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/pyproject.toml

@@ -15,6 +15,9 @@ dependencies = [
     "pydantic-settings>=2.6",
 ]
 
+[project.scripts]
+vision-recognize = "vision_service.cli:main"
+
 [project.optional-dependencies]
 # The real recognizer. Install with: uv sync --extra alpr
 #   fast-alpr is MIT (YOLOv9 detector + CCT OCR, both MIT) on ONNX Runtime — see the