As we have stated multiple times throughout this book, the Python programming language is on its way to becoming the most popular programming language. This is especially true in the field of computational image processing, which, most of the time, requires fast prototyping and designing, and significant automation capabilities.

As we will find out in the following section, digital images are represented in two-dimensional and three-dimensional matrices so that computers can process them easily. Consequently, most of the time, digital image processing involves matrix calculation. Multiple Python libraries and modules not only provide efficient matrix calculation options, but also interact seamlessly with other libraries that handle image reading/writing.

As we already know, automating tasks and making them concurrent are both Python's strong suit. This makes Python the prime candidate to implement your image processing applications. For this chapter, we will be working with two main Python libraries: OpenCV (which stands for Open Source Computer Vision), which is a library that provides image processing and computer vision options in C++, Java, and Python, and NumPy, which, as we know, is one of the most popular Python modules and performs efficient and parallelizable number-crunching calculations.