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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
Languages
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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

What this book covers

Chapter 1, Setting Up OpenCV, explains how to set up OpenCV 4 with Python 3 on various platforms. It also provides troubleshooting steps for common problems.

Chapter 2, Handling Files, Cameras, and GUIs, introduces OpenCV's I/O functionalities. It also discusses an object-oriented design for a GUI project that we will develop further in other chapters.

Chapter 3, Processing Images with OpenCV, presents some techniques required to alter images, such as manipulating colors, sharpening an image, marking contours of objects, and detecting geometric shapes.

Chapter 4, Depth Estimation and Segmentation, shows you how to use data from a depth camera to identify foreground and background regions, such that we can limit an effect to only the foreground or background.

Chapter 5, Detecting and Recognizing Faces, introduces some of OpenCV's functionality for face detection and recognition, along with the data files that define particular types of detectable objects.

Chapter 6, Retrieving Images and Searching Using Image Descriptors, shows how to describe the features of an image with the help of OpenCV, and how to make use of features to match and search for images.

Chapter 7, Building Custom Object Detectors, applies a combination of computer vision and machine learning algorithms to locate and classify objects in an image. It shows how to implement this combination of algorithms with OpenCV.

Chapter 8, Tracking Objects, demonstrates ways to track and predict the motion of people and objects in a video or live camera feed.

Chapter 9, Camera Models and Augmented Reality, enables you to build an augmented reality application that uses information about cameras, objects, and motion to superimpose 3D graphics atop tracked objects in real time.

Chapter 10, Introduction to Neural Networks with OpenCV, introduces you to artificial neural networks (ANNs) and deep neural networks (DNNs) in OpenCV, and illustrates their usage in real-world applications.

Appendix A, Bending Color Space with a Curves Filter, describes the concept of color curves and our implementation of them using SciPy.

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