掌握OpenCV 3：C++编程实现计算机视觉

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"《Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library》是由Adrian Kaehler和Gary Bradski合著的一本专业书籍，该书旨在帮助读者掌握OpenCV 3这一强大的计算机视觉库在C++编程中的应用。OpenCV，全称Open Source Computer Vision Library，是一个开源的计算机视觉和机器学习软件库，对于图像处理、物体识别、机器视觉等领域具有广泛的应用。本书涵盖了OpenCV 3的基础知识，包括如何安装和配置环境，以及如何使用其丰富的功能来处理图像数据，如图像预处理、特征检测、对象跟踪、人脸识别等。作者以浅显易懂的方式讲解了C++编程语言与OpenCV接口的结合，使得即使是初学者也能逐步理解并上手。书中还包含了大量的代码示例和实战项目，有助于读者通过实践提升技能。内容结构上，作者首先介绍OpenCV的基本概念，随后逐步深入到图像读取、显示、变换、滤波等操作。接着，讲解了各种图像特征提取方法，如SIFT、SURF、ORB等，以及如何构建基于这些特征的对象识别系统。此外，书中的章节还包括了机器学习在计算机视觉中的应用，如使用Haar cascades进行人脸检测，以及深度学习的基础应用。版权方面，这本书由Adrian Kaehler和Gary Bradski共同享有，并受到2017年的版权保护。《Learning OpenCV 3》的出版由O'Reilly Media负责，适用于教育、商业或销售推广用途。电子版也提供在线访问。如果你需要更多的信息，可以联系O'Reilly的公司或机构销售部门。编辑Dawn Schanafelt、索引者Ellen Troutman以及多位专业人员参与了本书的制作过程，确保了内容的质量和排版的专业性。该书于2016年12月首次发行，并不断更新以反映OpenCV技术的最新发展。《Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library》是一本全面而实用的教程，适合计算机视觉、机器学习或C++开发人员深入学习OpenCV，无论是初学者还是专业人士都能从中受益匪浅。通过阅读这本书，读者能够建立起扎实的计算机视觉基础，并能够在实际项目中灵活运用OpenCV的强大功能。"

Current GSoC Work 913

Community Contributions 915

OpenCV.org 916

Some AI Speculation 917

Afterword 920

A. Planar Subdivisions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 923

B. opencv_contrib. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 939

C. Calibration Patterns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 943

Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 949

Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 967

xiv | Table of Contents

• Give the reader a boost in implementing computer vision and machine learning

algorithms by providing many working code examples to start from

•

Suggest ways to fix some of the more advanced routines when something goes

wrong

This book documents OpenCV in a way that allows the reader to rapidly do interest‐

ing and fun things in computer vision. It gives an intuitive understanding of how the

algorithms work, which serves to guide the reader in designing and debugging vision

applications and also makes the formal descriptions of computer vision and machine

learning algorithms in other texts easier to comprehend and remember.

Who This Book Is For

This book contains descriptions, working code examples, and explanations of the

C++ computer vision tools contained in the OpenCV 3.x library. Thus, it should be

helpful to many different kinds of users:

Professionals and entrepreneurs

For practicing professionals who need to rapidly prototype or professionally

implement computer vision systems, the sample code provides a quick frame‐

work with which to start. Our descriptions of the algorithms can quickly teach or

remind the reader how they work. OpenCV 3.x sits on top of a hardware acceler‐

ation layer (HAL) so that implemented algorithms can run efficiently, seamlessly

taking advantage of a variety of hardware platforms.

Students

This is the text we wish had back in school. The intuitive explanations, detailed

documentation, and sample code will allow you to boot up faster in computer

vision, work on more interesting class projects, and ultimately contribute new

research to the field.

Teachers

Computer vision is a fast-moving field. We’ve found it effective to have students

rapidly cover an accessible text while the instructor fills in formal exposition

where needed and supplements that with current papers or guest lectures from

experts. The students can meanwhile start class projects earlier and attempt more

ambitious tasks.

Hobbyists

Computer vision is fun—here’s how to hack it.

We have a strong focus on giving readers enough intuition, documentation, and

working code to enable rapid implementation of real-time vision applications.

xvi | Preface

1 Always with a warning to more casual users that they may skip such sections.

What This Book Is Not

This book is not a formal text. We do go into mathematical detail at various points,

but it is all in the service of developing deeper intuitions behind the algorithms or to

clarify the implications of any assumptions built into those algorithms. We have not

attempted a formal mathematical exposition here and might even incur some wrath

along the way from those who do write formal expositions.

This book has more of an “applied” nature. It will certainly be of general help, but is

not aimed at any of the specialized niches in computer vision (e.g., medical imaging

or remote sensing analysis).

That said, we believe that by reading the explanations here first, a student will not

only learn the theory better, but remember it longer as well. Therefore, this book

would make a good adjunct text to a theoretical course and would be a great text for

an introductory or project-centric course.

About the Programs in This Book

All the program examples in this book are based on OpenCV version 3.x. The code

should work under Linux, Windows, and OS X. Using references online, OpenCV 3.x

has full support to run on Android and iOS. Source code for the examples in the

book can be fetched from this book’s website; source code for OpenCV is available on

GitHub; and prebuilt versions of OpenCV can be loaded from its SourceForge site.

OpenCV is under ongoing development, with official releases occurring quarterly. To

stay completely current, you should obtain your code updates from the aforemen‐

tioned GitHub site. OpenCV maintains a website at http://opencv.org; for developers,

there is a wiki at https://github.com/opencv/opencv/wiki.

Prerequisites

For the most part, readers need only know how to program in C++. Many of the

math sections in this book are optional and are labeled as such. The mathematics

involve simple algebra and basic matrix algebra, and assume some familiarity with

solution methods to least-squares optimization problems as well as some basic

knowledge of Gaussian distributions, Bayes’ law, and derivatives of simple functions.

The math in this book is in support of developing intuition for the algorithms. The

reader may skip the math and the algorithm descriptions, using only the function

definitions and code examples to get vision applications up and running.

Preface | xvii

How This Book Is Best Used

This text need not be read in order. It can serve as a kind of user manual: look up the

function when you need it, and read the function’s description if you want the gist of

how it works “under the hood.” However, the intent of this book is tutorial. It gives

you a basic understanding of computer vision along with details of how and when to

use selected algorithms.

This book is written to allow its use as an adjunct or primary textbook for an under‐

graduate or graduate course in computer vision. The basic strategy with this method

is for students to read the book for a rapid overview and then supplement that read‐

ing with more formal sections in other textbooks and with papers in the field. There

are exercises at the end of each chapter to help test the student’s knowledge and to

develop further intuitions.

You could approach this text in any of the following ways:

Grab bag

Go through Chapters 1–5 in the first sitting, and then just hit the appropriate

chapters or sections as you need them. This book does not have to be read in

sequence, except for Chapters 18 and 19 (which cover camera calibration and

stereo imaging) and Chapters 20, 21, and 22 (which cover machine learning).

Entrepreneurs and students doing project-based courses might go this way.

Good progress

Read just two chapters a week until you’ve covered Chapters 1–22 in 11 weeks

(Chapter 23 will go by in an instant). Start on projects and dive into details on

selected areas in the field, using additional texts and papers as appropriate.

The sprint

Cruise through the book as fast as your comprehension allows, covering Chap‐

ters 1–23. Then get started on projects and go into detail on selected areas in the

field using additional texts and papers. This is probably the choice for professio‐

nals, but it might also suit a more advanced computer vision course.

Chapter 20 is a brief chapter that gives general background on machine learning,

which is followed by Chapters 21 and 22, which give more details on the machine

learning algorithms implemented in OpenCV and how to use them. Of course,

machine learning is integral to object recognition and a big part of computer vision,

but it’s a field worthy of its own book. Professionals should find this text a suitable

launching point for further explorations of the literature—or for just getting down to

business with the code in that part of the library. The machine learning interface has

been substantially simplified and unified in OpenCV 3.x.

This is how we like to teach computer vision: sprint through the course content at a

level where the students get the gist of how things work; then get students started on

xviii | Preface

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