Book Image

Qt 5 and OpenCV 4 Computer Vision Projects

By : Zhuo Qingliang
4 (1)
Book Image

Qt 5 and OpenCV 4 Computer Vision Projects

4 (1)
By: Zhuo Qingliang

Overview of this book

OpenCV and Qt have proven to be a winning combination for developing cross-platform computer vision applications. By leveraging their power, you can create robust applications with both an intuitive graphical user interface (GUI) and high-performance capabilities. This book will help you learn through a variety of real-world projects on image processing, face and text recognition, object detection, and high-performance computing. You’ll be able to progressively build on your skills by working on projects of increasing complexity. You’ll begin by creating an image viewer application, building a user interface from scratch by adding menus, performing actions based on key-presses, and applying other functions. As you progress, the book will guide you through using OpenCV image processing and modification functions to edit an image with filters and transformation features. In addition to this, you’ll explore the complex motion analysis and facial landmark detection algorithms, which you can use to build security and face detection applications. Finally, you’ll learn to use pretrained deep learning models in OpenCV and GPUs to filter images quickly. By the end of this book, you will have learned how to effectively develop full-fledged computer vision applications with OpenCV and Qt.
Table of Contents (11 chapters)

Chapter 6, Object Detection in Real Time

  1. When we trained the cascade classifier for the faces of Boston bulls, we annotated the dog faces on each image by ourselves. The annotation process was very time-consuming. There is a tarball of annotation data for that dataset on its website: http://vision.stanford.edu/aditya86/ImageNetDogs/annotation.tar. Is it possible to generate the info.txt file from this annotation data by using a piece of code? How can this be done?

The annotation data in that tarball relates to the dogs' bodies, and not to the dogs' faces. So, we can't use it to train a classifier for the dogs' faces. However, if you want to train a classifier for the full bodies of the dogs, this can help. The data in that tarball is stored in XML format, and the annotation rectangles are the nodes with the //annotation/object/bndbox path, which we can extract...