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

Drawing a histogram


A histogram is a statistical graphic representation of variable distribution that allows us to understand the density estimation and probability distribution of data. A histogram is created by dividing the entire range of variable values into a small range of values, and then counting how many values fall into each interval.

 

If we apply this histogram concept to an image, it seems to be difficult to understand but, in fact, it is very simple. In a gray image, our variable values' ranges are each possible gray value (from 0 to 255), and the density is the number of pixels of the image that have this value. This means that we have to count the number of pixels of the image that have a value of 0, the number of pixels with a value of 1, and so on.

The callback function that shows the histogram of the input image is showHistoCallback ; this function calculates the histogram of each channel image and shows the result of each histogram channel in a new image.

Now, check the following...