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
Containerize the two non-desktop apps for the booth appliance. The desktop app stays
on its own tag-only release.yml.
- apps/server/Dockerfile: multi-stage node:22-alpine. `pnpm deploy --legacy --prod`
(NOT prune — the monorepo native better-sqlite3 won't resolve under a root prune)
yields a self-contained bundle; build stage adds node-gyp toolchain, runtime adds
libstdc++; non-root, healthcheck. Migrates the mounted DB on boot via a drizzle-kit-
free runtime migrator (packages/db/scripts/migrate-runtime.mjs) — drizzle-kit is a
devDep, pruned from prod.
- apps/server/src/static-spa.ts: Fastify serves the built React SPA (one container
serves API + UI). GET-only fallback to index.html, excludes /api + /health so it never
shadows the backend; a no-op in dev (no dist). Registered last in server.ts.
- apps/vision/Dockerfile: uv base, --extra alpr, model weights PRE-WARMED into the image
as the runtime user so fast_alpr boots offline (0 downloads at runtime). Engine env-
selected (VISION_RECOGNIZER stub|fast_alpr).
- Branch-aware: docker-compose.yml (base) + .dev.yml (build local, stub, ports) +
.prod.yml (pull pinned, fast_alpr, vision internal, restart always); REGISTRY/TAG from
env so a branch deploy pulls that branch's image.
- .gitea/workflows/build-images.yml: on push to dev/main, run the full turbo build+lint+
test gate, then buildx push both images to git.infra.msai.al/mca/parking_solution with
branch + branch-<sha> tags (registry cache; optional Komodo webhook behind KOMODO_ENABLED).
- .dockerignore excludes **/parking.sqlite* so the signed ledger is NEVER baked.
Verified locally (Docker 29): server image migrates + serves API+SPA (/health 200, /
+ /booth HTML, /api/nope JSON 404, no sqlite outside /data); vision image boots fast_alpr
with 0 runtime downloads; compose stack healthy with server→vision over the private network.
Wiki: new container-deployment.md; vision-service-packaging open Qs resolved; index + log.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
julian