opencvcourses/course-4 repository overview

⁠Course-4 Docker Image

This image consists of Python3 installation of OpenCV-4.4.0. It also comes with Tensorflow 2.3 preinstalled. You can use this image to run Python3 code on the terminal, or create Python3 notebooks.

⁠How to run

⁠Our favourite run command is:

⁠docker run -it --rm --mount type=bind,source="$(pwd)"/work,target=/work -p 8888:8888 opencvcourses/course-4

-it starts an interactive shell. This switch is always needed to start the container. Otherwise, it will start and stop instantly.
--rm specifies to kill the container after it is exited.
--mount creates persistent storage to save all the work. Read more about it here⁠.
-p is used to expose a container's port to the host.
Note: Run this command in the parent directory of work folder.

In this configuration, you can use all the functions of our image

⁠Minimal run command:

⁠docker run -it opencvcourses/course-4

This will start the container with an interactive shell. This is the most basic command to run the image.
Here, you can run python on the terminal.
In this mode, you can only access the terminal.

To use Jupyter Notebook as well, you'll need to expose a port from the container to the host. You can expose ports using the -p switch.

⁠docker run -it -p 8888:8888 opencvcourses/course-4

⁠How to use:

⁠Terminal:

You can create .py scripts and run them on the terminal.
Moreover, you can also run python code directly on the terminal using the python command.

⁠Notebooks:

Run the container and execute the following command to start a Jupyter Notebook session:
jupyter notebook --ip 0.0.0.0 --allow-root --no-browser