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Learning OpenCV Computer Vision with the OpenCV Library.pdf

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    Contents
    Preface
    Purpose
    Who This Book Is For
    What This Book Is Not
    About the Programs in This Book
    Prerequisites
    How This Book Is Best Used
    Conventions Used in This Book
    Using Code Examples
    Safari® Books Online
    We’d Like to Hear from You
    Acknowledgments
    Thanks for Help on OpenCV
    Thanks for Help on the Book
    Gary Adds . . .
    Adrian Adds . . .
    CHAPTER 1: Overview
    What Is OpenCV?
    Who Uses OpenCV?
    What Is Computer Vision?
    The Origin of OpenCV
    Speeding Up OpenCV with IPP
    Who Owns OpenCV?
    Downloading and Installing OpenCV
    Install
    Windows
    Linux
    MacOS X
    Getting the Latest OpenCV via CVS
    More OpenCV Documentation
    Documentation Available in HTML
    Documentation via the Wiki
    OpenCV Structure and Content
    Portability
    Exercises
    CHAPTER 2: Introduction to OpenCV
    Getting Started
    First Program—Display a Picture
    Second Program—AVI Video
    Moving Around
    A Simple Transformation
    A Not-So-Simple Transformation
    Input from a Camera
    Writing to an AVI File
    Onward
    Exercises
    CHAPTER 3: Getting to Know OpenCV
    OpenCV Primitive Data Types
    Matrix and Image Types
    CvMat Matrix Structure
    Accessing Data in Your Matrix
    The easy way
    The hard way
    The right way
    Arrays of Points
    IplImage Data Structure
    Accessing Image Data
    More on ROI and widthStep
    Matrix and Image Operators
    cvAbs, cvAbsDiff, and cvAbsDiffS
    cvAdd, cvAddS, cvAddWeighted, and alpha blending
    cvAnd and cvAndS
    cvAvg
    cvAvgSdv
    cvCalcCovarMatrix
    cvCmp and cvCmpS
    cvConvertScale
    cvConvertScaleAbs
    cvCopy
    cvCountNonZero
    cvCrossProduct
    cvCvtColor
    cvDet
    cvDiv
    cvDotProduct
    cvEigenVV
    cvFlip
    cvGEMM
    cvGetCol and cvGetCols
    cvGetDiag
    cvGetDims and cvGetDimSize
    cvGetRow and cvGetRows
    cvGetSize
    cvGetSubRect
    cvInRange and cvInRangeS
    cvInvert
    cvMahalonobis
    cvMax and cvMaxS
    cvMerge
    cvMin and cvMinS
    cvMinMaxLoc
    cvMul
    cvNot
    cvNorm
    cvNormalize
    cvOr and cvOrS
    cvReduce
    cvRepeat
    cvScale
    cvSet and cvSetZero
    cvSetIdentity
    cvSolve
    cvSplit
    cvSub
    cvSub, cvSubS, and cvSubRS
    cvSum
    cvSVD
    cvSVBkSb
    cvTrace
    cvTranspose and cvT
    cvXor and cvXorS
    cvZero
    Drawing Things
    Lines
    Circles and Ellipses
    Polygons
    Fonts and Text
    Data Persistence
    Integrated Performance Primitives
    Verifying Installation
    Summary
    Exercises
    CHAPTER 4: HighGUI
    A Portable Graphics Toolkit
    Creating a Window
    Loading an Image
    Displaying Images
    WaitKey
    Mouse Events
    Sliders, Trackbars, and Switches
    No Buttons
    Working with Video
    Reading Video
    Writing Video
    ConvertImage
    Exercises
    CHAPTER 5: Image Processing
    Overview
    Smoothing
    Image Morphology
    Dilation and Erosion
    Making Your Own Kernel
    More General Morphology
    Opening and closing
    Morphological gradient
    Top Hat and Black Hat
    Flood Fill
    Resize
    Image Pyramids
    Threshold
    Adaptive Threshold
    Exercises
    CHAPTER 6: Image Transforms
    Overview
    Convolution
    Convolution Boundaries
    Gradients and Sobel Derivatives
    Scharr Filter
    Laplace
    Canny
    Hough Transforms
    Hough Line Transform
    Hough Circle Transform
    Remap
    Stretch, Shrink, Warp, and Rotate
    Affine Transform
    Dense affine transformations
    cVWarpAffine performance
    Computing the affine map matrix
    Sparse affine transformations
    Perspective Transform
    Dense perspective transform
    Computing the perspective map matrix
    Sparse perspective transformations
    CartToPolar and PolarToCart
    LogPolar
    Discrete Fourier Transform (DFT)
    Spectrum Multiplication
    Convolution and DFT
    Discrete Cosine Transform (DCT)
    Integral Images
    Distance Transform
    Histogram Equalization
    Exercises
    CHAPTER 7: Histograms and Matching
    Basic Histogram Data Structure
    Accessing Histograms
    Basic Manipulations with Histograms
    Comparing Two Histograms
    Correlation (method = CV_COMP_CORREL)
    Chi-square (method = CV_COMP_CHISQR)
    Intersection (method = CV_COMP_INTERSECT)
    Bhattacharyya distance (method = CV_COMP_BHATTACHARYYA)
    Histogram Usage Examples
    Some More Complicated Stuff
    Earth Mover’s Distance
    Back Projection
    Patch-based back projection
    Template Matching
    Square difference matching method (method = CV_TM_SQDIFF)
    Correlation matching methods (method = CV_TM_CCORR)
    Correlation coefficient matching methods (method = CV_TM_CCOEFF)
    Normalized methods
    Exercises
    CHAPTER 8: Contours
    Memory Storage
    Sequences
    Creating a Sequence
    Deleting a Sequence
    Direct Access to Sequence Elements
    Slices, Copying, and Moving Data
    Using a Sequence As a Stack
    Inserting and Removing Elements
    Sequence Block Size
    Sequence Readers and Sequence Writers
    Sequences and Arrays
    Contour Finding
    Contours Are Sequences
    Freeman Chain Codes
    Drawing Contours
    A Contour Example
    Another Contour Example
    More to Do with Contours
    Polygon Approximations
    Summary Characteristics
    Length
    Bounding boxes
    Enclosing circles and ellipses
    Geometry
    Matching Contours
    Moments
    More About Moments
    Matching with Hu Moments
    Hierarchical Matching
    Contour Convexity and Convexity Defects
    Pairwise Geometrical Histograms
    Exercises
    CHAPTER 9: Image Parts and Segmentation
    Parts and Segments
    Background Subtraction
    Weaknesses of Background Subtraction
    Scene Modeling
    A Slice of Pixels
    Frame Differencing
    Averaging Background Method
    Accumulating means, variances, and covariances
    Advanced Background Method
    Structures
    Learning the background
    Learning with moving foreground objects
    Background differencing: Finding foreground objects
    Using the codebook background model
    A few more thoughts on codebook models
    Connected Components for Foreground Cleanup
    A quick test
    Comparing Background Methods
    Watershed Algorithm
    Image Repair by Inpainting
    Mean-Shift Segmentation
    Delaunay Triangulation, Voronoi Tesselation
    Creating a Delaunay or Voronoi Subdivision
    Navigating Delaunay Subdivisions
    Walking on edges
    Points from edges
    Method 1: Use an external point to locate an edge or vertex
    Method 2: Step through a sequence of points or edges
    Identifying the bounding triangle or edges on the convex hull and walking the hull
    Usage Examples
    Exercises
    CHAPTER 10: Tracking and Motion
    The Basics of Tracking
    Corner Finding
    Subpixel Corners
    Invariant Features
    Optical Flow
    Lucas-Kanade Method
    How Lucas-Kanade works
    Lucas-Kanade code
    Pyramid Lucas-Kanade code
    Dense Tracking Techniques
    Horn-Schunck method
    Block matching method
    Mean-Shift and Camshift Tracking
    Mean-Shift
    Camshift
    Motion Templates
    Estimators
    The Kalman Filter
    Some Kalman math
    Systems with dynamics
    Kalman equations
    OpenCV and the Kalman filter
    Kalman filter example code
    A Brief Note on the Extended Kalman Filter
    The Condensation Algorithm
    Exercises
    CHAPTER 11: Camera Models and Calibration
    Camera Model
    Basic Projective Geometry
    Lens Distortions
    Calibration
    Rotation Matrix and Translation Vector
    Chessboards
    Subpixel corners
    Drawing chessboard corners
    Homography
    Camera Calibration
    How many chess corners for how many parameters?
    What’s under the hood?
    Calibration function
    Computing extrinsics only
    Undistortion
    Putting Calibration All Together
    Rodrigues Transform
    Exercises
    CHAPTER 12: Projection and 3D Vision
    Projections
    Affine and Perspective Transformations
    Bird’s-Eye View Transform Example
    POSIT: 3D Pose Estimation
    Stereo Imaging
    Triangulation
    Epipolar Geometry
    The Essential and Fundamental Matrices
    Essential matrix math
    Fundamental matrix math
    How OpenCV handles all of this
    Computing Epipolar Lines
    Stereo Calibration
    Stereo Rectification
    Uncalibrated stereo rectification: Hartley’s algorithm
    Calibrated stereo rectification: Bouguet’s algorithm
    Rectification map
    Stereo Correspondence
    Stereo Calibration, Rectification, and Correspondence Code
    Depth Maps from 3D Reprojection
    Structure from Motion
    Fitting Lines in Two and Three Dimensions
    Exercises
    CHAPTER 13: Machine Learning
    What Is Machine Learning
    Training and Test Set
    Supervised and Unsupervised Data
    Generative and Discriminative Models
    OpenCV ML Algorithms
    Using Machine Learning in Vision
    Variable Importance
    Diagnosing Machine Learning Problems
    Cross-validation, bootstrapping, ROC curves, and confusion matrices
    Common Routines in the ML Library
    Training
    Prediction
    Controlling Training Iterations
    Mahalanobis Distance
    K-Means
    Problems and Solutions
    K-Means Code
    Naïve/Normal Bayes Classifier
    Naïve/Normal Bayes Code
    Binary Decision Trees
    Regression Impurity
    Classification Impurity
    Entropy impurity
    Gini impurity
    Misclassification impurity
    Decision Tree Usage
    Training the tree
    Decision Tree Results
    Boosting
    AdaBoost
    Boosting Code
    Random Trees
    Random Tree Code
    Using Random Trees
    Face Detection or Haar Classifier
    Supervised Learning and Boosting Theory
    Boosting in the Haar cascade
    Viola-Jones Classifier Theory
    Works well on . . .
    Code for Detecting Faces
    Learning New Objects
    Other Machine Learning Algorithms
    Expectation Maximization
    K-Nearest Neighbors
    Multilayer Perceptron
    Support Vector Machine
    Exercises
    CHAPTER 14: OpenCV’s Future
    Past and Future
    Directions
    Specific Items
    OpenCV for Artists
    Afterword
    Bibliography
    Index
    About the Authors
    Colophon
    Learning OpenCV Gary Bradski and Adrian Kaehler Beijing · Cambridge · Farnham · Köln · Sebastopol · Taipei · Tokyo
    Learning OpenCV by Gary Bradski and Adrian Kaehler Copyright © 2008 Gary Bradski and Adrian Kaehler. 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 (safari.oreilly.com). For more information, contact our corporate/institutional sales department: (800) 998-9938 or corporate@oreilly.com. Editor: Mike Loukides Production Editor: Rachel Monaghan Production Services: Newgen Publishing and Data Services Cover Designer: Karen Montgomery Interior Designer: David Futato Illustrator: Robert Romano Printing History: September 2008: First Edition. Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of O’Reilly Media, Inc. Learning OpenCV, the image of a giant peacock moth, and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc. was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information con- tained herein. This book uses Repkover,™ a durable and flexible lay-flat binding. ISBN: 978-0-596-51613-0 [M]
    Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1. 2. Overview What Is OpenCV? Who Uses OpenCV? What Is Computer Vision? The Origin of OpenCV Downloading and Installing OpenCV Getting the Latest OpenCV via CVS More OpenCV Documentation OpenCV Structure and Content Portability Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 1 2 6 8 10 11 13 14 15 Introduction to OpenCV Getting Started First Program—Display a Picture Second Program—AVI Video Moving Around A Simple Transformation A Not-So-Simple Transformation Input from a Camera Writing to an AVI File Onward Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 16 16 18 19 22 24 26 27 29 29 iii
    3. 4. 5. 6. Getting to Know OpenCV OpenCV Primitive Data Types CvMat Matrix Structure IplImage Data Structure Matrix and Image Operators Drawing Things Data Persistence Integrated Performance Primitives Summary Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 31 33 42 47 77 82 86 87 87 HighGUI A Portable Graphics Toolkit Creating a Window Loading an Image Displaying Images Working with Video ConvertImage Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 90 91 92 93 102 106 107 Image Processing Overview Smoothing Image Morphology Flood Fill Resize Image Pyramids Threshold Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 109 109 115 124 129 130 135 141 Image Transforms Overview Convolution Gradients and Sobel Derivatives Laplace Canny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 144 144 148 150 151 iv | Contents
    Hough Transforms Remap Stretch, Shrink, Warp, and Rotate CartToPolar and PolarToCart LogPolar Discrete Fourier Transform (DFT) Discrete Cosine Transform (DCT) Integral Images Distance Transform Histogram Equalization Exercises 153 162 163 172 174 177 182 182 185 186 190 7. 8. 9. Histograms and Matching Basic Histogram Data Structure Accessing Histograms Basic Manipulations with Histograms Some More Complicated Stuff Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 195 198 199 206 219 Contours Memory Storage Sequences Contour Finding Another Contour Example More to Do with Contours Matching Contours Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 222 223 234 243 244 251 262 Image Parts and Segmentation Parts and Segments Background Subtraction Watershed Algorithm Image Repair by Inpainting Mean-Shift Segmentation Delaunay Triangulation, Voronoi Tesselation Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 265 265 295 297 298 300 313 Contents | v
    10. 11. 12. 13. Tracking and Motion The Basics of Tracking Corner Finding Subpixel Corners Invariant Features Optical Flow Mean-Shift and Camshift Tracking Motion Templates Estimators The Condensation Algorithm Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 316 316 319 321 322 337 341 348 364 367 Camera Models and Calibration Camera Model Calibration Undistortion Putting Calibration All Together Rodrigues Transform Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 371 378 396 397 401 403 Projection and 3D Vision Projections Affine and Perspective Transformations POSIT: 3D Pose Estimation Stereo Imaging Structure from Motion Fitting Lines in Two and Three Dimensions Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 405 407 412 415 453 454 458 Machine Learning What Is Machine Learning Common Routines in the ML Library Mahalanobis Distance K-Means Naïve/Normal Bayes Classifier Binary Decision Trees Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 459 471 476 479 483 486 495 vi | Contents
    Random Trees Face Detection or Haar Classifier Other Machine Learning Algorithms Exercises 501 506 516 517 14. OpenCV’s Future Past and Future Directions OpenCV for Artists Afterword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521 521 522 525 526 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Contents | vii
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