Initital commit

.github/workflows/ci-checks.yml

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+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: CI checks
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  linting_testing:
+
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+      with:
+        submodules: true
+    - name: Set up Python 3
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements_dev.txt
+    - name: Run black
+      run: |
+        black --check main.py
+    - name: Run pylint
+      run: |
+        pylint  main.py

.github/workflows/release.yml

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+name: Release
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  docker:
+    runs-on: ubuntu-latest
+    steps:
+      -
+        name: Set up QEMU
+        uses: docker/setup-qemu-action@v1
+      -
+        name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v1
+      -
+        name: Login to GitHub Container Registry
+        uses: docker/login-action@v1 
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      -
+        name: Build and push
+        id: docker_build
+        uses: docker/build-push-action@v2
+        with:
+          file: Dockerfile
+          push: true
+          tags: |
+            ghcr.io/mo-rise/crowsnest-processor-video-yolov5:latest
+            ghcr.io/mo-rise/crowsnest-processor-video-yolov5:${{ github.event.release.tag_name }}
+      -
+        name: Image digest
+        run: echo ${{ steps.docker_build.outputs.digest }}

Dockerfile

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+FROM python:3.9-slim
+
+COPY requirements.txt requirements.txt
+
+RUN pip3 install -r requirements.txt
+
+WORKDIR /app
+
+COPY main.py main.py
+
+CMD ["python3", "main.py"]

main.py

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+"""Main entrypoint for this application"""
+import warnings
+import logging
+
+import yolov5
+from vidgear.gears import CamGear
+from vidgear.gears import WriteGear
+
+from environs import Env
+
+env = Env()
+
+SOURCE_STREAM: str = env("SOURCE_STREAM")
+SINK_STREAM: str = env("SINK_STREAM")
+YOLOV5_MODEL: str = env("YOLOV5_MODEL", default="yolov5s.pt")
+YOLOV5_CONFIDENCE_THRESHOLD: float = env.float(
+    "YOLOV5_CONFIDENCE_THRESHOLD", default=0.5, validate=lambda x: 0.0 <= x <= 1.0
+)
+YOLOV5_MAX_DETECTIONS: int = env.int(
+    "YOLOV5_MAX_DETECTIONS", default=10, validate=lambda x: x > 0
+)
+LOG_LEVEL = env.log_level("LOG_LEVEL", logging.WARNING)
+
+verbose = LOG_LEVEL <= logging.INFO
+
+# Setup logger
+logging.basicConfig(level=LOG_LEVEL)
+logging.captureWarnings(True)
+warnings.filterwarnings("once")
+LOGGER = logging.getLogger("crowsnest-processor-video-yolov5")
+
+### Configure yolov5 model
+MODEL = yolov5.load(YOLOV5_MODEL)
+
+MODEL.conf = 0.25  # NMS confidence threshold
+MODEL.iou = 0.45  # NMS IoU threshold
+MODEL.agnostic = False  # NMS class-agnostic
+MODEL.multi_label = False  # NMS multiple labels per box
+MODEL.max_det = 10  # maximum number of detections per image
+
+
+if __name__ == "__main__":
+
+    try:
+
+        # Open source stream, we always want the last frame since we
+        # cant guarantee to keep up with the source frame rate
+        source = CamGear(
+            source=SOURCE_STREAM,
+            colorspace="COLOR_BGR2RGB",
+            logging=verbose,
+            **{"THREADED_QUEUE_MODE": False}
+        ).start()
+
+        sink = WriteGear(
+            output_filename=SINK_STREAM,
+            logging=verbose,
+            **{"-f": "rtsp", "-rtsp_transport": "tcp"}
+        )
+
+        # Frame-wise loop
+        while True:
+            frame = source.read()
+            if frame is None:
+                break
+
+            # Do object detection
+            # pylint: disable=not-callable
+            result: yolov5.models.common.Detections = MODEL(
+                frame, size=max(frame.shape)
+            )
+
+            annotated_frame = result.render()[0]
+            sink.write(annotated_frame)
+
+    except Exception:  # pylint: disable=broad-except
+        LOGGER.exception("Fundamental failure...")
+
+    finally:
+        source.stop()
+        sink.close()

requirements.txt

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+vidgear
+yolov5
+environs

requirements_dev.txt

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+black
+pylint
+-r requirements.txt