OpenCV 2.3.1 图像处理权威指南：新增功能详解

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OpenCV 2.3.1 原汁原味参考文档是一份详尽且深入的教程，专为图形图像处理专业人员设计。它提供了OpenCV 2.3版本的详细介绍，强调了英文版的简洁易懂性，便于学习者快速理解和上手。该文档涵盖了OpenCV的核心功能、图像处理、高级用户界面与媒体I/O、视频分析、相机校准以及3D重建等多个关键模块。 1. **API概念** (1.1节)：这部分介绍了OpenCV的Application Programming Interface (API)，包括其设计理念，帮助读者理解如何通过这些接口进行高效地编程操作。 2. **核心功能** (2.1-2.9节)： - **基本结构** (2.1)：涵盖了OpenCV的基本数据结构，如Mat、Vector等，这些都是进行图像处理的基础。 - **动态结构** (2.3)：讨论了如何在运行时创建和操作动态数据结构，对于灵活处理复杂场景很有帮助。 - **数组操作** (2.4)：讲解了如何对数组进行读写、转换和数学运算等操作，是理解OpenCV核心功能的关键部分。 - **绘图函数** (2.5)：介绍了用于在图像上绘制线条、形状、文字等的函数，有助于视觉效果的实现。 - **XML/YAML持久化** (2.6-2.7)：探讨了如何将数据保存和加载到XML或YAML格式，这对于数据存储和迁移非常实用。 - **聚类** (2.8)：涉及到了数据聚类算法，用于在大规模数据集中进行分组和分类。 - **辅助和系统函数** (2.9)：列举了一些实用工具和宏，如内存管理、系统调用等。 3. **图像处理** (3.1-3.9节)： - **图像滤波** (3.1)：介绍了各种滤波器，如高斯滤波、中值滤波等，用于图像预处理。 - **几何变换** (3.2)：讲解了旋转、缩放、平移等操作，以及更复杂的仿射和透视变换。 - **其他变换** (3.3)：包括对比度增强、直方图均衡化等，提升图像质量。 - **直方图和结构分析** (3.4-3.5)：涉及了计算图像特征、边缘检测和纹理分析。 - **形状描述符** (3.5)：介绍了用于描述图像中物体形状的方法，如SIFT、SURF等。 - **平面细分** (3.6)：通过计算图像局部结构来实现图像分割。 - **运动分析与对象跟踪** (3.7-3.8)：涉及帧间匹配、光流法等技术，用于实时监控和跟踪对象。 - **特征检测** (3.8)：介绍如何检测图像中的关键点和兴趣区域。 - **目标检测** (3.9)：探讨了基于模板匹配、机器学习的物体识别技术。 4. **高级GUI与媒体I/O** (4.1-4.3节)： - **用户界面** (4.1)：指导如何创建交互式图形用户界面，方便用户与应用交互。 - **图像和视频读写** (4.2)：讲解了OpenCV支持的多种格式，以及如何读取和保存图像和视频数据。 - **Qt新功能** (4.3)：特别关注了与Qt库集成的新特性，扩展了GUI功能。 5. **视频分析** (5.1)：这部分着重于视频帧的处理和对象跟踪，常用于实时视频应用。 6. **相机校准与3D重建** (6.1)：介绍了如何校正相机参数，以及利用3D重建技术从多视图图像中提取三维信息。这份OpenCV 2.3.1参考文档是图像处理和计算机视觉领域不可或缺的学习资源，无论是初学者还是进阶开发者都能从中获益匪浅。通过阅读并实践其中的代码示例，用户可以掌握OpenCV的强大功能，应用于实际项目中。

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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