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feat(vision): add recognize CLI + verify fast-alpr end-to-end

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Add a dev CLI (uv run python -m vision_service.cli <image>) that runs a recognizer on
an image file and prints the parsed plate(s) + confidence + region — fast feedback with
no HTTP. Also a package.json `recognize` script and a vision-recognize entry point.

Verified fast-alpr for real: installed the `alpr` extra, downloaded the YOLOv9 + CCT
ONNX weights (~11MB, cached offline under ~/.cache), and ran recognition on the
project's test image → "5AU5341" at 1.000 confidence, region "Czech Republic", ~40ms
on CPU, via both the CLI and POST /analyze.

Fixes result parsing against the actual fast-alpr API: ocr.confidence is a LIST of
per-character confidences (not a scalar) — reduced to one plate confidence via the MIN
(a plate is only as trustworthy as its weakest character); also surface ocr.region.
Extracted the per-result mapping into a pure plate_from_alpr_result + _reduce_confidence
and unit-tested them (no model weights needed). 7 tests pass; ruff + mypy strict clean;
full turbo build/lint/test green.

Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
This commit is contained in:
Julian Çuni
2026-06-19 15:46:03 +02:00
parent 6933406ae3
commit 5cedcaefe1
7 changed files with 176 additions and 13 deletions
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@@ -30,6 +30,20 @@ uv sync --extra alpr # installs fast-alpr + onnxruntime (downloa
VISION_RECOGNIZER=fast_alpr uv run uvicorn vision_service.app:app --port 8089
```
Model weights (~11 MB: a YOLOv9 detector + CCT OCR) download on first use and cache under
`~/.cache/open-image-models` + `~/.cache/fast-plate-ocr` — offline after that.
### Quick test against an image (CLI, no HTTP)
```bash
uv run python -m vision_service.cli path/to/car.jpg # or: pnpm --filter @parking/vision recognize -- car.jpg
uv run python -m vision_service.cli car.jpg --ocr cct-s-v2-global-model # try another OCR model
```
Prints the parsed plate(s) + confidence + region as JSON. Confidence is the **min** of fast-alpr's
per-character confidences (a plate is only as trustworthy as its weakest character). Example output on
the fast-alpr test image: `5AU5341 (1.000) region "Czech Republic"` in ~40 ms on CPU.
`fast-alpr` is MIT (YOLOv9 detector + CCT OCR on ONNX Runtime). Swap `VISION_OCR_MODEL` to the 40+
country European model to benchmark Albanian plates. For GPU/NPU, install `onnxruntime-gpu` /
`-openvino` / `-directml` instead of `onnxruntime`.