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Mastering OpenCV with Practical Computer Vision Projects .pdf

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    Cover
    Copyright
    Credits
    About the Authors
    About the Reviewers
    www.PacktPub.com
    Table of Contents
    Preface
    Chapter 1: Cartoonifier and Skin Changer for Android
    Accessing the webcam
    Main camera processing loop for a desktop app
    Generating a black-and-white sketch
    Generating a color painting and a cartoon
    Generating an "evil" mode using edge filters
    Generating an "alien" mode using skin detection
    Skin-detection algorithm
    Showing the user where to put their face
    Implementation of the skin-color changer
    Porting from desktop to Android
    Setting up an Android project that uses OpenCV
    Color formats used for image processing on Android
    Input color format from the camera
    Output color format for display
    Adding the cartoonifier code to the Android NDK app
    Reviewing the Android app
    Cartoonifying the image when the user taps the screen
    Saving the image to a file and to the Android picture gallery
    Showing an Android notification message about a saved image
    Changing cartoon modes through the Android menu bar
    Reducing the random pepper noise from the sketch image
    Showing the FPS of the app
    Using a different camera resolution
    Customizing the app
    Summary
    Chapter 2: Marker-based Augmented Reality on iPhone or iPad
    Creating an iOS project that uses OpenCV
    Adding OpenCV framework
    Including OpenCV headers
    Application architecture
    Marker detection
    Marker identification
    Grayscale conversion
    Image binarization
    Contours detection
    Candidates search
    Marker code recognition
    Reading marker code
    Marker location refinement
    Placing a marker in 3D
    Camera calibration
    Marker pose estimation
    Rendering the 3D virtual object
    Creating the OpenGL rendering layer
    Rendering an AR scene
    Summary
    References
    Chapter 3: Marker-less Augmented Reality
    Marker-based versus marker-less AR
    Using feature descriptors to find an arbitrary image on video
    Feature extraction
    Definition of a pattern object
    Matching of feature points
    PatternDetector.cpp
    Outlier removal
    Cross-match filter
    Ratio test
    Homography estimation
    Homography refinement
    Putting it all together
    Pattern pose estimation
    PatternDetector.cpp
    Obtaining the camera-intrinsic matrix
    Pattern.cpp
    Application infrastructure
    ARPipeline.hpp
    ARPipeline.cpp
    Enabling support for 3D visualization in OpenCV
    Creating OpenGL windows using OpenCV
    Video capture using OpenCV
    Rendering augmented reality
    ARDrawingContext.hpp
    ARDrawingContext.cpp
    Demonstration
    main.cpp
    Summary
    References
    Chapter 4: Exploring Structure from Motion Using OpenCV
    Structure from Motion concepts
    Estimating the camera motion from a pair of images
    Point matching using rich feature descriptors
    Point matching using optical flow
    Finding camera matrices
    Reconstructing the scene
    Reconstruction from many views
    Refinement of the reconstruction
    Visualizing 3D point clouds with PCL
    Using the example code
    Summary
    References
    Chapter 5: Number Plate Recognition Using SVM and Neural Networks
    Introduction to ANPR
    ANPR algorithm
    Plate detection
    Segmentation
    Classification
    Plate recognition
    OCR segmentation
    Feature extraction
    OCR classification
    Evaluation
    Summary
    Chapter 6: Non-rigid Face Tracking
    Overview
    Utilities
    Object-oriented design
    Data collection: Image and video annotation
    Training data types
    Annotation tool
    Pre-annotated data (The MUCT dataset)
    Geometrical constraints
    Procrustes analysis
    Linear shape models
    A combined local-global representation
    Training and visualization
    Facial feature-detectors
    Correlation-based patch models
    Learning discriminative patch models
    Generative versus discriminative patch models
    Accounting for global geometric transformations
    Training and visualization
    Face detection and initialization
    Face tracking
    Face tracker implementation
    Training and visualization
    Generic versus person-specific models
    Summary
    References
    Chapter 7: 3D Head Pose Estimation Using AAM and POSIT
    Active Appearance Models overview
    Active Shape Models
    Getting the feel of PCA
    Triangulation
    Triangle texture warping
    Model Instantiation – playing with the Active Appearance Model
    AAM search and fitting
    POSIT
    Diving into POSIT
    POSIT and head model
    Tracking from webcam or video file
    Summary
    References
    Chapter 8: Face Recognition using Eigenfaces or Fisherfaces
    Introduction to face recognition and face detection
    Step 1: Face detection
    Implementing face detection using OpenCV
    Loading a Haar or LBP detector for object or face detection
    Accessing the webcam
    Detecting an object using the Haar or LBP Classifier
    Detecting the face
    Step 2: Face preprocessing
    Eye detection
    Eye search regions
    Step 3: Collecting faces and learning from them
    Collecting preprocessed faces for training
    Training the face recognition system from collected faces
    Viewing the learned knowledge
    Average face
    Eigenvalues, Eigenfaces, and Fisherfaces
    Step 4: Face recognition
    Face identification: Recognizing people from their face
    Face verification: Validating that it is the claimed person
    Finishing touches: Saving and loading files
    Finishing touches: Making a nice and interactive GUI
    Drawing the GUI elements
    Checking and handling mouse clicks
    Summary
    References
    Index
    Mastering OpenCV with Practical Computer Vision Projects Step-by-step tutorials to solve common real-world computer vision problems for desktop or mobile, from augmented reality and number plate recognition to face recognition and 3D head tracking Daniel Lélis Baggio Shervin Emami David Millán Escrivá Khvedchenia Ievgen Naureen Mahmood Jason Saragih Roy Shilkrot BIRMINGHAM - MUMBAI
    Mastering OpenCV with Practical Computer Vision Projects Copyright © 2012 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: December 2012 Production Reference: 2231112 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-84951-782-9 www.packtpub.com Cover Image by Neha Rajappan (neha.rajappan1@gmail.com)
    Credits Authors Daniel Lélis Baggio Shervin Emami David Millán Escrivá Khvedchenia Ievgen Naureen Mahmood Jason Saragih Roy Shilkrot Reviewers Kirill Kornyakov Luis Díaz Más Sebastian Montabone Acquisition Editor Usha Iyer Lead Technical Editor Ankita Shashi Technical Editors Sharvari Baet Prashant Salvi Copy Editors Brandt D'Mello Aditya Nair Alfida Paiva Project Coordinator Priya Sharma Proofreaders Chris Brown Martin Diver Indexer Hemangini Bari Tejal Soni Rekha Nair Graphics Valentina D'silva Aditi Gajjar Production Coordinator Arvindkumar Gupta Cover Work Arvindkumar Gupta
    About the Authors Daniel Lélis Baggio started his work in computer vision through medical image processing at InCor (Instituto do Coração – Heart Institute) in São Paulo, where he worked with intra-vascular ultrasound image segmentation. Since then, he has focused on GPGPU and ported the segmentation algorithm to work with NVIDIA's CUDA. He has also dived into six degrees of freedom head tracking with a natural user interface group through a project called ehci (http://code.google.com/p/ ehci/). He now works for the Brazilian Air Force. I'd like to thank God for the opportunity of working with computer vision. I try to understand the wonderful algorithms He has created for us to see. I also thank my family, and especially my wife, for all their support throughout the development of the book. I'd like to dedicate this book to my son Stefano. Shervin Emami (born in Iran) taught himself electronics and hobby robotics during his early teens in Australia. While building his first robot at the age of 15, he learned how RAM and CPUs work. He was so amazed by the concept that he soon designed and built a whole Z80 motherboard to control his robot, and wrote all the software purely in binary machine code using two push buttons for 0s and 1s. After learning that computers can be programmed in much easier ways such as assembly language and even high-level compilers, Shervin became hooked to computer programming and has been programming desktops, robots, and smartphones nearly every day since then. During his late teens he created Draw3D (http://draw3d.shervinemami.info/), a 3D modeler with 30,000 lines of optimized C and assembly code that rendered 3D graphics faster than all the commercial alternatives of the time; but he lost interest in graphics programming when 3D hardware acceleration became available.
    In University, Shervin took a subject on computer vision and became highly interested in it; so for his first thesis in 2003 he created a real-time face detection program based on Eigenfaces, using OpenCV (beta 3) for camera input. For his master's thesis in 2005 he created a visual navigation system for several mobile robots using OpenCV (v0.96). From 2008, he worked as a freelance Computer Vision Developer in Abu Dhabi and Philippines, using OpenCV for a large number of short-term commercial projects that included: • Detecting faces using Haar or Eigenfaces • Recognizing faces using Neural Networks, EHMM, or Eigenfaces • Detecting the 3D position and orientation of a face from a single photo using AAM and POSIT • Rotating a face in 3D using only a single photo • Face preprocessing and artificial lighting using any 3D direction from a single photo • Gender recognition • Facial expression recognition • Skin detection • Iris detection • Pupil detection • Eye-gaze tracking • Visual-saliency tracking • Histogram matching • Body-size detection • Shirt and bikini detection • Money recognition • Video stabilization • Face recognition on iPhone • Food recognition on iPhone • Marker-based augmented reality on iPhone (the second-fastest iPhone augmented reality app at the time).
    OpenCV was putting food on the table for Shervin's family, so he began giving back to OpenCV through regular advice on the forums and by posting free OpenCV tutorials on his website (http://www.shervinemami.info/openCV.html). In 2011, he contacted the owners of other free OpenCV websites to write this book. He also began working on computer vision optimization for mobile devices at NVIDIA, working closely with the official OpenCV developers to produce an optimized version of OpenCV for Android. In 2012, he also joined the Khronos OpenVL committee for standardizing the hardware acceleration of computer vision for mobile devices, on which OpenCV will be based in the future. I thank my wife Gay and my baby Luna for enduring the stress while I juggled my time between this book, working fulltime, and raising a family. I also thank the developers of OpenCV, who worked hard for many years to provide a high-quality product for free. David Millán Escrivá was eight years old when he wrote his first program on an 8086 PC with Basic language, which enabled the 2D plotting of basic equations. In 2005, he finished his studies in IT through the Universitat Politécnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96). He had a final project based on this subject and published it on HCI Spanish congress. He participated in Blender, an open source, 3D-software project, and worked in his first commercial movie Plumiferos - Aventuras voladoras as a Computer Graphics Software Developer. David now has more than 10 years of experience in IT, with experience in computer vision, computer graphics, and pattern recognition, working on different projects and startups, applying his knowledge of computer vision, optical character recognition, and augmented reality. He is the author of the "DamilesBlog" (http://blog.damiles.com), where he publishes research articles and tutorials about OpenCV, computer vision in general, and Optical Character Recognition algorithms.
    David has reviewed the book gnuPlot Cookbook by Lee Phillips and published by Packt Publishing. Thanks Izaskun and my daughter Eider for their patience and support. Os quiero pequeñas. I also thank Shervin for giving me this opportunity, the OpenCV team for their work, the support of Artres, and the useful help provided by Augmate. Khvedchenia Ievgen is a computer vision expert from Ukraine. He started his career with research and development of a camera-based driver assistance system for Harman International. He then began working as a Computer Vision Consultant for ESG. Nowadays, he is a self-employed developer focusing on the development of augmented reality applications. Ievgen is the author of the Computer Vision Talks blog (http://computer-vision-talks.com), where he publishes research articles and tutorials pertaining to computer vision and augmented reality. I would like to say thanks to my father who inspired me to learn programming when I was 14. His help can't be overstated. And thanks to my mom, who always supported me in all my undertakings. You always gave me a freedom to choose my own way in this life. Thanks, parents! Thanks to Kate, a woman who totally changed my life and made it extremely full. I'm happy we're together. Love you.
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