Learning OpenCV by Bradski, Gary -- Read -- Imperial Library of Trantor

Index

Learning OpenCV

SPECIAL OFFER: Upgrade this ebook with O’Reilly A Note Regarding Supplemental Files 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…

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

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

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 cvMahalanobis 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, 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

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

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

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

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

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

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

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

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

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

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

14. OpenCV's Future

Past and Future Directions

Specific Items

OpenCV for Artists Afterword

15. Bibliography Index About the Authors Colophon SPECIAL OFFER: Upgrade this ebook with O’Reilly

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