Suriyakumarvijayanayagam
c9be9b5807
Asked directly by the owner, about his own cameras, from his phone's connection. The answer is physics and the product was not giving it. A camera lives on the shop's LAN behind a router. 192.168.1.121 means "something on the network I am attached to" and nothing more - from mobile data, a hotel or head office it resolves to nobody, or to a completely different device holding that number. There is no route in from the internet and there must not be: an RTSP camera reachable from outside is how a shop's cameras end up being watched by strangers. That is why the product is split the way it is - the shop PC is the only machine on the camera's LAN, and every other surface reaches it outbound, which is what makes Watch live work from anywhere while nothing connects in. What was wrong is the message. "cannot reach 192.168.1.121:554 - Operation timed out" reads as a broken camera and sends somebody to re-type an address and a password that were always correct. _wrong_network_hint names the cause and separates two states that need opposite actions: on that network -> check the camera is powered on and the address is right somewhere else -> the COMPUTER is in the wrong place; no setting fixes it - The local address comes from a connected UDP socket that sends nothing. It only fixes a route so the kernel will name the source address. - The LAN ranges are spelled out, not is_private. That property also covers carrier-grade NAT and the documentation networks, and telling somebody who typed 203.0.113.9 that it is "on the shop's own network" is a confident wrong answer in the place people look first. Found by a test using that address as its example of a PUBLIC one. - A DNS name gets no hint: nothing can be concluded about camera.local from the string, and guessing is the failure mode this message exists to fix. - With no network at all it still names the cause and drops the comparison, rather than claiming to know which network this machine is on. 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.