])
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for gid, data in self.db.items():
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if gid in active_gids:
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continue
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dt = now - data['ts']
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if dt > TIEMPO_MAX_AUSENCIA:
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continue
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if not self._es_transito_posible(data, cam_id, now):
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continue
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if not data['firmas']:
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continue
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cross_cam = str(data['last_cam']) != str(cam_id)
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sim = self._sim_robusta(firma_hibrida, data['firmas'], cross_cam=cross_cam)
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misma_cam = str(data['last_cam']) == str(cam_id)
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es_vecino = str(data['last_cam']) in vecinos
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if misma_cam:
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umbral = UMBRAL_REID_MISMA_CAM
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elif es_vecino:
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umbral = UMBRAL_REID_VECINO
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else:
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umbral = UMBRAL_REID_NO_VECINO
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if sim > umbral:
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candidatos.append((sim, gid))
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if not candidatos:
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nid = self.next_gid
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self.next_gid += 1
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self._actualizar_sin_lock(nid, firma_hibrida, cam_id, now)
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return nid, False
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candidatos.sort(reverse=True)
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sim_1, best_gid = candidatos[0]
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if len(candidatos) >= 2:
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sim_2 = candidatos[1][0]
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margen = sim_1 - sim_2
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if margen < MARGEN_MINIMO_REID:
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nid = self.next_gid
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self.next_gid += 1
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self._actualizar_sin_lock(nid, firma_hibrida, cam_id, now)
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return nid, False
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self._actualizar_sin_lock(best_gid, firma_hibrida, cam_id, now)
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return best_gid, True
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def _actualizar_sin_lock(self, gid, firma_dict, cam_id, now):
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if gid not in self.db:
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self.db[gid] = {'firmas': [], 'last_cam': cam_id, 'ts': now}
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if firma_dict is not None:
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firmas_list = self.db[gid]['firmas']
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if not firmas_list:
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firmas_list.append(firma_dict)
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else:
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if firma_dict['calidad'] > (firmas_list[0]['calidad'] * 1.50):
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vieja_ancla = firmas_list[0]
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firmas_list[0] = firma_dict
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firma_dict = vieja_ancla
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if len(firmas_list) >= MAX_FIRMAS_MEMORIA:
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max_sim_interna = -1.0
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idx_redundante = 1
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for i in range(1, len(firmas_list)):
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sims_con_otras = [
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similitud_hibrida(firmas_list[i], firmas_list[j])
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for j in range(1, len(firmas_list)) if j != i
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]
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sim_promedio = float(np.mean(sims_con_otras)) if sims_con_otras else 0.0
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if sim_promedio > max_sim_interna:
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max_sim_interna = sim_promedio
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idx_redundante = i
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firmas_list[idx_redundante] = firma_dict
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else:
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firmas_list.append(firma_dict)
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self.db[gid]['last_cam'] = cam_id
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self.db[gid]['ts'] = now
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def actualizar(self, gid, firma, cam_id, now):
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with self.lock:
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self._actualizar_sin_lock(gid, firma, cam_id, now)
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# ──────────────────────────────────────────────────────────────────────────────
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# 4. GESTOR LOCAL
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# ──────────────────────────────────────────────────────────────────────────────
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def iou_overlap(boxA, boxB):
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xA = max(boxA[0], boxB[0]); yA = max(boxA[1], boxB[1])
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xB = min(boxA[2], boxB[2]); yB = min(boxA[3], boxB[3])
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inter = max(0, xB-xA) * max(0, yB-yA)
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areaA = (boxA[2]-boxA[0]) * (boxA[3]-boxA[1])
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areaB = (boxB[2]-boxB[0]) * (boxB[3]-boxB[1])
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return inter / (areaA + areaB - inter + 1e-6)
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class CamManager:
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def __init__(self, cam_id, global_mem):
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self.cam_id = cam_id
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self.global_mem = global_mem
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self.trackers = []
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def update(self, boxes, frame, now, turno_activo):
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for trk in self.trackers:
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trk.predict(turno_activo=turno_activo)
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if not turno_activo:
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return self.trackers
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matched, unmatched_dets, _ = self._asignar(boxes, now)
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fh, fw = frame.shape[:2]
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for t_idx, d_idx in matched:
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trk = self.trackers[t_idx]
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box = boxes[d_idx]
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en_grupo = any(
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other is not trk and iou_overlap(box, other.box) > 0.10
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for other in self.trackers
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)
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trk.update(box, en_grupo, now)
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active_gids_local = {t.gid for t in self.trackers if t.gid is not None}
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area_actual = (box[2] - box[0]) * (box[3] - box[1])
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if trk.gid is None and trk.listo_para_id:
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firma = extraer_firma_hibrida(frame, box)
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if firma is not None:
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gid, es_reid = self.global_mem.identificar_candidato(
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firma, self.cam_id, now, active_gids_local
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)
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trk.gid = gid
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trk.origen_global = es_reid
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trk.area_referencia = area_actual
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elif trk.gid is None and not trk.listo_para_id:
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|
firma_rapida = extraer_firma_hibrida(frame, box)
|
|
if firma_rapida is not None:
|
|
cx_nuevo = (box[0] + box[2]) / 2
|
|
cy_nuevo = (box[1] + box[3]) / 2
|
|
|
|
with self.global_mem.lock:
|
|
for gid_cand, data_cand in self.global_mem.db.items():
|
|
if gid_cand in active_gids_local:
|
|
continue
|
|
if str(data_cand.get('last_cam', '')) != str(self.cam_id):
|
|
continue
|
|
if (now - data_cand['ts']) > TIEMPO_REENTRADA:
|
|
continue
|
|
if not data_cand['firmas']:
|
|
continue
|
|
|
|
ultima_box = data_cand.get('last_box')
|
|
if ultima_box is None:
|
|
continue
|
|
|
|
cx_ult = (ultima_box[0] + ultima_box[2]) / 2
|
|
cy_ult = (ultima_box[1] + ultima_box[3]) / 2
|
|
distancia = np.sqrt((cx_nuevo-cx_ult)**2 + (cy_nuevo-cy_ult)**2)
|
|
|
|
if distancia < RADIO_REENTRADA:
|
|
sim = similitud_hibrida(firma_rapida, data_cand['firmas'][0])
|
|
if sim > UMBRAL_REID_MISMA_CAM:
|
|
trk.gid = gid_cand
|
|
trk.origen_global = True
|
|
trk.area_referencia = area_actual
|
|
trk.listo_para_id = True
|
|
self.global_mem._actualizar_sin_lock(
|
|
gid_cand, firma_rapida, self.cam_id, now
|
|
)
|
|
break
|
|
|
|
elif trk.gid is not None and not trk.en_grupo:
|
|
tiempo_ultima_firma = trk.ultimo_aprendizaje
|
|
if (now - tiempo_ultima_firma) > 1.5 and analizar_calidad(box):
|
|
x1b, y1b, x2b, y2b = map(int, box)
|
|
en_borde = (x1b < 15 or y1b < 15 or x2b > fw-15 or y2b > fh-15)
|
|
|
|
if not en_borde:
|
|
firma_nueva = extraer_firma_hibrida(frame, box)
|
|
if firma_nueva is not None:
|
|
with self.global_mem.lock:
|
|
if (trk.gid in self.global_mem.db
|
|
and self.global_mem.db[trk.gid]['firmas']):
|
|
|
|
firma_ancla = self.global_mem.db[trk.gid]['firmas'][0]
|
|
sim_coherencia = similitud_hibrida(firma_nueva, firma_ancla)
|
|
|
|
if sim_coherencia > 0.55:
|
|
es_coherente = True
|
|
for otro_gid, otro_data in self.global_mem.db.items():
|
|
if otro_gid == trk.gid or not otro_data['firmas']:
|
|
continue
|
|
sim_intruso = similitud_hibrida(
|
|
firma_nueva, otro_data['firmas'][0]
|
|
)
|
|
if sim_intruso > 0.55:
|
|
es_coherente = False
|
|
break
|
|
|
|
if es_coherente:
|
|
self.global_mem._actualizar_sin_lock(
|
|
trk.gid, firma_nueva, self.cam_id, now
|
|
)
|
|
trk.ultimo_aprendizaje = now
|
|
trk.aprendiendo = True
|
|
|
|
for trk in self.trackers:
|
|
if trk.time_since_update == 0 and trk.gid is not None:
|
|
with self.global_mem.lock:
|
|
if trk.gid in self.global_mem.db:
|
|
self.global_mem.db[trk.gid]['last_box'] = list(trk.box)
|
|
|
|
for d_idx in unmatched_dets:
|
|
self.trackers.append(KalmanTrack(boxes[d_idx], now))
|
|
|
|
vivos = []
|
|
for t in self.trackers:
|
|
x1, y1, x2, y2 = t.box
|
|
toca_borde = (x1 < 5 or y1 < 5 or x2 > fw-5 or y2 > fh-5)
|
|
tiempo_oculto = now - t.ts_ultima_deteccion
|
|
|
|
if toca_borde:
|
|
limite = TIEMPO_PACIENCIA_BORDE_CON_ID if t.gid else TIEMPO_PACIENCIA_BORDE_SIN_ID
|
|
else:
|
|
limite = TIEMPO_PACIENCIA_INTERIOR if t.gid else TIEMPO_PACIENCIA_INTERIOR_NUEVO
|
|
|
|
if tiempo_oculto < limite:
|
|
vivos.append(t)
|
|
|
|
self.trackers = vivos
|
|
return self.trackers
|
|
|
|
def _asignar(self, boxes, now):
|
|
n_trk = len(self.trackers)
|
|
n_det = len(boxes)
|
|
|
|
if n_trk == 0: return [], list(range(n_det)), []
|
|
if n_det == 0: return [], [], list(range(n_trk))
|
|
|
|
cost_mat = np.zeros((n_trk, n_det), dtype=np.float32)
|
|
|
|
for t, trk in enumerate(self.trackers):
|
|
for d, det in enumerate(boxes):
|
|
iou = iou_overlap(trk.box, det)
|
|
cx_t = (trk.box[0]+trk.box[2]) / 2
|
|
cy_t = (trk.box[1]+trk.box[3]) / 2
|
|
cx_d = (det[0]+det[2]) / 2
|
|
cy_d = (det[1]+det[3]) / 2
|
|
dist_norm = np.sqrt((cx_t-cx_d)**2 + (cy_t-cy_d)**2) / 550.0
|
|
|
|
area_trk = (trk.box[2]-trk.box[0]) * (trk.box[3]-trk.box[1])
|
|
area_det = (det[2]-det[0]) * (det[3]-det[1])
|
|
ratio_area = max(area_trk, area_det) / (min(area_trk, area_det) + 1e-6)
|
|
castigo_tam = (ratio_area - 1.0) * 0.4
|
|
|
|
tiempo_oculto = now - trk.ts_ultima_deteccion
|
|
if tiempo_oculto > (TIEMPO_TURNO_ROTATIVO * 2) and iou < 0.10:
|
|
fantasma_penalty = 5.0
|
|
else:
|
|
fantasma_penalty = 0.0
|
|
|
|
if iou >= 0.05 or dist_norm < 0.50:
|
|
cost_mat[t, d] = ((1.0 - iou)
|
|
+ (dist_norm * 2.0)
|
|
+ fantasma_penalty
|
|
+ castigo_tam)
|
|
else:
|
|
cost_mat[t, d] = 100.0
|
|
|
|
row_ind, col_ind = linear_sum_assignment(cost_mat)
|
|
matched, unmatched_dets, unmatched_trks = [], [], []
|
|
|
|
for r, c in zip(row_ind, col_ind):
|
|
if cost_mat[r, c] > 4.0:
|
|
unmatched_trks.append(r)
|
|
unmatched_dets.append(c)
|
|
else:
|
|
matched.append((r, c))
|
|
|
|
matched_t = {m[0] for m in matched}
|
|
matched_d = {m[1] for m in matched}
|
|
for t in range(n_trk):
|
|
if t not in matched_t: unmatched_trks.append(t)
|
|
for d in range(n_det):
|
|
if d not in matched_d: unmatched_dets.append(d)
|
|
|
|
return matched, unmatched_dets, unmatched_trks
|
|
|
|
# ──────────────────────────────────────────────────────────────────────────────
|
|
# 5. STREAM DE CÁMARA
|
|
# ──────────────────────────────────────────────────────────────────────────────
|
|
class CamStream:
|
|
def __init__(self, url):
|
|
self.url = url
|
|
self.cap = cv2.VideoCapture(url)
|
|
self.frame = None
|
|
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
|
|
threading.Thread(target=self._run, daemon=True).start()
|
|
|
|
def _run(self):
|
|
while True:
|
|
ret, f = self.cap.read()
|
|
if ret:
|
|
self.frame = f
|
|
time.sleep(0.01)
|
|
else:
|
|
time.sleep(2)
|
|
self.cap.open(self.url)
|
|
|
|
# ──────────────────────────────────────────────────────────────────────────────
|
|
# 6. DIBUJADO
|
|
# ──────────────────────────────────────────────────────────────────────────────
|
|
def dibujar_track(frame_show, trk):
|
|
try:
|
|
x1, y1, x2, y2 = map(int, trk.box)
|
|
except Exception:
|
|
return
|
|
|
|
if trk.gid is None:
|
|
color, label = C_CANDIDATO, f"?{trk.local_id}"
|
|
elif trk.en_grupo:
|
|
color, label = C_GRUPO, f"ID:{trk.gid} [grp]"
|
|
elif trk.aprendiendo:
|
|
color, label = C_APRENDIZAJE, f"ID:{trk.gid} [++]"
|
|
elif trk.origen_global:
|
|
color, label = C_GLOBAL, f"ID:{trk.gid} [re-id]"
|
|
else:
|
|
color, label = C_LOCAL, f"ID:{trk.gid}"
|
|
|
|
cv2.rectangle(frame_show, (x1, y1), (x2, y2), color, 2)
|
|
(tw, th), _ = cv2.getTextSize(label, FUENTE, 0.55, 1)
|
|
cv2.rectangle(frame_show, (x1, y1-th-6), (x1+tw+2, y1), color, -1)
|
|
cv2.putText(frame_show, label, (x1+1, y1-4), FUENTE, 0.55, (0, 0, 0), 1)
|
|
|
|
if trk.gid is None:
|
|
pct = min(trk.frames_buena_calidad / FRAMES_CALIDAD, 1.0)
|
|
bw = x2 - x1
|
|
cv2.rectangle(frame_show, (x1, y2+2), (x2, y2+7), (50, 50, 50), -1)
|
|
cv2.rectangle(frame_show, (x1, y2+2), (x1+int(bw*pct), y2+7), (0, 220, 220), -1)
|
|
|
|
# ──────────────────────────────────────────────────────────────────────────────
|
|
# 7. MAIN LOOP (para pruebas standalone)
|
|
# ──────────────────────────────────────────────────────────────────────────────
|
|
def main():
|
|
print("SmartSoft")
|
|
model = YOLO("yolov8n.pt")
|
|
global_mem = GlobalMemory()
|
|
managers = {str(c): CamManager(c, global_mem) for c in SECUENCIA}
|
|
cams = [CamStream(u) for u in URLS]
|
|
cv2.namedWindow("SmartSoft", cv2.WINDOW_AUTOSIZE)
|
|
|
|
idx = 0
|
|
while True:
|
|
now, tiles = time.time(), []
|
|
cam_ia = idx % len(cams)
|
|
|
|
for i, cam_obj in enumerate(cams):
|
|
frame = cam_obj.frame
|
|
cid = str(SECUENCIA[i])
|
|
|
|
if frame is None:
|
|
tiles.append(np.zeros((270, 480, 3), np.uint8))
|
|
continue
|
|
|
|
frame_show = cv2.resize(frame.copy(), (480, 270))
|
|
boxes = []
|
|
turno_activo = (i == cam_ia)
|
|
|
|
if turno_activo:
|
|
res = model.predict(
|
|
frame_show, conf=0.40, iou=0.50,
|
|
classes=[0], verbose=False, imgsz=480
|
|
)
|
|
if res[0].boxes:
|
|
boxes = res[0].boxes.xyxy.cpu().numpy().tolist()
|
|
|
|
tracks = managers[cid].update(boxes, frame_show, now, turno_activo)
|
|
|
|
for trk in tracks:
|
|
if trk.time_since_update == 0:
|
|
dibujar_track(frame_show, trk)
|
|
|
|
if turno_activo:
|
|
cv2.circle(frame_show, (460, 20), 6, (0, 0, 255), -1)
|
|
|
|
con_id = sum(1 for t in tracks if t.gid and t.time_since_update == 0)
|
|
cv2.putText(frame_show, f"CAM {cid} [{con_id} ID]",
|
|
(10, 28), FUENTE, 0.7, (255, 255, 255), 2)
|
|
tiles.append(frame_show)
|
|
|
|
if len(tiles) == 6:
|
|
cv2.imshow("SmartSoft", np.vstack([
|
|
np.hstack(tiles[0:3]),
|
|
np.hstack(tiles[3:6])
|
|
]))
|
|
|
|
idx += 1
|
|
if cv2.waitKey(1) == ord('q'):
|
|
break
|
|
|
|
cv2.destroyAllWindows()
|
|
|
|
if __name__ == "__main__":
|
|
main() |