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)

Implementing your first perceptron

Perceptrons are easy enough to be implemented from scratch. We can mimic the typical OpenCV or scikit-learn implementation of a classifier by creating a Perceptron object. This will allow us to initialize new perceptron objects that can learn from data via a fit method and make predictions via a separate predict method.

When we initialize a new perceptron object, we want to pass a learning rate (lr, or η in the previous section) and the number of iterations after which the algorithm should terminate (n_iter):

In [1]: import numpy as np
In [2]: class Perceptron(object):
... def __init__(self, lr=0.01, n_iter=10):
... = lr
... self.n_iter = n_iter

The fit method is where most of the work is done. This method should take as input some data samples (X) and their associated target labels (y). We will then create an array...