OpenCV学习之CvMat的用法详解及实例 目 录 1.初始化矩阵：...................................2.IplImage 到cvMat的转换.........................3.cvArr(IplImage或者cvMat)转化为cvMat...........4.图像直接操作...................................5.cvMat的直接操作................................6.间接访问cvMat..................................7.修改矩阵的形状——cvReshape的操作..............8.计算色彩距离...................................2 2 2 3 3 5 6 8 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 到cvMat的转换 方式一、cvGetMat方式： CvMat mathdr, *mat = cvGetMat( img, &mathdr ); 方式二、cvConvert方式： CvMat *mat = cvCreateMat( img->height, img->width, CV_64FC3 ); cvConvert( img, mat ); // #define cvConvert( src, dst ) cvConvertScale( (src), (dst), 1, 0 ) 3.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__; } 4.图像直接操作 方式一：直接数组操作 int col, row, z; uchar b, g, r; for( y = 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 ) 5.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1通道的数组对于CV_64FC1 (1 channel double)： 的读取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); 6.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 \ } 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\" ); } } 7.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 // 3 channels 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 ); 8.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：cvSplit->cvSub->cvMul->cvAdd 代码如下： 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
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\ return 0; }