opencv 矩陣學習

1.初始化矩陣：

方式一、逐點賦值式：

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、連接現有數組式：

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，因爲數據內存分配是由double定義的數組進行的。

2.IplImage 到cvMat的轉換

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)轉化爲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);
}
寫成函數爲：
// 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.圖像直接操作
方式一：直接數組操作 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
數組的直接操作比較鬱悶，這是由於其決定於數組的數據類型。

對於CV_32FC1 (1 channel float)：
CvMat* M = cvCreateMat( 4, 4, CV_32FC1 );
M->data.fl[ row * M->cols + col ] = (float)3.0;

對於CV_64FC1 (1 channel double)：
CvMat* M = cvCreateMat( 4, 4, CV_64FC1 );
M->data.db[ row * M->cols + col ] = 3.0;

一般的，對於1通道的數組：
CvMat* M = cvCreateMat( 4, 4, CV_64FC1 );
CV_MAT_ELEM( *M, double, row, col ) = 3.0;
注意double要根據數組的數據類型來傳入，這個宏對多通道無能爲力。

對於多通道：
看看這個宏的定義：#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;
更優化的方法是：
   #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];
        }
    }
}

對於多通道的數組，以下操作是推薦的：
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;
        }
    }
對於兩通道和四通道而言：
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.間接訪問cvMat
cvmGet/Set是訪問CV_32FC1 和 CV_64FC1型數組的最簡便的方式，其訪問速度和直接訪問幾乎相同
cvmSet( mat, row, col, value );
cvmGet( mat, row, col );
舉例：打印一個數組
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" );
    }
}

而對於其他的，比如是多通道的後者是其他數據類型的，cvGet/Set2D是個不錯的選擇
CvScalar scalar = cvGet2D( mat, row, col );
cvSet2D( mat, row, col, cvScalar( r, g, b ) );

注意：數據不能爲int，因爲cvGet2D得到的實質是double類型。
舉例：打印一個多通道矩陣：
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.修改矩陣的形狀——cvReshape的操作
經實驗表明矩陣操作的進行的順序是：首先滿足通道，然後滿足列，最後是滿足行。
注意：這和Matlab是不同的，Matlab是行、列、通道的順序。
我們在此舉例如下：
對於一通道：
 // 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
 
對於三通道
// 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.計算色彩距離
我們要計算img1,img2的每個像素的距離，用dist表示，定義如下
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 );
比較笨的思路是：cvSplit->cvSub->cvMul->cvAdd
代碼如下：
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 );

比較聰明的思路是
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);//將矩陣置0
//爲矩陣元素賦值
  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;
//獲得矩陣元素(0,2)的值
  float *p = (float*)cvPtr2D(mat, 0, 2);
  printf("%f/n",*p);
      return 0;
}