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首页OpenCV实战：解决现实世界计算机视觉问题全攻略

OpenCV实战：解决现实世界计算机视觉问题全攻略

Mastering

OpenCV

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"《Mastering OpenCV with Practical Computer Vision Projects》是一本实用的计算机视觉教程书籍，专为想要深入了解OpenCV并在实际项目中应用该技术的读者设计。作者包括Daniel Lélis Baggio、Shervin Emami、David Millán Escrivá、Khvedchenia Ievgen、Naureen Mahmood、Jason Saragih和Roy Shilkrot，他们共同为读者提供了从增强现实、车牌识别到人脸识别和3D头部追踪等众多现实世界问题的逐步解决方案。这本书针对桌面和移动平台，旨在帮助读者在计算机视觉领域深入实践。本书由Packt Publishing出版，版权日期为2012年，所有内容未经版权所有者事先书面许可，不得进行任何形式的复制、存储或传输。尽管作者和出版社已尽力确保书中信息的准确性，但本书销售时并不提供任何形式的担保，无论是明示还是暗示，都不对因本书内容导致的直接或间接损失负责。《Mastering OpenCV with Practical Computer Vision Projects》不仅涵盖了OpenCV的基本概念和技术，还提供了丰富的实例和实战项目，让学习者能够通过解决实际问题来掌握OpenCV的强大功能。无论是希望在游戏开发、机器人技术、安全监控等领域运用计算机视觉的开发者，还是对此感兴趣的学生，这本书都是一个宝贵的学习资源。通过阅读本书，读者将有机会提升自己的图像处理和机器视觉能力，从而在当今高度依赖视觉智能的时代保持竞争力。"

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Table of Contents

[ iii ]

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 ow 134

Finding camera matrices 139

Reconstructing the scene 143

Reconstruction from many views 147

Renement 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

Classication 173

Plate recognition 176

OCR segmentation 177

Feature extraction 178

OCR classication 181

Evaluation 185

Summary 188

Chapter 6: Non-rigid Face Tracking 189

Overview 191

Utilities 191

Object-oriented design 191

Table of Contents

[ iv ]

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-specic 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 tting 250

POSIT 253

Diving into POSIT 253

POSIT and head model 256

Tracking from webcam or video le 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

[ v ]

Detecting an object using the Haar or LBP Classier 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 identication: Recognizing people from their face 292

Face verication: Validating that it is the claimed person 292

Finishing touches: Saving and loading les 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

Preface

Mastering OpenCV with Practical Computer Vision Projects contains nine chapters, where

each chapter is a tutorial for an entire project from start to nish, based on OpenCV's

C++ interface including full source code. The author of each chapter was chosen for

their well-regarded online contributions to the OpenCV community on that topic,

and the book was reviewed by one of the main OpenCV developers. Rather than

explaining the basics of OpenCV functions, this is the rst book that shows how

to apply OpenCV to solve whole problems, including several 3D camera projects

(augmented reality, 3D Structure from Motion, Kinect interaction) and several facial

analysis projects (such as, skin detection, simple face and eye detection, complex facial

feature tracking, 3D head orientation estimation, and face recognition), therefore it

makes a great companion to existing OpenCV books.

What this book covers

Chapter 1, Cartoonier and Skin Changer for Android, contains a complete tutorial and

source code for both a desktop application and an Android app that automatically

generates a cartoon or painting from a real camera image, with several possible types

of cartoons including a skin color changer.

Chapter 2, Marker-based Augmented Reality on iPhone or iPad, contains a complete

tutorial on how to build a marker-based augmented reality (AR) application for

iPad and iPhone devices with an explanation of each step and source code.

Chapter 3, Marker-less Augmented Reality, contains a complete tutorial on how to

develop a marker-less augmented reality desktop application with an explanation

of what marker-less AR is and source code.

Chapter 4, Exploring Structure from Motion Using OpenCV, contains an introduction

to Structure from Motion (SfM) via an implementation of SfM concepts in OpenCV.

The reader will learn how to reconstruct 3D geometry from multiple 2D images and

estimate camera positions.

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