OpenCV 2.3.1官方参考手册：图像处理与计算机视觉

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"OpenCV2.3.1官方说明文档提供了对OpenCV库的全面介绍，是深入了解和使用这个开源计算机视觉库的重要参考资料。文档详细阐述了API概念、核心功能、图像处理、高级GUI和媒体I/O、视频分析、相机标定与三维重建以及特征提取等方面的知识。 1. API概念： API（Application Programming Interface）是OpenCV的核心组成部分，它定义了一系列函数、数据结构和宏，用于实现各种计算机视觉任务。API设计旨在提高效率，提供跨平台的兼容性，并支持多种编程语言。 2. 核心功能：核心功能模块包括基本结构、基本C结构与操作、动态结构、数组操作、绘制函数、XML/YAML持久化等。这些功能构成了OpenCV的基础，用于处理图像、矩阵运算、内存管理以及数据存储。 3. 图像处理（imgproc）：该模块包含图像滤波、几何变换、其他图像变换、直方图、结构分析和形状描述符、平面细分、运动分析与对象跟踪、特征检测和对象检测等功能。这些工具广泛应用于图像增强、物体识别和追踪等领域。 4. 高级GUI和媒体I/O（highgui）：这部分内容涉及用户界面的创建和管理，以及图像和视频的读写。OpenCV提供了简单易用的接口来实现图形用户界面，便于快速开发交互式应用，同时支持多媒体文件的输入输出。 5. 视频分析（video）：视频分析部分主要关注运动分析和对象跟踪。OpenCV提供了一系列算法和技术，用于从连续的视频帧中检测和追踪运动物体，这对于行为识别、监控系统等应用场景非常重要。 6. 相机标定与三维重建（calib3d）：相机标定是计算相机内参和外参的过程，对于获取真实世界坐标至关重要。三维重建则利用多视图几何学，将多个二维图像融合成三维场景模型。OpenCV提供了强大的工具和算法，支持这两项复杂的任务。 7. 特征提取（fe）：特征提取在图像识别和匹配中起着关键作用。OpenCV实现了多种经典的特征检测算法，如SIFT、SURF等，这些算法能够识别出图像中的显著点，为图像配准、物体识别等任务提供基础。综上，OpenCV2.3.1官方文档是学习和开发计算机视觉应用的宝贵资源，涵盖了从基础到高级的各种功能，适用于科研人员、工程师和学生等不同群体。通过深入学习和实践，开发者可以掌握强大的计算机视觉技术，实现复杂的应用场景。

The OpenCV Reference Manual, Release 2.3

Being derived from Vec<

Tp, 4> , Scalar

and Scalar can be used just as typical 4-element vectors. In addition,

they can be converted to/from CvScalar . The type Scalar is widely used in OpenCV to pass pixel values.

Range

class Range

Template class specifying a continuous subsequence (slice) of a sequence.

class Range

{

public:

...

int start, end;

};

The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes. Range(a,b) is

basically the same as a:b in Matlab or a..b in Python. As in Python, start is an inclusive left boundary of the range

and end is an exclusive right boundary of the range. Such a half-opened interval is usually denoted as [start, end) .

The static method Range::all() returns a special variable that means “the whole sequence” or “the whole range”,

just like ” : ” in Matlab or ” ... ” in Python. All the methods and functions in OpenCV that take Range support this

special Range::all() value. But, of course, in case of your own custom processing, you will probably have to check

and handle it explicitly:

void my

function(..., const Range& r, ....)

{

if(r == Range::all()) {

// process all the data

}

else {

// process [r.start, r.end)

}

Ptr

class Ptr

Template class for smart reference-counting pointers

template<typename

Tp> class Ptr

{

public:

// default constructor

Ptr();

// constructor that wraps the object pointer

Ptr(

obj);

// destructor: calls release()

~Ptr();

// copy constructor; increments ptr’s reference counter

Ptr(const Ptr& ptr);

// assignment operator; decrements own reference counter

// (with release()) and increments ptr’s reference counter

Ptr& operator = (const Ptr& ptr);

// increments reference counter

void addref();

12 Chapter 2. core. The Core Functionality

The OpenCV Reference Manual, Release 2.3

// decrements reference counter; when it becomes 0,

// delete

obj() is called

void release();

// user-specified custom object deletion operation.

// by default, "delete obj;" is called

void delete

obj();

// returns true if obj == 0;

bool empty() const;

// provide access to the object fields and methods

operator -> ();

const

operator -> () const;

// return the underlying object pointer;

// thanks to the methods, the Ptr<

Tp> can be

// used instead of

operator

();

operator const

() const;

protected:

// the encapsulated object pointer

obj;

// the associated reference counter

int

refcount;

};

The Ptr<

Tp> class is a template class that wraps pointers of the corresponding type. It is similar to shared

ptr that

is part of the Boost library ( http://www.boost.org/doc/libs/1_40_0/libs/smart_ptr/shared_ptr.htm ) and also part of the

C++0x standard.

This class provides the following options:

• Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or a C structure. For

some objects, like ﬁles, windows, mutexes, sockets, and others, a copy constructor or an assignment operator

are difﬁcult to deﬁne. For some other objects, like complex classiﬁers in OpenCV, copy constructors are absent

and not easy to implement. Finally, some of complex OpenCV and your own data structures may be written in

C. However, copy constructors and default constructors can simplify programming a lot. Besides, they are often

required (for example, by STL containers). By wrapping a pointer to such a complex object TObj to Ptr<TObj>

, you automatically get all of the necessary constructors and the assignment operator.

• O(1) complexity of the above-mentioned operations. While some structures, like std::vector, provide a copy

constructor and an assignment operator, the operations may take a considerable amount of time if the data

structures are large. But if the structures are put into Ptr<> , the overhead is small and independent of the data

size.

• Automatic destruction, even for C structures. See the example below with FILE

• Heterogeneous collections of objects. The standard STL and most other C++ and OpenCV containers can store

only objects of the same type and the same size. The classical solution to store objects of different types in the

same container is to store pointers to the base class base

class

instead but then you loose the automatic

memory management. Again, by using Ptr<base

class

t>() instead of the raw pointers, you can solve the

problem.

The Ptr class treats the wrapped object as a black box. The reference counter is allocated and managed separately.

The only thing the pointer class needs to know about the object is how to deallocate it. This knowledge is incapsulated

in the Ptr::delete

obj() method that is called when the reference counter becomes 0. If the object is a C++ class

instance, no additional coding is needed, because the default implementation of this method calls delete obj; .

However, if the object is deallocated in a different way, the specialized method should be created. For example, if you

want to wrap FILE , the delete

obj may be implemented as follows:

2.1. Basic Structures 13

The OpenCV Reference Manual, Release 2.3

data layout of the array M is deﬁned by the array M.step[] , so that the address of element (i

, ..., i

M.dims−1

) ,

where 0 ≤ i

< M.size[k] , is computed as:

addr(M

,...,i

M.dims−1

) = M.data + M.step[0] ∗ i

+ M.step[1] ∗ i

+ ... + M.step[M.dims − 1] ∗ i

M.dims−1

In case of a 2-dimensional array, the above formula is reduced to:

addr(M

i,j

) = M.data + M.step[0] ∗ i + M.step[1] ∗ j

Note that M.step[i] >= M.step[i+1] (in fact, M.step[i] >= M.step[i+1]

M.size[i+1] ). This means

that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, and so on.

M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() .

So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV 1.x. It is also

compatible with the majority of dense array types from the standard toolkits and SDKs, such as Numpy (ndarray),

Win32 (independent device bitmaps), and others, that is, with any array that uses steps (or strides) to compute the

position of a pixel. Due to this compatibility, it is possible to make a Mat header for user-allocated data and process it

in-place using OpenCV functions.

There are many different ways to create a Mat object. The most popular options are listed below:

• Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue])

constructor. A new array of the speciﬁed size and type is allocated. type has the same meaning as in the

cvCreateMat method. For example, CV

8UC1 means a 8-bit single-channel array, CV

32FC2 means a 2-channel

(complex) ﬂoating-point array, and so on.

// make a 7x7 complex matrix filled with 1+3j.

Mat M(7,7,CV

32FC2,Scalar(1,3));

// and now turn M to a 100x60 15-channel 8-bit matrix.

// The old content will be deallocated

M.create(100,60,CV

8UC(15));

As noted in the introduction to this chapter, create() allocates only a new array when the shape or type of the

current array are different from the speciﬁed ones.

• Create a multi-dimensional array:

// create a 100x100x100 8-bit array

int sz[] = {100, 100, 100};

Mat bigCube(3, sz, CV

8U, Scalar::all(0));

It passes the number of dimensions =1 to the Mat constructor but the created array will be 2-dimensional with

the number of columns set to 1. So, Mat::dims is always >= 2 (can also be 0 when the array is empty).

• Use a copy constructor or assignment operator where there can be an array or expression on the right side (see

below). As noted in the introduction, the array assignment is an O(1) operation because it only copies the header

and increases the reference counter. The Mat::clone() method can be used to get a full (deep) copy of the

array when you need it.

• Construct a header for a part of another array. It can be a single row, single column, several rows, several

columns, rectangular region in the array (called a minor in algebra) or a diagonal. Such operations are also O(1)

because the new header references the same data. You can actually modify a part of the array using this feature,

for example:

// add the 5-th row, multiplied by 3 to the 3rd row

M.row(3) = M.row(3) + M.row(5)

// now copy the 7-th column to the 1-st column

// M.col(1) = M.col(7); // this will not work

Mat M1 = M.col(1);

2.1. Basic Structures 15

The OpenCV Reference Manual, Release 2.3

M.col(7).copyTo(M1);

// create a new 320x240 image

Mat img(Size(320,240),CV

8UC3);

// select a ROI

Mat roi(img, Rect(10,10,100,100));

// fill the ROI with (0,255,0) (which is green in RGB space);

// the original 320x240 image will be modified

roi = Scalar(0,255,0);

Due to the additional datastart and dataend members, it is possible to compute a relative sub-array position

in the main container array using locateROI():

Mat A = Mat::eye(10, 10, CV

32S);

// extracts A columns, 1 (inclusive) to 3 (exclusive).

Mat B = A(Range::all(), Range(1, 3));

// extracts B rows, 5 (inclusive) to 9 (exclusive).

// that is, C ~ A(Range(5, 9), Range(1, 3))

Mat C = B(Range(5, 9), Range::all());

Size size; Point ofs;

C.locateROI(size, ofs);

// size will be (width=10,height=10) and the ofs will be (x=1, y=5)

As in case of whole matrices, if you need a deep copy, use the clone() method of the extracted sub-matrices.

• Make a header for user-allocated data. It can be useful to do the following:

1. Process “foreign” data using OpenCV (for example, when you implement a DirectShow* ﬁlter or a pro-

cessing module for gstreamer, and so on). For example:

void process

video

frame(const unsigned char

pixels,

int width, int height, int step)

{

Mat img(height, width, CV

8UC3, pixels, step);

GaussianBlur(img, img, Size(7,7), 1.5, 1.5);

}

2. Quickly initialize small matrices and/or get a super-fast element access.

double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}};

Mat M = Mat(3, 3, CV

64F, m).inv();

Partial yet very common cases of this user-allocated data case are conversions from CvMat and IplImage to

Mat. For this purpose, there are special constructors taking pointers to CvMat or IplImage and the optional ﬂag

indicating whether to copy the data or not.

Backward conversion from Mat to CvMat or IplImage is provided via cast operators Mat::operator

CvMat() const and Mat::operator IplImage(). The operators do NOT copy the data.

IplImage

img = cvLoadImage("greatwave.jpg", 1);

Mat mtx(img); // convert IplImage

-> Mat

CvMat oldmat = mtx; // convert Mat -> CvMat

Assert(oldmat.cols == img->width && oldmat.rows == img->height &&

oldmat.data.ptr == (uchar

)img->imageData && oldmat.step == img->widthStep);

• Use MATLAB-style array initializers, zeros(), ones(), eye(), for example:

// create a double-precision identity martix and add it to M.

M += Mat::eye(M.rows, M.cols, CV

64F);

16 Chapter 2. core. The Core Functionality

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