julian
20a3cb3e80
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
66 lines
3.5 KiB
Docker
66 lines
3.5 KiB
Docker
# syntax=docker/dockerfile:1.7
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# Parking VISION image: the Python/uv ANPR microservice. Build CONTEXT is apps/vision
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# (self-contained Python package; no monorepo deps). Ships WITH the `alpr` extra (real
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# fast-alpr/onnxruntime stack) but the engine is env-selected: VISION_RECOGNIZER=stub
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# (default, boots anywhere) or fast_alpr (prod). See wiki/decisions/container-deployment.md,
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# wiki/decisions/vision-service-packaging.md.
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# uv-provided Python 3.12 (matches apps/vision/.python-version).
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FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS base
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WORKDIR /app
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ENV UV_LINK_MODE=copy \
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UV_COMPILE_BYTECODE=1 \
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PYTHONUNBUFFERED=1
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# System libs the recognizer stack needs (opencv/onnxruntime): GL + glib. Kept minimal.
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends libgl1 libglib2.0-0 curl \
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&& rm -rf /var/lib/apt/lists/*
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# ---- deps: resolve + install the venv from the lockfile (cache-friendly) ----
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# Manifests first so the heavy `uv sync` layer caches across source edits.
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COPY pyproject.toml uv.lock .python-version ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --frozen --no-install-project --extra alpr
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# ---- project source ----
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COPY vision_service/ ./vision_service/
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COPY README.md ./
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# Vehicle stage weights (phase A): YOLOX-S, Apache-2.0, ~36 MB, baked into the image so the
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# air-gapped appliance never fetches at runtime and no operator-writable path holds a model
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# (vision-service-hardening.md). Best-effort at build: without network the stage stays off.
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ARG YOLOX_URL=https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.onnx
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RUN mkdir -p /app/models \
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&& (curl -fsSL -o /app/models/yolox_s.onnx "$YOLOX_URL" \
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|| (echo "[build] yolox weights not fetched (no network) — vehicle stage off" && rm -f /app/models/yolox_s.onnx))
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --frozen --extra alpr
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# Non-root runtime user, created BEFORE the model pre-warm so the weights cache lands in
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# this user's HOME (~/.cache) — the SAME path the runtime reads. (fast-alpr's
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# open-image-models caches under $HOME/.cache/open-image-models keyed to HOME, ignoring
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# HF_HOME/XDG_CACHE_HOME — so the pre-warm MUST run as the runtime user, not root.)
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RUN useradd --system --create-home --uid 999 vision \
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&& chown -R vision:vision /app
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USER vision
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# Pre-warm the fast-alpr model weights INTO the image (as the vision user → /home/vision/
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# .cache) so the prod recognizer is OFFLINE-first: ALPR() downloads weights on first
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# construction, which would otherwise need network on the appliance's first scan. Best-effort
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# — if the build host has no network this is skipped and weights fetch lazily at runtime.
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# NB: NO --mount=type=cache here — a BuildKit cache mount at ~/.cache is NOT committed to the
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# image layer, so the downloaded weights would vanish. They must write to the real layer.
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RUN uv run python -c "from fast_alpr import ALPR; ALPR()" \
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|| echo "[build] model pre-warm skipped (no network) — weights fetch at runtime"
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# Default to the stub recognizer (offline, no model load); override to fast_alpr in prod.
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ENV VISION_RECOGNIZER=stub \
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VISION_HOST=0.0.0.0 \
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VISION_PORT=8089 \
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VISION_VEHICLE_MODEL_PATH=/app/models/yolox_s.onnx
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EXPOSE 8089
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HEALTHCHECK --interval=30s --timeout=5s --start-period=20s --retries=3 \
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CMD python -c "import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://localhost:8089/health').status==200 else 1)" || exit 1
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CMD ["uv", "run", "uvicorn", "vision_service.app:app", "--host", "0.0.0.0", "--port", "8089"]
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