《LearningOpenCV》e版：英文教程指南

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《LearningOpenCV》是一本由Gary Bradski和Adrian Kaehler合著的专业级教程，专为学习OpenCV（Open Source Computer Vision Library）而设计。这本书在2008年首次出版，全面涵盖了OpenCV的核心概念和技术，适合计算机视觉领域的初学者和专业人士。OpenCV是一款广泛应用于机器视觉、图像处理、机器人技术以及计算机图形学的开源库，它在英文环境下提供了丰富的功能和API。本书的特点在于其详细且结构化的教学方式，包括清晰的导航目录，使读者能够系统地掌握OpenCV的基础理论和实践应用。作者们以其丰富的经验和深入浅出的讲解，引导读者从安装配置到实际项目开发的各个环节，例如图像读取、预处理、特征检测、目标识别、机器学习应用等，涵盖了计算机视觉的各个方面。版权方面，此书受到严格的版权保护，只能用于教育、商业或销售推广目的。此外，电子版也在O'Reilly Media的Safari在线平台提供方便的访问。如果你是企业或机构用户，可以通过电话或邮件联系O'Reilly的销售部门获取更多信息。编辑Mike Loukides参与了该书的编审工作，确保了内容的质量和一致性。生产编辑Rachel Monaghan和生产服务团队，如Newgen Publishing和Data Services，负责书籍的制作过程。封面设计由Karen Montgomery操刀，内部设计则由David Futato负责，插图由Robert Romano绘制，共同打造了一本专业且易于阅读的教材。《LearningOpenCV》不仅是一本技术手册，也是一本实用的手册，无论是对希望提升图像处理能力的工程师，还是希望探索计算机视觉潜力的研究者，都是一份不可或缺的参考资料。随着科技的发展，书中介绍的技术和工具在不断更新迭代，但其基础原理和方法将永远是理解现代计算机视觉技术的基石。因此，对于任何想要在这个领域有所建树的人来说，这本书是值得深入学习的宝贵资源。

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Preface

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Acknowledgments

A long-term open source e ort sees many people come and go, each contributing in dif-

ferent ways.  e list of contributors to this library is far too long to list here, but see the

.../opencv/docs/HTML/Contributors/doc_contributors.html  le that ships with OpenCV.

Thanks for Help on OpenCV

Intel is where the library was born and deserves great thanks for supporting this project

the whole way through. Open source needs a champion and enough development sup-

port in the beginning to achieve critical mass. Intel gave it both. There are not many other

companies where one could have started and maintained such a project through good

times and bad. Along the way, OpenCV helped give rise to—and now takes (optional)

advantage of—Intel’s Integrated Performance Primitives, which are hand-tuned assembly

language routines in vision, signal processing, speech, linear algebra, and more. Thus the

lives of a great commercial product and an open source product are intertwined.

Mark Holler, a research manager at Intel, allowed OpenCV to get started by knowingly

turning a blind eye to the inordinate amount of time being spent on an unofficial project

back in the library’s earliest days. As divine reward, he now grows wine up in Napa’s Mt.

Vieder area. Stuart Taylor in the Performance Libraries group at Intel enabled OpenCV

by letting us “borrow” part of his Russian software team. Richard Wirt was key to its

continued growth and survival. As the first author took on management responsibility

at Intel, lab director Bob Liang let OpenCV thrive; when Justin Rattner became CTO,

we were able to put OpenCV on a more firm foundation under Software Technology

Lab—supported by software guru Shinn-Horng Lee and indirectly under his manager,

Paul Wiley. Omid Moghadam helped advertise OpenCV in the early days. Mohammad

Haghighat and Bill Butera were great as technical sounding boards. Nuriel Amir, Denver

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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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