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Learning OpenCV 3 Computer Vision with Python (Update)

You're reading from   Learning OpenCV 3 Computer Vision with Python (Update) Unleash the power of computer vision with Python using OpenCV

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781785283840
Length 266 pages
Edition 1st Edition
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Toc

Table of Contents (11) Chapters Close

Preface 1. Setting Up OpenCV FREE CHAPTER 2. Handling Files, Cameras, and GUIs 3. Processing Images with OpenCV 3 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Detecting and Recognizing Objects 8. Tracking Objects 9. Neural Networks with OpenCV – an Introduction Index

Getting Haar cascade data


Once you have a copy of the source code of OpenCV 3, you will find a folder, data/haarcascades.

This folder contains all the XML files used by the OpenCV face detection engine to detect faces in still images, videos, and camera feeds.

Once you find haarcascades, create a directory for your project; in this folder, create a subfolder called cascades, and copy the following files from haarcascades into cascades:

haarcascade_profileface.xml
haarcascade_righteye_2splits.xml
haarcascade_russian_plate_number.xml
haarcascade_smile.xml
haarcascade_upperbody.xml

As their names suggest, these cascades are for tracking faces, eyes, noses, and mouths. They require a frontal, upright view of the subject. We will use them later when building a face detector. If you are curious about how these data sets are generated, refer to Appendix B, Generating Haar Cascades for Custom Targets, OpenCV Computer Vision with Python. With a lot of patience and a powerful computer, you can make your...

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