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

Cartoonize effect


The last section of this chapter is dedicated to creating another effect, called cartoonize; the purpose of this effect is to create an image that looks like a cartoon. To do this, we divide the algorithm into two steps: edge detection and color filtering.

The cartoonCallback function defines this effect, which has the following code:

void cartoonCallback(int state, void* userData) 
{ 
    /** EDGES **/ 
    // Apply median filter to remove possible noise 
    Mat imgMedian; 
    medianBlur(img, imgMedian, 7); 
 
    // Detect edges with canny 
    Mat imgCanny; 
    Canny(imgMedian, imgCanny, 50, 150); 
     
    // Dilate the edges 
    Mat kernel= getStructuringElement(MORPH_RECT, Size(2,2)); 
    dilate(imgCanny, imgCanny, kernel); 
 
    // Scale edges values to 1 and invert values 
    imgCanny= imgCanny/255; 
    imgCanny= 1-imgCanny; 
     
    // Use float values to allow multiply between 0 and 1 
    Mat imgCannyf; 
    imgCanny.convertTo(imgCannyf, CV_32FC3); ...