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
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backend/app/workers/__init__.py
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backend/app/workers/__init__.py
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backend/app/workers/pipeline.py
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backend/app/workers/pipeline.py
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"""
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Detection Pipeline Worker
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Pulls frames from a StreamWorker queue and runs:
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1. Vehicle detection (type, color, plate, person count)
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2. Face detection (embedding, identity match)
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3. Saves snapshots and events to DB
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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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import os
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from datetime import datetime, timezone
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from pathlib import Path
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from uuid import UUID
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, text
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from app.core.config import settings
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from app.core.database import AsyncSessionLocal
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from app.models.camera import Camera
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from app.models.vehicle import VehicleEvent, VehicleIdentity, VehicleType
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from app.models.person import FaceEvent, PersonIdentity
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from app.services.vehicle_detector import vehicle_detector
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from app.services.face_service import face_service
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from app.services.stream_manager import StreamWorker
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logger = logging.getLogger(__name__)
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class DetectionPipeline:
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"""
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Runs detection on frames for a single camera stream.
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"""
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def __init__(self, camera_id: UUID, camera_name: str, worker: StreamWorker):
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self.camera_id = camera_id
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self.camera_name = camera_name
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self.worker = worker
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self.snapshot_dir = Path(settings.STORAGE_PATH) / str(camera_id)
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self.snapshot_dir.mkdir(parents=True, exist_ok=True)
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self._running = False
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self._task = None
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async def start(self):
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self._running = True
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self._task = asyncio.create_task(self._loop())
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logger.info(f"[{self.camera_name}] Pipeline started")
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async def stop(self):
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self._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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async def _loop(self):
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loop = asyncio.get_event_loop()
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while self._running:
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try:
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frame_data = await asyncio.wait_for(
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self.worker.frame_queue.get(), timeout=2.0
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)
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except asyncio.TimeoutError:
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continue
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except asyncio.CancelledError:
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raise
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frame = frame_data["frame"]
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captured_at = frame_data["captured_at"]
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try:
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await loop.run_in_executor(
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None, self._process_frame, frame, captured_at
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)
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except Exception as e:
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logger.exception(f"[{self.camera_name}] Pipeline error: {e}")
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def _process_frame(self, frame, captured_at: datetime):
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"""Synchronous processing — runs in thread pool."""
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import asyncio
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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loop.run_until_complete(self._async_process(frame, captured_at))
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finally:
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loop.close()
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async def _async_process(self, frame, captured_at: datetime):
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"""Run detections and save to DB."""
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# --- Vehicle Detection ---
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vehicle_detections = vehicle_detector.detect(frame)
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# --- Face Detection ---
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face_detections = face_service.detect(frame)
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if not vehicle_detections and not face_detections:
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return
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# Save snapshot
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ts = captured_at.strftime("%Y%m%d_%H%M%S_%f")
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snapshot_filename = f"{ts}.jpg"
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snapshot_path = str(self.snapshot_dir / snapshot_filename)
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cv2.imwrite(snapshot_path, frame)
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async with AsyncSessionLocal() as db:
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vehicle_event_ids = []
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# Save vehicle events
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for det in vehicle_detections:
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identity_id = await self._get_or_create_vehicle_identity(
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db, det.plate_number, det.vehicle_type, det.color
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)
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# Save plate crop
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plate_snap = None
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if det.plate_crop is not None and det.plate_crop.size > 0:
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plate_snap = str(self.snapshot_dir / f"{ts}_plate.jpg")
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cv2.imwrite(plate_snap, det.plate_crop)
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event = VehicleEvent(
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camera_id=self.camera_id,
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identity_id=identity_id,
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plate_number=det.plate_number,
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plate_confidence=det.plate_confidence,
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vehicle_type=det.vehicle_type,
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color=det.color,
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person_count=det.person_count,
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detection_confidence=det.detection_confidence,
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vehicle_bbox=det.vehicle_bbox,
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plate_bbox=det.plate_bbox,
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snapshot_path=snapshot_path,
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plate_snapshot_path=plate_snap,
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captured_at=captured_at,
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)
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db.add(event)
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await db.flush()
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vehicle_event_ids.append(event.id)
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# Save face events
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for i, det in enumerate(face_detections):
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identity_id, similarity = await self._match_or_create_person(
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db, det.embedding
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)
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# Save face crop
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face_snap = None
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if det.face_crop is not None and det.face_crop.size > 0:
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face_snap = str(self.snapshot_dir / f"{ts}_face{i}.jpg")
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cv2.imwrite(face_snap, det.face_crop)
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embedding_list = face_service.embeddings_to_list(det.embedding) if det.embedding is not None else None
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event = FaceEvent(
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camera_id=self.camera_id,
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identity_id=identity_id,
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detection_confidence=det.detection_confidence,
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face_bbox=det.bbox,
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face_embedding=embedding_list,
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similarity_score=similarity,
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vehicle_event_id=vehicle_event_ids[0] if vehicle_event_ids else None,
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snapshot_path=snapshot_path,
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face_snapshot_path=face_snap,
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captured_at=captured_at,
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)
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db.add(event)
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await db.commit()
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async def _get_or_create_vehicle_identity(
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self, db: AsyncSession, plate_number, vehicle_type, color
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):
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"""Find existing vehicle identity by plate or create new one."""
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if not plate_number:
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return None
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result = await db.execute(
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select(VehicleIdentity).where(VehicleIdentity.plate_number == plate_number)
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)
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identity = result.scalar_one_or_none()
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if identity:
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identity.last_seen_at = datetime.now(timezone.utc)
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identity.total_sightings += 1
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if color and not identity.color:
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identity.color = color
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else:
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identity = VehicleIdentity(
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plate_number=plate_number,
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vehicle_type=vehicle_type or VehicleType.unknown,
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color=color,
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first_seen_at=datetime.now(timezone.utc),
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last_seen_at=datetime.now(timezone.utc),
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total_sightings=1,
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)
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db.add(identity)
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await db.flush()
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return identity.id
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async def _match_or_create_person(self, db: AsyncSession, embedding):
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"""
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Find closest matching person identity using pgvector cosine similarity.
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If no match above threshold, create new identity.
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Returns (identity_id, similarity_score).
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"""
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if embedding is None:
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return None, 0.0
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emb_list = face_service.embeddings_to_list(embedding)
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emb_str = f"[{','.join(str(x) for x in emb_list)}]"
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# pgvector cosine distance (1 - cosine_similarity)
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threshold = settings.FACE_SIMILARITY_THRESHOLD # distance threshold
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result = await db.execute(
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text(f"""
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SELECT id, 1 - (embedding <=> '{emb_str}'::vector) AS similarity
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FROM person_identities
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WHERE embedding IS NOT NULL
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AND (embedding <=> '{emb_str}'::vector) < :threshold
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ORDER BY embedding <=> '{emb_str}'::vector
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LIMIT 1
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"""),
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{"threshold": threshold}
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)
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row = result.fetchone()
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if row:
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identity_id, similarity = row
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# Update last seen
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await db.execute(
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text("UPDATE person_identities SET last_seen_at=NOW(), total_sightings=total_sightings+1 WHERE id=:id"),
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{"id": identity_id}
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)
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return identity_id, float(similarity)
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else:
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# New unknown person
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identity = PersonIdentity(
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first_seen_at=datetime.now(timezone.utc),
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last_seen_at=datetime.now(timezone.utc),
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total_sightings=1,
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embedding=emb_list,
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)
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db.add(identity)
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await db.flush()
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return identity.id, 0.0
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