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

Representing colors with hue, saturation, and brightness


In this chapter, we played with image colors. We used different color spaces and tried to identify image areas of uniform color. The RGB color space was initially considered, and although it is an effective representation for the capture and display of colors in electronic imaging systems, this representation is not very intuitive. Indeed, this is not the way humans think about colors; they most often describe colors in terms of their tint, brightness, or colorfulness (that is, whether it is a vivid or pastel color). A color space based on the concept of hue, saturation, and brightness has then been introduced to help users to specify the colors using properties that are more intuitive to them. In this recipe, we will explore the concepts of hue, saturation, and brightness as a means to describe colors.

How to do it...

The conversion of a BGR image into another color space is done using the cv::cvtColor function that was explored in...