Google Git
Sign in
ara-mdk / platform / external / opencv / 6acb9a7ea3d7564944e12cbc73a857b88c1301ee / . / cxcore / src / cxmatmul.cpp
blob: f6d31425ce0ed6ce16d753eb10c4f6d625f7410b [file] [log] [blame]
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "_cxcore.h"
/****************************************************************************************\
* cvGEMM *
\****************************************************************************************/
icvBLAS_GEMM_32f_t icvBLAS_GEMM_32f_p = 0;
icvBLAS_GEMM_64f_t icvBLAS_GEMM_64f_p = 0;
icvBLAS_GEMM_32fc_t icvBLAS_GEMM_32fc_p = 0;
icvBLAS_GEMM_64fc_t icvBLAS_GEMM_64fc_p = 0;
static void
icvGEMM_CopyBlock( const uchar* src, int src_step,
uchar* dst, int dst_step,
CvSize size, int pix_size )
{
int j;
size.width = size.width * (pix_size / sizeof(int));
for( ; size.height--; src += src_step, dst += dst_step )
{
for( j = 0; j <= size.width - 4; j += 4 )
{
int t0 = ((const int*)src)[j];
int t1 = ((const int*)src)[j+1];
((int*)dst)[j] = t0;
((int*)dst)[j+1] = t1;
t0 = ((const int*)src)[j+2];
t1 = ((const int*)src)[j+3];
((int*)dst)[j+2] = t0;
((int*)dst)[j+3] = t1;
}
for( ; j < size.width; j++ )
((int*)dst)[j] = ((const int*)src)[j];
}
}
static void
icvGEMM_TransposeBlock( const uchar* src, int src_step,
uchar* dst, int dst_step,
CvSize size, int pix_size )
{
int i, j;
for( i = 0; i < size.width; i++, dst += dst_step, src += pix_size )
{
const uchar* _src = src;
switch( pix_size )
{
case sizeof(int):
for( j = 0; j < size.height; j++, _src += src_step )
((int*)dst)[j] = ((int*)_src)[0];
break;
case sizeof(int)*2:
for( j = 0; j < size.height*2; j += 2, _src += src_step )
{
int t0 = ((int*)_src)[0];
int t1 = ((int*)_src)[1];
((int*)dst)[j] = t0;
((int*)dst)[j+1] = t1;
}
break;
case sizeof(int)*4:
for( j = 0; j < size.height*4; j += 4, _src += src_step )
{
int t0 = ((int*)_src)[0];
int t1 = ((int*)_src)[1];
((int*)dst)[j] = t0;
((int*)dst)[j+1] = t1;
t0 = ((int*)_src)[2];
t1 = ((int*)_src)[3];
((int*)dst)[j+2] = t0;
((int*)dst)[j+3] = t1;
}
break;
default:
assert(0);
return;
}
}
}
#define ICV_DEF_GEMM_SINGLE_MUL( flavor, arrtype, worktype ) \
static CvStatus CV_STDCALL \
icvGEMMSingleMul_##flavor( const arrtype* a_data, size_t a_step, \
const arrtype* b_data, size_t b_step, \
const arrtype* c_data, size_t c_step, \
arrtype* d_data, size_t d_step, \
CvSize a_size, CvSize d_size, \
double alpha, double beta, int flags ) \
{ \
int i, j, k, n = a_size.width, m = d_size.width, drows = d_size.height; \
const arrtype *_a_data = a_data, *_b_data = b_data, *_c_data = c_data; \
arrtype* a_buf = 0; \
size_t a_step0, a_step1, c_step0, c_step1, t_step; \
\
a_step /= sizeof(a_data[0]); \
b_step /= sizeof(b_data[0]); \
c_step /= sizeof(c_data[0]); \
d_step /= sizeof(d_data[0]); \
a_step0 = a_step; \
a_step1 = 1; \
\
if( !c_data ) \
c_step0 = c_step1 = 0; \
else if( !(flags & CV_GEMM_C_T) ) \
c_step0 = c_step, c_step1 = 1; \
else \
c_step0 = 1, c_step1 = c_step; \
\
if( flags & CV_GEMM_A_T ) \
{ \
CV_SWAP( a_step0, a_step1, t_step ); \
n = a_size.height; \
if( a_step > 1 && n > 1 ) \
a_buf = (arrtype*)cvStackAlloc(n*sizeof(a_data[0])); \
} \
\
if( n == 1 ) /* external product */ \
{ \
arrtype* b_buf = 0; \
\
if( a_step > 1 ) \
{ \
a_buf = (arrtype*)cvStackAlloc(drows*sizeof(a_data[0])); \
for( k = 0; k < drows; k++ ) \
a_buf[k] = a_data[a_step*k]; \
a_data = a_buf; \
} \
\
if( b_step > 1 ) \
{ \
b_buf = (arrtype*)cvStackAlloc(d_size.width*sizeof(b_buf[0]) ); \
for( j = 0; j < d_size.width; j++ ) \
b_buf[j] = b_data[j*b_step]; \
b_data = b_buf; \
} \
\
for( i = 0; i < drows; i++, _c_data += c_step0, \
d_data += d_step ) \
{ \
worktype al = worktype(a_data[i])*alpha; \
c_data = _c_data; \
for( j = 0; j <= d_size.width - 2; j += 2, c_data += 2*c_step1 )\
{ \
worktype s0 = al*b_data[j]; \
worktype s1 = al*b_data[j+1]; \
if( !c_data ) \
{ \
d_data[j] = arrtype(s0); \
d_data[j+1] = arrtype(s1); \
} \
else \
{ \
d_data[j] = arrtype(s0 + c_data[0]*beta); \
d_data[j+1] = arrtype(s1 + c_data[c_step1]*beta); \
} \
} \
\
for( ; j < d_size.width; j++, c_data += c_step1 ) \
{ \
worktype s0 = al*b_data[j]; \
if( !c_data ) \
d_data[j] = arrtype(s0); \
else \
d_data[j] = arrtype(s0 + c_data[0]*beta); \
} \
} \
} \
else if( flags & CV_GEMM_B_T ) /* A * Bt */ \
{ \
for( i = 0; i < drows; i++, _a_data += a_step0, \
_c_data += c_step0, \
d_data += d_step ) \
{ \
a_data = _a_data; \
b_data = _b_data; \
c_data = _c_data; \
\
if( a_buf ) \
{ \
for( k = 0; k < n; k++ ) \
a_buf[k] = a_data[a_step1*k]; \
a_data = a_buf; \
} \
\
for( j = 0; j < d_size.width; j++, b_data += b_step, \
c_data += c_step1 ) \
{ \
worktype s0(0), s1(0), s2(0), s3(0); \
\
for( k = 0; k <= n - 4; k += 4 ) \
{ \
s0 += worktype(a_data[k])*b_data[k]; \
s1 += worktype(a_data[k+1])*b_data[k+1]; \
s2 += worktype(a_data[k+2])*b_data[k+2]; \
s3 += worktype(a_data[k+3])*b_data[k+3]; \
} \
\
for( ; k < n; k++ ) \
s0 += worktype(a_data[k])*b_data[k]; \
s0 = (s0+s1+s2+s3)*alpha; \
\
if( !c_data ) \
d_data[j] = arrtype(s0); \
else \
d_data[j] = arrtype(s0 + c_data[0]*beta); \
} \
} \
} \
else if( d_size.width*sizeof(d_data[0]) <= 1600 ) \
{ \
for( i = 0; i < drows; i++, _a_data += a_step0, \
_c_data += c_step0, \
d_data += d_step ) \
{ \
a_data = _a_data, c_data = _c_data; \
\
if( a_buf ) \
{ \
for( k = 0; k < n; k++ ) \
a_buf[k] = a_data[a_step1*k]; \
a_data = a_buf; \
} \
\
for( j = 0; j <= m - 4; j += 4, c_data += 4*c_step1 ) \
{ \
const arrtype* b = _b_data + j; \
worktype s0(0), s1(0), s2(0), s3(0); \
\
for( k = 0; k < n; k++, b += b_step ) \
{ \
worktype a(a_data[k]); \
s0 += a * b[0]; s1 += a * b[1]; \
s2 += a * b[2]; s3 += a * b[3]; \
} \
\
if( !c_data ) \
{ \
d_data[j] = arrtype(s0*alpha); \
d_data[j+1] = arrtype(s1*alpha); \
d_data[j+2] = arrtype(s2*alpha); \
d_data[j+3] = arrtype(s3*alpha); \
} \
else \
{ \
s0 = s0*alpha; s1 = s1*alpha; \
s2 = s2*alpha; s3 = s3*alpha; \
d_data[j] = arrtype(s0 + c_data[0]*beta); \
d_data[j+1] = arrtype(s1 + c_data[c_step1]*beta); \
d_data[j+2] = arrtype(s2 + c_data[c_step1*2]*beta); \
d_data[j+3] = arrtype(s3 + c_data[c_step1*3]*beta); \
} \
} \
\
for( ; j < m; j++, c_data += c_step1 ) \
{ \
const arrtype* b = _b_data + j; \
worktype s0(0); \
\
for( k = 0; k < n; k++, b += b_step ) \
s0 += worktype(a_data[k]) * b[0]; \
\
s0 = s0*alpha; \
if( !c_data ) \
d_data[j] = arrtype(s0); \
else \
d_data[j] = arrtype(s0 + c_data[0]*beta); \
} \
} \
} \
else \
{ \
worktype* d_buf = (worktype*)cvStackAlloc(m*sizeof(d_buf[0])); \
\
for( i = 0; i < drows; i++, _a_data += a_step0, \
_c_data += c_step0, \
d_data += d_step ) \
{ \
a_data = _a_data; \
b_data = _b_data; \
c_data = _c_data; \
\
if( a_buf ) \
{ \
for( k = 0; k < n; k++ ) \
a_buf[k] = _a_data[a_step1*k]; \
a_data = a_buf; \
} \
\
for( j = 0; j < m; j++ ) \
d_buf[j] = worktype(0); \
\
for( k = 0; k < n; k++, b_data += b_step ) \
{ \
worktype al(a_data[k]); \
\
for( j = 0; j <= m - 4; j += 4 ) \
{ \
worktype t0 = d_buf[j] + b_data[j]*al; \
worktype t1 = d_buf[j+1] + b_data[j+1]*al; \
d_buf[j] = t0; \
d_buf[j+1] = t1; \
t0 = d_buf[j+2] + b_data[j+2]*al; \
t1 = d_buf[j+3] + b_data[j+3]*al; \
d_buf[j+2] = t0; \
d_buf[j+3] = t1; \
} \
\
for( ; j < m; j++ ) \
d_buf[j] += b_data[j]*al; \
} \
\
if( !c_data ) \
for( j = 0; j < m; j++ ) \
d_data[j] = arrtype(d_buf[j]*alpha); \
else \
for( j = 0; j < m; j++, c_data += c_step1 ) \
{ \
worktype t = d_buf[j]*alpha; \
d_data[j] = arrtype(t + c_data[0]*beta); \
} \
} \
} \
return CV_OK; \
}
#define ICV_DEF_GEMM_BLOCK_MUL( flavor, arrtype, worktype ) \
static CvStatus CV_STDCALL \
icvGEMMBlockMul_##flavor( const arrtype* a_data, size_t a_step, \
const arrtype* b_data, size_t b_step, \
worktype* d_data, size_t d_step, \
CvSize a_size, CvSize d_size, int flags ) \
{ \
int i, j, k, n = a_size.width, m = d_size.width; \
const arrtype *_a_data = a_data, *_b_data = b_data; \
arrtype* a_buf = 0; \
size_t a_step0, a_step1, t_step; \
int do_acc = flags & 16; \
\
a_step /= sizeof(a_data[0]); \
b_step /= sizeof(b_data[0]); \
d_step /= sizeof(d_data[0]); \
\
a_step0 = a_step; \
a_step1 = 1; \
\
if( flags & CV_GEMM_A_T ) \
{ \
CV_SWAP( a_step0, a_step1, t_step ); \
n = a_size.height; \
a_buf = (arrtype*)cvStackAlloc(n*sizeof(a_data[0])); \
} \
\
if( flags & CV_GEMM_B_T ) \
{ \
/* second operand is transposed */ \
for( i = 0; i < d_size.height; i++, _a_data += a_step0, \
d_data += d_step ) \
{ \
a_data = _a_data; b_data = _b_data; \
\
if( a_buf ) \
{ \
for( k = 0; k < n; k++ ) \
a_buf[k] = a_data[a_step1*k]; \
a_data = a_buf; \
} \
\
for( j = 0; j < d_size.width; j++, b_data += b_step ) \
{ \
worktype s0 = do_acc ? d_data[j]:worktype(0), s1(0);\
for( k = 0; k <= n - 2; k += 2 ) \
{ \
s0 += worktype(a_data[k])*b_data[k]; \
s1 += worktype(a_data[k+1])*b_data[k+1]; \
} \
\
for( ; k < n; k++ ) \
s0 += worktype(a_data[k])*b_data[k]; \
\
d_data[j] = s0 + s1; \
} \
} \
} \
else \
{ \
for( i = 0; i < d_size.height; i++, _a_data += a_step0, \
d_data += d_step ) \
{ \
a_data = _a_data, b_data = _b_data; \
\
if( a_buf ) \
{ \
for( k = 0; k < n; k++ ) \
a_buf[k] = a_data[a_step1*k]; \
a_data = a_buf; \
} \
\
for( j = 0; j <= m - 4; j += 4 ) \
{ \
worktype s0, s1, s2, s3; \
const arrtype* b = b_data + j; \
\
if( do_acc ) \
{ \
s0 = d_data[j]; s1 = d_data[j+1]; \
s2 = d_data[j+2]; s3 = d_data[j+3]; \
} \
else \
s0 = s1 = s2 = s3 = worktype(0); \
\
for( k = 0; k < n; k++, b += b_step ) \
{ \
worktype a(a_data[k]); \
s0 += a * b[0]; s1 += a * b[1]; \
s2 += a * b[2]; s3 += a * b[3]; \
} \
\
d_data[j] = s0; d_data[j+1] = s1; \
d_data[j+2] = s2; d_data[j+3] = s3; \
} \
\
for( ; j < m; j++ ) \
{ \
const arrtype* b = b_data + j; \
worktype s0 = do_acc ? d_data[j] : worktype(0); \
\
for( k = 0; k < n; k++, b += b_step ) \
s0 += worktype(a_data[k]) * b[0]; \
\
d_data[j] = s0; \
} \
} \
} \
\
return CV_OK; \
}
#define ICV_DEF_GEMM_STORE( flavor, arrtype, worktype ) \
static CvStatus CV_STDCALL \
icvGEMMStore_##flavor( const arrtype* c_data, size_t c_step, \
const worktype* d_buf, size_t d_buf_step, \
arrtype* d_data, size_t d_step, CvSize d_size,\
double alpha, double beta, int flags ) \
{ \
const arrtype* _c_data = c_data; \
int j; \
size_t c_step0, c_step1; \
\
c_step /= sizeof(c_data[0]); \
d_buf_step /= sizeof(d_buf[0]); \
d_step /= sizeof(d_data[0]); \
\
if( !c_data ) \
c_step0 = c_step1 = 0; \
else if( !(flags & CV_GEMM_C_T) ) \
c_step0 = c_step, c_step1 = 1; \
else \
c_step0 = 1, c_step1 = c_step; \
\
for( ; d_size.height--; _c_data += c_step0, \
d_buf += d_buf_step, \
d_data += d_step ) \
{ \
if( _c_data ) \
{ \
c_data = _c_data; \
for( j = 0; j <= d_size.width - 4; j += 4, c_data += 4*c_step1 )\
{ \
worktype t0 = alpha*d_buf[j]; \
worktype t1 = alpha*d_buf[j+1]; \
t0 += beta*worktype(c_data[0]); \
t1 += beta*worktype(c_data[c_step1]); \
d_data[j] = arrtype(t0); \
d_data[j+1] = arrtype(t1); \
t0 = alpha*d_buf[j+2]; \
t1 = alpha*d_buf[j+3]; \
t0 += beta*worktype(c_data[c_step1*2]); \
t1 += beta*worktype(c_data[c_step1*3]); \
d_data[j+2] = arrtype(t0); \
d_data[j+3] = arrtype(t1); \
} \
for( ; j < d_size.width; j++, c_data += c_step1 ) \
{ \
worktype t0 = alpha*d_buf[j]; \
d_data[j] = arrtype(t0 + beta*c_data[0]); \
} \
} \
else \
{ \
for( j = 0; j <= d_size.width - 4; j += 4 ) \
{ \
worktype t0 = alpha*d_buf[j]; \
worktype t1 = alpha*d_buf[j+1]; \
d_data[j] = arrtype(t0); \
d_data[j+1] = arrtype(t1); \
t0 = alpha*d_buf[j+2]; \
t1 = alpha*d_buf[j+3]; \
d_data[j+2] = arrtype(t0); \
d_data[j+3] = arrtype(t1); \
} \
for( ; j < d_size.width; j++ ) \
d_data[j] = arrtype(alpha*d_buf[j]); \
} \
} \
return CV_OK; \
}
ICV_DEF_GEMM_SINGLE_MUL( 32f_C1R, float, double)
ICV_DEF_GEMM_BLOCK_MUL( 32f_C1R, float, double)
ICV_DEF_GEMM_STORE( 32f_C1R, float, double)
ICV_DEF_GEMM_SINGLE_MUL( 64f_C1R, double, double)
ICV_DEF_GEMM_BLOCK_MUL( 64f_C1R, double, double)
ICV_DEF_GEMM_STORE( 64f_C1R, double, double)
ICV_DEF_GEMM_SINGLE_MUL( 32f_C2R, CvComplex32f, CvComplex64f)
ICV_DEF_GEMM_BLOCK_MUL( 32f_C2R, CvComplex32f, CvComplex64f)
ICV_DEF_GEMM_STORE( 32f_C2R, CvComplex32f, CvComplex64f)
ICV_DEF_GEMM_SINGLE_MUL( 64f_C2R, CvComplex64f, CvComplex64f)
ICV_DEF_GEMM_BLOCK_MUL( 64f_C2R, CvComplex64f, CvComplex64f)
ICV_DEF_GEMM_STORE( 64f_C2R, CvComplex64f, CvComplex64f)
typedef CvStatus (CV_STDCALL *CvGEMMSingleMulFunc)( const void* src1, size_t step1,
const void* src2, size_t step2, const void* src3, size_t step3,
void* dst, size_t dststep, CvSize srcsize, CvSize dstsize,
double alpha, double beta, int flags );
typedef CvStatus (CV_STDCALL *CvGEMMBlockMulFunc)( const void* src1, size_t step1,
const void* src2, size_t step2, void* dst, size_t dststep,
CvSize srcsize, CvSize dstsize, int flags );
typedef CvStatus (CV_STDCALL *CvGEMMStoreFunc)( const void* src1, size_t step1,
const void* src2, size_t step2, void* dst, size_t dststep,
CvSize dstsize, double alpha, double beta, int flags );
static void icvInitGEMMTable( CvBigFuncTable* single_mul_tab,
CvBigFuncTable* block_mul_tab,
CvBigFuncTable* store_tab )
{
single_mul_tab->fn_2d[CV_32FC1] = (void*)icvGEMMSingleMul_32f_C1R;
single_mul_tab->fn_2d[CV_64FC1] = (void*)icvGEMMSingleMul_64f_C1R;
single_mul_tab->fn_2d[CV_32FC2] = (void*)icvGEMMSingleMul_32f_C2R;
single_mul_tab->fn_2d[CV_64FC2] = (void*)icvGEMMSingleMul_64f_C2R;
block_mul_tab->fn_2d[CV_32FC1] = (void*)icvGEMMBlockMul_32f_C1R;
block_mul_tab->fn_2d[CV_64FC1] = (void*)icvGEMMBlockMul_64f_C1R;
block_mul_tab->fn_2d[CV_32FC2] = (void*)icvGEMMBlockMul_32f_C2R;
block_mul_tab->fn_2d[CV_64FC2] = (void*)icvGEMMBlockMul_64f_C2R;
store_tab->fn_2d[CV_32FC1] = (void*)icvGEMMStore_32f_C1R;
store_tab->fn_2d[CV_64FC1] = (void*)icvGEMMStore_64f_C1R;
store_tab->fn_2d[CV_32FC2] = (void*)icvGEMMStore_32f_C2R;
store_tab->fn_2d[CV_64FC2] = (void*)icvGEMMStore_64f_C2R;
}
CV_IMPL void
cvGEMM( const CvArr* Aarr, const CvArr* Barr, double alpha,
const CvArr* Carr, double beta, CvArr* Darr, int flags )
{
const int block_lin_size = 128;
const int block_size = block_lin_size * block_lin_size;
static CvBigFuncTable single_mul_tab, block_mul_tab, store_tab;
static int inittab = 0;
static double zero[] = {0,0,0,0};
static float zerof[] = {0,0,0,0};
uchar* buffer = 0;
int local_alloc = 0;
uchar* block_buffer = 0;
CV_FUNCNAME( "cvGEMM" );
__BEGIN__;
CvMat *A = (CvMat*)Aarr;
CvMat *B = (CvMat*)Barr;
CvMat *C = (CvMat*)Carr;
CvMat *D = (CvMat*)Darr;
int len = 0;
CvMat stub, stub1, stub2, stub3;
CvSize a_size, d_size;
int type;
if( !CV_IS_MAT( A ))
{
int coi = 0;
CV_CALL( A = cvGetMat( A, &stub1, &coi ));
if( coi != 0 )
CV_ERROR( CV_BadCOI, "" );
}
if( !CV_IS_MAT( B ))
{
int coi = 0;
CV_CALL( B = cvGetMat( B, &stub2, &coi ));
if( coi != 0 )
CV_ERROR( CV_BadCOI, "" );
}
if( !CV_IS_MAT( D ))
{
int coi = 0;
CV_CALL( D = cvGetMat( D, &stub, &coi ));
if( coi != 0 )
CV_ERROR( CV_BadCOI, "" );
}
if( beta == 0 )
C = 0;
if( C )
{
if( !CV_IS_MAT( C ))
{
int coi = 0;
CV_CALL( C = cvGetMat( C, &stub3, &coi ));
if( coi != 0 )
CV_ERROR( CV_BadCOI, "" );
}
if( !CV_ARE_TYPES_EQ( C, D ))
CV_ERROR( CV_StsUnmatchedFormats, "" );
if( ((flags&CV_GEMM_C_T) == 0 && (C->cols != D->cols || C->rows != D->rows)) ||
((flags&CV_GEMM_C_T) != 0 && (C->rows != D->cols || C->cols != D->rows)))
CV_ERROR( CV_StsUnmatchedSizes, "" );
if( (flags & CV_GEMM_C_T) != 0 && C->data.ptr == D->data.ptr )
{
cvTranspose( C, D );
C = D;
flags &= ~CV_GEMM_C_T;
}
}
else
{
C = &stub3;
C->data.ptr = 0;
C->step = 0;
C->type = CV_MAT_CONT_FLAG;
}
type = CV_MAT_TYPE(A->type);
if( !CV_ARE_TYPES_EQ( A, B ) || !CV_ARE_TYPES_EQ( A, D ) )
CV_ERROR( CV_StsUnmatchedFormats, "" );
a_size.width = A->cols;
a_size.height = A->rows;
d_size.width = D->cols;
d_size.height = D->rows;
switch( flags & (CV_GEMM_A_T|CV_GEMM_B_T) )
{
case 0:
len = B->rows;
if( a_size.width != len ||
B->cols != d_size.width ||
a_size.height != d_size.height )
CV_ERROR( CV_StsUnmatchedSizes, "" );
break;
case 1:
len = B->rows;
if( a_size.height != len ||
B->cols != d_size.width ||
a_size.width != d_size.height )
CV_ERROR( CV_StsUnmatchedSizes, "" );
break;
case 2:
len = B->cols;
if( a_size.width != len ||
B->rows != d_size.width ||
a_size.height != d_size.height )
CV_ERROR( CV_StsUnmatchedSizes, "" );
break;
case 3:
len = B->cols;
if( a_size.height != len ||
B->rows != d_size.width ||
a_size.width != d_size.height )
CV_ERROR( CV_StsUnmatchedSizes, "" );
break;
}
if( flags == 0 && 2 <= len && len <= 4 && (len == d_size.width || len == d_size.height) )
{
int i;
if( type == CV_64F )
{
double* d = D->data.db;
const double *a = A->data.db, *b = B->data.db, *c = C->data.db;
size_t d_step = D->step/sizeof(d[0]),
a_step = A->step/sizeof(a[0]),
b_step = B->step/sizeof(b[0]),
c_step = C->step/sizeof(c[0]);
if( !c )
c = zero;
switch( len )
{
case 2:
if( len == d_size.width && b != d )
{
for( i = 0; i < d_size.height; i++, d += d_step, a += a_step, c += c_step )
{
double t0 = a[0]*b[0] + a[1]*b[b_step];
double t1 = a[0]*b[1] + a[1]*b[b_step+1];
d[0] = t0*alpha + c[0]*beta;
d[1] = t1*alpha + c[1]*beta;
}
}
else if( a != d )
{
int c_step0 = 1;
if( c == zero )
{
c_step0 = 0;
c_step = 1;
}
for( i = 0; i < d_size.width; i++, d++, b++, c += c_step0 )
{
double t0 = a[0]*b[0] + a[1]*b[b_step];
double t1 = a[a_step]*b[0] + a[a_step+1]*b[b_step];
d[0] = t0*alpha + c[0]*beta;
d[d_step] = t1*alpha + c[c_step]*beta;
}
}
else
break;
EXIT;
case 3:
if( len == d_size.width && b != d )
{
for( i = 0; i < d_size.height; i++, d += d_step, a += a_step, c += c_step )
{
double t0 = a[0]*b[0] + a[1]*b[b_step] + a[2]*b[b_step*2];
double t1 = a[0]*b[1] + a[1]*b[b_step+1] + a[2]*b[b_step*2+1];
double t2 = a[0]*b[2] + a[1]*b[b_step+2] + a[2]*b[b_step*2+2];
d[0] = t0*alpha + c[0]*beta;
d[1] = t1*alpha + c[1]*beta;
d[2] = t2*alpha + c[2]*beta;
}
}
else if( a != d )
{
int c_step0 = 1;
if( c == zero )
{
c_step0 = 0;
c_step = 1;
}
for( i = 0; i < d_size.width; i++, d++, b++, c += c_step0 )
{
double t0 = a[0]*b[0] + a[1]*b[b_step] + a[2]*b[b_step*2];
double t1 = a[a_step]*b[0] + a[a_step+1]*b[b_step] + a[a_step+2]*b[b_step*2];
double t2 = a[a_step*2]*b[0] + a[a_step*2+1]*b[b_step] + a[a_step*2+2]*b[b_step*2];
d[0] = t0*alpha + c[0]*beta;
d[d_step] = t1*alpha + c[c_step]*beta;
d[d_step*2] = t2*alpha + c[c_step*2]*beta;
}
}
else
break;
EXIT;
case 4:
if( len == d_size.width && b != d )
{
for( i = 0; i < d_size.height; i++, d += d_step, a += a_step, c += c_step )
{
double t0 = a[0]*b[0] + a[1]*b[b_step] + a[2]*b[b_step*2] + a[3]*b[b_step*3];
double t1 = a[0]*b[1] + a[1]*b[b_step+1] + a[2]*b[b_step*2+1] + a[3]*b[b_step*3+1];
double t2 = a[0]*b[2] + a[1]*b[b_step+2] + a[2]*b[b_step*2+2] + a[3]*b[b_step*3+2];
double t3 = a[0]*b[3] + a[1]*b[b_step+3] + a[2]*b[b_step*2+3] + a[3]*b[b_step*3+3];
d[0] = t0*alpha + c[0]*beta;
d[1] = t1*alpha + c[1]*beta;
d[2] = t2*alpha + c[2]*beta;
d[3] = t3*alpha + c[3]*beta;
}
}
else if( d_size.width <= 16 && a != d )
{
int c_step0 = 1;
if( c == zero )
{
c_step0 = 0;
c_step = 1;
}
for( i = 0; i < d_size.width; i++, d++, b++, c += c_step0 )
{
double t0 = a[0]*b[0] + a[1]*b[b_step] + a[2]*b[b_step*2] + a[3]*b[b_step*3];
double t1 = a[a_step]*b[0] + a[a_step+1]*b[b_step] +
a[a_step+2]*b[b_step*2] + a[a_step+3]*b[b_step*3];
double t2 = a[a_step*2]*b[0] + a[a_step*2+1]*b[b_step] +
a[a_step*2+2]*b[b_step*2] + a[a_step*2+3]*b[b_step*3];
double t3 = a[a_step*3]*b[0] + a[a_step*3+1]*b[b_step] +
a[a_step*3+2]*b[b_step*2] + a[a_step*3+3]*b[b_step*3];
d[0] = t0*alpha + c[0]*beta;
d[d_step] = t1*alpha + c[c_step]*beta;
d[d_step*2] = t2*alpha + c[c_step*2]*beta;
d[d_step*3] = t3*alpha + c[c_step*3]*beta;
}
}
else
break;
EXIT;
}
}
if( type == CV_32F )
{
float* d = D->data.fl;
const float *a = A->data.fl, *b = B->data.fl, *c = C->data.fl;
size_t d_step = D->step/sizeof(d[0]),
a_step = A->step/sizeof(a[0]),
b_step = B->step/sizeof(b[0]),
c_step = C->step/sizeof(c[0]);
if( !c )
c = zerof;
switch( len )
{
case 2:
if( len == d_size.width && b != d )
{
for( i = 0; i < d_size.height; i++, d += d_step, a += a_step, c += c_step )
{
float t0 = a[0]*b[0] + a[1]*b[b_step];
float t1 = a[0]*b[1] + a[1]*b[b_step+1];
d[0] = (float)(t0*alpha + c[0]*beta);
d[1] = (float)(t1*alpha + c[1]*beta);
}
}
else if( a != d )
{
int c_step0 = 1;
if( c == zerof )
{
c_step0 = 0;
c_step = 1;
}
for( i = 0; i < d_size.width; i++, d++, b++, c += c_step0 )
{
float t0 = a[0]*b[0] + a[1]*b[b_step];
float t1 = a[a_step]*b[0] + a[a_step+1]*b[b_step];
d[0] = (float)(t0*alpha + c[0]*beta);
d[d_step] = (float)(t1*alpha + c[c_step]*beta);
}
}
else
break;
EXIT;
case 3:
if( len == d_size.width && b != d )
{
for( i = 0; i < d_size.height; i++, d += d_step, a += a_step, c += c_step )
{
float t0 = a[0]*b[0] + a[1]*b[b_step] + a[2]*b[b_step*2];
float t1 = a[0]*b[1] + a[1]*b[b_step+1] + a[2]*b[b_step*2+1];
float t2 = a[0]*b[2] + a[1]*b[b_step+2] + a[2]*b[b_step*2+2];
d[0] = (float)(t0*alpha + c[0]*beta);
d[1] = (float)(t1*alpha + c[1]*beta);
d[2] = (float)(t2*alpha + c[2]*beta);
}
}
else if( a != d )
{
int c_step0 = 1;
if( c == zerof )
{
c_step0 = 0;
c_step = 1;
}
for( i = 0; i < d_size.width; i++, d++, b++, c += c_step0 )
{
float t0 = a[0]*b[0] + a[1]*b[b_step] + a[2]*b[b_step*2];
float t1 = a[a_step]*b[0] + a[a_step+1]*b[b_step] + a[a_step+2]*b[b_step*2];
float t2 = a[a_step*2]*b[0] + a[a_step*2+1]*b[b_step] + a[a_step*2+2]*b[b_step*2];
d[0] = (float)(t0*alpha + c[0]*beta);
d[d_step] = (float)(t1*alpha + c[c_step]*beta);
d[d_step*2] = (float)(t2*alpha + c[c_step*2]*beta);
}
}
else
break;
EXIT;
case 4:
if( len == d_size.width && b != d )
{
for( i = 0; i < d_size.height; i++, d += d_step, a += a_step, c += c_step )
{
float t0 = a[0]*b[0] + a[1]*b[b_step] + a[2]*b[b_step*2] + a[3]*b[b_step*3];
float t1 = a[0]*b[1] + a[1]*b[b_step+1] + a[2]*b[b_step*2+1] + a[3]*b[b_step*3+1];
float t2 = a[0]*b[2] + a[1]*b[b_step+2] + a[2]*b[b_step*2+2] + a[3]*b[b_step*3+2];
float t3 = a[0]*b[3] + a[1]*b[b_step+3] + a[2]*b[b_step*2+3] + a[3]*b[b_step*3+3];
d[0] = (float)(t0*alpha + c[0]*beta);
d[1] = (float)(t1*alpha + c[1]*beta);
d[2] = (float)(t2*alpha + c[2]*beta);
d[3] = (float)(t3*alpha + c[3]*beta);
}
}
else if( len <= 16 && a != d )
{
int c_step0 = 1;
if( c == zerof )
{
c_step0 = 0;
c_step = 1;
}
for( i = 0; i < d_size.width; i++, d++, b++, c += c_step0 )
{
float t0 = a[0]*b[0] + a[1]*b[b_step] + a[2]*b[b_step*2] + a[3]*b[b_step*3];
float t1 = a[a_step]*b[0] + a[a_step+1]*b[b_step] +
a[a_step+2]*b[b_step*2] + a[a_step+3]*b[b_step*3];
float t2 = a[a_step*2]*b[0] + a[a_step*2+1]*b[b_step] +
a[a_step*2+2]*b[b_step*2] + a[a_step*2+3]*b[b_step*3];
float t3 = a[a_step*3]*b[0] + a[a_step*3+1]*b[b_step] +
a[a_step*3+2]*b[b_step*2] + a[a_step*3+3]*b[b_step*3];
d[0] = (float)(t0*alpha + c[0]*beta);
d[d_step] = (float)(t1*alpha + c[c_step]*beta);
d[d_step*2] = (float)(t2*alpha + c[c_step*2]*beta);
d[d_step*3] = (float)(t3*alpha + c[c_step*3]*beta);
}
}
else
break;
EXIT;
}
}
}
{
int b_step = B->step;
CvGEMMSingleMulFunc single_mul_func;
CvMat tmat, *D0 = D;
icvBLAS_GEMM_32f_t blas_func = 0;
if( !inittab )
{
icvInitGEMMTable( &single_mul_tab, &block_mul_tab, &store_tab );
inittab = 1;
}
single_mul_func = (CvGEMMSingleMulFunc)single_mul_tab.fn_2d[type];
if( !single_mul_func )
CV_ERROR( CV_StsUnsupportedFormat, "" );
if( D->data.ptr == A->data.ptr || D->data.ptr == B->data.ptr )
{
int buf_size = d_size.width*d_size.height*CV_ELEM_SIZE(type);
if( d_size.width <= CV_MAX_LOCAL_MAT_SIZE )
{
buffer = (uchar*)cvStackAlloc( buf_size );
local_alloc = 1;
}
else
CV_CALL( buffer = (uchar*)cvAlloc( buf_size ));
tmat = cvMat( d_size.height, d_size.width, type, buffer );
D = &tmat;
}
if( (d_size.width == 1 || len == 1) && !(flags & CV_GEMM_B_T) && CV_IS_MAT_CONT(B->type) )
{
b_step = d_size.width == 1 ? 0 : CV_ELEM_SIZE(type);
flags |= CV_GEMM_B_T;
}
if( (d_size.width | d_size.height | len) >= 16 && icvBLAS_GEMM_32f_p != 0 )
{
blas_func = type == CV_32FC1 ? (icvBLAS_GEMM_32f_t)icvBLAS_GEMM_32f_p :
type == CV_64FC1 ? (icvBLAS_GEMM_32f_t)icvBLAS_GEMM_64f_p :
type == CV_32FC2 ? (icvBLAS_GEMM_32f_t)icvBLAS_GEMM_32fc_p :
type == CV_64FC2 ? (icvBLAS_GEMM_32f_t)icvBLAS_GEMM_64fc_p : 0;
}
if( blas_func )
{
const char* transa = flags & CV_GEMM_A_T ? "t" : "n";
const char* transb = flags & CV_GEMM_B_T ? "t" : "n";
int lda, ldb, ldd;
if( C->data.ptr )
{
if( C->data.ptr != D->data.ptr )
{
if( !(flags & CV_GEMM_C_T) )
cvCopy( C, D );
else
cvTranspose( C, D );
}
}
if( CV_MAT_DEPTH(type) == CV_32F )
{
CvComplex32f _alpha, _beta;
lda = A->step/sizeof(float);
ldb = b_step/sizeof(float);
ldd = D->step/sizeof(float);
_alpha.re = (float)alpha;
_alpha.im = 0;
_beta.re = C->data.ptr ? (float)beta : 0;
_beta.im = 0;
if( CV_MAT_CN(type) == 2 )
lda /= 2, ldb /= 2, ldd /= 2;
blas_func( transb, transa, &d_size.width, &d_size.height, &len,
&_alpha, B->data.ptr, &ldb, A->data.ptr, &lda,
&_beta, D->data.ptr, &ldd );
}
else
{
CvComplex64f _alpha, _beta;
lda = A->step/sizeof(double);
ldb = b_step/sizeof(double);
ldd = D->step/sizeof(double);
_alpha.re = alpha;
_alpha.im = 0;
_beta.re = C->data.ptr ? beta : 0;
_beta.im = 0;
if( CV_MAT_CN(type) == 2 )
lda /= 2, ldb /= 2, ldd /= 2;
blas_func( transb, transa, &d_size.width, &d_size.height, &len,
&_alpha, B->data.ptr, &ldb, A->data.ptr, &lda,
&_beta, D->data.ptr, &ldd );
}
}
else if( ((d_size.height <= block_lin_size/2 || d_size.width <= block_lin_size/2) &&
len <= 10000) || len <= 10 ||
(d_size.width <= block_lin_size && d_size.height <= block_lin_size && len <= block_lin_size) )
{
single_mul_func( A->data.ptr, A->step, B->data.ptr, b_step,
C->data.ptr, C->step, D->data.ptr, D->step,
a_size, d_size, alpha, beta, flags );
}
else
{
int is_a_t = flags & CV_GEMM_A_T;
int is_b_t = flags & CV_GEMM_B_T;
int elem_size = CV_ELEM_SIZE(type);
int dk0_1, dk0_2;
int a_buf_size = 0, b_buf_size, d_buf_size;
uchar* a_buf = 0;
uchar* b_buf = 0;
uchar* d_buf = 0;
int i, j, k, di = 0, dj = 0, dk = 0;
int dm0, dn0, dk0;
int a_step0, a_step1, b_step0, b_step1, c_step0, c_step1;
int work_elem_size = elem_size << (CV_MAT_DEPTH(type) == CV_32F ? 1 : 0);
CvGEMMBlockMulFunc block_mul_func = (CvGEMMBlockMulFunc)block_mul_tab.fn_2d[type];
CvGEMMStoreFunc store_func = (CvGEMMStoreFunc)store_tab.fn_2d[type];
assert( block_mul_func && store_func );
if( !is_a_t )
a_step0 = A->step, a_step1 = elem_size;
else
a_step0 = elem_size, a_step1 = A->step;
if( !is_b_t )
b_step0 = b_step, b_step1 = elem_size;
else
b_step0 = elem_size, b_step1 = b_step;
if( !C->data.ptr )
{
c_step0 = c_step1 = 0;
flags &= ~CV_GEMM_C_T;
}
else if( !(flags & CV_GEMM_C_T) )
c_step0 = C->step, c_step1 = elem_size;
else
c_step0 = elem_size, c_step1 = C->step;
dm0 = MIN( block_lin_size, d_size.height );
dn0 = MIN( block_lin_size, d_size.width );
dk0_1 = block_size / dm0;
dk0_2 = block_size / dn0;
dk0 = MAX( dk0_1, dk0_2 );
dk0 = MIN( dk0, len );
if( dk0*dm0 > block_size )
dm0 = block_size / dk0;
if( dk0*dn0 > block_size )
dn0 = block_size / dk0;
dk0_1 = (dn0+dn0/8+2) & -2;
b_buf_size = (dk0+dk0/8+1)*dk0_1*elem_size;
d_buf_size = (dk0+dk0/8+1)*dk0_1*work_elem_size;
if( is_a_t )
{
a_buf_size = (dm0+dm0/8+1)*((dk0+dk0/8+2)&-2)*elem_size;
flags &= ~CV_GEMM_A_T;
}
CV_CALL( block_buffer = (uchar*)cvAlloc(a_buf_size + b_buf_size + d_buf_size));
d_buf = block_buffer;
b_buf = d_buf + d_buf_size;
if( is_a_t )
a_buf = b_buf + b_buf_size;
for( i = 0; i < d_size.height; i += di )
{
di = dm0;
if( i + di >= d_size.height || 8*(i + di) + di > 8*d_size.height )
di = d_size.height - i;
for( j = 0; j < d_size.width; j += dj )
{
uchar* _d = D->data.ptr + i*D->step + j*elem_size;
const uchar* _c = C->data.ptr + i*c_step0 + j*c_step1;
int _d_step = D->step;
dj = dn0;
if( j + dj >= d_size.width || 8*(j + dj) + dj > 8*d_size.width )
dj = d_size.width - j;
flags &= 15;
if( dk0 < len )
{
_d = d_buf;
_d_step = dj*work_elem_size;
}
for( k = 0; k < len; k += dk )
{
const uchar* _a = A->data.ptr + i*a_step0 + k*a_step1;
int _a_step = A->step;
const uchar* _b = B->data.ptr + k*b_step0 + j*b_step1;
int _b_step = b_step;
CvSize a_bl_size;
dk = dk0;
if( k + dk >= len || 8*(k + dk) + dk > 8*len )
dk = len - k;
if( !is_a_t )
a_bl_size.width = dk, a_bl_size.height = di;
else
a_bl_size.width = di, a_bl_size.height = dk;
if( a_buf && is_a_t )
{
int t;
_a_step = dk*elem_size;
icvGEMM_TransposeBlock( _a, A->step, a_buf, _a_step, a_bl_size, elem_size );
CV_SWAP( a_bl_size.width, a_bl_size.height, t );
_a = a_buf;
}
if( dj < d_size.width )
{
CvSize b_size;
if( !is_b_t )
b_size.width = dj, b_size.height = dk;
else
b_size.width = dk, b_size.height = dj;
_b_step = b_size.width*elem_size;
icvGEMM_CopyBlock( _b, b_step, b_buf, _b_step, b_size, elem_size );
_b = b_buf;
}
if( dk0 < len )
block_mul_func( _a, _a_step, _b, _b_step, _d, _d_step,
a_bl_size, cvSize(dj,di), flags );
else
single_mul_func( _a, _a_step, _b, _b_step, _c, C->step, _d, _d_step,
a_bl_size, cvSize(dj,di), alpha, beta, flags );
flags |= 16;
}
if( dk0 < len )
store_func( _c, C->step, _d, _d_step, D->data.ptr + i*D->step + j*elem_size,
D->step, cvSize(dj,di), alpha, beta, flags );
}
}
}
if( D0 != D )
CV_CALL( cvCopy( D, D0 ));
}
__END__;
if( buffer && !local_alloc )
cvFree( &buffer );
if( block_buffer )
cvFree( &block_buffer );
}
/****************************************************************************************\
* cvTransform *
\****************************************************************************************/
#define ICV_DEF_TRANSFORM_CASE_C1( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
{ \
for( i = 0; i < size.width; i++, dst += dst_cn ) \
{ \
const double* _mat = mat; \
double v0 = _ld_(src[i]); \
for( k = 0; k < dst_cn; k++, _mat += 2 ) \
{ \
temptype t0 = _cast_macro1_(_mat[0]*v0 + _mat[1]); \
dst[k] = _cast_macro2_(t0); \
} \
} \
src += size.width; \
}
#define ICV_DEF_DIAG_TRANSFORM_CASE_C1( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
for( i = 0; i < size.width; i++ ) \
{ \
double ft0; \
temptype t0; \
ft0 = mat[0]*_ld_(src[i]) + mat[1]; \
t0 = _cast_macro1_(ft0); \
dst[i] = _cast_macro2_(t0); \
}
#define ICV_DEF_TRANSFORM_CASE_C2( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
if( dst_cn == 2 ) \
{ \
for( i = 0; i < size.width*2; i += 2 ) \
{ \
double ft0, ft1; \
temptype t0, t1; \
ft0 = mat[0]*_ld_(src[i]) + mat[1]*_ld_(src[i+1]) + mat[2]; \
ft1 = mat[3]*_ld_(src[i]) + mat[4]*_ld_(src[i+1]) + mat[5]; \
t0 = _cast_macro1_(ft0); \
t1 = _cast_macro1_(ft1); \
dst[i] = _cast_macro2_(t0); \
dst[i+1] = _cast_macro2_(t1); \
} \
src += size.width*2; dst += size.width*2; \
} \
else \
for( i = 0; i < size.width; i++, src += 2, dst += dst_cn ) \
{ \
const double* _mat = mat; \
double v0 = _ld_(src[0]), v1 = src[1]; \
for( k = 0; k < dst_cn; k++, _mat += 3 ) \
{ \
temptype t0 = \
_cast_macro1_(_mat[0]*v0 + _mat[1]*v1 + _mat[2]); \
dst[k] = _cast_macro2_(t0); \
} \
}
#define ICV_DEF_DIAG_TRANSFORM_CASE_C2( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
for( i = 0; i < size.width*2; i += 2 ) \
{ \
double ft0, ft1; \
temptype t0, t1; \
ft0 = mat[0]*_ld_(src[i]) + mat[2]; \
ft1 = mat[4]*_ld_(src[i+1]) + mat[5]; \
t0 = _cast_macro1_(ft0); \
t1 = _cast_macro1_(ft1); \
dst[i] = _cast_macro2_(t0); \
dst[i+1] = _cast_macro2_(t1); \
}
#define ICV_DEF_TRANSFORM_CASE_C3( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
if( dst_cn == 3 ) \
{ \
for( i = 0; i < size.width*3; i += 3 ) \
{ \
double ft0, ft1, ft2; \
temptype t0, t1, t2; \
ft0 = mat[0]*_ld_(src[i]) + mat[1]*_ld_(src[i+1]) + \
mat[2]*_ld_(src[i+2]) + mat[3]; \
ft1 = mat[4]*_ld_(src[i]) + mat[5]*_ld_(src[i+1]) + \
mat[6]*_ld_(src[i+2]) + mat[7]; \
ft2 = mat[8]*_ld_(src[i]) + mat[9]*_ld_(src[i+1]) + \
mat[10]*_ld_(src[i+2]) + mat[11]; \
t0 = _cast_macro1_(ft0); \
t1 = _cast_macro1_(ft1); \
t2 = _cast_macro1_(ft2); \
dst[i] = _cast_macro2_(t0); \
dst[i+1] = _cast_macro2_(t1); \
dst[i+2] = _cast_macro2_(t2); \
} \
src += size.width*3; dst += size.width*3; \
} \
else if( dst_cn == 1 ) \
{ \
for( i = 0; i < size.width; i++, src += 3 ) \
{ \
temptype t0 = _cast_macro1_(mat[0]*_ld_(src[0]) + \
mat[1]*_ld_(src[1]) + mat[2]*_ld_(src[2]) + mat[3]); \
dst[i] = _cast_macro2_(t0); \
} \
dst += size.width; \
} \
else \
for( i = 0; i < size.width; i++, src += 3, dst += dst_cn ) \
{ \
const double* _mat = mat; \
double v0=_ld_(src[0]), v1=_ld_(src[1]), v2=_ld_(src[2]); \
for( k = 0; k < dst_cn; k++, _mat += 4 ) \
{ \
temptype t0 = _cast_macro1_(_mat[0]*v0 + \
_mat[1]*v1 + _mat[2]*v2 + _mat[3]); \
dst[k] = _cast_macro2_(t0); \
} \
}
#define ICV_DEF_DIAG_TRANSFORM_CASE_C3( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
for( i = 0; i < size.width*3; i += 3 ) \
{ \
double ft0, ft1, ft2; \
temptype t0, t1, t2; \
ft0 = mat[0]*_ld_(src[i]) + mat[3]; \
ft1 = mat[5]*_ld_(src[i+1]) + mat[7]; \
ft2 = mat[10]*_ld_(src[i+2]) + mat[11]; \
t0 = _cast_macro1_(ft0); \
t1 = _cast_macro1_(ft1); \
t2 = _cast_macro1_(ft2); \
dst[i] = _cast_macro2_(t0); \
dst[i+1] = _cast_macro2_(t1); \
dst[i+2] = _cast_macro2_(t2); \
}
#define ICV_DEF_TRANSFORM_CASE_C4( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
for( i = 0; i < size.width; i++, src += 4, dst += dst_cn ) \
{ \
const double* _mat = mat; \
double v0 = _ld_(src[0]), v1 = _ld_(src[1]), \
v2 = _ld_(src[2]), v3 = _ld_(src[3]); \
for( k = 0; k < dst_cn; k++, _mat += 5 ) \
{ \
temptype t0 =_cast_macro1_(_mat[0]*v0+_mat[1]*v1+ \
_mat[2]*v2+_mat[3]*v3+_mat[4]); \
dst[k] = _cast_macro2_(t0); \
} \
}
#define ICV_DEF_DIAG_TRANSFORM_CASE_C4( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
for( i = 0; i < size.width*4; i += 4 ) \
{ \
double ft0, ft1; \
temptype t0, t1; \
ft0 = mat[0]*_ld_(src[i]) + mat[4]; \
ft1 = mat[6]*_ld_(src[i+1]) + mat[9]; \
t0 = _cast_macro1_(ft0); \
t1 = _cast_macro1_(ft1); \
dst[i] = _cast_macro2_(t0); \
dst[i+1] = _cast_macro2_(t1); \
ft0 = mat[12]*_ld_(src[i+2]) + mat[14]; \
ft1 = mat[18]*_ld_(src[i+3]) + mat[19]; \
t0 = _cast_macro1_(ft0); \
t1 = _cast_macro1_(ft1); \
dst[i+2] = _cast_macro2_(t0); \
dst[i+3] = _cast_macro2_(t1); \
}
#define ICV_DEF_TRANSFORM_FUNC( flavor, arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_, cn )\
static CvStatus CV_STDCALL \
icvTransform_##flavor( const arrtype* src, int srcstep, \
arrtype* dst, int dststep, CvSize size, \
const double* mat, int dst_cn ) \
{ \
srcstep = srcstep/sizeof(src[0]) - size.width*cn; \
dststep = dststep/sizeof(dst[0]) - size.width*dst_cn; \
for( ; size.height--; src += srcstep, dst += dststep ) \
{ \
int i, k; \
ICV_DEF_TRANSFORM_CASE_C##cn( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
} \
\
return CV_OK; \
}
#define ICV_DEF_DIAG_TRANSFORM_FUNC( flavor, arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_, cn )\
static CvStatus CV_STDCALL \
icvDiagTransform_##flavor( const arrtype* src, int srcstep, \
arrtype* dst, int dststep, CvSize size, \
const double* mat ) \
{ \
srcstep /= sizeof(src[0]); \
dststep /= sizeof(dst[0]); \
for( ; size.height--; src += srcstep, dst += dststep ) \
{ \
int i; \
ICV_DEF_DIAG_TRANSFORM_CASE_C##cn( arrtype, temptype, _ld_, \
_cast_macro1_, _cast_macro2_ ) \
} \
\
return CV_OK; \
}
ICV_DEF_TRANSFORM_FUNC( 8u_C1R, uchar, int, CV_8TO32F, cvRound, CV_CAST_8U, 1 )
ICV_DEF_TRANSFORM_FUNC( 8u_C2R, uchar, int, CV_8TO32F, cvRound, CV_CAST_8U, 2 )
ICV_DEF_TRANSFORM_FUNC( 8u_C3R, uchar, int, CV_8TO32F, cvRound, CV_CAST_8U, 3 )
ICV_DEF_TRANSFORM_FUNC( 8u_C4R, uchar, int, CV_8TO32F, cvRound, CV_CAST_8U, 4 )
ICV_DEF_TRANSFORM_FUNC( 16u_C1R, ushort, int, CV_NOP, cvRound, CV_CAST_16U, 1 )
ICV_DEF_TRANSFORM_FUNC( 16u_C2R, ushort, int, CV_NOP, cvRound, CV_CAST_16U, 2 )
ICV_DEF_TRANSFORM_FUNC( 16u_C3R, ushort, int, CV_NOP, cvRound, CV_CAST_16U, 3 )
ICV_DEF_TRANSFORM_FUNC( 16u_C4R, ushort, int, CV_NOP, cvRound, CV_CAST_16U, 4 )
ICV_DEF_TRANSFORM_FUNC( 16s_C1R, short, int, CV_NOP, cvRound, CV_CAST_16S, 1 )
ICV_DEF_TRANSFORM_FUNC( 16s_C2R, short, int, CV_NOP, cvRound, CV_CAST_16S, 2 )
ICV_DEF_TRANSFORM_FUNC( 16s_C3R, short, int, CV_NOP, cvRound, CV_CAST_16S, 3 )
ICV_DEF_TRANSFORM_FUNC( 16s_C4R, short, int, CV_NOP, cvRound, CV_CAST_16S, 4 )
ICV_DEF_TRANSFORM_FUNC( 32s_C1R, int, int, CV_NOP, cvRound, CV_NOP, 1 )
ICV_DEF_TRANSFORM_FUNC( 32s_C2R, int, int, CV_NOP, cvRound, CV_NOP, 2 )
ICV_DEF_TRANSFORM_FUNC( 32s_C3R, int, int, CV_NOP, cvRound, CV_NOP, 3 )
ICV_DEF_TRANSFORM_FUNC( 32s_C4R, int, int, CV_NOP, cvRound, CV_NOP, 4 )
ICV_DEF_TRANSFORM_FUNC( 32f_C1R, float, double, CV_NOP, CV_NOP, CV_CAST_32F, 1 )
ICV_DEF_TRANSFORM_FUNC( 32f_C2R, float, double, CV_NOP, CV_NOP, CV_CAST_32F, 2 )
ICV_DEF_TRANSFORM_FUNC( 32f_C3R, float, double, CV_NOP, CV_NOP, CV_CAST_32F, 3 )
ICV_DEF_TRANSFORM_FUNC( 32f_C4R, float, double, CV_NOP, CV_NOP, CV_CAST_32F, 4 )
ICV_DEF_TRANSFORM_FUNC( 64f_C1R, double, double, CV_NOP, CV_NOP, CV_CAST_64F, 1 )
ICV_DEF_TRANSFORM_FUNC( 64f_C2R, double, double, CV_NOP, CV_NOP, CV_CAST_64F, 2 )
ICV_DEF_TRANSFORM_FUNC( 64f_C3R, double, double, CV_NOP, CV_NOP, CV_CAST_64F, 3 )
ICV_DEF_TRANSFORM_FUNC( 64f_C4R, double, double, CV_NOP, CV_NOP, CV_CAST_64F, 4 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 16u_C1R, ushort, int, CV_NOP, cvRound, CV_CAST_16U, 1 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 16u_C2R, ushort, int, CV_NOP, cvRound, CV_CAST_16U, 2 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 16u_C3R, ushort, int, CV_NOP, cvRound, CV_CAST_16U, 3 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 16u_C4R, ushort, int, CV_NOP, cvRound, CV_CAST_16U, 4 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 16s_C1R, short, int, CV_NOP, cvRound, CV_CAST_16S, 1 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 16s_C2R, short, int, CV_NOP, cvRound, CV_CAST_16S, 2 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 16s_C3R, short, int, CV_NOP, cvRound, CV_CAST_16S, 3 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 16s_C4R, short, int, CV_NOP, cvRound, CV_CAST_16S, 4 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 32s_C1R, int, int, CV_NOP, cvRound, CV_NOP, 1 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 32s_C2R, int, int, CV_NOP, cvRound, CV_NOP, 2 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 32s_C3R, int, int, CV_NOP, cvRound, CV_NOP, 3 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 32s_C4R, int, int, CV_NOP, cvRound, CV_NOP, 4 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 32f_C1R, float, double, CV_NOP, CV_NOP, CV_CAST_32F, 1 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 32f_C2R, float, double, CV_NOP, CV_NOP, CV_CAST_32F, 2 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 32f_C3R, float, double, CV_NOP, CV_NOP, CV_CAST_32F, 3 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 32f_C4R, float, double, CV_NOP, CV_NOP, CV_CAST_32F, 4 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 64f_C1R, double, double, CV_NOP, CV_NOP, CV_CAST_64F, 1 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 64f_C2R, double, double, CV_NOP, CV_NOP, CV_CAST_64F, 2 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 64f_C3R, double, double, CV_NOP, CV_NOP, CV_CAST_64F, 3 )
ICV_DEF_DIAG_TRANSFORM_FUNC( 64f_C4R, double, double, CV_NOP, CV_NOP, CV_CAST_64F, 4 )
#define icvTransform_8s_C1R 0
#define icvTransform_8s_C2R 0
#define icvTransform_8s_C3R 0
#define icvTransform_8s_C4R 0
#define icvDiagTransform_8s_C1R 0
#define icvDiagTransform_8s_C2R 0
#define icvDiagTransform_8s_C3R 0
#define icvDiagTransform_8s_C4R 0
#define icvDiagTransform_8u_C1R 0
#define icvDiagTransform_8u_C2R 0
#define icvDiagTransform_8u_C3R 0
#define icvDiagTransform_8u_C4R 0
CV_DEF_INIT_BIG_FUNC_TAB_2D( Transform, R )
CV_DEF_INIT_BIG_FUNC_TAB_2D( DiagTransform, R )
typedef CvStatus (CV_STDCALL * CvTransformFunc)(
const void* src, int srcstep,
void* dst, int dststep, CvSize size,
const void* mat, int dst_cn );
typedef CvStatus (CV_STDCALL * CvDiagTransformFunc)(
const void* src, int srcstep,
void* dst, int dststep, CvSize size,
const void* mat );
typedef CvStatus (CV_STDCALL * CvDiagTransformFunc)(
const void* src, int srcstep,
void* dst, int dststep, CvSize size,
const void* mat );
///////////////////// IPP transform functions //////////////////
icvColorTwist_8u_C3R_t icvColorTwist_8u_C3R_p = 0;
icvColorTwist_16u_C3R_t icvColorTwist_16u_C3R_p = 0;
icvColorTwist_16s_C3R_t icvColorTwist_16s_C3R_p = 0;
icvColorTwist_32f_C3R_t icvColorTwist_32f_C3R_p = 0;
icvColorTwist_32f_C4R_t icvColorTwist_32f_C4R_p = 0;
icvColorToGray_8u_C3C1R_t icvColorToGray_8u_C3C1R_p = 0;
icvColorToGray_16u_C3C1R_t icvColorToGray_16u_C3C1R_p = 0;
icvColorToGray_16s_C3C1R_t icvColorToGray_16s_C3C1R_p = 0;
icvColorToGray_32f_C3C1R_t icvColorToGray_32f_C3C1R_p = 0;
icvColorToGray_8u_AC4C1R_t icvColorToGray_8u_AC4C1R_p = 0;
icvColorToGray_16u_AC4C1R_t icvColorToGray_16u_AC4C1R_p = 0;
icvColorToGray_16s_AC4C1R_t icvColorToGray_16s_AC4C1R_p = 0;
icvColorToGray_32f_AC4C1R_t icvColorToGray_32f_AC4C1R_p = 0;
typedef CvStatus (CV_STDCALL * CvColorTwistIPPFunc)( const void* src, int srcstep,
void* dst, int dststep, CvSize size, const float* coeffs );
////////////////////////////////////////////////////////////////
CV_IMPL void
cvTransform( const CvArr* srcarr, CvArr* dstarr,
const CvMat* transmat, const CvMat* shiftvec )
{
static CvBigFuncTable transform_tab, diag_transform_tab;
static int inittab = 0;
CvMat* lut = 0;
CV_FUNCNAME( "cvTransform" );
__BEGIN__;
CvMat srcstub, *src = (CvMat*)srcarr;
CvMat dststub, *dst = (CvMat*)dstarr;
CvMat rotstub, *rot = (CvMat*)transmat;
CvMat shiftstub, *shift = (CvMat*)shiftvec;
CvSeq *src_seq = 0, *dst_seq = 0;
CvSeq hdr; // need only one copy of stub header & seqblock (either for src or dst)
CvSeqBlock block_hdr;
int i, j, type, cn, dst_cn;
int coi = 0, coi2 = 0;
double* buffer = (double*)cvStackAlloc( CV_CN_MAX*(CV_CN_MAX+1)*sizeof(buffer[0]) );
if( !inittab )
{
icvInitTransformRTable( &transform_tab );
icvInitDiagTransformRTable( &diag_transform_tab );
inittab = 1;
}
if( CV_IS_SEQ( src ))
{
src_seq = (CvSeq*)src;
if( CV_ELEM_SIZE(src_seq->flags) != src_seq->elem_size )
CV_ERROR( CV_StsUnsupportedFormat, "Unsupported type of sequence elements" );
}
else
CV_CALL( src = cvGetMat( src, &srcstub, &coi ));
if( CV_IS_SEQ( dst ))
{
dst_seq = (CvSeq*)dst;
if( CV_ELEM_SIZE(dst_seq->flags) != dst_seq->elem_size )
CV_ERROR( CV_StsUnsupportedFormat, "Unsupported type of sequence elements" );
}
else
CV_CALL( dst = cvGetMat( dst, &dststub, &coi2 ));
if( coi != 0 || coi2 != 0 )
CV_ERROR( CV_BadCOI, "" );
if( !CV_ARE_DEPTHS_EQ(src, dst) )
CV_ERROR( CV_StsUnmatchedFormats, "" );
if( src_seq || dst_seq )
{
if( !src_seq )
{
if( CV_IS_MAT_CONT(src->type) || (src->rows != 1 && src->cols != 1) )
CV_ERROR( CV_StsBadSize, "if eigher the source or destination is a sequence, "
"the other array must be also a sequence of continous 1d vector" );
src_seq = cvMakeSeqHeaderForArray( CV_MAT_TYPE(src->type), sizeof(hdr),
CV_ELEM_SIZE(src->type), src->data.ptr,
src->rows + src->cols + 1, &hdr, &block_hdr );
}
if( !dst_seq )
{
if( CV_IS_MAT_CONT(dst->type) || (dst->rows != 1 && dst->cols != 1) )
CV_ERROR( CV_StsBadSize, "if eigher the source or destination is a sequence, "
"the other array must be also a sequence of continous 1d vector" );
if( dst->rows + dst->cols - 1 != src_seq->total )
CV_ERROR( CV_StsUnmatchedFormats,
"source sequence and destination vector have different sizes" );
dst_seq = cvMakeSeqHeaderForArray( CV_MAT_TYPE(dst->type), sizeof(hdr),
CV_ELEM_SIZE(dst->type), dst->data.ptr,
dst->rows + dst->cols + 1, &hdr, &block_hdr );
}
else if( dst_seq->total != src_seq->total )
{
if( dst_seq->total > src_seq->total )
cvSeqPopMulti( dst_seq, 0, dst_seq->total - src_seq->total );
else
cvSeqPushMulti( dst_seq, 0, src_seq->total - dst_seq->total );
}
}
else if( !CV_ARE_SIZES_EQ( src, dst ))
CV_ERROR( CV_StsUnmatchedSizes, "" );
type = CV_MAT_TYPE( src->type );
cn = CV_MAT_CN( type );
dst_cn = CV_MAT_CN( dst->type );
if( cn > 4 || dst_cn > 4 )
CV_ERROR( CV_StsOutOfRange, "Both input and output array must have at most 4 channels" );
if( !CV_IS_MAT( rot ))
CV_CALL( rot = cvGetMat( rot, &rotstub, &coi ));
if( rot->rows != dst_cn )
CV_ERROR( CV_StsBadSize,
"The height of transmat matrix must be equal to number of channels" );
if( rot->cols == cn + 1 || rot->cols == cn )
{
if( CV_MAT_TYPE( rot->type ) == CV_64FC1 )
{
for( i = 0; i < dst_cn; i++ )
{
buffer[i*(cn+1) + cn] = 0;
for( j = 0; j < rot->cols; j++ )
buffer[i*(cn+1) + j] = ((double*)(rot->data.ptr + rot->step*i))[j];
}
}
else if( CV_MAT_TYPE( rot->type ) == CV_32FC1 )
{
for( i = 0; i < dst_cn; i++ )
{
buffer[i*(cn+1) + cn] = 0;
for( j = 0; j < rot->cols; j++ )
buffer[i*(cn+1) + j] = ((float*)(rot->data.ptr + rot->step*i))[j];
}
}
else
CV_ERROR( CV_StsUnsupportedFormat, "Rotation matrix must be 32fC1 or 64fC1" );
}
else
CV_ERROR( CV_StsUnmatchedSizes, "If the source array has <cn> channels, "
"the transformation matrix must have <cn> x <cn>+1 or <cn> x <cn> size" );
if( shift )
{
if( !CV_IS_MAT( shift ))
CV_CALL( shift = cvGetMat( shift, &shiftstub, &coi ));
if( CV_MAT_CN( shift->type ) * shift->cols * shift->rows == dst_cn &&
(shift->rows == 1 || shift->cols == 1) )
{
if( CV_MAT_DEPTH( shift->type ) == CV_64F )
{
int step = shift->step ? shift->step/sizeof(double) : 1;
for( i = 0; i < dst_cn; i++ )
buffer[i*(cn+1) + cn] += shift->data.db[i*step];
}
else if( CV_MAT_DEPTH( shift->type ) == CV_32F )
{
int step = shift->step ? shift->step/sizeof(float) : 1;
for( i = 0; i < dst_cn; i++ )
buffer[i*(cn+1) + cn] += shift->data.fl[i*step];
}
else
CV_ERROR( CV_StsUnsupportedFormat, "Shift vector must be 32f or 64f" );
}
else
{
CV_ERROR( CV_StsUnmatchedSizes,
"Shift (if present) must be 1 dimensional vector with the number "
"of elements equal to number of channels in the processed array" );
}
}
if( coi != 0 || coi2 != 0 )
CV_ERROR( CV_BadCOI, "" );
{
CvTransformFunc func = (CvTransformFunc)(transform_tab.fn_2d[type]);
CvDiagTransformFunc diag_func = 0;
CvLUT_TransformFunc lut_func = 0;
int diag_transform = 0;
CvColorTwistIPPFunc ipp_func = 0;
CvSize size;
float* ipp_coeffs = (float*)cvStackAlloc( 16*sizeof(ipp_coeffs[0]) );
if( !func )
CV_ERROR( CV_StsUnsupportedFormat, "" );
if( cn == dst_cn )
ipp_func = type == CV_8UC3 ? icvColorTwist_8u_C3R_p :
type == CV_16UC3 ? icvColorTwist_16u_C3R_p :
type == CV_16SC3 ? icvColorTwist_16s_C3R_p :
type == CV_32FC3 ? icvColorTwist_32f_C3R_p :
type == CV_32FC4 && fabs(buffer[4]) < DBL_EPSILON &&
fabs(buffer[9]) < DBL_EPSILON && fabs(buffer[14]) < DBL_EPSILON &&
fabs(buffer[19]) < DBL_EPSILON ? icvColorTwist_32f_C4R_p : 0;
else if( dst_cn == 1 && (cn == 3 || cn == 4) &&
buffer[0] >= 0 && buffer[1] >= 0 && buffer[2] >= 0 &&
buffer[0] + buffer[1] + buffer[2] <= 1.01 &&
fabs(buffer[3]) < DBL_EPSILON && (cn == 3 || fabs(buffer[4]) < DBL_EPSILON) )
{
if( cn == 3 )
ipp_func = type == CV_8UC3 ? icvColorToGray_8u_C3C1R_p :
type == CV_16UC3 ? icvColorToGray_16u_C3C1R_p :
type == CV_16SC3 ? icvColorToGray_16s_C3C1R_p :
type == CV_32FC3 ? icvColorToGray_32f_C3C1R_p : 0;
else
ipp_func = type == CV_8UC4 ? icvColorToGray_8u_AC4C1R_p :
type == CV_16UC4 ? icvColorToGray_16u_AC4C1R_p :
type == CV_16SC4 ? icvColorToGray_16s_AC4C1R_p :
type == CV_32FC4 ? icvColorToGray_32f_AC4C1R_p : 0;
}
if( dst_cn == cn )
{
diag_transform = 1;
for( i = 0; i < dst_cn; i++ )
for( j = 0; j < cn; j++ )
{
if( i != j && fabs(buffer[i*(cn+1) + j]) > DBL_EPSILON )
{
diag_transform = 0;
break;
}
}
if( diag_transform )
{
if( CV_MAT_DEPTH(type) == CV_8U )
{
CV_CALL( lut = cvCreateMat( 1, 256, type ));
for( i = 0; i < cn; i++ )
{
double a = buffer[i*(cn+1) + i], b = buffer[i*(cn+1) + cn];
uchar* ltab = lut->data.ptr;
for( j = 0; j < 256; j++ )
{
int t = cvRound(a*j + b);
ltab[j*cn + i] = CV_CAST_8U(t);
}
}
lut_func = cn == 1 ? (CvLUT_TransformFunc)icvLUT_Transform8u_8u_C1R :
cn == 2 ? (CvLUT_TransformFunc)icvLUT_Transform8u_8u_C2R :
cn == 3 ? (CvLUT_TransformFunc)icvLUT_Transform8u_8u_C3R :
(CvLUT_TransformFunc)icvLUT_Transform8u_8u_C4R;
}
else
diag_func = (CvDiagTransformFunc)(diag_transform_tab.fn_2d[type]);
}
}
if( ipp_func )
{
const double* ptr = buffer;
// fill cn x 4 ipp_coeffs array
for( i = 0; i < cn*4; i += 4, ptr += cn+1 )
{
float t0 = (float)ptr[0];
float t1 = (float)ptr[1];
ipp_coeffs[i] = t0;
ipp_coeffs[i+1] = t1;
t0 = (float)ptr[2];
t1 = (float)ptr[3];
ipp_coeffs[i+2] = t0;
ipp_coeffs[i+3] = t1;
}
}
if( !src_seq )
{
int srcstep = src->step;
int dststep = dst->step;
size = cvGetMatSize( src );
if( CV_IS_MAT_CONT( src->type & dst->type ))
{
size.width *= size.height;
size.height = 1;
srcstep = dststep = CV_STUB_STEP;
}
if( lut_func )
lut_func( src->data.ptr, src->step, dst->data.ptr,
dst->step, size, lut->data.ptr );
else if( ipp_func )
{
IPPI_CALL( ipp_func( src->data.ptr, srcstep, dst->data.ptr,
dststep, size, ipp_coeffs ));
}
else if( diag_transform )
diag_func( src->data.ptr, src->step, dst->data.ptr,
dst->step, size, buffer );
else
func( src->data.ptr, src->step, dst->data.ptr,
dst->step, size, buffer, dst_cn );
}
else
{
CvSeqBlock* src_block = src_seq->first;
CvSeqBlock* dst_block = dst_seq->first;
int src_idx = 0, dst_idx = 0;
int src_elem_size = CV_ELEM_SIZE(src_seq->flags);
int dst_elem_size = CV_ELEM_SIZE(dst_seq->flags);
for( i = src_seq->total; i > 0; )
{
int src_len = src_block->count - src_idx;
int dst_len = dst_block->count - dst_idx;
const void* srcptr = src_block->data + src_idx*src_elem_size;
void* dstptr = dst_block->data + dst_idx*dst_elem_size;
src_len = MIN(src_len, dst_len);
if( lut_func )
lut_func( srcptr, CV_STUB_STEP, dstptr, CV_STUB_STEP,
cvSize( src_len, 1 ), lut->data.ptr );
else if( ipp_func )
{
IPPI_CALL( ipp_func( srcptr, CV_STUB_STEP, dstptr, CV_STUB_STEP,
cvSize( src_len, 1 ), ipp_coeffs ));
}
else if( diag_transform )
diag_func( srcptr, CV_STUB_STEP, dstptr, CV_STUB_STEP,
cvSize( src_len, 1 ), buffer );
else
func( srcptr, CV_STUB_STEP, dstptr, CV_STUB_STEP,
cvSize( src_len, 1 ), buffer, dst_cn );
if( (src_idx += src_len) == src_block->count )
src_block = src_block->next, src_idx = 0;
if( (dst_idx += src_len) == dst_block->count )
dst_block = dst_block->next, dst_idx = 0;
i -= src_len;
}
}
}
__END__;
cvReleaseMat( &lut );
}
/****************************************************************************************\
* cvPerspectiveTransform *
\****************************************************************************************/
#define ICV_PERSPECTIVE_TRANSFORM_FUNC_2( flavor, arrtype ) \
static CvStatus CV_STDCALL \
icvPerspectiveTransform_##flavor##_C2R( const arrtype* src, int srcstep, \
arrtype* dst, int dststep, \
CvSize size, const double* mat ) \
{ \
int i; \
size.width *= 2; \
srcstep /= sizeof(src[0]); dststep /= sizeof(dst[0]); \
\
for( ; size.height--; src += srcstep, dst += dststep ) \
{ \
for( i = 0; i < size.width; i += 2 ) \
{ \
arrtype x = src[i], y = src[i + 1]; \
double w = x*mat[6] + y*mat[7] + mat[8]; \
\
if( fabs(w) > FLT_EPSILON ) \
{ \
w = 1./w; \
dst[i] = (arrtype)((x*mat[0] + y*mat[1] + mat[2]) * w); \
dst[i+1] = (arrtype)((x*mat[3] + y*mat[4] + mat[5]) * w); \
} \
else \
{ \
dst[i] = (arrtype)0; \
dst[i+1] = (arrtype)0; \
} \
} \
} \
\
return CV_OK; \
}
#define ICV_PERSPECTIVE_TRANSFORM_FUNC_3( flavor, arrtype ) \
static CvStatus CV_STDCALL \
icvPerspectiveTransform_##flavor##_C3R( const arrtype* src, int srcstep, \
arrtype* dst, int dststep, \
CvSize size, const double* mat ) \
{ \
int i; \
size.width *= 3; \
srcstep /= sizeof(src[0]); dststep /= sizeof(dst[0]); \
\
for( ; size.height--; src += srcstep, dst += dststep ) \
{ \
for( i = 0; i < size.width; i += 3 ) \
{ \
arrtype x = src[i], y = src[i + 1], z = src[i + 2]; \
double w = x*mat[12] + y*mat[13] + z*mat[14] + mat[15]; \
\
if( fabs(w) > FLT_EPSILON ) \
{ \
w = 1./w; \
dst[i] = (arrtype)((x*mat[0] + y*mat[1] + z*mat[2] + mat[3]) * w); \
dst[i+1] = (arrtype)((x*mat[4] + y*mat[5] + z*mat[6] + mat[7]) * w); \
dst[i+2] = (arrtype)((x*mat[8] + y*mat[9] + z*mat[10] + mat[11]) * w); \
} \
else \
{ \
dst[i] = (arrtype)0; \
dst[i+1] = (arrtype)0; \
dst[i+2] = (arrtype)0; \
} \
} \
} \
\
return CV_OK; \
}
ICV_PERSPECTIVE_TRANSFORM_FUNC_2( 32f, float )
ICV_PERSPECTIVE_TRANSFORM_FUNC_2( 64f, double )
ICV_PERSPECTIVE_TRANSFORM_FUNC_3( 32f, float )
ICV_PERSPECTIVE_TRANSFORM_FUNC_3( 64f, double )
static void icvInitPerspectiveTransformTable( CvFuncTable* tab2, CvFuncTable* tab3 )\
{ \
tab2->fn_2d[CV_32F] = (void*)icvPerspectiveTransform_32f_C2R; \
tab2->fn_2d[CV_64F] = (void*)icvPerspectiveTransform_64f_C2R; \
tab3->fn_2d[CV_32F] = (void*)icvPerspectiveTransform_32f_C3R; \
tab3->fn_2d[CV_64F] = (void*)icvPerspectiveTransform_64f_C3R; \
}
CV_IMPL void
cvPerspectiveTransform( const CvArr* srcarr, CvArr* dstarr, const CvMat* mat )
{
static CvFuncTable tab[2];
static int inittab = 0;
double buffer[16];
CV_FUNCNAME( "cvPerspectiveProject" );
__BEGIN__;
CvMat sstub, *src = (CvMat*)srcarr;
CvMat dstub, *dst = (CvMat*)dstarr;
int i, j, type, cn;
CvFunc2D_2A1P func = 0;
CvSize size;
if( !inittab )
{
icvInitPerspectiveTransformTable( &tab[0], &tab[1] );
inittab = 1;
}
if( !CV_IS_MAT( src ))
{
int coi = 0;
CV_CALL( src = cvGetMat( src, &sstub, &coi ));
if( coi != 0 )
CV_ERROR( CV_BadCOI, "" );
}
if( !CV_IS_MAT( dst ))
{
int coi = 0;
CV_CALL( dst = cvGetMat( dst, &dstub, &coi ));
if( coi != 0 )
CV_ERROR( CV_BadCOI, "" );
}
if( !CV_ARE_TYPES_EQ( src, dst ))
CV_ERROR( CV_StsUnmatchedFormats, "" );
if( !CV_ARE_SIZES_EQ( src, dst ))
CV_ERROR( CV_StsUnmatchedSizes, "" );
type = CV_MAT_TYPE( src->type );
cn = CV_MAT_CN( type );
if( cn != 2 && cn != 3 )
CV_ERROR( CV_BadNumChannels, cvUnsupportedFormat );
if( !CV_IS_MAT( mat ))
CV_ERROR( CV_StsBadArg, "Invalid transformation matrix" );
if( mat->rows != cn + 1 && mat->cols != mat->rows )
CV_ERROR( CV_StsBadSize,
"The size of transform matrix must be equal to number of channels" );
if( CV_MAT_TYPE( mat->type ) == CV_64FC1 )
{
for( i = 0; i <= cn; i++ )
{
for( j = 0; j <= cn; j++ )
buffer[i*(cn+1) + j] = ((double*)(mat->data.ptr + mat->step*i))[j];
}
}
else if( CV_MAT_TYPE( mat->type ) == CV_32FC1 )
{
for( i = 0; i <= cn; i++ )
{
for( j = 0; j <= cn; j++ )
buffer[i*(cn+1) + j] = ((float*)(mat->data.ptr + mat->step*i))[j];
}
}
else
{
CV_ERROR( CV_StsUnsupportedFormat, "Rotation matrix must be 32fC1 or 64fC1" );
}
func = (CvFunc2D_2A1P)tab[cn == 3].fn_2d[CV_MAT_DEPTH(type)];
if( !func )
CV_ERROR( CV_StsUnsupportedFormat, "" );
size = cvGetMatSize( src );
if( CV_IS_MAT_CONT( src->type & dst->type ))
{
size.width *= size.height;
size.height = 1;
}
IPPI_CALL( func( src->data.ptr, src->step, dst->data.ptr, dst->step, size, buffer));
CV_CHECK_NANS( dst );
__END__;
}
/****************************************************************************************\
* cvScaleAdd *
\****************************************************************************************/
#define ICV_DEF_MULADDC_CASE_C1( arrtype, temptype, src1, src2, dst, len ) \
{ \
int i; \
\
for( i = 0; i <= (len) - 4; i += 4 ) \
{ \
temptype t0 = (src1)[i]*s0 + (src2)[i]; \
temptype t1 = (src1)[i+1]*s0 + (src2)[i+1]; \
\
(dst)[i] = (arrtype)t0; \
(dst)[i+1] = (arrtype)t1; \
\
t0 = (src1)[i+2]*s0 + (src2)[i+2]; \
t1 = (src1)[i+3]*s0 + (src2)[i+3]; \
\
(dst)[i+2] = (arrtype)t0; \
(dst)[i+3] = (arrtype)t1; \
} \
\
for( ; i < (len); i++ ) \
{ \
temptype t0 = (src1)[i]*s0 + (src2)[i]; \
(dst)[i] = (arrtype)t0; \
} \
}
#define ICV_DEF_MULADDC_CASE_C2( arrtype, temptype, src1, src2, dst, len ) \
{ \
int i; \
\
for( i = 0; i <= (len) - 4; i += 4 ) \
{ \
temptype t0 = (src1)[i]*s0 - (src1)[i+1]*s1 + (src2)[i]; \
temptype t1 = (src1)[i]*s1 + (src1)[i+1]*s0 + (src2)[i+1]; \
\
(dst)[i] = (arrtype)t0; \
(dst)[i+1] = (arrtype)t1; \
\
t0 = (src1)[i+2]*s0 - (src1)[i+3]*s1 + (src2)[i+2]; \
t1 = (src1)[i+2]*s1 + (src1)[i+3]*s0 + (src2)[i+3]; \
\
(dst)[i+2] = (arrtype)t0; \
(dst)[i+3] = (arrtype)t1; \
} \
\
for( ; i < (len); i += 2 ) \
{ \
temptype t0 = (src1)[i]*s0 - (src1)[i+1]*s1 + (src2)[i]; \
temptype t1 = (src1)[i]*s1 + (src1)[i+1]*s0 + (src2)[i+1]; \
\
(dst)[i] = (arrtype)t0; \
(dst)[i+1] = (arrtype)t1; \
} \
}
#define ICV_DEF_MULADDS_FUNC( flavor, arrtype, scalartype, entry, cn ) \
static CvStatus CV_STDCALL \
icvMulAddC_##flavor( const arrtype* src1, int srcstep1, \
const arrtype* src2, int srcstep2, \
arrtype* dst, int dststep, CvSize size, \
const scalartype* scalar ) \
{ \
entry(scalartype); \
size.width *= (cn); \
srcstep1 /= sizeof(src1[0]); srcstep2 /= sizeof(src2[0]); \
dststep /= sizeof(dst[0]); \
\
for( ; size.height--; src1+=srcstep1, src2+=srcstep2, dst+=dststep ) \
{ \
ICV_DEF_MULADDC_CASE_C##cn( arrtype, scalartype, src1, src2, \
dst, size.width ) \
} \
\
return CV_OK; \
}
ICV_DEF_MULADDS_FUNC( 32f_C1R, float, double, CV_UN_ENTRY_C1, 1 )
ICV_DEF_MULADDS_FUNC( 32f_C2R, float, double, CV_UN_ENTRY_C2, 2 )
ICV_DEF_MULADDS_FUNC( 64f_C1R, double, double, CV_UN_ENTRY_C1, 1 )
ICV_DEF_MULADDS_FUNC( 64f_C2R, double, double, CV_UN_ENTRY_C2, 2 )
static void
icvInitMulAddCTable( CvBigFuncTable* tab )
{
tab->fn_2d[CV_32FC1] = (void*)icvMulAddC_32f_C1R;
tab->fn_2d[CV_32FC2] = (void*)icvMulAddC_32f_C2R;
tab->fn_2d[CV_64FC1] = (void*)icvMulAddC_64f_C1R;
tab->fn_2d[CV_64FC2] = (void*)icvMulAddC_64f_C2R;
}
CV_IMPL void
cvScaleAdd( const CvArr* srcarr1, CvScalar scale,
const CvArr* srcarr2, CvArr* dstarr )
{
static CvBigFuncTable muladds_tab;
static int inittab = 0;
CV_FUNCNAME( "cvScaleAdd" );
__BEGIN__;
CvMat stub1, *src1 = (CvMat*)srcarr1;
CvMat stub2, *src2 = (CvMat*)srcarr2;
CvMat stub, *dst = (CvMat*)dstarr;
CvSize size;
int type;
if( !CV_IS_MAT( src1 ) || !CV_IS_MAT(src2) || !CV_IS_MAT(dst))
{
int coi1 = 0, coi2 = 0, coi3 = 0;
CV_CALL( src1 = cvGetMat( src1, &stub1, &coi1 ));
CV_CALL( src2 = cvGetMat( src2, &stub2, &coi2 ));
CV_CALL( dst = cvGetMat( dst, &stub, &coi3 ));
if( coi1 + coi2 + coi3 != 0 )
CV_ERROR( CV_BadCOI, "" );
}
if( !CV_ARE_TYPES_EQ( src1, dst ) || !CV_ARE_TYPES_EQ( src2, dst ))
CV_ERROR( CV_StsUnmatchedFormats, "" );
if( !CV_ARE_SIZES_EQ( src1, dst ) || !CV_ARE_SIZES_EQ( src2, dst ))
CV_ERROR( CV_StsUnmatchedSizes, "" );
type = CV_MAT_TYPE( src1->type );
size = cvGetMatSize( src1 );
if( CV_IS_MAT_CONT( src1->type & src2->type & dst->type ))
{
size.width *= size.height;
if( size.width <= CV_MAX_INLINE_MAT_OP_SIZE )
{
if( type == CV_32FC1 )
{
float* mA = src1->data.fl;
float* mB = src2->data.fl;
float* mC = dst->data.fl;
do
{
mC[size.width - 1] = (float)(mA[size.width - 1]*scale.val[0] +
mB[size.width - 1]);
}
while( --size.width );
EXIT;
}
if( type == CV_64FC1 )
{
double* mA = src1->data.db;
double* mB = src2->data.db;
double* mC = dst->data.db;
do
{
mC[size.width - 1] = mA[size.width - 1]*scale.val[0] +
mB[size.width - 1];
}
while( --size.width );
EXIT;
}
}
size.height = 1;
}
if( !inittab )
{
icvInitMulAddCTable( &muladds_tab );
inittab = 1;
}
if( CV_MAT_CN(type) > 2 )
CV_ERROR( CV_StsOutOfRange, "The function only supports 1- and 2-channel arrays" );
{
CvFunc2D_3A1P func = (CvFunc2D_3A1P)(muladds_tab.fn_2d[type]);
if( !func )
CV_ERROR( CV_StsUnsupportedFormat, "" );
IPPI_CALL( func( src1->data.ptr, src1->step, src2->data.ptr, src2->step,
dst->data.ptr, dst->step, size, scale.val ));
}
CV_CHECK_NANS( dst );
__END__;
}
/****************************************************************************************\
* cvCalcCovarMatrix *
\****************************************************************************************/
#define ICV_DOT_PRODUCT_CASE( flavor, srctype, avgtype, load_macro ) \
static CvStatus CV_STDCALL \
icvDotProductShifted_##flavor##_C1R( const srctype* vec1, int vecstep1, \
const srctype* vec2, int vecstep2, \
const avgtype* avg, int avgstep, \
CvSize size, double* _result ) \
{ \
double result = 0; \
vecstep1 /= sizeof(vec1[0]); vecstep2 /= sizeof(vec2[0]); avgstep /= sizeof(avg[0]);\
\
for( ; size.height--; vec1 += vecstep1, vec2 += vecstep2, avg += avgstep ) \
{ \
int x; \
for( x = 0; x <= size.width - 4; x += 4 ) \
result += (load_macro(vec1[x]) - avg[x])*(load_macro(vec2[x]) - avg[x]) + \
(load_macro(vec1[x+1]) - avg[x+1])*(load_macro(vec2[x+1]) - avg[x+1]) + \
(load_macro(vec1[x+2]) - avg[x+2])*(load_macro(vec2[x+2]) - avg[x+2]) + \
(load_macro(vec1[x+3]) - avg[x+3])*(load_macro(vec2[x+3]) - avg[x+3]); \
for( ; x < size.width; x++ ) \
result += (load_macro(vec1[x]) - avg[x])*(load_macro(vec2[x]) - avg[x]); \
} \
\
*_result = result; \
return CV_OK; \
}
ICV_DOT_PRODUCT_CASE( 8u32f, uchar, float, CV_8TO32F )
ICV_DOT_PRODUCT_CASE( 8u64f, uchar, double, CV_8TO32F )
ICV_DOT_PRODUCT_CASE( 16u32f, ushort, float, CV_NOP )
ICV_DOT_PRODUCT_CASE( 16u64f, ushort, double, CV_NOP )
ICV_DOT_PRODUCT_CASE( 16s32f, short, float, CV_NOP )
ICV_DOT_PRODUCT_CASE( 16s64f, short, double, CV_NOP )
ICV_DOT_PRODUCT_CASE( 32f, float, float, CV_NOP )
ICV_DOT_PRODUCT_CASE( 32f64f, float, double, CV_NOP )
ICV_DOT_PRODUCT_CASE( 64f, double, double, CV_NOP )
static void icvInitDotProductShiftedTable( CvFuncTable* tabfl, CvFuncTable* tabdb )
{
tabfl->fn_2d[CV_8U] = (void*)icvDotProductShifted_8u32f_C1R;
tabfl->fn_2d[CV_8S] = 0;
tabfl->fn_2d[CV_16U] = (void*)icvDotProductShifted_16u32f_C1R;
tabfl->fn_2d[CV_16S] = (void*)icvDotProductShifted_16s32f_C1R;
tabfl->fn_2d[CV_32S] = 0;
tabfl->fn_2d[CV_32F] = (void*)icvDotProductShifted_32f_C1R;
tabfl->fn_2d[CV_64F] = 0;
tabdb->fn_2d[CV_8U] = (void*)icvDotProductShifted_8u64f_C1R;
tabdb->fn_2d[CV_8S] = 0;
tabdb->fn_2d[CV_16U] = (void*)icvDotProductShifted_16u64f_C1R;
tabdb->fn_2d[CV_16S] = (void*)icvDotProductShifted_16s64f_C1R;
tabdb->fn_2d[CV_32S] = 0;
tabdb->fn_2d[CV_32F] = (void*)icvDotProductShifted_32f64f_C1R;
tabdb->fn_2d[CV_64F] = (void*)icvDotProductShifted_64f_C1R;
}
#define ICV_EXT_PRODUCT_CASE( flavor, srctype, avgtype, load_macro ) \
static CvStatus CV_STDCALL \
icvExtProductShifted_##flavor##_C1R( const srctype* vec, int vecstep, \
const avgtype* avg, int avgstep, \
avgtype* dst, int dststep, \
CvSize size, avgtype* tempbuf ) \
{ \
int x, y, dstsize = size.width * size.height; \
\
vecstep /= sizeof(vec[0]); avgstep /= sizeof(avg[0]); \
for( y = 0; y < size.height; y++, vec += vecstep, avg += avgstep ) \
for( x = 0; x < size.width; x++ ) \
*tempbuf++ = load_macro(vec[x]) - avg[x]; \
tempbuf -= dstsize; \
\
dststep /= sizeof(dst[0]); \
for( y = 0; y < dstsize; y++, dst += dststep ) \
{ \
double ty = tempbuf[y]; \
for( x = 0; x <= y - 3; x += 4 ) \
{ \
double t0 = dst[x] + ty*tempbuf[x]; \
double t1 = dst[x+1] + ty*tempbuf[x+1]; \
dst[x] = (avgtype)t0; \
dst[x+1] = (avgtype)t1; \
t0 = dst[x+2] + ty*tempbuf[x+2]; \
t1 = dst[x+3] + ty*tempbuf[x+3]; \
dst[x+2] = (avgtype)t0; \
dst[x+3] = (avgtype)t1; \
} \
for( ; x <= y; x++ ) \
dst[x] = (avgtype)(dst[x] + ty*tempbuf[x]); \
} \
\
return CV_OK; \
}
ICV_EXT_PRODUCT_CASE( 8u32f, uchar, float, CV_8TO32F )
ICV_EXT_PRODUCT_CASE( 8u64f, uchar, double, CV_8TO32F )
ICV_EXT_PRODUCT_CASE( 16u32f, ushort, float, CV_NOP )
ICV_EXT_PRODUCT_CASE( 16u64f, ushort, double, CV_NOP )
ICV_EXT_PRODUCT_CASE( 16s32f, short, float, CV_NOP )
ICV_EXT_PRODUCT_CASE( 16s64f, short, double, CV_NOP )
ICV_EXT_PRODUCT_CASE( 32f, float, float, CV_NOP )
ICV_EXT_PRODUCT_CASE( 32f64f, float, double, CV_NOP )
ICV_EXT_PRODUCT_CASE( 64f, double, double, CV_NOP )
static void icvInitExtProductShiftedTable( CvFuncTable* tabfl, CvFuncTable* tabdb )
{
tabfl->fn_2d[CV_8U] = (void*)icvExtProductShifted_8u32f_C1R;
tabfl->fn_2d[CV_8S] = 0;
tabfl->fn_2d[CV_16U] = (void*)icvExtProductShifted_16u32f_C1R;
tabfl->fn_2d[CV_16S] = (void*)icvExtProductShifted_16s32f_C1R;
tabfl->fn_2d[CV_32S] = 0;
tabfl->fn_2d[CV_32F] = (void*)icvExtProductShifted_32f_C1R;
tabfl->fn_2d[CV_64F] = 0;
tabdb->fn_2d[CV_8U] = (void*)icvExtProductShifted_8u64f_C1R;
tabdb->fn_2d[CV_8S] = 0;
tabdb->fn_2d[CV_16U] = (void*)icvExtProductShifted_16u64f_C1R;
tabdb->fn_2d[CV_16S] = (void*)icvExtProductShifted_16s64f_C1R;
tabdb->fn_2d[CV_32S] = 0;
tabdb->fn_2d[CV_32F] = (void*)icvExtProductShifted_32f64f_C1R;
tabdb->fn_2d[CV_64F] = (void*)icvExtProductShifted_64f_C1R;
}
typedef struct vec_data
{
void* ptr;
int step;
}
vec_data;
CV_IMPL void
cvCalcCovarMatrix( const CvArr** vecarr, int count,
CvArr* covarr, CvArr* avgarr, int flags )
{
static CvFuncTable dot_tab[2];
static CvFuncTable ext_tab[2];
static int inittab = 0;
vec_data* vecdata = 0;
CvMat *tempvec = 0;
CV_FUNCNAME( "cvCalcCovarMatrix" );
__BEGIN__;
CvMat covstub, *cov = (CvMat*)covarr;
CvMat avgstub, *avg = (CvMat*)avgarr;
CvMat vecstub0, *vecmat = 0;
CvSize srcsize, contsize;
int srctype = 0, dsttype = 0;
int i, j;
int cont_flag, vec_delta = 0, vec_step = 0;
int is_covar_normal = (flags & CV_COVAR_NORMAL) != 0;
double scale;
if( !inittab )
{
icvInitDotProductShiftedTable( dot_tab + 0, dot_tab + 1 );
icvInitExtProductShiftedTable( ext_tab + 0, ext_tab + 1 );
inittab = 1;
}
if( !vecarr )
CV_ERROR( CV_StsNullPtr, "NULL vec pointer" );
CV_CALL( cov = cvGetMat( cov, &covstub ));
CV_CALL( avg = cvGetMat( avg, &avgstub ));
if( !CV_ARE_TYPES_EQ( cov, avg ))
CV_ERROR( CV_StsUnmatchedFormats,
"Covariation matrix and average vector should have the same types" );
dsttype = CV_MAT_TYPE( cov->type );
if( dsttype != CV_32FC1 && dsttype != CV_64FC1 )
CV_ERROR( CV_StsUnsupportedFormat, "Covariation matrix must be 32fC1 or 64fC1" );
if( cov->rows != cov->cols )
CV_ERROR( CV_StsBadSize, "Covariation matrix must be square" );
srcsize = cvGetMatSize( avg );
contsize.width = srcsize.width * srcsize.height;
contsize.height = 1;
cont_flag = avg->type;
if( flags & (CV_COVAR_ROWS|CV_COVAR_COLS) )
{
CV_CALL( vecmat = cvGetMat( vecarr[0], &vecstub0 ));
srctype = CV_MAT_TYPE(vecmat->type);
if( flags & CV_COVAR_COLS )
{
count = vecmat->cols;
if( avg->cols != 1 || avg->rows != vecmat->rows )
CV_ERROR( CV_StsUnmatchedSizes,
"The number of input vectors does not match to avg vector size" );
cont_flag = 0;
vec_delta = CV_ELEM_SIZE(vecmat->type);
vec_step = vecmat->step;
}
else
{
count = vecmat->rows;
if( avg->rows != 1 || avg->cols != vecmat->cols )
CV_ERROR( CV_StsUnmatchedSizes,
"The number of input vectors does not match to avg vector size" );
vec_delta = vecmat->step;
vec_step = CV_STUB_STEP;
}
if( !(flags & CV_COVAR_USE_AVG) )
CV_CALL( cvReduce( vecmat, avg, -1, CV_REDUCE_AVG ));
scale = !(flags & CV_COVAR_SCALE) ? 1. : 1./count;
cvMulTransposed( vecmat, cov, ((flags & CV_COVAR_ROWS)!=0) ^ ((flags & CV_COVAR_NORMAL)==0), avg, scale );
EXIT;
}
scale = !(flags & CV_COVAR_SCALE) ? 1. : 1./count;
if( is_covar_normal )
{
if( count <= 0 )
CV_ERROR( CV_StsBadSize,
"The number of vectors is zero or negative" );
if( cov->rows != contsize.width )
CV_ERROR( CV_StsUnmatchedSizes,
"The size of input vectors does not match with the size of covariation matrix" );
CV_CALL( tempvec = cvCreateMat( avg->rows, avg->cols, dsttype ));
}
else if( count != cov->rows )
CV_ERROR( CV_StsUnmatchedSizes,
"The vector count and covariance matrix size do not match" );
if( !(flags & (CV_COVAR_ROWS|CV_COVAR_COLS)) )
{
if( !(flags & CV_COVAR_USE_AVG) )
cvZero( avg );
CV_CALL( vecdata = (vec_data*)cvAlloc( count*sizeof(vecdata[0])));
for( i = 0; i < count; i++ )
{
CvMat vecstub, *vec = (CvMat*)vecarr[i];
CvMat* temp;
if( !CV_IS_MAT(vec) )
CV_CALL( vec = cvGetMat( vec, &vecstub ));
if( !CV_ARE_SIZES_EQ( vec, avg ))
CV_ERROR( CV_StsUnmatchedSizes,
"All input vectors and average vector must have the same size" );
vecdata[i].ptr = vec->data.ptr;
vecdata[i].step = vec->step;
cont_flag &= vec->type;
temp = vec;
if( i == 0 )
{
srctype = CV_MAT_TYPE( vec->type );
if( CV_MAT_CN( srctype ) != 1 )
CV_ERROR( CV_BadNumChannels, "All vectors must have a single channel" );
if( srctype != dsttype && !tempvec && !(flags & CV_COVAR_USE_AVG))
CV_CALL( tempvec = cvCreateMat( vec->rows, vec->cols, dsttype ));
}
else if( CV_MAT_TYPE(vec->type) != srctype )
CV_ERROR( CV_StsUnmatchedFormats,
"All input vectors must have the same type" );
if( !(flags & CV_COVAR_USE_AVG) )
{
if( tempvec )
{
temp = tempvec;
cvConvert( vec, temp );
}
cvAdd( temp, avg, avg );
}
}
if( !(flags & CV_COVAR_USE_AVG) )
cvScale( avg, avg, 1./count );
}
cont_flag = CV_IS_MAT_CONT( cont_flag );
if( cont_flag )
srcsize = contsize;
if( !is_covar_normal )
{
CvFunc2D_3A1P dot_func =
(CvFunc2D_3A1P)dot_tab[dsttype == CV_64FC1].fn_2d[CV_MAT_DEPTH(srctype)];
if( !dot_func )
CV_ERROR( CV_StsUnsupportedFormat,
"The format of input vectors is not supported" );
for( i = 0; i < count; i++ )
{
int a, b, delta;
if( !(i & 1) )
a = 0, b = i+1, delta = 1;
else
a = i, b = -1, delta = -1;
for( j = a; j != b; j += delta )
{
double result = 0;
void *v_i, *v_j;
int step_i, step_j;
if( !vecmat )
{
v_i = vecdata[i].ptr;
v_j = vecdata[j].ptr;
step_i = vecdata[i].step;
step_j = vecdata[j].step;
}
else
{
v_i = vecmat->data.ptr + vec_delta*i;
v_j = vecmat->data.ptr + vec_delta*j;
step_i = step_j = vec_step;
}
dot_func( v_i, step_i, v_j, step_j, avg->data.ptr, avg->step, srcsize, &result );
if( dsttype == CV_64FC1 )
{
((double*)(cov->data.ptr + i*cov->step))[j] =
((double*)(cov->data.ptr + j*cov->step))[i] = result*scale;
}
else
{
((float*)(cov->data.ptr + i*cov->step))[j] =
((float*)(cov->data.ptr + j*cov->step))[i] = (float)(result*scale);
}
}
}
}
else
{
uchar* cov_ptr = cov->data.ptr;
int cov_step = cov->step;
int cov_size = cov->rows;
CvFunc2D_3A1P ext_func =
(CvFunc2D_3A1P)ext_tab[dsttype == CV_64FC1].fn_2d[CV_MAT_DEPTH(srctype)];
if( !ext_func )
CV_ERROR( CV_StsUnsupportedFormat,
"The format of input vectors is not supported" );
cvZero( cov );
for( i = 0; i < count; i++ )
{
void* v;
int vstep;
if( !vecmat )
{
v = vecdata[i].ptr;
vstep = vecdata[i].step;
}
else
{
v = vecmat->data.ptr + vec_delta*i;
vstep = vec_step;
}
ext_func( v, vstep, avg->data.ptr, avg->step,
cov_ptr, cov_step, srcsize, tempvec->data.ptr );
}
if( dsttype == CV_64FC1 )
for( i = 0; i < cov_size; i++ )
for( j = 0; j <= i; j++ )
{
double* cov1 = ((double*)(cov_ptr + i*cov_step)) + j;
double* cov2 = ((double*)(cov_ptr + j*cov_step)) + i;
if( flags & CV_COVAR_SCALE )
*cov1 = *cov2 = *cov1*scale;
else
*cov2 = *cov1;
}
else
for( i = 0; i < cov_size; i++ )
for( j = 0; j <= i; j++ )
{
float* cov1 = ((float*)(cov_ptr + i*cov_step)) + j;
float* cov2 = ((float*)(cov_ptr + j*cov_step)) + i;
if( flags & CV_COVAR_SCALE )
*cov1 = *cov2 = (float)(*cov1*scale);
else
*cov2 = *cov1;
}
}
__END__;
cvFree( &vecdata );
cvReleaseMat( &tempvec );
}
/****************************************************************************************\
* cvMahalanobis *
\****************************************************************************************/
#define ICV_MAHALANOBIS( flavor, arrtype ) \
static CvStatus CV_STDCALL \
icvMahalanobis_##flavor##_C1R( const arrtype* mat, int matstep, \
const arrtype* vec, int len, double* _result ) \
{ \
int i, j; \
double result = 0; \
\
matstep /= sizeof(mat[0]); \
for( i = 0; i < len; i++, mat += matstep ) \
{ \
double row_sum = 0; \
for( j = 0; j <= len - 4; j += 4 ) \
row_sum += vec[j]*mat[j] + vec[j+1]*mat[j+1] + \
vec[j+2]*mat[j+2] + vec[j+3]*mat[j+3]; \
for( ; j < len; j++ ) \
row_sum += vec[j]*mat[j]; \
result += row_sum * vec[i]; \
} \
*_result = result; \
\
return CV_OK; \
}
ICV_MAHALANOBIS( 32f, float )
ICV_MAHALANOBIS( 64f, double )
static void icvInitMahalanobisTable( CvFuncTable* tab )
{
tab->fn_2d[CV_32F] = (void*)icvMahalanobis_32f_C1R;
tab->fn_2d[CV_64F] = (void*)icvMahalanobis_64f_C1R;
}
typedef CvStatus (CV_STDCALL * CvMahalanobisFunc)( const void* mat, int matstep,
const void* vec, int len, double* _result );
CV_IMPL double
cvMahalanobis( const CvArr* srcAarr, const CvArr* srcBarr, CvArr* matarr )
{
static CvFuncTable mahal_tab;
static int inittab = 0;
uchar* buffer = 0;
int local_alloc = 0;
double dist = 0;
CV_FUNCNAME( "cvMahalanobis" );
__BEGIN__;
int buf_size, elem_size, len;
CvMat stubA, *srcA = (CvMat*)srcAarr;
CvMat stubB, *srcB = (CvMat*)srcBarr;
CvMat stub, *mat = (CvMat*)matarr;
CvMat temp;
CvMahalanobisFunc func;
if( !inittab )
{
icvInitMahalanobisTable( &mahal_tab );
inittab = 1;
}
if( !CV_IS_MAT(srcA) )
CV_CALL( srcA = cvGetMat( srcA, &stubA ));
if( !CV_IS_MAT(srcB) )
CV_CALL( srcB = cvGetMat( srcB, &stubB ));
if( !CV_IS_MAT(mat) )
CV_CALL( mat = cvGetMat( mat, &stub ));
if( srcA->rows != 1 && srcA->cols != 1 )
CV_ERROR( CV_StsBadSize, "Input matrices must be 1-d vectors" );
len = srcA->rows + srcA->cols - 1;
if( !CV_ARE_SIZES_EQ(srcA,srcB) )
CV_ERROR( CV_StsUnmatchedSizes, "Input vectors have different sizes" );
if( mat->rows != len || mat->cols != len )
CV_ERROR( CV_StsUnmatchedSizes, "Input vectors and covariation matrix have different sizes" );
func = (CvMahalanobisFunc)mahal_tab.fn_2d[CV_MAT_DEPTH(srcA->type)];
if( CV_MAT_CN(srcA->type) > 1 || !func )
CV_ERROR( CV_StsUnsupportedFormat,
"Only single-channel floating-point vectors are supported" );
if( !CV_ARE_TYPES_EQ(srcA,srcB) || !CV_ARE_TYPES_EQ(srcA,mat) )
CV_ERROR( CV_StsUnmatchedSizes, "Input vectors have different sizes" );
elem_size = CV_ELEM_SIZE(srcA->type);
buf_size = len*elem_size;
if( buf_size <= CV_MAX_LOCAL_SIZE )
{
buffer = (uchar*)cvStackAlloc( buf_size );
local_alloc = 1;
}
else
{
CV_CALL( buffer = (uchar*)cvAlloc( buf_size ));
}
temp = cvMat( srcA->rows, srcA->cols, srcA->type, buffer );
CV_CALL( cvSub( srcA, srcB, &temp ));
IPPI_CALL( func( mat->data.ptr, mat->step, temp.data.ptr, len, &dist ));
dist = sqrt(dist);
__END__;
if( buffer && !local_alloc )
cvFree( &buffer );
return dist;
}
/****************************************************************************************\
* cvMulTransposed *
\****************************************************************************************/
#define ICV_DEF_MULTRANS_R_FUNC( flavor, srctype, dsttype, load_macro ) \
static CvStatus CV_STDCALL \
icvMulTransposedR_##flavor( const srctype* src, int srcstep, \
dsttype* dst, int dststep, \
const dsttype* delta, int deltastep, \
CvSize size, int delta_cols, double scale ) \
{ \
int i, j, k; \
dsttype* tdst = dst; \
dsttype* col_buf = 0; \
dsttype* delta_buf = 0; \
int local_alloc = 0; \
int buf_size = size.height*sizeof(dsttype); \
\
if( delta && delta_cols < size.width ) \
{ \
assert( delta_cols == 1 ); \
buf_size += 4*buf_size; \
} \
\
if( buf_size <= CV_MAX_LOCAL_SIZE ) \
{ \
col_buf = (dsttype*)cvStackAlloc( buf_size ); \
local_alloc = 1; \
} \
else \
{ \
col_buf = (dsttype*)cvAlloc( buf_size ); \
if( !col_buf ) \
return CV_OUTOFMEM_ERR; \
} \
\
srcstep /= sizeof(src[0]); dststep /= sizeof(dst[0]); \
deltastep /= sizeof(delta[0]); \
\
if( delta && delta_cols < size.width ) \
{ \
delta_buf = col_buf + size.height; \
for( i = 0; i < size.height; i++ ) \
delta_buf[i*4] = delta_buf[i*4+1] = \
delta_buf[i*4+2] = delta_buf[i*4+3] = delta[i*deltastep]; \
delta = delta_buf; \
deltastep = deltastep ? 4 : 0; \
} \
\
if( !delta ) \
for( i = 0; i < size.width; i++, tdst += dststep ) \
{ \
for( k = 0; k < size.height; k++ ) \
col_buf[k] = src[k*srcstep+i]; \
\
for( j = i; j <= size.width - 4; j += 4 ) \
{ \
double s0 = 0, s1 = 0, s2 = 0, s3 = 0; \
const srctype *tsrc = src + j; \
\
for( k = 0; k < size.height; k++, tsrc += srcstep ) \
{ \
double a = col_buf[k]; \
s0 += a * load_macro(tsrc[0]); \
s1 += a * load_macro(tsrc[1]); \
s2 += a * load_macro(tsrc[2]); \
s3 += a * load_macro(tsrc[3]); \
} \
\
tdst[j] = (dsttype)(s0*scale); \
tdst[j+1] = (dsttype)(s1*scale); \
tdst[j+2] = (dsttype)(s2*scale); \
tdst[j+3] = (dsttype)(s3*scale); \
} \
\
for( ; j < size.width; j++ ) \
{ \
double s0 = 0; \
const srctype *tsrc = src + j; \
\
for( k = 0; k < size.height; k++, tsrc += srcstep ) \
s0 += col_buf[k] * tsrc[0]; \
\
tdst[j] = (dsttype)(s0*scale); \
} \
} \
else \
for( i = 0; i < size.width; i++, tdst += dststep ) \
{ \
if( !delta_buf ) \
for( k = 0; k < size.height; k++ ) \
col_buf[k] = load_macro(src[k*srcstep+i]) - delta[k*deltastep+i]; \
else \
for( k = 0; k < size.height; k++ ) \
col_buf[k] = load_macro(src[k*srcstep+i]) - delta_buf[k*deltastep]; \
\
for( j = i; j <= size.width - 4; j += 4 ) \
{ \
double s0 = 0, s1 = 0, s2 = 0, s3 = 0; \
const srctype *tsrc = src + j; \
const dsttype *d = delta_buf ? delta_buf : delta + j; \
\
for( k = 0; k < size.height; k++, tsrc+=srcstep, d+=deltastep ) \
{ \
double a = col_buf[k]; \
s0 += a * (load_macro(tsrc[0]) - d[0]); \
s1 += a * (load_macro(tsrc[1]) - d[1]); \
s2 += a * (load_macro(tsrc[2]) - d[2]); \
s3 += a * (load_macro(tsrc[3]) - d[3]); \
} \
\
tdst[j] = (dsttype)(s0*scale); \
tdst[j+1] = (dsttype)(s1*scale); \
tdst[j+2] = (dsttype)(s2*scale); \
tdst[j+3] = (dsttype)(s3*scale); \
} \
\
for( ; j < size.width; j++ ) \
{ \
double s0 = 0; \
const srctype *tsrc = src + j; \
const dsttype *d = delta_buf ? delta_buf : delta + j; \
\
for( k = 0; k < size.height; k++, tsrc+=srcstep, d+=deltastep ) \
s0 += col_buf[k] * (load_macro(tsrc[0]) - d[0]); \
\
tdst[j] = (dsttype)(s0*scale); \
} \
} \
\
/* fill the lower part of the destination matrix */ \
for( i = 1; i < size.width; i++ ) \
for( j = 0; j < i; j++ ) \
dst[dststep*i + j] = dst[dststep*j + i]; \
\
if( col_buf && !local_alloc ) \
cvFree( &col_buf ); \
\
return CV_NO_ERR; \
}
#define ICV_DEF_MULTRANS_L_FUNC( flavor, srctype, dsttype, load_macro ) \
static CvStatus CV_STDCALL \
icvMulTransposedL_##flavor( const srctype* src, int srcstep, \
dsttype* dst, int dststep, \
dsttype* delta, int deltastep, \
CvSize size, int delta_cols, double scale ) \
{ \
int i, j, k; \
dsttype* tdst = dst; \
\
srcstep /= sizeof(src[0]); dststep /= sizeof(dst[0]); \
deltastep /= sizeof(delta[0]); \
\
if( !delta ) \
for( i = 0; i < size.height; i++, tdst += dststep ) \
for( j = i; j < size.height; j++ ) \
{ \
double s = 0; \
const srctype *tsrc1 = src + i*srcstep; \
const srctype *tsrc2 = src + j*srcstep; \
\
for( k = 0; k <= size.width - 4; k += 4 ) \
s += tsrc1[k]*tsrc2[k] + tsrc1[k+1]*tsrc2[k+1] + \
tsrc1[k+2]*tsrc2[k+2] + tsrc1[k+3]*tsrc2[k+3]; \
for( ; k < size.width; k++ ) \
s += tsrc1[k] * tsrc2[k]; \
tdst[j] = (dsttype)(s*scale); \
} \
else \
{ \
dsttype* row_buf = 0; \
int local_alloc = 0; \
int buf_size = size.width*sizeof(dsttype); \
dsttype delta_buf[4]; \
int delta_shift = delta_cols == size.width ? 4 : 0; \
\
if( buf_size <= CV_MAX_LOCAL_SIZE ) \
{ \
row_buf = (dsttype*)cvStackAlloc( buf_size ); \
local_alloc = 1; \
} \
else \
{ \
row_buf = (dsttype*)cvAlloc( buf_size ); \
if( !row_buf ) \
return CV_OUTOFMEM_ERR; \
} \
\
for( i = 0; i < size.height; i++, tdst += dststep ) \
{ \
const srctype *tsrc1 = src + i*srcstep; \
const dsttype *tdelta1 = delta + i*deltastep; \
\
if( delta_cols < size.width ) \
for( k = 0; k < size.width; k++ ) \
row_buf[k] = tsrc1[k] - tdelta1[0]; \
else \
for( k = 0; k < size.width; k++ ) \
row_buf[k] = tsrc1[k] - tdelta1[k]; \
\
for( j = i; j < size.height; j++ ) \
{ \
double s = 0; \
const srctype *tsrc2 = src + j*srcstep; \
const dsttype *tdelta2 = delta + j*deltastep; \
if( delta_cols < size.width ) \
{ \
delta_buf[0] = delta_buf[1] = \
delta_buf[2] = delta_buf[3] = tdelta2[0]; \
tdelta2 = delta_buf; \
} \
for( k = 0; k <= size.width-4; k += 4, tdelta2 += delta_shift ) \
s += row_buf[k]*(load_macro(tsrc2[k]) - tdelta2[0]) + \
row_buf[k+1]*(load_macro(tsrc2[k+1]) - tdelta2[1]) + \
row_buf[k+2]*(load_macro(tsrc2[k+2]) - tdelta2[2]) + \
row_buf[k+3]*(load_macro(tsrc2[k+3]) - tdelta2[3]); \
for( ; k < size.width; k++, tdelta2++ ) \
s += row_buf[k]*(load_macro(tsrc2[k]) - tdelta2[0]); \
tdst[j] = (dsttype)(s*scale); \
} \
} \
\
if( row_buf && !local_alloc ) \
cvFree( &row_buf ); \
} \
\
/* fill the lower part of the destination matrix */ \
for( j = 0; j < size.height - 1; j++ ) \
for( i = j; i < size.height; i++ ) \
dst[dststep*i + j] = dst[dststep*j + i]; \
\
return CV_NO_ERR; \
}
ICV_DEF_MULTRANS_R_FUNC( 8u32f, uchar, float, CV_8TO32F )
ICV_DEF_MULTRANS_R_FUNC( 8u64f, uchar, double, CV_8TO32F )
ICV_DEF_MULTRANS_R_FUNC( 32f, float, float, CV_NOP )
ICV_DEF_MULTRANS_R_FUNC( 32f64f, float, double, CV_NOP )
ICV_DEF_MULTRANS_R_FUNC( 64f, double, double, CV_NOP )
ICV_DEF_MULTRANS_R_FUNC( 16u32f, ushort, float, CV_NOP )
ICV_DEF_MULTRANS_R_FUNC( 16u64f, ushort, double, CV_NOP )
ICV_DEF_MULTRANS_R_FUNC( 16s32f, short, float, CV_NOP )
ICV_DEF_MULTRANS_R_FUNC( 16s64f, short, double, CV_NOP )
ICV_DEF_MULTRANS_L_FUNC( 8u32f, uchar, float, CV_8TO32F )
ICV_DEF_MULTRANS_L_FUNC( 8u64f, uchar, double, CV_8TO32F )
ICV_DEF_MULTRANS_L_FUNC( 32f, float, float, CV_NOP )
ICV_DEF_MULTRANS_L_FUNC( 32f64f, float, double, CV_NOP )
ICV_DEF_MULTRANS_L_FUNC( 64f, double, double, CV_NOP )
ICV_DEF_MULTRANS_L_FUNC( 16u32f, ushort, float, CV_NOP )
ICV_DEF_MULTRANS_L_FUNC( 16u64f, ushort, double, CV_NOP )
ICV_DEF_MULTRANS_L_FUNC( 16s32f, short, float, CV_NOP )
ICV_DEF_MULTRANS_L_FUNC( 16s64f, short, double, CV_NOP )
typedef CvStatus (CV_STDCALL * CvMulTransposedFunc)
( const void* src, int srcstep,
void* dst, int dststep, const void* delta,
int deltastep, CvSize size, int delta_cols, double scale );
CV_IMPL void
cvMulTransposed( const CvArr* srcarr, CvArr* dstarr,
int order, const CvArr* deltaarr, double scale )
{
const int gemm_level = 100; // boundary above which GEMM is faster.
CvMat* src2 = 0;
CV_FUNCNAME( "cvMulTransposed" );
__BEGIN__;
CvMat sstub, *src = (CvMat*)srcarr;
CvMat dstub, *dst = (CvMat*)dstarr;
CvMat deltastub, *delta = (CvMat*)deltaarr;
int stype, dtype;
if( !CV_IS_MAT( src ))
CV_CALL( src = cvGetMat( src, &sstub ));
if( !CV_IS_MAT( dst ))
CV_CALL( dst = cvGetMat( dst, &dstub ));
if( delta )
{
if( !CV_IS_MAT( delta ))
CV_CALL( delta = cvGetMat( delta, &deltastub ));
if( !CV_ARE_TYPES_EQ( dst, delta ))
CV_ERROR( CV_StsUnmatchedFormats, "" );
if( (delta->rows != src->rows && delta->rows != 1) ||
(delta->cols != src->cols && delta->cols != 1) )
CV_ERROR( CV_StsUnmatchedSizes, "" );
}
else
{
delta = &deltastub;
delta->data.ptr = 0;
delta->step = 0;
delta->rows = delta->cols = 0;
}
stype = CV_MAT_TYPE( src->type );
dtype = CV_MAT_TYPE( dst->type );
if( dst->rows != dst->cols )
CV_ERROR( CV_StsBadSize, "The destination matrix must be square" );
if( (order != 0 && src->cols != dst->cols) ||
(order == 0 && src->rows != dst->rows))
CV_ERROR( CV_StsUnmatchedSizes, "" );
if( src->data.ptr == dst->data.ptr || (stype == dtype &&
(dst->cols >= gemm_level && dst->rows >= gemm_level &&
src->cols >= gemm_level && src->rows >= gemm_level)))
{
if( deltaarr )
{
CV_CALL( src2 = cvCreateMat( src->rows, src->cols, src->type ));
cvRepeat( delta, src2 );
cvSub( src, src2, src2 );
src = src2;
}
cvGEMM( src, src, scale, 0, 0, dst, order == 0 ? CV_GEMM_B_T : CV_GEMM_A_T );
}
else
{
CvMulTransposedFunc func =
stype == CV_8U && dtype == CV_32F ?
(order ? (CvMulTransposedFunc)icvMulTransposedR_8u32f :
(CvMulTransposedFunc)icvMulTransposedL_8u32f) :
stype == CV_8U && dtype == CV_64F ?
(order ? (CvMulTransposedFunc)icvMulTransposedR_8u64f :
(CvMulTransposedFunc)icvMulTransposedL_8u64f) :
stype == CV_16U && dtype == CV_32F ?
(order ? (CvMulTransposedFunc)icvMulTransposedR_16u32f :
(CvMulTransposedFunc)icvMulTransposedL_16u32f) :
stype == CV_16U && dtype == CV_64F ?
(order ? (CvMulTransposedFunc)icvMulTransposedR_16u64f :
(CvMulTransposedFunc)icvMulTransposedL_16u64f) :
stype == CV_16S && dtype == CV_32F ?
(order ? (CvMulTransposedFunc)icvMulTransposedR_16s32f :
(CvMulTransposedFunc)icvMulTransposedL_16s32f) :
stype == CV_16S && dtype == CV_64F ?
(order ? (CvMulTransposedFunc)icvMulTransposedR_16s64f :
(CvMulTransposedFunc)icvMulTransposedL_16s64f) :
stype == CV_32F && dtype == CV_32F ?
(order ? (CvMulTransposedFunc)icvMulTransposedR_32f :
(CvMulTransposedFunc)icvMulTransposedL_32f) :
stype == CV_32F && dtype == CV_64F ?
(order ? (CvMulTransposedFunc)icvMulTransposedR_32f64f :
(CvMulTransposedFunc)icvMulTransposedL_32f64f) :
stype == CV_64F && dtype == CV_64F ?
(order ? (CvMulTransposedFunc)icvMulTransposedR_64f :
(CvMulTransposedFunc)icvMulTransposedL_64f) : 0;
if( !func )
CV_ERROR( CV_StsUnsupportedFormat, "" );
IPPI_CALL( func( src->data.ptr, src->step, dst->data.ptr, dst->step,
delta->data.ptr, delta->step, cvGetMatSize( src ),
delta->cols, scale ));
}
__END__;
if( src2 )
cvReleaseMat( &src2 );
}
/****************************************************************************************\
* cvDotProduct *
\****************************************************************************************/
#define ICV_DEF_DOT_PROD_FUNC_2D( flavor, arrtype, temptype, sumtype ) \
static CvStatus CV_STDCALL \
icvDotProduct_##flavor##_C1R( const arrtype* src1, int step1, \
const arrtype* src2, int step2, \
CvSize size, sumtype* _sum ) \
{ \
sumtype sum = 0; \
step1 /= sizeof(src1[0]); step2 /= sizeof(src2[0]); \
\
for( ; size.height--; src1 += step1, src2 += step2 ) \
{ \
int i; \
\
for( i = 0; i <= size.width - 4; i += 4 ) \
{ \
temptype t0 = (temptype)src1[i]*src2[i]; \
temptype t1 = (temptype)src1[i+1]*src2[i+1]; \
t0 += (temptype)src1[i+2]*src2[i+2]; \
t1 += (temptype)src1[i+3]*src2[i+3]; \
sum += t0 + t1; \
} \
\
for( ; i < size.width; i++ ) \
{ \
sum += (temptype)src1[i]*src2[i]; \
} \
} \
\
*_sum = sum; \
return CV_OK; \
}
ICV_DEF_DOT_PROD_FUNC_2D( 8u, uchar, int, int64 )
ICV_DEF_DOT_PROD_FUNC_2D( 16u, ushort, int64, int64 )
ICV_DEF_DOT_PROD_FUNC_2D( 16s, short, int64, int64 )
ICV_DEF_DOT_PROD_FUNC_2D( 32s, int, double, double )
ICV_DEF_DOT_PROD_FUNC_2D( 32f, float, double, double )
ICV_DEF_DOT_PROD_FUNC_2D( 64f, double, double, double )
#define icvDotProduct_8s_C1R 0
CV_DEF_INIT_FUNC_TAB_2D( DotProduct, C1R )
CV_IMPL double
cvDotProduct( const CvArr* srcAarr, const CvArr* srcBarr )
{
static CvFuncTable tab_2d;
static int inittab = 0;
Cv64suf result;
result.f = 0;
CV_FUNCNAME( "cvDotProduct" );
__BEGIN__;
CvMat stubA, *srcA = (CvMat*)srcAarr;
CvMat stubB, *srcB = (CvMat*)srcBarr;
CvSize size;
int type, depth;
CvFunc2D_2A1P func;
if( !inittab )
{
icvInitDotProductC1RTable( &tab_2d );
inittab = 1;
}
if( !CV_IS_MAT( srcA ))
{
int coi = 0;
CV_CALL( srcA = cvGetMat( srcA, &stubA, &coi ));
if( coi != 0 )
CV_ERROR( CV_BadCOI, "coi is not supported" );
}
if( srcBarr == srcAarr )
srcB = srcA;
else
{
if( !CV_IS_MAT( srcB ))
{
int coi = 0;
CV_CALL( srcB = cvGetMat( srcB, &stubB, &coi ));
if( coi != 0 )
CV_ERROR( CV_BadCOI, "coi is not supported" );
}
if( !CV_ARE_TYPES_EQ( srcA, srcB ))
CV_ERROR( CV_StsUnmatchedFormats, "" );
if( !CV_ARE_SIZES_EQ( srcA, srcB ))
CV_ERROR( CV_StsUnmatchedSizes, "" );
}
type = CV_MAT_TYPE( srcA->type );
size = cvGetMatSize( srcA );
size.width *= CV_MAT_CN( type );
depth = CV_MAT_DEPTH( type );
if( CV_IS_MAT_CONT( srcA->type & srcB->type ))
{
size.width *= size.height;
if( size.width <= CV_MAX_INLINE_MAT_OP_SIZE )
{
if( depth == CV_32F )
{
float* mA = srcA->data.fl;
float* mB = srcB->data.fl;
double sum = 0;
do
sum += (double)mA[size.width - 1]*mB[size.width - 1];
while( --size.width );
result.f = sum;
EXIT;
}
if( depth == CV_64F )
{
double* mA = srcA->data.db;
double* mB = srcB->data.db;
double sum = 0;
do
sum += mA[size.width - 1]*mB[size.width - 1];
while( --size.width );
result.f = sum;
EXIT;
}
}
size.height = 1;
}
func = (CvFunc2D_2A1P)(tab_2d.fn_2d[depth]);
if( !func )
CV_ERROR( CV_StsUnsupportedFormat, "" );
IPPI_CALL( func( srcA->data.ptr, srcA->step,
srcB->data.ptr, srcB->step,
size, &result ));
if( depth < CV_32S )
result.f = (double)result.i;
__END__;
return result.f;
}
/* End of file. */