OpenCV 3编程实战：打造高性能计算机视觉应用

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《OpenCV 3计算机视觉应用编程实战指南（第3版）》是一本专门针对OpenCV 3这一流行C++计算机视觉库的深入指南。本书由Robert Laganiere编著，由BIRMINGHAM-MUMBAI出版，旨在帮助读者构建高效且充分利用OpenCV 3功能的计算机视觉应用程序。此书是面向那些对计算机视觉技术感兴趣的开发者的实用工具，无论你是初学者还是经验丰富的开发者，都能在书中找到众多实用的代码示例和实践策略。第三版的《OpenCV 3计算机视觉应用编程Cookbook》汇集了大量的"配方"或"食谱"，涵盖从基础图像处理、特征检测、目标识别、机器学习到深度学习等广泛主题。作者通过深入浅出的方式，解释了如何将OpenCV 3的高效API和函数应用于实际项目中，帮助读者解决实际问题并提升技能。该书强调版权保护，所有内容未经Packt Publishing的书面许可不得复制、存储或以任何形式传输。尽管作者和出版社尽力确保信息的准确性，但书中提供的内容不附带任何保证，无论是明示的还是暗示的，都不承担因本书使用导致的直接或间接损失的责任。 Packt Publishing在书中准确地标注了提及的所有公司和产品的商标信息，体现了专业性和规范性。然而，由于技术更新迅速，部分章节可能反映了当时的技术状态，对于某些新出现的技术或API，读者可能需要结合官方文档进行补充学习。《OpenCV 3计算机视觉应用编程实战指南（第3版）》是一本实用的参考书籍，不仅适合想要掌握OpenCV 3的开发者，也是计算机视觉领域的宝贵参考资料，能够帮助读者在实践中提升计算机视觉项目的开发能力。通过阅读本书，读者将有机会深入理解OpenCV 3的核心功能，从而更好地将其融入到自己的项目中去。

[ viii ]

Introduction

291

Image formation

292

Computing the fundamental matrix of an image pair

295

Getting ready

295

How to do it…

296

How it works…

300

See also

301

Matching images using random sample consensus

301

How to do it…

302

How it works…

305

There's more…

307

Refining the fundamental matrix 307

Refining the matches 308

Computing a homography between two images

308

Getting ready

309

How to do it…

310

How it works…

313

There's more…

313

Generating image panoramas with the cv::Stitcher module 314

See also

315

Detecting a planar target in images

315

How to do it…

315

How it works…

319

See also

322

Chapter 11: Reconstructing 3D Scenes

323

Introduction

323

Digital image formation

324

Calibrating a camera

326

How to do it…

326

How it works…

332

There's more…

335

Calibration with known intrinsic parameters 335

Using a grid of circles for calibration 335

See also

335

Recovering camera pose

336

How to do it…

336

How it works…

339

There's more…

340

cv::Viz, a 3D Visualizer module 340

See also

342

Preface

Augmented reality, driving assistance, video monitoring; more and more applications are

now using computer vision and image analysis technologies, and yet we are still in the

infancy of the development of new computerized systems capable of understanding our

worlds through the sense of vision. And with the advent of powerful and affordable

computing devices and visual sensors, it has never been easier to create sophisticated

imaging applications. A multitude of software tools and libraries manipulating images and

videos are available, but for anyone who wishes to develop smart vision-based applications,

the OpenCV library is the tool to use. OpenCV (Open source Computer Vision) is an open

source library containing more than 500 optimized algorithms for image and video analysis.

Since its introduction in 1999, it has been largely adopted as the primary development tool

by the community of researchers and developers in computer vision. OpenCV was

originally developed at Intel by a team led by Gary Bradski as an initiative to advance

research in vision and promote the development of rich vision-based, CPU-intensive

applications. After a series of beta releases, version 1.0 was launched in 2006. A second

major release occurred in 2009 with the launch of OpenCV 2 that proposed important

changes, especially the new C++ interface, which we use in this book. In 2012, OpenCV

reshaped itself as a non-profit foundation (h t t p ://o p e n c v . o r g /) relying on crowdfunding

for its future development. OpenCV3 was introduced in 2013; changes were made mainly to

improve the usability of library. Its structure has been revised to remove the unnecessary

dependencies, large modules have been split into smaller ones and the API has been

refined. This book is the third edition of the OpenCV Computer Vision Application

Programming Cookbook and the first one that covers OpenCV version 3. All the programming

recipes of the previous editions have been reviewed and updated. We also have added new

content and new chapters to provide readers with even better coverage of the essential

functionalities of the library. This book covers many of the library’s features and

explains how to use them to accomplish specific tasks. Our objective is not to provide

detailed coverage of every option offered by the OpenCV functions and classes but rather to

give you the elements you need to build your applications from the ground up. We also

explore, in this book, fundamental concepts in image analysis and we describe some of the

important algorithms in computer vision. This book is an opportunity for you to get

introduced to the world of image and video analysis. But this is just the beginning. The

good news is that OpenCV continues to evolve and expand. Just consult the OpenCV online

documentation at h t t p ://o p e n c v . o r g / to stay updated about what the library can do for

you. You can also visit the author’s website at h t t p ://w w w . l a g a n i e r e . n a m e / for updated

information about this cookbook.

Preface

[ 2 ]

What this book covers

Chapter 1, Playing with Images, introduces the OpenCV library and shows you how to build

simple applications that can read and display images. It also introduces the basic OpenCV

data structures.

Chapter 2, Manipulating Pixels, explains how an image can be read. It describes different

methods for scanning an image in order to perform an operation on each of its pixels.

Chapter 3, Processing the Colors of an Image, consists of recipes presenting various object-

oriented design patterns that can help you to build better computer vision applications. It

also discusses the concept of colors in images.

Chapter 4, Counting the Pixels with Histograms, shows you how to compute image

histograms and how they can be used to modify an image. Different applications based on

histograms are presented that achieve image segmentation, object detection, and image

retrieval.

Chapter 5, Transforming Images with Morphological Operations, explores the concept of

mathematical morphology. It presents different operators and how they can be used to

detect edges, corners, and segments in images.

Chapter 6, Filtering the Images, teaches you the principle of frequency analysis and image

filtering. It shows how low-pass and high-pass filters can be applied to images and presents

the concept of derivative operators.

Chapter 7, Extracting Lines, Contours, and Components, focuses on the detection of geometric

image features. It explains how to extract contours, lines and connected components in an

image.

Chapter 8, Detecting Interest Points, describes various feature point detector in images.

Chapter 9, Describing and Matching Interest Points, explains how descriptors of interest

points can be computed and used to match points between images.

Chapter 10, Estimating Projective Relations in Images, explores the projective relations that

exist between two images in the same scene. It also describes how to detect specific targets

in an image.

Chapter 11, Reconstructing 3D scenes, allows you to reconstruct the 3D elements of a scene

from multiple images and recover the camera pose. It also includes a description of the

camera calibration process.

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