OpenCV之CvMat、Mat、IplImage之间相互转换实例(转)
OpenCV学习之CvMat的用法详解及实例
CvMat是OpenCV比较基础的函数。初学者应该掌握并熟练应用。但是我认为计算机专业学习的方法是,不断的总结并且提炼,同时还要做大量的实践,如编码,才能记忆深刻,体会深刻,从而引导自己想更高层次迈进。
1.初始化矩阵:
方式一、逐点赋值式:
CvMat* mat = cvCreateMat( 2, 2, CV_64FC1 ); cvZero( mat ); cvmSet( mat, 0, 0, 1 ); cvmSet( mat, 0, 1, 2 ); cvmSet( mat, 1, 0, 3 ); cvmSet( mat, 2, 2, 4 ); cvReleaseMat( &mat );
方式二、连接现有数组式:
double a[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }; CvMat mat = cvMat( 3, 4, CV_64FC1, a ); // 64FC1 for double // 不需要cvReleaseMat,因为数据内存分配是由double定义的数组进行的。
2.IplImage <----->cvMat的转换
A.CvMat-> IplImage
IplImage* img = cvCreateImage(cvGetSize(mat),8,1); cvGetImage(matI,img); cvSaveImage("rice1.bmp",img); B.IplImage -> CvMat IplImage* img = cvLoadimage("leda.jpg",1);
法2:CvMat *mat = cvCreateMat( img->height, img->width, CV_64FC3 );
cvConvert( img, mat );
法1:CvMat mathdr;
CvMat *mat = cvGetMat( img, &mathdr );
3.IplImage <--->Mat的转换
(1)将IplImage----- > Mat类型
Mat::Mat(const IplImage* img, bool copyData=false);
默认情况下,新的Mat类型与原来的IplImage类型共享图像数据,转换只是创建一个Mat矩阵头。当将参数copyData设为true后,就会复制整个图像数据。
例:
IplImage*iplImg = cvLoadImage("greatwave.jpg", 1); Matmtx(iplImg); // IplImage* ->Mat 共享数据 // or : Mat mtx = iplImg; 或者是:Mat mtx(iplImg,0); // 0是不复制影像,也就是iplImg的data共用同个记意位置,header各自有
(2)将Mat类型转换-----> IplImage类型
同样只是创建图像头,而没有复制数据。
例:
IplImage ipl_img = img; // Mat -> IplImage IplImage*-> BYTE* BYTE* data= img->imageData;
4.CvMat<--->Mat的转换
(1)将CvMat类型转换为Mat类型
B.CvMat->Mat
与IplImage的转换类似,可以选择是否复制数据。
CvMat*m= cvCreatMat(int rows ,int cols , int type); Mat::Mat(const CvMat* m, bool copyData=false);
在openCV中,没有向量(vector)的数据结构。任何时候,但我们要表示向量时,用矩阵数据表示即可。
但是,CvMat类型与我们在线性代数课程上学的向量概念相比,更抽象,比如CvMat的元素数据类型并不仅限于基础数据类型,比如,下面创建一个二维数据矩阵:
CvMat*m= cvCreatMat(int rows ,int cols , int type);
这里的type可以是任意的预定义数据类型,比如RGB或者别的多通道数据。这样我们便可以在一个CvMat矩阵上表示丰富多彩的图像了。
(2)将Mat类型转换为CvMat类型
与IplImage的转换类似,不复制数据,只创建矩阵头。
例:
// 假设Mat类型的imgMat图像数据存在 CvMat cvMat = imgMat; // Mat -> CvMat
5.cv::Mat--->const cvArr*
cvArr * 数组的指针。就是opencv里面的一种类型。
Mat img; const CvArr* s=(CvArr*)&img;
上面就可以了,CvArr是Mat的虚基类,所有直接强制转换就可以了
void cvResize( src 就是之前的lplimage类型的一个指针变量
6.cvArr(IplImage或者cvMat)转化为cvMat
方式一、cvGetMat方式:
int coi = 0; cvMat *mat = (CvMat*)arr; if( !CV_IS_MAT(mat) ) { mat = cvGetMat( mat, &matstub, &coi ); if (coi != 0) reutn; // CV_ERROR_FROM_CODE(CV_BadCOI); }
写成函数为:
// This is just an example of function // to support both IplImage and cvMat as an input CVAPI( void ) cvIamArr( const CvArr* arr ) { CV_FUNCNAME( "cvIamArr" ); __BEGIN__; CV_ASSERT( mat == NULL ); CvMat matstub, *mat = (CvMat*)arr; int coi = 0; if( !CV_IS_MAT(mat) ) { CV_CALL( mat = cvGetMat( mat, &matstub, &coi ) ); if (coi != 0) CV_ERROR_FROM_CODE(CV_BadCOI); } // Process as cvMat __END__; }
7.图像直接操作
方式一:直接数组操作 int col, row, z;
uchar b, g, r; for( row = 0; row < img->height; y++ ) { for ( col = 0; col < img->width; col++ ) { b = img->imageData[img->widthStep * row + col * 3] g = img->imageData[img->widthStep * row + col * 3 + 1]; r = img->imageData[img->widthStep * row + col * 3 + 2]; } }
方式二:宏操作:
int row, col; uchar b, g, r; for( row = 0; row < img->height; row++ ) { for ( col = 0; col < img->width; col++ ) { b = CV_IMAGE_ELEM( img, uchar, row, col * 3 ); g = CV_IMAGE_ELEM( img, uchar, row, col * 3 + 1 ); r = CV_IMAGE_ELEM( img, uchar, row, col * 3 + 2 ); } }
注:CV_IMAGE_ELEM( img, uchar, row, col * img->nChannels + ch )
8.cvMat的直接操作
数组的直接操作比较郁闷,这是由于其决定于数组的数据类型。
对于CV_32FC1 (1 channel float):
CvMat* M = cvCreateMat( 4, 4, CV_32FC1 ); M->data.fl[ row * M->cols + col ] = (float)3.0;
对于CV_64FC1 (1 channel double):
CvMat* M = cvCreateMat( 4, 4, CV_64FC1 ); M->data.db[ row * M->cols + col ] = 3.0;
一般的,对于1通道的数组:
CvMat* M = cvCreateMat( 4, 4, CV_64FC1 ); CV_MAT_ELEM( *M, double, row, col ) = 3.0;
注意double要根据数组的数据类型来传入,这个宏对多通道无能为力。
对于多通道:
看看这个宏的定义:
#define CV_MAT_ELEM_CN( mat, elemtype, row, col ) \ (*(elemtype*)((mat).data.ptr + (size_t)(mat).step*(row) + sizeof(elemtype)*(col))) if( CV_MAT_DEPTH(M->type) == CV_32F ) CV_MAT_ELEM_CN( *M, float, row, col * CV_MAT_CN(M->type) + ch ) = 3.0; if( CV_MAT_DEPTH(M->type) == CV_64F ) CV_MAT_ELEM_CN( *M, double, row, col * CV_MAT_CN(M->type) + ch ) = 3.0;
更优化的方法是:
#define CV_8U 0 #define CV_8S 1 #define CV_16U 2 #define CV_16S 3 #define CV_32S 4 #define CV_32F 5 #define CV_64F 6 #define CV_USRTYPE1 7 int elem_size = CV_ELEM_SIZE( mat->type ); for( col = start_col; col < end_col; col++ ) { for( row = 0; row < mat->rows; row++ ) { for( elem = 0; elem < elem_size; elem++ ) { (mat->data.ptr + ((size_t)mat->step * row) + (elem_size * col))[elem] = (submat->data.ptr + ((size_t)submat->step * row) + (elem_size * (col - start_col)))[elem]; } } }
对于多通道的数组,以下操作是推荐的:
for(row=0; row< mat->rows; row++) { p = mat->data.fl + row * (mat->step/4); for(col = 0; col < mat->cols; col++) { *p = (float) row+col; *(p+1) = (float) row+col+1; *(p+2) =(float) row+col+2; p+=3; } }
对于两通道和四通道而言:
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);
9.间接访问cvMat
cvmGet/Set是访问CV_32FC1 和 CV_64FC1型数组的最简便的方式,其访问速度和直接访问几乎相同
cvmSet( mat, row, col, value );
cvmGet( mat, row, col );
举例:打印一个数组
inline void cvDoubleMatPrint( const CvMat* mat ) { int i, j; for( i = 0; i < mat->rows; i++ ) { for( j = 0; j < mat->cols; j++ ) { printf( "%f ",cvmGet( mat, i, j ) ); } printf( "\n" ); } }
而对于其他的,比如是多通道的后者是其他数据类型的,cvGet/Set2D是个不错的选择
CvScalar scalar = cvGet2D( mat, row, col );
cvSet2D( mat, row, col, cvScalar( r, g, b ) );
注意:数据不能为int,因为cvGet2D得到的实质是double类型。
举例:打印一个多通道矩阵:
inline void cv3DoubleMatPrint( const CvMat* mat ) { int i, j; for( i = 0; i < mat->rows; i++ ) { for( j = 0; j < mat->cols; j++ ) { CvScalar scal = cvGet2D( mat, i, j ); printf( "(%f,%f,%f) ", scal.val[0], scal.val[1], scal.val[2] ); } printf( "\n" ); } }
10.修改矩阵的形状——cvReshape的操作
经实验表明矩阵操作的进行的顺序是:首先满足通道,然后满足列,最后是满足行。
注意:这和Matlab是不同的,Matlab是行、列、通道的顺序。
我们在此举例如下:
对于一通道:
// 1 channel CvMat *mat, mathdr; double data[] = { 11, 12, 13, 14, 21, 22, 23, 24, 31, 32, 33, 34 }; CvMat* orig = &cvMat( 3, 4, CV_64FC1, data ); //11 12 13 14 //21 22 23 24 //31 32 33 34 mat = cvReshape( orig, &mathdr, 1, 1 ); // new_ch, new_rows cvDoubleMatPrint( mat ); // above // 11 12 13 14 21 22 23 24 31 32 33 34 mat = cvReshape( mat, &mathdr, 1, 3 ); // new_ch, new_rows cvDoubleMatPrint( mat ); // above //11 12 13 14 //21 22 23 24 //31 32 33 34 mat = cvReshape( orig, &mathdr, 1, 12 ); // new_ch, new_rows cvDoubleMatPrint( mat ); // above // 11 // 12 // 13 // 14 // 21 // 22 // 23 // 24 // 31 // 32 // 33 // 34 mat = cvReshape( mat, &mathdr, 1, 3 ); // new_ch, new_rows cvDoubleMatPrint( mat ); // above //11 12 13 14 //21 22 23 24 //31 32 33 34 mat = cvReshape( orig, &mathdr, 1, 2 ); // new_ch, new_rows cvDoubleMatPrint( mat ); // above //11 12 13 14 21 22 //23 24 31 32 33 34 mat = cvReshape( mat, &mathdr, 1, 3 ); // new_ch, new_rows cvDoubleMatPrint( mat ); // above //11 12 13 14 //21 22 23 24 //31 32 33 34 mat = cvReshape( orig, &mathdr, 1, 6 ); // new_ch, new_rows cvDoubleMatPrint( mat ); // above // 11 12 // 13 14 // 21 22 // 23 24 // 31 32 // 33 34 mat = cvReshape( mat, &mathdr, 1, 3 ); // new_ch, new_rows cvDoubleMatPrint( mat ); // above //11 12 13 14 //21 22 23 24 //31 32 33 34 // Use cvTranspose and cvReshape( mat, &mathdr, 1, 2 ) to get // 11 23 // 12 24 // 13 31 // 14 32 // 21 33 // 22 34 // Use cvTranspose again when to recover
对于三通道:
CvMat mathdr, *mat; double data[] = { 111, 112, 113, 121, 122, 123,211, 212, 213, 221, 222, 223 }; CvMat* orig = &cvMat( 2, 2, CV_64FC3, data ); //(111,112,113) (121,122,123) //(211,212,213) (221,222,223) mat = cvReshape( orig, &mathdr, 3, 1 ); // new_ch, new_rows cv3DoubleMatPrint( mat ); // above // (111,112,113) (121,122,123) (211,212,213) (221,222,223) // concatinate in column first order mat = cvReshape( orig, &mathdr, 1, 1 );// new_ch, new_rows cvDoubleMatPrint( mat ); // above // 111 112 113 121 122 123 211 212 213 221 222 223 // concatinate in channel first, column second, row third mat = cvReshape( orig, &mathdr, 1, 3); // new_ch, new_rows cvDoubleMatPrint( mat ); // above //111 112 113 121 //122 123 211 212 //213 221 222 223 // channel first, column second, row third mat = cvReshape( orig, &mathdr, 1, 4 ); // new_ch, new_rows cvDoubleMatPrint( mat ); // above //111 112 113 //121 122 123 //211 212 213 //221 222 223 // channel first, column second, row third // memorize this transform because this is useful to // add (or do something) color channels CvMat* mat2 = cvCreateMat( mat->cols, mat->rows, mat->type ); cvTranspose( mat, mat2 ); cvDoubleMatPrint( mat2 ); // above //111 121 211 221 //112 122 212 222 //113 123 213 223 cvReleaseMat( &mat2 );
11.计算色彩距离
我们要计算img1,img2的每个像素的距离,用dist表示,定义如下
IplImage *img1 = cvCreateImage( cvSize(w,h), IPL_DEPTH_8U, 3 ); IplImage *img2 = cvCreateImage( cvSize(w,h), IPL_DEPTH_8U, 3 ); CvMat *dist = cvCreateMat( h, w, CV_64FC1 );
比较笨的思路是:
代码如下:
cvSplit->cvSub->cvMul->cvAdd
IplImage *img1B = cvCreateImage( cvGetSize(img1), img1->depth, 1 ); IplImage *img1G = cvCreateImage( cvGetSize(img1), img1->depth, 1 ); IplImage *img1R = cvCreateImage( cvGetSize(img1), img1->depth, 1 ); IplImage *img2B = cvCreateImage( cvGetSize(img1), img1->depth, 1 ); IplImage *img2G = cvCreateImage( cvGetSize(img1), img1->depth, 1 ); IplImage *img2R = cvCreateImage( cvGetSize(img1), img1->depth, 1 ); IplImage *diff = cvCreateImage( cvGetSize(img1), IPL_DEPTH_64F, 1 ); cvSplit( img1, img1B, img1G, img1R ); cvSplit( img2, img2B, img2G, img2R ); cvSub( img1B, img2B, diff ); cvMul( diff, diff, dist ); cvSub( img1G, img2G, diff ); cvMul( diff, diff, diff); cvAdd( diff, dist, dist ); cvSub( img1R, img2R, diff ); cvMul( diff, diff, diff ); cvAdd( diff, dist, dist ); cvReleaseImage( &img1B ); cvReleaseImage( &img1G ); cvReleaseImage( &img1R ); cvReleaseImage( &img2B ); cvReleaseImage( &img2G ); cvReleaseImage( &img2R ); cvReleaseImage( &diff );
比较聪明的思路是:
int D = img1->nChannels; // D: Number of colors (dimension) int N = img1->width * img1->height; // N: number of pixels CvMat mat1hdr, *mat1 = cvReshape( img1, &mat1hdr, 1, N ); // N x D(colors) CvMat mat2hdr, *mat2 = cvReshape( img2, &mat2hdr, 1, N ); // N x D(colors) CvMat diffhdr, *diff = cvCreateMat( N, D, CV_64FC1 ); // N x D, temporal buff cvSub( mat1, mat2, diff ); cvMul( diff, diff, diff ); dist = cvReshape( dist, &disthdr, 1, N ); // nRow x nCol to N x 1 cvReduce( diff, dist, 1, CV_REDUCE_SUM ); // N x D to N x 1 dist = cvReshape( dist, &disthdr, 1, img1->height ); // Restore N x 1 to nRow x nCol cvReleaseMat( &diff ); #pragma comment( lib, "cxcore.lib" ) #include "cv.h" #include <stdio.h> int main() { CvMat* mat = cvCreateMat(3,3,CV_32FC1); cvZero(mat);//将矩阵置0 //为矩阵元素赋值 CV_MAT_ELEM( *mat, float, 0, 0 ) = 1.f; CV_MAT_ELEM( *mat, float, 0, 1 ) = 2.f; CV_MAT_ELEM( *mat, float, 0, 2 ) = 3.f; CV_MAT_ELEM( *mat, float, 1, 0 ) = 4.f; CV_MAT_ELEM( *mat, float, 1, 1 ) = 5.f; CV_MAT_ELEM( *mat, float, 1, 2 ) = 6.f; CV_MAT_ELEM( *mat, float, 2, 0 ) = 7.f; CV_MAT_ELEM( *mat, float, 2, 1 ) = 8.f; CV_MAT_ELEM( *mat, float, 2, 2 ) = 9.f; //获得矩阵元素(0,2)的值 float *p = (float*)cvPtr2D(mat, 0, 2); printf("%f\n",*p); return 0; }