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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
If you are a Java and Android developer looking to enhance your skills by learning the latest features of OpenCV Android application programming, then this book is for you.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 212
Veröffentlichungsjahr: 2015
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Table of Contents
Mastering OpenCV Android Application Programming
Mastering OpenCV Android Application Programming
Copyright © 2015 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 authors, 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: July 2015
Production reference: 1230715
Published by Packt Publishing Ltd.
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ISBN 978-1-78398-820-4
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Credits
Authors
Salil Kapur
Nisarg Thakkar
Reviewers
Radhakrishna Dasari
Noritsuna Imamura
Ashwin Kachhara
André Moreira de Souza
Commissioning Editor
Kartikey Pandey
Acquisition Editors
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Content Development Editors
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Technical Editor
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Indexer
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Graphics
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Cover Work
Nitesh Thakur
About the Authors
Salil Kapur is a software engineer at Microsoft. He earned his bachelor's degree in computer science from Birla Institute of Technology and Science, Pilani.
He has a passion for programming and is always excited to try out new technologies. His interests lie in computer vision, networks, and developing scalable systems. He is an open source enthusiast and has contributed to libraries such as SimpleCV, BinPy, and Krita.
When he is not working, he spends most of his time on Quora and Hacker News. He loves to play basketball and ultimate frisbee. He can be reached at <[email protected]>.
Nisarg Thakkar is a software developer and a tech enthusiast in general. He primarily programs in C++ and Java. He has extensive experience in Android app development and computer vision application development using OpenCV. He has also contributed to an OpenCV project and works on its development during his free time. His interests lie in stereo vision, virtual reality, and exploiting the Android platform for noncommercial projects that benefit the people who cannot afford the conventional solutions.
He was also the subcoordinator of the Mobile App Club at his university. He was also the cofounder of two start-ups at his college, which he started with his group of friends. One of these start-ups has developed Android apps for hotels, while the other is currently working on building a better contact manager app for the Android platform.
Nisarg Thakkar is currently studying at BITS Pilani, K. K. Birla Goa campus, where he will be graduating with a degree in engineering (hons.) in computer science in May 2016. He can be reached at <[email protected]>.
About the Reviewers
Radhakrishna Dasari is a computer science PhD student at the State University of New York in Buffalo. He works at Ubiquitous Multimedia Lab, whose director is Dr. Chang Wen Chen. His research spans computer vision and machine learning with an emphasis on multimedia applications. He intends to pursue a research career in computer vision and loves to teach.
Noritsuna Imamura is a specialist in embedded Linux/Android-based computer vision. He is the main person of SIProp (http://siprop.org/).
His main works are as follows:
He can be reached at <[email protected]>.
Ashwin Kachhara graduated from IIT Bombay in June 2015 and is currently pursuing his master's at Georgia Tech, Atlanta. Over the past 5 years, he has been developing software for different platforms, including AVR, Android, Microsoft Kinect, and the Oculus Rift. His professional interests span Mixed Reality, Wearable Technologies, graphics, and computer vision. He has previously worked as an intern at the SONY Head Mounted Display (HMD) division in Tokyo and at the National University of Singapore's Interactive and Digital Media Institute (IDMI). He is a virtual reality enthusiast and enjoys rollerblading and karaoke when he is not writing awesome code.
André Moreira de Souza is a PhD candidate in computer science, with an emphasis on computer graphics from the Pontifical Catholic University of Rio de Janeiro (Brazil).
He graduated with a bachelor of computer science degree from Universidade Federal do Maranhão (UFMA) in Brazil. During his undergraduate degree, he was a member of Labmint's research team and worked with medical imaging, specifically, breast cancer detection and diagnosis using image processing.
Currently, he works as a researcher and system analyst at Instituto Tecgraf, one of the major research and development labs in computer graphics in Brazil. He has been working extensively with PHP, HTML, and CSS since 2007; nowadays, he develops projects in C++11/C++14, along with SQLite, Qt, Boost, and OpenGL. More information about him can be acquired by visiting his personal website at www.andredsm.com.
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Preface
This book will help you get started with OpenCV on the Android platform in no time. It explains the various computer vision algorithms conceptually, as well as their implementation on the Android platform. This book is an invaluable resource if you are looking forward to implementing computer vision modules on new or existing Android apps.
What this book covers
Chapter 1, Applying Effects to Images, includes some of the basic preprocessing algorithms used in various computer vision applications. This chapter also explains how you can integrate OpenCV to your existing projects.
Chapter 2, Detecting Basic Features in Images, covers the detection of primary features such as edges, corners, lines, and circles in images.
Chapter 3, Detecting Objects, dives deep into feature detection, using more advanced algorithms to detect and describe features in order to uniquely match them to features in other objects.
Chapter 4, Drilling Deeper into Object Detection – Using Cascade Classifiers, explains the detection of general objects, such as faces/eyes in images and videos.
Chapter 5, Tracking Objects in Videos, covers the concepts of optical flow as a motion detector and implements the Lucas-Kanade-Tomasi tracker to track objects in a video.
Chapter 6, Working with Image Alignment and Stitching, covers the basic concepts of image alignment and image stitching to create a panoramic scene image.
Chapter 7, Bringing Your Apps to Life with OpenCV Machine Learning, explains how machine learning can be used in computer vision applications. In this chapter, we take a look at some common machine learning algorithms and their implementation in Android.
Chapter 8, Troubleshooting and Best Practices, covers some of the common errors and issues that developers face while building their applications. It also unfolds some good practices that can make the application more efficient.
Chapter 9, Developing a Document Scanning App, uses various algorithms that have been explained across various chapters to build a complete system to scan documents, regardless of what angle you click the image at.
What you need for this book
For this book, you need a system with at least 1 GB RAM. Windows, OS X, and Linux are the currently supported operating systems for Android development.
Who this book is for
If you are a Java and Android developer and looking to enhance your skills by learning the latest features of OpenCV Android application programming, then this book is for you.
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Chapter 1. Applying Effects to Images
Generally, an image contains more information than required for any particular task. For this reason, we need to preprocess the images so that they contain only as much information as required for the application, thereby reducing the computing time needed.
In this chapter, we will learn about the different preprocessing operations, which are as follows:
At the end of this chapter, we will see how you can integrate OpenCV into your existing Android applications.
Before we take a look at the various feature detection algorithms and their implementations, let's first build a basic Android application to which we will keep adding feature detection algorithms, as we go through this chapter.
Getting started
When we see an image, we perceive it as colors and objects. However, a computer vision system sees it as a matrix of numbers (see the following image). These numbers are interpreted differently, depending on the color model used. The computer cannot directly detect patterns or objects in the image. The aim of computer vision systems is to interpret this matrix of numbers as an object of a particular type.
Representation of a binary image
Setting up OpenCV
OpenCV is the short form of Open Source Computer Vision library. It is the most widely used computer vision library. It is a collection of commonly used functions that perform operations related to computer vision. OpenCV has been natively written in C/C++, but has wrappers for Python, Java, and any JVM language, which is designed to create the Java byte code, such as Scala and Clojure. Since most of the Android app development is done in C++/Java, OpenCV has also been ported as an SDK that developers can use to implement it in their apps and make them vision enabled.
We will now take a look at how to get started with setting up OpenCV for the Android platform, and start our journey. We will use Android Studio as our IDE of choice, but any other IDE should work just as well with slight modifications. Follow these steps in order to get started:
Storing images in OpenCV
OpenCV stores images as a custom object called Mat. This object stores the information such as rows, columns, data, and so on that can be used to uniquely identify and recreate the image when required. Different images contain different amounts of data. For example, a colored image contains more data than a grayscale version of the same image. This is because a colored image is a 3-channel image when using the RGB model, and a grayscale image is a 1-channel image. The following figures show how 1-channel and multichannel (here, RGB) images are stored (these images are taken from docs.opencv.org).
A 1-channel representation of an image is shown as follows:
A grayscale (1-channel) image representation:
A more elaborate form of an image is the RGB representation, which is shown as follows:
A RGB (3-channel) image representation
In the grayscale image, the numbers represent the intensity of that particular color. They are represented on a scale of 0-255 when using integer representations, with 0 being pure black and 255 being pure white. If we use a floating point representation, the pixels are represented on a scale of 0-1, with 0 being pure black and 1 being pure white. In an RGB image in OpenCV, the first channel corresponds to blue color, second channel corresponds to green color, and the third channel corresponds to red color. Thus, each channel represents the intensity of any particular color. As we know that red, green, and blue are primary colors, they can be combined in different proportions to generate any color visible to the human eye. The following figure shows the different colors and their respective RGB equivalents in an integer format:
Now that we have seen how an image is represented in computing terms, we will see how we can modify the pixel values so that they need less computation time when using them for the actual task at hand.
