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
