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