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

apps/vision/Dockerfile

@@ -14,7 +14,7 @@ ENV UV_LINK_MODE=copy \
 
 # System libs the recognizer stack needs (opencv/onnxruntime): GL + glib. Kept minimal.
 RUN apt-get update \
-    && apt-get install -y --no-install-recommends libgl1 libglib2.0-0 \
+    && apt-get install -y --no-install-recommends libgl1 libglib2.0-0 curl \
     && rm -rf /var/lib/apt/lists/*
 
 # ---- deps: resolve + install the venv from the lockfile (cache-friendly) ----
@@ -26,6 +26,13 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # ---- project source ----
 COPY vision_service/ ./vision_service/
 COPY README.md ./
+# Vehicle stage weights (phase A): YOLOX-S, Apache-2.0, ~36 MB, baked into the image so the
+# air-gapped appliance never fetches at runtime and no operator-writable path holds a model
+# (vision-service-hardening.md). Best-effort at build: without network the stage stays off.
+ARG YOLOX_URL=https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.onnx
+RUN mkdir -p /app/models \
+    && (curl -fsSL -o /app/models/yolox_s.onnx "$YOLOX_URL" \
+        || (echo "[build] yolox weights not fetched (no network) — vehicle stage off" && rm -f /app/models/yolox_s.onnx))
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv sync --frozen --extra alpr
 
@@ -49,7 +56,8 @@ RUN uv run python -c "from fast_alpr import ALPR; ALPR()" \
 # Default to the stub recognizer (offline, no model load); override to fast_alpr in prod.
 ENV VISION_RECOGNIZER=stub \
     VISION_HOST=0.0.0.0 \
-    VISION_PORT=8089
+    VISION_PORT=8089 \
+    VISION_VEHICLE_MODEL_PATH=/app/models/yolox_s.onnx
 EXPOSE 8089
 HEALTHCHECK --interval=30s --timeout=5s --start-period=20s --retries=3 \
   CMD python -c "import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://localhost:8089/health').status==200 else 1)" || exit 1