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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
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Authors (2):
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Joe Minichino Joe Minichino
Author Profile Icon Joe Minichino
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

Creating modules

To help us build an interactive demo of a depth camera, we will reuse much of the Cameo project that we developed in Chapter 2, Handling Files, Cameras, and GUIs, and Chapter 3, Processing Images with OpenCV. As you will recall, we designed Cameo to support various kinds of input, so we can easily adapt it to support depth cameras in particular. We will add code that analyzes the depth layers in an image in order to find the main region, such as the face of a person sitting in front of the camera. Having found this region, we will paint everything else black. This type of effect is sometimes used in chat applications to hide the background so that users have more privacy.

Some of the code to manipulate depth-camera data will be reusable outside Cameo.py, so we should separate it into a new module. Let's create a depth.py file in the same directory as Cameo...

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