Book Image

Computer Vision with OpenCV 3 and Qt5

By : Amin Ahmadi Tazehkandi
4 (1)
Book Image

Computer Vision with OpenCV 3 and Qt5

4 (1)
By: Amin Ahmadi Tazehkandi

Overview of this book

Developers have been using OpenCV library to develop computer vision applications for a long time. However, they now need a more effective tool to get the job done and in a much better and modern way. Qt is one of the major frameworks available for this task at the moment. This book will teach you to develop applications with the combination of OpenCV 3 and Qt5, and how to create cross-platform computer vision applications. We’ll begin by introducing Qt, its IDE, and its SDK. Next you’ll learn how to use the OpenCV API to integrate both tools, and see how to configure Qt to use OpenCV. You’ll go on to build a full-fledged computer vision application throughout the book. Later, you’ll create a stunning UI application using the Qt widgets technology, where you’ll display the images after they are processed in an efficient way. At the end of the book, you’ll learn how to convert OpenCV Mat to Qt QImage. You’ll also see how to efficiently process images to filter them, transform them, detect or track objects as well as analyze video. You’ll become better at developing OpenCV applications.
Table of Contents (19 chapters)
Title Page
Dedication
Packt Upsell
Foreword
Contributors
Preface

All about the Mat class


In the previous chapters, you experienced using the Mat class of the OpenCV framework very briefly, but we're going to dig a little bit deeper now. The Mat class, which borrows its name from the matrix, is an n-dimensional array capable of storing and handling different mathematical data types in single or multiple channels. To simplify this further, let's take a look at what an image is in terms of computer vision. An in computer vision is a matrix (therefore a two-dimensional array) of pixels, with a specified width (number of columns in the matrix) and height (number of rows in the matrix). Furthermore, a pixel in a grayscale image can be represented with a single number (therefore a single channel), with a minimum value (usually 0) representing the black and a maximum value (usually 255, which is the highest number possible with one byte) representing the white, and all values in between corresponding to different gray intensities accordingly. Look at the following...