Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Image color equalization


In this section, we are going to learn how to equalize a color image. Image equalization, or histogram equalization, tries to obtain a histogram with a uniform distribution of values. The result of equalization is an increase in the contrast of an image. Equalization allows lower local contrast areas to gain high contrast, spreading out the most frequent intensities. This method is very useful when the image is extremely dark or bright and there is a very small difference between the background and foreground. Using histogram equalization, we increase the contrast and the details that are over- or under-exposed. This technique is very useful in medical images, such as X-rays.

However, there are two main disadvantages to this method: the increase in background noise and a consequent decrease in useful signals. We can see the effect of equalization in the following photograph, and the histogram changes and spreads when increasing the image contrast:

Let's implement our...