feat(vision): vehicle stage, phase A — YOLOX-S (Apache-2.0 ONNX) beside the plate recognizer

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

wiki/log.md

@@ -3091,3 +3091,12 @@ category + threshold; the desk pre-selects the mapped category and shows the rea
 a confident, pricier-mapped read with a cheaper choice signs `anomaly carwash.categoryDowngrade`
 (both categories/prices, operator, snapshot) — never blocks. No recognizer emits body_type yet.
 Tests in carwash.test.ts. Updated [[venue-modules]] (As built), [[opencv-anpr-service]].
+
+## [2026-09-06] ingest | Vision vehicle stage, phase A: YOLOX-S beside the plate recognizer
+`vision_service/vehicle.py` (YOLOX ONNX on onnxruntime: letterbox, grid decode, NMS, COCO
+car/motorcycle/bus/truck → vocabulary, one vehicle per frame — the box holding the plate, else the
+largest) + `WithVehicle` composition over any plate recognizer; `VISION_VEHICLE_MODEL_PATH` (unset =
+off), input size, detector floor; Dockerfile bakes yolox_s.onnx (best-effort curl) and sets the
+path; compose forwards it (empty = off). Measured on four real dev entry frames: car at 0.83–0.88,
+~240–330 ms, empty lane → none. Tests: tests/test_vehicle.py (pure post-processing + composition +
+missing-model health). Updated [[opencv-anpr-service]], [[venue-modules]].