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

Implementation of the skin color changer


Rather than detecting the skin color and then the region with that skin color, we can use OpenCV's floodFill() function, which is similar to the bucket fill tool in most image editing software. We know that the regions in the middle of the screen should be skin pixels (since we asked the user to put their face in the middle), so to change the whole face to have green skin, we can just apply a green flood fill on the center pixel, which will always color some parts of the face green. In reality, the color, saturation, and brightness are likely to be different in different parts of the face, so a flood fill will rarely cover all the skin pixels of a face unless the threshold is so low that it also covers unwanted pixels outside of the face. So instead of applying a single flood fill in the center of the image, let's apply a flood fill on six different points around the face that should be skin pixels.

A nice feature of OpenCV's floodFill() is that it...