This is the code repository for Machine Learning for OpenCV, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
Machine Learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of Machine Learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
In [1]: import numpy
... numpy.__version__
Out[1]: '1.11.3'
You will need a computer, Python Anaconda, and enthusiasm. Lots of enthusiasm. Why Python?, you may ask. The answer is simple: It has become the de facto language of data science, thanks to its great number of open-source libraries and tools to process and interact with data. One of these tools is the Python Anaconda distribution, which provides all the scientific computing libraries we could possibly ask for, such as NumPy, SciPy, Matplotlib, Scikit-Learn, and Pandas. In addition, installing OpenCV is essentially a one-liner. No more flipping switches in ccmake or compiling from scratch! We will talk about how to install Python Anaconda in Chapter 1, A Taste of Machine Learning. If you have mostly been using OpenCV in combination with C++, that's fine. But, at least for the purpose of this book, I would strongly suggest that you switch to Python. C++ is fine when your task is to develop high-performance code or real-time applications. But, when it comes to picking up a new skill, I believe Python to be the fundamentally better choice of language, because you can do more by typing less. Rather than getting annoyed by the syntactic subtleties of C++, or wasting hours trying to convert data from one format into another, Python will help you concentrate on the topic at hand: to become an expert in machine learning.