OpenCV 2.3.1官方手册：从入门到高级功能概览

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OpenCV-ref-man-2.3.1是一份详尽的OpenCV开发文档，针对版本2.3.1提供，发布日期为2011年10月15日。这份手册涵盖了OpenCV的核心功能和高级特性，对深入理解和实践OpenCV编程至关重要。 1. **介绍** (Introduction) 开篇部分概述了OpenCV（Open Source Computer Vision Library）的基本概念，它是一个强大的计算机视觉库，广泛应用于图像处理、机器学习和实时视频分析等领域。本部分可能讨论了库的设计理念，以及如何通过API进行高效地开发应用。 2. **核心功能 - The Core Functionality** - **基本结构** (Basic Structures): 介绍了OpenCV中的关键数据结构，如Mat（矩阵）、vector等，这些是所有图像处理操作的基础。 - **动态结构** (Dynamic Structures): 讲述了如何创建和操作动态数据结构，以及它们在内存管理中的角色。 - **数组操作** (Operations on Arrays): 包括数组的创建、修改、复制和转换等操作，是数据处理的核心内容。 - **绘图函数** (Drawing Functions): 提供了绘制图形、线条、文本和其他形状的工具，用于可视化结果或交互式用户界面。 - **XML/YAML持久化** (XML/YAML Persistence): 存储和读取数据到XML或YAML格式，便于数据交换和持久化。 - **系统和辅助函数** (Utility and System Functions and Macros): 包括内存管理、错误处理和其他实用工具，有助于简化编程流程。 3. **图像处理 - imgproc.ImageProcessing** - **图像过滤** (Image Filtering): 如平滑滤波、锐化等，用于改善图像质量。 - **几何变换** (Geometric Image Transformations): 如旋转、缩放、仿射变换等，用于调整图像空间。 - **其他变换** (Miscellaneous Image Transformations): 包括图像金字塔、直方图均衡化等。 - **结构分析与形状描述符** (Structural Analysis and Shape Descriptors): 如边缘检测、轮廓分析，以及计算形状特征。 - **平面细分（仅限CAPI）** (Planar Subdivisions - CAPI): 高级图像分割技术，可能涉及图形算法。 - **运动分析与对象跟踪** (Motion Analysis and Object Tracking): 基于帧间的差异或特征匹配进行对象追踪。 - **特征检测** (Feature Detection): 如SIFT、SURF等高级特征检测方法。 - **物体检测** (Object Detection): 利用机器学习模型识别特定对象。 4. **高级别GUI和媒体I/O - highgui.High-level GUI and Media I/O** - **用户界面** (User Interface): 介绍如何构建图形用户界面，与用户交互并显示图像。 - **图像和视频读写** (Reading and Writing Images and Video): 操作OpenCV支持的各种图像和视频格式。 - **Qt新功能** (Qt New Functions): 如果有，这部分可能会提及与Qt库集成的高级GUI功能。 5. **视频分析 - video.VideoAnalysis** 重点在于视频流的处理，包括运动分析和对象跟踪等实时应用的技术。 6. **相机校准和三维重建 - calib3d.Camera Calibration and 3D Reconstruction** 介绍了如何校准相机参数，并基于校准结果进行三维重建。 7. **最后的章节可能涉及** (Final Chapters): 更专业的领域如特征匹配、立体视觉、深度估计等高级主题。 OpenCV-ref-man-2.3.1是一份全面的指南，不仅适合初学者了解库的基础操作，也适合有经验的开发者深入探索其丰富的功能和高级算法。通过阅读这份文档，开发者可以掌握从图像处理到计算机视觉的多种技能，以便构建出强大而高效的视觉应用。

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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