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

Main camera processing loop for a desktop app


If you want to display a GUI window on the screen using OpenCV, you call the cv::namedWindow() function and then the cv::imshow() function for each image, but you must also call cv::waitKey() once per frame, otherwise your windows will not update at all! Calling cv::waitKey(0) waits forever until the user hits a key in the window, but a positive number such as waitKey(20) or higher will wait for at least that many milliseconds.

Put this main loop in the main.cpp file, as the basis of your real-time camera app:

while (true) { 
    // Grab the next camera frame. 
    cv::Mat cameraFrame; 
    camera >> cameraFrame; 
    if (cameraFrame.empty()) { 
        std::cerr<<"ERROR: Couldn't grab a camera frame."<< 
        std::endl; 
        exit(1); 
    } 
    // Create a blank output image, that we will draw onto. 
    cv::Mat displayedFrame(cameraFrame.size(), cv::CV_8UC3); 

    // Run the cartoonifier filter on the camera frame....