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/schemas.py

@@ -22,8 +22,12 @@ class BBox(BaseModel):
 
 class PlateResult(BaseModel):
     text: str
+    # The plate's confidence = the MIN of fast-alpr's per-character confidences (a plate
+    # is only as trustworthy as its weakest character). See recognizer.py.
     confidence: float = Field(ge=0.0, le=1.0)
     bbox: BBox | None = None
+    # Predicted issuing region/country (advisory; fast-alpr's global model emits this).
+    region: str | None = None
 
 
 class VehicleResult(BaseModel):