Book Image

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

By : Robert Laganiere
Book Image

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

By: Robert Laganiere

Overview of this book

Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you’ll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you’ll also get acquainted with recent approaches in machine learning and object classification.
Table of Contents (21 chapters)
OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Converting color representations


The RGB color space is based on the use of the red, green, and blue additive primary colors. We saw in the first recipe of this chapter that these primaries have been chosen because they can produce a good range of colors well aligned with the human visual system. It is often the default color space in digital imagery because this is the way color images are acquired, that is, through the use of red, green, and blue filters. Additionally, the red, green, and blue channels are normalized such that when combined in equal amounts, a gray-level intensity is obtained, that is, from black (0,0,0) to white (255,255,255).

Unfortunately, computing the distance between the colors using the RGB color space is not the best way to measure the similarity between two given colors. Indeed, RGB is not a perceptually uniform color space. This means that two colors at a given distance might look very similar, while two other colors separated by the same distance might look very...