This chapter was an important milestone since it introduced the concepts required for linking OpenCV and Qt frameworks together. In this chapter, we learned all about the Mat class and its variants. We learned about the new Transparent API in OpenCV and how using the UMat class can improve the performance of our computer vision applications. We also learned about reading and writing images and videos, and also capturing video frames from cameras and network-based video feeds. Later, we moved on to learning about the Qt capabilities and classes that are related to computer vision and working with images. The QImage class in Qt, which is the equivalent of the Mat class in OpenCV, was introduced in this chapter. We also learned about the QPixmap and QPainter classes, and several other Qt classes. While doing just that, we also learned how to create a custom Qt widget and use the QPainter class to draw on a QImage class. Finally, we ended the chapter with an introduction to Qt classes...
Computer Vision with OpenCV 3 and Qt5
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Computer Vision with OpenCV 3 and Qt5
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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.
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Current Title:
Computer Vision with OpenCV 3 and Qt5
Table of Contents (19 chapters)
Title Page
Dedication
Packt Upsell
Foreword
Contributors
Preface
Free Chapter
Introduction to OpenCV and Qt
Creating Our First Qt and OpenCV Project
Creating a Comprehensive Qt+OpenCV Project
Mat and QImage
The Graphics View Framework
Image Processing in OpenCV
Features and Descriptors
Multithreading
Video Analysis
Debugging and Testing
Linking and Deployment
Qt Quick Applications
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Customer Reviews