Suriyakumarvijayanayagam
9182f70442
Every id in the schema is a uuid and stays one. What was wrong was putting one in front of a person: RecordVisit named every new customer 'Visitor ' || left(id::text, 8), so the arrivals feed, the shop PC and the mobile app all read "Visitor 3446ec35" - the string a shop assistant reads to a colleague and types into a search box. label is a stored column staff can overwrite and SearchVisitors matches on, so formatting around it in a front end would have left the data wrong on three surfaces. Migration 012 adds a per-client visitors.number, taken from a counter on clients with UPDATE ... RETURNING inside the visit transaction. Per client rather than global: a global sequence would tell any customer who signs up how many people the whole platform has ever seen, from their own first visitor number. The backfill numbers existing rows by first_seen_at and relabels only the eight-hex pattern the old statement produced, so a human-typed name is never overwritten. Three of the four things anyone addresses by URL already had a human name and the API simply refused it - a site has a slug, a camera has the id the engine knows it by. refs.go accepts either form anywhere an id is taken; a uuid resolves with no lookup, so every URL a client already stored keeps working. - An ambiguous camera name resolves to nothing, never to a guess: two shops may each have an "Office1" and acting on the first row would edit the wrong shop's camera. - 404 on a path, 400 on a query filter. /api/visits answered fine and it was the filter that was wrong. - site and site_id are both accepted everywhere now. They differed per endpoint, and an unknown query parameter is silently ignored, so getting it the wrong way round returned the whole estate. - The search matches V-13, which is what the product now shows. Two bugs found by running it rather than testing it: - 'Visitor ' || $2::text beside number = $2 makes Postgres deduce two types for one parameter and refuse the insert. It compiled and passed every in-memory test; the first real database rejected it, along with the existing face tests that share the path. - The fallback avatar said "V1" for Visitor 13, Visitor 10 and Visitor 15 alike, and read as the V-1 reference for a fourth person. It shows the number now. The prop is customerRef, not ref - React reserves that name and it would never have arrived. Verified on the live database and through the running API: 13 hex labels became Visitor 1-13 in first-seen order, two typed names left alone, and the same customer reachable by uuid, V-13 and 13. 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.