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Learning OpenCV 4 Computer Vision with Python 3

You're reading from   Learning OpenCV 4 Computer Vision with Python 3 Get to grips with tools, techniques, and algorithms for computer vision and machine learning

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781789531619
Length 372 pages
Edition 3rd Edition
Languages
Tools
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Authors (2):
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Joe Minichino Joe Minichino
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Joe Minichino
Joseph Howse Joseph Howse
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Joseph Howse
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Table of Contents (13) Chapters Close

Preface 1. Setting Up OpenCV 2. Handling Files, Cameras, and GUIs FREE CHAPTER 3. Processing Images with OpenCV 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Building Custom Object Detectors 8. Tracking Objects 9. Camera Models and Augmented Reality 10. Introduction to Neural Networks with OpenCV 11. Other Book You May Enjoy Appendix A: Bending Color Space with the Curves Filter

Implementing the demo application

We are going to implement our demo in a single script, ImageTrackingDemo.py, 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, 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.

Without further ado, let's dive into the script's implementation.

Importing modules

...
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