OpenCV开源计算机视觉库详解

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"Learning OpenCV 2nd Early Release.pdf" OpenCV，全称为“开源计算机视觉库”，是一个在C++基础上编写的开源计算机视觉库。它可在Linux、Windows、MacOS X、iOS和Android等操作系统上运行，并提供了Python、Java、Ruby、Matlab等多种语言的接口。OpenCV的主要目标是实现计算效率，特别强调实时应用。为了实现这一目标，从算法到多核及CPU指令层面，都进行了优化，支持SSE、MMX、AVX、NEON、OpenMP和TBB等硬件加速技术。对于Intel架构，OpenCV能够利用Intel的集成性能原语（IPP）库进行更深度的优化，IPP包含各种算法领域的低级优化例程。OpenCV会在运行时自动选择并使用IPP的适当指令。此外，OpenCV的GPU模块还提供了针对Nvidia GPU的CUDA加速版本的许多算法以及对通用GPU的OpenCL优化版本。 OpenCV的设计理念是提供易于使用的计算机视觉基础设施，帮助开发者实现复杂的图像处理和计算机视觉任务。它包含了广泛的函数库，涵盖了图像处理、特征检测、物体识别、图像分割、运动分析等多个领域。例如，它可以用于人脸识别、车牌识别、图像拼接、实时视频分析等实际应用场景。 OpenCV的另一个特点是对实时性有很强的关注，这使得它非常适合于嵌入式系统和移动设备，如智能手机和平板电脑。其跨平台的特性使得开发人员能够在不同操作系统和设备上无缝迁移代码。此外，OpenCV社区活跃，开发者可以从中获取丰富的教程、示例代码和文档，以及来自全球的专家和同行的帮助。通过OpenCV，初学者和专业开发者都能快速掌握计算机视觉的核心概念和技术，进而创建出高效、实用的计算机视觉应用程序。 OpenCV是一个功能强大且灵活的工具，无论你是想要入门计算机视觉，还是进行高级研究，都能在OpenCV中找到所需的支持。它为开发者提供了实现复杂视觉任务的可能，同时保持了良好的性能和易用性，是计算机视觉领域不可或缺的重要资源。

• imgproc: The image processing module contains basic transformations on images,

including filters and similar convolutional operators.

• highgui: This HighGUI module contains user interface functions which can be

used to display images or take simple user input. It can be thought of as a very

light weight window UI toolkit.

• video: The video library contains the functions you need to read and write video

streams.

• calib3d: This module contains implementations of algorithms you will need to

calibrate single cameras as well as stereo or multi-camera arrays.

• features2d: The features2d module contains algorithms for detecting, describing,

and matching keypoint features.

• objdetect: This module contains algorithms for detecting specific objects, such as

faces or pedestrians. You can train the detectors to detect other objects as well.

• ml: The Machine Learning Library is actually an entire library in itself, and

contains a wide array of machine learning algorithms implemented in such as way

as to work with the natural data structures of OpenCV.

• flann: FLANN stands for “Fast Library for Approximate Nearest Neighbors”.

This library contains methods you will not likely use directly, but which are used

by other functions in other modules for doing nearest neighbor searches in large

data sets.

• gpu: The GPU library contains implementations of most of the rest of the library

functions optimized for operation on CUDA GPUs. There are also some functions

which are only implemented for GPU operation. Some of these provide excellent

results but require computational resources sufficiently high that implementation

on non-GPU hardware would provide little utility.

• photo: This is a relatively new module which contains tools useful for

computational photography.

• stitching: This entire module implements a sophisticated image stitching pipeline.

This is new functionality in the library but, like the ‘photo’ module is a place

where future growth is expected.

• nonfree: OpenCV contains some implementations of algorithms which are

patented or are otherwise burdened by some usage restrictions (e.g., the SIFT

algorithm). Those algorithms are segregated off to their own module, so that you

will know that you will need to do some kind of special work in order to use them

in a commercial product.

• contrib: This module contains new things that have yet to be blessed into the

whole of the library.

• legacy: This module contains old things that have yet to be banished from the

library altogether.

• ocl: The OCL module is a newer module, which could be considered analogous to

the GPU module, except that it relies on OpenCL, a Khronos standard for open

parallel computing. Though less featured than the GPU module at this time, the

OCL module aims to provide implementations which can run on any GPU or

other device supporting OpenCL. (This is in contrast to the GPU module which

explicitly makes use of the Nvidia CUDA toolkit and so will only work on Nvidia

GPU devices.)

Introduction to OpenCV 2.x

Include files

After installing the OpenCV library and setting up our programming environment, our next task is to make

something interesting happen with code. In order to do this, we’ll have to discuss header files. Fortunately,

the headers reflect the new, modular structure of OpenCV introduced in Chapter 1. The main header file of

interest is …/include/opencv2/opencv.hpp. This header file just calls the header files for each OpenCV

module:

#include "opencv2/core/core_c.h"

Old C data structures and arithmetic routines.

#include "opencv2/core/core.hpp"

New C++ data structures and arithmetic routines.

#include "opencv2/flann/miniflann.hpp"

Approximate nearest neighbor matching functions. (Mostly for internal use)

#include "opencv2/imgproc/imgproc_c.h"

Old C image processing functions.

#include "opencv2/imgproc/imgproc.hpp"

New C++ image processing functions.

#include "opencv2/video/photo.hpp"

Algorithms specific to handling and restoring photographs.

#include "opencv2/video/video.hpp"

Video tracking and background segmentation routines.

#include "opencv2/features2d/features2d.hpp"

Two-dimensional feature tracking support.

#include "opencv2/objdetect/objdetect.hpp"

Cascade face detector; latent SVM; HoG; planar patch detector.

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#include "opencv2/calib3d/calib3d.hpp"

Calibration and stereo.

#include "opencv2/ml/ml.hpp"

Machine learning: clustering, pattern recognition.

#include "opencv2/highgui/highgui_c.h"

Old C image display, sliders, mouse interaction, I/O.

#include "opencv2/highgui/highgui.hpp"

New C++ image display, sliders, buttons, mouse, I/O.

#include "opencv2/contrib/contrib.hpp"

User-contributed code: flesh detection, fuzzy mean-shift tracking, spin images, self-similar features.

You may use the include file opencv.hpp to include any and every possible OpenCV function but, since it

includes everything, it will cause compile time to be slower. If you are only using, say, image processing

functions, compile time will be faster if you only include opencv2/imgproc/imgproc.hpp. These include

files are located on disk under the …/modules directory. For example, imgproc.hpp is located at

…/modules/imgproc/include/opencv2/imgproc/imgproc.hpp. Similarly, the sources for the functions

themselves are located under their corresponding src directory. For example, cv::Canny() in the

imgproc module is located in …/modules/improc/src/canny.cpp.

With the above include files, we can start our first C++ OpenCV program.

Legacy code such as the older blob tracking, hmm face detection, condensation tracker,

and eigen objects can be included using opencv2/legacy/legacy.hpp, which is located in

…/modules/legacy/include/opencv2/legacy/legacy.hpp.

First Program—Display a Picture

OpenCV provides utilities for reading from a wide array of image file types, as well as from video and

cameras. These utilities are part of a toolkit called HighGUI, which is included in the OpenCV package. On

the http://opencv.org site, you can go to the tutorial pages off of the documentation links at

http://docs.opencv.org/doc/tutorials/tutorials.html to see tutorials on various aspects of using OpenCV.

In the tutorial section, the “introduction to OpenCV” tutorial explains how to set up OpenCV for

various combinations of operating systems and development tools.

We will use an example from the “highgui module” to create a simple program that opens an image and

displays it on the screen (Example 2-1).

Example 2-1: A simple OpenCV program that loads an image from disk and displays it on the screen

#include <opencv2/opencv.hpp> //Include file for every supported OpenCV function

int main( int argc, char** argv ) {

cv::Mat img = cv::imread(argv[1],-1);

if( img.empty() ) return -1;

cv::namedWindow( "Example1", cv::WINDOW_AUTOSIZE );

cv::imshow( "Example1", img );

cv::waitKey( 0 );

cv::destroyWindow( "Example1" );

}

Note that OpenCV functions live within a namespace called cv. To call OpenCV functions, you must

explicitly tell the compiler that you are talking about the cv namespace by prepending cv:: to each function

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call. To get out of this bookkeeping chore, we can employ the using namespace cv; directive as

shown in Example 2-2

. This tells the compiler to assume that functions might belong to that namespace.

Note also the difference in include files between Example 2-1 and Example 2-2; in the former, we used the

general include opencv.hpp, whereas in the latter, we used only the necessary include file to improve

compile time.

Example 2-2: Same as Example 2-1 but employing the “using namespace” directive. For faster compile, we

use only the needed header file, not the generic opencv.hpp.

#include "opencv2/highgui/highgui.hpp"

using namespace cv;

int main( int argc, char** argv ) {

Mat img = imread( argv[1], -1 );

if( img.empty() ) return -1;

namedWindow( "Example2", WINDOW_AUTOSIZE );

imshow( "Example2", img );

waitKey( 0 );

destroyWindow( "Example2" );

}

When compiled and run from the command line with a single argument, Example 2-1 loads an image into

memory and displays it on the screen. It then waits until the user presses a key, at which time it closes the

window and exits. Let’s go through the program line by line and take a moment to understand what each

command is doing.

cv::Mat img = cv::imread( argv[1], -1 );

This line loads the image.

The function cv::imread() is a high-level routine that determines the file

format to be loaded based on the file name; it also automatically allocates the memory needed for the image

data structure. Note that cv::imread() can read a wide variety of image formats, including BMP, DIB,

JPEG, JPE, PNG, PBM, PGM, PPM, SR, RAS, and TIFF. A cv::Mat structure is returned. This structure

is the OpenCV construct with which you will deal the most. OpenCV uses this structure to handle all kinds

of images: single-channel, multichannel, integer-valued, floating-point-valued, and so on. The line

immediately following

if( img.empty() ) return -1;

checks to see if an image was in fact read. Another high-level function, cv::namedWindow(), opens a

window on the screen that can contain and display an image.

cv::namedWindow( "Example2", cv::WINDOW_AUTOSIZE );

This function, provided by the HighGUI library, also assigns a name to the window (in this case,

"Example2"). Future HighGUI calls that interact with this window will refer to it by this name.

The second argument to cv::namedWindow() defines window properties. It may be set either to 0 (the

default value) or to cv::WINDOW_AUTOSIZE. In the former case, the size of the window will be the

same regardless of the image size, and the image will be scaled to fit within the window. In the latter case,

Of course, once you do this, you risk conflicting names with other potential namespaces. If the function foo()

exists, say, in the cv and std namespaces, you must specify which function you are talking about using either

cv::foo() or std::foo() as you intend. In this book, other than in our specific example of Example 2-2, we

will use the explicit form cv:: for objects in the OpenCV namespace, as this is generally considered to be better

programming style.

A proper program would check for the existence of argv[1] and, in its absence, deliver an instructional error

message for the user. We will abbreviate such necessities in this book and assume that the reader is cultured enough to

understand the importance of error-handling code.

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