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

MeanShift and CamShift


What we learned until now in this chapter, apart from the use cases that we already saw, was meant to prepare us for correctly using the and CamShift algorithms, since they extensively benefit from histograms and back-projection images. But what are the and CAMShift algorithms?

Let's start with the MeanShift and then move on to CamShift, which is basically the enhanced version of the same algorithm. So, a very practical definition for MeanShift (as it is stated in the current OpenCV documentation) is the following:

Finds an object on a back projection image

That's quite a simple yet practical definition of the MeanShift algorithm, and we are going to stick to that more or less when we work with it. However, it's worth noting the underlying algorithm, since it helps with using it easily and much more efficiently. To start describing how MeanShift works, first, we need to think about the white pixels in a back-projection image (or binary images in general) as scattered...