julian
5cedcaefe1
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
63 lines
1.9 KiB
TOML
63 lines
1.9 KiB
TOML
[project]
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name = "parking-vision"
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version = "0.0.0"
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description = "Host-side ANPR / vehicle-verification microservice for the parking system (separate process; localhost HTTP)."
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requires-python = ">=3.10,<4.0"
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# Core deps are LIGHT on purpose: the service boots, serves /health, and answers
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# /analyze in stub mode with ONLY these. The heavy recognizer stack (fast-alpr +
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# onnxruntime + model weights) is the optional `alpr` extra, so `uv sync` and the test
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# suite work offline without downloading models. See
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# wiki/decisions/vision-service-packaging.md + wiki/entities/opencv-anpr-service.md.
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dependencies = [
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"fastapi>=0.115",
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"uvicorn[standard]>=0.32",
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"pydantic>=2.9",
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"pydantic-settings>=2.6",
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]
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[project.scripts]
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vision-recognize = "vision_service.cli:main"
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[project.optional-dependencies]
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# The real recognizer. Install with: uv sync --extra alpr
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# fast-alpr is MIT (YOLOv9 detector + CCT OCR, both MIT) on ONNX Runtime — see the
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# recognizer evaluation in wiki/entities/opencv-anpr-service.md. onnxruntime is the
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# CPU backend; swap for onnxruntime-gpu / -openvino / -directml on capable hardware.
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alpr = [
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"fast-alpr>=0.4.0",
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"onnxruntime>=1.19",
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]
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[dependency-groups]
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# Dev tooling (uv installs these by default for local work; excluded from the runtime image).
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dev = [
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"ruff>=0.8",
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"pytest>=8.3",
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"httpx>=0.27", # FastAPI TestClient transport
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"mypy>=1.13",
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]
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[tool.ruff]
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line-length = 110
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target-version = "py310"
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[tool.ruff.lint]
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# A pragmatic default set: pyflakes, pycodestyle, isort, bugbear, pyupgrade.
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select = ["E", "F", "I", "B", "UP"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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[tool.mypy]
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python_version = "3.10"
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strict = true
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# fast-alpr / onnxruntime ship without type stubs; don't fail typecheck on the optional stack.
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ignore_missing_imports = true
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["vision_service"]
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