julian
20a3cb3e80
Fills /analyze vehicle.body_type + confidence (car / motorcycle / bus / truck from COCO, mapped to the shared vocabulary) for the Car Wash desk's category suggestion (venue-modules.md §Vehicle category from vision). Advisory: the operator decides, a confident downgrade is flagged, nothing is gated on it. - vision_service/vehicle.py: pure numpy/cv2 letterbox (pad 114, raw BGR), stride-grid decode, class-agnostic NMS, one vehicle per frame (the box holding the plate's centre, else the largest); YoloxVehicleDetector on onnxruntime CPU, 2 intra-op threads. - recognizer.py: WithVehicle composes the stage over any plate recognizer (stub included); a failing stage yields vehicle=null + a "vehicle: …" note in /health.detail — never costs the plate read. model_version reads "<plate>+yolox:yolox_s.onnx@640". - settings: VISION_VEHICLE_MODEL_PATH (unset = off), _INPUT_SIZE (640), _MIN_CONFIDENCE (0.4, the detector's floor; the flag threshold is site config). - Dockerfile bakes yolox_s.onnx (best-effort curl at build; no network → stage off) and sets the path; compose forwards it (empty = off); .env.example documents it. - Measured on four real dev entry frames (DS-2CD1047G3H, 2560×1440): car at 0.83–0.88 in ~240–330 ms; empty lane with a person → none. - tests/test_vehicle.py: decode/NMS/pick/letterbox on synthetic tensors, the composition, and a missing-model /health. Wiki: opencv-anpr-service, venue-modules, log. Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
32 lines
1.8 KiB
Bash
32 lines
1.8 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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