julian
f7a262ac9a
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apps/trainer (parking-trainer): inspect / train / evaluate / publish. Reads the wash collector's SQLite + crops read-only off its volume; time split (validation = newest slice); thin classes dropped; damped class weights; `features` mode (frozen ImageNet backbone, on-disk feature cache, seconds to retrain) and `finetune` mode (light augmentation). CPU-only torch from PyTorch's wheel index. ONNX export checked against the torch model; NO model file below the validation floor (exit 3, report still written); exit 2 = not enough labels. `evaluate` scores a shipped model on labels reviewed after training + the unlabelled pile; `publish` PUTs a version folder to a Gitea generic package. Light core deps; the `train` extra is heavy — CI syncs without it, torch tests skip. apps/vision: BodyTypeClassifier (bodytype.onnx + sidecar = the preprocessing contract: crop margin, input size, RGB 0-255, normalisation inside the graph) and RefinedVehicleDetector over YOLOX — refines only `car` or a class the classifier trained on, min-confidence, `detector_class` on the result; path set but no file = phase B off without an error; a broken file is a health detail. models/bodytype.version (tracked, empty) pins the published version the Dockerfile fetches at build (BuildKit secret; a pin that cannot be fetched fails the build). Verified: a trainer model gives identical probabilities inside the vision service; both images built and smoke-tested. Delivery: parking-trainer image in build-images.yml, the `trainer` compose profile on the collector stack (CPU, read-only data, TRAINER_OUT), commented TRAINER_OUT/PUBLISH_TOKEN in the wash-collector stack, .dockerignore for both Python contexts, trainer deps synced in CI. Wiki: bodytype-classifier-training rewritten as built (+ one fleet model not per site, secrets/access, where the crops live), opencv-anpr-service §Phase B, vision-review-outbox, vision-service-packaging, fleet-deployment-komodo, index, log. Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
74 lines
2.8 KiB
Python
74 lines
2.8 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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# The vehicle's box in frame pixels — the crop a reviewer sees / a classifier eats.
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bbox: BBox | None = None
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# Phase B: the detector's coarse class when the body-type classifier ran on this crop
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# (body_type is then the classifier's answer if confident, else the detector's).
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detector_class: 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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