Learning OpenCV 3 Computer vision in C++ with the OpenCV library (Adrian Kaehler, Gary Bradski) (Z-Library)

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Adrian Kaehler & Gary Bradski Learning OpenCV 3 COMPUTER VISION IN C++ WITH THE OPENCV LIBRARY

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Adrian Kaehler and Gary Bradski Learning OpenCV 3 Computer Vision in C++ with the OpenCV Library Boston Farnham Sebastopol TokyoBeijing

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978-1-491-93799-0 [M] Learning OpenCV 3 by Adrian Kaehler and Gary Bradski Copyright © 2017 Adrian Kaehler, Gary Bradski. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://www.oreilly.com/safari). For more information, contact our corpo‐ rate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Editor: Dawn Schanafelt Indexer: Ellen Troutman Production Editor: Kristen Brown Interior Designer: David Futato Copyeditor: Rachel Monaghan Cover Designer: Karen Montgomery Proofreader: James Fraleigh Illustrator: Rebecca Demarest December 2016: First Edition Revision History for the First Edition 2016-12-09: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781491937990 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Learning OpenCV 3, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

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Table of Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv 1. Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 What Is OpenCV? 1 Who Uses OpenCV? 2 What Is Computer Vision? 3 The Origin of OpenCV 6 OpenCV Block Diagram 8 Speeding Up OpenCV with IPP 9 Who Owns OpenCV? 10 Downloading and Installing OpenCV 10 Installation 10 Getting the Latest OpenCV via Git 13 More OpenCV Documentation 13 Supplied Documentation 14 Online Documentation and the Wiki 14 OpenCV Contribution Repository 17 Downloading and Building Contributed Modules 17 Portability 18 Summary 19 Exercises 19 2. Introduction to OpenCV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Include Files 21 Resources 22 First Program—Display a Picture 23 Second Program—Video 25 Moving Around 27 iii

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A Simple Transformation 31 A Not-So-Simple Transformation 32 Input from a Camera 35 Writing to an AVI File 36 Summary 38 Exercises 38 3. Getting to Know OpenCV Data Types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 The Basics 41 OpenCV Data Types 41 Overview of the Basic Types 42 Basic Types: Getting Down to Details 44 Helper Objects 52 Utility Functions 60 The Template Structures 67 Summary 68 Exercises 69 4. Images and Large Array Types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Dynamic and Variable Storage 71 The cv::Mat Class: N-Dimensional Dense Arrays 72 Creating an Array 73 Accessing Array Elements Individually 78 The N-ary Array Iterator: NAryMatIterator 81 Accessing Array Elements by Block 84 Matrix Expressions: Algebra and cv::Mat 85 Saturation Casting 87 More Things an Array Can Do 88 The cv::SparseMat Class: Sparse Arrays 89 Accessing Sparse Array Elements 90 Functions Unique to Sparse Arrays 92 Template Structures for Large Array Types 94 Summary 97 Exercises 97 5. Array Operations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 More Things You Can Do with Arrays 99 cv::abs() 102 cv::absdiff() 103 cv::add() 103 cv::addWeighted() 104 cv::bitwise_and() 106 iv | Table of Contents

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cv::bitwise_not() 107 cv::bitwise_or() 107 cv::bitwise_xor() 108 cv::calcCovarMatrix() 108 cv::cartToPolar() 110 cv::checkRange() 111 cv::compare() 111 cv::completeSymm() 112 cv::convertScaleAbs() 112 cv::countNonZero() 113 cv::cvarrToMat() 113 cv::dct() 114 cv::dft() 115 cv::cvtColor() 117 cv::determinant() 119 cv::divide() 120 cv::eigen() 120 cv::exp() 121 cv::extractImageCOI() 121 cv::flip() 122 cv::gemm() 122 cv::getConvertElem() and cv::getConvertScaleElem() 123 cv::idct() 124 cv::idft() 124 cv::inRange() 124 cv::insertImageCOI() 125 cv::invert() 126 cv::log() 126 cv::LUT() 127 cv::magnitude() 127 cv::Mahalanobis() 128 cv::max() 129 cv::mean() 130 cv::meanStdDev() 130 cv::merge() 131 cv::min() 131 cv::minMaxIdx() 132 cv::minMaxLoc() 133 cv::mixChannels() 134 cv::mulSpectrums() 136 cv::multiply() 136 cv::mulTransposed() 136 Table of Contents | v

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cv::norm() 137 cv::normalize() 139 cv::perspectiveTransform() 140 cv::phase() 141 cv::polarToCart() 142 cv::pow() 142 cv::randu() 143 cv::randn() 143 cv::randShuffle() 144 cv::reduce() 144 cv::repeat() 145 cv::scaleAdd() 146 cv::setIdentity() 146 cv::solve() 147 cv::solveCubic() 148 cv::solvePoly() 149 cv::sort() 149 cv::sortIdx() 149 cv::split() 150 cv::sqrt() 150 cv::subtract() 152 cv::sum() 152 cv::trace() 152 cv::transform() 153 cv::transpose() 153 Summary 154 Exercises 154 6. Drawing and Annotating. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Drawing Things 157 Line Art and Filled Polygons 158 Fonts and Text 165 Summary 167 Exercises 167 7. Functors in OpenCV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Objects That “Do Stuff” 169 Principal Component Analysis (cv::PCA) 169 Singular Value Decomposition (cv::SVD) 173 Random Number Generator (cv::RNG) 176 Summary 179 Exercises 180 vi | Table of Contents

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8. Image, Video, and Data Files. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 HighGUI: Portable Graphics Toolkit 183 Working with Image Files 185 Loading and Saving Images 185 A Note About Codecs 188 Compression and Decompression 188 Working with Video 189 Reading Video with the cv::VideoCapture Object 190 Writing Video with the cv::VideoWriter Object 196 Data Persistence 198 Writing to a cv::FileStorage 198 Reading from a cv::FileStorage 200 cv::FileNode 201 Summary 204 Exercises 204 9. Cross-Platform and Native Windows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Working with Windows 207 HighGUI Native Graphical User Interface 208 Working with the Qt Backend 220 Integrating OpenCV with Full GUI Toolkits 232 Summary 247 Exercises 247 10. Filters and Convolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Overview 249 Before We Begin 249 Filters, Kernels, and Convolution 249 Border Extrapolation and Boundary Conditions 251 Threshold Operations 255 Otsu’s Algorithm 258 Adaptive Threshold 259 Smoothing 261 Simple Blur and the Box Filter 262 Median Filter 265 Gaussian Filter 266 Bilateral Filter 267 Derivatives and Gradients 269 The Sobel Derivative 269 Scharr Filter 272 The Laplacian 273 Image Morphology 275 Table of Contents | vii

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Dilation and Erosion 276 The General Morphology Function 281 Opening and Closing 281 Morphological Gradient 285 Top Hat and Black Hat 287 Making Your Own Kernel 289 Convolution with an Arbitrary Linear Filter 290 Applying a General Filter with cv::filter2D() 291 Applying a General Separable Filter with cv::sepFilter2D 292 Kernel Builders 292 Summary 294 Exercises 294 11. General Image Transforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Overview 299 Stretch, Shrink, Warp, and Rotate 299 Uniform Resize 300 Image Pyramids 302 Nonuniform Mappings 306 Affine Transformation 308 Perspective Transformation 313 General Remappings 316 Polar Mappings 317 LogPolar 318 Arbitrary Mappings 322 Image Repair 323 Inpainting 324 Denoising 325 Histogram Equalization 328 cv::equalizeHist(): Contrast equalization 331 Summary 331 Exercises 332 12. Image Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Overview 335 Discrete Fourier Transform 336 cv::dft(): The Discrete Fourier Transform 336 cv::idft(): The Inverse Discrete Fourier Transform 339 cv::mulSpectrums(): Spectrum Multiplication 339 Convolution Using Discrete Fourier Transforms 340 cv::dct(): The Discrete Cosine Transform 342 cv::idct(): The Inverse Discrete Cosine Transform 343 viii | Table of Contents

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Integral Images 343 cv::integral() for Standard Summation Integral 346 cv::integral() for Squared Summation Integral 346 cv::integral() for Tilted Summation Integral 346 The Canny Edge Detector 347 cv::Canny() 349 Hough Transforms 349 Hough Line Transform 349 Hough Circle Transform 354 Distance Transformation 358 cv::distanceTransform() for Unlabeled Distance Transform 359 cv::distanceTransform() for Labeled Distance Transform 360 Segmentation 360 Flood Fill 361 Watershed Algorithm 365 Grabcuts 366 Mean-Shift Segmentation 368 Summary 370 Exercises 371 13. Histograms and Templates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Histogram Representation in OpenCV 376 cv::calcHist(): Creating a Histogram from Data 377 Basic Manipulations with Histograms 380 Histogram Normalization 380 Histogram Threshold 380 Finding the Most Populated Bin 380 Comparing Two Histograms 382 Histogram Usage Examples 385 Some More Sophisticated Histograms Methods 388 Earth Mover’s Distance 389 Back Projection 394 Template Matching 397 Square Difference Matching Method (cv::TM_SQDIFF) 399 Normalized Square Difference Matching Method (cv::TM_SQDIFF_NORMED) 400 Correlation Matching Methods (cv::TM_CCORR) 400 Normalized Cross-Correlation Matching Method (cv::TM_CCORR_NORMED) 400 Correlation Coefficient Matching Methods (cv::TM_CCOEFF) 400 Normalized Correlation Coefficient Matching Method (cv::TM_CCOEFF_NORMED) 401 Table of Contents | ix

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Summary 404 Exercises 404 14. Contours. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Contour Finding 407 Contour Hierarchies 408 Drawing Contours 413 A Contour Example 414 Another Contour Example 416 Fast Connected Component Analysis 417 More to Do with Contours 420 Polygon Approximations 420 Geometry and Summary Characteristics 421 Geometrical Tests 428 Matching Contours and Images 429 Moments 429 More About Moments 431 Matching and Hu Moments 435 Using Shape Context to Compare Shapes 436 Summary 441 Exercises 442 15. Background Subtraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 Overview of Background Subtraction 445 Weaknesses of Background Subtraction 446 Scene Modeling 447 A Slice of Pixels 447 Frame Differencing 451 Averaging Background Method 452 Accumulating Means, Variances, and Covariances 458 A More Advanced Background Subtraction Method 467 Structures 470 Learning the Background 472 Learning with Moving Foreground Objects 474 Background Differencing: Finding Foreground Objects 475 Using the Codebook Background Model 477 A Few More Thoughts on Codebook Models 477 Connected Components for Foreground Cleanup 477 A Quick Test 481 Comparing Two Background Methods 483 OpenCV Background Subtraction Encapsulation 485 The cv::BackgroundSubtractor Base Class 485 x | Table of Contents

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KaewTraKuPong and Bowden Method 486 Zivkovic Method 488 Summary 490 Exercises 491 16. Keypoints and Descriptors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Keypoints and the Basics of Tracking 493 Corner Finding 494 Introduction to Optical Flow 498 Lucas-Kanade Method for Sparse Optical Flow 500 Generalized Keypoints and Descriptors 511 Optical Flow, Tracking, and Recognition 513 How OpenCV Handles Keypoints and Descriptors, the General Case 514 Core Keypoint Detection Methods 526 Keypoint Filtering 571 Matching Methods 573 Displaying Results 580 Summary 583 Exercises 584 17. Tracking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Concepts in Tracking 587 Dense Optical Flow 588 The Farnebäck Polynomial Expansion Algorithm 589 The Dual TV-L1 Algorithm 592 The Simple Flow Algorithm 596 Mean-Shift and Camshift Tracking 600 Mean-Shift 601 Camshift 604 Motion Templates 605 Estimators 613 The Kalman Filter 615 A Brief Note on the Extended Kalman Filter 633 Summary 634 Exercises 634 18. Camera Models and Calibration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 Camera Model 638 The Basics of Projective Geometry 641 Rodrigues Transform 643 Lens Distortions 644 Calibration 648 Table of Contents | xi

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Rotation Matrix and Translation Vector 650 Calibration Boards 652 Homography 660 Camera Calibration 665 Undistortion 677 Undistortion Maps 678 Converting Undistortion Maps Between Representations with cv::convertMaps() 679 Computing Undistortion Maps with cv::initUndistortRectifyMap() 680 Undistorting an Image with cv::remap() 682 Undistortion with cv::undistort() 683 Sparse Undistortion with cv::undistortPoints() 683 Putting Calibration All Together 684 Summary 687 Exercises 688 19. Projection and Three-Dimensional Vision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691 Projections 692 Affine and Perspective Transformations 694 Bird’s-Eye-View Transform Example 695 Three-Dimensional Pose Estimation 700 Pose Estimation from a Single Camera 700 Stereo Imaging 703 Triangulation 704 Epipolar Geometry 708 The Essential and Fundamental Matrices 710 Computing Epipolar Lines 720 Stereo Calibration 721 Stereo Rectification 726 Stereo Correspondence 737 Stereo Calibration, Rectification, and Correspondence Code Example 752 Depth Maps from Three-Dimensional Reprojection 759 Structure from Motion 761 Fitting Lines in Two and Three Dimensions 762 Summary 765 Exercises 766 20. The Basics of Machine Learning in OpenCV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769 What Is Machine Learning? 770 Training and Test Sets 770 Supervised and Unsupervised Learning 771 Generative and Discriminative Models 773 xii | Table of Contents

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OpenCV ML Algorithms 774 Using Machine Learning in Vision 776 Variable Importance 778 Diagnosing Machine Learning Problems 779 Legacy Routines in the ML Library 785 K-Means 786 Mahalanobis Distance 793 Summary 797 Exercises 797 21. StatModel: The Standard Model for Learning in OpenCV. . . . . . . . . . . . . . . . . . . . . . . . . 799 Common Routines in the ML Library 799 Training and the cv::ml::TrainData Structure 802 Prediction 809 Machine Learning Algorithms Using cv::StatModel 810 Naïve/Normal Bayes Classifier 810 Binary Decision Trees 816 Boosting 830 Random Trees 837 Expectation Maximization 842 K-Nearest Neighbors 846 Multilayer Perceptron 849 Support Vector Machine 859 Summary 870 Exercises 871 22. Object Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 875 Tree-Based Object Detection Techniques 875 Cascade Classifiers 876 Supervised Learning and Boosting Theory 879 Learning New Objects 888 Object Detection Using Support Vector Machines 897 Latent SVM for Object Detection 898 The Bag of Words Algorithm and Semantic Categorization 901 Summary 907 Exercises 907 23. Future of OpenCV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 909 Past and Present 909 OpenCV 3.x 910 How Well Did Our Predictions Go Last Time? 911 Future Functions 912 Table of Contents | xiii

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Current GSoC Work 913 Community Contributions 915 OpenCV.org 916 Some AI Speculation 917 Afterword 920 A. Planar Subdivisions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 923 B. opencv_contrib. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 939 C. Calibration Patterns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 943 Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 949 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 967 xiv | Table of Contents

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Preface This book provides a working guide to the C++ Open Source Computer Vision Library (OpenCV) version 3.x and gives a general background on the field of com‐ puter vision sufficient to help readers use OpenCV effectively. Purpose of This Book Computer vision is a rapidly growing field largely because of four trends: • The advent of mobile phones put millions of cameras in people’s hands. • The Internet and search engines aggregated the resulting giant flows of image and video data into huge databases. • Computer processing power became a cheap commodity. • Vision algorithms themselves became more mature (now with the advent of deep neural networks, which OpenCV is increasingly supporting; see dnn at opencv_contrib [opencv_contrib]). OpenCV has played a role in the growth of computer vision by enabling hundreds of thousands of people to do more productive work in vision. OpenCV 3.x now allows students, researchers, professionals, and entrepreneurs to efficiently implement projects and jump-start research by providing them with a coherent C++ computer vision architecture that is optimized over many platforms. The purpose of this book is to: • Comprehensively document OpenCV by detailing what function calling conven‐ tions really mean and how to use them correctly • Give the reader an intuitive understanding of how the vision algorithms work • Give the reader some sense of what algorithm to use and when to use it Preface | xv

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• Give the reader a boost in implementing computer vision and machine learning algorithms by providing many working code examples to start from • Suggest ways to fix some of the more advanced routines when something goes wrong This book documents OpenCV in a way that allows the reader to rapidly do interest‐ ing and fun things in computer vision. It gives an intuitive understanding of how the algorithms work, which serves to guide the reader in designing and debugging vision applications and also makes the formal descriptions of computer vision and machine learning algorithms in other texts easier to comprehend and remember. Who This Book Is For This book contains descriptions, working code examples, and explanations of the C++ computer vision tools contained in the OpenCV 3.x library. Thus, it should be helpful to many different kinds of users: Professionals and entrepreneurs For practicing professionals who need to rapidly prototype or professionally implement computer vision systems, the sample code provides a quick frame‐ work with which to start. Our descriptions of the algorithms can quickly teach or remind the reader how they work. OpenCV 3.x sits on top of a hardware acceler‐ ation layer (HAL) so that implemented algorithms can run efficiently, seamlessly taking advantage of a variety of hardware platforms. Students This is the text we wish had back in school. The intuitive explanations, detailed documentation, and sample code will allow you to boot up faster in computer vision, work on more interesting class projects, and ultimately contribute new research to the field. Teachers Computer vision is a fast-moving field. We’ve found it effective to have students rapidly cover an accessible text while the instructor fills in formal exposition where needed and supplements that with current papers or guest lectures from experts. The students can meanwhile start class projects earlier and attempt more ambitious tasks. Hobbyists Computer vision is fun—here’s how to hack it. We have a strong focus on giving readers enough intuition, documentation, and working code to enable rapid implementation of real-time vision applications. xvi | Preface

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1 Always with a warning to more casual users that they may skip such sections. What This Book Is Not This book is not a formal text. We do go into mathematical detail at various points,1 but it is all in the service of developing deeper intuitions behind the algorithms or to clarify the implications of any assumptions built into those algorithms. We have not attempted a formal mathematical exposition here and might even incur some wrath along the way from those who do write formal expositions. This book has more of an “applied” nature. It will certainly be of general help, but is not aimed at any of the specialized niches in computer vision (e.g., medical imaging or remote sensing analysis). That said, we believe that by reading the explanations here first, a student will not only learn the theory better, but remember it longer as well. Therefore, this book would make a good adjunct text to a theoretical course and would be a great text for an introductory or project-centric course. About the Programs in This Book All the program examples in this book are based on OpenCV version 3.x. The code should work under Linux, Windows, and OS X. Using references online, OpenCV 3.x has full support to run on Android and iOS. Source code for the examples in the book can be fetched from this book’s website; source code for OpenCV is available on GitHub; and prebuilt versions of OpenCV can be loaded from its SourceForge site. OpenCV is under ongoing development, with official releases occurring quarterly. To stay completely current, you should obtain your code updates from the aforemen‐ tioned GitHub site. OpenCV maintains a website at http://opencv.org; for developers, there is a wiki at https://github.com/opencv/opencv/wiki. Prerequisites For the most part, readers need only know how to program in C++. Many of the math sections in this book are optional and are labeled as such. The mathematics involve simple algebra and basic matrix algebra, and assume some familiarity with solution methods to least-squares optimization problems as well as some basic knowledge of Gaussian distributions, Bayes’ law, and derivatives of simple functions. The math in this book is in support of developing intuition for the algorithms. The reader may skip the math and the algorithm descriptions, using only the function definitions and code examples to get vision applications up and running. Preface | xvii

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How This Book Is Best Used This text need not be read in order. It can serve as a kind of user manual: look up the function when you need it, and read the function’s description if you want the gist of how it works “under the hood.” However, the intent of this book is tutorial. It gives you a basic understanding of computer vision along with details of how and when to use selected algorithms. This book is written to allow its use as an adjunct or primary textbook for an under‐ graduate or graduate course in computer vision. The basic strategy with this method is for students to read the book for a rapid overview and then supplement that read‐ ing with more formal sections in other textbooks and with papers in the field. There are exercises at the end of each chapter to help test the student’s knowledge and to develop further intuitions. You could approach this text in any of the following ways: Grab bag Go through Chapters 1–5 in the first sitting, and then just hit the appropriate chapters or sections as you need them. This book does not have to be read in sequence, except for Chapters 18 and 19 (which cover camera calibration and stereo imaging) and Chapters 20, 21, and 22 (which cover machine learning). Entrepreneurs and students doing project-based courses might go this way. Good progress Read just two chapters a week until you’ve covered Chapters 1–22 in 11 weeks (Chapter 23 will go by in an instant). Start on projects and dive into details on selected areas in the field, using additional texts and papers as appropriate. The sprint Cruise through the book as fast as your comprehension allows, covering Chap‐ ters 1–23. Then get started on projects and go into detail on selected areas in the field using additional texts and papers. This is probably the choice for professio‐ nals, but it might also suit a more advanced computer vision course. Chapter 20 is a brief chapter that gives general background on machine learning, which is followed by Chapters 21 and 22, which give more details on the machine learning algorithms implemented in OpenCV and how to use them. Of course, machine learning is integral to object recognition and a big part of computer vision, but it’s a field worthy of its own book. Professionals should find this text a suitable launching point for further explorations of the literature—or for just getting down to business with the code in that part of the library. The machine learning interface has been substantially simplified and unified in OpenCV 3.x. This is how we like to teach computer vision: sprint through the course content at a level where the students get the gist of how things work; then get students started on xviii | Preface