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

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); 

Note

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

Note

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 of channels permitted ranges from 1 to CV_CN_MAX.

The initialization does not set up the data values, and hence...