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Learn OpenCV 4 by Building Projects

Learn OpenCV 4 by Building Projects

By : Millán Escrivá, Vinícius G. Mendonça, Joshi
2.5 (2)
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Learn OpenCV 4 by Building Projects

Learn OpenCV 4 by Building Projects

2.5 (2)
By: Millán Escrivá, Vinícius G. Mendonça, Joshi

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. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module. By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
Table of Contents (14 chapters)
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Basic matrix operations

In this section, we will learn a number of basic and important matrix operations that we can apply to images or any matrix data. We learned how to load an image and store it in a Mat variable, but we can create Mat manually. The most common constructor is giving the matrix a size and type, as follows:

Mat a= Mat(Size(5,5), CV_32F); 
You can create a new matrix linking with a stored buffer from third-party libraries without copying data using this constructor: Mat(size, type, pointer_to_buffer).

The types supported depend on the type of number you want to store and the number of channels. The most common types are as follows:

CV_8UC1 
CV_8UC3 
CV_8UC4 
CV_32FC1 
CV_32FC3 
CV_32FC4
You can create any type of matrix using CV_number_typeC(n), where the number_type is 8 bits unsigned (8U) to 64 float (64F), and where (n) is the number of channels; the number...

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