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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

By : Millán Escrivá, Robert Laganiere
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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

5 (1)
By: Millán Escrivá, Robert Laganiere

Overview of this book

OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work with recipes to implement a variety of tasks. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by guiding you through setting up OpenCV, and explaining how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection. By the end of this book, you'll have the skills you need to confidently implement a range of computer vision algorithms to meet the technical requirements of your complex CV projects.
Table of Contents (17 chapters)
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Scanning an image with iterators

In object-oriented programming, looping over a data collection is usually done using iterators. Iterators are specialized classes that are built to go over each element of a collection, hiding how the iteration over each element is specifically done for a given collection. This application of the information-hiding principle makes scanning a collection easier and safer. In addition, it makes it similar in form no matter what type of collection is used. The Standard Template Library (STL) has an iterator class associated with each of its collection classes. OpenCV then offers a cv::Mat iterator class that is compatible with the standard iterators found in the C++ STL.

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In this recipe...

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