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

Lomography effect


In this section, we will create another image effect, a photographic effect that is commonly used in different mobile applications, such as Google Camera or Instagram.

In this section, we will discover how to use a Look up Table or LUT. We will discuss LUTs later in this chapter.

We will 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 callback lomoCallback and has the following code:

void lomoCallback(int state, void* userData)
{
    Mat result;

    const double exponential_e = std::exp(1.0);
    // Create Lookup 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;
    split(img, bgr);
    LUT(bgr[2],...