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Computer Vision with OpenCV 3 and Qt5

Computer Vision with OpenCV 3 and Qt5

By : Amin Ahmadi Tazehkandi
4.5 (11)
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Computer Vision with OpenCV 3 and Qt5

Computer Vision with OpenCV 3 and Qt5

4.5 (11)
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 (14 chapters)
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Image transformation capabilities


In this section, you will learn about the transformation capabilities available in OpenCV. In general, there are two image transformation categories in OpenCV, called geometric and miscellaneous (which simply means everything else) transformations if you take a look at the OpenCV documentation. The reason for this is explained here.

Geometric transformations, as it can be guessed from their name, deal mostly with geometric properties of images, such as their size, orientation, shape, and so on. Note that a geometric transformation does not change the contents of the image, but it merely changes the form and shape of it by moving around the pixels of an depending on the geometric transformation type. Same as what we saw with filtering images in the beginning of the previous section, geometric transformation functions also need to deal with the extrapolation of pixels outside of an image, or, simply put, making an assumption about the non-existing pixels...

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