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OpenCV 4 Computer Vision Application Programming Cookbook

You're reading from   OpenCV 4 Computer Vision Application Programming Cookbook Build complex computer vision applications with OpenCV and C++

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789340723
Length 494 pages
Edition 4th Edition
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Authors (2):
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Robert Laganiere Robert Laganiere
Author Profile Icon Robert Laganiere
Robert Laganiere
David Millán Escrivá David Millán Escrivá
Author Profile Icon David Millán Escrivá
David Millán Escrivá
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Toc

Table of Contents (17) Chapters Close

Preface 1. Playing with Images FREE CHAPTER 2. Manipulating the Pixels 3. Processing Color Images with Classes 4. Counting the Pixels with Histograms 5. Transforming Images with Morphological Operations 6. Filtering the Images 7. Extracting Lines, Contours, and Components 8. Detecting Interest Points 9. Describing and Matching Interest Points 10. Estimating Projective Relations in Images 11. Reconstructing 3D Scenes 12. Processing Video Sequences 13. Tracking Visual Motion 14. Learning from Examples 15. OpenCV Advanced Features 16. Other Books You May Enjoy

Computing the image histogram

An image is composed of pixels and each pixel, can contain one value (one channel) that generates a gray image, or can contain three values (RGB or three channels) that generate a color image. Each channel contains values from 0 to 255 (from a black to a saturated channel, in the case of a one-channel pixel from black to white). Depending on the content of the image, you will get different amounts of each gray value.

A histogram is a simple table that gives you the number of pixels that have a given value in an image (or sometimes, a set of images). The histogram of a gray-level image will, therefore, have 256 entries (or bins). Bin 0 gives you the number of pixels that have the value 0, bin 1 gives you the number of pixels that have the value 1, and so on. Obviously, if you sum all of the entries of a histogram, you should get the total number of...

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