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)

The Gazer application

In order to delve into camera handling, video processing, and motion analysis, we will develop a brand new application. Besides learning about these topics, we will also get an application that has many pragmatic features: being able to record video through a webcam, monitor for our home security, and notify us on our mobile if a suspicious motion is detected. Let's clarify its features, which are as follows:

  • Open a webcam and play the video that's been captured from it in real time
  • Record video from the webcam by clicking on a start/stop button
  • Show a list of saved videos
  • Detect motion, save video, and send notifications to our mobile phone if suspicious motion is detected
  • Show some information about the cameras and the application's status

After these features have been clarified, we can design the UI. Again, we will use the open source...