feat(vision): scaffold apps/vision ANPR microservice (FastAPI, stub recognizer)

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

apps/vision/README.md

@@ -0,0 +1,54 @@
+# @parking/vision — host-side ANPR / vehicle-verification service
+
+A **separate process** (Python + FastAPI) the Node backend calls over **localhost HTTP** with a
+camera snapshot, returning a licence-plate read (and, later, vehicle-attribute verification — the
+anti-plate-spoofing witness). Recognition is **advisory, never the sole authority** to open a
+barrier: if this service is down or unsure, the host falls back to the ticket path.
+
+Lives inside the Turborepo at `apps/vision/` but is **not a JS package** — Python deps are managed by
+`uv`/`pyproject.toml`; the `package.json` is a thin shim so `turbo run lint/test` includes it. See
+`wiki/decisions/vision-service-packaging.md` and `wiki/entities/opencv-anpr-service.md`.
+
+## Run
+
+```bash
+# from apps/vision/ — install the light core (boots in stub mode, no model downloads)
+uv sync
+
+# dev server with reload (or: pnpm --filter @parking/vision dev)
+uv run uvicorn vision_service.app:app --reload --port 8089
+
+# checks
+uv run ruff check .
+uv run pytest -q
+```
+
+### Enable the real recognizer (fast-alpr)
+
+```bash
+uv sync --extra alpr                 # installs fast-alpr + onnxruntime (downloads model weights)
+VISION_RECOGNIZER=fast_alpr uv run uvicorn vision_service.app:app --port 8089
+```
+
+`fast-alpr` is MIT (YOLOv9 detector + CCT OCR on ONNX Runtime). Swap `VISION_OCR_MODEL` to the 40+
+country European model to benchmark Albanian plates. For GPU/NPU, install `onnxruntime-gpu` /
+`-openvino` / `-directml` instead of `onnxruntime`.
+
+## API
+
+- `GET /health` → `{ status, recognizer, ready, model_version, detail? }`
+- `POST /analyze` (body = raw image bytes, `Content-Type: application/octet-stream`) →
+  `{ plate: {text, confidence, bbox}|null, plates[], vehicle: null, low_confidence, model_version, took_ms }`
+
+The Node side POSTs `Snapshot.bytes` directly (no multipart). `vehicle` is scaffolded but not yet
+populated — fast-alpr is plate-only; the vehicle stage (Job 2) is built later on the same runtime.
+
+## Config (env, prefix `VISION_`)
+
+| Var | Default | Meaning |
+| --- | --- | --- |
+| `VISION_RECOGNIZER` | `stub` | `stub` (no models) or `fast_alpr` (real) |
+| `VISION_PORT` | `8089` | listen port |
+| `VISION_DETECTOR_MODEL` | `yolo-v9-t-384-license-plate-end2end` | fast-alpr detector |
+| `VISION_OCR_MODEL` | `cct-xs-v2-global-model` | fast-alpr OCR |
+| `VISION_MIN_CONFIDENCE` | `0.5` | below this → `low_confidence=true` |