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Content Summary
OpenCV plays an important role in the field of computer vision. As a cross-platform computer Vision Library based on open source distribution, OPENCV implements many common algorithms for image processing and computer vision. The introduction to OPENCV3 programming is an index of the most commonly used and core component modules of the current version of OpenCV, with a brief introduction to the powerful features, performance, and new features in OpenCV2 and OpenCV3. Book Matching OpenCV2 and OpenCV3 two versions of the sample code package, containing a total of more than 200 detailed comments of the program source code and the idea description. Readers can retrace, quickly get started and learn in depth by technical direction.
The introduction to OPENCV3 programming requires the reader to have basic C + + knowledge, suitable for research in computer vision and related fields of students and teachers, first contact with OpenCV but have a certain basic knowledge-C + + programming, and has been OPENCV 1.0 programming experience, Want to quickly understand and get started with OpenCV2, OpenCV3 programming in the field of computer vision professionals. "Introduction to OpenCV3 Programming" is also suitable for image processing, computer vision Amateurs, open source project enthusiasts to the new version of OPENCV reference manual.
The download links for "Sample programs", "EXE executables", and "book Color Maps" in the "Getting Started with OpenCV3 programming" can be obtained by scanning the QR code in the back of the OpenCV3 of "Getting started with programming."
Directory
First part Quick Start OpenCV 1
The 1th chapter encounters OpenCV 3
1.1 OpenCV Peripheral Concept Cognition 4
1.1.1 Image processing, computer vision and OpenCV 4
1.1.2 OpenCV Overview 4
Origin and development of 1.1.3 5
1.1.4 Application Overview 6
1.2 OPENCV Basic Architecture Analysis 7
What did 1.3 OpenCV3 bring 11
1.3.1 Changes in Project architecture 11
1.3.2 Some strategies for upgrading OpenCV2 code to OpenCV3 error 12
1.4 OpenCV Download, installation and configuration 14
1.4.1 Preparation: Download and install the integrated development environment 14
1.4.2 First step: Download and install the OpenCV SDK 15
1.4.3 Step Two: Configure environment variables 16
1.4.4 Step Three: Configuration of the project include (include) directory 17
1.4.5 Step Fourth: Configuration of the Engineering Library (LIB) directory 21
1.4.6 Fifth Step: Configuration of the link library 22
1.4.7 Sixth step: Add OpenCV dynamic link library under Windows folder 25
1.4.8 Seventh Step: Final Test 26
1.4.9 problems and solutions that may be encountered 27
1.5 Quick Start OpenCV image processing 28
1.5.1 First Program: Image display 29
1.5.2 Second Program: Image corrosion 30
1.5.3 Third Program: image Blur 31
1.5.4 Fourth program: Canny edge detection 32
1.6 OpenCV Video Operation Basics 34
1.6.1 Reading and playing video 34
1.6.2 Call Camera Capture Image 35
1.7 Summary of this chapter 38
2nd. Pre-departure cognitive preparation 39
2.1 OpenCV Official Routine guide and appreciation 40
2.1.1 Color target tracking: Camshift 41
2.1.2 Light Flow: Optical Flow 42
2.1.3-point tracker: Lkdemo 43
2.1.4 Face Recognition: Objectdetection 43
2.1.5 Support vector Machine Boot 44
2.2 Open Source charm: Compile OpenCV source code 45
2.2.1 Download Install CMake 45
2.2.2 using CMake to generate OPENCV source code engineering Solutions 46
2.2.3 compiling OPENCV source code 50
2.3 "OPENCV.HPP" header file Cognition 53
2.4 Naming Conventions 54
2.5 argc and argv parameter doubts 56
2.5.1 ARGC and argv in main function 56
2.5.2 the specific meaning of argc and argv 57
2.5.3 Several instructions for the main function in Visual Studio 58
2.5.4 Summary 59
2.6 Format output function printf () Brief Analysis 59
2.6.1 format output: printf () function 59
2.6.2 Sample Program: Example of use of printf functions 60
2.7 Smart Display OPENCV version 61 currently in use
2.8 Summary of this chapter 61
The 3rd Chapter Highgui graphical user interface Preliminary 63
3.1 loading, displaying and outputting images to a file 64
3.1.1 Namespace of OpenCV 64
A brief analysis of 3.1.2 Mat class 64
Overview of loading and displaying 3.1.3 images 65
3.1.4 Image loading: Imread () function 65
3.1.5 Image display: Imshow () function 66
3.1.6 about Inputarray Type 67
3.1.7 Create window: Namedwindow () function 67
3.1.8 output image to file: Imwrite () function 68
3.1.9 Integrated Sample program: Image loading, display and output 70
3.2 Creation and use of Sliders 73
3.2.1 Create slider bar: Createtrackbar () function 73
3.2.2 Get the position of the current track bar: Gettrackbarpos () function 76
3.3 Mouse Operation 76
3.4 Summary of this chapter 80
The second part of core components 83
4th Chapter OPENCV data structure and basic drawing 85
4.1 Base Image Container Mat 86
4.1.1 Digital Image Storage Overview 86
Use of the 4.1.2 Mat structure 86
Storage method for 4.1.3 pixel values 88
4.1.4 Seven ways to explicitly create Mat objects 89
Format output method in 4.1.5 OpenCV 91
4.1.6 output other commonly used data structures 94
4.1.7 Example Program: Use of the base image container Mat class 95
4.2 Common data structures and functions 95
4.2.1 Points: Point class 96
4.2.2 Color representation: Scalar class 96
4.2.3 Size: Size class 96
4.2.4 the representation of a rectangle: Rect class 97
4.2.5 Color Space Conversion: Cvtcolor () function 98
4.2.6 Other common points of knowledge 100
4.3 Drawing of basic graphics 100
4.3.1 of the DrawEllipse () function 101
4.3.2 of the Drawfilledcircle () function 102
4.3.3 of the DrawPolygon () function 102
4.3.4 of the DrawLine () function 103
4.3.5 The main function 104
4.4 Summary of this chapter 106
5th Chapter Core Components Advanced 107
5.1 Accessing pixels in an image 108
5.1.1 image in memory storage mode 108
5.1.2 color space reduced by 108
5.1.3 Lut function: Look up table Operation 109
5.1.4 Chronograph function 110
5.1.5 three methods of accessing pixels in an image 110
5.1.6 Sample Program 114
5.2 Roi Area Image Overlay & image blending 114
5.2.1 Area of interest: ROI 115
5.2.2 Linear blending Operation 116
5.2.3 computed array weighted sum: addweighted () function 117
5.2.4 Composite Example: primary image blending 120
5.3 Separating color channels, multi-channel image blending 125
5.3.1 Channel separation: Split () function 125
5.3.2 Channel Merge: Merge () function 126
5.3.3 Sample program: Multi-channel image blending 127
5.4 Image contrast, brightness value adjustment 131
5.4.1 Theoretical Basis 131
5.4.2 accessing pixels in a picture 131
5.4.3 Sample program: Image contrast, brightness value adjustment 132
5.5 Discrete Fourier transform 135
Principle of 5.5.1 discrete Fourier transform 135
5.5.2 DFT () function detailed 136
5.5.3 return DFT optimal dimension size: Getoptimaldftsize () function 137
5.5.4 Extending the image boundary: Copymakeborder () function 137
5.5.5 calculating the amplitude of two-dimensional vectors: magnitude () function 138
5.5.6 Calculating natural logarithm: log () function 138
5.5.7 Matrix Normalization: Normalize () function 138
5.5.8 sample program: Discrete Fourier transform 139
5.6 Input and output XML and Yaml files 144
5.6.1 Introduction to XML and YAML files 144
5.6.2 the use of Filestorage class operation files Boot 144
5.6.3 Sample program: Writing XML and YAML files 147
5.6.4 Sample program: XML and Yaml file reads 148
5.7 Summary of this chapter 150
Part III Mastering IMGPROC components 151
The 6th Chapter Image processing 153
6.1 Linear Filter: Box filter, mean filter, Gaussian filter 154
6.1.1 Smoothing 154
6.1.2 Image Filter and filter 154
Introduction to 6.1.3 Linear filters 155
6.1.4 Filtering and blurring 155
6.1.5 neighborhood operator and linear neighborhood filtering 155
6.1.6 Box Filter (Box filter) 156
6.1.7 mean-value filter 157
6.1.8 Gaussian Filter 159
6.1.9 Linear Filter related OPENCV source code Analysis 160
Analysis of source code of Gaussianblur function in 6.1.10 OpenCV 164
6.1.11 Linear filtering Core API function 165
6.1.12 Image Linear Filter Synthesis Example 170
6.2 Nonlinear Filtering: Median filtering, bilateral filtering 175
6.2.1 Nonlinear Filtering Overview 175
6.2.2 Median filter 175
6.2.3 Bilateral filtering 177
6.2.4 Nonlinear filtering related core API functions 178
5 Examples of image filtering in 6.2.5 OpenCV 181
6.3 Morphological Filtering (1): Corrosion and expansion 187
6.3.1 Morphology Overview 187
6.3.2 Expansion 188
6.3.3 Corrosion 189
6.3.4 related OPENCV Source code Analysis Traceability 190
6.3.5 related core API functions explained 191
6.3.6 Integrated Example: corrosion and expansion 195
6.4 Morphological Filtering (2): Open operation, closed operation, morphological gradient, top hat, black Hat 198
6.4.1 Open Operation 199
6.4.2 closed Operation 200
6.4.3 Morphological Gradient 200
6.4.4 Top Hat 201
6.4.5 Black Hat 202
6.4.6 Morphological filter OPENCV source code Analysis Traceability 203
6.4.7 Core API functions: Morphologyex () 205
6.4.8 examples of each morphological operation use list 206
6.4.9 Integrated Example: morphological filtering 208
6.5 Diffuse water filling 214
6.5.1 definition of diffuse water filling 214
The basic idea of 6.5.2 diffuse water filling method 214
6.5.3 implementation of the diffuse fill algorithm: FloodFill function 214
6.5.4 Composite Example: Diffuse fill 216
6.6 Image Pyramid and image size scaling 223
6.6.1 Introduction 223
6.6.2 about Image Pyramids 223
6.6.3 Gauss Pyramid 225
6.6.4 Laplace Pyramid 226
6.6.5 Size Adjustment: Resize () function 227
6.6.6 image pyramid-related API functions 230
6.6.7 Integrated Example: Image pyramid and image size scaling 234
6.7 Threshold Value 237
6.7.1 Fixed threshold operation: Threshold () function 238
6.7.2 Adaptive threshold Operation: Adaptivethreshold () function 239
6.7.3 Sample program: Basic threshold Action 240
6.8 Summary of this chapter 244
The 7th Chapter 247 Image Transformation
7.1 Edge detection based on OPENCV 248
General steps for 7.1.1 Edge detection 248
7.1.2 Canny operator 248
7.1.3 Sobel operator 253
7.1.4 Laplacian operator 256
7.1.5 ScHARR Filter 259
7.1.6 Integrated Example: Edge detection 262
7.2 Huffman Transform 267
7.2.1 Hough Transform Overview 267
The Hough Line transformation in 7.2.2 OpenCV 268
Principle of 7.2.3 Hough Line Transformation 268
7.2.4 Standard Hough Transform: houghlines () function 270
7.2.5 cumulative probability Hough Transform: houghlinesp () function 272
7.2.6 Hough Circular Transform 274
Principle of 7.2.7 Hough gradient method 275
Disadvantage of 7.2.8 Hof gradient method 276
7.2.9 Hof Circle Transform: Houghcircles () function 276
7.2.10 Synthesis Example: Hough Transform 278
7.3 Remapping 281
7.3.1 The concept of remapping 281
7.3.2 Implementation remapping: Remap () function 282
7.3.3 Basic Sample program: Basic Remapping 283
7.3.4 Integrated Sample program: Implementing Multiple Remapping 285
7.4 Affine Transformation 289
7.4.1 Understanding affine Transformations 289
The method of 7.4.2 affine transformation 290
7.4.3 affine transformation: Warpaffine () function 291
7.4.4 computing Two-dimensional rotation transformation matrix: getrotationmatrix2d () function 292
7.4.5 Example Program: affine transformation 292
7.5 Histogram equalization 295
7.5.1 Histogram equalization Concepts and features 296
7.5.2 to achieve histogram equalization: Equalizehist () function 297
7.5.3 Sample program: Histogram equalization 298
7.6 Summary of this chapter 300
8th Chapter Image Contour and image segmentation repair 303
8.1 Finding and drawing outlines 304
8.1.1 Looking for contour: findcontours () function 304
8.1.2 Drawing contour: drawcontours () function 305
8.1.3 Basic Sample program: Contour Lookup 306
8.1.4 Integrated Sample program: Find and draw outlines 308
8.2 Finding the convex hull of an object 312
8.2.1 Convex Bag 312
8.2.2 finding convex hull: convexhull () function 313
8.2.3 Basic Sample program: Convex Hull Detection Basics 313
8.2.4 Integrated Sample program: Finding and drawing the convex hull of an object 315
8.3 Using polygons to enclose outlines 318
8.3.1 return outer rectangle boundary: Boundingrect () function 318
8.3.2 Finding the minimum bounding rectangle: minarearect () function 318
8.3.3 finding the smallest enclosing circle: minenclosingcircle () function 318
8.3.4 fitting Two-dimensional point set with ellipse: Fitellipse () function 319
8.3.5 approximation of the polygonal curve: APPROXPOLYDP () function 319
8.3.6 Basic Sample program: Creating rectangular boundaries that surround outlines 319
8.3.7 Basic Sample program: Creating circular boundaries that surround outlines 321
8.3.8 Integrated Sample program: Using polygons to surround outlines 324
8.4 Moment of Image 327
Calculation of 8.4.1 moment: Moments () function 328
8.4.2 Calculate Contour Area: Contourarea () function 328
8.4.3 Calculating Contour Length: arclength () function 328
8.4.4 Integrated Sample program: Find and draw image Contour moment 329
8.5 Watershed Algorithm 333
8.5.1 Implementing the Watershed Algorithm: Watershed () function 334
8.5.2 Integrated Sample program: Watershed algorithm 334
8.6 Image Patching 338
8.6.1 Implementing Image patching: Inpaint () function 340
8.6.2 Integrated Sample program: Image patching 341
8.7 Summary of this chapter 343
9th Chapter histogram and match 345
9.1 Image Histogram Overview 346
9.2 Calculation and drawing of histogram 347
9.2.1 Calculation histogram: calchist () function 347
9.2.2 Find maximum value: Minmaxloc () function 348
9.2.3 Sample Program: Drawing h-s Histogram 348
9.2.4 Sample program: Calculate and draw an image one-dimensional histogram 350
9.2.5 Sample program: Draw RGB Tri-color histogram 352
9.3 Histogram Comparison 355
9.3.1 Contrast histogram: comparehist () function 355
9.3.2 Sample Program: Histogram comparison 356
9.4 Reverse Projection 
9.4.1 Introduction 
How the 9.4.2 reverse projection works
The role of 9.4.3 reverse projection 361
9.4.4 result of Reverse projection 361
9.4.5 Calculate Reverse projection: Calcbackproject () function 361
9.4.6 Channel copy: Mixchannels () function 362
9.4.7 Integrated Program: Reverse projection 363
9.5 Template Matching 367
Concept and principle of 9.5.1 template matching 367
9.5.2 Implementing template matching: Matchtemplate () function 367
9.5.3 Composite Example: template matching 369
9.6 Summary of this chapter 373
Part IV in-depth feature2d components 375
10th. Corner Detection 377
10.1 Harris Corner Point Detection 378
10.1.1 points of interest and corner 378
10.1.2 Corner Point Detection 378
10.1.3 Harris Corner Point detection 379
10.1.4 implementation of Harris Corner Detection: Cornerharris () function 379
10.1.5 Integrated Example: Harris Corner Point Detection and drawing 381
10.2 Shi-tomasi Corner Point detection 384
10.2.1 Shi-tomasi Corner Point Detection Overview 384
10.2.2 determining the image strong corner point: Goodfeaturestotrack () function 384
10.2.3 Integrated Example: Shi-tomasi Corner point Detection 385
10.3 Sub-pixel corner detection 388
10.3.1 Background Overview 388
10.3.2 looking for Subpixel corners: Cornersubpix () function 389
10.3.3 Integrated Example: sub-pixel corner detection 389
10.4 Summary of this chapter 392
11th. Feature detection and matching 395
11.1 Surf feature point detection 396
11.1.1 Surf Algorithm Overview 396
11.1.2 Surf Algorithm principle 396
11.1.3 Surf related OPENCV source Analysis 400
11.1.4 drawing key points: drawkeypoints () function 401
11.1.5 KeyPoint Class 402
11.1.6 Sample program: Surf feature point detection 402
11.2 Surf Feature Extraction 405
11.2.1 Draw match points: drawmatches () function 405
11.2.2 Bruteforcematcher class Source Analysis 407
11.2.3 Sample program: Surf feature extraction 408
11.3 feature point matching using Flann 410
Simple analysis of 11.3.1 Flannbasedmatcher class 410
11.3.2 find the best match: Descriptormatcher::match method 411
11.3.3 Sample program: Using Flann for feature point matching 411
11.3.4 Integrated Sample program: Flann combines surf to describe and match key points 413
11.3.5 Integrated Sample program: Sift with brute force matching for key points description and extraction 417
11.4 Finding known Objects 420
11.4.1 Looking for Perspective transformations: findhomography () function 421
11.4.2 A Perspective Matrix Transformation: Perspectivetransform () function 421
11.4.3 Sample program: Finding known objects 422
11.5 Orb Feature Extraction 425
11.5.1 Orb Algorithm Overview 425
11.5.2 Related Concept Cognition 425
11.5.3 Orb class related source simple analysis 426
11.5.4 Example Program: Orb algorithm description and match 426
11.6 Summary of this chapter 430
Appendix 433
A1 Companion Sample program listing 433
A2 additional list of programs included with the book 436
A3 Book core functions list 439
A4 Mat class functions list 442
A4.1 Constructor: Mat::mat 442
A4.2 destructor Mat::~mat 444
A4.3 Mat class member function 444
Main references 447
About the author
Mao, the network ID "Light ink". Microsoft VC Field Most valuable Experts (MVP), China's top ten outstanding It Bloggers 2013, Nanjing University of Aeronautics and Astronautics 2014 Ten outstanding Youth. CSDN Blog expert, 2013 CSDN blog star, in Csdn to write a number of years, blog views more than 2.52 million visitors. He graduated from the National Aeronautics and Astronautics University of Ukraine, and Nanjing University of Aeronautics and Astronautics. Published book "Journey by Dream: Windows game programming from scratch", personal deeds have been many times by the People's Daily, Xinhua, Phoenix, NetEase News, Sina News, Jinling evening, modern Express and other mainstream media reports.
Objective
Computer vision is a field that has matured in recent years. With the advent of powerful and affordable computing devices, creating complex image applications is never as easy as it is today. OpenCV plays an important role in the field of computer vision, which is a cross-platform computer Vision Library based on open source distribution, which realizes many general algorithms in image processing and computer vision. Since its inception in 1999, OPENCV has been regarded as the preferred tool by scholars and developers in the field of computer vision and has become one of the most powerful research tools in computer vision.
The OPENCV was originally developed by a team from Intel. After a series of beta releases, the official version of OpenCV 1.0 was finally released on October 19, 2006.
October 1, 2009, OpenCV 2.0, it brings a new C + + interface, the ability to OpenCV unlimited amplification. In the 2.0 era, OPENCV added new platform support, including iOS and andriod, GPU acceleration via cuda and OpenCL, an interface for Python and Java users, and a continuous, artistically integrated system based on GitHub and Buildbot. A stable and easy-to-use OPENCV 2.4.x used by many companies and schools around the world.
August 21, 2014, OpenCV 3.0 Alpha released, it brings a new project architecture changes, announcing the advent of a new era of computer vision. Like other large projects, OpenCV3 has abandoned the overall unified architecture, using kernel + plug-in architecture to make its subject more stable, while additional libraries can be more flexible to maintain high-speed development and iteration.
The book is derived from the author on the csdn of the "OpenCV Introductory Tutorial" series of blog articles, since the first published on February 24, 2014, has been the vast majority of OPENCV enthusiasts of the extensive attention and support, the cumulative reading volume exceeded more than 400,000 people. Many readers have a strong desire to assemble the content into a book and add more new content. So, after the author half a year his pen, there is now the birth of this book.
As an entry-level OPENCV programming textbook, this book takes a detailed annotated program code as the main line, starting with the relevant functions, classes, and data structures of the 4 components of the new OpenCV core, Highgui, Improc, and feature2d. Learn more about the new version of the OPENCV will encounter a variety of problems, and provide detailed practical code as a reference. The purpose of this book is to enable more users to skillfully use the new C + + interface OpenCV2 or OpenCV3, to understand the differences in the details of OpenCV2 and OpenCV3 to promote the popularity of the new OpenCV in the world.
The contents of this book are arranged
This book is divided into 4 parts, 11 chapters, the content of the summary is listed below.
The 1th chapter encounters OpenCV: Introduces OpenCV's periphery concept, analyzes the basic structure of OPENCV, and explains the new features of OpenCV3. This chapter focuses on the OPENCV download, installation and configuration process, after the configuration is complete, to lead you to formally appreciate the charm of OPENCV, explained the 4 OPENCV image processing applet, and instruct you to learn how to use OPENCV operation video and call the camera.
The 2nd chapter of the pre-departure cognitive preparation: The OPENCV official routine guide learning and appreciation, explaining how to compile OpenCV source code, and introduced to some of the surrounding concepts of cognition.
The 3rd Chapter Highgui graphical user interface: The image loading, display and output to the file for detailed analysis, explaining the OPENCV in the creation and use of sliders, and how to interact with the mouse.
The 4th Chapter OPENCV data structure and the basic drawing: Explain the opencv commonly used in the figure and the basic drawing operation.
5th. Core Components Advanced: Explain some advanced knowledge points of core modules, such as manipulating pixels in images, blending images, separating color channels, adjusting contrast and brightness of images, making discrete Fourier transforms, and input and output XML and YAML files.
The 6th chapter is image processing: Learning various methods of image processing using OPENCV, including box filter, mean filter and Gaussian filter which belong to linear filter, which belong to the median filter, bilateral filter, and two basic morphological operations-expansion and corrosion; 5 advanced morphological filtering operations-open operation, closed operation, Morphological gradients, cap caps and Black Caps; In addition, there are diffuse fill algorithm, image pyramid, image scaling, thresholding.
The 7th chapter is about image transformation: Several types of image transformation methods are explained. Including the canny operator, Sobel operator, Laplace operator and ScHARR filter used for edge detection using OPENCV, the Hough Line transform, Hough Circle Transformation, remapping and affine transformation, and histogram equalization for Image feature extraction.
8th Chapter Image Contour and image segmentation repair: How to find the contour and draw the contour, how to find the convex hull of the object, use the polygon to surround the contour, and calculate the moment of an image. In addition, the method of watershed algorithm and image patching operation is introduced.
The 9th chapter histogram and matching: Explain the image histogram related programming skills, as well as histogram contrast, reverse projection and template matching technology.
10th Corner Detection: Explain Harris corner detection and Shi-tomasi corner detection, as well as a sub-pixel corner detection method.
The 11th chapter features detection and matching: using OpenCV2 to explain and implement the surf, SIFT and Orb feature detection method, and on the basis of Flann feature matching, further realizes the use of homography mapping to find known objects.
A reader for reading this book
Research students and teachers in computer vision and related fields
This book has detailed content, annotated detailed code, will help you through the OPENCV to study computer vision theory, writing papers, through graduation design, completion of scientific research projects of powerful tools. At the same time, this book is suitable as a teaching book for university computer Vision course.
The first contact with OPENCV, the researchers with a certain basic knowledge C + + programming
As a programming tutorial targeting the new OPENCV standard for quick Start, this book requires just a few basic C/s + + programming languages. If you already have these basics and are interested in computer vision, then this book is for you.
Already have OpenCV1.0 programming experience, want to quickly understand and get started OpenCV2, OpenCV3 programming of computer vision professionals
If you've ever used OpenCV1.0, or studied OpenCV1.0 's classic Classics learning OpenCV, you'll feel very gracious. You will find the new OPENCV with more powerful and convenient features to help you do more with less.
Want to have a new version of OPENCV Interface reference book computer vision enthusiasts
This book gives a detailed explanation of the common classes and functions since OpenCV2, including OpenCV3, and provides a "book Core function List" in the appendix for retrieval. You'll quickly find the functions, data structures, and classes you need to use in your book.
OpenCV enthusiasts who want to have a huge amount of detailed annotated OpenCV2, OpenCV3 sample program code
This book contains the OpenCV2 version of the 95 book mainline sample program source code, 21 sample program source code, OPENCV3 version of the 95 book mainline sample program source code. OpenCV2, OpenCV3 version Two code provides separate download. These program codes are commented in detail and in a structured manner, and provide an. exe file that can be run independently for quick viewing of program effects for easy viewing and retrieval. You'll find the reference code you need in a huge sample program to speed up your research and learning.
Amateur in the field of image processing and computer vision
Sea-wide by diving, sky-high-flying birds, computer vision in the field of the Treasury to explore.
Open Source Project Enthusiasts
OpenCV as a fully free and open source code Library of computer vision, with a total of millions of lines of code for you to study, this book will guide you to learn their mentor.
Sample program description for this book
The sample program of this book was originally developed under the OpenCV 2.4.9 (April 15, 2014 release), and the first edition was written based on OpenCV2.4.9. During the manuscript writing and revision process, it coincided with the release of OpenCV3.0 Alpha (August 21, 2014) and OpenCV3 Beta (November 11, 2014), so this book, in its review and revision process (December 1, 2014), decided to stand at the top of the wave, To OpenCV2-based, adding OpenCV3 features, so that the book can be qualified OpenCV2 and OpenCV3 two editions of the role of the textbook. This is why the book has OpenCV2 and OpenCV3 two separate versions of the sample program.
Two versions, detailed comments of more than 100 sample program source code is the soul of this book, the case of the example program is summarized below.
This book contains the OpenCV2 version of the 95 book mainline sample program source code, 21 sample program source code, as well as the OpenCV3 version of the 95 book mainline sample program source code.
OpenCV2, OpenCV3 version Two code provides separate download.
The OpenCV2 version of the sample program was developed and tested in WINDOWS7 64-bit flagship, Visual Stuido 2010, OpenCV2.4.9, and theoretically supports all versions of the OPENCV2 series for compilation.
The OpenCV3 version of the sample program was developed and tested in WINDOWS7 64-bit flagship, Visual Stuido 2010, OpenCV3.0 Beta, and theoretically supports a full version of OpenCV3, which is currently released.
The program source code is commented in detail and methodically.
Additional. exe files that can be run independently are provided for quick viewing of program effects and for easy retrieval.
The sample code for this book is downloaded in the following ways.
Scan the cover of the book after the two-dimensional code of the port, get.
In the author's blog, click the download link posted in the appropriate book Maintenance blog post.
Download in the official website of the electronic industry press.
Thanks
First of all, I need to thank my mentor, Professor autobiography of Nanjing University of Aeronautics and Astronautics, who has also personally participated in writing some chapters of the book. In the master's degree, if there is no mentor inculcation, I will not meet with OPENCV, there will be no publication of this book.
Thanks to my fellow comrades, Wang Bihui and Wu Songsen, who have contributed to the perfection of this book, are part of the chapters in this book.
Thanks to the OPENCV development team for developing such a powerful and stable, easy-to-use, open-source visual library for the world, and continuously maintaining and updating it.
I thank my parents for raising my adult, thank you for the attentive of the family, you are my strongest backing.
Thanks to my alma mater, Nanjing University of Aeronautics and Astronautics gave me a reluctant mediocrity, up and down Quest heart.
Thanks to the Daiquan teachers of Nanjing University of Aeronautics and Astronautics for their help and support in publishing this book.
Thanks to the NSFC Youth Science Foundation Project "Research on key technology of new single-stator two-DOF ultrasonic motor and its driving intelligent gimbal System" (Project approval number: 51205193) provides financial support for the theoretical research of this book.
Thanks to the Chen Xiaomou and Ding Yijong editors of the publishing house of the electronics industry press, they have made a deep impression on the professional and rigorous attitude of the publication.
Finally, I need to thank the many readers on my blog, is your hope for this book and enthusiastic message let me have to complete the book of motivation and courage.
Communication and Errata
Due to the limited level of the editor, even after many times the book proofreading, there will inevitably be omissions. Hope that before the book you, can be enthusiastic to point out the mistakes in the book, so that in the next edition of the books printed, can be a more perfect and rigorous appearance, presented in front of everyone. In addition, you have to believe that you are not a person in the fight, in the author's blog, you can find with their like-minded many enthusiasts who love computer vision programming technology. We can communicate together and learn to improve.
Finally, I hope everyone in this book help, can be very good to get started and master the new version of OpenCV.
May this book for the new version of OPENCV in the domestic popularization and development in the world, offer humble strength.
Light Ink
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