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#include "_cv.h"
void
icvCrossCorr( const CvArr* _img, const CvArr* _templ, CvArr* _corr, CvPoint anchor )
{
const double block_scale = 4.5;
const int min_block_size = 256;
CvMat* dft_img[CV_MAX_THREADS] = {0};
CvMat* dft_templ = 0;
void* buf[CV_MAX_THREADS] = {0};
int k, num_threads = 0;
CV_FUNCNAME( "icvCrossCorr" );
__BEGIN__;
CvMat istub, *img = (CvMat*)_img;
CvMat tstub, *templ = (CvMat*)_templ;
CvMat cstub, *corr = (CvMat*)_corr;
CvSize dftsize, blocksize;
int depth, templ_depth, corr_depth, max_depth = CV_32F,
cn, templ_cn, corr_cn, buf_size = 0,
tile_count_x, tile_count_y, tile_count;
CV_CALL( img = cvGetMat( img, &istub ));
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_16U &&
CV_MAT_DEPTH( img->type ) != CV_32F )
CV_ERROR( CV_StsUnsupportedFormat,
"The function supports only 8u, 16u and 32f data types" );
if( !CV_ARE_DEPTHS_EQ( img, templ ) && CV_MAT_DEPTH( templ->type ) != CV_32F )
CV_ERROR( CV_StsUnsupportedFormat,
"Template (kernel) must be of the same depth as the input image, or be 32f" );
if( !CV_ARE_DEPTHS_EQ( img, corr ) && CV_MAT_DEPTH( corr->type ) != CV_32F &&
CV_MAT_DEPTH( corr->type ) != CV_64F )
CV_ERROR( CV_StsUnsupportedFormat,
"The output image must have the same depth as the input image, or be 32f/64f" );
if( (!CV_ARE_CNS_EQ( img, corr ) || CV_MAT_CN(templ->type) > 1) &&
(CV_MAT_CN( corr->type ) > 1 || !CV_ARE_CNS_EQ( img, templ)) )
CV_ERROR( CV_StsUnsupportedFormat,
"The output must have the same number of channels as the input (when the template has 1 channel), "
"or the output must have 1 channel when the input and the template have the same number of channels" );
depth = CV_MAT_DEPTH(img->type);
cn = CV_MAT_CN(img->type);
templ_depth = CV_MAT_DEPTH(templ->type);
templ_cn = CV_MAT_CN(templ->type);
corr_depth = CV_MAT_DEPTH(corr->type);
corr_cn = CV_MAT_CN(corr->type);
max_depth = MAX( max_depth, templ_depth );
max_depth = MAX( max_depth, depth );
max_depth = MAX( max_depth, corr_depth );
if( depth > CV_8U )
max_depth = CV_64F;
if( img->cols < templ->cols || img->rows < templ->rows )
CV_ERROR( CV_StsUnmatchedSizes,
"Such a combination of image and template/filter size is not supported" );
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)" );
blocksize.width = cvRound(templ->cols*block_scale);
blocksize.width = MAX( blocksize.width, min_block_size - templ->cols + 1 );
blocksize.width = MIN( blocksize.width, corr->cols );
blocksize.height = cvRound(templ->rows*block_scale);
blocksize.height = MAX( blocksize.height, min_block_size - templ->rows + 1 );
blocksize.height = MIN( blocksize.height, corr->rows );
dftsize.width = cvGetOptimalDFTSize(blocksize.width + templ->cols - 1);
if( dftsize.width == 1 )
dftsize.width = 2;
dftsize.height = cvGetOptimalDFTSize(blocksize.height + templ->rows - 1);
if( dftsize.width <= 0 || dftsize.height <= 0 )
CV_ERROR( CV_StsOutOfRange, "the input arrays are too big" );
// recompute block size
blocksize.width = dftsize.width - templ->cols + 1;
blocksize.width = MIN( blocksize.width, corr->cols );
blocksize.height = dftsize.height - templ->rows + 1;
blocksize.height = MIN( blocksize.height, corr->rows );
CV_CALL( dft_templ = cvCreateMat( dftsize.height*templ_cn, dftsize.width, max_depth ));
num_threads = cvGetNumThreads();
for( k = 0; k < num_threads; k++ )
CV_CALL( dft_img[k] = cvCreateMat( dftsize.height, dftsize.width, max_depth ));
if( templ_cn > 1 && templ_depth != max_depth )
buf_size = templ->cols*templ->rows*CV_ELEM_SIZE(templ_depth);
if( cn > 1 && depth != max_depth )
buf_size = MAX( buf_size, (blocksize.width + templ->cols - 1)*
(blocksize.height + templ->rows - 1)*CV_ELEM_SIZE(depth));
if( (corr_cn > 1 || cn > 1) && corr_depth != max_depth )
buf_size = MAX( buf_size, blocksize.width*blocksize.height*CV_ELEM_SIZE(corr_depth));
if( buf_size > 0 )
{
for( k = 0; k < num_threads; k++ )
CV_CALL( buf[k] = cvAlloc(buf_size) );
}
// compute DFT of each template plane
for( k = 0; k < templ_cn; k++ )
{
CvMat dstub, *src, *dst, temp;
CvMat* planes[] = { 0, 0, 0, 0 };
int yofs = k*dftsize.height;
src = templ;
dst = cvGetSubRect( dft_templ, &dstub, cvRect(0,yofs,templ->cols,templ->rows));
if( templ_cn > 1 )
{
planes[k] = templ_depth == max_depth ? dst :
cvInitMatHeader( &temp, templ->rows, templ->cols, templ_depth, buf[0] );
cvSplit( templ, planes[0], planes[1], planes[2], planes[3] );
src = planes[k];
planes[k] = 0;
}
if( dst != src )
cvConvert( src, dst );
if( dft_templ->cols > templ->cols )
{
cvGetSubRect( dft_templ, dst, cvRect(templ->cols, yofs,
dft_templ->cols - templ->cols, templ->rows) );
cvZero( dst );
}
cvGetSubRect( dft_templ, dst, cvRect(0,yofs,dftsize.width,dftsize.height) );
cvDFT( dst, dst, CV_DXT_FORWARD + CV_DXT_SCALE, templ->rows );
}
tile_count_x = (corr->cols + blocksize.width - 1)/blocksize.width;
tile_count_y = (corr->rows + blocksize.height - 1)/blocksize.height;
tile_count = tile_count_x*tile_count_y;
{
#ifdef _OPENMP
#pragma omp parallel for num_threads(num_threads) schedule(dynamic)
#endif
// calculate correlation by blocks
for( k = 0; k < tile_count; k++ )
{
int thread_idx = cvGetThreadNum();
int x = (k%tile_count_x)*blocksize.width;
int y = (k/tile_count_x)*blocksize.height;
int i, yofs;
CvMat sstub, dstub, *src, *dst, temp;
CvMat* planes[] = { 0, 0, 0, 0 };
CvMat* _dft_img = dft_img[thread_idx];
void* _buf = buf[thread_idx];
CvSize csz = { blocksize.width, blocksize.height }, isz;
int x0 = x - anchor.x, y0 = y - anchor.y;
int x1 = MAX( 0, x0 ), y1 = MAX( 0, y0 ), x2, y2;
csz.width = MIN( csz.width, corr->cols - x );
csz.height = MIN( csz.height, corr->rows - y );
isz.width = csz.width + templ->cols - 1;
isz.height = csz.height + templ->rows - 1;
x2 = MIN( img->cols, x0 + isz.width );
y2 = MIN( img->rows, y0 + isz.height );
for( i = 0; i < cn; i++ )
{
CvMat dstub1, *dst1;
yofs = i*dftsize.height;
src = cvGetSubRect( img, &sstub, cvRect(x1,y1,x2-x1,y2-y1) );
dst = cvGetSubRect( _dft_img, &dstub,
cvRect(0,0,isz.width,isz.height) );
dst1 = dst;
if( x2 - x1 < isz.width || y2 - y1 < isz.height )
dst1 = cvGetSubRect( _dft_img, &dstub1,
cvRect( x1 - x0, y1 - y0, x2 - x1, y2 - y1 ));
if( cn > 1 )
{
planes[i] = dst1;
if( depth != max_depth )
planes[i] = cvInitMatHeader( &temp, y2 - y1, x2 - x1, depth, _buf );
cvSplit( src, planes[0], planes[1], planes[2], planes[3] );
src = planes[i];
planes[i] = 0;
}
if( dst1 != src )
cvConvert( src, dst1 );
if( dst != dst1 )
cvCopyMakeBorder( dst1, dst, cvPoint(x1 - x0, y1 - y0), IPL_BORDER_REPLICATE );
if( dftsize.width > isz.width )
{
cvGetSubRect( _dft_img, dst, cvRect(isz.width, 0,
dftsize.width - isz.width,dftsize.height) );
cvZero( dst );
}
cvDFT( _dft_img, _dft_img, CV_DXT_FORWARD, isz.height );
cvGetSubRect( dft_templ, dst,
cvRect(0,(templ_cn>1?yofs:0),dftsize.width,dftsize.height) );
cvMulSpectrums( _dft_img, dst, _dft_img, CV_DXT_MUL_CONJ );
cvDFT( _dft_img, _dft_img, CV_DXT_INVERSE, csz.height );
src = cvGetSubRect( _dft_img, &sstub, cvRect(0,0,csz.width,csz.height) );
dst = cvGetSubRect( corr, &dstub, cvRect(x,y,csz.width,csz.height) );
if( corr_cn > 1 )
{
planes[i] = src;
if( corr_depth != max_depth )
{
planes[i] = cvInitMatHeader( &temp, csz.height, csz.width,
corr_depth, _buf );
cvConvert( src, planes[i] );
}
cvMerge( planes[0], planes[1], planes[2], planes[3], dst );
planes[i] = 0;
}
else
{
if( i == 0 )
cvConvert( src, dst );
else
{
if( max_depth > corr_depth )
{
cvInitMatHeader( &temp, csz.height, csz.width,
corr_depth, _buf );
cvConvert( src, &temp );
src = &temp;
}
cvAcc( src, dst );
}
}
}
}
}
__END__;
cvReleaseMat( &dft_templ );
for( k = 0; k < num_threads; k++ )
{
cvReleaseMat( &dft_img[k] );
cvFree( &buf[k] );
}
}
/***************************** 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( img->rows < templ->rows || img->cols < templ->cols )
{
CvMat* t;
CV_SWAP( img, templ, t );
}
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]);
if( templ_norm < DBL_EPSILON && method == CV_TM_CCOEFF_NORMED )
{
cvSet( result, cvScalarAll(1.) );
EXIT;
}
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(MAX(wnd_sum2 - wnd_mean2,0))*templ_norm;
if( t > DBL_EPSILON )
{
num /= t;
if( fabs(num) > 1. )
num = num > 0 ? 1 : -1;
}
else
num = method != CV_TM_SQDIFF_NORMED || num < DBL_EPSILON ? 0 : 1;
}
rrow[j] = (float)num;
}
}
__END__;
cvReleaseMat( &sum );
cvReleaseMat( &sqsum );
}
/* End of file. */