7c43e4580d
- 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
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
"""
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FaceService — InsightFace-based face detection, embedding, and identity matching.
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Uses pgvector cosine similarity for fast 1:N identity search.
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"""
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import cv2
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import numpy as np
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import logging
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from typing import Optional
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from dataclasses import dataclass, field
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from uuid import UUID
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from app.core.config import settings
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logger = logging.getLogger(__name__)
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@dataclass
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class FaceDetection:
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bbox: dict = field(default_factory=dict) # {"x","y","w","h"}
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embedding: Optional[np.ndarray] = None # 512-dim vector
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detection_confidence: float = 0.0
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face_crop: Optional[np.ndarray] = None
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matched_identity_id: Optional[UUID] = None
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similarity_score: float = 0.0
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class FaceService:
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def __init__(self):
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self._app = None
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self._ready = False
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def load(self):
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"""Load InsightFace model — call once at startup."""
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try:
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from insightface.app import FaceAnalysis
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self._app = FaceAnalysis(
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name=settings.FACE_MODEL,
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allowed_modules=["detection", "recognition"],
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)
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self._app.prepare(
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ctx_id=settings.GPU_DEVICE,
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det_size=(640, 640),
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)
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self._ready = True
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logger.info(f"InsightFace loaded (model={settings.FACE_MODEL})")
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except Exception as e:
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logger.error(f"Failed to load InsightFace: {e}")
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def detect(self, frame: np.ndarray) -> list[FaceDetection]:
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"""
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Detect all faces in a frame and extract embeddings.
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Returns list of FaceDetection objects.
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"""
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if not self._ready or self._app is None:
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return []
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try:
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faces = self._app.get(frame)
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except Exception as e:
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logger.debug(f"Face detection error: {e}")
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return []
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detections = []
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for face in faces:
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bbox = face.bbox.astype(int)
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x1, y1, x2, y2 = bbox[0], bbox[1], bbox[2], bbox[3]
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det = FaceDetection(
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bbox={"x": x1, "y": y1, "w": x2 - x1, "h": y2 - y1},
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embedding=face.normed_embedding, # Already L2-normalized
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detection_confidence=float(face.det_score),
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face_crop=frame[max(0, y1):y2, max(0, x1):x2],
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)
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detections.append(det)
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return detections
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def cosine_similarity(self, emb1: np.ndarray, emb2: np.ndarray) -> float:
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"""Cosine similarity between two normalized embeddings."""
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return float(np.dot(emb1, emb2))
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def embeddings_to_list(self, embedding: np.ndarray) -> list[float]:
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"""Convert numpy embedding to list for pgvector storage."""
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return embedding.tolist()
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# Singleton
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face_service = FaceService()
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