abraham
762699f41e
- pet_telegram.py: cliente de Telegram con cola y hilo de envio propio, para que la red nunca bloquee la vision ni la escucha de audio. Reintentos con retroceso exponencial, respeto del retry_after ante limites de frecuencia y descarte de los avisos mas viejos si la cola se llena. - pet_alerts.py: punto unico de reparto de avisos. Toma el fotograma mas reciente, dibuja la caja de la mascota detectada y la marca de tiempo, guarda la captura en capturas/ y la manda a Telegram. Anti-repeticion de 10 s en consola y de ALERT_COOLDOWN (60 s) para capturas y Telegram. - scripts/telegram_check.py: valida el token, deduce el chat_id del ultimo mensaje recibido por el bot y manda mensaje y foto de prueba. - pet_speed.py: integra el reparto de avisos y anade capturas y estado de Telegram a las estadisticas. - Readme.md: seccion de Telegram y estado de la Fase B.
420 lines
16 KiB
Python
420 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Pet Monitor - Visión por computador con cámara IP Tapo (RTSP) + NPU Hailo.
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- Ingesta RTSP de vídeo en un hilo aparte, que descarta los fotogramas viejos
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y expone siempre el más reciente (evita la latencia acumulada típica de RTSP).
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- Detección de gato/perro (clases COCO 15 y 16) con YOLOv8s sobre la NPU Hailo
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y alerta de carrera según el desplazamiento del centroide entre fotogramas.
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- Escucha de ladridos sobre el audio de la propia cámara (ver pet_audio.py),
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en un segundo hilo y con su propia conexión RTSP.
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- Avisos por consola y a Telegram con captura anotada (ver pet_alerts.py).
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- Reconexión automática con backoff en todos los canales.
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Variables en .env (junto a este script):
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CAMERA_IP, CAMERA_USER, CAMERA_PW (obligatorias: cuenta de cámara Tapo)
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CAMERA_STREAM (opcional: stream2 = 720p, stream1 = 2K)
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CAMERA_RTSP_URL (opcional: URL RTSP completa, tiene prioridad)
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PET_AUDIO (opcional: 0 desactiva la escucha de ladridos)
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BARK_THRESHOLD, BARK_COOLDOWN (opcional: umbral y anti-repetición del ladrido)
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TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID (opcional: avisos a Telegram; el chat se
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deduce solo si no se indica)
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ALERT_COOLDOWN (opcional: segundos entre capturas de un aviso)
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SNAPSHOTS, SNAPSHOT_DIR (opcional: guardar las capturas en disco)
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Ejecución: uv run pet_speed.py
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"""
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import math
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import os
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import signal
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import threading
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import time
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import cv2
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import numpy as np
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from picamera2.devices.hailo import Hailo, hailo_architecture
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from pet_alerts import AlertDispatcher
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from pet_audio import AudioListener, BarkDetector
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from pet_common import build_rtsp_url, load_env, mask_url
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from pet_telegram import TelegramNotifier
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# --- Configuración -------------------------------------------------------
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# Clases COCO: 15 = Cat (Gato), 16 = Dog (Perro)
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TARGET_CLASSES = {15: "Gato", 16: "Perro"}
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CONF_THRESHOLD = 0.45 # Confianza mínima para considerar una detección
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SPEED_THRESHOLD = 0.30 # Velocidad normalizada por segundo (fracción del frame)
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DEFAULT_STREAM = "stream2" # stream1 = 2304x1296@15, stream2 = 1280x720@15
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STATS_INTERVAL = 10.0 # Segundos entre líneas de estadísticas
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BARK_THRESHOLD = 0.15 # Puntuación de Bark a partir de la cual se avisa
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BARK_COOLDOWN = 15.0 # Segundos mínimos entre dos avisos de ladrido
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ALERT_COOLDOWN = 60.0 # Segundos mínimos entre capturas del mismo tipo de aviso
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SNAPSHOT_DIR = "capturas"
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# Orden de color que espera el HEF. Los modelos de Hailo Model Zoo se compilan
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# para RGB; con PET_COLOR_ORDER=bgr se invierte (útil para comparar precisión).
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COLOR_ORDER = os.environ.get("PET_COLOR_ORDER", "rgb").lower()
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LETTERBOX_FILL = (114, 114, 114)
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# Latencia baja en RTSP: forzar TCP evita el jitter y las pérdidas de UDP.
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# (No se usa 'stimeout': fue eliminado en FFmpeg 6, que es el que trae OpenCV 4.10)
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os.environ.setdefault("OPENCV_FFMPEG_CAPTURE_OPTIONS", "rtsp_transport;tcp")
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# --- Utilidades ----------------------------------------------------------
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def get_hailo_model_path():
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"""Elige el HEF de YOLOv8s que corresponde a la arquitectura detectada."""
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arch = hailo_architecture()
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if arch == "HAILO8":
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candidates = ["/usr/share/hailo-models/yolov8s_h8.hef", "/usr/share/hailo-models/yolov8s_h8l.hef"]
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else:
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candidates = ["/usr/share/hailo-models/yolov8s_h8l.hef", "/usr/share/hailo-models/yolov8s_h8.hef"]
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for path in candidates:
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if os.path.exists(path):
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return path
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raise FileNotFoundError("No se encontró ningún modelo YOLOv8 HEF en /usr/share/hailo-models/")
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def letterbox(frame, width, height):
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"""
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Reescala manteniendo la relación de aspecto y rellena hasta width x height.
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Devuelve (imagen, escala, pad_x, pad_y) para poder deshacer la transformación.
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"""
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src_h, src_w = frame.shape[:2]
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scale = min(width / src_w, height / src_h)
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new_w, new_h = int(round(src_w * scale)), int(round(src_h * scale))
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resized = cv2.resize(frame, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
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pad_x, pad_y = (width - new_w) // 2, (height - new_h) // 2
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padded = cv2.copyMakeBorder(
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resized, pad_y, height - new_h - pad_y, pad_x, width - new_w - pad_x,
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cv2.BORDER_CONSTANT, value=LETTERBOX_FILL,
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)
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return padded, scale, pad_x, pad_y
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def box_to_source(box, scale, pad_x, pad_y, src_w, src_h):
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"""
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Convierte una caja normalizada del modelo (letterbox) a píxeles del frame
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original. Hailo NMS devuelve [ymin, xmin, ymax, xmax, confidence].
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"""
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ymin, xmin, ymax, xmax = box[:4]
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model_w = src_w * scale + 2 * pad_x
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model_h = src_h * scale + 2 * pad_y
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x1 = (xmin * model_w - pad_x) / scale
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x2 = (xmax * model_w - pad_x) / scale
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y1 = (ymin * model_h - pad_y) / scale
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y2 = (ymax * model_h - pad_y) / scale
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return (
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float(min(max(x1, 0.0), src_w)),
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float(min(max(y1, 0.0), src_h)),
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float(min(max(x2, 0.0), src_w)),
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float(min(max(y2, 0.0), src_h)),
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)
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# --- Ingesta de vídeo ----------------------------------------------------
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class RtspVideoStream:
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"""
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Hilo lector de RTSP: mantiene siempre el último frame disponible.
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Si el bucle principal va más lento que la cámara, los frames intermedios se
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descartan solos (no se encolan), así la detección trabaja sobre imagen en
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vivo y no sobre un buffer con retardo creciente.
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"""
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def __init__(self, url, reconnect_delay=2.0, max_reconnect_delay=30.0):
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self._url = url
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self._reconnect_delay = reconnect_delay
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self._max_reconnect_delay = max_reconnect_delay
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self._lock = threading.Lock()
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self._new_frame = threading.Event()
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self._stop = threading.Event()
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self._thread = None
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self._seq = 0
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self._frame = None
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self._timestamp = 0.0
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self.frames_read = 0
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self.reconnects = 0
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self.connected = False
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self.capture_fps = 0.0
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def start(self):
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self._thread = threading.Thread(target=self._loop, name="rtsp-reader", daemon=True)
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self._thread.start()
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return self
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def _open_capture(self):
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cap = cv2.VideoCapture(self._url, cv2.CAP_FFMPEG)
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cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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if not cap.isOpened():
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cap.release()
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return None
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return cap
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def _loop(self):
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cap = None
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delay = self._reconnect_delay
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window_start = time.time()
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window_frames = 0
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while not self._stop.is_set():
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if cap is None:
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cap = self._open_capture()
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if cap is None:
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self.connected = False
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print(f"[WARN] No hay stream RTSP; reintentando en {delay:.0f}s", flush=True)
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self._stop.wait(delay)
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delay = min(delay * 2, self._max_reconnect_delay)
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self.reconnects += 1
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continue
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self.connected = True
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delay = self._reconnect_delay
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window_start, window_frames = time.time(), 0
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print("[INFO] Stream RTSP conectado.", flush=True)
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ok, frame = cap.read()
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if not ok or frame is None:
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print("[WARN] Lectura RTSP interrumpida; reconectando...", flush=True)
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cap.release()
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cap = None
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self.connected = False
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self.reconnects += 1
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self._stop.wait(delay)
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continue
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now = time.time()
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window_frames += 1
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elapsed = now - window_start
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if elapsed >= 2.0:
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self.capture_fps = window_frames / elapsed
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window_start, window_frames = now, 0
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with self._lock:
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self._seq += 1
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self._frame = frame
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self._timestamp = now
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self.frames_read += 1
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self._new_frame.set()
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if cap is not None:
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cap.release()
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def wait_for_frame(self, last_seq, timeout=1.0):
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"""
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Bloquea hasta que haya un frame más nuevo que last_seq.
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Devuelve (seq, frame, timestamp) o (last_seq, None, None) si expira.
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"""
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deadline = time.time() + timeout
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while not self._stop.is_set() and time.time() < deadline:
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with self._lock:
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seq, frame, ts = self._seq, self._frame, self._timestamp
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if seq > last_seq and frame is not None:
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return seq, frame, ts
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self._new_frame.wait(0.05)
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self._new_frame.clear()
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return last_seq, None, None
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def latest_frame(self):
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"""
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Copia del último fotograma recibido, para las capturas de alerta.
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Se copia fuera del cerrojo porque el hilo lector reemplaza el array en
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lugar de modificarlo, así que la referencia no cambia bajo los pies.
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"""
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with self._lock:
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frame = self._frame
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return None if frame is None else frame.copy()
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def stop(self):
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self._stop.set()
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self._new_frame.set()
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if self._thread is not None:
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self._thread.join(timeout=5.0)
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# --- Arranque de los canales de aviso ------------------------------------
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def start_telegram(env):
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"""Arranca el envío a Telegram, o explica por qué no se puede."""
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token = env.get("TELEGRAM_BOT_TOKEN")
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if not token:
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print("[INFO] Telegram no configurado (falta TELEGRAM_BOT_TOKEN); los avisos solo saldrán por consola.")
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return None
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try:
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notifier = TelegramNotifier(token, env.get("TELEGRAM_CHAT_ID") or None).start()
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except Exception as exc: # noqa: BLE001
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print(f"[WARN] Telegram desactivado: {exc}")
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return None
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print("[INFO] Telegram activo.")
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return notifier
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def start_audio_listener(url, env, alerts):
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"""
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Arranca el hilo de audio, o explica por qué no se puede.
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Devolver None es un caso normal (no un error fatal): el monitor sigue
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funcionando solo con vídeo si falta el modelo o se desactiva a propósito.
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"""
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if env.get("PET_AUDIO", "1") == "0":
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print("[INFO] Escucha de ladridos desactivada (PET_AUDIO=0).")
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return None
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try:
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detector = BarkDetector()
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except Exception as exc: # noqa: BLE001
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print(f"[WARN] Audio desactivado: {exc}")
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return None
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threshold = float(env.get("BARK_THRESHOLD", BARK_THRESHOLD))
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cooldown = float(env.get("BARK_COOLDOWN", BARK_COOLDOWN))
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def avisa(ladrido, grupo, _cuando):
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alerts.alert("ladrido", f"🔊 [ALERTA] ¡Ladrido detectado! Bark {ladrido:.2f} (grupo canino {grupo:.2f})")
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listener = AudioListener(url, detector, on_bark=avisa, threshold=threshold, cooldown=cooldown).start()
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print(f"[INFO] Escucha de ladridos activa (umbral Bark {threshold:.2f}, aviso cada {cooldown:.0f}s como mínimo).")
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return listener
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# --- Bucle principal -----------------------------------------------------
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def main():
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env = load_env()
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stream_name = env.get("CAMERA_STREAM", DEFAULT_STREAM)
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url = build_rtsp_url(env, stream_name)
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model_path = get_hailo_model_path()
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print(f"[INFO] Cargando modelo Hailo: {model_path} ...")
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hailo = Hailo(model_path)
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model_h, model_w, _ = hailo.get_input_shape()
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print(f"[INFO] Modelo cargado. Resolución de entrada: {model_w}x{model_h}")
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print(f"[INFO] Cámara Tapo: {mask_url(url)}")
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stream = RtspVideoStream(url).start()
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notifier = start_telegram(env)
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alerts = AlertDispatcher(
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stream,
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notifier=notifier,
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snapshot_dir=env.get("SNAPSHOT_DIR", SNAPSHOT_DIR),
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save_snapshots=env.get("SNAPSHOTS", "1") != "0",
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capture_cooldown=float(env.get("ALERT_COOLDOWN", ALERT_COOLDOWN)),
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)
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audio = start_audio_listener(url, env, alerts)
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print("[INFO] Monitorizando mascotas (Gatos y Perros)... Presiona Ctrl+C para salir.\n")
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running = threading.Event()
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running.set()
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def handle_signal(signum, _frame):
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print(f"\n[INFO] Señal {signum} recibida; deteniendo...", flush=True)
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running.clear()
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signal.signal(signal.SIGINT, handle_signal)
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signal.signal(signal.SIGTERM, handle_signal)
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last_seq = 0
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last_centroid = None
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last_frame_time = 0.0
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last_stats = time.time()
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frames_processed = 0
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inference_time = 0.0
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try:
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while running.is_set():
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seq, frame, frame_time = stream.wait_for_frame(last_seq)
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if frame is None:
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continue
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last_seq = seq
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letterboxed, scale, pad_x, pad_y = letterbox(frame, model_w, model_h)
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if COLOR_ORDER == "bgr":
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model_input = np.ascontiguousarray(letterboxed)
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else:
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model_input = np.ascontiguousarray(cv2.cvtColor(letterboxed, cv2.COLOR_BGR2RGB))
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started = time.time()
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detections_by_class = hailo.run(model_input)
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inference_time += time.time() - started
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frames_processed += 1
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src_h, src_w = frame.shape[:2]
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dt = frame_time - last_frame_time if last_frame_time else 0.0
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pet_found = False
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for class_id, label in TARGET_CLASSES.items():
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if class_id >= len(detections_by_class):
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continue
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for det in detections_by_class[class_id]:
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confidence = det[4]
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if confidence <= CONF_THRESHOLD:
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continue
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pet_found = True
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x1, y1, x2, y2 = box_to_source(det, scale, pad_x, pad_y, src_w, src_h)
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centroid = ((x1 + x2) / 2.0 / src_w, (y1 + y2) / 2.0 / src_h)
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# Cajas del fotograma actual, para anotar la captura del aviso
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alerts.set_pet_boxes([(label, float(confidence), (x1, y1, x2, y2))])
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if last_centroid and dt > 0:
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dist = math.hypot(centroid[0] - last_centroid[0], centroid[1] - last_centroid[1])
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speed = dist / dt
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if speed > SPEED_THRESHOLD:
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alerts.alert(
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"carrera",
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f"⚠️ [ALERTA] ¡{label} corriendo! Vel: {speed:.2f} norm/s (Conf: {confidence:.2f})",
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)
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else:
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alerts.alert(
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"calmado",
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f"🐾 [OK] {label} detectado (calmado). Vel: {speed:.2f} norm/s (Conf: {confidence:.2f})",
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)
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last_centroid = centroid
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break # Evaluamos la mascota principal detectada
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if pet_found:
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break
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if not pet_found:
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last_centroid = None
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alerts.set_pet_boxes([])
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last_frame_time = frame_time
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now = time.time()
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if now - last_stats >= STATS_INTERVAL:
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avg_inf = inference_time / frames_processed * 1000 if frames_processed else 0.0
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linea = (
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f"[STATS] captura {stream.capture_fps:.1f} fps | análisis "
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f"{frames_processed / (now - last_stats):.1f} fps | inferencia {avg_inf:.1f} ms "
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f"| reconexiones {stream.reconnects}"
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)
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if audio is not None:
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linea += (
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f" || audio: Bark máx {audio.take_max_bark():.2f} (umbral {audio.threshold:.2f}) "
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f"| ventanas {audio.windows} | ladridos {audio.alerts}"
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)
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linea += f" || {alerts.stats()}"
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print(linea)
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last_stats = now
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frames_processed = 0
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inference_time = 0.0
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finally:
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stream.stop()
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if audio is not None:
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audio.stop()
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if notifier is not None:
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notifier.stop()
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hailo.close()
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print("[INFO] Streams cerrados y acelerador Hailo liberado correctamente.")
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if __name__ == "__main__":
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main()
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