julian
5e1395db18
The app plumbing for venue-modules.md §"Vehicle category from vision"; the model is the open half (no bundled recognizer emits body_type yet, so the desk shows nothing until phase A lands in the vision service). - Shared: VEHICLE_CLASSES vocabulary, VehicleRead, CARWASH_VISION_THRESHOLD_DEFAULT, reason code carwash.categoryDowngrade; settings/order/lookup views carry the read. - Vision contract: /analyze vehicle.body_type + confidence (service schema); the Node client normalises to the vocabulary and drops the rest. - Record: snapshot.ts stores the read in the plate's device_events row (or its own when the plate was unreadable); vehicleForIdentity() resolves it like the plate. - Car wash: carwash_categories.vision_classes (site mapping "car, sedan → Vetura"), carwash_config.vision_threshold (signed config_change when it moves), four vision columns on orders — migration 0030. Lookup returns vision + suggestedCategoryId. - Desk pre-selects the mapped category and shows the read + snapshot thumbnail; Setup offers class chips per category and the threshold. Operator decides. - Flag: a read at/above the threshold whose mapped category prices HIGHER than the chosen one signs one `anomaly` (both categories/prices, operator, snapshot) and stores its id on the order. Equal/upgrade/unsure/unmapped → nothing. Recorded only, never blocks, no reason prompt (user, 2026-09-06). Tests in carwash.test.ts; wiki venue-modules (As built), opencv-anpr-service, log. Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
69 lines
2.5 KiB
Python
69 lines
2.5 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. `body_type` is ADVISORY: the Node server records it
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beside the plate and the Car Wash desk pre-selects the site category it maps to; the
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operator decides, a disagreement is flagged, nothing is ever gated on it. Values come
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from the shared vocabulary (car, sedan, hatchback, suv, minivan, pickup, van, truck,
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bus, motorcycle) — anything else is ignored by Node. Phase A (a COCO detector) emits
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car/truck/bus/motorcycle; the finer classes need the body-type classifier. Not yet
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produced by any bundled recognizer."""
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colour: str | None = None
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body_type: str | None = None
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# Confidence of `body_type` (0–1). Node compares it to the site's threshold.
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confidence: float | None = Field(default=None, ge=0.0, le=1.0)
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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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