Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

Copyright
Table of Contents
Preface
	Purpose of This Book
		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
	O’Reilly Safari
	We’d Like to Hear from You
	Acknowledgments
		Thanks for Help on OpenCV
		Thanks for Help on This Book
		Adrian Adds...
		Gary Adds...
Chapter 1. Overview
	What Is OpenCV?
	Who Uses OpenCV?
	What Is Computer Vision?
	The Origin of OpenCV
		OpenCV Block Diagram
		Speeding Up OpenCV with IPP
		Who Owns OpenCV?
	Downloading and Installing OpenCV
		Installation
	Getting the Latest OpenCV via Git
	More OpenCV Documentation
		Supplied Documentation
		Online Documentation and the Wiki
	OpenCV Contribution Repository
		Downloading and Building Contributed Modules
	Portability
	Summary
	Exercises
Chapter 2. Introduction to OpenCV
	Include Files
		Resources
	First Program—Display a Picture
	Second Program—Video
	Moving Around
	A Simple Transformation
	A Not-So-Simple Transformation
	Input from a Camera
	Writing to an AVI File
	Summary
	Exercises
Chapter 3. Getting to Know OpenCV Data Types
	The Basics
	OpenCV Data Types
		Overview of the Basic Types
		Basic Types: Getting Down to Details
		Helper Objects
		Utility Functions
		The Template Structures
	Summary
	Exercises
Chapter 4. Images and Large Array Types
	Dynamic and Variable Storage
		The cv::Mat Class: N-Dimensional Dense Arrays
		Creating an Array
		Accessing Array Elements Individually
		The N-ary Array Iterator: NAryMatIterator
		Accessing Array Elements by Block
		Matrix Expressions: Algebra and cv::Mat
		Saturation Casting
		More Things an Array Can Do
		The cv::SparseMat Class: Sparse Arrays
		Accessing Sparse Array Elements
		Functions Unique to Sparse Arrays
		Template Structures for Large Array Types
	Summary
	Exercises
Chapter 5. Array Operations
	More Things You Can Do with Arrays
		cv::abs()
		cv::absdiff()
		cv::add()
		cv::addWeighted()
		cv::bitwise_and()
		cv::bitwise_not()
		cv::bitwise_or()
		cv::bitwise_xor()
		cv::calcCovarMatrix()
		cv::cartToPolar()
		cv::checkRange()
		cv::compare()
		cv::completeSymm()
		cv::convertScaleAbs()
		cv::countNonZero()
		cv::cvarrToMat()
		cv::dct()
		cv::dft()
		cv::cvtColor()
		cv::determinant()
		cv::divide()
		cv::eigen()
		cv::exp()
		cv::extractImageCOI()
		cv::flip()
		cv::gemm()
		cv::getConvertElem() and cv::getConvertScaleElem()
		cv::idct()
		cv::idft()
		cv::inRange()
		cv::insertImageCOI()
		cv::invert()
		cv::log()
		cv::LUT()
		cv::magnitude()
		cv::Mahalanobis()
		cv::max()
		cv::mean()
		cv::meanStdDev()
		cv::merge()
		cv::min()
		cv::minMaxIdx()
		cv::minMaxLoc()
		cv::mixChannels()
		cv::mulSpectrums()
		cv::multiply()
		cv::mulTransposed()
		cv::norm()
		cv::normalize()
		cv::perspectiveTransform()
		cv::phase()
		cv::polarToCart()
		cv::pow()
		cv::randu()
		cv::randn()
		cv::randShuffle()
		cv::reduce()
		cv::repeat()
		cv::scaleAdd()
		cv::setIdentity()
		cv::solve()
		cv::solveCubic()
		cv::solvePoly()
		cv::sort()
		cv::sortIdx()
		cv::split()
		cv::sqrt()
		cv::subtract()
		cv::sum()
		cv::trace()
		cv::transform()
		cv::transpose()
	Summary
	Exercises
Chapter 6. Drawing and Annotating
	Drawing Things
		Line Art and Filled Polygons
		Fonts and Text
	Summary
	Exercises
Chapter 7. Functors in OpenCV
	Objects That “Do Stuff”
		Principal Component Analysis (cv::PCA)
		Singular Value Decomposition (cv::SVD)
		Random Number Generator (cv::RNG)
	Summary
	Exercises
Chapter 8. Image, Video, and Data Files
	HighGUI: Portable Graphics Toolkit
	Working with Image Files
		Loading and Saving Images
		A Note About Codecs
		Compression and Decompression
	Working with Video
		Reading Video with the cv::VideoCapture Object
		Writing Video with the cv::VideoWriter Object
	Data Persistence
		Writing to a cv::FileStorage
		Reading from a cv::FileStorage
		cv::FileNode
	Summary
	Exercises
Chapter 9. Cross-Platform and Native Windows
	Working with Windows
		HighGUI Native Graphical User Interface
		Working with the Qt Backend
		Integrating OpenCV with Full GUI Toolkits
	Summary
	Exercises
Chapter 10. Filters and Convolution
	Overview
	Before We Begin
		Filters, Kernels, and Convolution
		Border Extrapolation and Boundary Conditions
	Threshold Operations
		Otsu’s Algorithm
		Adaptive Threshold
	Smoothing
		Simple Blur and the Box Filter
		Median Filter
		Gaussian Filter
		Bilateral Filter
	Derivatives and Gradients
		The Sobel Derivative
		Scharr Filter
		The Laplacian
	Image Morphology
		Dilation and Erosion
		The General Morphology Function
		Opening and Closing
		Morphological Gradient
		Top Hat and Black Hat
		Making Your Own Kernel
	Convolution with an Arbitrary Linear Filter
		Applying a General Filter with cv::filter2D()
		Applying a General Separable Filter with cv::sepFilter2D
		Kernel Builders
	Summary
	Exercises
Chapter 11. General Image Transforms
	Overview
	Stretch, Shrink, Warp, and Rotate
		Uniform Resize
		Image Pyramids
		Nonuniform Mappings
		Affine Transformation
		Perspective Transformation
	General Remappings
		Polar Mappings
		LogPolar
		Arbitrary Mappings
	Image Repair
		Inpainting
		Denoising
	Histogram Equalization
		cv::equalizeHist(): Contrast equalization
	Summary
	Exercises
Chapter 12. Image Analysis
	Overview
	Discrete Fourier Transform
		cv::dft(): The Discrete Fourier Transform
		cv::idft(): The Inverse Discrete Fourier Transform
		cv::mulSpectrums(): Spectrum Multiplication
		Convolution Using Discrete Fourier Transforms
		cv::dct(): The Discrete Cosine Transform
		cv::idct(): The Inverse Discrete Cosine Transform
	Integral Images
		cv::integral() for Standard Summation Integral
		cv::integral() for Squared Summation Integral
		cv::integral() for Tilted Summation Integral
	The Canny Edge Detector
		cv::Canny()
	Hough Transforms
		Hough Line Transform
		Hough Circle Transform
	Distance Transformation
		cv::distanceTransform() for Unlabeled Distance Transform
		cv::distanceTransform() for Labeled Distance Transform
	Segmentation
		Flood Fill
		Watershed Algorithm
		Grabcuts
		Mean-Shift Segmentation
	Summary
	Exercises
Chapter 13. Histograms and Templates
	Histogram Representation in OpenCV
		cv::calcHist(): Creating a Histogram from Data
	Basic Manipulations with Histograms
		Histogram Normalization
		Histogram Threshold
		Finding the Most Populated Bin
		Comparing Two Histograms
		Histogram Usage Examples
	Some More Sophisticated Histograms Methods
		Earth Mover’s Distance
		Back Projection
	Template Matching
		Square Difference Matching Method (cv::TM_SQDIFF)
		Normalized Square Difference Matching Method (cv::TM_SQDIFF_NORMED)
		Correlation Matching Methods (cv::TM_CCORR)
		Normalized Cross-Correlation Matching Method (cv::TM_CCORR_NORMED)
		Correlation Coefficient Matching Methods (cv::TM_CCOEFF)
		Normalized Correlation Coefficient Matching Method (cv::TM_CCOEFF_NORMED)
	Summary
	Exercises
Chapter 14. Contours
	Contour Finding
		Contour Hierarchies
		Drawing Contours
		A Contour Example
		Another Contour Example
		Fast Connected Component Analysis
	More to Do with Contours
		Polygon Approximations
		Geometry and Summary Characteristics
		Geometrical Tests
	Matching Contours and Images
		Moments
		More About Moments
		Matching and Hu Moments
		Using Shape Context to Compare Shapes
	Summary
	Exercises
Chapter 15. Background Subtraction
	Overview of Background Subtraction
	Weaknesses of Background Subtraction
	Scene Modeling
		A Slice of Pixels
		Frame Differencing
	Averaging Background Method
		Accumulating Means, Variances, and Covariances
	A More Advanced Background Subtraction 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 Two Background Methods
	OpenCV Background Subtraction Encapsulation
		The cv::BackgroundSubtractor Base Class
		KaewTraKuPong and Bowden Method
		Zivkovic Method
	Summary
	Exercises
Chapter 16. Keypoints and Descriptors
	Keypoints and the Basics of Tracking
		Corner Finding
		Introduction to Optical Flow
		Lucas-Kanade Method for Sparse Optical Flow
	Generalized Keypoints and Descriptors
		Optical Flow, Tracking, and Recognition
		How OpenCV Handles Keypoints and Descriptors, the General Case
		Core Keypoint Detection Methods
		Keypoint Filtering
		Matching Methods
		Displaying Results
	Summary
	Exercises
Chapter 17. Tracking
	Concepts in Tracking
	Dense Optical Flow
		The Farnebäck Polynomial Expansion Algorithm
		The Dual TV-L1 Algorithm
		The Simple Flow Algorithm
	Mean-Shift and Camshift Tracking
		Mean-Shift
		Camshift
	Motion Templates
	Estimators
		The Kalman Filter
		A Brief Note on the Extended Kalman Filter
	Summary
	Exercises
Chapter 18. Camera Models and Calibration
	Camera Model
		The Basics of Projective Geometry
		Rodrigues Transform
		Lens Distortions
	Calibration
		Rotation Matrix and Translation Vector
		Calibration Boards
		Homography
		Camera Calibration
	Undistortion
		Undistortion Maps
		Converting Undistortion Maps Between Representations with cv::convertMaps()
		Computing Undistortion Maps with cv::initUndistortRectifyMap()
		Undistorting an Image with cv::remap()
		Undistortion with cv::undistort()
		Sparse Undistortion with cv::undistortPoints()
	Putting Calibration All Together
	Summary
	Exercises
Chapter 19. Projection and Three-Dimensional Vision
	Projections
	Affine and Perspective Transformations
		Bird’s-Eye-View Transform Example
	Three-Dimensional Pose Estimation
		Pose Estimation from a Single Camera
	Stereo Imaging
		Triangulation
		Epipolar Geometry
		The Essential and Fundamental Matrices
		Computing Epipolar Lines
		Stereo Calibration
		Stereo Rectification
		Stereo Correspondence
		Stereo Calibration, Rectification, and Correspondence Code Example
		Depth Maps from Three-Dimensional Reprojection
	Structure from Motion
	Fitting Lines in Two and Three Dimensions
	Summary
	Exercises
Chapter 20. The Basics of Machine Learning in OpenCV
	What Is Machine Learning?
		Training and Test Sets
		Supervised and Unsupervised Learning
		Generative and Discriminative Models
		OpenCV ML Algorithms
		Using Machine Learning in Vision
		Variable Importance
		Diagnosing Machine Learning Problems
	Legacy Routines in the ML Library
		K-Means
		Mahalanobis Distance
	Summary
	Exercises
Chapter 21. StatModel: The Standard Model for Learning in OpenCV
	Common Routines in the ML Library
		Training and the cv::ml::TrainData Structure
		Prediction
	Machine Learning Algorithms Using cv::StatModel
		Naïve/Normal Bayes Classifier
		Binary Decision Trees
		Boosting
		Random Trees
		Expectation Maximization
		K-Nearest Neighbors
		Multilayer Perceptron
		Support Vector Machine
	Summary
	Exercises
Chapter 22. Object Detection
	Tree-Based Object Detection Techniques
		Cascade Classifiers
		Supervised Learning and Boosting Theory
		Learning New Objects
	Object Detection Using Support Vector Machines
		Latent SVM for Object Detection
		The Bag of Words Algorithm and Semantic Categorization
	Summary
	Exercises
Chapter 23. Future of OpenCV
	Past and Present
		OpenCV 3.x
	How Well Did Our Predictions Go Last Time?
	Future Functions
		Current GSoC Work
		Community Contributions
		OpenCV.org
	Some AI Speculation
	Afterword
Appendix A. Planar Subdivisions
	Delaunay Triangulation, Voronoi Tesselation
		Creating a Delaunay or Voronoi Subdivision
		Navigating Delaunay Subdivisions
		Usage Examples
	Exercises
Appendix B. opencv_contrib
	An Overview of the opencv_contrib Modules
		Contents of opencv_contrib
Appendix C. Calibration Patterns
	Calibration Patterns Used by OpenCV
Bibliography
Index
About the Authors
Colophon