Suriyakumarvijayanayagam
6068b2c3c7
POST /api/auth/password. The cost of its absence was measured today rather than argued: rotating three production accounts took a shell on the host, three round trips, and briefly left a PLATFORM ADMIN - the account that reads every company on the estate - with the password PASTE_IT_HERE, because a placeholder in a pasted command was taken literally and there was no way to correct it from the product. A manager could always reset somebody ELSE's password. A platform admin could be reset by nobody: they have no client, so the team routes are not theirs, and `provision user` on the host was the only route. For software that puts accounts on shop-floor PCs and staff phones, this is not a feature - it is what makes every other credential decision recoverable. Three decisions: - **authed, not tenantOnly.** A session is not a company's data, and the account with no company is precisely the one that had no route. Scoping this by client would have reproduced the hole it exists to close, which is also why SetUserPassword is not scoped by client the way ResetMemberPassword beside it is. The user id comes from the verified session, never the request, so there is nothing to point at anyone else. - **The current password is required.** An access token lives twelve hours and travels on devices that get lost and shared; without this a stolen one owns the account permanently instead of until it expires. - **Every OTHER session is revoked, and the caller's is kept.** Somebody changing their password because they believe it is known must not have to wonder whether the device that already had it is still signed in - and must not be signed out of the one in their hand while dealing with it. A failure there is logged, not returned: the password IS changed by then, and reporting an error would send them to retry with a current password that no longer exists. The suite's login() helper fatals on anything but 200, which is right everywhere else and useless here - half of what these tests assert is that a password has STOPPED working. loginCode() returns the status. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01KGcjxF1cNLcuwc3DAPcnfj
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.