Learning OpenCV 3 Application Development

Samyak Datta

• Publisher: Packt Publishing
• Year: 2016
• ISBN:
  9781784391454
• Edition: 1
• Topic: Informatica
• Subtopic: Visione artificiale e riconoscimento di schemi

Build, create, and deploy your own computer vision applications with the power of OpenCV

About This Book

• This book provides hands-on examples that cover the major features that are part of any important Computer Vision application
• It explores important algorithms that allow you to recognize faces, identify objects, extract features from images, help your system make meaningful predictions from visual data, and much more
• All the code examples in the book are based on OpenCV 3.1 – the latest version

Who This Book Is For

This is the perfect book for anyone who wants to dive into the exciting world of image processing and computer vision. This book is aimed at programmers with a working knowledge of C++. Prior knowledge of OpenCV or Computer Vision/Machine Learning is not required.

What You Will Learn

• Explore the steps involved in building a typical computer vision/machine learning application
• Understand the relevance of OpenCV at every stage of building an application
• Harness the vast amount of information that lies hidden in images into the apps you build
• Incorporate visual information in your apps to create more appealing software
• Get acquainted with how large-scale and popular image editing apps such as Instagram work behind the scenes by getting a glimpse of how the image filters in apps can be recreated using simple operations in OpenCV
• Appreciate how difficult it is for a computer program to perform tasks that are trivial for human beings
• Get to know how to develop applications that perform face detection, gender detection from facial images, and handwritten character (digit) recognition

In Detail

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you're a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++.

At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You'll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations.

Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code!

The concluding sections touch upon OpenCV's Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!

Style and approach

This book takes a very hands-on approach to developing an end-to-end application with OpenCV. To avoid being too theoretical, the description of concepts are accompanied simultaneously by the development of applications. Throughout the course of the book, the projects and practical, real-life examples are explained and developed step by step in sync with the theory.

Learning OpenCV 3 Application Development

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Learning OpenCV 3 Application Development

Copyright © 2016 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: December 2016

Production reference: 1131216

Published by Packt Publishing Ltd.

Livery Place

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Birmingham B3 2PB, UK.

ISBN 978-1-78439-145-4

www.packtpub.com

Credits

Author Samyak Datta	Copy Editor Safis Editing
Reviewer Nikolaus Gradwohl	Project Coordinator Sheejal Shah
Commissioning Editor Kunal Parikh	Proofreader Safis Editing
Acquisition Editor Sonali Vernekar	Indexer Rekha Nair
Content Development Editor Nikhil Borkar	Graphics Abhinash Sahu
Technical Editor Hussain Kanchwala	Production Coordinator Shraddha Falebhai

About the Author

Samyak Datta has a bachelor's and a master's degree in Computer Science from the Indian Institute of Technology, Roorkee. He is a computer vision and machine learning enthusiast. His first contact with OpenCV was in 2013 when he was working on his master's thesis, and since then, there has been no looking back. He has contributed to OpenCV's GitHub repository. Over the course of his undergraduate and master's degrees, Samyak has had the opportunity to engage with both the industry and research. He worked with Google India and Media.net (Directi) as a software engineering intern, where he was involved with projects ranging from machine learning and natural language processing to computer vision. As of 2016, he is working at the Center for Visual Information Technology (CVIT) at the Indian Institute of Information Technology, Hyderabad.

About the Reviewer

Nikolaus Gradwohl was born 1976 in Vienna, Austria and always wanted to become an inventor like Gyro Gearloose. When he got his first Atari, he figured out that being a computer programmer is the closest he could get to that dream. He has written programs for nearly anything that can be programmed, ranging from 8-bit microcontrollers to mainframes, for a living. In his free time, he collects programming languages and operating systems.

He is the author of the book Processing 2: Creative Coding Hotshot. You can see some of his work on his blog at http://www.local-guru.net/.

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Preface

The mission of this book is to explain to a novice the steps involved in building and deploying an end-to-end application in the domain of computer vision using OpenCV/C++. The book will start with instructions about installing the library and end with the reader having developed an application that does something tangible and useful in computer vision/machine learning. All concepts included in the text have been selected because of their frequent use and relevance in practical computer vision-based projects. To avoid being too theoretical, the description of concepts is accompanied simultaneously by the development of some end-to-end applications. The projects will be explained and developed step-by-step during the entire course of the text, as and when relevant theoretical concepts will be introduced. This will help the readers grasp the practical applications of the concept under study while not losing sight of the big-picture.

What this book covers

Chapter 1, Laying the Foundation, will lay the foundation for the basic data structures and containers in OpenCV for example, the Mat, Rect, Point and Scalar objects . It also explains the need for each of them as a separate data types by offering an insight into the possible use cases. A major portion of the chapter focuses on how OpenCV uses the Mat object to store images so that your code can access them, basics of scanning images using the Mat object and the concept of R, G and B color channels. After the readers are comfortable with working with images, we introduce the concept of pixel-based image transformations and show examples of simple code that can achieve contrast/brightness modification using simple Mat object traversals.

Chapter 2, Image Filtering, progresses to slightly advanced pixel traversal algorithms. We introduce the concepts of masks and image filtering and talk about the standard filters such as box filter, median filter, and Gaussian blur in OpenCV. Also, we will mathematically explain the concepts of convolution and correlation and show how a generic filter can be written using OpenCV’s filter2D method. As a practical use case, we implement a popular and interesting image manipulation technique called the Vignette filter.

Chapter 3, Image Thresholding, talks about image thresholding, which is yet another process that frequently comes up in the solution to most computer vision problems. In this chapter, the readers are made to understand that the algorithms that fall into this domain basically involve operations that produce a binary image from a grayscale one. The different variants such as binary and inverted binary, are available as part of the OpenCV imgproc module are explained in detail. The chapter will also briefly touch upon the concepts of erosion and dilation (morphological transformations) as steps that takes place after thresholding.

Chapter 4, Image Histograms, talks about aggregating pixel values by discussing image histograms and histogram-related operations on images such as calculating and displaying histograms, the concept of color and multi-dimensional histograms.

Chapter 5, Image Derivatives and Edge Detection, focuses on other types of information that can be extracted from the pixels in our image. The readers are introduced to the concept of image derivatives and the discussion then moves on to the application of image derivatives in edge detection algorithms. A demonstration of the edge detection methods in OpenCV for example Sobel, Canny and Laplacian is presented. As a small practical use-case of the Laplacian, we demonstrate how it can be used to detect the blurriness of images.

Chapter 6, Face Detection Using OpenCV, talks about one of the most popular and ubiquitous problems in the computer vision community--detecting objects, specifically faces in images. The main motivation of this chapter is to take a look at the Haar-cascade classifier algorithm, which is used to detect faces and then go on to show how the complex algorithm can be run using a single line of OpenCV code. At this point, we introduce our programming project of predicting gender of a person from a facial image. The reader is made to realize that any system involving analysis of facial images (be it face recognition or gender, age or ethnicity prediction) requires an accurate and robust face detection as its first step.

Chapter 7, Affine Transformations and Face Alignment, serves as a natural successor to the preceding chapter. After detecting faces from images, the readers will be taught about the post-processing steps that are undertaken--geometric (Affine) transformations such resizing, cropping and rotating images. The need for such transformations is explained. All images in the gender classification project have to go through this step before feature extraction.

Chapter 8, Feature Descriptors in OpenCV, introduces the notion of feature descriptors. The readers realize that in order to infer meaningful information from images, one must construct appropriate features from the pixel values. In other words, images have to be converted into feature vectors before feeding them into a machine learning algorithm. To that end, the chapter also goes on to introduce the concept of local binary pattern as a feature a...