Suriyakumarvijayanayagam
ce0223006b
012 turned three descriptive columns into identifiers other systems keep: in agent.json on a shop counter, in a saved URL, in a scheduled report. All three were already treated as stable and none of it was enforced. - clients.slug is an MQTT topic segment the broker ACL is written against. Rename one and that tenant's whole estate is silently refused by the broker, with no way to tell the agents. - sites.slug is what a shop PC calls itself - agent.json holds "site_id": "chennai", never the uuid. A rename orphans the PC from the shop it is standing in. - site_cameras.camera_id lands in visits.camera_id, which is text and not a foreign key. A rename orphans every visit already attributed to the old name: the footfall is still there and no longer joins to a camera. This was half-enforced in handleUpdateCamera and nowhere else, which is the shape of a rule that holds until somebody adds a second write path. - visitors.number is assigned once from the tenant's counter and read back as V-42. A trigger, not a CHECK: a CHECK cannot see the old row and the rule is about the transition. The DISPLAY name is deliberately not frozen - "TeNext Chennai", "Front door" - it is what a person reads, nothing keys on it, and a system that cannot fix a typo in a shop's name has confused the two. Also records why the uuid stays where a slug would do. The length was never the problem; needing it was, and that is fixed. Replacing it would touch eight foreign keys on a live database to shorten a field clients are already told not to use, and a sequential id would make any future tenancy hole walkable by counting. It is NOT because ids must be minted offline - sites, visitors and visits are all created server-side with a database in hand, and claiming otherwise would defend the status quo rather than explain it. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01HViLj9gYNRtSr7YVZmW5sn
Behavision
Production face recognition over RTSP. Watches camera streams, detects and tracks faces, recognizes known people, auto-enrolls new visitors, records visit history, and serves a live dashboard + JSON API.
Clean-room rewrite of the previous Camera/ and pattern_reg/ projects:
same core ideas, correct engineering.
Quick start (Windows)
cd D:\NEARLE\Behavision
python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt
python -m behavision setup-models # downloads YuNet, copies ArcFace etc. from the old project
python -m behavision run # dashboard at http://localhost:8010
Camera credentials live in .env (gitignored) — never in code or YAML.
So do the dashboard credentials: set BEHAVISION_API_USER and
BEHAVISION_API_PASSWORD, or let the server generate one into
data/api_credentials.txt on first boot. A routable api.host is never
served without HTTP Basic auth; 127.0.0.1 is left open.
To test without a camera, set webcam: 0 on a camera in
config/default.yaml.
Enroll a person by name from photos:
python -m behavision enroll --name "Alice" --images C:\photos\alice\
Architecture
behavision/
├── config.py typed config: YAML + ${ENV} expansion, validated (pydantic)
├── capture.py RTSP/webcam reader thread: latest-frame slot, TCP transport,
│ exponential-backoff reconnect, percent-encoded credentials
├── detection.py YuNet face detector (OpenCV) → boxes + 5 landmarks, clipped
├── recognition.py ArcFace ONNX encoder (correct (x-127.5)/127.5 RGB preprocessing,
│ unit-norm output) + clamped face-quality scoring
├── tracking.py IoU tracker: identity decided once per TRACK, not per frame
├── gallery/
│ ├── index.py FAISS IndexFlatIP (exact cosine) with identical numpy fallback
│ ├── store.py SQLite (WAL): identities, embeddings, sightings — source of truth
│ └── service.py three-zone matching: match / ambiguous(do nothing) / enroll
├── attributes.py optional age, gender, emotion on the aligned chip
├── events.py async event bus → log / webhook / rate-limited email sinks
├── engine.py one worker thread per camera, shared models + gallery
├── api.py FastAPI: dashboard, MJPEG stream, identities, events, stats
└── __main__.py CLI: run | enroll | setup-models
Pipeline
RTSP ──► capture ──► detect (YuNet) ──► track (IoU)
│ once per track, quality-gated
▼
align (Umeyama 5-pt) ──► ArcFace ──► cosine search
│
┌───────────────────────────┼──────────────────────────┐
sim ≥ 0.42 0.32 ≤ sim < 0.42 sim < 0.32
known person ambiguous → retry new visitor
sighting + event on a better frame auto-enroll + event
Design decisions (and the failure they prevent)
| Decision | Prevents |
|---|---|
Per-camera FaceDetector, shared thread-safe encoder |
cv2 input-size race between camera workers |
| HTTP Basic on every route, escaped dashboard output | open biometric API on the LAN; stored XSS via identity labels |
| Percent-encoded credentials, URL built from parts | @ in password silently breaking the stream (old bug) |
| Track-level identity, sighting cooldown | one user registered per frame (old bug) |
Exact IndexFlatIP on unit vectors, -1 guarded |
inverted L2 threshold + wrong-person metadata[-1] (old bugs) |
| SQLite as source of truth, index rebuilt at boot | index/metadata drift, untrained-IVF crash (old bugs) |
| Three-zone thresholds with ambiguous no-op | duplicate identities and wrong merges |
| One color conversion, ArcFace-native normalization | off-distribution embeddings making thresholds meaningless (old bug) |
| All quality terms clamped to [0,1] | unreachable registration threshold (old bug) |
| Readiness-guarded API, sinks off the hot path | startup crashes, notification stalls |
API
| Method | Path | Purpose |
|---|---|---|
| GET | / |
live dashboard |
| GET | /api/health, /api/stats |
liveness / metrics |
| GET | /api/cameras/{id}/stream.mjpeg |
annotated live stream |
| GET | /api/cameras/{id}/frame.jpg |
latest annotated frame |
| GET | /api/identities, /api/sightings, /api/events |
data |
| PATCH | /api/identities/{id} |
rename a visitor ({"label": "Alice"}) |
| DELETE | /api/identities/{id} |
forget a person (embeddings removed) |
Tests
pip install pytest
pytest tests -q
Configuration
Everything lives in config/default.yaml; ${VAR} placeholders resolve
from the environment (.env is loaded first). Thresholds:
recognition.match_threshold(default 0.42): raise for fewer false matches, lower for fewer duplicates.recognition.min_enroll_quality(0.65): how good a face must look (sharpness, size, lighting, frontality) before a new identity is minted.tracking.min_hits_for_id(4): frames a face must persist before we spend an embedding on it — filters passers-by and phantom detections.cameras[].max_width(1280): frames are downscaled at ingest — full 3MP streams waste memory and detector time.
Recognition models
The encoder picks the first usable model in models/:
arcface_int8.onnx → w600k_mbf.onnx (MobileFaceNet, 13 MB, downloaded
automatically) → arcface.onnx (r100, 260 MB, copied from the old project;
needs ~1.5 GB free RAM to load). Pin one with recognition.model_file.
Every stored embedding is tagged with the model that produced it, and only
embeddings from the active model are searched — different encoders'
vectors are numerically incompatible and never mix.