julian
5cedcaefe1
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
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
"""Dev CLI to test a recognizer against an image file — no HTTP, fast feedback.
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uv run python -m vision_service.cli path/to/car.jpg
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uv run python -m vision_service.cli car.jpg --recognizer stub # contract only
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uv run python -m vision_service.cli car.jpg --ocr cct-s-v2-global-model
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Defaults to the `fast_alpr` recognizer (the point of this tool). Prints the parsed
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plate result as JSON. If the `alpr` extra isn't installed it says so and exits non-zero
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rather than silently using the stub. See wiki/entities/opencv-anpr-service.md.
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"""
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from __future__ import annotations
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import argparse
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import sys
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from pathlib import Path
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from .recognizer import build_recognizer
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from .settings import Settings
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def main(argv: list[str] | None = None) -> int:
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parser = argparse.ArgumentParser(prog="vision-recognize", description="Run a recognizer on an image.")
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parser.add_argument("image", type=Path, help="path to an image file (JPEG/PNG) with a plate")
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parser.add_argument(
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"--recognizer",
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choices=["fast_alpr", "stub"],
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default="fast_alpr",
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help="which recognizer to use (default: fast_alpr)",
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)
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parser.add_argument("--detector", default=None, help="override the fast-alpr detector model name")
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parser.add_argument("--ocr", default=None, help="override the fast-alpr OCR model name")
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args = parser.parse_args(argv)
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if not args.image.is_file():
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print(f"error: no such file: {args.image}", file=sys.stderr)
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return 2
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settings = Settings(recognizer=args.recognizer)
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if args.detector:
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settings.detector_model = args.detector
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if args.ocr:
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settings.ocr_model = args.ocr
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rec = build_recognizer(settings)
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if not rec.ready:
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err = getattr(rec, "error", "unavailable")
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print(
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f"error: recognizer '{args.recognizer}' not ready: {err}\n"
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"hint: install the models with uv sync --extra alpr",
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file=sys.stderr,
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)
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return 1
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image_bytes = args.image.read_bytes()
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result = rec.analyze(image_bytes)
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# Pydantic v2: model_dump_json gives a clean, stable rendering.
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print(result.model_dump_json(indent=2))
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if result.plate is None:
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print("\n(no plate detected)", file=sys.stderr)
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else:
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flag = " [LOW CONFIDENCE]" if result.low_confidence else ""
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print(
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f"\n→ {result.plate.text} ({result.plate.confidence:.3f}){flag} in {result.took_ms:.1f} ms",
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file=sys.stderr,
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)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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