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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 Hardcover
Published in Jun 2026
Publisher
ISBN-13 9781803230221
Length
Edition 1st 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
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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

Getting Haar cascade data

Your installation of OpenCV 5 should contain a subfolder called data. The path to this folder is stored in an OpenCV variable called cv2.data.haarcascades.

The data 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.

From the data folder, we will use the following cascade files:

haarcascade_frontalface_default.xml
haarcascade_eye.xml

As their names suggest, these cascades are for detecting faces and eyes. 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 cascade files are generated, you can find...

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