Program advanced computer vision applications in Python using different features of the OpenCV library
Practical end-to-end project covering an important computer vision problem
All projects in the book include a step-by-step guide to create computer vision applications

Who This Book Is For

This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV's concepts and Python libraries. Basic knowledge of Python programming is expected and assumed.

What You Will Learn

Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning
Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
Learn feature extraction and feature matching for tracking arbitrary objects of interest
Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
Track visually salient objects by searching for and focusing on important regions of an image
Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs)
Recognize street signs using a multi-class adaptation of support vector machines (SVMs)
Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features

In Detail

OpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions

This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.

By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.

Style and approach

This book covers independent hands-on projects that teach important computer vision concepts like image processing and machine learning for OpenCV with multiple examples.

About the Author

Michael Beyeler
Michael Beyeler is a Postdoctoral Fellow in Neuroengineering and Data Science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis ("bionic eye"). His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. Michael is the author of OpenCV with Python Blueprints, a practical guide for building advanced computer vision projects. He is also an active contributor to several open-source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android.

Michael received a Ph.D. in Computer Science from the University of California, Irvine as well as a M.Sc. in Biomedical Engineering and a B.Sc. in Electrical Engineering from ETH Zurich, Switzerland. When he is not "nerding out" on brains, he can be found on top of a snowy mountain, in front of a live band, or behind the piano.

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