/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
// Copyright (C) 2014-2015, Itseez Inc., 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 the copyright holders 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 <sstream>
#include "precomp.hpp"
#include "opencl_kernels_core.hpp"
#include "opencv2/core/opencl/runtime/opencl_clamdblas.hpp"
#include "opencv2/core/opencl/runtime/opencl_core.hpp"
#include "intel_gpu_gemm.inl.hpp"
namespace cv
{
/****************************************************************************************\
* GEMM *
\****************************************************************************************/
static void
GEMM_CopyBlock( const uchar* src, size_t src_step,
uchar* dst, size_t dst_step,
Size size, size_t pix_size )
{
int j;
size.width *= (int)(pix_size / sizeof(int));
for( ; size.height--; src += src_step, dst += dst_step )
{
j=0;
#if CV_ENABLE_UNROLLED
for( ; 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;
}
#endif
for( ; j < size.width; j++ )
((int*)dst)[j] = ((const int*)src)[j];
}
}
static void
GEMM_TransposeBlock( const uchar* src, size_t src_step,
uchar* dst, size_t dst_step,
Size size, size_t 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;
}
}
}
template<typename T, typename WT> static void
GEMMSingleMul( const T* a_data, size_t a_step,
const T* b_data, size_t b_step,
const T* c_data, size_t c_step,
T* d_data, size_t d_step,
Size a_size, Size 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 T *_a_data = a_data, *_b_data = b_data, *_c_data = c_data;
cv::AutoBuffer<T> _a_buf;
T* 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 & GEMM_3_T) )
c_step0 = c_step, c_step1 = 1;
else
c_step0 = 1, c_step1 = c_step;
if( flags & GEMM_1_T )
{
CV_SWAP( a_step0, a_step1, t_step );
n = a_size.height;
if( a_step > 1 && n > 1 )
{
_a_buf.allocate(n);
a_buf = _a_buf.data();
}
}
if( n == 1 ) /* external product */
{
cv::AutoBuffer<T> _b_buf;
T* b_buf = 0;
if( a_step > 1 && a_size.height > 1 )
{
_a_buf.allocate(drows);
a_buf = _a_buf.data();
for( k = 0; k < drows; k++ )
a_buf[k] = a_data[a_step*k];
a_data = a_buf;
}
if( b_step > 1 )
{
_b_buf.allocate(d_size.width);
b_buf = _b_buf.data();
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 )
{
WT al = WT(a_data[i])*alpha;
c_data = _c_data;
for( j = 0; j <= d_size.width - 2; j += 2, c_data += 2*c_step1 )
{
WT s0 = al*WT(b_data[j]);
WT s1 = al*WT(b_data[j+1]);
if( !c_data )
{
d_data[j] = T(s0);
d_data[j+1] = T(s1);
}
else
{
d_data[j] = T(s0 + WT(c_data[0])*beta);
d_data[j+1] = T(s1 + WT(c_data[c_step1])*beta);
}
}
for( ; j < d_size.width; j++, c_data += c_step1 )
{
WT s0 = al*WT(b_data[j]);
if( !c_data )
d_data[j] = T(s0);
else
d_data[j] = T(s0 + WT(c_data[0])*beta);
}
}
}
else if( flags & GEMM_2_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 )
{
WT s0(0), s1(0), s2(0), s3(0);
k = 0;
#if CV_ENABLE_UNROLLED
for( ; k <= n - 4; k += 4 )
{
s0 += WT(a_data[k])*WT(b_data[k]);
s1 += WT(a_data[k+1])*WT(b_data[k+1]);
s2 += WT(a_data[k+2])*WT(b_data[k+2]);
s3 += WT(a_data[k+3])*WT(b_data[k+3]);
}
#endif
for( ; k < n; k++ )
s0 += WT(a_data[k])*WT(b_data[k]);
s0 = (s0+s1+s2+s3)*alpha;
if( !c_data )
d_data[j] = T(s0);
else
d_data[j] = T(s0 + WT(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 T* b = _b_data + j;
WT s0(0), s1(0), s2(0), s3(0);
for( k = 0; k < n; k++, b += b_step )
{
WT a(a_data[k]);
s0 += a * WT(b[0]); s1 += a * WT(b[1]);
s2 += a * WT(b[2]); s3 += a * WT(b[3]);
}
if( !c_data )
{
d_data[j] = T(s0*alpha);
d_data[j+1] = T(s1*alpha);
d_data[j+2] = T(s2*alpha);
d_data[j+3] = T(s3*alpha);
}
else
{
s0 = s0*alpha; s1 = s1*alpha;
s2 = s2*alpha; s3 = s3*alpha;
d_data[j] = T(s0 + WT(c_data[0])*beta);
d_data[j+1] = T(s1 + WT(c_data[c_step1])*beta);
d_data[j+2] = T(s2 + WT(c_data[c_step1*2])*beta);
d_data[j+3] = T(s3 + WT(c_data[c_step1*3])*beta);
}
}
for( ; j < m; j++, c_data += c_step1 )
{
const T* b = _b_data + j;
WT s0(0);
for( k = 0; k < n; k++, b += b_step )
s0 += WT(a_data[k]) * WT(b[0]);
s0 = s0*alpha;
if( !c_data )
d_data[j] = T(s0);
else
d_data[j] = T(s0 + WT(c_data[0])*beta);
}
}
}
else
{
cv::AutoBuffer<WT> _d_buf(m);
WT* d_buf = _d_buf.data();
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] = WT(0);
for( k = 0; k < n; k++, b_data += b_step )
{
WT al(a_data[k]);
j=0;
#if CV_ENABLE_UNROLLED
for(; j <= m - 4; j += 4 )
{
WT t0 = d_buf[j] + WT(b_data[j])*al;
WT t1 = d_buf[j+1] + WT(b_data[j+1])*al;
d_buf[j] = t0;
d_buf[j+1] = t1;
t0 = d_buf[j+2] + WT(b_data[j+2])*al;
t1 = d_buf[j+3] + WT(b_data[j+3])*al;
d_buf[j+2] = t0;
d_buf[j+3] = t1;
}
#endif
for( ; j < m; j++ )
d_buf[j] += WT(b_data[j])*al;
}
if( !c_data )
for( j = 0; j < m; j++ )
d_data[j] = T(d_buf[j]*alpha);
else
for( j = 0; j < m; j++, c_data += c_step1 )
{
WT t = d_buf[j]*alpha;
d_data[j] = T(t + WT(c_data[0])*beta);
}
}
}
}
template<typename T, typename WT> static void
GEMMBlockMul( const T* a_data, size_t a_step,
const T* b_data, size_t b_step,
WT* d_data, size_t d_step,
Size a_size, Size d_size, int flags )
{
int i, j, k, n = a_size.width, m = d_size.width;
const T *_a_data = a_data, *_b_data = b_data;
cv::AutoBuffer<T> _a_buf;
T* 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 & GEMM_1_T )
{
CV_SWAP( a_step0, a_step1, t_step );
n = a_size.height;
_a_buf.allocate(n);
a_buf = _a_buf.data();
}
if( flags & GEMM_2_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 )
{
WT s0 = do_acc ? d_data[j]:WT(0), s1(0);
for( k = 0; k <= n - 2; k += 2 )
{
s0 += WT(a_data[k])*WT(b_data[k]);
s1 += WT(a_data[k+1])*WT(b_data[k+1]);
}
for( ; k < n; k++ )
s0 += WT(a_data[k])*WT(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 )
{
WT s0, s1, s2, s3;
const T* 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 = WT(0);
for( k = 0; k < n; k++, b += b_step )
{
WT a(a_data[k]);
s0 += a * WT(b[0]); s1 += a * WT(b[1]);
s2 += a * WT(b[2]); s3 += a * WT(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 T* b = b_data + j;
WT s0 = do_acc ? d_data[j] : WT(0);
for( k = 0; k < n; k++, b += b_step )
s0 += WT(a_data[k]) * WT(b[0]);
d_data[j] = s0;
}
}
}
}
template<typename T, typename WT> static void
GEMMStore( const T* c_data, size_t c_step,
const WT* d_buf, size_t d_buf_step,
T* d_data, size_t d_step, Size d_size,
double alpha, double beta, int flags )
{
const T* _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 & GEMM_3_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;
j=0;
#if CV_ENABLE_UNROLLED
for(; j <= d_size.width - 4; j += 4, c_data += 4*c_step1 )
{
WT t0 = alpha*d_buf[j];
WT t1 = alpha*d_buf[j+1];
t0 += beta*WT(c_data[0]);
t1 += beta*WT(c_data[c_step1]);
d_data[j] = T(t0);
d_data[j+1] = T(t1);
t0 = alpha*d_buf[j+2];
t1 = alpha*d_buf[j+3];
t0 += beta*WT(c_data[c_step1*2]);
t1 += beta*WT(c_data[c_step1*3]);
d_data[j+2] = T(t0);
d_data[j+3] = T(t1);
}
#endif
for( ; j < d_size.width; j++, c_data += c_step1 )
{
WT t0 = alpha*d_buf[j];
d_data[j] = T(t0 + WT(c_data[0])*beta);
}
}
else
{
j = 0;
#if CV_ENABLE_UNROLLED
for( ; j <= d_size.width - 4; j += 4 )
{
WT t0 = alpha*d_buf[j];
WT t1 = alpha*d_buf[j+1];
d_data[j] = T(t0);
d_data[j+1] = T(t1);
t0 = alpha*d_buf[j+2];
t1 = alpha*d_buf[j+3];
d_data[j+2] = T(t0);
d_data[j+3] = T(t1);
}
#endif
for( ; j < d_size.width; j++ )
d_data[j] = T(alpha*d_buf[j]);
}
}
}
typedef void (*GEMMSingleMulFunc)( const void* src1, size_t step1,
const void* src2, size_t step2, const void* src3, size_t step3,
void* dst, size_t dststep, Size srcsize, Size dstsize,
double alpha, double beta, int flags );
typedef void (*GEMMBlockMulFunc)( const void* src1, size_t step1,
const void* src2, size_t step2, void* dst, size_t dststep,
Size srcsize, Size dstsize, int flags );
typedef void (*GEMMStoreFunc)( const void* src1, size_t step1,
const void* src2, size_t step2, void* dst, size_t dststep,
Size dstsize, double alpha, double beta, int flags );
static void GEMMSingleMul_32f( const float* a_data, size_t a_step,
const float* b_data, size_t b_step,
const float* c_data, size_t c_step,
float* d_data, size_t d_step,
Size a_size, Size d_size,
double alpha, double beta, int flags )
{
GEMMSingleMul<float,double>(a_data, a_step, b_data, b_step, c_data,
c_step, d_data, d_step, a_size, d_size,
alpha, beta, flags);
}
static void GEMMSingleMul_64f( const double* a_data, size_t a_step,
const double* b_data, size_t b_step,
const double* c_data, size_t c_step,
double* d_data, size_t d_step,
Size a_size, Size d_size,
double alpha, double beta, int flags )
{
GEMMSingleMul<double,double>(a_data, a_step, b_data, b_step, c_data,
c_step, d_data, d_step, a_size, d_size,
alpha, beta, flags);
}
static void GEMMSingleMul_32fc( const Complexf* a_data, size_t a_step,
const Complexf* b_data, size_t b_step,
const Complexf* c_data, size_t c_step,
Complexf* d_data, size_t d_step,
Size a_size, Size d_size,
double alpha, double beta, int flags )
{
GEMMSingleMul<Complexf,Complexd>(a_data, a_step, b_data, b_step, c_data,
c_step, d_data, d_step, a_size, d_size,
alpha, beta, flags);
}
static void GEMMSingleMul_64fc( const Complexd* a_data, size_t a_step,
const Complexd* b_data, size_t b_step,
const Complexd* c_data, size_t c_step,
Complexd* d_data, size_t d_step,
Size a_size, Size d_size,
double alpha, double beta, int flags )
{
GEMMSingleMul<Complexd,Complexd>(a_data, a_step, b_data, b_step, c_data,
c_step, d_data, d_step, a_size, d_size,
alpha, beta, flags);
}
static void GEMMBlockMul_32f( const float* a_data, size_t a_step,
const float* b_data, size_t b_step,
double* d_data, size_t d_step,
Size a_size, Size d_size, int flags )
{
GEMMBlockMul(a_data, a_step, b_data, b_step, d_data, d_step, a_size, d_size, flags);
}
static void GEMMBlockMul_64f( const double* a_data, size_t a_step,
const double* b_data, size_t b_step,
double* d_data, size_t d_step,
Size a_size, Size d_size, int flags )
{
GEMMBlockMul(a_data, a_step, b_data, b_step, d_data, d_step, a_size, d_size, flags);
}
static void GEMMBlockMul_32fc( const Complexf* a_data, size_t a_step,
const Complexf* b_data, size_t b_step,
Complexd* d_data, size_t d_step,
Size a_size, Size d_size, int flags )
{
GEMMBlockMul(a_data, a_step, b_data, b_step, d_data, d_step, a_size, d_size, flags);
}
static void GEMMBlockMul_64fc( const Complexd* a_data, size_t a_step,
const Complexd* b_data, size_t b_step,
Complexd* d_data, size_t d_step,
Size a_size, Size d_size, int flags )
{
GEMMBlockMul(a_data, a_step, b_data, b_step, d_data, d_step, a_size, d_size, flags);
}
static void GEMMStore_32f( const float* c_data, size_t c_step,
const double* d_buf, size_t d_buf_step,
float* d_data, size_t d_step, Size d_size,
double alpha, double beta, int flags )
{
GEMMStore(c_data, c_step, d_buf, d_buf_step, d_data, d_step, d_size, alpha, beta, flags);
}
static void GEMMStore_64f( const double* c_data, size_t c_step,
const double* d_buf, size_t d_buf_step,
double* d_data, size_t d_step, Size d_size,
double alpha, double beta, int flags )
{
GEMMStore(c_data, c_step, d_buf, d_buf_step, d_data, d_step, d_size, alpha, beta, flags);
}
static void GEMMStore_32fc( const Complexf* c_data, size_t c_step,
const Complexd* d_buf, size_t d_buf_step,
Complexf* d_data, size_t d_step, Size d_size,
double alpha, double beta, int flags )
{
GEMMStore(c_data, c_step, d_buf, d_buf_step, d_data, d_step, d_size, alpha, beta, flags);
}
static void GEMMStore_64fc( const Complexd* c_data, size_t c_step,
const Complexd* d_buf, size_t d_buf_step,
Complexd* d_data, size_t d_step, Size d_size,
double alpha, double beta, int flags )
{
GEMMStore(c_data, c_step, d_buf, d_buf_step, d_data, d_step, d_size, alpha, beta, flags);
}
#ifdef HAVE_CLAMDBLAS
static bool ocl_gemm_amdblas( InputArray matA, InputArray matB, double alpha,
InputArray matC, double beta, OutputArray matD, int flags )
{
int type = matA.type(), esz = CV_ELEM_SIZE(type);
bool haveC = matC.kind() != cv::_InputArray::NONE;
Size sizeA = matA.size(), sizeB = matB.size(), sizeC = haveC ? matC.size() : Size(0, 0);
bool atrans = (flags & GEMM_1_T) != 0, btrans = (flags & GEMM_2_T) != 0, ctrans = (flags & GEMM_3_T) != 0;
if (atrans)
sizeA = Size(sizeA.height, sizeA.width);
if (btrans)
sizeB = Size(sizeB.height, sizeB.width);
if (haveC && ctrans)
sizeC = Size(sizeC.height, sizeC.width);
Size sizeD(sizeB.width, sizeA.height);
CV_Assert( matB.type() == type && (!haveC || matC.type() == type) );
CV_Assert( sizeA.width == sizeB.height && (!haveC || sizeC == sizeD) );
matD.create(sizeD, type);
if ( matA.offset() % esz != 0 || matA.step() % esz != 0 ||
matB.offset() % esz != 0 || matB.step() % esz != 0 ||
(haveC && (matC.offset() % esz != 0 || matC.step() % esz != 0)) )
return false;
UMat A = matA.getUMat(), B = matB.getUMat(), D = matD.getUMat();
if (!ocl::internal::isCLBuffer(A) || !ocl::internal::isCLBuffer(B) || !ocl::internal::isCLBuffer(D))
{
return false;
}
if (haveC)
{
UMat C = matC.getUMat();
if (!ocl::internal::isCLBuffer(C))
return false;
}
if (haveC)
ctrans ? transpose(matC, D) : matC.copyTo(D);
else
D.setTo(Scalar::all(0));
int M = sizeD.height, N = sizeD.width, K = sizeA.width;
int lda = (int)A.step / esz, ldb = (int)B.step / esz, ldc = (int)D.step / esz;
int offa = (int)A.offset / esz, offb = (int)B.offset / esz, offc = (int)D.offset / esz;
cl_command_queue clq = (cl_command_queue)ocl::Queue::getDefault().ptr();
clAmdBlasTranspose transA = atrans ? clAmdBlasTrans : clAmdBlasNoTrans;
clAmdBlasTranspose transB = btrans ? clAmdBlasTrans : clAmdBlasNoTrans;
clAmdBlasOrder order = clAmdBlasRowMajor;
clAmdBlasStatus status = clAmdBlasSuccess;
if (type == CV_32FC1)
status = clAmdBlasSgemmEx(order, transA, transB, M, N, K,
(cl_float)alpha, (const cl_mem)A.handle(ACCESS_READ), offa, lda,
(const cl_mem)B.handle(ACCESS_READ), offb, ldb,
(cl_float)beta, (cl_mem)D.handle(ACCESS_RW), offc, ldc,
1, &clq, 0, NULL, NULL);
else if (type == CV_64FC1)
status = clAmdBlasDgemmEx(order, transA, transB, M, N, K,
alpha, (const cl_mem)A.handle(ACCESS_READ), offa, lda,
(const cl_mem)B.handle(ACCESS_READ), offb, ldb,
beta, (cl_mem)D.handle(ACCESS_RW), offc, ldc,
1, &clq, 0, NULL, NULL);
else if (type == CV_32FC2)
{
cl_float2 alpha_2 = { { (cl_float)alpha, 0 } };
cl_float2 beta_2 = { { (cl_float)beta, 0 } };
status = clAmdBlasCgemmEx(order, transA, transB, M, N, K,
alpha_2, (const cl_mem)A.handle(ACCESS_READ), offa, lda,
(const cl_mem)B.handle(ACCESS_READ), offb, ldb,
beta_2, (cl_mem)D.handle(ACCESS_RW), offc, ldc,
1, &clq, 0, NULL, NULL);
}
else if (type == CV_64FC2)
{
cl_double2 alpha_2 = { { alpha, 0 } };
cl_double2 beta_2 = { { beta, 0 } };
status = clAmdBlasZgemmEx(order, transA, transB, M, N, K,
alpha_2, (const cl_mem)A.handle(ACCESS_READ), offa, lda,
(const cl_mem)B.handle(ACCESS_READ), offb, ldb,
beta_2, (cl_mem)D.handle(ACCESS_RW), offc, ldc,
1, &clq, 0, NULL, NULL);
}
else
CV_Error(Error::StsUnsupportedFormat, "");
return status == clAmdBlasSuccess;
}
#endif
#ifdef HAVE_OPENCL
static bool ocl_gemm( InputArray matA, InputArray matB, double alpha,
InputArray matC, double beta, OutputArray matD, int flags )
{
int depth = matA.depth(), cn = matA.channels();
int type = CV_MAKETYPE(depth, cn);
CV_Assert_N( type == matB.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
const ocl::Device & dev = ocl::Device::getDefault();
bool doubleSupport = dev.doubleFPConfig() > 0;
if (!doubleSupport && depth == CV_64F)
return false;
bool haveC = matC.kind() != cv::_InputArray::NONE;
Size sizeA = matA.size(), sizeB = matB.size(), sizeC = haveC ? matC.size() : Size(0, 0);
bool atrans = (flags & GEMM_1_T) != 0, btrans = (flags & GEMM_2_T) != 0, ctrans = (flags & GEMM_3_T) != 0;
CV_Assert( !haveC || matC.type() == type );
Size sizeD(((btrans)? sizeB.height : sizeB.width),
((atrans)? sizeA.width : sizeA.height));
matD.create(sizeD, type);
UMat A = matA.getUMat(), B = matB.getUMat(), D = matD.getUMat();
if (!dev.intelSubgroupsSupport() || (depth == CV_64F) || cn != 1)
{
String opts;
if (atrans)
sizeA = Size(sizeA.height, sizeA.width);
if (btrans)
sizeB = Size(sizeB.height, sizeB.width);
if (haveC && ctrans)
sizeC = Size(sizeC.height, sizeC.width);
CV_Assert( sizeA.width == sizeB.height && (!haveC || sizeC == sizeD) );
int max_wg_size = (int)dev.maxWorkGroupSize();
int block_size = (max_wg_size / (32*cn) < 32) ? (max_wg_size / (16*cn) < 16) ? (max_wg_size / (8*cn) < 8) ? 1 : 8 : 16 : 32;
if (atrans)
A = A.t();
if (btrans)
B = B.t();
if (haveC)
ctrans ? transpose(matC, D) : matC.copyTo(D);
int vectorWidths[] = { 4, 4, 2, 2, 1, 4, cn, -1 };
int kercn = ocl::checkOptimalVectorWidth(vectorWidths, B, D);
opts += format(" -D T=%s -D T1=%s -D WT=%s -D cn=%d -D kercn=%d -D LOCAL_SIZE=%d%s%s%s",
ocl::typeToStr(type), ocl::typeToStr(depth), ocl::typeToStr(CV_MAKETYPE(depth, kercn)),
cn, kercn, block_size,
(sizeA.width % block_size !=0) ? " -D NO_MULT" : "",
haveC ? " -D HAVE_C" : "",
doubleSupport ? " -D DOUBLE_SUPPORT" : "");
ocl::Kernel k("gemm", cv::ocl::core::gemm_oclsrc, opts);
if (k.empty())
return false;
if (depth == CV_64F)
k.args(ocl::KernelArg::ReadOnlyNoSize(A),
ocl::KernelArg::ReadOnlyNoSize(B, cn, kercn),
ocl::KernelArg::ReadWrite(D, cn, kercn),
sizeA.width, alpha, beta);
else
k.args(ocl::KernelArg::ReadOnlyNoSize(A),
ocl::KernelArg::ReadOnlyNoSize(B, cn, kercn),
ocl::KernelArg::ReadWrite(D, cn, kercn),
sizeA.width, (float)alpha, (float)beta);
size_t globalsize[2] = { (size_t)sizeD.width * cn / kercn, (size_t)sizeD.height};
size_t localsize[2] = { (size_t)block_size, (size_t)block_size};
return k.run(2, globalsize, block_size!=1 ? localsize : NULL, false);
}
else
{
if (haveC && beta != 0.0)
{
ctrans ? transpose(matC, D) : matC.copyTo(D);
}
else
{
beta = 0.0;
}
return intel_gpu_gemm(A, sizeA,
B, sizeB,
D, sizeD,
alpha,
beta,
atrans, btrans);
}
}
#endif
static void gemmImpl( Mat A, Mat B, double alpha,
Mat C, double beta, Mat D, int flags )
{
CV_INSTRUMENT_REGION();
const int block_lin_size = 128;
const int block_size = block_lin_size * block_lin_size;
static double zero[] = {0,0,0,0};
static float zerof[] = {0,0,0,0};
Size a_size = A.size(), d_size;
int i, len = 0, type = A.type();
switch( flags & (GEMM_1_T|GEMM_2_T) )
{
case 0:
d_size = Size( B.cols, a_size.height );
len = B.rows;
break;
case 1:
d_size = Size( B.cols, a_size.width );
len = B.rows;
break;
case 2:
d_size = Size( B.rows, a_size.height );
len = B.cols;
break;
case 3:
d_size = Size( B.rows, a_size.width );
len = B.cols;
break;
}
if( flags == 0 && 2 <= len && len <= 4 && (len == d_size.width || len == d_size.height) )
{
if( type == CV_32F )
{
float* d = D.ptr<float>();
const float *a = A.ptr<float>(),
*b = B.ptr<float>(),
*c = (const float*)C.data;
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.data ? C.step/sizeof(c[0]) : 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;
return;
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;
return;
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;
return;
}
}
if( type == CV_64F )
{
double* d = D.ptr<double>();
const double *a = A.ptr<double>(),
*b = B.ptr<double>(),
*c = (const double*)C.data;
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.data ? C.step/sizeof(c[0]) : 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;
return;
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;
return;
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;
return;
}
}
}
{
size_t b_step = B.step;
GEMMSingleMulFunc singleMulFunc;
GEMMBlockMulFunc blockMulFunc;
GEMMStoreFunc storeFunc;
Mat *matD = &D;
const uchar* Cdata = C.data;
size_t Cstep = C.data ? (size_t)C.step : 0;
AutoBuffer<uchar> buf;
if( type == CV_32FC1 )
{
singleMulFunc = (GEMMSingleMulFunc)GEMMSingleMul_32f;
blockMulFunc = (GEMMBlockMulFunc)GEMMBlockMul_32f;
storeFunc = (GEMMStoreFunc)GEMMStore_32f;
}
else if( type == CV_64FC1 )
{
singleMulFunc = (GEMMSingleMulFunc)GEMMSingleMul_64f;
blockMulFunc = (GEMMBlockMulFunc)GEMMBlockMul_64f;
storeFunc = (GEMMStoreFunc)GEMMStore_64f;
}
else if( type == CV_32FC2 )
{
singleMulFunc = (GEMMSingleMulFunc)GEMMSingleMul_32fc;
blockMulFunc = (GEMMBlockMulFunc)GEMMBlockMul_32fc;
storeFunc = (GEMMStoreFunc)GEMMStore_32fc;
}
else
{
CV_Assert( type == CV_64FC2 );
singleMulFunc = (GEMMSingleMulFunc)GEMMSingleMul_64fc;
blockMulFunc = (GEMMBlockMulFunc)GEMMBlockMul_64fc;
storeFunc = (GEMMStoreFunc)GEMMStore_64fc;
}
if( (d_size.width == 1 || len == 1) && !(flags & GEMM_2_T) && B.isContinuous() )
{
b_step = d_size.width == 1 ? 0 : CV_ELEM_SIZE(type);
flags |= GEMM_2_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 & GEMM_1_T ? "t" : "n";
const char* transb = flags & GEMM_2_T ? "t" : "n";
int lda, ldb, ldd;
if( C->data.ptr )
{
if( C->data.ptr != D->data.ptr )
{
if( !(flags & GEMM_3_T) )
cvCopy( C, D );
else
cvTranspose( C, D );
}
}
if( CV_MAT_DEPTH(type) == CV_32F )
{
Complex32f _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) )
{
singleMulFunc( A.ptr(), A.step, B.ptr(), b_step, Cdata, Cstep,
matD->ptr(), matD->step, a_size, d_size, alpha, beta, flags );
}
else
{
int is_a_t = flags & GEMM_1_T;
int is_b_t = flags & GEMM_2_T;
int elem_size = CV_ELEM_SIZE(type);
int dk0_1, dk0_2;
size_t a_buf_size = 0, b_buf_size, d_buf_size;
uchar* a_buf = 0;
uchar* b_buf = 0;
uchar* d_buf = 0;
int j, k, di = 0, dj = 0, dk = 0;
int dm0, dn0, dk0;
size_t 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);
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.empty() )
{
c_step0 = c_step1 = 0;
flags &= ~GEMM_3_T;
}
else if( !(flags & GEMM_3_T) )
c_step0 = C.step, c_step1 = elem_size;
else
c_step0 = elem_size, c_step1 = C.step;
dm0 = std::min( block_lin_size, d_size.height );
dn0 = std::min( block_lin_size, d_size.width );
dk0_1 = block_size / dm0;
dk0_2 = block_size / dn0;
dk0 = std::min( dk0_1, dk0_2 );
dk0 = std::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 = (size_t)(dk0+dk0/8+1)*dk0_1*elem_size;
d_buf_size = (size_t)(dk0+dk0/8+1)*dk0_1*work_elem_size;
if( is_a_t )
{
a_buf_size = (size_t)(dm0+dm0/8+1)*((dk0+dk0/8+2)&-2)*elem_size;
flags &= ~GEMM_1_T;
}
buf.allocate(d_buf_size + b_buf_size + a_buf_size);
d_buf = buf.data();
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 = matD->ptr() + i*matD->step + j*elem_size;
const uchar* _c = Cdata + i*c_step0 + j*c_step1;
size_t _d_step = matD->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.ptr() + i*a_step0 + k*a_step1;
size_t _a_step = A.step;
const uchar* _b = B.ptr() + k*b_step0 + j*b_step1;
size_t _b_step = b_step;
Size 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 )
{
_a_step = dk*elem_size;
GEMM_TransposeBlock( _a, A.step, a_buf, _a_step, a_bl_size, elem_size );
std::swap( a_bl_size.width, a_bl_size.height );
_a = a_buf;
}
if( dj < d_size.width )
{
Size 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;
GEMM_CopyBlock( _b, b_step, b_buf, _b_step, b_size, elem_size );
_b = b_buf;
}
if( dk0 < len )
blockMulFunc( _a, _a_step, _b, _b_step, _d, _d_step,
a_bl_size, Size(dj,di), flags );
else
singleMulFunc( _a, _a_step, _b, _b_step, _c, Cstep,
_d, _d_step, a_bl_size, Size(dj,di), alpha, beta, flags );
flags |= 16;
}
if( dk0 < len )
storeFunc( _c, Cstep, _d, _d_step,
matD->ptr(i) + j*elem_size,
matD->step, Size(dj,di), alpha, beta, flags );
}
}
}
}
}
template <typename fptype>inline static void
callGemmImpl(const fptype *src1, size_t src1_step, const fptype *src2, size_t src2_step, fptype alpha,
const fptype *src3, size_t src3_step, fptype beta, fptype *dst, size_t dst_step, int m_a, int n_a, int n_d, int flags, int type)
{
CV_StaticAssert(GEMM_1_T == CV_HAL_GEMM_1_T, "Incompatible GEMM_1_T flag in HAL");
CV_StaticAssert(GEMM_2_T == CV_HAL_GEMM_2_T, "Incompatible GEMM_2_T flag in HAL");
CV_StaticAssert(GEMM_3_T == CV_HAL_GEMM_3_T, "Incompatible GEMM_3_T flag in HAL");
int b_m, b_n, c_m, c_n, m_d;
if(flags & GEMM_2_T)
{
b_m = n_d;
if(flags & GEMM_1_T )
{
b_n = m_a;
m_d = n_a;
}
else
{
b_n = n_a;
m_d = m_a;
}
}
else
{
b_n = n_d;
if(flags & GEMM_1_T )
{
b_m = m_a;
m_d = n_a;
}
else
{
m_d = m_a;
b_m = n_a;
}
}
if(flags & GEMM_3_T)
{
c_m = n_d;
c_n = m_d;
}
else
{
c_m = m_d;
c_n = n_d;
}
Mat A, B, C;
if(src1 != NULL)
A = Mat(m_a, n_a, type, (void*)src1, src1_step);
if(src2 != NULL)
B = Mat(b_m, b_n, type, (void*)src2, src2_step);
if(src3 != NULL && beta != 0.0)
C = Mat(c_m, c_n, type, (void*)src3, src3_step);
Mat D(m_d, n_d, type, (void*)dst, dst_step);
gemmImpl(A, B, alpha, C, beta, D, flags);
}
}
void cv::hal::gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags)
{
CALL_HAL(gemm32f, cv_hal_gemm32f, src1, src1_step, src2, src2_step, alpha, src3, src3_step, beta, dst, dst_step, m_a, n_a, n_d, flags)
callGemmImpl(src1, src1_step, src2, src2_step, alpha, src3, src3_step, beta, dst, dst_step, m_a, n_a, n_d, flags, CV_32F);
}
void cv::hal::gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags)
{
CALL_HAL(gemm64f, cv_hal_gemm64f, src1, src1_step, src2, src2_step, alpha, src3, src3_step, beta, dst, dst_step, m_a, n_a, n_d, flags)
callGemmImpl(src1, src1_step, src2, src2_step, alpha, src3, src3_step, beta, dst, dst_step, m_a, n_a, n_d, flags, CV_64F);
}
CV_EXPORTS void cv::hal::gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags)
{
CALL_HAL(gemm32fc, cv_hal_gemm32fc, src1, src1_step, src2, src2_step, alpha, src3, src3_step, beta, dst, dst_step, m_a, n_a, n_d, flags)
callGemmImpl(src1, src1_step, src2, src2_step, alpha, src3, src3_step, beta, dst, dst_step, m_a, n_a, n_d, flags, CV_32FC2);
}
CV_EXPORTS void cv::hal::gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags)
{
CALL_HAL(gemm64fc, cv_hal_gemm64fc, src1, src1_step, src2, src2_step, alpha, src3, src3_step, beta, dst, dst_step, m_a, n_a, n_d, flags)
callGemmImpl(src1, src1_step, src2, src2_step, alpha, src3, src3_step, beta, dst, dst_step, m_a, n_a, n_d, flags, CV_64FC2);
}
void cv::gemm( InputArray matA, InputArray matB, double alpha,
InputArray matC, double beta, OutputArray _matD, int flags )
{
#ifdef HAVE_CLAMDBLAS
CV_OCL_RUN(ocl::haveAmdBlas() && matA.dims() <= 2 && matB.dims() <= 2 && matC.dims() <= 2 && _matD.isUMat() &&
matA.cols() > 20 && matA.rows() > 20 && matB.cols() > 20, // since it works incorrect for small sizes
ocl_gemm_amdblas(matA, matB, alpha, matC, beta, _matD, flags))
#endif
#ifdef HAVE_OPENCL
CV_OCL_RUN(_matD.isUMat() && matA.dims() <= 2 && matB.dims() <= 2 && matC.dims() <= 2,
ocl_gemm(matA, matB, alpha, matC, beta, _matD, flags))
#endif
Mat A = matA.getMat(), B = matB.getMat(), C = beta != 0.0 ? matC.getMat() : Mat();
Size a_size = A.size(), d_size;
int len = 0, type = A.type();
CV_Assert_N( type == B.type(), (type == CV_32FC1 || type == CV_64FC1 || type == CV_32FC2 || type == CV_64FC2) );
switch( flags & (GEMM_1_T|GEMM_2_T) )
{
case 0:
d_size = Size( B.cols, a_size.height );
len = B.rows;
CV_Assert( a_size.width == len );
break;
case 1:
d_size = Size( B.cols, a_size.width );
len = B.rows;
CV_Assert( a_size.height == len );
break;
case 2:
d_size = Size( B.rows, a_size.height );
len = B.cols;
CV_Assert( a_size.width == len );
break;
case 3:
d_size = Size( B.rows, a_size.width );
len = B.cols;
CV_Assert( a_size.height == len );
break;
}
if( !C.empty() )
{
CV_Assert_N( C.type() == type,
(((flags&GEMM_3_T) == 0 && C.rows == d_size.height && C.cols == d_size.width) ||
((flags&GEMM_3_T) != 0 && C.rows == d_size.width && C.cols == d_size.height)));
}
_matD.create( d_size.height, d_size.width, type );
Mat D = _matD.getMat();
if( (flags & GEMM_3_T) != 0 && C.data == D.data )
{
transpose( C, C );
flags &= ~GEMM_3_T;
}
Mat *DProxyPtr = &D, DProxy;
if( D.data == A.data || D.data == B.data )
{
DProxy = Mat(d_size.height, d_size.width, D.type());
DProxyPtr = &DProxy;
}
if( type == CV_32FC1 )
hal::gemm32f(A.ptr<float>(), A.step, B.ptr<float>(), B.step, static_cast<float>(alpha),
C.ptr<float>(), C.step, static_cast<float>(beta),
DProxyPtr->ptr<float>(), DProxyPtr->step,
a_size.height, a_size.width, DProxyPtr->cols, flags);
else if( type == CV_64FC1 )
hal::gemm64f(A.ptr<double>(), A.step, B.ptr<double>(), B.step, alpha,
C.ptr<double>(), C.step, beta,
DProxyPtr->ptr<double>(), DProxyPtr->step,
a_size.height, a_size.width, DProxyPtr->cols, flags);
else if( type == CV_32FC2 )
hal::gemm32fc(A.ptr<float>(), A.step, B.ptr<float>(), B.step, static_cast<float>(alpha),
C.ptr<float>(), C.step, static_cast<float>(beta),
DProxyPtr->ptr<float>(), DProxyPtr->step,
a_size.height, a_size.width, DProxyPtr->cols, flags);
else
{
CV_Assert( type == CV_64FC2 );
hal::gemm64fc(A.ptr<double>(), A.step, B.ptr<double>(), B.step, alpha,
C.ptr<double>(), C.step, beta,
D.ptr<double>(), D.step,
a_size.height, a_size.width, DProxyPtr->cols, flags);
}
if(DProxyPtr != &D)
DProxyPtr->copyTo(D);
}
/****************************************************************************************\
* Transform *
\****************************************************************************************/
namespace cv
{
template<typename T, typename WT> static void
transform_( const T* src, T* dst, const WT* m, int len, int scn, int dcn )
{
int x;
if( scn == 2 && dcn == 2 )
{
for( x = 0; x < len*2; x += 2 )
{
WT v0 = src[x], v1 = src[x+1];
T t0 = saturate_cast<T>(m[0]*v0 + m[1]*v1 + m[2]);
T t1 = saturate_cast<T>(m[3]*v0 + m[4]*v1 + m[5]);
dst[x] = t0; dst[x+1] = t1;
}
}
else if( scn == 3 && dcn == 3 )
{
for( x = 0; x < len*3; x += 3 )
{
WT v0 = src[x], v1 = src[x+1], v2 = src[x+2];
T t0 = saturate_cast<T>(m[0]*v0 + m[1]*v1 + m[2]*v2 + m[3]);
T t1 = saturate_cast<T>(m[4]*v0 + m[5]*v1 + m[6]*v2 + m[7]);
T t2 = saturate_cast<T>(m[8]*v0 + m[9]*v1 + m[10]*v2 + m[11]);
dst[x] = t0; dst[x+1] = t1; dst[x+2] = t2;
}
}
else if( scn == 3 && dcn == 1 )
{
for( x = 0; x < len; x++, src += 3 )
dst[x] = saturate_cast<T>(m[0]*src[0] + m[1]*src[1] + m[2]*src[2] + m[3]);
}
else if( scn == 4 && dcn == 4 )
{
for( x = 0; x < len*4; x += 4 )
{
WT v0 = src[x], v1 = src[x+1], v2 = src[x+2], v3 = src[x+3];
T t0 = saturate_cast<T>(m[0]*v0 + m[1]*v1 + m[2]*v2 + m[3]*v3 + m[4]);
T t1 = saturate_cast<T>(m[5]*v0 + m[6]*v1 + m[7]*v2 + m[8]*v3 + m[9]);
dst[x] = t0; dst[x+1] = t1;
t0 = saturate_cast<T>(m[10]*v0 + m[11]*v1 + m[12]*v2 + m[13]*v3 + m[14]);
t1 = saturate_cast<T>(m[15]*v0 + m[16]*v1 + m[17]*v2 + m[18]*v3 + m[19]);
dst[x+2] = t0; dst[x+3] = t1;
}
}
else
{
for( x = 0; x < len; x++, src += scn, dst += dcn )
{
const WT* _m = m;
int j, k;
for( j = 0; j < dcn; j++, _m += scn + 1 )
{
WT s = _m[scn];
for( k = 0; k < scn; k++ )
s += _m[k]*src[k];
dst[j] = saturate_cast<T>(s);
}
}
}
}
#if CV_SIMD128 && !defined(__aarch64__)
static inline void
load3x3Matrix(const float* m, v_float32x4& m0, v_float32x4& m1, v_float32x4& m2, v_float32x4& m3)
{
m0 = v_float32x4(m[0], m[4], m[8], 0);
m1 = v_float32x4(m[1], m[5], m[9], 0);
m2 = v_float32x4(m[2], m[6], m[10], 0);
m3 = v_float32x4(m[3], m[7], m[11], 0);
}
#endif
#if CV_SIMD128
static inline v_int16x8
v_matmulvec(const v_int16x8 &v0, const v_int16x8 &m0, const v_int16x8 &m1, const v_int16x8 &m2, const v_int32x4 &m3, const int BITS)
{
// v0 : 0 b0 g0 r0 b1 g1 r1 ?
v_int32x4 t0 = v_dotprod(v0, m0); // a0 b0 a1 b1
v_int32x4 t1 = v_dotprod(v0, m1); // c0 d0 c1 d1
v_int32x4 t2 = v_dotprod(v0, m2); // e0 f0 e1 f1
v_int32x4 t3 = v_setzero_s32();
v_int32x4 s0, s1, s2, s3;
v_transpose4x4(t0, t1, t2, t3, s0, s1, s2, s3);
s0 = s0 + s1 + m3; // B0 G0 R0 ?
s2 = s2 + s3 + m3; // B1 G1 R1 ?
s0 = s0 >> BITS;
s2 = s2 >> BITS;
v_int16x8 result = v_pack(s0, v_setzero_s32()); // B0 G0 R0 0 0 0 0 0
result = v_reinterpret_as_s16(v_reinterpret_as_s64(result) << 16); // 0 B0 G0 R0 0 0 0 0
result = result | v_pack(v_setzero_s32(), s2); // 0 B0 G0 R0 B1 G1 R1 0
return result;
}
#endif
static void
transform_8u( const uchar* src, uchar* dst, const float* m, int len, int scn, int dcn )
{
#if CV_SIMD128
const int BITS = 10, SCALE = 1 << BITS;
const float MAX_M = (float)(1 << (15 - BITS));
if( hasSIMD128() && scn == 3 && dcn == 3 &&
std::abs(m[0]) < MAX_M && std::abs(m[1]) < MAX_M && std::abs(m[2]) < MAX_M && std::abs(m[3]) < MAX_M*256 &&
std::abs(m[4]) < MAX_M && std::abs(m[5]) < MAX_M && std::abs(m[6]) < MAX_M && std::abs(m[7]) < MAX_M*256 &&
std::abs(m[8]) < MAX_M && std::abs(m[9]) < MAX_M && std::abs(m[10]) < MAX_M && std::abs(m[11]) < MAX_M*256 )
{
const int nChannels = 3;
const int cWidth = v_int16x8::nlanes;
// faster fixed-point transformation
short m00 = saturate_cast<short>(m[0]*SCALE), m01 = saturate_cast<short>(m[1]*SCALE),
m02 = saturate_cast<short>(m[2]*SCALE), m10 = saturate_cast<short>(m[4]*SCALE),
m11 = saturate_cast<short>(m[5]*SCALE), m12 = saturate_cast<short>(m[6]*SCALE),
m20 = saturate_cast<short>(m[8]*SCALE), m21 = saturate_cast<short>(m[9]*SCALE),
m22 = saturate_cast<short>(m[10]*SCALE);
int m03 = saturate_cast<int>((m[3]+0.5f)*SCALE), m13 = saturate_cast<int>((m[7]+0.5f)*SCALE ),
m23 = saturate_cast<int>((m[11]+0.5f)*SCALE);
v_int16x8 m0 = v_int16x8(0, m00, m01, m02, m00, m01, m02, 0);
v_int16x8 m1 = v_int16x8(0, m10, m11, m12, m10, m11, m12, 0);
v_int16x8 m2 = v_int16x8(0, m20, m21, m22, m20, m21, m22, 0);
v_int32x4 m3 = v_int32x4(m03, m13, m23, 0);
int x = 0;
for (; x <= (len - cWidth) * nChannels; x += cWidth * nChannels)
{
// load 8 pixels
v_int16x8 v0 = v_reinterpret_as_s16(v_load_expand(src + x));
v_int16x8 v1 = v_reinterpret_as_s16(v_load_expand(src + x + cWidth));
v_int16x8 v2 = v_reinterpret_as_s16(v_load_expand(src + x + cWidth * 2));
v_int16x8 v3;
// rotate and pack
v3 = v_rotate_right<1>(v2); // 0 b6 g6 r6 b7 g7 r7 0
v2 = v_rotate_left <5>(v2, v1); // 0 b4 g4 r4 b5 g5 r5 0
v1 = v_rotate_left <3>(v1, v0); // 0 b2 g2 r2 b3 g3 r3 0
v0 = v_rotate_left <1>(v0); // 0 b0 g0 r0 b1 g1 r1 0
// multiply with matrix and normalize
v0 = v_matmulvec(v0, m0, m1, m2, m3, BITS); // 0 B0 G0 R0 B1 G1 R1 0
v1 = v_matmulvec(v1, m0, m1, m2, m3, BITS); // 0 B2 G2 R2 B3 G3 R3 0
v2 = v_matmulvec(v2, m0, m1, m2, m3, BITS); // 0 B4 G4 R4 B5 G5 R5 0
v3 = v_matmulvec(v3, m0, m1, m2, m3, BITS); // 0 B6 G6 R6 B7 G7 R7 0
// narrow down as uint8x16
v_uint8x16 z0 = v_pack_u(v0, v_setzero_s16()); // 0 B0 G0 R0 B1 G1 R1 0 0 0 0 0 0 0 0 0
v_uint8x16 z1 = v_pack_u(v1, v_setzero_s16()); // 0 B2 G2 R2 B3 G3 R3 0 0 0 0 0 0 0 0 0
v_uint8x16 z2 = v_pack_u(v2, v_setzero_s16()); // 0 B4 G4 R4 B5 G5 R5 0 0 0 0 0 0 0 0 0
v_uint8x16 z3 = v_pack_u(v3, v_setzero_s16()); // 0 B6 G6 R6 B7 G7 R7 0 0 0 0 0 0 0 0 0
// rotate and pack
z0 = v_reinterpret_as_u8(v_reinterpret_as_u64(z0) >> 8) | v_reinterpret_as_u8(v_reinterpret_as_u64(z1) << 40); // B0 G0 R0 B1 G1 R1 B2 G2 0 0 0 0 0 0 0 0
z1 = v_reinterpret_as_u8(v_reinterpret_as_u64(z1) >> 24) | v_reinterpret_as_u8(v_reinterpret_as_u64(z2) << 24); // R2 B3 G3 R3 B4 G4 R4 B5 0 0 0 0 0 0 0 0
z2 = v_reinterpret_as_u8(v_reinterpret_as_u64(z2) >> 40) | v_reinterpret_as_u8(v_reinterpret_as_u64(z3) << 8); // G5 R6 B6 G6 R6 B7 G7 R7 0 0 0 0 0 0 0 0
// store on memory
v_store_low(dst + x, z0);
v_store_low(dst + x + cWidth, z1);
v_store_low(dst + x + cWidth * 2, z2);
}
for( ; x < len * nChannels; x += nChannels )
{
int v0 = src[x], v1 = src[x+1], v2 = src[x+2];
uchar t0 = saturate_cast<uchar>((m00*v0 + m01*v1 + m02*v2 + m03)>>BITS);
uchar t1 = saturate_cast<uchar>((m10*v0 + m11*v1 + m12*v2 + m13)>>BITS);
uchar t2 = saturate_cast<uchar>((m20*v0 + m21*v1 + m22*v2 + m23)>>BITS);
dst[x] = t0; dst[x+1] = t1; dst[x+2] = t2;
}
return;
}
#endif
transform_(src, dst, m, len, scn, dcn);
}
static void
transform_16u( const ushort* src, ushort* dst, const float* m, int len, int scn, int dcn )
{
#if CV_SIMD128 && !defined(__aarch64__)
if( hasSIMD128() && scn == 3 && dcn == 3 )
{
const int nChannels = 3;
const int cWidth = v_float32x4::nlanes;
v_int16x8 delta = v_int16x8(0, -32768, -32768, -32768, -32768, -32768, -32768, 0);
v_float32x4 m0, m1, m2, m3;
load3x3Matrix(m, m0, m1, m2, m3);
m3 -= v_float32x4(32768.f, 32768.f, 32768.f, 0.f);
int x = 0;
for( ; x <= (len - cWidth) * nChannels; x += cWidth * nChannels )
{
// load 4 pixels
v_uint16x8 v0_16 = v_load(src + x); // b0 g0 r0 b1 g1 r1 b2 g2
v_uint16x8 v2_16 = v_load_low(src + x + cWidth * 2); // r2 b3 g3 r3 ? ? ? ?
// expand to 4 vectors
v_uint32x4 v0_32, v1_32, v2_32, v3_32, dummy_32;
v_expand(v_rotate_right<3>(v0_16), v1_32, dummy_32); // b1 g1 r1
v_expand(v_rotate_right<1>(v2_16), v3_32, dummy_32); // b3 g3 r3
v_expand(v_rotate_right<6>(v0_16, v2_16), v2_32, dummy_32); // b2 g2 r2
v_expand(v0_16, v0_32, dummy_32); // b0 g0 r0
// convert to float32x4
v_float32x4 x0 = v_cvt_f32(v_reinterpret_as_s32(v0_32)); // b0 g0 r0
v_float32x4 x1 = v_cvt_f32(v_reinterpret_as_s32(v1_32)); // b1 g1 r1
v_float32x4 x2 = v_cvt_f32(v_reinterpret_as_s32(v2_32)); // b2 g2 r2
v_float32x4 x3 = v_cvt_f32(v_reinterpret_as_s32(v3_32)); // b3 g3 r3
// multiply and convert back to int32x4
v_int32x4 y0, y1, y2, y3;
y0 = v_round(v_matmuladd(x0, m0, m1, m2, m3)); // B0 G0 R0
y1 = v_round(v_matmuladd(x1, m0, m1, m2, m3)); // B1 G1 R1
y2 = v_round(v_matmuladd(x2, m0, m1, m2, m3)); // B2 G2 R2
y3 = v_round(v_matmuladd(x3, m0, m1, m2, m3)); // B3 G3 R3
// narrow down to int16x8
v_int16x8 v0 = v_add_wrap(v_pack(v_rotate_left<1>(y0), y1), delta); // 0 B0 G0 R0 B1 G1 R1 0
v_int16x8 v2 = v_add_wrap(v_pack(v_rotate_left<1>(y2), y3), delta); // 0 B2 G2 R2 B3 G3 R3 0
// rotate and pack
v0 = v_rotate_right<1>(v0) | v_rotate_left<5>(v2); // B0 G0 R0 B1 G1 R1 B2 G2
v2 = v_rotate_right<3>(v2); // R2 B3 G3 R3 0 0 0 0
// store 4 pixels
v_store(dst + x, v_reinterpret_as_u16(v0));
v_store_low(dst + x + cWidth * 2, v_reinterpret_as_u16(v2));
}
for( ; x < len * nChannels; x += nChannels )
{
float v0 = src[x], v1 = src[x + 1], v2 = src[x + 2];
ushort t0 = saturate_cast<ushort>(m[0] * v0 + m[1] * v1 + m[2] * v2 + m[3]);
ushort t1 = saturate_cast<ushort>(m[4] * v0 + m[5] * v1 + m[6] * v2 + m[7]);
ushort t2 = saturate_cast<ushort>(m[8] * v0 + m[9] * v1 + m[10] * v2 + m[11]);
dst[x] = t0; dst[x + 1] = t1; dst[x + 2] = t2;
}
return;
}
#endif
transform_(src, dst, m, len, scn, dcn);
}
static void
transform_32f( const float* src, float* dst, const float* m, int len, int scn, int dcn )
{
#if CV_SIMD128 && !defined(__aarch64__)
if( hasSIMD128() )
{
int x = 0;
if( scn == 3 && dcn == 3 )
{
const int cWidth = 3;
v_float32x4 m0, m1, m2, m3;
load3x3Matrix(m, m0, m1, m2, m3);
for( ; x < (len - 1)*cWidth; x += cWidth )
{
v_float32x4 x0 = v_load(src + x);
v_float32x4 y0 = v_matmuladd(x0, m0, m1, m2, m3);
v_store_low(dst + x, y0);
dst[x + 2] = v_combine_high(y0, y0).get0();
}
for( ; x < len*cWidth; x += cWidth )
{
float v0 = src[x], v1 = src[x+1], v2 = src[x+2];
float t0 = saturate_cast<float>(m[0]*v0 + m[1]*v1 + m[2]*v2 + m[3]);
float t1 = saturate_cast<float>(m[4]*v0 + m[5]*v1 + m[6]*v2 + m[7]);
float t2 = saturate_cast<float>(m[8]*v0 + m[9]*v1 + m[10]*v2 + m[11]);
dst[x] = t0; dst[x+1] = t1; dst[x+2] = t2;
}
return;
}
if( scn == 4 && dcn == 4 )
{
const int cWidth = 4;
v_float32x4 m0 = v_float32x4(m[0], m[5], m[10], m[15]);
v_float32x4 m1 = v_float32x4(m[1], m[6], m[11], m[16]);
v_float32x4 m2 = v_float32x4(m[2], m[7], m[12], m[17]);
v_float32x4 m3 = v_float32x4(m[3], m[8], m[13], m[18]);
v_float32x4 m4 = v_float32x4(m[4], m[9], m[14], m[19]);
for( ; x < len*cWidth; x += cWidth )
{
v_float32x4 x0 = v_load(src + x);
v_float32x4 y0 = v_matmul(x0, m0, m1, m2, m3) + m4;
v_store(dst + x, y0);
}
return;
}
}
#endif
transform_(src, dst, m, len, scn, dcn);
}
static void
transform_8s(const schar* src, schar* dst, const float* m, int len, int scn, int dcn)
{
transform_(src, dst, m, len, scn, dcn);
}
static void
transform_16s(const short* src, short* dst, const float* m, int len, int scn, int dcn)
{
transform_(src, dst, m, len, scn, dcn);
}
static void
transform_32s(const int* src, int* dst, const double* m, int len, int scn, int dcn)
{
transform_(src, dst, m, len, scn, dcn);
}
static void
transform_64f(const double* src, double* dst, const double* m, int len, int scn, int dcn)
{
transform_(src, dst, m, len, scn, dcn);
}
template<typename T, typename WT> static void
diagtransform_( const T* src, T* dst, const WT* m, int len, int cn, int )
{
int x;
if( cn == 2 )
{
for( x = 0; x < len*2; x += 2 )
{
T t0 = saturate_cast<T>(m[0]*src[x] + m[2]);
T t1 = saturate_cast<T>(m[4]*src[x+1] + m[5]);
dst[x] = t0; dst[x+1] = t1;
}
}
else if( cn == 3 )
{
for( x = 0; x < len*3; x += 3 )
{
T t0 = saturate_cast<T>(m[0]*src[x] + m[3]);
T t1 = saturate_cast<T>(m[5]*src[x+1] + m[7]);
T t2 = saturate_cast<T>(m[10]*src[x+2] + m[11]);
dst[x] = t0; dst[x+1] = t1; dst[x+2] = t2;
}
}
else if( cn == 4 )
{
for( x = 0; x < len*4; x += 4 )
{
T t0 = saturate_cast<T>(m[0]*src[x] + m[4]);
T t1 = saturate_cast<T>(m[6]*src[x+1] + m[9]);
dst[x] = t0; dst[x+1] = t1;
t0 = saturate_cast<T>(m[12]*src[x+2] + m[14]);
t1 = saturate_cast<T>(m[18]*src[x+3] + m[19]);
dst[x+2] = t0; dst[x+3] = t1;
}
}
else
{
for( x = 0; x < len; x++, src += cn, dst += cn )
{
const WT* _m = m;
for( int j = 0; j < cn; j++, _m += cn + 1 )
dst[j] = saturate_cast<T>(src[j]*_m[j] + _m[cn]);
}
}
}
static void
diagtransform_8u(const uchar* src, uchar* dst, const float* m, int len, int scn, int dcn)
{
diagtransform_(src, dst, m, len, scn, dcn);
}
static void
diagtransform_8s(const schar* src, schar* dst, const float* m, int len, int scn, int dcn)
{
diagtransform_(src, dst, m, len, scn, dcn);
}
static void
diagtransform_16u(const ushort* src, ushort* dst, const float* m, int len, int scn, int dcn)
{
diagtransform_(src, dst, m, len, scn, dcn);
}
static void
diagtransform_16s(const short* src, short* dst, const float* m, int len, int scn, int dcn)
{
diagtransform_(src, dst, m, len, scn, dcn);
}
static void
diagtransform_32s(const int* src, int* dst, const double* m, int len, int scn, int dcn)
{
diagtransform_(src, dst, m, len, scn, dcn);
}
static void
diagtransform_32f(const float* src, float* dst, const float* m, int len, int scn, int dcn)
{
diagtransform_(src, dst, m, len, scn, dcn);
}
static void
diagtransform_64f(const double* src, double* dst, const double* m, int len, int scn, int dcn)
{
diagtransform_(src, dst, m, len, scn, dcn);
}
typedef void (*TransformFunc)( const uchar* src, uchar* dst, const uchar* m, int, int, int );
static TransformFunc getTransformFunc(int depth)
{
static TransformFunc transformTab[] =
{
(TransformFunc)transform_8u, (TransformFunc)transform_8s, (TransformFunc)transform_16u,
(TransformFunc)transform_16s, (TransformFunc)transform_32s, (TransformFunc)transform_32f,
(TransformFunc)transform_64f, 0
};
return transformTab[depth];
}
static TransformFunc getDiagTransformFunc(int depth)
{
static TransformFunc diagTransformTab[] =
{
(TransformFunc)diagtransform_8u, (TransformFunc)diagtransform_8s, (TransformFunc)diagtransform_16u,
(TransformFunc)diagtransform_16s, (TransformFunc)diagtransform_32s, (TransformFunc)diagtransform_32f,
(TransformFunc)diagtransform_64f, 0
};
return diagTransformTab[depth];
}
}
void cv::transform( InputArray _src, OutputArray _dst, InputArray _mtx )
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat(), m = _mtx.getMat();
int depth = src.depth(), scn = src.channels(), dcn = m.rows;
CV_Assert( scn == m.cols || scn + 1 == m.cols );
bool isDiag = false;
_dst.create( src.size(), CV_MAKETYPE(depth, dcn) );
Mat dst = _dst.getMat();
int mtype = depth == CV_32S || depth == CV_64F ? CV_64F : CV_32F;
AutoBuffer<double> _mbuf;
double* mbuf;
if( !m.isContinuous() || m.type() != mtype || m.cols != scn + 1 )
{
_mbuf.allocate(dcn*(scn+1));
mbuf = _mbuf.data();
Mat tmp(dcn, scn+1, mtype, mbuf);
memset(tmp.ptr(), 0, tmp.total()*tmp.elemSize());
if( m.cols == scn+1 )
m.convertTo(tmp, mtype);
else
{
Mat tmppart = tmp.colRange(0, m.cols);
m.convertTo(tmppart, mtype);
}
m = tmp;
}
else
mbuf = m.ptr<double>();
if( scn == dcn )
{
int i, j;
double eps = mtype == CV_32F ? FLT_EPSILON : DBL_EPSILON;
if( scn == 1 )
{
double alpha, beta;
if( mtype == CV_32F )
alpha = m.at<float>(0), beta = m.at<float>(1);
else
alpha = m.at<double>(0), beta = m.at<double>(1);
src.convertTo(dst, dst.type(), alpha, beta);
return;
}
for( i = 0, isDiag = true; isDiag && i < scn; i++ )
{
for( j = 0; isDiag && j < scn; j++ )
{
double v = mtype == CV_32F ? m.at<float>(i, j) : m.at<double>(i, j);
if( i != j && fabs(v) > eps )
isDiag = false;
}
}
}
TransformFunc func = isDiag ? getDiagTransformFunc(depth): getTransformFunc(depth);
CV_Assert( func != 0 );
const Mat* arrays[] = {&src, &dst, 0};
uchar* ptrs[2] = {};
NAryMatIterator it(arrays, ptrs);
size_t i, total = it.size;
for( i = 0; i < it.nplanes; i++, ++it )
func( ptrs[0], ptrs[1], (uchar*)mbuf, (int)total, scn, dcn );
}
/****************************************************************************************\
* Perspective Transform *
\****************************************************************************************/
namespace cv
{
template<typename T> static void
perspectiveTransform_( const T* src, T* dst, const double* m, int len, int scn, int dcn )
{
const double eps = FLT_EPSILON;
int i;
if( scn == 2 && dcn == 2 )
{
for( i = 0; i < len*2; i += 2 )
{
T x = src[i], y = src[i + 1];
double w = x*m[6] + y*m[7] + m[8];
if( fabs(w) > eps )
{
w = 1./w;
dst[i] = (T)((x*m[0] + y*m[1] + m[2])*w);
dst[i+1] = (T)((x*m[3] + y*m[4] + m[5])*w);
}
else
dst[i] = dst[i+1] = (T)0;
}
}
else if( scn == 3 && dcn == 3 )
{
for( i = 0; i < len*3; i += 3 )
{
T x = src[i], y = src[i + 1], z = src[i + 2];
double w = x*m[12] + y*m[13] + z*m[14] + m[15];
if( fabs(w) > eps )
{
w = 1./w;
dst[i] = (T)((x*m[0] + y*m[1] + z*m[2] + m[3]) * w);
dst[i+1] = (T)((x*m[4] + y*m[5] + z*m[6] + m[7]) * w);
dst[i+2] = (T)((x*m[8] + y*m[9] + z*m[10] + m[11]) * w);
}
else
dst[i] = dst[i+1] = dst[i+2] = (T)0;
}
}
else if( scn == 3 && dcn == 2 )
{
for( i = 0; i < len; i++, src += 3, dst += 2 )
{
T x = src[0], y = src[1], z = src[2];
double w = x*m[8] + y*m[9] + z*m[10] + m[11];
if( fabs(w) > eps )
{
w = 1./w;
dst[0] = (T)((x*m[0] + y*m[1] + z*m[2] + m[3])*w);
dst[1] = (T)((x*m[4] + y*m[5] + z*m[6] + m[7])*w);
}
else
dst[0] = dst[1] = (T)0;
}
}
else
{
for( i = 0; i < len; i++, src += scn, dst += dcn )
{
const double* _m = m + dcn*(scn + 1);
double w = _m[scn];
int j, k;
for( k = 0; k < scn; k++ )
w += _m[k]*src[k];
if( fabs(w) > eps )
{
_m = m;
for( j = 0; j < dcn; j++, _m += scn + 1 )
{
double s = _m[scn];
for( k = 0; k < scn; k++ )
s += _m[k]*src[k];
dst[j] = (T)(s*w);
}
}
else
for( j = 0; j < dcn; j++ )
dst[j] = 0;
}
}
}
static void
perspectiveTransform_32f(const float* src, float* dst, const double* m, int len, int scn, int dcn)
{
perspectiveTransform_(src, dst, m, len, scn, dcn);
}
static void
perspectiveTransform_64f(const double* src, double* dst, const double* m, int len, int scn, int dcn)
{
perspectiveTransform_(src, dst, m, len, scn, dcn);
}
}
void cv::perspectiveTransform( InputArray _src, OutputArray _dst, InputArray _mtx )
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat(), m = _mtx.getMat();
int depth = src.depth(), scn = src.channels(), dcn = m.rows-1;
CV_Assert( scn + 1 == m.cols );
CV_Assert( depth == CV_32F || depth == CV_64F );
_dst.create( src.size(), CV_MAKETYPE(depth, dcn) );
Mat dst = _dst.getMat();
const int mtype = CV_64F;
AutoBuffer<double> _mbuf;
double* mbuf = m.ptr<double>();
if( !m.isContinuous() || m.type() != mtype )
{
_mbuf.allocate((dcn+1)*(scn+1));
mbuf = _mbuf.data();
Mat tmp(dcn+1, scn+1, mtype, mbuf);
m.convertTo(tmp, mtype);
m = tmp;
}
TransformFunc func = depth == CV_32F ?
(TransformFunc)perspectiveTransform_32f :
(TransformFunc)perspectiveTransform_64f;
CV_Assert( func != 0 );
const Mat* arrays[] = {&src, &dst, 0};
uchar* ptrs[2] = {};
NAryMatIterator it(arrays, ptrs);
size_t i, total = it.size;
for( i = 0; i < it.nplanes; i++, ++it )
func( ptrs[0], ptrs[1], (uchar*)mbuf, (int)total, scn, dcn );
}
/****************************************************************************************\
* ScaleAdd *
\****************************************************************************************/
namespace cv
{
static void scaleAdd_32f(const float* src1, const float* src2, float* dst,
int len, float* _alpha)
{
float alpha = *_alpha;
int i = 0;
#if CV_SIMD
v_float32 v_alpha = vx_setall_f32(alpha);
const int cWidth = v_float32::nlanes;
for (; i <= len - cWidth; i += cWidth)
v_store(dst + i, v_muladd(vx_load(src1 + i), v_alpha, vx_load(src2 + i)));
vx_cleanup();
#endif
for (; i < len; i++)
dst[i] = src1[i] * alpha + src2[i];
}
static void scaleAdd_64f(const double* src1, const double* src2, double* dst,
int len, double* _alpha)
{
double alpha = *_alpha;
int i = 0;
#if CV_SIMD_64F
v_float64 a2 = vx_setall_f64(alpha);
const int cWidth = v_float64::nlanes;
for (; i <= len - cWidth; i += cWidth)
v_store(dst + i, v_muladd(vx_load(src1 + i), a2, vx_load(src2 + i)));
vx_cleanup();
#endif
for (; i < len; i++)
dst[i] = src1[i] * alpha + src2[i];
}
typedef void (*ScaleAddFunc)(const uchar* src1, const uchar* src2, uchar* dst, int len, const void* alpha);
#ifdef HAVE_OPENCL
static bool ocl_scaleAdd( InputArray _src1, double alpha, InputArray _src2, OutputArray _dst, int type )
{
const ocl::Device & d = ocl::Device::getDefault();
bool doubleSupport = d.doubleFPConfig() > 0;
Size size = _src1.size();
int depth = CV_MAT_DEPTH(type);
if ( (!doubleSupport && depth == CV_64F) || size != _src2.size() )
return false;
_dst.create(size, type);
int cn = CV_MAT_CN(type), wdepth = std::max(depth, CV_32F);
int kercn = ocl::predictOptimalVectorWidthMax(_src1, _src2, _dst),
rowsPerWI = d.isIntel() ? 4 : 1;
char cvt[2][50];
ocl::Kernel k("KF", ocl::core::arithm_oclsrc,
format("-D OP_SCALE_ADD -D BINARY_OP -D dstT=%s -D DEPTH_dst=%d -D workT=%s -D convertToWT1=%s"
" -D srcT1=dstT -D srcT2=dstT -D convertToDT=%s -D workT1=%s"
" -D wdepth=%d%s -D rowsPerWI=%d",
ocl::typeToStr(CV_MAKE_TYPE(depth, kercn)), depth,
ocl::typeToStr(CV_MAKE_TYPE(wdepth, kercn)),
ocl::convertTypeStr(depth, wdepth, kercn, cvt[0]),
ocl::convertTypeStr(wdepth, depth, kercn, cvt[1]),
ocl::typeToStr(wdepth), wdepth,
doubleSupport ? " -D DOUBLE_SUPPORT" : "", rowsPerWI));
if (k.empty())
return false;
UMat src1 = _src1.getUMat(), src2 = _src2.getUMat(), dst = _dst.getUMat();
ocl::KernelArg src1arg = ocl::KernelArg::ReadOnlyNoSize(src1),
src2arg = ocl::KernelArg::ReadOnlyNoSize(src2),
dstarg = ocl::KernelArg::WriteOnly(dst, cn, kercn);
if (wdepth == CV_32F)
k.args(src1arg, src2arg, dstarg, (float)alpha);
else
k.args(src1arg, src2arg, dstarg, alpha);
size_t globalsize[2] = { (size_t)dst.cols * cn / kercn, ((size_t)dst.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
#endif
}
void cv::scaleAdd( InputArray _src1, double alpha, InputArray _src2, OutputArray _dst )
{
CV_INSTRUMENT_REGION();
int type = _src1.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
CV_Assert( type == _src2.type() );
CV_OCL_RUN(_src1.dims() <= 2 && _src2.dims() <= 2 && _dst.isUMat(),
ocl_scaleAdd(_src1, alpha, _src2, _dst, type))
if( depth < CV_32F )
{
addWeighted(_src1, alpha, _src2, 1, 0, _dst, depth);
return;
}
Mat src1 = _src1.getMat(), src2 = _src2.getMat();
CV_Assert(src1.size == src2.size);
_dst.create(src1.dims, src1.size, type);
Mat dst = _dst.getMat();
float falpha = (float)alpha;
void* palpha = depth == CV_32F ? (void*)&falpha : (void*)α
ScaleAddFunc func = depth == CV_32F ? (ScaleAddFunc)scaleAdd_32f : (ScaleAddFunc)scaleAdd_64f;
if (src1.isContinuous() && src2.isContinuous() && dst.isContinuous())
{
size_t len = src1.total()*cn;
func(src1.ptr(), src2.ptr(), dst.ptr(), (int)len, palpha);
return;
}
const Mat* arrays[] = {&src1, &src2, &dst, 0};
uchar* ptrs[3] = {};
NAryMatIterator it(arrays, ptrs);
size_t i, len = it.size*cn;
for( i = 0; i < it.nplanes; i++, ++it )
func( ptrs[0], ptrs[1], ptrs[2], (int)len, palpha );
}
/****************************************************************************************\
* Covariation Matrix *
\****************************************************************************************/
void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean, int flags, int ctype )
{
CV_INSTRUMENT_REGION();
CV_Assert_N( data, nsamples > 0 );
Size size = data[0].size();
int sz = size.width * size.height, esz = (int)data[0].elemSize();
int type = data[0].type();
Mat mean;
ctype = std::max(std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), _mean.depth()), CV_32F);
if( (flags & CV_COVAR_USE_AVG) != 0 )
{
CV_Assert( _mean.size() == size );
if( _mean.isContinuous() && _mean.type() == ctype )
mean = _mean.reshape(1, 1);
else
{
_mean.convertTo(mean, ctype);
mean = mean.reshape(1, 1);
}
}
Mat _data(nsamples, sz, type);
for( int i = 0; i < nsamples; i++ )
{
CV_Assert_N( data[i].size() == size, data[i].type() == type );
if( data[i].isContinuous() )
memcpy( _data.ptr(i), data[i].ptr(), sz*esz );
else
{
Mat dataRow(size.height, size.width, type, _data.ptr(i));
data[i].copyTo(dataRow);
}
}
calcCovarMatrix( _data, covar, mean, (flags & ~(CV_COVAR_ROWS|CV_COVAR_COLS)) | CV_COVAR_ROWS, ctype );
if( (flags & CV_COVAR_USE_AVG) == 0 )
_mean = mean.reshape(1, size.height);
}
void cv::calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mean, int flags, int ctype )
{
CV_INSTRUMENT_REGION();
if(_src.kind() == _InputArray::STD_VECTOR_MAT || _src.kind() == _InputArray::STD_ARRAY_MAT)
{
std::vector<cv::Mat> src;
_src.getMatVector(src);
CV_Assert( src.size() > 0 );
Size size = src[0].size();
int type = src[0].type();
ctype = std::max(std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), _mean.depth()), CV_32F);
Mat _data(static_cast<int>(src.size()), size.area(), type);
int i = 0;
for(std::vector<cv::Mat>::iterator each = src.begin(); each != src.end(); ++each, ++i )
{
CV_Assert_N( (*each).size() == size, (*each).type() == type );
Mat dataRow(size.height, size.width, type, _data.ptr(i));
(*each).copyTo(dataRow);
}
Mat mean;
if( (flags & CV_COVAR_USE_AVG) != 0 )
{
CV_Assert( _mean.size() == size );
if( mean.type() != ctype )
{
mean = _mean.getMat();
_mean.create(mean.size(), ctype);
Mat tmp = _mean.getMat();
mean.convertTo(tmp, ctype);
mean = tmp;
}
mean = _mean.getMat().reshape(1, 1);
}
calcCovarMatrix( _data, _covar, mean, (flags & ~(CV_COVAR_ROWS|CV_COVAR_COLS)) | CV_COVAR_ROWS, ctype );
if( (flags & CV_COVAR_USE_AVG) == 0 )
{
mean = mean.reshape(1, size.height);
mean.copyTo(_mean);
}
return;
}
Mat data = _src.getMat(), mean;
CV_Assert( ((flags & CV_COVAR_ROWS) != 0) ^ ((flags & CV_COVAR_COLS) != 0) );
bool takeRows = (flags & CV_COVAR_ROWS) != 0;
int type = data.type();
int nsamples = takeRows ? data.rows : data.cols;
CV_Assert( nsamples > 0 );
Size size = takeRows ? Size(data.cols, 1) : Size(1, data.rows);
if( (flags & CV_COVAR_USE_AVG) != 0 )
{
mean = _mean.getMat();
ctype = std::max(std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), mean.depth()), CV_32F);
CV_Assert( mean.size() == size );
if( mean.type() != ctype )
{
_mean.create(mean.size(), ctype);
Mat tmp = _mean.getMat();
mean.convertTo(tmp, ctype);
mean = tmp;
}
}
else
{
ctype = std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), CV_32F);
reduce( _src, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype );
mean = _mean.getMat();
}
mulTransposed( data, _covar, ((flags & CV_COVAR_NORMAL) == 0) ^ takeRows,
mean, (flags & CV_COVAR_SCALE) != 0 ? 1./nsamples : 1, ctype );
}
/****************************************************************************************\
* Mahalanobis *
\****************************************************************************************/
double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
{
CV_INSTRUMENT_REGION();
Mat v1 = _v1.getMat(), v2 = _v2.getMat(), icovar = _icovar.getMat();
int type = v1.type(), depth = v1.depth();
Size sz = v1.size();
int i, j, len = sz.width*sz.height*v1.channels();
AutoBuffer<double> buf(len);
double result = 0;
CV_Assert_N( type == v2.type(), type == icovar.type(),
sz == v2.size(), len == icovar.rows && len == icovar.cols );
sz.width *= v1.channels();
if( v1.isContinuous() && v2.isContinuous() )
{
sz.width *= sz.height;
sz.height = 1;
}
if( depth == CV_32F )
{
const float* src1 = v1.ptr<float>();
const float* src2 = v2.ptr<float>();
size_t step1 = v1.step/sizeof(src1[0]);
size_t step2 = v2.step/sizeof(src2[0]);
double* diff = buf.data();
const float* mat = icovar.ptr<float>();
size_t matstep = icovar.step/sizeof(mat[0]);
for( ; sz.height--; src1 += step1, src2 += step2, diff += sz.width )
{
for( i = 0; i < sz.width; i++ )
diff[i] = src1[i] - src2[i];
}
diff = buf.data();
for( i = 0; i < len; i++, mat += matstep )
{
double row_sum = 0;
j = 0;
#if CV_ENABLE_UNROLLED
for(; j <= len - 4; j += 4 )
row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] +
diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3];
#endif
for( ; j < len; j++ )
row_sum += diff[j]*mat[j];
result += row_sum * diff[i];
}
}
else if( depth == CV_64F )
{
const double* src1 = v1.ptr<double>();
const double* src2 = v2.ptr<double>();
size_t step1 = v1.step/sizeof(src1[0]);
size_t step2 = v2.step/sizeof(src2[0]);
double* diff = buf.data();
const double* mat = icovar.ptr<double>();
size_t matstep = icovar.step/sizeof(mat[0]);
for( ; sz.height--; src1 += step1, src2 += step2, diff += sz.width )
{
for( i = 0; i < sz.width; i++ )
diff[i] = src1[i] - src2[i];
}
diff = buf.data();
for( i = 0; i < len; i++, mat += matstep )
{
double row_sum = 0;
j = 0;
#if CV_ENABLE_UNROLLED
for(; j <= len - 4; j += 4 )
row_sum += diff[j]*mat[j] + diff[j+1]*mat[j+1] +
diff[j+2]*mat[j+2] + diff[j+3]*mat[j+3];
#endif
for( ; j < len; j++ )
row_sum += diff[j]*mat[j];
result += row_sum * diff[i];
}
}
else
CV_Error( CV_StsUnsupportedFormat, "" );
return std::sqrt(result);
}
/****************************************************************************************\
* MulTransposed *
\****************************************************************************************/
namespace cv
{
template<typename sT, typename dT> static void
MulTransposedR( const Mat& srcmat, Mat& dstmat, const Mat& deltamat, double scale )
{
int i, j, k;
const sT* src = srcmat.ptr<sT>();
dT* dst = dstmat.ptr<dT>();
const dT* delta = deltamat.ptr<dT>();
size_t srcstep = srcmat.step/sizeof(src[0]);
size_t dststep = dstmat.step/sizeof(dst[0]);
size_t deltastep = deltamat.rows > 1 ? deltamat.step/sizeof(delta[0]) : 0;
int delta_cols = deltamat.cols;
Size size = srcmat.size();
dT* tdst = dst;
dT* col_buf = 0;
dT* delta_buf = 0;
int buf_size = size.height*sizeof(dT);
AutoBuffer<uchar> buf;
if( delta && delta_cols < size.width )
{
assert( delta_cols == 1 );
buf_size *= 5;
}
buf.allocate(buf_size);
col_buf = (dT*)buf.data();
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 sT *tsrc = src + j;
for( k = 0; k < size.height; k++, tsrc += srcstep )
{
double a = col_buf[k];
s0 += a * tsrc[0];
s1 += a * tsrc[1];
s2 += a * tsrc[2];
s3 += a * tsrc[3];
}
tdst[j] = (dT)(s0*scale);
tdst[j+1] = (dT)(s1*scale);
tdst[j+2] = (dT)(s2*scale);
tdst[j+3] = (dT)(s3*scale);
}
for( ; j < size.width; j++ )
{
double s0 = 0;
const sT *tsrc = src + j;
for( k = 0; k < size.height; k++, tsrc += srcstep )
s0 += (double)col_buf[k] * tsrc[0];
tdst[j] = (dT)(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] = src[k*srcstep+i] - delta[k*deltastep+i];
else
for( k = 0; k < size.height; k++ )
col_buf[k] = 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 sT *tsrc = src + j;
const dT *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 * (tsrc[0] - d[0]);
s1 += a * (tsrc[1] - d[1]);
s2 += a * (tsrc[2] - d[2]);
s3 += a * (tsrc[3] - d[3]);
}
tdst[j] = (dT)(s0*scale);
tdst[j+1] = (dT)(s1*scale);
tdst[j+2] = (dT)(s2*scale);
tdst[j+3] = (dT)(s3*scale);
}
for( ; j < size.width; j++ )
{
double s0 = 0;
const sT *tsrc = src + j;
const dT *d = delta_buf ? delta_buf : delta + j;
for( k = 0; k < size.height; k++, tsrc+=srcstep, d+=deltastep )
s0 += (double)col_buf[k] * (tsrc[0] - d[0]);
tdst[j] = (dT)(s0*scale);
}
}
}
template<typename sT, typename dT> static void
MulTransposedL( const Mat& srcmat, Mat& dstmat, const Mat& deltamat, double scale )
{
int i, j, k;
const sT* src = srcmat.ptr<sT>();
dT* dst = dstmat.ptr<dT>();
const dT* delta = deltamat.ptr<dT>();
size_t srcstep = srcmat.step/sizeof(src[0]);
size_t dststep = dstmat.step/sizeof(dst[0]);
size_t deltastep = deltamat.rows > 1 ? deltamat.step/sizeof(delta[0]) : 0;
int delta_cols = deltamat.cols;
Size size = srcmat.size();
dT* tdst = dst;
if( !delta )
for( i = 0; i < size.height; i++, tdst += dststep )
for( j = i; j < size.height; j++ )
{
double s = 0;
const sT *tsrc1 = src + i*srcstep;
const sT *tsrc2 = src + j*srcstep;
for( k = 0; k <= size.width - 4; k += 4 )
s += (double)tsrc1[k]*tsrc2[k] + (double)tsrc1[k+1]*tsrc2[k+1] +
(double)tsrc1[k+2]*tsrc2[k+2] + (double)tsrc1[k+3]*tsrc2[k+3];
for( ; k < size.width; k++ )
s += (double)tsrc1[k] * tsrc2[k];
tdst[j] = (dT)(s*scale);
}
else
{
dT delta_buf[4];
int delta_shift = delta_cols == size.width ? 4 : 0;
AutoBuffer<uchar> buf(size.width*sizeof(dT));
dT* row_buf = (dT*)buf.data();
for( i = 0; i < size.height; i++, tdst += dststep )
{
const sT *tsrc1 = src + i*srcstep;
const dT *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 sT *tsrc2 = src + j*srcstep;
const dT *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 += (double)row_buf[k]*(tsrc2[k] - tdelta2[0]) +
(double)row_buf[k+1]*(tsrc2[k+1] - tdelta2[1]) +
(double)row_buf[k+2]*(tsrc2[k+2] - tdelta2[2]) +
(double)row_buf[k+3]*(tsrc2[k+3] - tdelta2[3]);
for( ; k < size.width; k++, tdelta2++ )
s += (double)row_buf[k]*(tsrc2[k] - tdelta2[0]);
tdst[j] = (dT)(s*scale);
}
}
}
}
typedef void (*MulTransposedFunc)(const Mat& src, Mat& dst, const Mat& delta, double scale);
}
void cv::mulTransposed( InputArray _src, OutputArray _dst, bool ata,
InputArray _delta, double scale, int dtype )
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat(), delta = _delta.getMat();
const int gemm_level = 100; // boundary above which GEMM is faster.
int stype = src.type();
dtype = std::max(std::max(CV_MAT_DEPTH(dtype >= 0 ? dtype : stype), delta.depth()), CV_32F);
CV_Assert( src.channels() == 1 );
if( !delta.empty() )
{
CV_Assert_N( delta.channels() == 1,
(delta.rows == src.rows || delta.rows == 1),
(delta.cols == src.cols || delta.cols == 1));
if( delta.type() != dtype )
delta.convertTo(delta, dtype);
}
int dsize = ata ? src.cols : src.rows;
_dst.create( dsize, dsize, dtype );
Mat dst = _dst.getMat();
if( src.data == dst.data || (stype == dtype &&
(dst.cols >= gemm_level && dst.rows >= gemm_level &&
src.cols >= gemm_level && src.rows >= gemm_level)))
{
Mat src2;
const Mat* tsrc = &src;
if( !delta.empty() )
{
if( delta.size() == src.size() )
subtract( src, delta, src2 );
else
{
repeat(delta, src.rows/delta.rows, src.cols/delta.cols, src2);
subtract( src, src2, src2 );
}
tsrc = &src2;
}
gemm( *tsrc, *tsrc, scale, Mat(), 0, dst, ata ? GEMM_1_T : GEMM_2_T );
}
else
{
MulTransposedFunc func = 0;
if(stype == CV_8U && dtype == CV_32F)
{
if(ata)
func = MulTransposedR<uchar,float>;
else
func = MulTransposedL<uchar,float>;
}
else if(stype == CV_8U && dtype == CV_64F)
{
if(ata)
func = MulTransposedR<uchar,double>;
else
func = MulTransposedL<uchar,double>;
}
else if(stype == CV_16U && dtype == CV_32F)
{
if(ata)
func = MulTransposedR<ushort,float>;
else
func = MulTransposedL<ushort,float>;
}
else if(stype == CV_16U && dtype == CV_64F)
{
if(ata)
func = MulTransposedR<ushort,double>;
else
func = MulTransposedL<ushort,double>;
}
else if(stype == CV_16S && dtype == CV_32F)
{
if(ata)
func = MulTransposedR<short,float>;
else
func = MulTransposedL<short,float>;
}
else if(stype == CV_16S && dtype == CV_64F)
{
if(ata)
func = MulTransposedR<short,double>;
else
func = MulTransposedL<short,double>;
}
else if(stype == CV_32F && dtype == CV_32F)
{
if(ata)
func = MulTransposedR<float,float>;
else
func = MulTransposedL<float,float>;
}
else if(stype == CV_32F && dtype == CV_64F)
{
if(ata)
func = MulTransposedR<float,double>;
else
func = MulTransposedL<float,double>;
}
else if(stype == CV_64F && dtype == CV_64F)
{
if(ata)
func = MulTransposedR<double,double>;
else
func = MulTransposedL<double,double>;
}
if( !func )
CV_Error( CV_StsUnsupportedFormat, "" );
func( src, dst, delta, scale );
completeSymm( dst, false );
}
}
/****************************************************************************************\
* Dot Product *
\****************************************************************************************/
namespace cv
{
template<typename T> double
dotProd_(const T* src1, const T* src2, int len)
{
int i = 0;
double result = 0;
#if CV_ENABLE_UNROLLED
for( ; i <= len - 4; i += 4 )
result += (double)src1[i]*src2[i] + (double)src1[i+1]*src2[i+1] +
(double)src1[i+2]*src2[i+2] + (double)src1[i+3]*src2[i+3];
#endif
for( ; i < len; i++ )
result += (double)src1[i]*src2[i];
return result;
}
static double dotProd_8u(const uchar* src1, const uchar* src2, int len)
{
double r = 0;
#if ARITHM_USE_IPP
CV_IPP_RUN(IPP_VERSION_X100 > 201800 || cv::ipp::getIppTopFeatures() != ippCPUID_SSE42, CV_INSTRUMENT_FUN_IPP(ippiDotProd_8u64f_C1R, src1, len*sizeof(uchar), src2, len*sizeof(uchar), ippiSize(len, 1), &r) >= 0, r);
#endif
int i = 0;
#if CV_SIMD
int len0 = len & -v_uint16::nlanes, blockSize0 = (1 << 15), blockSize;
while (i < len0)
{
blockSize = std::min(len0 - i, blockSize0);
v_int32 v_sum = vx_setzero_s32();
const int cWidth = v_uint16::nlanes;
int j = 0;
for (; j <= blockSize - cWidth * 2; j += cWidth * 2)
{
v_uint16 v_src10, v_src20, v_src11, v_src21;
v_expand(vx_load(src1 + j), v_src10, v_src11);
v_expand(vx_load(src2 + j), v_src20, v_src21);
v_sum += v_dotprod(v_reinterpret_as_s16(v_src10), v_reinterpret_as_s16(v_src20));
v_sum += v_dotprod(v_reinterpret_as_s16(v_src11), v_reinterpret_as_s16(v_src21));
}
for (; j <= blockSize - cWidth; j += cWidth)
{
v_int16 v_src10 = v_reinterpret_as_s16(vx_load_expand(src1 + j));
v_int16 v_src20 = v_reinterpret_as_s16(vx_load_expand(src2 + j));
v_sum += v_dotprod(v_src10, v_src20);
}
r += (double)v_reduce_sum(v_sum);
src1 += blockSize;
src2 += blockSize;
i += blockSize;
}
vx_cleanup();
#elif CV_NEON
if( cv::checkHardwareSupport(CV_CPU_NEON) )
{
int len0 = len & -8, blockSize0 = (1 << 15), blockSize;
uint32x4_t v_zero = vdupq_n_u32(0u);
CV_DECL_ALIGNED(16) uint buf[4];
while( i < len0 )
{
blockSize = std::min(len0 - i, blockSize0);
uint32x4_t v_sum = v_zero;
int j = 0;
for( ; j <= blockSize - 16; j += 16 )
{
uint8x16_t v_src1 = vld1q_u8(src1 + j), v_src2 = vld1q_u8(src2 + j);
uint16x8_t v_src10 = vmovl_u8(vget_low_u8(v_src1)), v_src20 = vmovl_u8(vget_low_u8(v_src2));
v_sum = vmlal_u16(v_sum, vget_low_u16(v_src10), vget_low_u16(v_src20));
v_sum = vmlal_u16(v_sum, vget_high_u16(v_src10), vget_high_u16(v_src20));
v_src10 = vmovl_u8(vget_high_u8(v_src1));
v_src20 = vmovl_u8(vget_high_u8(v_src2));
v_sum = vmlal_u16(v_sum, vget_low_u16(v_src10), vget_low_u16(v_src20));
v_sum = vmlal_u16(v_sum, vget_high_u16(v_src10), vget_high_u16(v_src20));
}
for( ; j <= blockSize - 8; j += 8 )
{
uint16x8_t v_src1 = vmovl_u8(vld1_u8(src1 + j)), v_src2 = vmovl_u8(vld1_u8(src2 + j));
v_sum = vmlal_u16(v_sum, vget_low_u16(v_src1), vget_low_u16(v_src2));
v_sum = vmlal_u16(v_sum, vget_high_u16(v_src1), vget_high_u16(v_src2));
}
vst1q_u32(buf, v_sum);
r += buf[0] + buf[1] + buf[2] + buf[3];
src1 += blockSize;
src2 += blockSize;
i += blockSize;
}
}
#endif
return r + dotProd_(src1, src2, len - i);
}
static double dotProd_8s(const schar* src1, const schar* src2, int len)
{
double r = 0.0;
int i = 0;
#if CV_SIMD
int len0 = len & -v_int16::nlanes, blockSize0 = (1 << 14), blockSize;
while (i < len0)
{
blockSize = std::min(len0 - i, blockSize0);
v_int32 v_sum = vx_setzero_s32();
const int cWidth = v_int16::nlanes;
int j = 0;
for (; j <= blockSize - cWidth * 2; j += cWidth * 2)
{
v_int16 v_src10, v_src20, v_src11, v_src21;
v_expand(vx_load(src1 + j), v_src10, v_src11);
v_expand(vx_load(src2 + j), v_src20, v_src21);
v_sum += v_dotprod(v_src10, v_src20);
v_sum += v_dotprod(v_src11, v_src21);
}
for (; j <= blockSize - cWidth; j += cWidth)
{
v_int16 v_src10 = vx_load_expand(src1 + j);
v_int16 v_src20 = vx_load_expand(src2 + j);
v_sum += v_dotprod(v_src10, v_src20);
}
r += (double)v_reduce_sum(v_sum);
src1 += blockSize;
src2 += blockSize;
i += blockSize;
}
vx_cleanup();
#elif CV_NEON
if( cv::checkHardwareSupport(CV_CPU_NEON) )
{
int len0 = len & -8, blockSize0 = (1 << 14), blockSize;
int32x4_t v_zero = vdupq_n_s32(0);
CV_DECL_ALIGNED(16) int buf[4];
while( i < len0 )
{
blockSize = std::min(len0 - i, blockSize0);
int32x4_t v_sum = v_zero;
int j = 0;
for( ; j <= blockSize - 16; j += 16 )
{
int8x16_t v_src1 = vld1q_s8(src1 + j), v_src2 = vld1q_s8(src2 + j);
int16x8_t v_src10 = vmovl_s8(vget_low_s8(v_src1)), v_src20 = vmovl_s8(vget_low_s8(v_src2));
v_sum = vmlal_s16(v_sum, vget_low_s16(v_src10), vget_low_s16(v_src20));
v_sum = vmlal_s16(v_sum, vget_high_s16(v_src10), vget_high_s16(v_src20));
v_src10 = vmovl_s8(vget_high_s8(v_src1));
v_src20 = vmovl_s8(vget_high_s8(v_src2));
v_sum = vmlal_s16(v_sum, vget_low_s16(v_src10), vget_low_s16(v_src20));
v_sum = vmlal_s16(v_sum, vget_high_s16(v_src10), vget_high_s16(v_src20));
}
for( ; j <= blockSize - 8; j += 8 )
{
int16x8_t v_src1 = vmovl_s8(vld1_s8(src1 + j)), v_src2 = vmovl_s8(vld1_s8(src2 + j));
v_sum = vmlal_s16(v_sum, vget_low_s16(v_src1), vget_low_s16(v_src2));
v_sum = vmlal_s16(v_sum, vget_high_s16(v_src1), vget_high_s16(v_src2));
}
vst1q_s32(buf, v_sum);
r += buf[0] + buf[1] + buf[2] + buf[3];
src1 += blockSize;
src2 += blockSize;
i += blockSize;
}
}
#endif
return r + dotProd_(src1, src2, len - i);
}
static double dotProd_16u(const ushort* src1, const ushort* src2, int len)
{
#if ARITHM_USE_IPP
double r = 0;
CV_IPP_RUN_FAST(CV_INSTRUMENT_FUN_IPP(ippiDotProd_16u64f_C1R, src1, len*sizeof(ushort), src2, len*sizeof(ushort), ippiSize(len, 1), &r) >= 0, r);
#endif
return dotProd_(src1, src2, len);
}
static double dotProd_16s(const short* src1, const short* src2, int len)
{
#if ARITHM_USE_IPP && (IPP_VERSION_X100 != 900) // bug in IPP 9.0.0
double r = 0;
CV_IPP_RUN_FAST(CV_INSTRUMENT_FUN_IPP(ippiDotProd_16s64f_C1R, src1, len*sizeof(short), src2, len*sizeof(short), ippiSize(len, 1), &r) >= 0, r);
#endif
return dotProd_(src1, src2, len);
}
static double dotProd_32s(const int* src1, const int* src2, int len)
{
#if ARITHM_USE_IPP
double r = 0;
CV_IPP_RUN_FAST(CV_INSTRUMENT_FUN_IPP(ippiDotProd_32s64f_C1R, src1, len*sizeof(int), src2, len*sizeof(int), ippiSize(len, 1), &r) >= 0, r);
#endif
return dotProd_(src1, src2, len);
}
static double dotProd_32f(const float* src1, const float* src2, int len)
{
double r = 0.0;
#if ARITHM_USE_IPP
CV_IPP_RUN_FAST(CV_INSTRUMENT_FUN_IPP(ippiDotProd_32f64f_C1R, src1, len*sizeof(float), src2, len*sizeof(float), ippiSize(len, 1), &r, ippAlgHintFast) >= 0, r);
#endif
int i = 0;
#if CV_SIMD
int len0 = len & -v_float32::nlanes, blockSize0 = (1 << 13), blockSize;
while (i < len0)
{
blockSize = std::min(len0 - i, blockSize0);
v_float32 v_sum = vx_setzero_f32();
int j = 0;
int cWidth = v_float32::nlanes;
for (; j <= blockSize - cWidth; j += cWidth)
v_sum = v_muladd(vx_load(src1 + j), vx_load(src2 + j), v_sum);
r += v_reduce_sum(v_sum);
src1 += blockSize;
src2 += blockSize;
i += blockSize;
}
vx_cleanup();
#endif
return r + dotProd_(src1, src2, len - i);
}
static double dotProd_64f(const double* src1, const double* src2, int len)
{
#if ARITHM_USE_IPP
double r = 0;
CV_IPP_RUN_FAST(CV_INSTRUMENT_FUN_IPP(ippsDotProd_64f, src1, src2, len, &r) >= 0, r);
#endif
return dotProd_(src1, src2, len);
}
typedef double (*DotProdFunc)(const uchar* src1, const uchar* src2, int len);
static DotProdFunc getDotProdFunc(int depth)
{
static DotProdFunc dotProdTab[] =
{
(DotProdFunc)GET_OPTIMIZED(dotProd_8u), (DotProdFunc)GET_OPTIMIZED(dotProd_8s),
(DotProdFunc)dotProd_16u, (DotProdFunc)dotProd_16s,
(DotProdFunc)dotProd_32s, (DotProdFunc)GET_OPTIMIZED(dotProd_32f),
(DotProdFunc)dotProd_64f, 0
};
return dotProdTab[depth];
}
double Mat::dot(InputArray _mat) const
{
CV_INSTRUMENT_REGION();
Mat mat = _mat.getMat();
int cn = channels();
DotProdFunc func = getDotProdFunc(depth());
CV_Assert_N( mat.type() == type(), mat.size == size, func != 0 );
if( isContinuous() && mat.isContinuous() )
{
size_t len = total()*cn;
if( len == (size_t)(int)len )
return func(data, mat.data, (int)len);
}
const Mat* arrays[] = {this, &mat, 0};
uchar* ptrs[2] = {};
NAryMatIterator it(arrays, ptrs);
int len = (int)(it.size*cn);
double r = 0;
for( size_t i = 0; i < it.nplanes; i++, ++it )
r += func( ptrs[0], ptrs[1], len );
return r;
}
}
/****************************************************************************************\
* Earlier API *
\****************************************************************************************/
CV_IMPL void cvGEMM( const CvArr* Aarr, const CvArr* Barr, double alpha,
const CvArr* Carr, double beta, CvArr* Darr, int flags )
{
cv::Mat A = cv::cvarrToMat(Aarr), B = cv::cvarrToMat(Barr);
cv::Mat C, D = cv::cvarrToMat(Darr);
if( Carr )
C = cv::cvarrToMat(Carr);
CV_Assert_N( (D.rows == ((flags & CV_GEMM_A_T) == 0 ? A.rows : A.cols)),
(D.cols == ((flags & CV_GEMM_B_T) == 0 ? B.cols : B.rows)),
D.type() == A.type() );
gemm( A, B, alpha, C, beta, D, flags );
}
CV_IMPL void
cvTransform( const CvArr* srcarr, CvArr* dstarr,
const CvMat* transmat, const CvMat* shiftvec )
{
cv::Mat m = cv::cvarrToMat(transmat), src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
if( shiftvec )
{
cv::Mat v = cv::cvarrToMat(shiftvec).reshape(1,m.rows),
_m(m.rows, m.cols + 1, m.type()), m1 = _m.colRange(0,m.cols), v1 = _m.col(m.cols);
m.convertTo(m1, m1.type());
v.convertTo(v1, v1.type());
m = _m;
}
CV_Assert_N( dst.depth() == src.depth(), dst.channels() == m.rows );
cv::transform( src, dst, m );
}
CV_IMPL void
cvPerspectiveTransform( const CvArr* srcarr, CvArr* dstarr, const CvMat* mat )
{
cv::Mat m = cv::cvarrToMat(mat), src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert_N( dst.type() == src.type(), dst.channels() == m.rows-1 );
cv::perspectiveTransform( src, dst, m );
}
CV_IMPL void cvScaleAdd( const CvArr* srcarr1, CvScalar scale,
const CvArr* srcarr2, CvArr* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert_N( src1.size == dst.size, src1.type() == dst.type() );
cv::scaleAdd( src1, scale.val[0], cv::cvarrToMat(srcarr2), dst );
}
CV_IMPL void
cvCalcCovarMatrix( const CvArr** vecarr, int count,
CvArr* covarr, CvArr* avgarr, int flags )
{
cv::Mat cov0 = cv::cvarrToMat(covarr), cov = cov0, mean0, mean;
CV_Assert_N( vecarr != 0, count >= 1 );
if( avgarr )
mean = mean0 = cv::cvarrToMat(avgarr);
if( (flags & CV_COVAR_COLS) != 0 || (flags & CV_COVAR_ROWS) != 0 )
{
cv::Mat data = cv::cvarrToMat(vecarr[0]);
cv::calcCovarMatrix( data, cov, mean, flags, cov.type() );
}
else
{
std::vector<cv::Mat> data(count);
for( int i = 0; i < count; i++ )
data[i] = cv::cvarrToMat(vecarr[i]);
cv::calcCovarMatrix( &data[0], count, cov, mean, flags, cov.type() );
}
if( mean.data != mean0.data && mean0.data )
mean.convertTo(mean0, mean0.type());
if( cov.data != cov0.data )
cov.convertTo(cov0, cov0.type());
}
CV_IMPL double
cvMahalanobis( const CvArr* srcAarr, const CvArr* srcBarr, const CvArr* matarr )
{
return cv::Mahalanobis(cv::cvarrToMat(srcAarr),
cv::cvarrToMat(srcBarr), cv::cvarrToMat(matarr));
}
CV_IMPL void
cvMulTransposed( const CvArr* srcarr, CvArr* dstarr,
int order, const CvArr* deltaarr, double scale )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst0 = cv::cvarrToMat(dstarr), dst = dst0, delta;
if( deltaarr )
delta = cv::cvarrToMat(deltaarr);
cv::mulTransposed( src, dst, order != 0, delta, scale, dst.type());
if( dst.data != dst0.data )
dst.convertTo(dst0, dst0.type());
}
CV_IMPL double cvDotProduct( const CvArr* srcAarr, const CvArr* srcBarr )
{
return cv::cvarrToMat(srcAarr).dot(cv::cvarrToMat(srcBarr));
}
CV_IMPL void
cvCalcPCA( const CvArr* data_arr, CvArr* avg_arr, CvArr* eigenvals, CvArr* eigenvects, int flags )
{
cv::Mat data = cv::cvarrToMat(data_arr), mean0 = cv::cvarrToMat(avg_arr);
cv::Mat evals0 = cv::cvarrToMat(eigenvals), evects0 = cv::cvarrToMat(eigenvects);
cv::Mat mean = mean0, evals = evals0, evects = evects0;
cv::PCA pca;
pca.mean = mean;
pca.eigenvalues = evals;
pca.eigenvectors = evects;
pca(data, (flags & CV_PCA_USE_AVG) ? mean : cv::Mat(),
flags, !evals.empty() ? evals.rows + evals.cols - 1 : 0);
if( pca.mean.size() == mean.size() )
pca.mean.convertTo( mean, mean.type() );
else
{
cv::Mat temp; pca.mean.convertTo( temp, mean.type() );
transpose( temp, mean );
}
evals = pca.eigenvalues;
evects = pca.eigenvectors;
int ecount0 = evals0.cols + evals0.rows - 1;
int ecount = evals.cols + evals.rows - 1;
CV_Assert_N( (evals0.cols == 1 || evals0.rows == 1),
ecount0 <= ecount,
evects0.cols == evects.cols,
evects0.rows == ecount0 );
cv::Mat temp = evals0;
if( evals.rows == 1 )
evals.colRange(0, ecount0).convertTo(temp, evals0.type());
else
evals.rowRange(0, ecount0).convertTo(temp, evals0.type());
if( temp.data != evals0.data )
transpose(temp, evals0);
evects.rowRange(0, ecount0).convertTo( evects0, evects0.type() );
// otherwise some datatype's or size's were incorrect, so the output arrays have been reallocated
CV_Assert( mean0.data == mean.data );
}
CV_IMPL void
cvProjectPCA( const CvArr* data_arr, const CvArr* avg_arr,
const CvArr* eigenvects, CvArr* result_arr )
{
cv::Mat data = cv::cvarrToMat(data_arr), mean = cv::cvarrToMat(avg_arr);
cv::Mat evects = cv::cvarrToMat(eigenvects), dst0 = cv::cvarrToMat(result_arr), dst = dst0;
cv::PCA pca;
pca.mean = mean;
int n;
if( mean.rows == 1 )
{
CV_Assert_N(dst.cols <= evects.rows, dst.rows == data.rows);
n = dst.cols;
}
else
{
CV_Assert_N(dst.rows <= evects.rows, dst.cols == data.cols);
n = dst.rows;
}
pca.eigenvectors = evects.rowRange(0, n);
cv::Mat result = pca.project(data);
if( result.cols != dst.cols )
result = result.reshape(1, 1);
result.convertTo(dst, dst.type());
CV_Assert(dst0.data == dst.data);
}
CV_IMPL void
cvBackProjectPCA( const CvArr* proj_arr, const CvArr* avg_arr,
const CvArr* eigenvects, CvArr* result_arr )
{
cv::Mat data = cv::cvarrToMat(proj_arr), mean = cv::cvarrToMat(avg_arr);
cv::Mat evects = cv::cvarrToMat(eigenvects), dst0 = cv::cvarrToMat(result_arr), dst = dst0;
cv::PCA pca;
pca.mean = mean;
int n;
if( mean.rows == 1 )
{
CV_Assert_N(data.cols <= evects.rows, dst.rows == data.rows);
n = data.cols;
}
else
{
CV_Assert_N(data.rows <= evects.rows, dst.cols == data.cols);
n = data.rows;
}
pca.eigenvectors = evects.rowRange(0, n);
cv::Mat result = pca.backProject(data);
result.convertTo(dst, dst.type());
CV_Assert(dst0.data == dst.data);
}
/* End of file. */