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You're reading from OpenCV 4 with Python Blueprints Build creative computer vision projects with the latest version of OpenCV 4 and Python 3

Product type Paperback

Published in Mar 2020

Publisher Packt

ISBN-13 9781789801811

Length 366 pages

Edition 2nd Edition

Languages

Python

Tools

OpenCV

Concepts

Computer Vision

Authors (4):

Michael Beyeler (USD)

Dr. Menua Gevorgyan

Michael Beyeler

Arsen Mamikonyan

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Table of Contents (14) Chapters

Preface

1. Fun with Filters

2. Hand Gesture Recognition Using a Kinect Depth Sensor FREE CHAPTER

3. Finding Objects via Feature Matching and Perspective Transforms

4. 3D Scene Reconstruction Using Structure from Motion

5. Using Computational Photography with OpenCV

6. Tracking Visually Salient Objects

7. Learning to Recognize Traffic Signs

8. Learning to Recognize Facial Emotions

9. Learning to Classify and Localize Objects

10. Learning to Detect and Track Objects

11. Profiling and Accelerating Your Apps

Accelerating with Numba

12. Setting Up a Docker Container

Defining a Dockerfile

13. Other Books You May Enjoy

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Getting started

This chapter has been tested with OpenCV 4.1.1.

Note that you might have to obtain the so-called extra modules from https://github.com/Itseez/opencv_contrib.

We install OpenCV with OPENCV_ENABLE_NONFREE and the OPENCV_EXTRA_MODULES_PATH variable set in order to get Speeded-Up Robust Features (SURF) and the Fast Library for Approximate Nearest Neighbors (FLANN) installed. You can also use the Docker files available in the repository, which contain all the required installations.

Additionally, note that you may have to obtain a license to use SURF in commercial applications.

The code for this chapter can be found in the GitHub book repository available at https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition/tree/master/chapter3.

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Authors (4)

Michael Beyeler (USD)

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. 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 PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland.

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Dr. Menua Gevorgyan

Dr. Menua Gevorgyan is an experienced researcher with a demonstrated history of working in the information technology and services industry. He is skilled in computer vision, deep learning, machine learning, and data science as well as having a lot of experience with OpenCV and Python programming. He is interested in machine perception and machine understanding problems, and wonders if it is possible to make a machine perceive the world as a human does.

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Mamikonyan

Arsen Mamikonyan is an experienced machine learning specialist with demonstrated work experience in Silicon Valley and London, and teaching experience at the American University of Armenia. He is skilled in applied machine learning and data science and has built real-life applications using Python and OpenCV, among others. He holds a master's degree in engineering (MEng) with a concentration on artificial intelligence from the Massachusetts Institute of Technology.

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Michael Beyeler