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>
81 lines
3.4 KiB
Markdown
81 lines
3.4 KiB
Markdown
# Running the demo
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Two surfaces: the **web client** (open it in a browser) and the **iOS/Android app**
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(scan screen). Both are clients of the same recognition server.
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## 1. Start the server
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```bash
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cd recognization
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YOLO_OFFLINE=1 .venv/bin/python code/app.py --port 8080 --classify-workers 8
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```
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Ready when `curl -s http://127.0.0.1:8080/v1/health` answers (about 25 s: the model
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and the catalog load, then the OCR engine reports itself).
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Flags that matter for a demo:
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| Flag | Why |
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| --- | --- |
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| `--classify-workers 8` | More parallel classification; the demo's bottleneck. |
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| `--host 0.0.0.0` | Only if a phone or iPad on the LAN must reach it (set `DORAYAKI_API_TOKEN` too). |
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| `--no-tags` | Disables price-tag OCR entirely if it is fighting for CPU. |
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| `DORAYAKI_OCR=none` | Same, via environment. |
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## 2. The web client
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Open <http://127.0.0.1:8080>.
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- **Live** — pick a camera (or “Video file…”), press Start. Boxes appear as
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products are recognised, with name and price; teal boxes mark the shelf-edge
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price tags; amber means “check this”; red marks empty shelf space.
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- **Photo** — a still shelf image: boxes paint immediately, identities fill in,
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the algorithm selector re-classifies, and tapping a product opens the card with
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the catalog photo, stock, and the tag reading compared against the catalog.
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## 3. The app (iOS simulator, no camera needed)
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The app can drive a **synthetic store walk**: the server renders a virtual camera
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sweeping a real shelf photo — pan a row left to right, step up, pan back — so the
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scan screen behaves like an employee walking the shelf.
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Start the server on the port the app expects (8090 is the Debug default; 8097 is
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what the verification scripts used — pass the override if you change it):
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```bash
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YOLO_OFFLINE=1 .venv/bin/python code/app.py --port 8090 --host 127.0.0.1 --classify-workers 8
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```
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Then run the app in the simulator and open **Scan**. Press the scan control: the
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stage shows the walk moving, green boxes with names appear, teal tag boxes carry
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prices, and tapping a product opens the card.
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On a real device, replace the walk with the camera: the app uses the device camera
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and the same server (a LAN IP needs HTTPS or an ATS exception — see
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`docs/API.md`).
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## 4. The demo configuration (measured)
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Everything below was measured on this machine, 2026-09-24, one client, 1280×720
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frames, the synthetic walk:
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| Setting | Result |
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| --- | --- |
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| live method `orb` | **7.2 results/s, 178 labelled tracks, 21 distinct SKUs, first label 0.7 s** |
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| live method `hybrid` | 1.4–9.9/s depending on load, 4–13 tracks, first label 3–5 s |
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| walk `pan=0.02` | a row sweeps in ~8 s — reads like a person |
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| walk `pan=0.06` | ~3 s per row — a time-lapse |
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| OCR cadence (live) | 1 rail band every 30 frames; more than that drops the frame rate |
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So: **ORB for live labelling, Hybrid for “Show matching”** (tap-to-inspect), and
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the slower walk pace. The method menu in both clients switches between them live.
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## 5. What to say, honestly
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Working today: live detection and tracking, product identity with names and
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prices, price-tag reading and the tag-vs-catalog comparison, empty-shelf gaps,
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tap-to-inspect with the catalog photo and stock, and the walk.
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Not built yet: configurable shelf rules (facing counts, brand blocks), the
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fix → rescan → verify loop, and a head-office dashboard. Do not claim those on
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camera.
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