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
120 lines
4.8 KiB
Python
120 lines
4.8 KiB
Python
"""Per-camera recognition gates.
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The gates describe a *view*, not a preference. An overhead corridor camera
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where genuine faces measure 0.32-0.45 and an entrance camera at head height
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where they measure 0.70-0.82 cannot share one enrollment gate, and a real
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site has both — so one global number is guaranteed wrong somewhere.
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"""
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import numpy as np
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import pytest
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from behavision.config import (CameraConfig, CameraTuning, Config,
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RecognitionSection)
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from behavision.gallery import Gallery, IdentityStore, VectorIndex
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DIM = 16
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def _unit(seed):
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rng = np.random.default_rng(seed)
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v = rng.normal(size=DIM).astype(np.float32)
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return v / np.linalg.norm(v)
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@pytest.fixture
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def gallery(tmp_path):
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store = IdentityStore(tmp_path / "t.db")
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gal = Gallery(store, VectorIndex(DIM),
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RecognitionSection(sighting_cooldown_seconds=0.0))
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yield gal
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store.close()
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# -- merging ------------------------------------------------------------
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def test_merged_overrides_only_what_is_set():
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base = RecognitionSection()
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merged = base.merged(CameraTuning(min_enroll_quality=0.40))
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assert merged.min_enroll_quality == 0.40
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assert merged.match_threshold == base.match_threshold
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def test_merging_does_not_mutate_the_global_section():
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"""Every camera merges off the same object; an in-place update would let
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one camera's tuning leak into every other camera."""
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base = RecognitionSection()
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base.merged(CameraTuning(min_enroll_quality=0.40))
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assert base.min_enroll_quality == 0.65
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def test_empty_tuning_returns_the_global_section_itself():
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base = RecognitionSection()
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assert base.merged(CameraTuning()) is base
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assert base.merged(None) is base
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def test_a_camera_cannot_invert_enroll_and_match():
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"""config.py's invariant has to hold per camera too, or one camera makes
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decisions that contradict the numbers driving every other one."""
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with pytest.raises(ValueError):
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RecognitionSection().merged(CameraTuning(match_threshold=0.10))
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def test_tuning_survives_the_camera_json_round_trip():
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cam = CameraConfig(id="door", host="10.0.0.5",
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tuning=CameraTuning(min_enroll_quality=0.40))
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revived = CameraConfig.model_validate(cam.model_dump(mode="json"))
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assert revived.tuning.min_enroll_quality == 0.40
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def test_a_camera_without_tuning_still_loads():
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cam = CameraConfig(id="plain", host="10.0.0.6")
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assert cam.tuning.min_enroll_quality is None
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assert RecognitionSection().merged(cam.tuning).min_enroll_quality == 0.65
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# -- effect on the shared gallery ---------------------------------------
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def test_a_loose_camera_enrolls_a_face_the_global_gate_refuses(gallery):
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"""The measured Office1 case: real faces at 0.45 against a 0.65 gate."""
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emb = _unit(1)
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assert gallery.resolve(emb, quality=0.45, camera_id="hall").kind == "skipped"
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overhead = RecognitionSection().merged(CameraTuning(min_enroll_quality=0.40))
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res = gallery.resolve(emb, quality=0.45, camera_id="hall", rcfg=overhead)
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assert res.kind == "new"
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def test_one_cameras_override_does_not_leak_to_another(gallery):
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loose = RecognitionSection().merged(CameraTuning(min_enroll_quality=0.40))
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gallery.resolve(_unit(1), quality=0.45, camera_id="overhead", rcfg=loose)
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# A different, unmodified camera must still apply the global gate. Seed 4
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# sits at 0.033 to seed 1 — a genuinely different person, so the refusal
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# can only come from the quality gate. (These are 16-d fixtures; random
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# vectors that small are far less orthogonal than the real 512-d ones,
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# so the seed has to be picked, not assumed.)
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assert gallery.resolve(_unit(4), quality=0.45,
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camera_id="door").kind == "skipped"
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def test_reinforcement_honours_the_calling_cameras_gate(gallery):
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loose = RecognitionSection().merged(CameraTuning(min_enroll_quality=0.40))
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new = gallery.resolve(_unit(1), quality=0.9, camera_id="overhead",
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rcfg=loose)
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# Reinforcement only stores a view that is confidently this person
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# (>= enroll 0.32) yet not a near-duplicate (< reinforce 0.55). This
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# mixture measures 0.451 against the stored vector — inside that window.
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view = _unit(1) * 0.3 + _unit(3) * 0.7
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view = (view / np.linalg.norm(view)).astype(np.float32)
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# 0.45 is under the global gate but over this camera's.
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assert not gallery.reinforce_identity(new.identity_id, view, 0.45)
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assert gallery.reinforce_identity(new.identity_id, view, 0.45, rcfg=loose)
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def test_worker_resolves_its_own_gates_at_construction():
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cfg = Config()
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cam = CameraConfig(id="overhead", host="10.0.0.7",
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tuning=CameraTuning(min_enroll_quality=0.40))
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merged = cfg.recognition.merged(cam.tuning)
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assert merged.min_enroll_quality == 0.40
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assert cfg.recognition.min_enroll_quality == 0.65
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