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

Appendix 1. Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Mastering OpenCV 3 Daniel Lelis Baggio, Shervin Emami, David Millán Escrivá, Khvedchenia Ievgen, Jason Saragih, Roy Shilkrot

ISBN: 978-1-78646-717-1

  • Execute basic image processing operations and cartoonify an image
  • Build an OpenCV project natively with Raspberry Pi and cross-compile it for Raspberry Pi.text
  • Extend the natural feature tracking algorithm to support the tracking of multiple image targets on a video
  • Use OpenCV 3’s new 3D visualization framework to illustrate the 3D scene geometry
  • Create an application for Automatic Number Plate Recognition (ANPR) using a support vector machine and Artificial Neural Networks
  • Train and predict pattern-recognition algorithms to decide whether an image is a number plate
  • Use POSIT for the six degrees of freedom head pose
  • Train a face recognition database using deep learning and recognize faces from that database

Machine Learning for OpenCV Michael Beyeler

ISBN: 978-1-78398-028-4

  • Explore and make effective use of OpenCV's Machine Learning module
  • Learn deep learning for computer vision with Python
  • Master linear regression and regularization techniques
  • Classify objects such as flower species, handwritten digits, and pedestrians
  • Explore the effective use of support vector machines, boosted decision trees, and random forests
  • Get acquainted with neural networks and Deep Learning to address real-world problems
  • Discover hidden structures in your data using k-means clustering
  • Get to grips with data pre-processing and feature engineering