精通OpenCV 3(Mastering OpenCV 3) – jashliao部落格

精通OpenCV 3(Mastering OpenCV 3)

資料來源:https://github.com/PacktPublishing/Mastering-OpenCV3-Second-Edition

http://www.drmaster.com.tw/bookinfo.asp?BookID=MP11713
https://www.cs.ccu.edu.tw/~damon/photo/,OpenCV/,Mastering_OpenCV.pdf

GITHUB: https://github.com/jash-git/Mastering-OpenCV-3

前言

第1章　在樹莓派上建立卡通化器和膚色轉換器

第2章　使用OpenCV探索運動恢復結構

第3章　使用支援向量機和神經網路進行車牌辨識

第4章　非剛性人臉追蹤

第5章　使用AAM和POSIT進行3D頭部姿勢估算

第6章　使用Eigenface或Fisherface進行人臉辨識

Preface 1
Chapter 1: Cartoonifier and Skin Changer for Android 7
    Accessing the webcam 9
    Main camera processing loop for a desktop app 10
    Generating a black-and-white sketch 11
    Generating a color painting and a cartoon 12
    Generating an “evil” mode using edge filters 14
    Generating an “alien” mode using skin detection 16
    Skin-detection algorithm 16
    Showing the user where to put their face 17
    Implementation of the skin-color changer 19
    Porting from desktop to Android 24
    Setting up an Android project that uses OpenCV 24
    Color formats used for image processing on Android 25
    Input color format from the camera 25
    Output color format for display 26
    Adding the cartoonifier code to the Android NDK app 28
    Reviewing the Android app 30
    Cartoonifying the image when the user taps the screen 31
    Saving the image to a file and to the Android picture gallery 33
    Showing an Android notification message about a saved image 36
    Changing cartoon modes through the Android menu bar 37
    Reducing the random pepper noise from the sketch image 40
    Showing the FPS of the app 43
    Using a different camera resolution 43
    Customizing the app 44
    Summary 45
    Table of Contents
Chapter 2: Marker-based Augmented Reality on iPhone or iPad 47
    Creating an iOS project that uses OpenCV 48
    Adding OpenCV framework 49
    Including OpenCV headers 51
    Application architecture 52
    Marker detection 62
    Marker identification 64
    Grayscale conversion 64
    Image binarization 65
    Contours detection 66
    Candidates search 67
    Marker code recognition 72
    Reading marker code 72
    Marker location refinement 74
    Placing a marker in 3D 76
    Camera calibration 76
    Marker pose estimation 78
    Rendering the 3D virtual object 82
    Creating the OpenGL rendering layer 82
    Rendering an AR scene 85
    Summary 92
    References 92
Chapter 3: Marker-less Augmented Reality 93
    Marker-based versus marker-less AR 94
    Using feature descriptors to find an arbitrary image on video 95
    Feature extraction 95
    Definition of a pattern object 98
    Matching of feature points 98
    PatternDetector.cpp 99
    Outlier removal 100
    Cross-match filter 101
    Ratio test 101
    Homography estimation 102
    Homography refinement 104
    Putting it all together 107
    Pattern pose estimation 108
    PatternDetector.cpp 108
    Obtaining the camera-intrinsic matrix 110
    Pattern.cpp 113
    Application infrastructure 114
    ARPipeline.hpp 115
    ARPipeline.cpp 115
    Enabling support for 3D visualization in OpenCV 116
    Table of Contents
    Creating OpenGL windows using OpenCV 118
    Video capture using OpenCV 118
    Rendering augmented reality 119
    ARDrawingContext.hpp 119
    ARDrawingContext.cpp 120
    Demonstration 122
    main.cpp 123
    Summary 126
    References 127
Chapter 4: Exploring Structure from Motion Using OpenCV 129
    Structure from Motion concepts 130
    Estimating the camera motion from a pair of images 132
    Point matching using rich feature descriptors 132
    Point matching using optical flow 134
    Finding camera matrices 139
    Reconstructing the scene 143
    Reconstruction from many views 147
    Refinement of the reconstruction 151
    Visualizing 3D point clouds with PCL 155
    Using the example code 158
    Summary 159
    References 160
Chapter 5: Number Plate Recognition Using SVM and
    Neural Networks 161
    Introduction to ANPR 161
    ANPR algorithm 163
    Plate detection 166
    Segmentation 167
    Classification 173
    Plate recognition 176
    OCR segmentation 177
    Feature extraction 178
    OCR classification 181
    Evaluation 185
    Summary 188
    Chapter 6: Non-rigid Face Tracking 189
    Overview 191
    Utilities 191
    Object-oriented design 191
    Table of Contents
    Data collection: Image and video annotation 193
    Training data types 194
    Annotation tool 198
    Pre-annotated data (The MUCT dataset) 198
    Geometrical constraints 199
    Procrustes analysis 202
    Linear shape models 205
    A combined local-global representation 207
    Training and visualization 209
    Facial feature detectors 212
    Correlation-based patch models 214
    Learning discriminative patch models 214
    Generative versus discriminative patch models 218
    Accounting for global geometric transformations 219
    Training and visualization 222
    Face detection and initialization 224
    Face tracking 228
    Face tracker implementation 229
    Training and visualization 231
    Generic versus person-specific models 232
    Summary 233
    References 233
Chapter 7: 3D Head Pose Estimation Using AAM and POSIT 235
    Active Appearance Models overview 236
    Active Shape Models 238
    Getting the feel of PCA 240
    Triangulation 245
    Triangle texture warping 247
    Model Instantiation – playing with the Active Appearance Model 249
    AAM search and fitting 250
    POSIT 253
    Diving into POSIT 253
    POSIT and head model 256
    Tracking from webcam or video file 257
    Summary 259
    References 260
Chapter 8: Face Recognition using Eigenfaces or Fisherfaces 261
    Introduction to face recognition and face detection 261
    Step 1: Face detection 263
    Implementing face detection using OpenCV 264
    Loading a Haar or LBP detector for object or face detection 265
    Accessing the webcam 266
    Table of Contents
    Detecting an object using the Haar or LBP Classifier 266
    Detecting the face 268
    Step 2: Face preprocessing 270
    Eye detection 271
    Eye search regions 272
    Step 3: Collecting faces and learning from them 281
    Collecting preprocessed faces for training 283
    Training the face recognition system from collected faces 285
    Viewing the learned knowledge 287
    Average face 289
    Eigenvalues, Eigenfaces, and Fisherfaces 290
    Step 4: Face recognition 292
    Face identification: Recognizing people from their face 292
    Face verification: Validating that it is the claimed person 292
    Finishing touches: Saving and loading files 295
    Finishing touches: Making a nice and interactive GUI 295
    Drawing the GUI elements 297
    Checking and handling mouse clicks 306
    Summary 308
    References 309
    Index 311