Summary

In this first chapter, we covered the main steps to set up OpenCV and Python to build your computer vision projects. At the beginning of this chapter, we quickly looked at the main concepts in this book – Artificial Intelligence, machine learning, neural networks, and deep learning. Then we explored the OpenCV library, including the history of the library and its main modules. As OpenCV and other packages can be installed in many operating systems and in different ways, we covered the main approaches.

Specifically, we saw how to install Python, OpenCV, and other packages globally or in a virtual environment. In connection with the installation of the packages, we introduced Anaconda/Miniconda and Conda, because we can also create and manage virtual environments. Additionally, Anaconda/Miniconda comes with many open source scientific packages, including SciPy and NumPy.

We explored the main packages for scientific computing, data science, machine learning, and computer vision, because they offer powerful computational tools. Then we discussed the Python-specific IDEs, including PyCharm (the de facto Python IDE environment). PyCharm (and other IDEs) can help us create virtual environments in a very intuitive way. We also looked at Jupyter Notebooks, because it can be a good tool for the readers of this book. In the next chapters, more Jupyter Notebooks will be created to give you a better understanding of this useful tool. Finally, we explored an OpenCV and Python project structure, covering the main files that should be included. Then we built our first Python and OpenCV sample project, where we saw the commands to build, run, and test this project.

In the next chapter, you will start to write your first scripts as you get better acquainted with the OpenCV library. You will see some basic concepts necessary to start coding your computer vision projects (for example, understanding main image concepts, the coordinate system in OpenCV, and accessing and manipulating pixels in OpenCV).