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OpenCV Computer Vision Application Programming Cookbook Second Edition

You're reading from   OpenCV Computer Vision Application Programming Cookbook Second Edition Over 50 recipes to help you build computer vision applications in C++ using the OpenCV library

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Product type Paperback
Published in Aug 2014
Publisher Packt
ISBN-13 9781782161486
Length 374 pages
Edition 1st Edition
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Author (1):
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Robert Laganiere Robert Laganiere
Author Profile Icon Robert Laganiere
Robert Laganiere
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Toc

Table of Contents (13) Chapters Close

Preface 1. Playing with Images FREE CHAPTER 2. Manipulating Pixels 3. Processing Color Images with Classes 4. Counting the Pixels with Histograms 5. Transforming Images with Morphological Operations 6. Filtering the Images 7. Extracting Lines, Contours, and Components 8. Detecting Interest Points 9. Describing and Matching Interest Points 10. Estimating Projective Relations in Images 11. Processing Video Sequences Index

Processing the video frames

In this recipe, our objective is to apply some processing function to each of the frames of a video sequence. We will do this by encapsulating the OpenCV video capture framework into our own class. Among other things, this class will allow us to specify a function that will be called each time a new frame is extracted.

How to do it...

What we want is to be able to specify a processing function (a callback function) that will be called for each frame of a video sequence. This function can be defined as receiving a cv::Mat instance and outputting a processed frame. Therefore, in our framework, the processing function must have the following signature to be a valid callback:

void processFrame(cv::Mat& img, cv::Mat& out);

As an example of such a processing function, consider the following simple function that computes the Canny edges of an input image:

void canny(cv::Mat& img, cv::Mat& out) {
  // Convert to gray
  if (img.channels()==3)
    cv::cvtColor...
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