Learning OpenCV：权威指南

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"Learning OpenCV，这是一本关于OpenCV的学习教材，由Gary Bradski和Adrian Kaehler撰写，是2008年的第一版。该书由O'Reilly Media出版，适合教育、商业或销售推广使用。" OpenCV（开源计算机视觉库）是一个广泛应用于图像处理和计算机视觉领域的强大工具，它提供了各种算法和功能，用于实时的图像分析、识别以及机器学习任务。《Learning OpenCV》这本书是学习这个库的理想资源，由Gary Bradski和Adrian Kaehler两位专家合作完成，他们具有深厚的理论知识和实践经验。在书中，作者详细介绍了OpenCV的基本概念和核心功能，包括图像的读取与显示、图像的基本操作（如滤波、色彩空间转换）、特征检测（如SIFT、SURF）、物体识别和追踪、以及如何构建复杂的计算机视觉系统。同时，书中的例子和代码示例都是用C++编写的，同时也涵盖了Python接口，适合不同编程背景的读者。此外，书中还讲解了OpenCV库中的机器学习模块，如支持向量机（SVM）和神经网络，这些在现代计算机视觉应用中至关重要。对于希望深入研究深度学习的读者，虽然此书可能不涉及最新的深度学习框架（如TensorFlow或PyTorch），但它提供的基础将有助于理解和应用这些框架。编辑Mike Loukides和生产编辑Rachel Monaghan确保了书籍内容的准确性和易读性。封面设计和内页设计均出自专业设计师之手，使得这本教材既具有专业性又不失吸引力。《Learning OpenCV》是入门和提升OpenCV技能的理想教材，无论你是计算机视觉新手还是有一定经验的开发者，都能从中受益。通过阅读此书，你将能够掌握OpenCV的核心功能，并有能力开发出自己的计算机视觉项目。同时，书中提及的实例和练习可以让你在实践中深化理解，从而在图像处理和计算机视觉领域建立起坚实的基础。

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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