feat: initial BantayCam scaffold
- FastAPI backend with async SQLAlchemy - Camera RTSP management (add, start, stop) - Vehicle detection (YOLO + fast-alpr) - Type: car, motorcycle, truck, jeepney - Color detection (HSV) - License plate OCR - Motorcycle person count - Face detection + InsightFace ArcFace embedding - pgvector identity grouping (auto-cluster same face) - Vehicle + person movement trail APIs - Docker Compose with pgvector/pg16 - Models: Camera, VehicleIdentity, VehicleEvent, PersonIdentity, FaceEvent
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Nemo
153
backend/app/services/stream_manager.py
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153
backend/app/services/stream_manager.py
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"""
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StreamManager — Manages RTSP stream connections and dispatches frames to workers.
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Each camera runs in its own asyncio task with a frame queue.
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"""
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import asyncio
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import cv2
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import logging
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from typing import Dict, Optional
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from uuid import UUID
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from datetime import datetime
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from app.core.config import settings
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logger = logging.getLogger(__name__)
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class StreamWorker:
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"""
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Handles one RTSP camera stream.
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Reads frames and puts them into a queue for the AI pipeline to consume.
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"""
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def __init__(self, camera_id: UUID, camera_name: str, rtsp_url: str):
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self.camera_id = camera_id
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self.camera_name = camera_name
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self.rtsp_url = rtsp_url
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self.frame_queue: asyncio.Queue = asyncio.Queue(maxsize=10)
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self.is_running = False
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self._task: Optional[asyncio.Task] = None
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self.frame_count = 0
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self.last_frame_at: Optional[datetime] = None
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self.error: Optional[str] = None
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async def start(self):
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self.is_running = True
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self._task = asyncio.create_task(self._read_loop())
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logger.info(f"[{self.camera_name}] Stream started")
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async def stop(self):
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self.is_running = False
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if self._task:
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self._task.cancel()
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try:
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await self._task
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except asyncio.CancelledError:
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pass
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logger.info(f"[{self.camera_name}] Stream stopped")
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async def _read_loop(self):
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"""Read frames from RTSP in a thread pool (OpenCV is blocking)."""
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loop = asyncio.get_event_loop()
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while self.is_running:
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try:
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cap = await loop.run_in_executor(
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None,
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lambda: cv2.VideoCapture(self.rtsp_url)
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)
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if not cap.isOpened():
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self.error = "Cannot open RTSP stream"
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logger.error(f"[{self.camera_name}] {self.error}")
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await asyncio.sleep(5)
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continue
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self.error = None
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frame_idx = 0
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while self.is_running:
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ret, frame = await loop.run_in_executor(None, cap.read)
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if not ret:
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logger.warning(f"[{self.camera_name}] Frame read failed, reconnecting...")
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break
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frame_idx += 1
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self.frame_count += 1
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self.last_frame_at = datetime.utcnow()
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# Skip frames for performance
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if frame_idx % settings.PROCESS_EVERY_N_FRAMES != 0:
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continue
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# Non-blocking put — drop frame if queue is full
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try:
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self.frame_queue.put_nowait({
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"frame": frame,
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"frame_idx": frame_idx,
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"captured_at": self.last_frame_at,
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})
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except asyncio.QueueFull:
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pass # AI pipeline is slow — drop frame
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cap.release()
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except asyncio.CancelledError:
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raise
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except Exception as e:
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self.error = str(e)
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logger.exception(f"[{self.camera_name}] Stream error: {e}")
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await asyncio.sleep(5)
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class StreamManager:
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"""
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Singleton that manages all active camera streams.
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"""
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_instance = None
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def __init__(self):
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self._workers: Dict[str, StreamWorker] = {}
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@classmethod
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def get(cls) -> "StreamManager":
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if cls._instance is None:
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cls._instance = StreamManager()
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return cls._instance
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async def start_stream(self, camera_id: UUID, camera_name: str, rtsp_url: str):
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key = str(camera_id)
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if key in self._workers:
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await self.stop_stream(camera_id)
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worker = StreamWorker(camera_id, camera_name, rtsp_url)
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self._workers[key] = worker
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await worker.start()
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return worker
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async def stop_stream(self, camera_id: UUID):
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key = str(camera_id)
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if key in self._workers:
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await self._workers[key].stop()
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del self._workers[key]
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def get_worker(self, camera_id: UUID) -> Optional[StreamWorker]:
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return self._workers.get(str(camera_id))
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def get_all_status(self) -> list:
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return [
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{
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"camera_id": str(k),
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"is_running": w.is_running,
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"frame_count": w.frame_count,
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"last_frame_at": w.last_frame_at,
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"error": w.error,
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}
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for k, w in self._workers.items()
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]
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async def stop_all(self):
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for worker in list(self._workers.values()):
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await worker.stop()
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self._workers.clear()
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