深度学习OpenCV实战指南

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"Learning OpenCV 中文版，一本全面介绍OpenCV库的书籍，由Gary Bradski和Adrian Kaehler撰写，内容高清且包含目录。" OpenCV（开源计算机视觉库）是一个强大的工具包，主要用于图像处理和计算机视觉领域。该库由一系列C++函数和类组成，同时也支持Python、Java和其他语言。"Learning OpenCV"这本书是学习这个库的理想资源，它适合初学者以及有一定基础的开发者，提供了深入浅出的讲解和实践示例。书中内容可能包括以下几个主要知识点： 1. 基础概念：介绍计算机视觉的基本概念，如图像表示、颜色空间、几何变换等，这些都是理解和应用OpenCV的基础。 2. 安装与环境配置：详细指导如何在不同操作系统上安装和配置OpenCV，包括编译源代码和使用预编译的库。 3. 图像处理：涵盖图像的读取、显示、保存，以及基本的图像操作，如滤波、边缘检测（如Canny算法）、形态学操作等。 4. 特征检测：介绍各种特征检测算法，如SIFT、SURF、ORB等，这些在物体识别和追踪中非常关键。 5. 对象检测：讲解Haar特征和HOG特征在人脸识别和行人检测中的应用，以及如何使用Adaboost训练自定义分类器。 6. 视频分析：探讨帧间操作，如运动估计、背景减除、光流计算，这些都是视频处理的核心技术。 7. 机器学习与深度学习：介绍OpenCV中的机器学习库，如支持向量机（SVM），以及如何结合深度学习框架（如TensorFlow或PyTorch）进行更复杂的图像识别任务。 8. 实时应用：讲解如何将OpenCV集成到实际项目中，如机器人导航、无人机视觉、增强现实等。 9. 案例研究：提供真实世界的例子，帮助读者理解OpenCV在实际问题中的应用。 10. 编程实践：每章都配有丰富的代码示例，读者可以跟随这些示例进行实践，加深对理论知识的理解。通过阅读"Learning OpenCV"，读者不仅可以掌握OpenCV库的使用，还能了解到计算机视觉领域的最新发展和技术趋势。这本书对于希望在图像处理、机器视觉或相关领域开展工作的个人或团队来说，是一本不可多得的参考资料。

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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