opencv中mat,cvmat,Iplimage结构体定义以及格式互相转换

opencv中常见的与图像操作有关的数据容器有Mat,cvMat和IplImage,这三种类型都可以代表和显示图像,但是,Mat类型侧重于计算,数学性较高,openCV对Mat类型的计算也进行了优化。而CvMat和IplImage类型更侧重于“图像”,opencv对其中的图像操作(缩放、单通道提取、图像阈值操作等)进行了优化。在opencv2.0之前,opencv是完全用C实现的,但是,IplImage类型与CvMat类型的关系类似于面向对象中的继承关系。实际上,CvMat之上还有一个更抽象的基类----CvArr,这在源代码中会常见。

1. IplImage

opencv中的图像信息头,该结构体定义:


typedef struct _IplImage 
{ 
int nSize; 
int ID; 
int nChannels; 
int alphaChannel; 
int depth; 

char colorModel[4]; 
char channelSeq[4]; 
int dataOrder; 
int origin; 
int align; 

int width; 
int height; 

struct _IplROI *roi; 
struct _IplImage *maskROI; 
void *imageId; 
struct _IplTileInfo *tileInfo; 

int imageSize; 
char *imageData; 
int widthStep; 
int BorderMode[4]; 
int BorderConst[4]; 

char *imageDataOrigin; 
} IplImage;

dataOrder中的两个取值:交叉存取颜色通道是颜色数据排列将会是BGRBGR...的交错排列。分开的颜色通道是有几个颜色通道就分几个颜色平面存储。roi是IplROI结构体,该结构体包含了xOffset,yOffset,height,width,coi成员变量,其中xOffset,yOffset是x,y坐标,coi代表channel of interest(感兴趣的通道),非0的时候才有效。访问图像中的数据元素,分间接存储和直接存储,当图像元素为浮点型时,(uchar *) 改为 (float *): 

ViewCode
 IplImage* img=cvLoadImage("lena.jpg", 1);
CvScalar s; 
s=cvGet2D(img,i,j); 
cvSet2D(img,i,j,s); 


IplImage* img; //malloc memory by cvLoadImage or cvCreateImage
for(int row = 0; row < img->height; row++)
{
for (int col = 0; col < img->width; col++)
{
b = CV_IMAGE_ELEM(img, UCHAR, row, col * img->nChannels + 0); 
g = CV_IMAGE_ELEM(img, UCHAR, row, col * img->nChannels + 1); 
r = CV_IMAGE_ELEM(img, UCHAR, row, col * img->nChannels + 2);
}
}


IplImage* img; //malloc memory by cvLoadImage or cvCreateImage
uchar b, g, r; // 3 channels
for(int row = 0; row < img->height; row++)
{
for (int col = 0; col < img->width; col++)
{
b = ((uchar *)(img->imageData + row * img->widthStep))[col * img->nChannels + 0]; 
g = ((uchar *)(img->imageData + row * img->widthStep))[col * img->nChannels + 1]; 
r = ((uchar *)(img->imageData + row * img->widthStep))[col * img->nChannels + 2];
}
}
  初始化使用IplImage *,是一个指向结构体IplImage的指针: 
IplImage * cvLoadImage(constchar * filename, int//load images from specified image 
IplImage * cvCreateImage(CvSize size, int depth, int channels); //allocate memory

2.CvMat

首先,我们需要知道,第一,在OpenCV中没有向量(vector)结构。任何时候需要向量,都只需要一个列矩阵(如果需要一个转置或者共轭向量,则需要一个行矩阵)。第二,OpenCV矩阵的概念与我们在线性代数课上学习的概念相比,更抽象,尤其是矩阵的元素,并非只能取简单的数值类型,可以是多通道的值。CvMat 的结构: 

typedef struct CvMat 
{ 
int type; 
int step; 
int* refcount; 
union {
uchar* ptr;
short* s;
int* i;
float* fl;
double* db;
} data; 
union {
int rows;
int height;
};
union {
int cols; 
int width;
};
} CvMat; 

 创建CvMat数据: 

View Code
CvMat * cvCreateMat(int rows, int cols, int type); 
CV_INLine CvMat cvMat((int rows, int cols, int type, void* data CV_DEFAULT); 
CvMat * cvInitMatHeader(CvMat * mat, int rows, int cols, int type, void * data CV_DEFAULT(NULL), int step CV_DEFAULT(CV_AUTOSTEP)); 

 对矩阵数据进行访问: 

cvmSet( CvMat* mat, int row, int col, double value);
cvmGet( const CvMat* mat, int row, int col );


CvScalar cvGet2D(const CvArr * arr, int idx0, int idx1); //CvArr只作为函数的形参void cvSet2D(CvArr* arr, int idx0, int idx1, CvScalar value);
CvMat * cvmat = cvCreateMat(4, 4, CV_32FC1);
cvmat->data.fl[row * cvmat->cols + col] = (float)3.0;


CvMat * cvmat = cvCreateMat(4, 4, CV_64FC1);
cvmat->data.db[row * cvmat->cols + col] = 3.0;
CvMat * cvmat = cvCreateMat(4, 4, CV_64FC1);
CV_MAT_ELEM(*cvmat, double, row, col) = 3.0; 
if (CV_MAT_DEPTH(cvmat->type) == CV_32F)
CV_MAT_ELEM_CN(*cvmat, float, row, col * CV_MAT_CN(cvmat->type) + ch) = (float)3.0; // ch为通道值
if (CV_MAT_DEPTH(cvmat->type) == CV_64F)
CV_MAT_ELEM_CN(*cvmat, double, row, col * CV_MAT_CN(cvmat->type) + ch) = 3.0; // ch为通道值
for (int row = 0; row < cvmat->rows; row++)
{ 
p = cvmat ->data.fl + row * (cvmat->step / 4);
for (int col = 0; col < cvmat->cols; col++) 
{ 
*p = (float) row + col; 
*(p+1) = (float)row + col + 1; 
*(p+2) = (float)row + col + 2; 
p += 3; 
}
}

CvMat * vector = cvCreateMat(1,3, CV_32SC2);CV_MAT_ELEM(*vector, CvPoint, 0, 0) = cvPoint(100,100);

CvMat * vector = cvCreateMat(1,3, CV_64FC4);CV_MAT_ELEM(*vector, CvScalar, 0, 0) = CvScalar(0, 0, 0, 0);
CvMat* M1 = cvCreateMat( 4, 4,CV_32FC1);
CvMat* M2;
M2=cvCloneMat(M1);

 

3.Mat

Mat是opencv2.0推出的处理图像的新的数据结构,现在越来越有趋势取代之前的cvMat和lplImage,相比之下Mat最大的好处就是能够更加方便的进行内存管理,不再需要程序员手动管理内存的释放。opencv2.3中提到Mat是一个多维的密集数据数组,可以用来处理向量和矩阵、图像、直方图等等常见的多维数据。 

class CV_EXPORTS Mat
{

publicint flags;(Note :目前还不知道flags做什么用的)
int dims; 
int rows,cols; 
uchar *data; 
int * refcount; 
...

};
 

 从以上结构体可以看出Mat也是一个矩阵头,默认不分配内存,只是指向一块内存(注意读写保护)。初始化使用create函数或者Mat构造函数,以下整理自opencv2.3.1 Manual:

Mat(nrows, ncols, type, fillValue]); 
M.create(nrows, ncols, type);
例子:
Mat M(7,7,CV_32FC2,Scalar(1,3)); 
M.create(100, 60, CV_8UC(15)); 
int sz[] = {100, 100, 100}; 
Mat bigCube(3, sz, CV_8U, Scalar:all(0));
double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}};
Mat M = Mat(3, 3, CV_64F, m).inv();
Mat img(Size(320,240),CV_8UC3); 
Mat img(height, width, CV_8UC3, pixels, step); 
IplImage* img = cvLoadImage("greatwave.jpg", 1);
Mat mtx(img,0); // convert IplImage* -> Mat; 
访问Mat的数据元素:
Mat M;
M.row(3) = M.row(3) + M.row(5) * 3; 


Mat M1 = M.col(1);
M.col(7).copyTo(M1); 


Mat M;
M.at<double>(i,j); 
M.at(uchar)(i,j); 
Vec3i bgr1 = M.at(Vec3b)(i,j) 
Vec3s bgr2 = M.at(Vec3s)(i,j) 
Vec3w bgr3 = M.at(Vec3w)(i,j) 


double sum = 0.0f;
for(int row = 0; row < M.rows; row++)
{ 
constdouble * Mi = M.ptr<double>(row); 
for (int col = 0; col < M.cols; col++) 
sum += std::max(Mi[j], 0.);
}


double sum=0;
MatConstIterator<double> it = M.begin<double>(), it_end = M.end<double>();
for(; it != it_end; ++it) 
sum += std::max(*it, 0.);
Mat可进行Matlab风格的矩阵操作,如初始化的时候可以用initializers,zeros(), ones(), eye(). 除以上内容之外,Mat还有有3个重要的方法:
View Code
Mat mat = imread(const String* filename); // 读取图像
imshow(conststring frameName, InputArray mat); // 显示图像
imwrite (conststring& filename, InputArray img); //储存图像

 

4. CvMat, Mat, IplImage之间的互相转换

IpIImage -> CvMat

CvMat matheader;
CvMat * mat = cvGetMat(img, &matheader);

CvMat * mat = cvCreateMat(img->height, img->width, CV_64FC3);
cvConvert(img, mat)
IplImage -> Mat
Mat::Mat(const IplImage* img, bool copyData=false);
例子:
IplImage* iplImg = cvLoadImage("greatwave.jpg", 1);
Mat mtx(iplImg); 
  
 Mat -> IplImage
Mat M
IplImage iplimage = M;
 CvMat -> Mat
Mat::Mat(const CvMat* m, bool copyData=false); 
 Mat -> CvMat
例子(假设Mat类型的imgMat图像数据存在):
CvMat cvMat = imgMat;/*Mat -> CvMat, 类似转换到IplImage,不复制数据只创建矩阵

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