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#include "precomp.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 )
{
for( j = 0; j <= size.width - 4; j += 4 )
{
int t0 = ((const int*)src)[j];
int t1 = ((const int*)src)[j+1];
((int*)dst)[j] = t0;
((int*)dst)[j+1] = t1;
t0 = ((const int*)src)[j+2];
t1 = ((const int*)src)[j+3];
((int*)dst)[j+2] = t0;
((int*)dst)[j+3] = t1;
}
for( ; j < size.width; j++ )
((int*)dst)[j] = ((const int*)src)[j];
}
}
static void
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;
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 = (T*)cvStackAlloc(n*sizeof(a_data[0]));
}
if( n == 1 ) /* external product */
{
T* b_buf = 0;
if( a_step > 1 && a_size.height > 1 )
{
a_buf = (T*)cvStackAlloc(drows*sizeof(a_data[0]));
for( k = 0; k < drows; k++ )
a_buf[k] = a_data[a_step*k];
a_data = a_buf;
}
if( b_step > 1 )
{
b_buf = (T*)cvStackAlloc(d_size.width*sizeof(b_buf[0]) );
for( j = 0; j < d_size.width; j++ )
b_buf[j] = b_data[j*b_step];
b_data = b_buf;
}
for( i = 0; i < drows; i++, _c_data += c_step0, d_data += d_step )
{
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);
for( k = 0; 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]);
}
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
{
WT* d_buf = (WT*)cvStackAlloc(m*sizeof(d_buf[0]));
for( i = 0; i < drows; i++, _a_data += a_step0, _c_data += c_step0, d_data += d_step )
{
a_data = _a_data;
b_data = _b_data;
c_data = _c_data;
if( a_buf )
{
for( k = 0; k < n; k++ )
a_buf[k] = _a_data[a_step1*k];
a_data = a_buf;
}
for( j = 0; j < m; j++ )
d_buf[j] = WT(0);
for( k = 0; k < n; k++, b_data += b_step )
{
WT al(a_data[k]);
for( j = 0; 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;
}
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;
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 = (T*)cvStackAlloc(n*sizeof(a_data[0]));
}
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;
for( j = 0; 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);
}
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
{
for( j = 0; 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);
}
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);
}
void gemm( const Mat& matA, const Mat& matB, double alpha,
const Mat& matC, double beta, Mat& D, int flags )
{
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};
Mat A = matA, B = matB;
const Mat* C = matC.data && beta != 0 ? &matC : 0;
Size a_size = A.size(), d_size;
int i, len = 0, type = A.type();
CV_Assert( 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 )
{
CV_Assert( 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)));
if( (flags & GEMM_3_T) != 0 && C->data == D.data )
{
transpose( D, D );
flags &= ~GEMM_3_T;
}
}
D.create( d_size.height, d_size.width, type );
if( flags == 0 && 2 <= len && len <= 4 && (len == d_size.width || len == d_size.height) )
{
if( type == CV_32F )
{
float* d = (float*)D.data;
const float *a = (const float*)A.data,
*b = (const float*)B.data,
*c = (const float*)(C ? C->data : 0);
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 ? 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 = (double*)D.data;
const double *a = (const double*)A.data,
*b = (const double*)B.data,
*c = (const double*)(C ? C->data : 0);
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 ? 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, tmat;
const uchar* Cdata = C ? C->data : 0;
size_t Cstep = C ? 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.data == A.data || D.data == B.data )
{
buf.allocate(d_size.width*d_size.height*CV_ELEM_SIZE(type));
tmat = Mat(d_size.height, d_size.width, type, (uchar*)buf );
matD = &tmat;
}
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.data, A.step, B.data, b_step, Cdata, Cstep,
matD->data, 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;
int 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 )
{
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 = (dk0+dk0/8+1)*dk0_1*elem_size;
d_buf_size = (dk0+dk0/8+1)*dk0_1*work_elem_size;
if( is_a_t )
{
a_buf_size = (dm0+dm0/8+1)*((dk0+dk0/8+2)&-2)*elem_size;
flags &= ~GEMM_1_T;
}
buf.allocate(a_buf_size + b_buf_size + d_buf_size);
d_buf = (uchar*)buf;
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->data + 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.data + i*a_step0 + k*a_step1;
size_t _a_step = A.step;
const uchar* _b = B.data + 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->data + i*matD->step + j*elem_size,
matD->step, Size(dj,di), alpha, beta, flags );
}
}
}
if( matD != &D )
matD->copyTo(D);
}
}
/****************************************************************************************\
* Transform *
\****************************************************************************************/
template<typename T, typename WT> static void
transformC1_( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
Size size = getContinuousSize( srcmat, dstmat );
const WT* m = (const WT*)tmat.data;
int dst_cn = dstmat.channels();
int x, y, k;
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
const WT* _m = m;
for( k = 0; k < dst_cn; k++, dst++, _m += 2 )
for( x = 0; x < size.width; x++ )
dst[x*dst_cn] = saturate_cast<T>(_m[0]*src[x] + _m[1]);
}
}
template<typename T, typename WT> static void
transformC2_( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
Size size = getContinuousSize( srcmat, dstmat );
const WT* m = (const WT*)tmat.data;
int dst_cn = dstmat.channels();
int x, y, k;
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
if( dst_cn == 2 )
for( x = 0; x < size.width*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
{
const WT* _m = m;
for( k = 0; k < dst_cn; k++, dst++, _m += 3 )
for( x = 0; x < size.width; x++ )
dst[x*dst_cn] = saturate_cast<T>(_m[0]*src[x*2] +
_m[1]*src[x*2+1] + _m[2]);
}
}
}
template<typename T, typename WT> static void
transformC3_( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
Size size = getContinuousSize( srcmat, dstmat );
const WT* m = (const WT*)tmat.data;
int dst_cn = dstmat.channels();
int x, y, k;
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
if( dst_cn == 3 )
for( x = 0; x < size.width*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( dst_cn == 1 )
for( x = 0; x < size.width; x++, src += 3 )
dst[x] = saturate_cast<T>(m[0]*src[0] + m[1]*src[1] + m[2]*src[2] + m[3]);
else
{
const WT* _m = m;
for( k = 0; k < dst_cn; k++, dst++, _m += 4 )
for( x = 0; x < size.width; x++ )
dst[x*dst_cn] = saturate_cast<T>(_m[0]*src[x*3] +
_m[1]*src[x*3+1] + _m[2]*src[x*3+2] + _m[3]);
}
}
}
template<typename T, typename WT> static void
transformC4_( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
Size size = getContinuousSize( srcmat, dstmat );
const WT* m = (const WT*)tmat.data;
int dst_cn = dstmat.channels();
int x, y, k;
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
if( dst_cn == 4 )
for( x = 0; x < size.width*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
{
const WT* _m = m;
for( k = 0; k < dst_cn; k++, dst++, _m += 5 )
for( x = 0; x < size.width; x++ )
dst[x*dst_cn] = saturate_cast<T>(_m[0]*src[x*4] + _m[1]*src[x*4+1] +
_m[2]*src[x*4+2] + _m[3]*src[x*4+3] + _m[4]);
}
}
}
#if CV_SSE2
static inline void
load3x3Matrix( const float* m, __m128& m0, __m128& m1, __m128& m2, __m128& m3 )
{
m0 = _mm_setr_ps(m[0], m[4], m[8], 0);
m1 = _mm_setr_ps(m[1], m[5], m[9], 0);
m2 = _mm_setr_ps(m[2], m[6], m[10], 0);
m3 = _mm_setr_ps(m[3], m[7], m[11], 0);
}
static inline void
load4x4Matrix( const float* m, __m128& m0, __m128& m1, __m128& m2, __m128& m3, __m128& m4 )
{
m0 = _mm_setr_ps(m[0], m[5], m[10], m[15]);
m1 = _mm_setr_ps(m[1], m[6], m[11], m[16]);
m2 = _mm_setr_ps(m[2], m[7], m[12], m[17]);
m3 = _mm_setr_ps(m[3], m[8], m[13], m[18]);
m4 = _mm_setr_ps(m[4], m[9], m[14], m[19]);
}
template<> void
transformC3_<uchar, float>( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
typedef uchar T;
typedef float WT;
const int BITS = 10, SCALE = 1 << BITS;
const float MAX_M = (float)(1 << (15 - BITS));
Size size = getContinuousSize( srcmat, dstmat );
const float* m = (const float*)tmat.data;
int dst_cn = dstmat.channels();
int x, y, k;
if( checkHardwareSupport(CV_CPU_SSE2) && dst_cn == 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 )
{
// 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);
__m128i m0 = _mm_setr_epi16(0, m00, m01, m02, m00, m01, m02, 0);
__m128i m1 = _mm_setr_epi16(0, m10, m11, m12, m10, m11, m12, 0);
__m128i m2 = _mm_setr_epi16(0, m20, m21, m22, m20, m21, m22, 0);
__m128i m3 = _mm_setr_epi32(m03, m13, m23, 0);
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
int x = 0;
for( ; x <= (size.width - 8)*3; x += 8*3 )
{
__m128i z = _mm_setzero_si128(), t0, t1, t2, r0, r1;
__m128i v0 = _mm_loadl_epi64((const __m128i*)(src + x));
__m128i v1 = _mm_loadl_epi64((const __m128i*)(src + x + 8));
__m128i v2 = _mm_loadl_epi64((const __m128i*)(src + x + 16)), v3;
v0 = _mm_unpacklo_epi8(v0, z); // b0 g0 r0 b1 g1 r1 b2 g2
v1 = _mm_unpacklo_epi8(v1, z); // r2 b3 g3 r3 b4 g4 r4 b5
v2 = _mm_unpacklo_epi8(v2, z); // g5 r5 b6 g6 r6 b7 g7 r7
v3 = _mm_srli_si128(v2, 2); // ? b6 g6 r6 b7 g7 r7 0
v2 = _mm_or_si128(_mm_slli_si128(v2, 10), _mm_srli_si128(v1, 6)); // ? b4 g4 r4 b5 g5 r5 ?
v1 = _mm_or_si128(_mm_slli_si128(v1, 6), _mm_srli_si128(v0, 10)); // ? b2 g2 r2 b3 g3 r3 ?
v0 = _mm_slli_si128(v0, 2); // 0 b0 g0 r0 b1 g1 r1 ?
// process pixels 0 & 1
t0 = _mm_madd_epi16(v0, m0); // a0 b0 a1 b1
t1 = _mm_madd_epi16(v0, m1); // c0 d0 c1 d1
t2 = _mm_madd_epi16(v0, m2); // e0 f0 e1 f1
v0 = _mm_unpacklo_epi32(t0, t1); // a0 c0 b0 d0
t0 = _mm_unpackhi_epi32(t0, t1); // a1 b1 c1 d1
t1 = _mm_unpacklo_epi32(t2, z); // e0 0 f0 0
t2 = _mm_unpackhi_epi32(t2, z); // e1 0 f1 0
r0 = _mm_add_epi32(_mm_add_epi32(_mm_unpacklo_epi64(v0, t1), _mm_unpackhi_epi64(v0,t1)), m3); // B0 G0 R0 0
r1 = _mm_add_epi32(_mm_add_epi32(_mm_unpacklo_epi64(t0, t2), _mm_unpackhi_epi64(t0,t2)), m3); // B1 G1 R1 0
r0 = _mm_srai_epi32(r0, BITS);
r1 = _mm_srai_epi32(r1, BITS);
v0 = _mm_packus_epi16(_mm_packs_epi32(_mm_slli_si128(r0, 4), r1), z); // 0 B0 G0 R0 B1 G1 R1 0
// process pixels 2 & 3
t0 = _mm_madd_epi16(v1, m0); // a0 b0 a1 b1
t1 = _mm_madd_epi16(v1, m1); // c0 d0 c1 d1
t2 = _mm_madd_epi16(v1, m2); // e0 f0 e1 f1
v1 = _mm_unpacklo_epi32(t0, t1); // a0 c0 b0 d0
t0 = _mm_unpackhi_epi32(t0, t1); // a1 b1 c1 d1
t1 = _mm_unpacklo_epi32(t2, z); // e0 0 f0 0
t2 = _mm_unpackhi_epi32(t2, z); // e1 0 f1 0
r0 = _mm_add_epi32(_mm_add_epi32(_mm_unpacklo_epi64(v1, t1), _mm_unpackhi_epi64(v1,t1)), m3); // B2 G2 R2 0
r1 = _mm_add_epi32(_mm_add_epi32(_mm_unpacklo_epi64(t0, t2), _mm_unpackhi_epi64(t0,t2)), m3); // B3 G3 R3 0
r0 = _mm_srai_epi32(r0, BITS);
r1 = _mm_srai_epi32(r1, BITS);
v1 = _mm_packus_epi16(_mm_packs_epi32(_mm_slli_si128(r0, 4), r1), z); // 0 B2 G2 R2 B3 G3 R3 0
// process pixels 4 & 5
t0 = _mm_madd_epi16(v2, m0); // a0 b0 a1 b1
t1 = _mm_madd_epi16(v2, m1); // c0 d0 c1 d1
t2 = _mm_madd_epi16(v2, m2); // e0 f0 e1 f1
v2 = _mm_unpacklo_epi32(t0, t1); // a0 c0 b0 d0
t0 = _mm_unpackhi_epi32(t0, t1); // a1 b1 c1 d1
t1 = _mm_unpacklo_epi32(t2, z); // e0 0 f0 0
t2 = _mm_unpackhi_epi32(t2, z); // e1 0 f1 0
r0 = _mm_add_epi32(_mm_add_epi32(_mm_unpacklo_epi64(v2, t1), _mm_unpackhi_epi64(v2,t1)), m3); // B4 G4 R4 0
r1 = _mm_add_epi32(_mm_add_epi32(_mm_unpacklo_epi64(t0, t2), _mm_unpackhi_epi64(t0,t2)), m3); // B5 G5 R5 0
r0 = _mm_srai_epi32(r0, BITS);
r1 = _mm_srai_epi32(r1, BITS);
v2 = _mm_packus_epi16(_mm_packs_epi32(_mm_slli_si128(r0, 4), r1), z); // 0 B4 G4 R4 B5 G5 R5 0
// process pixels 6 & 7
t0 = _mm_madd_epi16(v3, m0); // a0 b0 a1 b1
t1 = _mm_madd_epi16(v3, m1); // c0 d0 c1 d1
t2 = _mm_madd_epi16(v3, m2); // e0 f0 e1 f1
v3 = _mm_unpacklo_epi32(t0, t1); // a0 c0 b0 d0
t0 = _mm_unpackhi_epi32(t0, t1); // a1 b1 c1 d1
t1 = _mm_unpacklo_epi32(t2, z); // e0 0 f0 0
t2 = _mm_unpackhi_epi32(t2, z); // e1 0 f1 0
r0 = _mm_add_epi32(_mm_add_epi32(_mm_unpacklo_epi64(v3, t1), _mm_unpackhi_epi64(v3,t1)), m3); // B6 G6 R6 0
r1 = _mm_add_epi32(_mm_add_epi32(_mm_unpacklo_epi64(t0, t2), _mm_unpackhi_epi64(t0,t2)), m3); // B7 G7 R7 0
r0 = _mm_srai_epi32(r0, BITS);
r1 = _mm_srai_epi32(r1, BITS);
v3 = _mm_packus_epi16(_mm_packs_epi32(_mm_slli_si128(r0, 4), r1), z); // 0 B6 G6 R6 B7 G7 R7 0
v0 = _mm_or_si128(_mm_srli_si128(v0, 1), _mm_slli_si128(v1, 5));
v1 = _mm_or_si128(_mm_srli_si128(v1, 3), _mm_slli_si128(v2, 3));
v2 = _mm_or_si128(_mm_srli_si128(v2, 5), _mm_slli_si128(v3, 1));
_mm_storel_epi64((__m128i*)(dst + x), v0);
_mm_storel_epi64((__m128i*)(dst + x + 8), v1);
_mm_storel_epi64((__m128i*)(dst + x + 16), v2);
}
for( ; x < size.width*3; x += 3 )
{
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;
}
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
if( dst_cn == 1 )
for( x = 0; x < size.width; x++, src += 3 )
dst[x] = saturate_cast<T>(m[0]*CV_8TO32F(src[0]) +
m[1]*CV_8TO32F(src[1]) + m[2]*CV_8TO32F(src[2]) + m[3]);
else
{
const WT* _m = m;
for( k = 0; k < dst_cn; k++, dst++, _m += 4 )
for( x = 0; x < size.width; x++ )
dst[x*dst_cn] = saturate_cast<T>(_m[0]*CV_8TO32F(src[x*3]) +
_m[1]*CV_8TO32F(src[x*3+1]) + _m[2]*CV_8TO32F(src[x*3+2]) + _m[3]);
}
}
}
template<> void
transformC3_<ushort, float>( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
typedef ushort T;
typedef float WT;
Size size = getContinuousSize( srcmat, dstmat );
const float* m = (const float*)tmat.data;
int dst_cn = dstmat.channels();
int x, y, k;
if( checkHardwareSupport(CV_CPU_SSE2) && dst_cn == 3 )
{
__m128 m0, m1, m2, m3;
__m128i delta = _mm_setr_epi16(0,-32768,-32768,-32768,-32768,-32768,-32768,0);
load3x3Matrix(m, m0, m1, m2, m3);
m3 = _mm_sub_ps(m3, _mm_setr_ps(32768.f, 32768.f, 32768.f, 0.f));
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
int x = 0;
for( ; x <= (size.width - 4)*3; x += 4*3 )
{
__m128i z = _mm_setzero_si128();
__m128i v0 = _mm_loadu_si128((const __m128i*)(src + x)), v1;
__m128i v2 = _mm_loadl_epi64((const __m128i*)(src + x + 8)), v3;
v1 = _mm_unpacklo_epi16(_mm_srli_si128(v0, 6), z); // b1 g1 r1
v3 = _mm_unpacklo_epi16(_mm_srli_si128(v2, 2), z); // b3 g3 r3
v2 = _mm_or_si128(_mm_srli_si128(v0, 12), _mm_slli_si128(v2, 4));
v0 = _mm_unpacklo_epi16(v0, z); // b0 g0 r0
v2 = _mm_unpacklo_epi16(v2, z); // b2 g2 r2
__m128 x0 = _mm_cvtepi32_ps(v0), x1 = _mm_cvtepi32_ps(v1);
__m128 x2 = _mm_cvtepi32_ps(v2), x3 = _mm_cvtepi32_ps(v3);
__m128 y0 = _mm_add_ps(_mm_add_ps(_mm_add_ps(
_mm_mul_ps(m0, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(0,0,0,0))),
_mm_mul_ps(m1, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(1,1,1,1)))),
_mm_mul_ps(m2, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(2,2,2,2)))), m3);
__m128 y1 = _mm_add_ps(_mm_add_ps(_mm_add_ps(
_mm_mul_ps(m0, _mm_shuffle_ps(x1,x1,_MM_SHUFFLE(0,0,0,0))),
_mm_mul_ps(m1, _mm_shuffle_ps(x1,x1,_MM_SHUFFLE(1,1,1,1)))),
_mm_mul_ps(m2, _mm_shuffle_ps(x1,x1,_MM_SHUFFLE(2,2,2,2)))), m3);
__m128 y2 = _mm_add_ps(_mm_add_ps(_mm_add_ps(
_mm_mul_ps(m0, _mm_shuffle_ps(x2,x2,_MM_SHUFFLE(0,0,0,0))),
_mm_mul_ps(m1, _mm_shuffle_ps(x2,x2,_MM_SHUFFLE(1,1,1,1)))),
_mm_mul_ps(m2, _mm_shuffle_ps(x2,x2,_MM_SHUFFLE(2,2,2,2)))), m3);
__m128 y3 = _mm_add_ps(_mm_add_ps(_mm_add_ps(
_mm_mul_ps(m0, _mm_shuffle_ps(x3,x3,_MM_SHUFFLE(0,0,0,0))),
_mm_mul_ps(m1, _mm_shuffle_ps(x3,x3,_MM_SHUFFLE(1,1,1,1)))),
_mm_mul_ps(m2, _mm_shuffle_ps(x3,x3,_MM_SHUFFLE(2,2,2,2)))), m3);
v0 = _mm_cvtps_epi32(y0); v1 = _mm_cvtps_epi32(y1);
v2 = _mm_cvtps_epi32(y2); v3 = _mm_cvtps_epi32(y3);
v0 = _mm_add_epi16(_mm_packs_epi32(_mm_slli_si128(v0,4), v1), delta); // 0 b0 g0 r0 b1 g1 r1 0
v2 = _mm_add_epi16(_mm_packs_epi32(_mm_slli_si128(v2,4), v3), delta); // 0 b2 g2 r2 b3 g3 r3 0
v1 = _mm_or_si128(_mm_srli_si128(v0,2), _mm_slli_si128(v2,10)); // b0 g0 r0 b1 g1 r1 b2 g2
v2 = _mm_srli_si128(v2, 6); // r2 b3 g3 r3 0 0 0 0
_mm_storeu_si128((__m128i*)(dst + x), v1);
_mm_storel_epi64((__m128i*)(dst + x + 8), v2);
}
for( ; x < size.width*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;
}
}
return;
}
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
if( dst_cn == 1 )
for( x = 0; x < size.width; x++, src += 3 )
dst[x] = saturate_cast<T>(m[0]*src[0] + m[1]*src[1] + m[2]*src[2] + m[3]);
else
{
const WT* _m = m;
for( k = 0; k < dst_cn; k++, dst++, _m += 4 )
for( x = 0; x < size.width; x++ )
dst[x*dst_cn] = saturate_cast<T>(_m[0]*src[x*3] + _m[1]*src[x*3+1] + _m[2]*src[x*3+2] + _m[3]);
}
}
}
template<> void
transformC3_<float, float>( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
typedef float T;
typedef float WT;
Size size = getContinuousSize( srcmat, dstmat );
const float* m = (const float*)tmat.data;
int dst_cn = dstmat.channels();
int x, y, k;
if( checkHardwareSupport(CV_CPU_SSE2) && dst_cn == 3 )
{
__m128 m0, m1, m2, m3;
load3x3Matrix(m, m0, m1, m2, m3);
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
int x = 0;
for( ; x < (size.width - 1)*3; x += 3 )
{
__m128 x0 = _mm_loadu_ps(src + x);
__m128 y0 = _mm_add_ps(_mm_add_ps(_mm_add_ps(
_mm_mul_ps(m0, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(0,0,0,0))),
_mm_mul_ps(m1, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(1,1,1,1)))),
_mm_mul_ps(m2, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(2,2,2,2)))), m3);
_mm_storel_pi((__m64*)(dst + x), y0);
_mm_store_ss(dst + x + 2, _mm_movehl_ps(y0,y0));
}
for( ; x < size.width*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;
}
}
return;
}
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
if( dst_cn == 1 )
for( x = 0; x < size.width; x++, src += 3 )
dst[x] = saturate_cast<T>(m[0]*src[0] + m[1]*src[1] + m[2]*src[2] + m[3]);
else
{
const WT* _m = m;
for( k = 0; k < dst_cn; k++, dst++, _m += 4 )
for( x = 0; x < size.width; x++ )
dst[x*dst_cn] = saturate_cast<T>(_m[0]*src[x*3] + _m[1]*src[x*3+1] + _m[2]*src[x*3+2] + _m[3]);
}
}
}
template<> void
transformC4_<float, float>( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
typedef float T;
typedef float WT;
Size size = getContinuousSize( srcmat, dstmat );
const WT* m = (const WT*)tmat.data;
int dst_cn = dstmat.channels();
int x, y, k;
if( checkHardwareSupport(CV_CPU_SSE2) && dst_cn == 4 )
{
__m128 m0, m1, m2, m3, m4;
load4x4Matrix(m, m0, m1, m2, m3, m4);
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
for( x = 0; x < size.width*4; x += 4 )
{
__m128 x0 = _mm_loadu_ps(src + x);
__m128 y0 = _mm_add_ps(_mm_add_ps(_mm_add_ps(_mm_add_ps(
_mm_mul_ps(m0, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(0,0,0,0))),
_mm_mul_ps(m1, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(1,1,1,1)))),
_mm_mul_ps(m2, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(2,2,2,2)))),
_mm_mul_ps(m3, _mm_shuffle_ps(x0,x0,_MM_SHUFFLE(3,3,3,3)))), m4);
_mm_storeu_ps(dst + x, y0);
}
}
return;
}
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
const WT* _m = m;
for( k = 0; k < dst_cn; k++, dst++, _m += 5 )
for( x = 0; x < size.width; x++ )
dst[x*dst_cn] = saturate_cast<T>(_m[0]*src[x*4] + _m[1]*src[x*4+1] +
_m[2]*src[x*4+2] + _m[3]*src[x*4+3] + _m[4]);
}
}
#endif
template<typename T, typename WT> static void
diagtransC2_( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
Size size = getContinuousSize( srcmat, dstmat );
const WT* m = (const WT*)tmat.data;
int x, y;
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
for( x = 0; x < size.width*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;
}
}
}
template<typename T, typename WT> static void
diagtransC3_( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
Size size = getContinuousSize( srcmat, dstmat );
const WT* m = (const WT*)tmat.data;
int x, y;
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
for( x = 0; x < size.width*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;
}
}
}
template<typename T, typename WT> static void
diagtransC4_( const Mat& srcmat, Mat& dstmat, Mat& tmat )
{
Size size = getContinuousSize( srcmat, dstmat );
const WT* m = (const WT*)tmat.data;
int x, y;
for( y = 0; y < size.height; y++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*y);
T* dst = (T*)(dstmat.data + dstmat.step*y);
for( x = 0; x < size.width*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;
}
}
}
typedef void (*TransformFunc)( const Mat &src, Mat& dst, Mat& M );
void transform( const Mat& src, Mat& dst, const Mat& _m )
{
static TransformFunc tab[2][32] =
{
{transformC1_<uchar, float>, 0, transformC1_<ushort, float>, transformC1_<short,float>,
transformC1_<int, double>, transformC1_<float, float>, transformC1_<double, double>, 0,
transformC2_<uchar, float>, 0, transformC2_<ushort, float>, transformC2_<short,float>,
transformC2_<int, double>, transformC2_<float, float>, transformC2_<double, double>, 0,
transformC3_<uchar, float>, 0, transformC3_<ushort, float>, transformC3_<short,float>,
transformC3_<int, double>, transformC3_<float, float>, transformC3_<double, double>, 0,
transformC4_<uchar, float>, 0, transformC4_<ushort, float>, transformC4_<short,float>,
transformC4_<int, double>, transformC4_<float, float>, transformC4_<double, double>, 0},
{0, 0, 0, 0, 0, 0, 0, 0,
0, 0, diagtransC2_<ushort, float>, diagtransC2_<short,float>,
diagtransC2_<int, double>, diagtransC2_<float, float>, diagtransC2_<double, double>, 0,
0, 0, diagtransC3_<ushort, float>, diagtransC3_<short,float>,
diagtransC3_<int, double>, diagtransC3_<float, float>, diagtransC3_<double, double>, 0,
0, 0, diagtransC4_<ushort, float>, diagtransC4_<short,float>,
diagtransC4_<int, double>, diagtransC4_<float, float>, diagtransC4_<double, double>, 0}
};
int type = src.type(), depth = src.depth(), scn = src.channels(), dcn = _m.rows;
bool isDiag = false;
CV_Assert( (scn == _m.cols || scn + 1 == _m.cols) && scn <= 4 && dcn <= 4 );
double mbuf[20] = {0};
Mat m = _m;
dst.create( src.size(), CV_MAKETYPE(depth, dcn) );
Size size = getContinuousSize( src, dst );
int mtype = depth == CV_32S || depth == CV_64F ? CV_64F : CV_32F;
if( !_m.isContinuous() || _m.type() != mtype || _m.cols != scn + 1 )
{
m = Mat(dcn, scn + 1, mtype, mbuf);
Mat tmat_part = m.colRange(0, _m.cols);
_m.convertTo(tmat_part, mtype);
}
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 = ((float*)m.data)[0], beta = ((float*)m.data)[1];
else
alpha = ((double*)m.data)[0], beta = ((double*)m.data)[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 ? ((float*)m.data)[i*(scn+1)+j] :
((double*)m.data)[i*(scn+1)+j];
if( i != j && fabs(v) > eps )
isDiag = false;
}
}
if( isDiag && depth == CV_8U )
{
Mat lut(1, 256, CV_8UC(scn));
for( i = 0; i < scn; i++ )
{
uchar* data = lut.data + i;
double val, delta;
if( mtype == CV_32F )
{
val = ((float*)m.data)[i*(scn+1) + scn];
delta = ((float*)m.data)[i*(scn+1) + i];
}
else
{
val = ((double*)m.data)[i*(scn+1) + scn];
delta = ((double*)m.data)[i*(scn+1) + i];
}
for( j = 0; j < 256; j++, val += delta )
{
int ival = cvRound(val);
data[j*scn] = CV_CAST_8U(ival);
}
}
LUT( src, lut, dst );
return;
}
}
TransformFunc func = tab[isDiag][type];
CV_Assert( func != 0 );
func( src, dst, m );
}
/****************************************************************************************\
* Perspective Transform *
\****************************************************************************************/
template<typename T> static void
perspectiveTransform2_( const Mat& srcmat, Mat& dstmat, const double* mat )
{
Size size = getContinuousSize( srcmat, dstmat, srcmat.channels() );
for( int i = 0; i < size.height; i++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*i);
T* dst = (T*)(dstmat.data + dstmat.step*i);
for( int j = 0; j < size.width; j += 2 )
{
T x = src[j], y = src[j + 1];
double w = x*mat[6] + y*mat[7] + mat[8];
if( fabs(w) > FLT_EPSILON )
{
w = 1./w;
dst[j] = (T)((x*mat[0] + y*mat[1] + mat[2])*w);
dst[j+1] = (T)((x*mat[3] + y*mat[4] + mat[5])*w);
}
else
dst[j] = dst[j+1] = (T)0;
}
}
}
template<typename T> static void
perspectiveTransform3_( const Mat& srcmat, Mat& dstmat, const double* mat )
{
Size size = getContinuousSize( srcmat, dstmat, srcmat.channels() );
for( int i = 0; i < size.height; i++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*i);
T* dst = (T*)(dstmat.data + dstmat.step*i);
for( int j = 0; j < size.width; j += 3 )
{
T x = src[j], y = src[j + 1], z = src[j + 2];
double w = x*mat[12] + y*mat[13] + z*mat[14] + mat[15];
if( fabs(w) > FLT_EPSILON )
{
w = 1./w;
dst[j] = (T)((x*mat[0] + y*mat[1] + z*mat[2] + mat[3]) * w);
dst[j+1] = (T)((x*mat[4] + y*mat[5] + z*mat[6] + mat[7]) * w);
dst[j+2] = (T)((x*mat[8] + y*mat[9] + z*mat[10] + mat[11]) * w);
}
else
dst[j] = dst[j+1] = dst[j+2] = (T)0;
}
}
}
template<typename T> static void
perspectiveTransform23_( const Mat& srcmat, Mat& dstmat, const double* mat )
{
Size size = getContinuousSize( srcmat, dstmat, srcmat.channels() );
for( int i = 0; i < size.height; i++ )
{
const T* src = (const T*)(srcmat.data + srcmat.step*i);
T* dst = (T*)(dstmat.data + dstmat.step*i);
for( int j = 0; j < size.width; j++, src += 3, dst += 2 )
{
T x = src[0], y = src[1], z = src[2];
double w = x*mat[8] + y*mat[9] + z*mat[10] + mat[11];
if( fabs(w) > FLT_EPSILON )
{
w = 1./w;
dst[0] = (T)((x*mat[0] + y*mat[1] + z*mat[2] + mat[3])*w);
dst[1] = (T)((x*mat[4] + y*mat[5] + z*mat[6] + mat[7])*w);
}
else
dst[0] = dst[1] = (T)0;
}
}
}
typedef void (*PerspectiveTransformFunc)(const Mat& src, Mat& dst, const double* mat );
void perspectiveTransform( const Mat& src, Mat& dst, const Mat& _m )
{
int depth = src.depth(), scn = src.channels(), dcn = _m.rows-1;
CV_Assert( (depth == CV_32F || depth == CV_64F) && scn+1 == _m.cols && scn <= 4 &&
((scn == 2 && dcn == 2) || (scn == 3 && dcn == 3) || (scn == 2 && dcn == 3)));
double mbuf[16] = {0};
Mat tmat;
const double* m = (const double*)_m.data;
dst.create( src.size(), CV_MAKETYPE(depth, dcn) );
if( !_m.isContinuous() || _m.type() != CV_64F )
{
tmat = Mat(dcn + 1, scn + 1, CV_64F, mbuf);
_m.convertTo(tmat, CV_64F);
m = (const double*)tmat.data;
}
PerspectiveTransformFunc func = 0;
if( scn == 2 && dcn == 2 )
{
if(depth == CV_32F)
func = perspectiveTransform2_<float>;
else
func = perspectiveTransform2_<double>;
}
else if( scn == 2 && dcn == 3 )
{
if(depth == CV_32F)
func = perspectiveTransform23_<float>;
else
func = perspectiveTransform23_<double>;
}
else if( scn == 3 && dcn == 3 )
{
if(depth == CV_32F)
func = perspectiveTransform3_<float>;
else
func = perspectiveTransform3_<double>;
}
else
CV_Error( CV_StsNotImplemented, "Only 2->2, 2->3 and 3->3 perspective transformation is implemented" );
func( src, dst, m );
}
/****************************************************************************************\
* ScaleAdd *
\****************************************************************************************/
void scaleAdd( const Mat& src1, double alpha, const Mat& src2, Mat& dst )
{
int type = src1.type(), depth = CV_MAT_DEPTH(type);
CV_Assert( src1.size() == src2.size() && type == src2.type() );
dst.create( src1.size(), type );
Size size = getContinuousSize( src1, src2, dst, src1.channels() );
if( depth == CV_32F )
{
const float *s1 = (const float*)src1.data, *s2 = (const float*)src2.data;
float* d = (float*)dst.data;
size_t step1 = src1.step/sizeof(s1[0]), step2 = src2.step/sizeof(s2[0]);
size_t step = dst.step/sizeof(d[0]);
float a = (float)alpha;
if( size.width == 1 )
{
for( ; size.height--; s1 += step1, s2 += step2, d += step )
d[0] = s1[0]*a + s2[0];
return;
}
for( ; size.height--; s1 += step1, s2 += step2, d += step )
{
int i;
for( i = 0; i <= size.width - 4; i += 4 )
{
float t0 = s1[i]*a + s2[i];
float t1 = s1[i+1]*a + s2[i+1];
d[i] = t0;
d[i+1] = t1;
t0 = s1[i+2]*a + s2[i+2];
t1 = s1[i+3]*a + s2[i+3];
d[i+2] = t0;
d[i+3] = t1;
}
for( ; i < size.width; i++ )
d[i] = s1[i]*a + s2[i];
}
}
else if( depth == CV_64F )
{
const double *s1 = (const double*)src1.data, *s2 = (const double*)src2.data;
double* d = (double*)dst.data;
size_t step1 = src1.step/sizeof(s1[0]), step2 = src2.step/sizeof(s2[0]);
size_t step = dst.step/sizeof(d[0]);
if( size.width == 1 )
{
for( ; size.height--; s1 += step1, s2 += step2, d += step )
d[0] = s1[0]*alpha + s2[0];
return;
}
for( ; size.height--; s1 += step1, s2 += step2, d += step )
{
int i;
for( i = 0; i <= size.width - 4; i += 4 )
{
double t0 = s1[i]*alpha + s2[i];
double t1 = s1[i+1]*alpha + s2[i+1];
d[i] = t0;
d[i+1] = t1;
t0 = s1[i+2]*alpha + s2[i+2];
t1 = s1[i+3]*alpha + s2[i+3];
d[i+2] = t0;
d[i+3] = t1;
}
for( ; i < size.width; i++ )
d[i] = s1[i]*alpha + s2[i];
}
}
else
addWeighted(src1, alpha, src2, 1, 0, dst);
}
/****************************************************************************************\
* Covariation Matrix *
\****************************************************************************************/
void calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean, int flags, int ctype )
{
CV_Assert( 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( data[i].size() == size && data[i].type() == type );
if( data[i].isContinuous() )
memcpy( _data.ptr(i), data[i].data, 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 calcCovarMatrix( const Mat& data, Mat& covar, Mat& _mean, int flags, int ctype )
{
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);
Mat mean = _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.type() != ctype )
_mean.convertTo(mean, ctype);
}
else
{
reduce( data, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype );
mean = _mean;
}
mulTransposed( data, covar, ((flags & CV_COVAR_NORMAL) == 0) ^ takeRows,
mean, (flags & CV_COVAR_SCALE) != 0 ? 1./nsamples : 1, ctype );
}
/****************************************************************************************\
* Mahalanobis *
\****************************************************************************************/
double Mahalanobis( const Mat& v1, const Mat& v2, const Mat& icovar )
{
int type = v1.type(), depth = v1.depth();
Size sz = v1.size();
int i, j, len = sz.width*sz.height*v1.channels();
AutoBuffer<uchar> buf(len*v1.elemSize());
double result = 0;
CV_Assert( type == v2.type() && type == icovar.type() &&
sz == v2.size() && len == icovar.rows && len == icovar.cols );
if( v1.isContinuous() && v2.isContinuous() )
{
sz.width *= sz.height;
sz.height = 1;
}
if( depth == CV_32F )
{
const float* src1 = (const float*)v1.data;
const float* src2 = (const float*)v2.data;
size_t step1 = v1.step/sizeof(src1[0]);
size_t step2 = v2.step/sizeof(src2[0]);
float* diff = (float*)(uchar*)buf;
const float* mat = (const float*)icovar.data;
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 = (float*)(uchar*)buf;
for( i = 0; i < len; i++, mat += matstep )
{
double row_sum = 0;
for( j = 0; 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];
for( ; j < len; j++ )
row_sum += diff[j]*mat[j];
result += row_sum * diff[i];
}
}
else if( depth == CV_64F )
{
const double* src1 = (const double*)v1.data;
const double* src2 = (const double*)v2.data;
size_t step1 = v1.step/sizeof(src1[0]);
size_t step2 = v2.step/sizeof(src2[0]);
double* diff = (double*)(uchar*)buf;
const double* mat = (const double*)icovar.data;
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 = (double*)(uchar*)buf;
for( i = 0; i < len; i++, mat += matstep )
{
double row_sum = 0;
for( j = 0; 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];
for( ; j < len; j++ )
row_sum += diff[j]*mat[j];
result += row_sum * diff[i];
}
}
else
CV_Error( CV_StsUnsupportedFormat, "" );
return std::sqrt(result);
}
/****************************************************************************************\
* cvMulTransposed *
\****************************************************************************************/
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 = (const sT*)srcmat.data;
dT* dst = (dT*)dstmat.data;
const dT* delta = (const dT*)deltamat.data;
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*)(uchar*)buf;
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 += 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 += 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 = (const sT*)srcmat.data;
dT* dst = (dT*)dstmat.data;
const dT* delta = (const dT*)deltamat.data;
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 += tsrc1[k]*tsrc2[k] + tsrc1[k+1]*tsrc2[k+1] +
tsrc1[k+2]*tsrc2[k+2] + tsrc1[k+3]*tsrc2[k+3];
for( ; k < size.width; k++ )
s += tsrc1[k] * tsrc2[k];
tdst[j] = (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*)(uchar*)buf;
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 += row_buf[k]*(tsrc2[k] - tdelta2[0]) +
row_buf[k+1]*(tsrc2[k+1] - tdelta2[1]) +
row_buf[k+2]*(tsrc2[k+2] - tdelta2[2]) +
row_buf[k+3]*(tsrc2[k+3] - tdelta2[3]);
for( ; k < size.width; k++, tdelta2++ )
s += 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 mulTransposed( const Mat& src, Mat& dst, bool ata,
const Mat& _delta, double scale, int dtype )
{
const int gemm_level = 100; // boundary above which GEMM is faster.
int stype = src.type();
Mat delta = _delta;
dtype = std::max(std::max(CV_MAT_DEPTH(dtype >= 0 ? dtype : stype), delta.depth()), CV_32F);
CV_Assert( src.channels() == 1 );
if( delta.data )
{
CV_Assert( 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 );
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.data )
{
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 *
\****************************************************************************************/
template<typename T, typename WT, typename ST> static double
dotprod_( const Mat& srcmat1, const Mat& srcmat2 )
{
const T *src1 = (const T*)srcmat1.data, *src2 = (const T*)srcmat2.data;
size_t step1 = srcmat1.step/sizeof(src1[0]), step2 = srcmat2.step/sizeof(src2[0]);
ST sum = 0;
Size size = getContinuousSize( srcmat1, srcmat2, srcmat1.channels() );
if( size.width == 1 )
{
WT t = 0;
for( ; size.height--; src1 += step1, src2 += step2 )
t += (WT)src1[0]*src2[0];
sum += t;
}
else
{
for( ; size.height--; src1 += step1, src2 += step2 )
{
int i;
WT t = 0;
for( i = 0; i <= size.width - 4; i += 4 )
{
sum += (WT)src1[i]*src2[i] +
(WT)src1[i+1]*src2[i+1] +
(WT)src1[i+2]*src2[i+2] +
(WT)src1[i+3]*src2[i+3];
}
for( ; i < size.width; i++ )
t += (WT)src1[i]*src2[i];
sum += t;
}
}
return (double)sum;
}
typedef double (*DotProductFunc)(const Mat& src1, const Mat& src2);
double Mat::dot(const Mat& mat) const
{
static DotProductFunc tab[] = {
dotprod_<uchar, int, int64>, 0,
dotprod_<ushort, double, double>,
dotprod_<short, double, double>,
dotprod_<int, double, double>,
dotprod_<float, double, double>,
dotprod_<double, double, double>, 0 };
DotProductFunc func = tab[depth()];
CV_Assert( mat.type() == type() && mat.size() == size() && func != 0 );
return func( *this, mat );
}
/****************************************************************************************\
* PCA *
\****************************************************************************************/
PCA::PCA() {}
PCA::PCA(const Mat& data, const Mat& mean, int flags, int maxComponents)
{
operator()(data, mean, flags, maxComponents);
}
PCA& PCA::operator()(const Mat& data, const Mat& _mean, int flags, int maxComponents)
{
int covar_flags = CV_COVAR_SCALE;
int i, len, in_count;
Size mean_sz;
CV_Assert( data.channels() == 1 );
if( flags & CV_PCA_DATA_AS_COL )
{
len = data.rows;
in_count = data.cols;
covar_flags |= CV_COVAR_COLS;
mean_sz = Size(1, len);
}
else
{
len = data.cols;
in_count = data.rows;
covar_flags |= CV_COVAR_ROWS;
mean_sz = Size(len, 1);
}
int count = std::min(len, in_count), out_count = count;
if( maxComponents > 0 )
out_count = std::min(count, maxComponents);
// "scrambled" way to compute PCA (when cols(A)>rows(A)):
// B = A'A; B*x=b*x; C = AA'; C*y=c*y -> AA'*y=c*y -> A'A*(A'*y)=c*(A'*y) -> c = b, x=A'*y
if( len <= in_count )
covar_flags |= CV_COVAR_NORMAL;
int ctype = std::max(CV_32F, data.depth());
mean.create( mean_sz, ctype );
Mat covar( count, count, ctype );
if( _mean.data )
{
CV_Assert( _mean.size() == mean_sz );
_mean.convertTo(mean, ctype);
}
calcCovarMatrix( data, covar, mean, covar_flags, ctype );
eigen( covar, eigenvalues, eigenvectors );
if( !(covar_flags & CV_COVAR_NORMAL) )
{
// CV_PCA_DATA_AS_ROW: cols(A)>rows(A). x=A'*y -> x'=y'*A
// CV_PCA_DATA_AS_COL: rows(A)>cols(A). x=A''*y -> x'=y'*A'
Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
if( data.type() != ctype || tmp_mean.data == mean.data )
{
data.convertTo( tmp_data, ctype );
subtract( tmp_data, tmp_mean, tmp_data );
}
else
{
subtract( data, tmp_mean, tmp_mean );
tmp_data = tmp_mean;
}
Mat evects1(count, len, ctype);
gemm( eigenvectors, tmp_data, 1, Mat(), 0, evects1,
(flags & CV_PCA_DATA_AS_COL) ? CV_GEMM_B_T : 0);
eigenvectors = evects1;
// normalize eigenvectors
for( i = 0; i < out_count; i++ )
{
Mat vec = eigenvectors.row(i);
normalize(vec, vec);
}
}
if( count > out_count )
{
// use clone() to physically copy the data and thus deallocate the original matrices
eigenvalues = eigenvalues.rowRange(0,out_count).clone();
eigenvectors = eigenvectors.rowRange(0,out_count).clone();
}
return *this;
}
void PCA::project(const Mat& data, Mat& result) const
{
CV_Assert( mean.data && eigenvectors.data &&
((mean.rows == 1 && mean.cols == data.cols) || (mean.cols == 1 && mean.rows == data.rows)));
Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
int ctype = mean.type();
if( data.type() != ctype || tmp_mean.data == mean.data )
{
data.convertTo( tmp_data, ctype );
subtract( tmp_data, tmp_mean, tmp_data );
}
else
{
subtract( data, tmp_mean, tmp_mean );
tmp_data = tmp_mean;
}
if( mean.rows == 1 )
gemm( tmp_data, eigenvectors, 1, Mat(), 0, result, GEMM_2_T );
else
gemm( eigenvectors, tmp_data, 1, Mat(), 0, result, 0 );
}
Mat PCA::project(const Mat& data) const
{
Mat result;
project(data, result);
return result;
}
void PCA::backProject(const Mat& data, Mat& result) const
{
CV_Assert( mean.data && eigenvectors.data &&
((mean.rows == 1 && eigenvectors.rows == data.cols) ||
(mean.cols == 1 && eigenvectors.rows == data.rows)));
Mat tmp_data, tmp_mean;
data.convertTo(tmp_data, mean.type());
if( mean.rows == 1 )
{
tmp_mean = repeat(mean, data.rows, 1);
gemm( tmp_data, eigenvectors, 1, tmp_mean, 1, result, 0 );
}
else
{
tmp_mean = repeat(mean, 1, data.cols);
gemm( eigenvectors, tmp_data, 1, tmp_mean, 1, result, GEMM_1_T );
}
}
Mat PCA::backProject(const Mat& data) const
{
Mat result;
backProject(data, result);
return result;
}
}
/****************************************************************************************\
* 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( (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( 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( 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( 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( 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.data ? 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( (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(dst.cols <= evects.rows && dst.rows == data.rows);
n = dst.cols;
}
else
{
CV_Assert(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(data.cols <= evects.rows && dst.rows == data.rows);
n = data.cols;
}
else
{
CV_Assert(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. */