OpenCV 4 with Python Blueprints: Build creative computer vision projects with the latest version of OpenCV 4 and Python 3 , Second Edition

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Hand Gesture Recognition Using a Kinect Depth Sensor

The goal of this chapter is to develop an app that detects and tracks simple hand gestures in real time, using the output of a depth sensor, such as that of a Microsoft Kinect 3D sensor or an ASUS Xtion sensor. The app will analyze each captured frame to perform the following tasks:

Hand region segmentation: The user's hand region will be extracted in each frame by analyzing the depth map output of the Kinect sensor, which is done by thresholding, applying some morphological operations, and finding connected components.
Hand shape analysis: The shape of the segmented hand region will be analyzed by determining contours, convex hull, and convexity defects.
Hand gesture recognition: The number of extended fingers will be determined based on the hand contour's convexity defects, and the gesture will be classified accordingly (with no extended fingers corresponding to a fist, and five extended fingers corresponding to an open hand).

Gesture recognition is an ever-popular topic in computer science. This is because it not only enables humans to communicate with machines (Human-Machine Interaction (HMI)) but also constitutes the first step for machines to begin understanding human body language. With affordable sensors such as Microsoft Kinect or Asus Xtion and open source software such as OpenKinect and OpenNI, it has never been easier to get started in the field yourself. So, what shall we do with all this technology?

In this chapter, we will cover the following topics:

Planning the app
Setting up the app
Tracking hand gestures in real time
Understanding hand region segmentation
Performing hand shape analysis
Performing hand gesture recognition

The beauty of the algorithm that we are going to implement in this chapter is that it works well for many hand gestures, yet it is simple enough to run in real time on a generic laptop. Also, if we want, we can easily extend it to incorporate more complicated hand-pose estimations.

Once you complete the app, you will understand how to use depth sensors in your own apps. You will learn how to compose shapes of interest with OpenCV from the depth information, as well as understanding how to analyze shapes with OpenCV, using their geometric properties.

Key benefits

Understand how to capture high-quality image data, detect and track objects, and process the actions of animals or humans

Implement your learning in different areas of computer vision

Explore advanced concepts in OpenCV such as machine learning, artificial neural network, and augmented reality

Description

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. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks. You’ll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you’ll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you’ll understand how to align images, and detect and track objects using neural networks. By the end of this OpenCV Python book, you’ll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs.

What you will learn

Generate real-time visual effects using 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 to track arbitrary objects of interest

Reconstruct a 3D real-world scene using 2D camera motion and camera reprojection techniques

Detect faces using a cascade classifier and identify emotions in human faces using multilayer perceptrons

Classify, localize, and detect objects with deep neural networks

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David Buniatyan Nov 20, 2020

The book goes through application-specific examples and brings advanced concepts within their context. Ideal for practitioners who are interested in quickly seeing results and learning computer vision techniques as they build.

Amazon Verified review

David Oct 01, 2020

I really enjoyed the book. As a Machine Learning R&D engineer with about 5 years of experience I think it is well written and best suited for anyone who interested in computer vision, but mostly for the ones who are looking to build their own app. Let me go through the points I liked:- Each chapter is built around an independent application, such as Finding an Object or Facial Emotions detection. This opens a reader the possibility to start right off from the problem they have in mind.- The book describes neatly and with a good visual support important ML algorithms such as SVM, backpropagation, CNNs- The book contains guides and instructions not only from the algorithmic side, but from the developer's as well. Thus setting up an environment will not cause any problems.

Matthew Emerick Apr 17, 2020

Disclaimer: The publisher asked me to review this book and gave me a review copy. I promise to be 100% honest in how I feel about this book, both the good and the less so.Overview:This book is for the intermediate or advanced Python programmer who is interested in computer vision with OpenCV. It's a computer vision book that lets you build projects to gain hands on experience in the field as well as a portfolio to show off to get hired. Outside of the libraries the book lists, you will need a depth sensor for the project in Chapter 2. If you can't or won't buy one, you can still do the remaining projects.What I Like:The very first chapter gives a good introduction to using OpenCV with some traditional computer vision work. This gives the reader some confidence when moving forward. The rest of the book flows from intermediate to advanced quite well with increasingly more difficult programs. Each chapter's project focuses on a different ability of the OpenCV library.What I Didn't LikeThere isn't much to dislike about this book. I do wish that there were more projects, I'm a bit of a sucker for that. I also wish that there were suggestions as how to further extend the code or additional code exercises. That would have been great for the programmers transitioning from students with textbooks to software developers.What I Would Like to SeeI touched upon this in the last section, but the book could have been longer. Or it could have been broken up into two books of the same length, one for intermediate programmer and the other for advanced. Thankfully, there are other books to fill in the space, as well as plenty to find online.Overall, this is a great book. I give it an easy 5 stars out of 5. It does exactly what is says it is going to and fills a gap that I had been trying to fill. Well done!

SJ Sep 07, 2020

The authors did a good job of explaining various applications that can be build using OpenCV. OpenCV is a widely used Vision-based library but finding a proper guide that can explain all applications is hard to find. If you are looking for a place for a good start to learn traditional vision and write them as code, this is a book to go.

OpenCV 4 with Python Blueprints: Build creative computer vision projects with the latest version of OpenCV 4 and Python 3 , Second Edition

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