Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

By : Joseph Howse, Joe Minichino
5 (2)
Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

5 (2)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You'll be able to put theory into practice by building apps with OpenCV 5 and Python 3. You'll start by setting up OpenCV 5 with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying images, videos, and camera feeds. From taking you through image processing, video analysis, depth estimation, and segmentation, to helping you gain practice by building a GUI app, this book ensures you'll have opportunities for hands-on activities. You'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning, which will enable you to create and use object detectors and even track moving objects in real time. Later, you'll develop your skills in augmented reality and real-world 3D navigation. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age, and you'll deploy your solutions to the Cloud. By the end of this book, you'll have the skills you need to execute real-world computer vision projects.
Table of Contents (12 chapters)
Free Chapter
Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning
Appendix A: Bending Color Space with the Curves Filter

Implementing the demo application

We are going to implement our demo in a single script,, which will contain the following components:

  1. Import statements
  2. A helper function for a custom grayscale conversion
  3. Helper functions to convert keypoints from 2D to 3D space
  4. An application class, ImageTrackingDemo, which will encapsulate a model of the camera and lens, a model of the reference image, a Kalman filter, 6DOF tracking results (including the translation and both the Rodrigues and Euler representations of the rotation), and an application loop that will track the image and draw a simple AR visualization
  5. A main function to launch the application

The script will depend on one other file, reference_image.png, which will represent the image that we want to track.

By preparing a reference image in advance, and by loading it from file at runtime, we can ensure that its technical qualities are good: it has a high resolution (important for close-up tracking), it is properly...