OpenCV实战指南：从入门到精通

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《学习OpenCV》是一本由Gary Bradski和Adrian Kaehler合著的专业指南，专为想要掌握开源计算机视觉库（OpenCV）的读者精心编撰。本书不仅提供了一个实用的工作手册，让读者能够深入理解OpenCV的功能，还为计算机视觉领域的基础知识提供了足够的背景，使读者能够有效地利用这一强大的工具。该书的核心内容围绕OpenCV库中的计算机视觉工具展开，通过详细的描述、实际编码示例和详尽的解释，涵盖了图像处理、特征检测、机器学习等核心技术。无论你是软件工程师、研究者、还是对计算机视觉技术感兴趣的业余爱好者，都能在本书中找到所需的信息和实践指导。书中不仅注重理论知识的传授，更强调通过实例操作来提升技能，确保读者能够在实践中迅速上手。作者Gary Bradski以其在计算机视觉领域的深厚经验和Adrian Kaehler的专业视角，共同构建了这本书的结构，使之既适合初学者系统学习，也适合有经验的开发人员查阅参考。书中涵盖了OpenCV的各个版本和应用场景，如实时视频分析、物体识别、人脸识别、图像稳定性和增强现实等领域，帮助读者应对日益增长的计算机视觉挑战。《学习OpenCV》不仅是学习OpenCV的最佳入门书籍，而且是提高计算机视觉编程能力的实用手册。对于希望在这个领域深入发展的专业人士，它提供了丰富的项目案例和实战演练，有助于培养读者的创新思维和解决问题的能力。此外，由于OpenCV在全球范围内广泛应用，读者可以通过本书了解最新的技术和趋势，保持与行业的同步发展。《学习OpenCV》是一本全面且实用的计算机视觉教程，无论是作为独立的学习资源，还是作为教学辅助材料，都能为读者带来极大的价值。通过深入阅读和实践，读者不仅能掌握OpenCV的核心技术，还能对整个计算机视觉领域有更深入的理解，从而在各自的领域中取得成功。

Preface

Dash, John Mark Agosta, and Marzia Polito were of key assistance in launching the ma-

chine learning library. Rainer Lienhart, Jean-Yves Bouguet, Radek Grzeszczuk, and Ara

Nefian were able technical contributors to OpenCV and great colleagues along the way;

the first is now a professor, the second is now making use of OpenCV in some well-known

Google projects, and the others are staffing research labs and start-ups. There were many

other technical contributors too numerous to name.

On the software side, some individuals stand out for special mention, especially on the

Russian software team. Chief among these is the Russian lead programmer Vadim Pisare-

vsky, who developed large parts of the library and also managed and nurtured the library

through the lean times when boom had turned to bust; he, if anyone, is the true hero of the

library. His technical insights have also been of great help during the writing of this book.

Giving him managerial support and protection in the lean years was Valery Kuriakin, a

man of great talent and intellect. Victor Eruhimov was there in the beginning and stayed

through most of it. We thank Boris Chudinovich for all of the contour components.

Finally, very special thanks go to Willow Garage [WG], not only for its steady  nancial

backing to OpenCV’s future development but also for supporting one author (and pro-

viding the other with snacks and beverages) during the  nal period of writing this book.

Thanks for Help on the Book

While preparing this book, we had several key people contributing advice, reviews, and

suggestions. Thanks to John Markoff, Technology Reporter at the New York Times for

encouragement, key contacts, and general writing advice born of years in the trenches.

To our reviewers, a special thanks go to Evgeniy Bart, physics postdoc at CalTech, who

made many helpful comments on every chapter; Kjerstin Williams at Applied Minds,

who did detailed proofs and verification until the end; John Hsu at Willow Garage, who

went through all the example code; and Vadim Pisarevsky, who read each chapter in de-

tail, proofed the function calls and the code, and also provided several coding examples.

There were many other partial reviewers. Jean-Yves Bouguet at Google was of great help

in discussions on the calibration and stereo chapters. Professor Andrew Ng at Stanford

University provided useful early critiques of the machine learning chapter. There were

numerous other reviewers for various chapters—our thanks to all of them. Of course,

any errors result from our own ignorance or misunderstanding, not from the advice we

received.

Finally, many thanks go to our editor, Michael Loukides, for his early support, numer-

ous edits, and continued enthusiasm over the long haul.

Gary Adds . . .

With three young kids at home, my wife Sonya put in more work to enable this book than

I did. Deep thanks and love—even OpenCV gives her recognition, as you can see in the

face detection section example image. Further back, my technical beginnings started with

the physics department at the University of Oregon followed by undergraduate years at

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xvi

Preface

UC Berkeley. For graduate school, I’d like to thank my advisor Steve Grossberg and Gail

Carpenter at the Center for Adaptive Systems, Boston University, where I first cut my

academic teeth. Though they focus on mathematical modeling of the brain and I have

ended up firmly on the engineering side of AI, I think the perspectives I developed there

have made all the difference. Some of my former colleagues in graduate school are still

close friends and gave advice, support, and even some editing of the book: thanks to

Frank Guenther, Andrew Worth, Steve Lehar, Dan Cruthirds, Allen Gove, and Krishna

Govindarajan.

I specially thank Stanford University, where I’m currently a consulting professor in the

AI and Robotics lab. Having close contact with the best minds in the world definitely

rubs off, and working with Sebastian Thrun and Mike Montemerlo to apply OpenCV

on Stanley (the robot that won the $2M DARPA Grand Challenge) and with Andrew Ng

on STAIR (one of the most advanced personal robots) was more technological fun than

a person has a right to have. It’s a department that is currently hitting on all cylinders

and simply a great environment to be in. In addition to Sebastian Thrun and Andrew Ng

there, I thank Daphne Koller for setting high scientific standards, and also for letting me

hire away some key interns and students, as well as Kunle Olukotun and Christos Kozy-

rakis for many discussions and joint work. I also thank Oussama Khatib, whose work on

control and manipulation has inspired my current interests in visually guided robotic

manipulation. Horst Haussecker at Intel Research was a great colleague to have, and his

own experience in writing a book helped inspire my effort.

Finally, thanks once again to Willow Garage for allowing me to pursue my lifelong ro-

botic dreams in a great environment featuring world-class talent while also supporting

my time on this book and supporting OpenCV itself.

Adrian Adds . . .

Coming from a background in theoretical physics, the arc that brought me through su-

percomputer design and numerical computing on to machine learning and computer vi-

sion has been a long one. Along the way, many individuals stand out as key contributors. I

have had many wonderful teachers, some formal instructors and others informal guides.

I should single out Professor David Dorfan of UC Santa Cruz and Hartmut Sadrozinski of

SLAC for their encouragement in the beginning, and Norman Christ for teaching me the

fine art of computing with the simple edict that “if you can not make the computer do it,

you don’t know what you are talking about”. Special thanks go to James Guzzo, who let me

spend time on this sort of thing at Intel—even though it was miles from what I was sup-

posed to be doing—and who encouraged my participation in the Grand Challenge during

those years. Finally, I want to thank Danny Hillis for creating the kind of place where all of

this technology can make the leap to wizardry and for encouraging my work on the book

while at Applied Minds.

I also would like to thank Stanford University for the extraordinary amount of support I

have received from them over the years. From my work on the Grand Challenge team with

Sebastian Thrun to the STAIR Robot with Andrew Ng, the Stanford AI Lab was always

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CHAPTER 1

Overview

What Is OpenCV?

OpenCV [OpenCV] is an open source (see http://opensource.org) computer vision library

available from http://SourceForge.net/projects/opencvlibrary.  e library is written in C

and C++ and runs under Linux, Windows and Mac OS X.  ere is active development

on interfaces for Python, Ruby, Matlab, and other languages.

OpenCV was designed for computational e ciency and with a strong focus on real-

time applications. OpenCV is written in optimized C and can take advantage of mul-

ticore processors. If you desire further automatic optimization on Intel architectures

[Intel], you can buy Intel’s Integrated Performance Primitives (IPP) libraries [IPP], which

consist of low-level optimized routines in many di erent algorithmic areas. OpenCV

automatically uses the appropriate IPP library at runtime if that library is installed.

One of OpenCV’s goals is to provide a simple-to-use computer vision infrastructure

that helps people build fairly sophisticated vision applications quickly.  e OpenCV

library contains over 500 functions that span many areas in vision, including factory

product inspection, medical imaging, security, user interface, camera calibration, stereo

vision, and robotics. Because computer vision and machine learning o en go hand-in-

hand, OpenCV also contains a full, general-purpose Machine Learning Library (MLL).

 is sublibrary is focused on statistical pattern recognition and clustering.  e MLL is

highly useful for the vision tasks that are at the core of OpenCV’s mission, but it is gen-

eral enough to be used for any machine learning problem.

Who Uses OpenCV?

Most computer scientists and practical programmers are aware of some facet of the role

that computer vision plays. But few people are aware of all the ways in which computer

vision is used. For example, most people are somewhat aware of its use in surveillance,

and many also know that it is increasingly being used for images and video on the Web.

A few have seen some use of computer vision in game interfaces. Yet few people realize

that most aerial and street-map images (such as in Google’s Street View) make heavy

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