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《Learning OpenCV 3》學習OpenCV 3 範例/電子檔備份 GITHUH: https://github.com/jash-git/Learning-OpenCV-3 資料來源:http://www.1024ebook.com/book/7041 http://file.allitebooks.com/20170108/Learning OpenCV 3.pdf https://github.com/oreillymedia/Learning-OpenCV-3_examples 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     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     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     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 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     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     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     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     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     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     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     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