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.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Building an Image Viewer
Building an Image Viewer
Technical requirements
Designing the user interface
Starting the project from scratch
Setting up the full user interface
Implementing the functions for the actions
Summary
Questions
2
Editing Images Like a Pro
Editing Images Like a Pro
Technical requirements
The ImageEditor application
Blurring images using OpenCV
Adding features using Qt's plugin mechanism
Editing images like a pro
Summary
Questions
3
Home Security Applications
Home Security Applications
Technical requirements
The Gazer application
Accessing cameras
Saving videos
Motion analysis with OpenCV
Summary
Questions
4
Fun with Faces
Fun with Faces
Technical requirements
The Facetious application
Detecting faces using cascade classifiers
Detecting facial landmarks
Applying masks to faces
Summary
Questions
5
Optical Character Recognition
Optical Character Recognition
Technical requirements
Creating Literacy
OCR with Tesseract
Detecting text areas with OpenCV
Recognizing characters on the screen
Summary
Questions
6
Object Detection in Real Time
Object Detection in Real Time
Technical requirements
Detecting objects using OpenCV
Detecting objects using a cascade classifier
Training a cascade classifier
Detecting objects using deep learning models
About real time
Summary
Questions
7
Real-Time Car Detection and Distance Measurement
Real-Time Car Detection and Distance Measurement
Technical requirements
Car detection in real time
Distance measurement
Measuring the distance between cars or between the car and the camera
Summary
Questions
8
Using OpenGL for the High-Speed Filtering of Images
Using OpenGL for the High-Speed Filtering of Images
Technical requirements
Hello OpenGL
OpenGL in Qt
Filtering images with OpenGL
Using OpenGL with OpenCV
Summary
Further reading
9
Assessments
Assessments
Chapter 1, Building an Image Viewer
Chapter 2, Editing Images Like a Pro
Chapter 3, Home Security Applications
Chapter 4, Fun with Faces
Chapter 5, Optical Character Recognition
Chapter 6, Object Detection in Real Time
Chapter 7, Real-Time Car Detection and Distance Measurement
10
Other Books You May Enjoy
Other Books You May Enjoy
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Questions

Try these questions to test your knowledge from this chapter:

  1. When we trained the cascade classifier for the faces of the Boston Bulls, we annotated the dog faces on each image by ourselves. The annotation process cost us much time. There is a tarball of annotation data for that dataset at this website: http://vision.stanford.edu/aditya86/ImageNetDogs/annotation.tar. Could we generate the info.txt file from this annotation data via a piece of code? How would we do that?
  2. Try to find a pretrained (fast/faster) R-CNN model and a pretrained SSD model. Run them and compare their performance to YOLOv3.
  3. Could we use YOLOv3 to detect a certain kind of object, but not all the 80 classes of objects?
Previous Section
End of Chapter 6
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