OpenCV 2.3: The Comprehensive Image Processing Guide

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"OpenCV 2.3 文档英文版，是针对计算机视觉库OpenCV 2.3的参考资料，适合OpenCV开发者用于学习和开发。文档内容涵盖OpenCV的基本结构、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3D重建以及特征提取等多个方面，提供了丰富的操作数组、滤波、几何变换、聚类、用户界面、对象检测等功能的详细介绍。" OpenCV是一个开源的计算机视觉库，它包含了众多图像和视频处理的函数，广泛应用于图像分析、机器学习和计算机视觉相关的项目。OpenCV 2.3版本的文档详细介绍了该库的各个组成部分和使用方法。 1. **基本结构**：这部分介绍了OpenCV中基本的数据结构，如IplImage和CvMat，这些是处理图像数据的基础。此外，还涉及基本的C结构体和操作，包括向量、矩阵和多维数组的操作。 2. **动态结构**：讲解了如何创建、修改和管理动态数据结构，如Mat和Vector，它们在处理图像和视频流时非常有用。 3. **数组操作**：涵盖了一系列对数组进行操作的函数，包括基本的数学运算、逻辑运算以及转换等，这对于图像处理中的像素操作至关重要。 4. **绘图函数**：提供了绘制图形、线条、文本等图像元素的方法，对于可视化结果或创建用户界面非常实用。 5. **XML/YAML持久化**：介绍了如何使用OpenCV保存和加载配置、模型参数等数据到XML或YAML格式的文件中，方便程序的配置管理和复用。 6. **聚类算法**：如K-Means等，用于将数据集分成不同的类别或群体，常用于图像分割和对象识别。 7. **辅助和系统函数及宏**：提供了一些系统级别的功能，如错误处理、类型转换和性能计时，以及方便开发的宏定义。 8. **图像处理**：包括各种图像滤波、几何变换（如平移、旋转、缩放）、颜色空间转换、直方图计算、边缘检测等，这些都是图像分析的核心技术。 9. **高级GUI和媒体I/O**：详细阐述了如何使用OpenCV构建用户界面，以及读取和写入图像和视频文件，还包括了与Qt库的集成。 10. **视频分析**：主要讨论运动分析和对象追踪，包括背景减除、光流估计等，这些都是视频监控和运动检测的关键技术。 11. **相机标定与3D重建**：这部分讲解了如何校准相机，消除镜头畸变，以及如何从二维图像恢复三维信息，是机器人视觉和增强现实应用的基础。 12. **特征提取**：如SIFT、SURF等，这些特征描述符用于图像匹配、物体识别和跟踪，是计算机视觉领域的重要工具。 13. **对象检测**：介绍了预训练的分类器如Haar特征和HOG描述符，用于人脸检测、行人检测等目标检测任务。这个文档是OpenCV开发者的重要参考资料，不仅包含了详尽的API介绍，还有实例代码和理论解释，帮助开发者理解和利用OpenCV的强大功能。通过深入学习，开发者可以有效地实现图像处理、模式识别、目标跟踪等复杂任务。

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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