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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

Getting Haar cascade data

The OpenCV 4 source code, or your installation of a prepackaged build of OpenCV 4, should contain a subfolder called data/haarcascades. If you are unable to locate this, refer back to Chapter 1, Setting Up OpenCV, for instructions on obtaining the OpenCV 4 source code.

The data/haarcascades folder contains XML files that can be loaded by an OpenCV class called cv2.CascadeClassifier. An instance of this class interprets a given XML file as a Haar cascade, which provides a detection model for a type of object such as a face. cv2.CascadeClassifier can detect this type of object in any image. As usual, we could obtain a still image from a file, or we could obtain a series of frames from a video file or a video camera.

Once you find data/haarcascades, create a directory elsewhere for your project; in this folder, create a subfolder called cascades, and copy...

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