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Water bodies detection

Goal

  • Build the container image
  • Run the water bodies detection in the container image tagged localhost/detect-water-bodies:latest.

Lab

This step has a dedicated lab available at /workspace/quickwin/practice-labs/Container.ipynb

The container recipe

The water bodies detection has a recipe to build the container image.

The detect_water_bodies step container image recipe is:

detect-water-body/Dockerfile
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FROM docker.io/python:3.10-slim

RUN pip install --no-cache-dir \
    rasterio \
    click \
    pystac \
    loguru \
    pyproj \
    shapely \
    scikit-image \
    pystac \
    rio_stac && \
    python -c "import rasterio"

ADD app.py /app/app.py

ENTRYPOINT []

Building the container:

Build the container images with:

terminal
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export WORKSPACE=/workspace/quickwin

command -v podman >/dev/null 2>&1 && { 

    podman build --format docker -t localhost/detect-water-body:latest ${WORKSPACE}/water-bodies/command-line-tools/detect-water-body

} || command -v docker >/dev/null 2>&1 && { 

    docker build -t localhost/detect-water-body:latest ${WORKSPACE}/water-bodies/command-line-tools/detect-water-body 
}

Expected outcome

The local container registry lists the built images:

(base) jovyan@coder-mrossi:~/runs$ podman images | grep localhost
localhost/detect-water-body  latest      534f3f10c06e  11 minutes ago  530 MB

How to run a step in a container

We'll use podman container engine (docker is also fine).

The command to run the detect-water-body step in the container is:

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podman run \
    -i \
    --userns=keep-id \
    --mount=type=bind,source=/workspace/quickwin/runs,target=/runs \
    --workdir=/runs \
    --read-only=true \
    --user=1001:100 \
    --rm \
    --env=HOME=/runs \
    --env=PYTHONPATH=/app \
    localhost/detect-water-body:latest \
    python \
    -m \
    app \
    --aoi \
    "-121.399,39.834,-120.74,40.472" \
    --band \
    green \
    --band \
    nir \
    --epsg \
    "EPSG:4326" \
    --input-item \
    https://earth-search.aws.element84.com/v0/collections/sentinel-s2-l2a-cogs/items/S2B_10TFK_20210713_0_L2A

Let's break down what this command does:

  • podman run: This is the command to run a container.
  • -i: This flag makes the container interactive, allowing you to interact with it via the terminal.
  • --userns=keep-id: It instructs podman to keep the user namespace ID. --mount=type=bind,source=/workspace/runs,target=/runs: This option mounts a directory from the host system to the container. In this case, it mounts the /workspace/runs directory on the host to the /runs directory inside the container.
  • --workdir=/runs: Sets the working directory inside the container to /runs.
  • --read-only=true: Makes the file system inside the container read-only, meaning you can't write or modify files inside the container.
  • --user=1001:100: Specifies the user and group IDs to be used within the container.
  • --rm: This flag tells podman to remove the container after it has finished running.
  • --env=HOME=/runs: Sets the HOME environment variable inside the container to /runs.
  • --env=PYTHONPATH=/app: Sets the PYTHONPATH environment variable inside the container to /app.
  • localhost/detect-water-body:latest: This is the name of the container image that you want to run. It's pulling the image from the local container registry with the name "detect-water-body" and the "latest" tag.
  • python -m app: This is the command to run inside the container. It runs a Python module named "app".
  • --aoi "-121.399,39.834,-120.74,40.472": This provides command-line arguments to the Python module. It specifies the area of interest (AOI) as a bounding box.
  • --band green: Specifies the band to be extracted from the Sentinel-2 acquisition. In this case, it's the "green" band.
  • --band nir: Specifies the band to be extracted from the Sentinel-2 acquisition. In this case, it's the "nir" band.
  • --epsg "EPSG:4326": Specifies the EPSG code, which defines the coordinate system used for the aoi command-line argument.
  • --input-item ...: Specifies the input STAC item URL. This particular URL points to a Sentinel-2 image hosted on AWS Earth Search.

Expected outcome

The folder /workspace/quickwin/runs contains: 

(base) jovyan@jupyter-mrossi--training:~/quickwin$ tree runs/
runs/
├── S2B_10TFK_20210713_0_L2A
│   ├── S2B_10TFK_20210713_0_L2A.json
│   └── otsu.tif
├── catalog.json
└── otsu.tif

1 directory, 4 files