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/pyproject.toml

@@ -0,0 +1,59 @@
+[project]
+name = "parking-vision"
+version = "0.0.0"
+description = "Host-side ANPR / vehicle-verification microservice for the parking system (separate process; localhost HTTP)."
+requires-python = ">=3.10,<4.0"
+# Core deps are LIGHT on purpose: the service boots, serves /health, and answers
+# /analyze in stub mode with ONLY these. The heavy recognizer stack (fast-alpr +
+# onnxruntime + model weights) is the optional `alpr` extra, so `uv sync` and the test
+# suite work offline without downloading models. See
+# wiki/decisions/vision-service-packaging.md + wiki/entities/opencv-anpr-service.md.
+dependencies = [
+    "fastapi>=0.115",
+    "uvicorn[standard]>=0.32",
+    "pydantic>=2.9",
+    "pydantic-settings>=2.6",
+]
+
+[project.optional-dependencies]
+# The real recognizer. Install with: uv sync --extra alpr
+#   fast-alpr is MIT (YOLOv9 detector + CCT OCR, both MIT) on ONNX Runtime — see the
+#   recognizer evaluation in wiki/entities/opencv-anpr-service.md. onnxruntime is the
+#   CPU backend; swap for onnxruntime-gpu / -openvino / -directml on capable hardware.
+alpr = [
+    "fast-alpr>=0.4.0",
+    "onnxruntime>=1.19",
+]
+
+[dependency-groups]
+# Dev tooling (uv installs these by default for local work; excluded from the runtime image).
+dev = [
+    "ruff>=0.8",
+    "pytest>=8.3",
+    "httpx>=0.27",          # FastAPI TestClient transport
+    "mypy>=1.13",
+]
+
+[tool.ruff]
+line-length = 110
+target-version = "py310"
+
+[tool.ruff.lint]
+# A pragmatic default set: pyflakes, pycodestyle, isort, bugbear, pyupgrade.
+select = ["E", "F", "I", "B", "UP"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+
+[tool.mypy]
+python_version = "3.10"
+strict = true
+# fast-alpr / onnxruntime ship without type stubs; don't fail typecheck on the optional stack.
+ignore_missing_imports = true
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["vision_service"]