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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
40 lines
2.3 KiB
Bash
40 lines
2.3 KiB
Bash
# apps/vision — the ANPR microservice's own env (copy to apps/vision/.env).
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# This is the PYTHON SERVICE's config only. The Node server has its OWN VISION_* vars
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# (in apps/server/.env) — keep the two .env files SEPARATE (they share the VISION_
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# prefix but are different processes). See wiki/entities/opencv-anpr-service.md "Configuration".
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# Recognizer: "stub" (no models, recognizes nothing — boots anywhere, for dev/CI) or
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# "fast_alpr" (the real MIT YOLOv9+CCT/ONNX stack — needs `uv sync --extra alpr`).
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VISION_RECOGNIZER=fast_alpr
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# Bind. On the appliance prefer 127.0.0.1 — the Node backend is the only caller, so the
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# /analyze endpoint should NOT be reachable off-host. (0.0.0.0 only if you must.)
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VISION_HOST=127.0.0.1
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VISION_PORT=8089
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# fast-alpr models (only used when recognizer=fast_alpr). The defaults won the Albanian
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# benchmark; change the OCR to european-plates-mobile-vit-v2-model only to re-test.
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VISION_DETECTOR_MODEL=yolo-v9-t-384-license-plate-end2end
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VISION_OCR_MODEL=cct-xs-v2-global-model
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# Confidence floor — a best plate below this is flagged low_confidence so the Node side
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# treats it as advisory and falls back to the ticket path. Keep in sync with the server.
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VISION_MIN_CONFIDENCE=0.5
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# Vehicle stage (phase A): a YOLOX ONNX graph (Apache-2.0) run on the same frame after the
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# plate read; fills /analyze `vehicle.body_type` (car/truck/bus/motorcycle) + confidence for
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# the Car Wash desk's category suggestion. Unset = off. The Docker image bakes the weights
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# at /app/models/yolox_s.onnx; locally: curl the release file into apps/vision/models/.
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# https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.onnx
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# VISION_VEHICLE_MODEL_PATH=models/yolox_s.onnx
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# VISION_VEHICLE_INPUT_SIZE=640
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# VISION_VEHICLE_MIN_CONFIDENCE=0.4
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# Phase B: the body-type classifier (sedan/hatchback/suv/… on the detector's crop), trained
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# by apps/trainer on the reviewer's labels. The Docker image bakes it at
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# /app/models/bodytype.onnx (+ .json sidecar) when models/bodytype.version pins a published
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# version; locally copy a trainer output folder's two files into apps/vision/models/.
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# Path set but no file = stage off (the normal state before the first model).
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# VISION_VEHICLE_CLASSIFIER_PATH=models/bodytype.onnx
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# VISION_VEHICLE_CLASSIFIER_MIN_CONFIDENCE=0.6
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