Book Image

Machine Learning for OpenCV

By : Michael Beyeler
Book Image

Machine Learning for OpenCV

By: Michael Beyeler

Overview of this book

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. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Table of Contents (13 chapters)

Using decision trees for regression

Although we have so far focused on using decision trees in classification tasks, you can also use them for regression. But you will need to use scikit-learn again, as OpenCV does not provide this flexibility. We therefore only briefly review its functionality here.

Let's say we wanted to use a decision tree to fit a sin wave. To make things interesting, we will also add some noise to the data points using NumPy's random number generator:

In [1]: import numpy as np
... rng = np.random.RandomState(42)

We then create 100 x values between 0 and 5, and calculate the corresponding sin values:

In [2]: X = np.sort(5 * rng.rand(100, 1), axis=0)
... y = np.sin(X).ravel()

We then add noise to every other data point in y (using y[::2]), scaled by 0.5 so we don't introduce too much jitter:

In [3]: y[::2] += 0.5 * (0.5 - rng.rand(50...