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

Template matching


The OpenCV framework offers different methods for object detection, tracking, and counting. Template matching is one of the most basic methods of object detection in OpenCV, yet, if it's used correctly and in conjunction with good threshold values, it can be used to effectively detect and count objects in an image. It is done by using a single function in OpenCV called the matchTemplate function.

The matchTemplate function takes an image as the input parameter. Consider it the image that will be searched for the object (or better yet, the scene that may contain the template) that we are interested in. It also takes a template as the second parameter. This template is also an image, but it's the one that will be searched for within the first image parameter. Another parameter required by this function, which is also the most important parameter and the one that decides the template matching method, is the method parameter, which can be one of the entries in the cv::TemplateMatchModes...