abraham
60002a90a7
- pet_speed.py: ingesta RTSP en hilo aparte, que descarta fotogramas antiguos y se reconecta con backoff exponencial; escalado letterbox a 640x640 con desmapeo de cajas y centroide al fotograma original; configuracion por .env (con CAMERA_STREAM y CAMERA_RTSP_URL opcionales); cierre limpio con SIGINT y SIGTERM. - Readme.md: seccion de la camara Tapo (cuenta de camara, streams, rendimiento medido); se corrige Hailo-8L por Hailo-8, que es lo que reporta hailortcli. - .gitignore: excluir los logs del runtime Hailo.
362 lines
14 KiB
Python
362 lines
14 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-8.
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Sustituye la cámara CSI interna de la Raspberry Pi por la cámara Tapo de red:
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- Ingesta RTSP en un hilo aparte, que descarta los frames viejos y expone
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siempre el más reciente (evita la latencia acumulada típica de RTSP).
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- Reconexión automática con backoff si la cámara se cae o cambia de IP.
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- Inferencia YOLOv8s sobre la NPU Hailo (HEF del sistema).
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- Detección de gato/perro (clases COCO 15 y 16) y alerta de carrera según
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el desplazamiento del centroide entre frames consecutivos.
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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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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 urllib.parse
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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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# --- 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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# 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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ENV_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".env")
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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 load_env(path=ENV_PATH):
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"""Lee un .env sencillo (CLAVE=valor) sin dependencias externas."""
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env = {}
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if os.path.exists(path):
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with open(path) as fh:
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for line in fh:
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line = line.strip()
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if not line or line.startswith("#") or "=" not in line:
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continue
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key, value = line.split("=", 1)
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env[key.strip()] = value.strip().strip('"').strip("'")
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# Las variables de entorno reales tienen prioridad sobre el fichero, para
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# poder sobreescribirlas desde systemd o desde una prueba puntual.
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for key in ("CAMERA_IP", "CAMERA_USER", "CAMERA_PW", "CAMERA_STREAM", "CAMERA_RTSP_URL"):
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if os.environ.get(key):
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env[key] = os.environ[key]
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return env
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def mask_url(url):
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"""Oculta las credenciales de la URL para poder mostrarla por consola."""
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parts = urllib.parse.urlsplit(url)
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if not parts.hostname:
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return "***"
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port = f":{parts.port}" if parts.port else ""
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return f"{parts.scheme}://***@{parts.hostname}{port}{parts.path}"
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def build_rtsp_url(env, stream):
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"""Construye la URL RTSP de la Tapo, escapando usuario y contraseña."""
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if env.get("CAMERA_RTSP_URL"):
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return env["CAMERA_RTSP_URL"]
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ip = env.get("CAMERA_IP")
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user = env.get("CAMERA_USER")
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password = env.get("CAMERA_PW")
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missing = [k for k, v in (("CAMERA_IP", ip), ("CAMERA_USER", user), ("CAMERA_PW", password)) if not v]
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if missing:
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raise SystemExit(f"[ERROR] Faltan variables en {ENV_PATH}: {', '.join(missing)}")
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return "rtsp://%s:%s@%s:554/%s" % (
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urllib.parse.quote(user, safe=""),
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urllib.parse.quote(password, safe=""),
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ip,
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stream,
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)
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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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clip_x = lambda v: float(min(max(v, 0.0), src_w)) # noqa: E731
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clip_y = lambda v: float(min(max(v, 0.0), src_h)) # noqa: E731
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return clip_x(x1), clip_y(y1), clip_x(x2), clip_y(y2)
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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 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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# --- 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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# Ocultamos la contraseña al mostrarla por consola
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print(f"[INFO] Cámara Tapo: {mask_url(url)}")
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stream = RtspVideoStream(url).start()
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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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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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print(f"⚠️ [ALERTA] ¡{label} corriendo! Vel: {speed:.2f} norm/s (Conf: {confidence:.2f})")
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else:
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print(f"🐾 [OK] {label} detectado (calmado). Vel: {speed:.2f} norm/s (Conf: {confidence:.2f})")
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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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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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print(
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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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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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hailo.close()
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print("[INFO] Stream RTSP cerrado y acelerador Hailo liberado correctamente.")
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if __name__ == "__main__":
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main()
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