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
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
import numpy as np
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import pytest
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import behavision.gallery.index as index_mod
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from behavision.gallery.index import VectorIndex
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def _unit(v):
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v = np.asarray(v, dtype=np.float32)
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return v / np.linalg.norm(v)
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@pytest.fixture(params=["numpy", "faiss"])
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def make_index(request, monkeypatch):
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if request.param == "numpy":
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monkeypatch.setattr(index_mod, "_HAVE_FAISS", False)
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elif not index_mod._HAVE_FAISS:
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pytest.skip("faiss not installed")
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return lambda dim=8: VectorIndex(dim)
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def test_empty_index_returns_no_matches(make_index):
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idx = make_index()
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assert idx.search(_unit(np.ones(8))) == []
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def test_add_search_finds_nearest(make_index):
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idx = make_index()
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a = _unit([1, 0, 0, 0, 0, 0, 0, 0])
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b = _unit([0, 1, 0, 0, 0, 0, 0, 0])
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idx.add([10, 20], np.vstack([a, b]))
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results = idx.search(_unit([0.9, 0.1, 0, 0, 0, 0, 0, 0]), k=2)
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assert results[0][0] == 10
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assert results[0][1] > results[1][1]
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assert results[0][1] == pytest.approx(1.0, abs=0.05)
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def test_remove(make_index):
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idx = make_index()
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a = _unit(np.arange(1, 9))
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idx.add([7], a.reshape(1, -1))
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idx.remove([7])
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assert len(idx) == 0
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assert idx.search(a) == []
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