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

Lomography effect


In this section, we are going to create another image effect, which is a photograph effect that is very common in different mobile applications, such as Google Camera or Instagram. We are going to discover how to use a look-up table (LUT). We will go through LUTs later in this same section. We are going to learn how to add an over image, in this case a dark halo, to create our desired effect. The function that implements this effect is the lomoCallback callback and it has the following code:

void lomoCallback(int state, void* userData) 
{ 
    Mat result; 
 
    const double exponential_e = std::exp(1.0); 
    // Create Look-up table for color curve effect 
    Mat lut(1, 256, CV_8UC1); 
    for (int i=0; i<256; i++) 
    { 
        float x= (float)i/256.0;  
        lut.at<uchar>(i)= cvRound( 256 * (1/(1 + pow(exponential_e, -((x-0.5)/0.1)) )) ); 
    } 
    
    // Split the image channels and apply curve transform only to red channel 
    vector<Mat> bgr...