opencvcourses/opencv repository overview

This image consists of CPP and Python3 installation of OpenCV-4.4.0. You can use it to run CPP and Python3 code on the terminal, or create CPP and 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/opencv:440

-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/opencv:440
This will start the container with an interactive shell. This is the most basic command to run the image.
Here, you can run python, or c++ code (using cmake), or even cling.
To start cling, enter cling on the terminal and code in c++ like python.
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.

⁠How to use:

⁠cmake

You can run CPP codes using cmake. Try the sample code available in our course.

⁠Jupyter Notebook:

Start the container and execute the below command to start a Jupyter Notebook session:
jupyter notebook --ip 0.0.0.0 --allow-root --no-browser
You can create Python3 or CPP notebooks in jupyter notebook.

⁠CPP in Jupyter Notebook

Xeus-cling is the CPP interpreter kernel. Coding is similar in xeus-cling, but you need to include a few helper header files. They are:

#include "/usr/local/lib/includeLibraries.h"
#include "/usr/local/lib/displayImages.h"
#include "/usr/local/lib/matplotlibcpp.h"

Check the course contents on how to print in cpp using matplotlib-cpp