www.pudn.com > OpenCV-Intel.zip > cvtemplmatch.cpp
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#include "_cv.h"
#define ICV_DECL_CROSSCORR_DIRECT( flavor, arrtype, corrtype, worktype ) \
static CvStatus CV_STDCALL \
icvCrossCorrDirect_##flavor##_CnR( const arrtype* img0, int imgstep, \
CvSize imgsize, const arrtype* templ0, int templstep, CvSize templsize, \
corrtype* corr, int corrstep, CvSize corrsize, int cn ) \
{ \
int x, i, j; \
double* corrrow = 0; \
\
if( templsize.height > 1 ) \
corrrow = (double*)cvStackAlloc( corrsize.width*sizeof(corrrow[0]));\
templsize.width *= cn; \
imgsize.width *= cn; \
corrstep /= sizeof(corr[0]); \
imgstep /= sizeof(img0[0]); \
templstep /= sizeof(templ0[0]); \
\
for( ; corrsize.height--; corr += corrstep, img0 += imgstep ) \
{ \
const arrtype* img = img0; \
const arrtype* templ = templ0; \
\
for( i = 0; i < templsize.height; i++, img += imgstep, \
templ += templstep ) \
{ \
for( x = 0; x < corrsize.width; x++, img += cn ) \
{ \
worktype sum = 0; \
int len = MIN( templsize.width, imgsize.width - x*cn ); \
double t; \
for( j = 0; j <= len - 4; j += 4 ) \
sum += (worktype)(img[j])*templ[j] + \
(worktype)(img[j+1])*templ[j+1] + \
(worktype)(img[j+2])*templ[j+2] + \
(worktype)(img[j+3])*templ[j+3]; \
\
for( ; j < len; j++ ) \
sum += (worktype)(img[j])*templ[j]; \
t = sum; \
if( i > 0 ) \
t += corrrow[x]; \
if( i < templsize.height-1 ) \
corrrow[x] = t; \
else \
corr[x] = (corrtype)t; \
} \
\
img -= corrsize.width*cn; \
} \
} \
\
return CV_OK; \
}
ICV_DECL_CROSSCORR_DIRECT( 8u32f, uchar, float, int )
ICV_DECL_CROSSCORR_DIRECT( 32f, float, float, double )
ICV_DECL_CROSSCORR_DIRECT( 64f, double, double, double )
typedef CvStatus (CV_STDCALL * CvCrossCorrDirectFunc)(
const void* img, int imgstep, CvSize imgsize,
const void* templ, int templstep, CvSize templsize,
void* corr, int corrstep, CvSize corrsize, int cn );
static void
icvCrossCorr( const CvArr* _img, const CvArr* _templ, CvArr* _corr )
{
CvMat* dft_img = 0;
CvMat* dft_templ = 0;
CvMat* temp = 0;
CV_FUNCNAME( "cvCrossCorr" );
__BEGIN__;
CvMat stub, *img = (CvMat*)_img;
CvMat tstub, *templ = (CvMat*)_templ;
CvMat cstub, *corr = (CvMat*)_corr;
CvSize imgsize, templsize, corrsize, dftsize;
int i, depth, cn, corr_type;
double O_direct, O_fast;
CV_CALL( img = cvGetMat( img, &stub ));
CV_CALL( templ = cvGetMat( templ, &tstub ));
CV_CALL( corr = cvGetMat( corr, &cstub ));
if( CV_MAT_DEPTH( img->type ) != CV_8U &&
CV_MAT_DEPTH( img->type ) != CV_32F )
CV_ERROR( CV_StsUnsupportedFormat,
"The function supports only 8u and 32f data types" );
if( CV_MAT_TYPE( corr->type ) != CV_32FC1 &&
CV_MAT_TYPE( corr->type ) != CV_64FC1 )
CV_ERROR( CV_StsUnsupportedFormat,
"The correlation output should be single-channel floating-point array" );
if( !CV_ARE_TYPES_EQ( img, templ ))
CV_ERROR( CV_StsUnmatchedSizes, "image and template should have the same type" );
if( img->cols < templ->cols || img->rows < templ->rows )
{
CvMat* t;
CV_SWAP( img, templ, t );
}
if( img->cols < templ->cols || img->rows < templ->rows )
CV_ERROR( CV_StsUnmatchedSizes,
"Neither of the two input arrays is smaller than the other" );
if( corr->rows > img->rows + templ->rows - 1 ||
corr->cols > img->cols + templ->cols - 1 )
CV_ERROR( CV_StsUnmatchedSizes,
"output image should not be greater than (W + w - 1)x(H + h - 1)" );
depth = CV_MAT_DEPTH(img->type);
cn = CV_MAT_CN(img->type);
corr_type = CV_MAT_TYPE(corr->type);
imgsize = cvGetMatSize(img);
templsize = cvGetSize(templ);
corrsize = cvGetSize(corr);
dftsize.width = cvGetOptimalDFTSize(imgsize.width + templsize.width - 1);
if( dftsize.width == 1 )
dftsize.width = 2;
dftsize.height = cvGetOptimalDFTSize(imgsize.height + templsize.height - 1);
if( dftsize.width <= 0 || dftsize.height <= 0 )
CV_ERROR( CV_StsOutOfRange, "the input arrays are too big" );
// determine which method to use for correlation.
O_direct = (double)corrsize.width * corrsize.height *
(double)templsize.width * templsize.height; // approximate formulae
// calculate it in two steps to avoid icc remark
O_fast = (imgsize.height + templsize.height + corrsize.height)*
(double)dftsize.width*log((double)dftsize.width);
O_fast = (O_fast + 3*dftsize.width*(double)dftsize.height*log((double)dftsize.height))/CV_LOG2;
if( O_direct < O_fast )
{
CvCrossCorrDirectFunc corr_func = 0;
if( depth == CV_8U && corr_type == CV_32F )
corr_func = (CvCrossCorrDirectFunc)icvCrossCorrDirect_8u32f_CnR;
else if( depth == CV_32F && corr_type == CV_32F )
corr_func = (CvCrossCorrDirectFunc)icvCrossCorrDirect_32f_CnR;
else if( depth == CV_64F && corr_type == CV_64F )
corr_func = (CvCrossCorrDirectFunc)icvCrossCorrDirect_64f_CnR;
else
CV_ERROR( CV_StsUnsupportedFormat,
"Unsupported combination of input and output formats" );
IPPI_CALL( corr_func( img->data.ptr, img->step, imgsize,
templ->data.ptr, templ->step, templsize,
corr->data.ptr, corr->step, corrsize, cn ));
}
else
{
CV_CALL( dft_img = cvCreateMat( dftsize.height, dftsize.width, corr_type ));
CV_CALL( dft_templ = cvCreateMat( dftsize.height, dftsize.width, corr_type ));
if( cn > 1 && depth != CV_MAT_DEPTH(corr_type) )
{
CV_CALL( temp = cvCreateMat( imgsize.height, imgsize.width, depth ));
}
for( i = 0; i < cn; i++ )
{
CvMat dftstub, srcstub;
CvMat* src = img, *dst = &dftstub;
CvMat* planes[] = { 0, 0, 0, 0 };
cvGetSubRect( dft_img, dst, cvRect(0,0,img->cols,img->rows));
if( cn > 1 )
{
planes[i] = temp ? temp : dst;
cvSplit( img, planes[0], planes[1], planes[2], planes[3] );
src = planes[i];
}
if( dst != src )
{
if( CV_ARE_TYPES_EQ(src, dst) )
cvCopy( src, dst );
else
cvConvert( src, dst );
}
cvGetSubRect( dft_img, dst, cvRect(img->cols, 0,
dft_img->cols - img->cols, img->rows) );
cvZero( dst );
cvDFT( dft_img, dft_img, CV_DXT_FORWARD, img->rows );
src = templ;
cvGetSubRect( dft_templ, dst, cvRect(0,0,templ->cols,templ->rows));
if( cn > 1 )
{
planes[i] = dst;
if( temp )
{
planes[i] = &srcstub;
cvGetSubRect( temp, planes[i], cvRect(0,0,templ->cols,templ->rows) );
}
cvSplit( templ, planes[0], planes[1], planes[2], planes[3] );
src = planes[i];
}
if( dst != src )
{
if( CV_ARE_TYPES_EQ(src, dst) )
cvCopy( src, dst );
else
cvConvert( src, dst );
}
cvGetSubRect( dft_templ, dst, cvRect(templ->cols, 0,
dft_templ->cols - templ->cols, templ->rows) );
cvZero( dst );
cvDFT( dft_templ, dft_templ, CV_DXT_FORWARD, templ->rows );
cvMulSpectrums( dft_img, dft_templ, dft_img, CV_DXT_MUL_CONJ );
cvDFT( dft_img, dft_img, CV_DXT_INV_SCALE );
cvGetSubRect( dft_img, dst, cvRect(0,0,corr->cols,corr->rows) );
if( i == 0 )
cvCopy( dst, corr );
else
cvAdd( corr, dst, corr );
}
}
__END__;
cvReleaseMat( &dft_img );
cvReleaseMat( &dft_templ );
cvReleaseMat( &temp );
}
/***************************** IPP Match Template Functions ******************************/
icvCrossCorrValid_Norm_8u32f_C1R_t icvCrossCorrValid_Norm_8u32f_C1R_p = 0;
icvCrossCorrValid_NormLevel_8u32f_C1R_t icvCrossCorrValid_NormLevel_8u32f_C1R_p = 0;
icvSqrDistanceValid_Norm_8u32f_C1R_t icvSqrDistanceValid_Norm_8u32f_C1R_p = 0;
icvCrossCorrValid_Norm_32f_C1R_t icvCrossCorrValid_Norm_32f_C1R_p = 0;
icvCrossCorrValid_NormLevel_32f_C1R_t icvCrossCorrValid_NormLevel_32f_C1R_p = 0;
icvSqrDistanceValid_Norm_32f_C1R_t icvSqrDistanceValid_Norm_32f_C1R_p = 0;
typedef CvStatus (CV_STDCALL * CvTemplMatchIPPFunc)
( const void* img, int imgstep, CvSize imgsize,
const void* templ, int templstep, CvSize templsize,
void* result, int rstep );
/*****************************************************************************************/
CV_IMPL void
cvMatchTemplate( const CvArr* _img, const CvArr* _templ, CvArr* _result, int method )
{
CvMat* sum = 0;
CvMat* sqsum = 0;
CV_FUNCNAME( "cvMatchTemplate" );
__BEGIN__;
int coi1 = 0, coi2 = 0;
int depth, cn;
int i, j, k;
CvMat stub, *img = (CvMat*)_img;
CvMat tstub, *templ = (CvMat*)_templ;
CvMat rstub, *result = (CvMat*)_result;
CvScalar templ_mean = cvScalarAll(0);
double templ_norm = 0, templ_sum2 = 0;
int idx = 0, idx2 = 0;
double *p0, *p1, *p2, *p3;
double *q0, *q1, *q2, *q3;
double inv_area;
int sum_step, sqsum_step;
int num_type = method == CV_TM_CCORR || method == CV_TM_CCORR_NORMED ? 0 :
method == CV_TM_CCOEFF || method == CV_TM_CCOEFF_NORMED ? 1 : 2;
int is_normed = method == CV_TM_CCORR_NORMED ||
method == CV_TM_SQDIFF_NORMED ||
method == CV_TM_CCOEFF_NORMED;
CV_CALL( img = cvGetMat( img, &stub, &coi1 ));
CV_CALL( templ = cvGetMat( templ, &tstub, &coi2 ));
CV_CALL( result = cvGetMat( result, &rstub ));
if( CV_MAT_DEPTH( img->type ) != CV_8U &&
CV_MAT_DEPTH( img->type ) != CV_32F )
CV_ERROR( CV_StsUnsupportedFormat,
"The function supports only 8u and 32f data types" );
if( !CV_ARE_TYPES_EQ( img, templ ))
CV_ERROR( CV_StsUnmatchedSizes, "image and template should have the same type" );
if( CV_MAT_TYPE( result->type ) != CV_32FC1 )
CV_ERROR( CV_StsUnsupportedFormat, "output image should have 32f type" );
if( result->rows != img->rows - templ->rows + 1 ||
result->cols != img->cols - templ->cols + 1 )
CV_ERROR( CV_StsUnmatchedSizes, "output image should be (W - w + 1)x(H - h + 1)" );
if( method < CV_TM_SQDIFF || method > CV_TM_CCOEFF_NORMED )
CV_ERROR( CV_StsBadArg, "unknown comparison method" );
depth = CV_MAT_DEPTH(img->type);
cn = CV_MAT_CN(img->type);
if( is_normed && cn == 1 && templ->rows > 8 && templ->cols > 8 &&
img->rows > templ->cols && img->cols > templ->cols )
{
CvTemplMatchIPPFunc ipp_func =
depth == CV_8U ?
(method == CV_TM_SQDIFF_NORMED ? (CvTemplMatchIPPFunc)icvSqrDistanceValid_Norm_8u32f_C1R_p :
method == CV_TM_CCORR_NORMED ? (CvTemplMatchIPPFunc)icvCrossCorrValid_Norm_8u32f_C1R_p :
(CvTemplMatchIPPFunc)icvCrossCorrValid_NormLevel_8u32f_C1R_p) :
(method == CV_TM_SQDIFF_NORMED ? (CvTemplMatchIPPFunc)icvSqrDistanceValid_Norm_32f_C1R_p :
method == CV_TM_CCORR_NORMED ? (CvTemplMatchIPPFunc)icvCrossCorrValid_Norm_32f_C1R_p :
(CvTemplMatchIPPFunc)icvCrossCorrValid_NormLevel_32f_C1R_p);
if( ipp_func )
{
CvSize img_size = cvGetMatSize(img), templ_size = cvGetMatSize(templ);
IPPI_CALL( ipp_func( img->data.ptr, img->step ? img->step : CV_STUB_STEP,
img_size, templ->data.ptr,
templ->step ? templ->step : CV_STUB_STEP,
templ_size, result->data.ptr,
result->step ? result->step : CV_STUB_STEP ));
for( i = 0; i < result->rows; i++ )
{
float* rrow = (float*)(result->data.ptr + i*result->step);
for( j = 0; j < result->cols; j++ )
{
if( fabs(rrow[j]) > 1. )
rrow[j] = rrow[j] < 0 ? -1.f : 1.f;
}
}
EXIT;
}
}
CV_CALL( icvCrossCorr( img, templ, result ));
if( method == CV_TM_CCORR )
EXIT;
inv_area = 1./((double)templ->rows * templ->cols);
CV_CALL( sum = cvCreateMat( img->rows + 1, img->cols + 1,
CV_MAKETYPE( CV_64F, cn )));
if( method == CV_TM_CCOEFF )
{
CV_CALL( cvIntegral( img, sum, 0, 0 ));
CV_CALL( templ_mean = cvAvg( templ ));
q0 = q1 = q2 = q3 = 0;
}
else
{
CvScalar _templ_sdv = cvScalarAll(0);
CV_CALL( sqsum = cvCreateMat( img->rows + 1, img->cols + 1,
CV_MAKETYPE( CV_64F, cn )));
CV_CALL( cvIntegral( img, sum, sqsum, 0 ));
CV_CALL( cvAvgSdv( templ, &templ_mean, &_templ_sdv ));
templ_norm = CV_SQR(_templ_sdv.val[0]) + CV_SQR(_templ_sdv.val[1]) +
CV_SQR(_templ_sdv.val[2]) + CV_SQR(_templ_sdv.val[3]);
templ_sum2 = templ_norm +
CV_SQR(templ_mean.val[0]) + CV_SQR(templ_mean.val[1]) +
CV_SQR(templ_mean.val[2]) + CV_SQR(templ_mean.val[3]);
if( num_type != 1 )
{
templ_mean = cvScalarAll(0);
templ_norm = templ_sum2;
}
templ_sum2 /= inv_area;
templ_norm = sqrt(templ_norm);
templ_norm /= sqrt(inv_area); // care of accuracy here
q0 = (double*)sqsum->data.ptr;
q1 = q0 + templ->cols*cn;
q2 = (double*)(sqsum->data.ptr + templ->rows*sqsum->step);
q3 = q2 + templ->cols*cn;
}
p0 = (double*)sum->data.ptr;
p1 = p0 + templ->cols*cn;
p2 = (double*)(sum->data.ptr + templ->rows*sum->step);
p3 = p2 + templ->cols*cn;
sum_step = sum ? sum->step / sizeof(double) : 0;
sqsum_step = sqsum ? sqsum->step / sizeof(double) : 0;
for( i = 0; i < result->rows; i++ )
{
float* rrow = (float*)(result->data.ptr + i*result->step);
idx = i * sum_step;
idx2 = i * sqsum_step;
for( j = 0; j < result->cols; j++, idx += cn, idx2 += cn )
{
double num = rrow[j], t;
double wnd_mean2 = 0, wnd_sum2 = 0;
if( num_type == 1 )
{
for( k = 0; k < cn; k++ )
{
t = p0[idx+k] - p1[idx+k] - p2[idx+k] + p3[idx+k];
wnd_mean2 += CV_SQR(t);
num -= t*templ_mean.val[k];
}
wnd_mean2 *= inv_area;
}
if( is_normed || num_type == 2 )
{
for( k = 0; k < cn; k++ )
{
t = q0[idx2+k] - q1[idx2+k] - q2[idx2+k] + q3[idx2+k];
wnd_sum2 += t;
}
if( num_type == 2 )
num = wnd_sum2 - 2*num + templ_sum2;
}
if( is_normed )
{
t = sqrt(wnd_sum2 - wnd_mean2)*templ_norm;
if( fabs(t) < DBL_EPSILON )
num = num_type == 2 ? 1 : -1;
else
{
num /= t;
if( fabs(num) > 1. )
num = num > 0 ? 1 : -1;
}
}
rrow[j] = (float)num;
}
}
__END__;
cvReleaseMat( &sum );
cvReleaseMat( &sqsum );
}
/* End of file. */