Book Image

OpenCV By Example

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

OpenCV By Example

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

Overview of this book

Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.
Table of Contents (18 chapters)
OpenCV By Example
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Drawing a histogram


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

If we apply this histogram concept to an image, it seems to be complex to understand, but it is really very simple. In a gray image, our variable values can take any possible gray value ranging from 0 to 255, and the density is the number of pixels in the image that have this value. This means that we have to count the number of image pixels that have the value 0, count the number of pixels of value 1, and so on.

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

Now, let's check...