julian
6933406ae3
Skeleton of the host-side vision service per the packaging decision: a Python/FastAPI app at apps/vision/, uv-managed, wired into the Turbo graph via a thin package.json shim (dev/lint/test/build → uv/uvicorn/ruff/pytest). A per-package turbo.json sets build outputs [] so the no-op build is warning-free. Endpoints: GET /health (readiness + model version) and POST /analyze (raw octet-stream body, so Node POSTs Snapshot.bytes directly; empty→400, oversize→413, recognizer-not-ready→503). The recognizer is a Protocol with a StubRecognizer (no models, boots/tests offline — the dev/CI default) and a FastAlprRecognizer (the real MIT YOLOv9+CCT/ONNX stack, lazily imported; missing models ⇒ ready=False, not a crash) — the device-adapter pattern applied to the model. fast-alpr + onnxruntime are an optional `alpr` extra, so `uv sync` needs no model download. Verified: turbo run lint|test|build includes @parking/vision and stays green; uv run mypy strict-clean; uvicorn boots and serves /health + /analyze live; pnpm workspace 6→7. Not built yet: the Node VisionClient adapter, a Dockerfile + model fetch, and Job 2 (vehicle verification). Updates the packaging decision (As-scaffolded) + log. Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
57 lines
1.6 KiB
Python
57 lines
1.6 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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confidence: float = Field(ge=0.0, le=1.0)
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bbox: BBox | 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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