Suriyakumarvijayanayagam
dad04e8cda
Five components that ship as one product:
- behavision/ the recognition engine. RTSP ingest, YuNet detection, IoU
tracking, ArcFace embeddings, a FAISS/SQLite gallery, and a
FastAPI dashboard. Identity is decided once per TRACK from an
average of at least three embeddings, never per frame.
- agent/ the Go edge agent: supervises the engine, holds a durable
spool, and drains it to MQTT. Nothing is acked before the
broker confirms.
- desktop/ the shop PC application (Wails + React + tray).
- server/ the cloud API, MQTT consumer, reports and assistant.
- web/ platform.loyaly.ai, the head-office app, embedded in the
server binary.
The gallery stores 512-float embeddings and timestamps - no images unless
`app.store_faces` is switched on. Those embeddings are biometric personal
data under GDPR and India's DPDP: template inversion reconstructs a
recognisable face from an ArcFace vector, so data/behavision.db is treated
as a biometric database and DELETE /api/visitors/{id} is a real erasure.
CLAUDE.md carries the reasoning behind every non-obvious decision here,
including the ones that were measured and the ones that were wrong first.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HViLj9gYNRtSr7YVZmW5sn
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
"""cv2.FaceDetectorYN caches its input size and is not thread-safe, so camera
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workers must not share one. Skipped when the model is absent, matching the
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faiss-optional pattern in test_index.py — the suite stays runnable with no
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models installed."""
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import threading
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from pathlib import Path
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import numpy as np
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import pytest
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from behavision.config import Config
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from behavision.detection import YUNET_FILENAME, FaceDetector
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MODELS = Path(__file__).resolve().parent.parent / "models"
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pytestmark = pytest.mark.skipif(not (MODELS / YUNET_FILENAME).exists(),
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reason="YuNet model not installed")
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def _detector():
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d = Config().detection
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return FaceDetector(MODELS, d.score_threshold, d.nms_threshold,
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d.max_faces, d.min_face_px)
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def _hammer(det, size, errors, n=40):
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w, h = size
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frame = np.zeros((h, w, 3), dtype=np.uint8)
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for _ in range(n):
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try:
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det.detect(frame)
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except Exception as exc: # noqa: BLE001 - cv2 raises on size mismatch
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errors.append(exc)
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return
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def _race(det_a, det_b):
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errors = []
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threads = [threading.Thread(target=_hammer, args=(det_a, (1280, 720), errors)),
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threading.Thread(target=_hammer, args=(det_b, (640, 480), errors))]
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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return errors
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def test_a_shared_detector_really_does_race():
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"""Guards the premise: if this ever stops failing, the test below is
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proving nothing and the per-camera split can be revisited."""
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shared = _detector()
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assert _race(shared, shared), "expected a shared detector to race"
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def test_per_camera_detectors_do_not_race():
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assert _race(_detector(), _detector()) == []
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