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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

Detecting people with HOG descriptors

OpenCV comes with a class called cv2.HOGDescriptor, which is capable of performing people detection. The interface has some similarities to the cv2.CascadeClassifier class that we used in Chapter 5, Detecting and Recognizing Faces. However, unlike cv2.CascadeClassifier, cv2.HOGDescriptor sometimes returns nested detection rectangles. In other words, cv2.HOGDescriptor might tell us that it detected one person whose bounding rectangle is located completely inside another person's bounding rectangle. This situation really is possible; for example, a child could be standing in front of an adult, and the child's bounding rectangle could be completely inside the adult's bounding rectangle. However, in a typical situation, nested detections are probably errors, so cv2.HOGDescriptor is often used along with code to filter out any nested...

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