Captures frames from a V4L2 camera and publishes them as std/media/v1/Image: an Arrow struct of width, height, encoding and data, so a receiver needs nothing from the message metadata. For a compressed encoding the recorded dimensions are the image's, not the length of the payload. Based on opencv-video-capture from dora-hub, with three changes. The output is the typed image struct rather than a flat UInt8 buffer plus metadata. Camera selection by id reads /dev/v4l/by-id/ only; the upstream macOS system_profiler and Windows PowerShell enumeration is not carried. CAPTURE_FRAMES bounds a run so a test ends on its own, in place of upstream's ten-second CI cutoff -- a timeout would have made the CI step exit non-zero. CI=true keeps a failed capture from raising, publishing a labelled black frame instead, which is what lets dataflow.yml run on a machine with no camera. Two dataflows, run one at a time, as dora has no cross-dataflow wiring: dataflow.yml is the camera alone and the CI gate; dataflow_view.yml wires it into dora-image-view and is not run by CI, since it fetches that node over the network and needs a tag to exist there.
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# Gates that need no camera: the dataflow still matches the typed contract in
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# dora-node.yml and still runs end to end, the unit tests pass, and the docs
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# lint. CI=true is set by the runner, which is what makes a failed capture
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# publish a placeholder frame instead of raising.
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name: validate
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# A pull request would otherwise run this twice on the same commit: once for
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# the branch push and once for the PR.
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on:
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push:
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branches: [main]
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pull_request:
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env:
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DORA_VERSION: v1.0.1
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DORA_TARGET: x86_64-unknown-linux-gnu
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jobs:
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validate:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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# The opencv-python wheel links against libGL at import time, so cv2
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# cannot even be imported on the bare runner image.
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- name: Install the OpenCV shared libraries
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run: |
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sudo apt-get update
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sudo apt-get install -y --no-install-recommends libgl1 libglib2.0-0
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- uses: astral-sh/setup-uv@v5
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with:
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enable-cache: true
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# The CLI is a single static binary pinned to DORA_VERSION, so the cache
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# hit is exact and the download only happens when the version changes.
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- name: Cache the dora CLI
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id: dora-cache
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uses: actions/cache@v4
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with:
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path: ~/.local/bin/dora
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key: dora-cli-${{ env.DORA_VERSION }}-${{ env.DORA_TARGET }}
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- name: Install the dora CLI
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if: steps.dora-cache.outputs.cache-hit != 'true'
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run: |
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curl -fsSL -o dora-cli.tar.gz \
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"https://github.com/dora-rs/dora/releases/download/${DORA_VERSION}/dora-cli-${DORA_TARGET}.tar.gz"
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tar xzf dora-cli.tar.gz
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mkdir -p "$HOME/.local/bin"
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install -m 755 "dora-cli-${DORA_TARGET}/dora" "$HOME/.local/bin/dora"
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rm -rf dora-cli.tar.gz "dora-cli-${DORA_TARGET}"
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- name: Put the dora CLI on PATH
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run: echo "$HOME/.local/bin" >> "$GITHUB_PATH"
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- run: dora --version
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# --locked fails if uv.lock is out of date with pyproject.toml, so a
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# dependency change that was never locked cannot reach main.
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- name: Install
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run: uv sync --locked --group dev
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- run: uv run ruff check .
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- run: uv run pymarkdown scan *.md
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- run: uv run pytest -q
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- name: Validate the node manifest
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run: dora validate --node-manifest dora-node.yml
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# strict_types: true in the dataflow turns a type warning into a non-zero
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# exit, so a mismatched annotation fails here rather than at runtime.
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- name: Validate the dataflow
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run: dora validate dataflow.yml
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# dataflow_view.yml is deliberately not run: it fetches dora-image-view
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# over the network and needs a tag to exist there.
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# CAPTURE_FRAMES bounds the run so it ends on its own; CI=true is already
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# set by the runner, which makes a failed capture publish a placeholder
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# frame rather than raising on a machine with no camera.
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- name: Run the dataflow headless
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env:
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CAPTURE_FRAMES: 30
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run: dora run dataflow.yml --uv
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