Being the avid user of OpenCV that I believe you are, I probably don't have to convince you about the power of OpenCV.

Built to provide a common infrastructure for computer vision applications, OpenCV has become a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. According to their own documentation, OpenCV has a user community of more than 47,000 people and has been downloaded over seven million times. That's pretty impressive! As an open source project, it is very easy for researchers, businesses, and government bodies to utilize and modify already available code.

That being said, a number of open source machine learning libraries have popped up as part of the recent machine learning boom that provide far more functionality than OpenCV. A prominent example is scikit-learn, which provides a number of state-of-the-art machine learning algorithms as well as a wealth of online tutorials and code snippets. As OpenCV was developed mainly to provide computer vision algorithms, its machine learning functionality is restricted to a single module, called ml. As we will see in this book, OpenCV still provides a number of state-of-the-art algorithms, but sometimes lacks a bit in functionality. In these rare cases, instead of reinventing the wheel, we will simply use scikit-learn for our purposes.

Last but not least, installing OpenCV using the Python Anaconda distribution is essentially a one-liner as we'll see in the following sections.