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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

By : Joe Minichino, Joseph Howse
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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

5 (1)
By: Joe Minichino, Joseph Howse

Overview of this book

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.
Table of Contents (12 chapters)
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Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning
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Appendix A: Bending Color Space with the Curves Filter

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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