OpenCV 3入门指南：C++与计算机视觉实战

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"《Learning OpenCV 3》是一本由Adrian Kaehler和Gary Bradski合著的计算机视觉教程，它全面介绍了如何使用OpenCV 3这一强大的跨平台计算机视觉库。OpenCV是一款基于BSD许可的开源软件，它的设计目标是提供一个轻量级且高效的工具集，支持多种操作系统，包括Linux、Windows、Android和Mac OS。这个库主要由C函数和少量C++类构成，这使得它在性能优化方面表现出色。书中深入讲解了如何在C++环境中利用OpenCV进行各种图像处理和计算机视觉任务。内容涵盖了从基础概念如图像读取和显示，到高级功能如特征检测、对象识别、机器学习和深度学习的实现。作者通过丰富的代码示例和实用案例，帮助读者掌握如何构建实际应用，无论是在实时视频分析、机器人导航，还是在图像内容理解等领域。除了C++接口，OpenCV还提供了Python、Ruby和MATLAB等其他语言的接口，这极大地扩展了其适用范围，使得不同编程背景的开发者都能方便地接入和利用其功能。学习这本书，读者不仅能了解到OpenCV的底层机制，还能学习到如何在实践中有效地集成这些技术。版权信息显示，该书于2017年出版，适合用于教育、商业或销售推广用途。O'Reilly Media公司负责编辑、制作和发行，书中包括了详细的索引、专业的内部设计以及严谨的校对，确保了内容的专业性和易读性。对于希望深入了解计算机视觉技术，特别是使用OpenCV的开发者和研究者来说，《Learning OpenCV 3》无疑是一本不可多得的参考资料。"

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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