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
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
"""The /analyze response contract — the shape the Node VisionClient adapter consumes.
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Mirrors the first-cut API in wiki/entities/opencv-anpr-service.md:
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{ plate: {text, confidence, bbox}|null, vehicle: {...}|null, modelVersion, tookMs }
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Job 2 (vehicle attributes) is scaffolded as an optional field, not yet populated —
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fast-alpr is plate-only; the vehicle stage is built later on the same ONNX runtime.
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"""
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from __future__ import annotations
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from pydantic import BaseModel, Field
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class BBox(BaseModel):
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"""Plate bounding box in pixels (top-left origin)."""
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x1: int
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y1: int
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x2: int
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y2: int
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class PlateResult(BaseModel):
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text: str
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# The plate's confidence = the MIN of fast-alpr's per-character confidences (a plate
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# is only as trustworthy as its weakest character). See recognizer.py.
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confidence: float = Field(ge=0.0, le=1.0)
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bbox: BBox | None = None
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# Predicted issuing region/country (advisory; fast-alpr's global model emits this).
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region: str | None = None
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class VehicleResult(BaseModel):
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"""Job 2 — vehicle attributes / fingerprint (anti-spoofing). Not yet produced."""
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colour: str | None = None
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body_type: str | None = None
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make: str | None = None
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model: str | None = None
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class AnalyzeResponse(BaseModel):
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# The single best plate, or null when none was found.
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plate: PlateResult | None = None
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# All plates found (a frame may contain several vehicles).
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plates: list[PlateResult] = Field(default_factory=list)
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vehicle: VehicleResult | None = None
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# True when the best plate is below the confidence floor — Node should treat the
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# read as advisory only and prefer the ticket path. See fail-state-safety.
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low_confidence: bool = False
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model_version: str
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took_ms: float
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class HealthResponse(BaseModel):
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status: str
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recognizer: str
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ready: bool
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model_version: str
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detail: str | None = None
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