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

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

apps/vision/vision_service/cli.py

@@ -0,0 +1,72 @@
+"""Dev CLI to test a recognizer against an image file — no HTTP, fast feedback.
+
+    uv run python -m vision_service.cli path/to/car.jpg
+    uv run python -m vision_service.cli car.jpg --recognizer stub        # contract only
+    uv run python -m vision_service.cli car.jpg --ocr cct-s-v2-global-model
+
+Defaults to the `fast_alpr` recognizer (the point of this tool). Prints the parsed
+plate result as JSON. If the `alpr` extra isn't installed it says so and exits non-zero
+rather than silently using the stub. See wiki/entities/opencv-anpr-service.md.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+from .recognizer import build_recognizer
+from .settings import Settings
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(prog="vision-recognize", description="Run a recognizer on an image.")
+    parser.add_argument("image", type=Path, help="path to an image file (JPEG/PNG) with a plate")
+    parser.add_argument(
+        "--recognizer",
+        choices=["fast_alpr", "stub"],
+        default="fast_alpr",
+        help="which recognizer to use (default: fast_alpr)",
+    )
+    parser.add_argument("--detector", default=None, help="override the fast-alpr detector model name")
+    parser.add_argument("--ocr", default=None, help="override the fast-alpr OCR model name")
+    args = parser.parse_args(argv)
+
+    if not args.image.is_file():
+        print(f"error: no such file: {args.image}", file=sys.stderr)
+        return 2
+
+    settings = Settings(recognizer=args.recognizer)
+    if args.detector:
+        settings.detector_model = args.detector
+    if args.ocr:
+        settings.ocr_model = args.ocr
+
+    rec = build_recognizer(settings)
+    if not rec.ready:
+        err = getattr(rec, "error", "unavailable")
+        print(
+            f"error: recognizer '{args.recognizer}' not ready: {err}\n"
+            "hint: install the models with  uv sync --extra alpr",
+            file=sys.stderr,
+        )
+        return 1
+
+    image_bytes = args.image.read_bytes()
+    result = rec.analyze(image_bytes)
+    # Pydantic v2: model_dump_json gives a clean, stable rendering.
+    print(result.model_dump_json(indent=2))
+
+    if result.plate is None:
+        print("\n(no plate detected)", file=sys.stderr)
+    else:
+        flag = "  [LOW CONFIDENCE]" if result.low_confidence else ""
+        print(
+            f"\n→ {result.plate.text}  ({result.plate.confidence:.3f}){flag}  in {result.took_ms:.1f} ms",
+            file=sys.stderr,
+        )
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())