e67ab8bfb4
From the deep review's hygiene list: - The demo replay (a still recognition replayed as if it were live tracking) moves out of app.py into demo_replay.py, and every payload it produces is marked `synthetic: true`. It had been committed inside an unrelated feature commit, which made a film-making device look like part of the server. - `/v1/health` reports the OCR engine and whether it is available. Without it an operator could not tell a broken OCR from a shelf with no readable tags. - A failed OCR pass is counted and logged instead of swallowed: `TagService.stats()` now carries `passes_failed` and `last_pass_error`. - The live classification budget goes back to the measured production defaults (16 in flight, 2 attempts); a demo walk over a nearly static shelf asks for more through `--live-inflight` / `--live-attempts` rather than silently changing the default for everyone. 162 tests pass. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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Running the demo
Two surfaces: the web client (open it in a browser) and the iOS/Android app (scan screen). Both are clients of the same recognition server.
1. Start the server
cd recognization
YOLO_OFFLINE=1 .venv/bin/python code/app.py --port 8080 --classify-workers 8
Ready when curl -s http://127.0.0.1:8080/v1/health answers (about 25 s: the model
and the catalog load, then the OCR engine reports itself).
Flags that matter for a demo:
| Flag | Why |
|---|---|
--classify-workers 8 |
More parallel classification; the demo's bottleneck. |
--host 0.0.0.0 |
Only if a phone or iPad on the LAN must reach it (set DORAYAKI_API_TOKEN too). |
--no-tags |
Disables price-tag OCR entirely if it is fighting for CPU. |
DORAYAKI_OCR=none |
Same, via environment. |
2. The web client
Open http://127.0.0.1:8080.
- Live — pick a camera (or “Video file…”), press Start. Boxes appear as products are recognised, with name and price; teal boxes mark the shelf-edge price tags; amber means “check this”; red marks empty shelf space.
- Photo — a still shelf image: boxes paint immediately, identities fill in, the algorithm selector re-classifies, and tapping a product opens the card with the catalog photo, stock, and the tag reading compared against the catalog.
3. The app (iOS simulator, no camera needed)
The app can drive a synthetic store walk: the server renders a virtual camera sweeping a real shelf photo — pan a row left to right, step up, pan back — so the scan screen behaves like an employee walking the shelf.
Start the server on the port the app expects (8090 is the Debug default; 8097 is what the verification scripts used — pass the override if you change it):
YOLO_OFFLINE=1 .venv/bin/python code/app.py --port 8090 --host 127.0.0.1 --classify-workers 8
Then run the app in the simulator and open Scan. Press the scan control: the stage shows the walk moving, green boxes with names appear, teal tag boxes carry prices, and tapping a product opens the card.
On a real device, replace the walk with the camera: the app uses the device camera
and the same server (a LAN IP needs HTTPS or an ATS exception — see
docs/API.md).
4. The demo configuration (measured)
Everything below was measured on this machine, 2026-09-24, one client, 1280×720 frames, the synthetic walk:
| Setting | Result |
|---|---|
live method orb |
7.2 results/s, 178 labelled tracks, 21 distinct SKUs, first label 0.7 s |
live method hybrid |
1.4–9.9/s depending on load, 4–13 tracks, first label 3–5 s |
walk pan=0.02 |
a row sweeps in ~8 s — reads like a person |
walk pan=0.06 |
~3 s per row — a time-lapse |
| OCR cadence (live) | 1 rail band every 30 frames; more than that drops the frame rate |
So: ORB for live labelling, Hybrid for “Show matching” (tap-to-inspect), and the slower walk pace. The method menu in both clients switches between them live.
5. What to say, honestly
Working today: live detection and tracking, product identity with names and prices, price-tag reading and the tag-vs-catalog comparison, empty-shelf gaps, tap-to-inspect with the catalog photo and stock, and the walk.
Not built yet: configurable shelf rules (facing counts, brand blocks), the fix → rescan → verify loop, and a head-office dashboard. Do not claim those on camera.