The operator's category choice is a hypothesis, not truth (user, 2026-09-06): each wash
order with a vehicle read queues a package for a trusted reviewer over the private overlay
(Netbird); the verdict becomes the phase-B training label and the per-operator error rate.
wiki/concepts/vision-review-outbox.md.
- Boxes: the vision service returns the vehicle bbox; snapshot.ts stores the vehicle and
plate boxes on the read as FRACTIONS of the analysed frame (the stored snapshot is a
downscaled copy); vehicleForIdentity() returns them.
- carwash_review_outbox (migration 0031) + review-outbox.ts: crop = detector box + 8 %
margin, ≤ 640 px, plate blurred in place from the plate box; payload carries a
pseudonymous booth id and a keyed operator hash — no site name, no plate, no OSD, no
bystanders; multipart POST with a per-booth bearer; 2xx → sent (image dropped);
400/404/413/415/422 → abandoned; anything else → backoff 1 min·2^n capped 6 h; voided
orders and items older than 14 days abandoned unsent. Nothing queued while unconfigured.
- Enqueue is fire-and-forget off the intake path in createOrder; the loop runs every
CARWASH_REVIEW_INTERVAL_SEC (60) and stops on close.
- GET /api/carwash/review/status (site:read) + a "Remote review" line in Setup → Car wash.
- Env CARWASH_REVIEW_URL / _TOKEN / _BOOTH_ID (all three or off) documented in
.env.example and forwarded by compose.
- Tests: review-outbox.test.ts (crop + blur on a synthetic frame, config/pseudonyms,
queue/drain/backoff/abandon, through the app). Wiki: new concept page, index,
venue-modules As built, log. The collector is not built.
Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
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
The app plumbing for venue-modules.md §"Vehicle category from vision"; the model is the
open half (no bundled recognizer emits body_type yet, so the desk shows nothing until
phase A lands in the vision service).
- Shared: VEHICLE_CLASSES vocabulary, VehicleRead, CARWASH_VISION_THRESHOLD_DEFAULT,
reason code carwash.categoryDowngrade; settings/order/lookup views carry the read.
- Vision contract: /analyze vehicle.body_type + confidence (service schema); the Node
client normalises to the vocabulary and drops the rest.
- Record: snapshot.ts stores the read in the plate's device_events row (or its own when
the plate was unreadable); vehicleForIdentity() resolves it like the plate.
- Car wash: carwash_categories.vision_classes (site mapping "car, sedan → Vetura"),
carwash_config.vision_threshold (signed config_change when it moves), four vision
columns on orders — migration 0030. Lookup returns vision + suggestedCategoryId.
- Desk pre-selects the mapped category and shows the read + snapshot thumbnail; Setup
offers class chips per category and the threshold. Operator decides.
- Flag: a read at/above the threshold whose mapped category prices HIGHER than the chosen
one signs one `anomaly` (both categories/prices, operator, snapshot) and stores its id on
the order. Equal/upgrade/unsure/unmapped → nothing. Recorded only, never blocks, no
reason prompt (user, 2026-09-06).
Tests in carwash.test.ts; wiki venue-modules (As built), opencv-anpr-service, log.
Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
Add a dev CLI (uv run python -m vision_service.cli <image>) that runs a recognizer on
an image file and prints the parsed plate(s) + confidence + region — fast feedback with
no HTTP. Also a package.json `recognize` script and a vision-recognize entry point.
Verified fast-alpr for real: installed the `alpr` extra, downloaded the YOLOv9 + CCT
ONNX weights (~11MB, cached offline under ~/.cache), and ran recognition on the
project's test image → "5AU5341" at 1.000 confidence, region "Czech Republic", ~40ms
on CPU, via both the CLI and POST /analyze.
Fixes result parsing against the actual fast-alpr API: ocr.confidence is a LIST of
per-character confidences (not a scalar) — reduced to one plate confidence via the MIN
(a plate is only as trustworthy as its weakest character); also surface ocr.region.
Extracted the per-result mapping into a pure plate_from_alpr_result + _reduce_confidence
and unit-tested them (no model weights needed). 7 tests pass; ruff + mypy strict clean;
full turbo build/lint/test green.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Skeleton of the host-side vision service per the packaging decision: a Python/FastAPI
app at apps/vision/, uv-managed, wired into the Turbo graph via a thin package.json
shim (dev/lint/test/build → uv/uvicorn/ruff/pytest). A per-package turbo.json sets
build outputs [] so the no-op build is warning-free.
Endpoints: GET /health (readiness + model version) and POST /analyze (raw
octet-stream body, so Node POSTs Snapshot.bytes directly; empty→400, oversize→413,
recognizer-not-ready→503). The recognizer is a Protocol with a StubRecognizer (no
models, boots/tests offline — the dev/CI default) and a FastAlprRecognizer (the real
MIT YOLOv9+CCT/ONNX stack, lazily imported; missing models ⇒ ready=False, not a crash)
— the device-adapter pattern applied to the model. fast-alpr + onnxruntime are an
optional `alpr` extra, so `uv sync` needs no model download.
Verified: turbo run lint|test|build includes @parking/vision and stays green; uv run
mypy strict-clean; uvicorn boots and serves /health + /analyze live; pnpm workspace
6→7. Not built yet: the Node VisionClient adapter, a Dockerfile + model fetch, and
Job 2 (vehicle verification). Updates the packaging decision (As-scaffolded) + log.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
julian