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Learning OpenCV 5 Computer Vision with Python

You're reading from   Learning OpenCV 5 Computer Vision with Python Tackle computer vision and machine learning with the newest tools, techniques and algorithms

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Product type Paperback
Published in Jul 2025
Publisher Packt
ISBN-13 9781803230221
Length
Edition 4th Edition
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Authors (2):
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Joe Minichino Joe Minichino
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Joe Minichino
Joseph Howse Joseph Howse
Author Profile Icon Joseph Howse
Joseph Howse
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Table of Contents (12) Chapters Close

1. Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning FREE CHAPTER
2. Setting Up OpenCV 3. Handling Files, Cameras, and GUIs 4. Processing Images with OpenCV 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. OpenCV Applications at Scale 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 (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...

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