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opencv/modules/core/src/matrix_operations.cpp
Maksim Shabunin 6d5ac67681 Restored IPP call reduction
2019-07-31 15:41:22 +03:00

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#include "opencv2/core/mat.hpp"
#include "opencv2/core/types_c.h"
#include "opencl_kernels_core.hpp"
#include "precomp.hpp"
#undef HAVE_IPP
#undef CV_IPP_RUN_FAST
#define CV_IPP_RUN_FAST(f, ...)
#undef CV_IPP_RUN
#define CV_IPP_RUN(c, f, ...)
/*************************************************************************************************\
Matrix Operations
\*************************************************************************************************/
void cv::swap( Mat& a, Mat& b )
{
std::swap(a.flags, b.flags);
std::swap(a.dims, b.dims);
std::swap(a.rows, b.rows);
std::swap(a.cols, b.cols);
std::swap(a.data, b.data);
std::swap(a.datastart, b.datastart);
std::swap(a.dataend, b.dataend);
std::swap(a.datalimit, b.datalimit);
std::swap(a.allocator, b.allocator);
std::swap(a.u, b.u);
std::swap(a.size.p, b.size.p);
std::swap(a.step.p, b.step.p);
std::swap(a.step.buf[0], b.step.buf[0]);
std::swap(a.step.buf[1], b.step.buf[1]);
if( a.step.p == b.step.buf )
{
a.step.p = a.step.buf;
a.size.p = &a.rows;
}
if( b.step.p == a.step.buf )
{
b.step.p = b.step.buf;
b.size.p = &b.rows;
}
}
void cv::hconcat(const Mat* src, size_t nsrc, OutputArray _dst)
{
CV_INSTRUMENT_REGION();
if( nsrc == 0 || !src )
{
_dst.release();
return;
}
int totalCols = 0, cols = 0;
for( size_t i = 0; i < nsrc; i++ )
{
CV_Assert( src[i].dims <= 2 &&
src[i].rows == src[0].rows &&
src[i].type() == src[0].type());
totalCols += src[i].cols;
}
_dst.create( src[0].rows, totalCols, src[0].type());
Mat dst = _dst.getMat();
for( size_t i = 0; i < nsrc; i++ )
{
Mat dpart = dst(Rect(cols, 0, src[i].cols, src[i].rows));
src[i].copyTo(dpart);
cols += src[i].cols;
}
}
void cv::hconcat(InputArray src1, InputArray src2, OutputArray dst)
{
CV_INSTRUMENT_REGION();
Mat src[] = {src1.getMat(), src2.getMat()};
hconcat(src, 2, dst);
}
void cv::hconcat(InputArray _src, OutputArray dst)
{
CV_INSTRUMENT_REGION();
std::vector<Mat> src;
_src.getMatVector(src);
hconcat(!src.empty() ? &src[0] : 0, src.size(), dst);
}
void cv::vconcat(const Mat* src, size_t nsrc, OutputArray _dst)
{
CV_TRACE_FUNCTION_SKIP_NESTED()
if( nsrc == 0 || !src )
{
_dst.release();
return;
}
int totalRows = 0, rows = 0;
for( size_t i = 0; i < nsrc; i++ )
{
CV_Assert(src[i].dims <= 2 &&
src[i].cols == src[0].cols &&
src[i].type() == src[0].type());
totalRows += src[i].rows;
}
_dst.create( totalRows, src[0].cols, src[0].type());
Mat dst = _dst.getMat();
for( size_t i = 0; i < nsrc; i++ )
{
Mat dpart(dst, Rect(0, rows, src[i].cols, src[i].rows));
src[i].copyTo(dpart);
rows += src[i].rows;
}
}
void cv::vconcat(InputArray src1, InputArray src2, OutputArray dst)
{
CV_INSTRUMENT_REGION();
Mat src[] = {src1.getMat(), src2.getMat()};
vconcat(src, 2, dst);
}
void cv::vconcat(InputArray _src, OutputArray dst)
{
CV_INSTRUMENT_REGION();
std::vector<Mat> src;
_src.getMatVector(src);
vconcat(!src.empty() ? &src[0] : 0, src.size(), dst);
}
//////////////////////////////////////// set identity ////////////////////////////////////////////
#ifdef HAVE_OPENCL
namespace cv {
static bool ocl_setIdentity( InputOutputArray _m, const Scalar& s )
{
int type = _m.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type), kercn = cn, rowsPerWI = 1;
int sctype = CV_MAKE_TYPE(depth, cn == 3 ? 4 : cn);
if (ocl::Device::getDefault().isIntel())
{
rowsPerWI = 4;
if (cn == 1)
{
kercn = std::min(ocl::predictOptimalVectorWidth(_m), 4);
if (kercn != 4)
kercn = 1;
}
}
ocl::Kernel k("setIdentity", ocl::core::set_identity_oclsrc,
format("-D T=%s -D T1=%s -D cn=%d -D ST=%s -D kercn=%d -D rowsPerWI=%d",
ocl::memopTypeToStr(CV_MAKE_TYPE(depth, kercn)),
ocl::memopTypeToStr(depth), cn,
ocl::memopTypeToStr(sctype),
kercn, rowsPerWI));
if (k.empty())
return false;
UMat m = _m.getUMat();
k.args(ocl::KernelArg::WriteOnly(m, cn, kercn),
ocl::KernelArg::Constant(Mat(1, 1, sctype, s)));
size_t globalsize[2] = { (size_t)m.cols * cn / kercn, ((size_t)m.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
}
#endif
void cv::setIdentity( InputOutputArray _m, const Scalar& s )
{
CV_INSTRUMENT_REGION();
CV_Assert( _m.dims() <= 2 );
CV_OCL_RUN(_m.isUMat(),
ocl_setIdentity(_m, s))
Mat m = _m.getMat();
int rows = m.rows, cols = m.cols, type = m.type();
if( type == CV_32FC1 )
{
float* data = m.ptr<float>();
float val = (float)s[0];
size_t step = m.step/sizeof(data[0]);
for( int i = 0; i < rows; i++, data += step )
{
for( int j = 0; j < cols; j++ )
data[j] = 0;
if( i < cols )
data[i] = val;
}
}
else if( type == CV_64FC1 )
{
double* data = m.ptr<double>();
double val = s[0];
size_t step = m.step/sizeof(data[0]);
for( int i = 0; i < rows; i++, data += step )
{
for( int j = 0; j < cols; j++ )
data[j] = j == i ? val : 0;
}
}
else
{
m = Scalar(0);
m.diag() = s;
}
}
//////////////////////////////////////////// trace ///////////////////////////////////////////
cv::Scalar cv::trace( InputArray _m )
{
CV_INSTRUMENT_REGION();
Mat m = _m.getMat();
CV_Assert( m.dims <= 2 );
int type = m.type();
int nm = std::min(m.rows, m.cols);
if( type == CV_32FC1 )
{
const float* ptr = m.ptr<float>();
size_t step = m.step/sizeof(ptr[0]) + 1;
double _s = 0;
for( int i = 0; i < nm; i++ )
_s += ptr[i*step];
return _s;
}
if( type == CV_64FC1 )
{
const double* ptr = m.ptr<double>();
size_t step = m.step/sizeof(ptr[0]) + 1;
double _s = 0;
for( int i = 0; i < nm; i++ )
_s += ptr[i*step];
return _s;
}
return cv::sum(m.diag());
}
////////////////////////////////////// transpose /////////////////////////////////////////
namespace cv
{
template<typename T> static void
transpose_( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz )
{
int i=0, j, m = sz.width, n = sz.height;
#if CV_ENABLE_UNROLLED
for(; i <= m - 4; i += 4 )
{
T* d0 = (T*)(dst + dstep*i);
T* d1 = (T*)(dst + dstep*(i+1));
T* d2 = (T*)(dst + dstep*(i+2));
T* d3 = (T*)(dst + dstep*(i+3));
for( j = 0; j <= n - 4; j += 4 )
{
const T* s0 = (const T*)(src + i*sizeof(T) + sstep*j);
const T* s1 = (const T*)(src + i*sizeof(T) + sstep*(j+1));
const T* s2 = (const T*)(src + i*sizeof(T) + sstep*(j+2));
const T* s3 = (const T*)(src + i*sizeof(T) + sstep*(j+3));
d0[j] = s0[0]; d0[j+1] = s1[0]; d0[j+2] = s2[0]; d0[j+3] = s3[0];
d1[j] = s0[1]; d1[j+1] = s1[1]; d1[j+2] = s2[1]; d1[j+3] = s3[1];
d2[j] = s0[2]; d2[j+1] = s1[2]; d2[j+2] = s2[2]; d2[j+3] = s3[2];
d3[j] = s0[3]; d3[j+1] = s1[3]; d3[j+2] = s2[3]; d3[j+3] = s3[3];
}
for( ; j < n; j++ )
{
const T* s0 = (const T*)(src + i*sizeof(T) + j*sstep);
d0[j] = s0[0]; d1[j] = s0[1]; d2[j] = s0[2]; d3[j] = s0[3];
}
}
#endif
for( ; i < m; i++ )
{
T* d0 = (T*)(dst + dstep*i);
j = 0;
#if CV_ENABLE_UNROLLED
for(; j <= n - 4; j += 4 )
{
const T* s0 = (const T*)(src + i*sizeof(T) + sstep*j);
const T* s1 = (const T*)(src + i*sizeof(T) + sstep*(j+1));
const T* s2 = (const T*)(src + i*sizeof(T) + sstep*(j+2));
const T* s3 = (const T*)(src + i*sizeof(T) + sstep*(j+3));
d0[j] = s0[0]; d0[j+1] = s1[0]; d0[j+2] = s2[0]; d0[j+3] = s3[0];
}
#endif
for( ; j < n; j++ )
{
const T* s0 = (const T*)(src + i*sizeof(T) + j*sstep);
d0[j] = s0[0];
}
}
}
template<typename T> static void
transposeI_( uchar* data, size_t step, int n )
{
for( int i = 0; i < n; i++ )
{
T* row = (T*)(data + step*i);
uchar* data1 = data + i*sizeof(T);
for( int j = i+1; j < n; j++ )
std::swap( row[j], *(T*)(data1 + step*j) );
}
}
typedef void (*TransposeFunc)( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz );
typedef void (*TransposeInplaceFunc)( uchar* data, size_t step, int n );
#define DEF_TRANSPOSE_FUNC(suffix, type) \
static void transpose_##suffix( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size sz ) \
{ transpose_<type>(src, sstep, dst, dstep, sz); } \
\
static void transposeI_##suffix( uchar* data, size_t step, int n ) \
{ transposeI_<type>(data, step, n); }
DEF_TRANSPOSE_FUNC(8u, uchar)
DEF_TRANSPOSE_FUNC(16u, ushort)
DEF_TRANSPOSE_FUNC(8uC3, Vec3b)
DEF_TRANSPOSE_FUNC(32s, int)
DEF_TRANSPOSE_FUNC(16uC3, Vec3s)
DEF_TRANSPOSE_FUNC(32sC2, Vec2i)
DEF_TRANSPOSE_FUNC(32sC3, Vec3i)
DEF_TRANSPOSE_FUNC(32sC4, Vec4i)
DEF_TRANSPOSE_FUNC(32sC6, Vec6i)
DEF_TRANSPOSE_FUNC(32sC8, Vec8i)
static TransposeFunc transposeTab[] =
{
0, transpose_8u, transpose_16u, transpose_8uC3, transpose_32s, 0, transpose_16uC3, 0,
transpose_32sC2, 0, 0, 0, transpose_32sC3, 0, 0, 0, transpose_32sC4,
0, 0, 0, 0, 0, 0, 0, transpose_32sC6, 0, 0, 0, 0, 0, 0, 0, transpose_32sC8
};
static TransposeInplaceFunc transposeInplaceTab[] =
{
0, transposeI_8u, transposeI_16u, transposeI_8uC3, transposeI_32s, 0, transposeI_16uC3, 0,
transposeI_32sC2, 0, 0, 0, transposeI_32sC3, 0, 0, 0, transposeI_32sC4,
0, 0, 0, 0, 0, 0, 0, transposeI_32sC6, 0, 0, 0, 0, 0, 0, 0, transposeI_32sC8
};
#ifdef HAVE_OPENCL
static bool ocl_transpose( InputArray _src, OutputArray _dst )
{
const ocl::Device & dev = ocl::Device::getDefault();
const int TILE_DIM = 32, BLOCK_ROWS = 8;
int type = _src.type(), cn = CV_MAT_CN(type), depth = CV_MAT_DEPTH(type),
rowsPerWI = dev.isIntel() ? 4 : 1;
UMat src = _src.getUMat();
_dst.create(src.cols, src.rows, type);
UMat dst = _dst.getUMat();
String kernelName("transpose");
bool inplace = dst.u == src.u;
if (inplace)
{
CV_Assert(dst.cols == dst.rows);
kernelName += "_inplace";
}
else
{
// check required local memory size
size_t required_local_memory = (size_t) TILE_DIM*(TILE_DIM+1)*CV_ELEM_SIZE(type);
if (required_local_memory > ocl::Device::getDefault().localMemSize())
return false;
}
ocl::Kernel k(kernelName.c_str(), ocl::core::transpose_oclsrc,
format("-D T=%s -D T1=%s -D cn=%d -D TILE_DIM=%d -D BLOCK_ROWS=%d -D rowsPerWI=%d%s",
ocl::memopTypeToStr(type), ocl::memopTypeToStr(depth),
cn, TILE_DIM, BLOCK_ROWS, rowsPerWI, inplace ? " -D INPLACE" : ""));
if (k.empty())
return false;
if (inplace)
k.args(ocl::KernelArg::ReadWriteNoSize(dst), dst.rows);
else
k.args(ocl::KernelArg::ReadOnly(src),
ocl::KernelArg::WriteOnlyNoSize(dst));
size_t localsize[2] = { TILE_DIM, BLOCK_ROWS };
size_t globalsize[2] = { (size_t)src.cols, inplace ? ((size_t)src.rows + rowsPerWI - 1) / rowsPerWI : (divUp((size_t)src.rows, TILE_DIM) * BLOCK_ROWS) };
if (inplace && dev.isIntel())
{
localsize[0] = 16;
localsize[1] = dev.maxWorkGroupSize() / localsize[0];
}
return k.run(2, globalsize, localsize, false);
}
#endif
#ifdef HAVE_IPP
static bool ipp_transpose( Mat &src, Mat &dst )
{
CV_INSTRUMENT_REGION_IPP();
int type = src.type();
typedef IppStatus (CV_STDCALL * IppiTranspose)(const void * pSrc, int srcStep, void * pDst, int dstStep, IppiSize roiSize);
typedef IppStatus (CV_STDCALL * IppiTransposeI)(const void * pSrcDst, int srcDstStep, IppiSize roiSize);
IppiTranspose ippiTranspose = 0;
IppiTransposeI ippiTranspose_I = 0;
if (dst.data == src.data && dst.cols == dst.rows)
{
CV_SUPPRESS_DEPRECATED_START
ippiTranspose_I =
type == CV_8UC1 ? (IppiTransposeI)ippiTranspose_8u_C1IR :
type == CV_8UC3 ? (IppiTransposeI)ippiTranspose_8u_C3IR :
type == CV_8UC4 ? (IppiTransposeI)ippiTranspose_8u_C4IR :
type == CV_16UC1 ? (IppiTransposeI)ippiTranspose_16u_C1IR :
type == CV_16UC3 ? (IppiTransposeI)ippiTranspose_16u_C3IR :
type == CV_16UC4 ? (IppiTransposeI)ippiTranspose_16u_C4IR :
type == CV_16SC1 ? (IppiTransposeI)ippiTranspose_16s_C1IR :
type == CV_16SC3 ? (IppiTransposeI)ippiTranspose_16s_C3IR :
type == CV_16SC4 ? (IppiTransposeI)ippiTranspose_16s_C4IR :
type == CV_32SC1 ? (IppiTransposeI)ippiTranspose_32s_C1IR :
type == CV_32SC3 ? (IppiTransposeI)ippiTranspose_32s_C3IR :
type == CV_32SC4 ? (IppiTransposeI)ippiTranspose_32s_C4IR :
type == CV_32FC1 ? (IppiTransposeI)ippiTranspose_32f_C1IR :
type == CV_32FC3 ? (IppiTransposeI)ippiTranspose_32f_C3IR :
type == CV_32FC4 ? (IppiTransposeI)ippiTranspose_32f_C4IR : 0;
CV_SUPPRESS_DEPRECATED_END
}
else
{
ippiTranspose =
type == CV_8UC1 ? (IppiTranspose)ippiTranspose_8u_C1R :
type == CV_8UC3 ? (IppiTranspose)ippiTranspose_8u_C3R :
type == CV_8UC4 ? (IppiTranspose)ippiTranspose_8u_C4R :
type == CV_16UC1 ? (IppiTranspose)ippiTranspose_16u_C1R :
type == CV_16UC3 ? (IppiTranspose)ippiTranspose_16u_C3R :
type == CV_16UC4 ? (IppiTranspose)ippiTranspose_16u_C4R :
type == CV_16SC1 ? (IppiTranspose)ippiTranspose_16s_C1R :
type == CV_16SC3 ? (IppiTranspose)ippiTranspose_16s_C3R :
type == CV_16SC4 ? (IppiTranspose)ippiTranspose_16s_C4R :
type == CV_32SC1 ? (IppiTranspose)ippiTranspose_32s_C1R :
type == CV_32SC3 ? (IppiTranspose)ippiTranspose_32s_C3R :
type == CV_32SC4 ? (IppiTranspose)ippiTranspose_32s_C4R :
type == CV_32FC1 ? (IppiTranspose)ippiTranspose_32f_C1R :
type == CV_32FC3 ? (IppiTranspose)ippiTranspose_32f_C3R :
type == CV_32FC4 ? (IppiTranspose)ippiTranspose_32f_C4R : 0;
}
IppiSize roiSize = { src.cols, src.rows };
if (ippiTranspose != 0)
{
if (CV_INSTRUMENT_FUN_IPP(ippiTranspose, src.ptr(), (int)src.step, dst.ptr(), (int)dst.step, roiSize) >= 0)
return true;
}
else if (ippiTranspose_I != 0)
{
if (CV_INSTRUMENT_FUN_IPP(ippiTranspose_I, dst.ptr(), (int)dst.step, roiSize) >= 0)
return true;
}
return false;
}
#endif
}
void cv::transpose( InputArray _src, OutputArray _dst )
{
CV_INSTRUMENT_REGION();
int type = _src.type(), esz = CV_ELEM_SIZE(type);
CV_Assert( _src.dims() <= 2 && esz <= 32 );
CV_OCL_RUN(_dst.isUMat(),
ocl_transpose(_src, _dst))
Mat src = _src.getMat();
if( src.empty() )
{
_dst.release();
return;
}
_dst.create(src.cols, src.rows, src.type());
Mat dst = _dst.getMat();
// handle the case of single-column/single-row matrices, stored in STL vectors.
if( src.rows != dst.cols || src.cols != dst.rows )
{
CV_Assert( src.size() == dst.size() && (src.cols == 1 || src.rows == 1) );
src.copyTo(dst);
return;
}
CV_IPP_RUN_FAST(ipp_transpose(src, dst))
if( dst.data == src.data )
{
TransposeInplaceFunc func = transposeInplaceTab[esz];
CV_Assert( func != 0 );
CV_Assert( dst.cols == dst.rows );
func( dst.ptr(), dst.step, dst.rows );
}
else
{
TransposeFunc func = transposeTab[esz];
CV_Assert( func != 0 );
func( src.ptr(), src.step, dst.ptr(), dst.step, src.size() );
}
}
////////////////////////////////////// completeSymm /////////////////////////////////////////
void cv::completeSymm( InputOutputArray _m, bool LtoR )
{
CV_INSTRUMENT_REGION();
Mat m = _m.getMat();
size_t step = m.step, esz = m.elemSize();
CV_Assert( m.dims <= 2 && m.rows == m.cols );
int rows = m.rows;
int j0 = 0, j1 = rows;
uchar* data = m.ptr();
for( int i = 0; i < rows; i++ )
{
if( !LtoR ) j1 = i; else j0 = i+1;
for( int j = j0; j < j1; j++ )
memcpy(data + (i*step + j*esz), data + (j*step + i*esz), esz);
}
}
cv::Mat cv::Mat::cross(InputArray _m) const
{
Mat m = _m.getMat();
int tp = type(), d = CV_MAT_DEPTH(tp);
CV_Assert( dims <= 2 && m.dims <= 2 && size() == m.size() && tp == m.type() &&
((rows == 3 && cols == 1) || (cols*channels() == 3 && rows == 1)));
Mat result(rows, cols, tp);
if( d == CV_32F )
{
const float *a = (const float*)data, *b = (const float*)m.data;
float* c = (float*)result.data;
size_t lda = rows > 1 ? step/sizeof(a[0]) : 1;
size_t ldb = rows > 1 ? m.step/sizeof(b[0]) : 1;
c[0] = a[lda] * b[ldb*2] - a[lda*2] * b[ldb];
c[1] = a[lda*2] * b[0] - a[0] * b[ldb*2];
c[2] = a[0] * b[ldb] - a[lda] * b[0];
}
else if( d == CV_64F )
{
const double *a = (const double*)data, *b = (const double*)m.data;
double* c = (double*)result.data;
size_t lda = rows > 1 ? step/sizeof(a[0]) : 1;
size_t ldb = rows > 1 ? m.step/sizeof(b[0]) : 1;
c[0] = a[lda] * b[ldb*2] - a[lda*2] * b[ldb];
c[1] = a[lda*2] * b[0] - a[0] * b[ldb*2];
c[2] = a[0] * b[ldb] - a[lda] * b[0];
}
return result;
}
////////////////////////////////////////// reduce ////////////////////////////////////////////
namespace cv
{
template<typename T, typename ST, class Op> static void
reduceR_( const Mat& srcmat, Mat& dstmat )
{
typedef typename Op::rtype WT;
Size size = srcmat.size();
size.width *= srcmat.channels();
AutoBuffer<WT> buffer(size.width);
WT* buf = buffer.data();
ST* dst = dstmat.ptr<ST>();
const T* src = srcmat.ptr<T>();
size_t srcstep = srcmat.step/sizeof(src[0]);
int i;
Op op;
for( i = 0; i < size.width; i++ )
buf[i] = src[i];
for( ; --size.height; )
{
src += srcstep;
i = 0;
#if CV_ENABLE_UNROLLED
for(; i <= size.width - 4; i += 4 )
{
WT s0, s1;
s0 = op(buf[i], (WT)src[i]);
s1 = op(buf[i+1], (WT)src[i+1]);
buf[i] = s0; buf[i+1] = s1;
s0 = op(buf[i+2], (WT)src[i+2]);
s1 = op(buf[i+3], (WT)src[i+3]);
buf[i+2] = s0; buf[i+3] = s1;
}
#endif
for( ; i < size.width; i++ )
buf[i] = op(buf[i], (WT)src[i]);
}
for( i = 0; i < size.width; i++ )
dst[i] = (ST)buf[i];
}
template<typename T, typename ST, class Op> static void
reduceC_( const Mat& srcmat, Mat& dstmat )
{
typedef typename Op::rtype WT;
Size size = srcmat.size();
int cn = srcmat.channels();
size.width *= cn;
Op op;
for( int y = 0; y < size.height; y++ )
{
const T* src = srcmat.ptr<T>(y);
ST* dst = dstmat.ptr<ST>(y);
if( size.width == cn )
for( int k = 0; k < cn; k++ )
dst[k] = src[k];
else
{
for( int k = 0; k < cn; k++ )
{
WT a0 = src[k], a1 = src[k+cn];
int i;
for( i = 2*cn; i <= size.width - 4*cn; i += 4*cn )
{
a0 = op(a0, (WT)src[i+k]);
a1 = op(a1, (WT)src[i+k+cn]);
a0 = op(a0, (WT)src[i+k+cn*2]);
a1 = op(a1, (WT)src[i+k+cn*3]);
}
for( ; i < size.width; i += cn )
{
a0 = op(a0, (WT)src[i+k]);
}
a0 = op(a0, a1);
dst[k] = (ST)a0;
}
}
}
}
typedef void (*ReduceFunc)( const Mat& src, Mat& dst );
}
#define reduceSumR8u32s reduceR_<uchar, int, OpAdd<int> >
#define reduceSumR8u32f reduceR_<uchar, float, OpAdd<int> >
#define reduceSumR8u64f reduceR_<uchar, double,OpAdd<int> >
#define reduceSumR16u32f reduceR_<ushort,float, OpAdd<float> >
#define reduceSumR16u64f reduceR_<ushort,double,OpAdd<double> >
#define reduceSumR16s32f reduceR_<short, float, OpAdd<float> >
#define reduceSumR16s64f reduceR_<short, double,OpAdd<double> >
#define reduceSumR32f32f reduceR_<float, float, OpAdd<float> >
#define reduceSumR32f64f reduceR_<float, double,OpAdd<double> >
#define reduceSumR64f64f reduceR_<double,double,OpAdd<double> >
#define reduceMaxR8u reduceR_<uchar, uchar, OpMax<uchar> >
#define reduceMaxR16u reduceR_<ushort,ushort,OpMax<ushort> >
#define reduceMaxR16s reduceR_<short, short, OpMax<short> >
#define reduceMaxR32f reduceR_<float, float, OpMax<float> >
#define reduceMaxR64f reduceR_<double,double,OpMax<double> >
#define reduceMinR8u reduceR_<uchar, uchar, OpMin<uchar> >
#define reduceMinR16u reduceR_<ushort,ushort,OpMin<ushort> >
#define reduceMinR16s reduceR_<short, short, OpMin<short> >
#define reduceMinR32f reduceR_<float, float, OpMin<float> >
#define reduceMinR64f reduceR_<double,double,OpMin<double> >
#ifdef HAVE_IPP
static inline bool ipp_reduceSumC_8u16u16s32f_64f(const cv::Mat& srcmat, cv::Mat& dstmat)
{
int sstep = (int)srcmat.step, stype = srcmat.type(),
ddepth = dstmat.depth();
IppiSize roisize = { srcmat.size().width, 1 };
typedef IppStatus (CV_STDCALL * IppiSum)(const void * pSrc, int srcStep, IppiSize roiSize, Ipp64f* pSum);
typedef IppStatus (CV_STDCALL * IppiSumHint)(const void * pSrc, int srcStep, IppiSize roiSize, Ipp64f* pSum, IppHintAlgorithm hint);
IppiSum ippiSum = 0;
IppiSumHint ippiSumHint = 0;
if(ddepth == CV_64F)
{
ippiSum =
stype == CV_8UC1 ? (IppiSum)ippiSum_8u_C1R :
stype == CV_8UC3 ? (IppiSum)ippiSum_8u_C3R :
stype == CV_8UC4 ? (IppiSum)ippiSum_8u_C4R :
stype == CV_16UC1 ? (IppiSum)ippiSum_16u_C1R :
stype == CV_16UC3 ? (IppiSum)ippiSum_16u_C3R :
stype == CV_16UC4 ? (IppiSum)ippiSum_16u_C4R :
stype == CV_16SC1 ? (IppiSum)ippiSum_16s_C1R :
stype == CV_16SC3 ? (IppiSum)ippiSum_16s_C3R :
stype == CV_16SC4 ? (IppiSum)ippiSum_16s_C4R : 0;
ippiSumHint =
stype == CV_32FC1 ? (IppiSumHint)ippiSum_32f_C1R :
stype == CV_32FC3 ? (IppiSumHint)ippiSum_32f_C3R :
stype == CV_32FC4 ? (IppiSumHint)ippiSum_32f_C4R : 0;
}
if(ippiSum)
{
for(int y = 0; y < srcmat.size().height; y++)
{
if(CV_INSTRUMENT_FUN_IPP(ippiSum, srcmat.ptr(y), sstep, roisize, dstmat.ptr<Ipp64f>(y)) < 0)
return false;
}
return true;
}
else if(ippiSumHint)
{
for(int y = 0; y < srcmat.size().height; y++)
{
if(CV_INSTRUMENT_FUN_IPP(ippiSumHint, srcmat.ptr(y), sstep, roisize, dstmat.ptr<Ipp64f>(y), ippAlgHintAccurate) < 0)
return false;
}
return true;
}
return false;
}
static inline void reduceSumC_8u16u16s32f_64f(const cv::Mat& srcmat, cv::Mat& dstmat)
{
CV_IPP_RUN_FAST(ipp_reduceSumC_8u16u16s32f_64f(srcmat, dstmat));
cv::ReduceFunc func = 0;
if(dstmat.depth() == CV_64F)
{
int sdepth = CV_MAT_DEPTH(srcmat.type());
func =
sdepth == CV_8U ? (cv::ReduceFunc)cv::reduceC_<uchar, double, cv::OpAdd<double> > :
sdepth == CV_16U ? (cv::ReduceFunc)cv::reduceC_<ushort, double, cv::OpAdd<double> > :
sdepth == CV_16S ? (cv::ReduceFunc)cv::reduceC_<short, double, cv::OpAdd<double> > :
sdepth == CV_32F ? (cv::ReduceFunc)cv::reduceC_<float, double, cv::OpAdd<double> > : 0;
}
CV_Assert(func);
func(srcmat, dstmat);
}
#endif
#define reduceSumC8u32s reduceC_<uchar, int, OpAdd<int> >
#define reduceSumC8u32f reduceC_<uchar, float, OpAdd<int> >
#define reduceSumC16u32f reduceC_<ushort,float, OpAdd<float> >
#define reduceSumC16s32f reduceC_<short, float, OpAdd<float> >
#define reduceSumC32f32f reduceC_<float, float, OpAdd<float> >
#define reduceSumC64f64f reduceC_<double,double,OpAdd<double> >
#ifdef HAVE_IPP
#define reduceSumC8u64f reduceSumC_8u16u16s32f_64f
#define reduceSumC16u64f reduceSumC_8u16u16s32f_64f
#define reduceSumC16s64f reduceSumC_8u16u16s32f_64f
#define reduceSumC32f64f reduceSumC_8u16u16s32f_64f
#else
#define reduceSumC8u64f reduceC_<uchar, double,OpAdd<int> >
#define reduceSumC16u64f reduceC_<ushort,double,OpAdd<double> >
#define reduceSumC16s64f reduceC_<short, double,OpAdd<double> >
#define reduceSumC32f64f reduceC_<float, double,OpAdd<double> >
#endif
#ifdef HAVE_IPP
#define REDUCE_OP(favor, optype, type1, type2) \
static inline bool ipp_reduce##optype##C##favor(const cv::Mat& srcmat, cv::Mat& dstmat) \
{ \
if((srcmat.channels() == 1)) \
{ \
int sstep = (int)srcmat.step; \
typedef Ipp##favor IppType; \
IppiSize roisize = ippiSize(srcmat.size().width, 1);\
for(int y = 0; y < srcmat.size().height; y++)\
{\
if(CV_INSTRUMENT_FUN_IPP(ippi##optype##_##favor##_C1R, srcmat.ptr<IppType>(y), sstep, roisize, dstmat.ptr<IppType>(y)) < 0)\
return false;\
}\
return true;\
}\
return false; \
} \
static inline void reduce##optype##C##favor(const cv::Mat& srcmat, cv::Mat& dstmat) \
{ \
CV_IPP_RUN_FAST(ipp_reduce##optype##C##favor(srcmat, dstmat)); \
cv::reduceC_ < type1, type2, cv::Op##optype < type2 > >(srcmat, dstmat); \
}
#endif
#ifdef HAVE_IPP
REDUCE_OP(8u, Max, uchar, uchar)
REDUCE_OP(16u, Max, ushort, ushort)
REDUCE_OP(16s, Max, short, short)
REDUCE_OP(32f, Max, float, float)
#else
#define reduceMaxC8u reduceC_<uchar, uchar, OpMax<uchar> >
#define reduceMaxC16u reduceC_<ushort,ushort,OpMax<ushort> >
#define reduceMaxC16s reduceC_<short, short, OpMax<short> >
#define reduceMaxC32f reduceC_<float, float, OpMax<float> >
#endif
#define reduceMaxC64f reduceC_<double,double,OpMax<double> >
#ifdef HAVE_IPP
REDUCE_OP(8u, Min, uchar, uchar)
REDUCE_OP(16u, Min, ushort, ushort)
REDUCE_OP(16s, Min, short, short)
REDUCE_OP(32f, Min, float, float)
#else
#define reduceMinC8u reduceC_<uchar, uchar, OpMin<uchar> >
#define reduceMinC16u reduceC_<ushort,ushort,OpMin<ushort> >
#define reduceMinC16s reduceC_<short, short, OpMin<short> >
#define reduceMinC32f reduceC_<float, float, OpMin<float> >
#endif
#define reduceMinC64f reduceC_<double,double,OpMin<double> >
#ifdef HAVE_OPENCL
namespace cv {
static bool ocl_reduce(InputArray _src, OutputArray _dst,
int dim, int op, int op0, int stype, int dtype)
{
const int min_opt_cols = 128, buf_cols = 32;
int sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype),
ddepth = CV_MAT_DEPTH(dtype), ddepth0 = ddepth;
const ocl::Device &defDev = ocl::Device::getDefault();
bool doubleSupport = defDev.doubleFPConfig() > 0;
size_t wgs = defDev.maxWorkGroupSize();
bool useOptimized = 1 == dim && _src.cols() > min_opt_cols && (wgs >= buf_cols);
if (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F))
return false;
if (op == CV_REDUCE_AVG)
{
if (sdepth < CV_32S && ddepth < CV_32S)
ddepth = CV_32S;
}
const char * const ops[4] = { "OCL_CV_REDUCE_SUM", "OCL_CV_REDUCE_AVG",
"OCL_CV_REDUCE_MAX", "OCL_CV_REDUCE_MIN" };
int wdepth = std::max(ddepth, CV_32F);
if (useOptimized)
{
size_t tileHeight = (size_t)(wgs / buf_cols);
if (defDev.isIntel())
{
static const size_t maxItemInGroupCount = 16;
tileHeight = min(tileHeight, defDev.localMemSize() / buf_cols / CV_ELEM_SIZE(CV_MAKETYPE(wdepth, cn)) / maxItemInGroupCount);
}
char cvt[3][40];
cv::String build_opt = format("-D OP_REDUCE_PRE -D BUF_COLS=%d -D TILE_HEIGHT=%zu -D %s -D dim=1"
" -D cn=%d -D ddepth=%d"
" -D srcT=%s -D bufT=%s -D dstT=%s"
" -D convertToWT=%s -D convertToBufT=%s -D convertToDT=%s%s",
buf_cols, tileHeight, ops[op], cn, ddepth,
ocl::typeToStr(sdepth),
ocl::typeToStr(ddepth),
ocl::typeToStr(ddepth0),
ocl::convertTypeStr(ddepth, wdepth, 1, cvt[0]),
ocl::convertTypeStr(sdepth, ddepth, 1, cvt[1]),
ocl::convertTypeStr(wdepth, ddepth0, 1, cvt[2]),
doubleSupport ? " -D DOUBLE_SUPPORT" : "");
ocl::Kernel k("reduce_horz_opt", ocl::core::reduce2_oclsrc, build_opt);
if (k.empty())
return false;
UMat src = _src.getUMat();
Size dsize(1, src.rows);
_dst.create(dsize, dtype);
UMat dst = _dst.getUMat();
if (op0 == CV_REDUCE_AVG)
k.args(ocl::KernelArg::ReadOnly(src),
ocl::KernelArg::WriteOnlyNoSize(dst), 1.0f / src.cols);
else
k.args(ocl::KernelArg::ReadOnly(src),
ocl::KernelArg::WriteOnlyNoSize(dst));
size_t localSize[2] = { (size_t)buf_cols, (size_t)tileHeight};
size_t globalSize[2] = { (size_t)buf_cols, (size_t)src.rows };
return k.run(2, globalSize, localSize, false);
}
else
{
char cvt[2][40];
cv::String build_opt = format("-D %s -D dim=%d -D cn=%d -D ddepth=%d"
" -D srcT=%s -D dstT=%s -D dstT0=%s -D convertToWT=%s"
" -D convertToDT=%s -D convertToDT0=%s%s",
ops[op], dim, cn, ddepth, ocl::typeToStr(useOptimized ? ddepth : sdepth),
ocl::typeToStr(ddepth), ocl::typeToStr(ddepth0),
ocl::convertTypeStr(ddepth, wdepth, 1, cvt[0]),
ocl::convertTypeStr(sdepth, ddepth, 1, cvt[0]),
ocl::convertTypeStr(wdepth, ddepth0, 1, cvt[1]),
doubleSupport ? " -D DOUBLE_SUPPORT" : "");
ocl::Kernel k("reduce", ocl::core::reduce2_oclsrc, build_opt);
if (k.empty())
return false;
UMat src = _src.getUMat();
Size dsize(dim == 0 ? src.cols : 1, dim == 0 ? 1 : src.rows);
_dst.create(dsize, dtype);
UMat dst = _dst.getUMat();
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnly(src),
temparg = ocl::KernelArg::WriteOnlyNoSize(dst);
if (op0 == CV_REDUCE_AVG)
k.args(srcarg, temparg, 1.0f / (dim == 0 ? src.rows : src.cols));
else
k.args(srcarg, temparg);
size_t globalsize = std::max(dsize.width, dsize.height);
return k.run(1, &globalsize, NULL, false);
}
}
}
#endif
void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
{
CV_INSTRUMENT_REGION();
CV_Assert( _src.dims() <= 2 );
int op0 = op;
int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype);
if( dtype < 0 )
dtype = _dst.fixedType() ? _dst.type() : stype;
dtype = CV_MAKETYPE(dtype >= 0 ? dtype : stype, cn);
int ddepth = CV_MAT_DEPTH(dtype);
CV_Assert( cn == CV_MAT_CN(dtype) );
CV_Assert( op == CV_REDUCE_SUM || op == CV_REDUCE_MAX ||
op == CV_REDUCE_MIN || op == CV_REDUCE_AVG );
CV_OCL_RUN(_dst.isUMat(),
ocl_reduce(_src, _dst, dim, op, op0, stype, dtype))
// Fake reference to source. Resolves issue 8693 in case of src == dst.
UMat srcUMat;
if (_src.isUMat())
srcUMat = _src.getUMat();
Mat src = _src.getMat();
_dst.create(dim == 0 ? 1 : src.rows, dim == 0 ? src.cols : 1, dtype);
Mat dst = _dst.getMat(), temp = dst;
if( op == CV_REDUCE_AVG )
{
op = CV_REDUCE_SUM;
if( sdepth < CV_32S && ddepth < CV_32S )
{
temp.create(dst.rows, dst.cols, CV_32SC(cn));
ddepth = CV_32S;
}
}
ReduceFunc func = 0;
if( dim == 0 )
{
if( op == CV_REDUCE_SUM )
{
if(sdepth == CV_8U && ddepth == CV_32S)
func = GET_OPTIMIZED(reduceSumR8u32s);
else if(sdepth == CV_8U && ddepth == CV_32F)
func = reduceSumR8u32f;
else if(sdepth == CV_8U && ddepth == CV_64F)
func = reduceSumR8u64f;
else if(sdepth == CV_16U && ddepth == CV_32F)
func = reduceSumR16u32f;
else if(sdepth == CV_16U && ddepth == CV_64F)
func = reduceSumR16u64f;
else if(sdepth == CV_16S && ddepth == CV_32F)
func = reduceSumR16s32f;
else if(sdepth == CV_16S && ddepth == CV_64F)
func = reduceSumR16s64f;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceSumR32f32f);
else if(sdepth == CV_32F && ddepth == CV_64F)
func = reduceSumR32f64f;
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceSumR64f64f;
}
else if(op == CV_REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMaxR8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMaxR16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceMaxR16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceMaxR32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMaxR64f;
}
else if(op == CV_REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMinR8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMinR16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceMinR16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceMinR32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMinR64f;
}
}
else
{
if(op == CV_REDUCE_SUM)
{
if(sdepth == CV_8U && ddepth == CV_32S)
func = GET_OPTIMIZED(reduceSumC8u32s);
else if(sdepth == CV_8U && ddepth == CV_32F)
func = reduceSumC8u32f;
else if(sdepth == CV_8U && ddepth == CV_64F)
func = reduceSumC8u64f;
else if(sdepth == CV_16U && ddepth == CV_32F)
func = reduceSumC16u32f;
else if(sdepth == CV_16U && ddepth == CV_64F)
func = reduceSumC16u64f;
else if(sdepth == CV_16S && ddepth == CV_32F)
func = reduceSumC16s32f;
else if(sdepth == CV_16S && ddepth == CV_64F)
func = reduceSumC16s64f;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceSumC32f32f);
else if(sdepth == CV_32F && ddepth == CV_64F)
func = reduceSumC32f64f;
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceSumC64f64f;
}
else if(op == CV_REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMaxC8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMaxC16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceMaxC16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceMaxC32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMaxC64f;
}
else if(op == CV_REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMinC8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMinC16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceMinC16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceMinC32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMinC64f;
}
}
if( !func )
CV_Error( CV_StsUnsupportedFormat,
"Unsupported combination of input and output array formats" );
func( src, temp );
if( op0 == CV_REDUCE_AVG )
temp.convertTo(dst, dst.type(), 1./(dim == 0 ? src.rows : src.cols));
}
//////////////////////////////////////// sort ///////////////////////////////////////////
namespace cv
{
template<typename T> static void sort_( const Mat& src, Mat& dst, int flags )
{
AutoBuffer<T> buf;
int n, len;
bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
bool inplace = src.data == dst.data;
bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
if( sortRows )
n = src.rows, len = src.cols;
else
{
n = src.cols, len = src.rows;
buf.allocate(len);
}
T* bptr = buf.data();
for( int i = 0; i < n; i++ )
{
T* ptr = bptr;
if( sortRows )
{
T* dptr = dst.ptr<T>(i);
if( !inplace )
{
const T* sptr = src.ptr<T>(i);
memcpy(dptr, sptr, sizeof(T) * len);
}
ptr = dptr;
}
else
{
for( int j = 0; j < len; j++ )
ptr[j] = src.ptr<T>(j)[i];
}
std::sort( ptr, ptr + len );
if( sortDescending )
{
for( int j = 0; j < len/2; j++ )
std::swap(ptr[j], ptr[len-1-j]);
}
if( !sortRows )
for( int j = 0; j < len; j++ )
dst.ptr<T>(j)[i] = ptr[j];
}
}
#ifdef HAVE_IPP
typedef IppStatus (CV_STDCALL *IppSortFunc)(void *pSrcDst, int len, Ipp8u *pBuffer);
static IppSortFunc getSortFunc(int depth, bool sortDescending)
{
if (!sortDescending)
return depth == CV_8U ? (IppSortFunc)ippsSortRadixAscend_8u_I :
depth == CV_16U ? (IppSortFunc)ippsSortRadixAscend_16u_I :
depth == CV_16S ? (IppSortFunc)ippsSortRadixAscend_16s_I :
depth == CV_32S ? (IppSortFunc)ippsSortRadixAscend_32s_I :
depth == CV_32F ? (IppSortFunc)ippsSortRadixAscend_32f_I :
depth == CV_64F ? (IppSortFunc)ippsSortRadixAscend_64f_I :
0;
else
return depth == CV_8U ? (IppSortFunc)ippsSortRadixDescend_8u_I :
depth == CV_16U ? (IppSortFunc)ippsSortRadixDescend_16u_I :
depth == CV_16S ? (IppSortFunc)ippsSortRadixDescend_16s_I :
depth == CV_32S ? (IppSortFunc)ippsSortRadixDescend_32s_I :
depth == CV_32F ? (IppSortFunc)ippsSortRadixDescend_32f_I :
depth == CV_64F ? (IppSortFunc)ippsSortRadixDescend_64f_I :
0;
}
static bool ipp_sort(const Mat& src, Mat& dst, int flags)
{
CV_INSTRUMENT_REGION_IPP();
bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
bool inplace = (src.data == dst.data);
int depth = src.depth();
IppDataType type = ippiGetDataType(depth);
IppSortFunc ippsSortRadix_I = getSortFunc(depth, sortDescending);
if(!ippsSortRadix_I)
return false;
if(sortRows)
{
AutoBuffer<Ipp8u> buffer;
int bufferSize;
if(ippsSortRadixGetBufferSize(src.cols, type, &bufferSize) < 0)
return false;
buffer.allocate(bufferSize);
if(!inplace)
src.copyTo(dst);
for(int i = 0; i < dst.rows; i++)
{
if(CV_INSTRUMENT_FUN_IPP(ippsSortRadix_I, (void*)dst.ptr(i), dst.cols, buffer.data()) < 0)
return false;
}
}
else
{
AutoBuffer<Ipp8u> buffer;
int bufferSize;
if(ippsSortRadixGetBufferSize(src.rows, type, &bufferSize) < 0)
return false;
buffer.allocate(bufferSize);
Mat row(1, src.rows, src.type());
Mat srcSub;
Mat dstSub;
Rect subRect(0,0,1,src.rows);
for(int i = 0; i < src.cols; i++)
{
subRect.x = i;
srcSub = Mat(src, subRect);
dstSub = Mat(dst, subRect);
srcSub.copyTo(row);
if(CV_INSTRUMENT_FUN_IPP(ippsSortRadix_I, (void*)row.ptr(), dst.rows, buffer.data()) < 0)
return false;
row = row.reshape(1, dstSub.rows);
row.copyTo(dstSub);
}
}
return true;
}
#endif
template<typename _Tp> class LessThanIdx
{
public:
LessThanIdx( const _Tp* _arr ) : arr(_arr) {}
bool operator()(int a, int b) const { return arr[a] < arr[b]; }
const _Tp* arr;
};
template<typename T> static void sortIdx_( const Mat& src, Mat& dst, int flags )
{
AutoBuffer<T> buf;
AutoBuffer<int> ibuf;
bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
CV_Assert( src.data != dst.data );
int n, len;
if( sortRows )
n = src.rows, len = src.cols;
else
{
n = src.cols, len = src.rows;
buf.allocate(len);
ibuf.allocate(len);
}
T* bptr = buf.data();
int* _iptr = ibuf.data();
for( int i = 0; i < n; i++ )
{
T* ptr = bptr;
int* iptr = _iptr;
if( sortRows )
{
ptr = (T*)(src.data + src.step*i);
iptr = dst.ptr<int>(i);
}
else
{
for( int j = 0; j < len; j++ )
ptr[j] = src.ptr<T>(j)[i];
}
for( int j = 0; j < len; j++ )
iptr[j] = j;
std::sort( iptr, iptr + len, LessThanIdx<T>(ptr) );
if( sortDescending )
{
for( int j = 0; j < len/2; j++ )
std::swap(iptr[j], iptr[len-1-j]);
}
if( !sortRows )
for( int j = 0; j < len; j++ )
dst.ptr<int>(j)[i] = iptr[j];
}
}
#ifdef HAVE_IPP
typedef IppStatus (CV_STDCALL *IppSortIndexFunc)(const void* pSrc, Ipp32s srcStrideBytes, Ipp32s *pDstIndx, int len, Ipp8u *pBuffer);
static IppSortIndexFunc getSortIndexFunc(int depth, bool sortDescending)
{
if (!sortDescending)
return depth == CV_8U ? (IppSortIndexFunc)ippsSortRadixIndexAscend_8u :
depth == CV_16U ? (IppSortIndexFunc)ippsSortRadixIndexAscend_16u :
depth == CV_16S ? (IppSortIndexFunc)ippsSortRadixIndexAscend_16s :
depth == CV_32S ? (IppSortIndexFunc)ippsSortRadixIndexAscend_32s :
depth == CV_32F ? (IppSortIndexFunc)ippsSortRadixIndexAscend_32f :
0;
else
return depth == CV_8U ? (IppSortIndexFunc)ippsSortRadixIndexDescend_8u :
depth == CV_16U ? (IppSortIndexFunc)ippsSortRadixIndexDescend_16u :
depth == CV_16S ? (IppSortIndexFunc)ippsSortRadixIndexDescend_16s :
depth == CV_32S ? (IppSortIndexFunc)ippsSortRadixIndexDescend_32s :
depth == CV_32F ? (IppSortIndexFunc)ippsSortRadixIndexDescend_32f :
0;
}
static bool ipp_sortIdx( const Mat& src, Mat& dst, int flags )
{
CV_INSTRUMENT_REGION_IPP();
bool sortRows = (flags & 1) == SORT_EVERY_ROW;
bool sortDescending = (flags & SORT_DESCENDING) != 0;
int depth = src.depth();
IppDataType type = ippiGetDataType(depth);
IppSortIndexFunc ippsSortRadixIndex = getSortIndexFunc(depth, sortDescending);
if(!ippsSortRadixIndex)
return false;
if(sortRows)
{
AutoBuffer<Ipp8u> buffer;
int bufferSize;
if(ippsSortRadixIndexGetBufferSize(src.cols, type, &bufferSize) < 0)
return false;
buffer.allocate(bufferSize);
for(int i = 0; i < src.rows; i++)
{
if(CV_INSTRUMENT_FUN_IPP(ippsSortRadixIndex, (const void*)src.ptr(i), (Ipp32s)src.step[1], (Ipp32s*)dst.ptr(i), src.cols, buffer.data()) < 0)
return false;
}
}
else
{
Mat dstRow(1, dst.rows, dst.type());
Mat dstSub;
Rect subRect(0,0,1,src.rows);
AutoBuffer<Ipp8u> buffer;
int bufferSize;
if(ippsSortRadixIndexGetBufferSize(src.rows, type, &bufferSize) < 0)
return false;
buffer.allocate(bufferSize);
Ipp32s srcStep = (Ipp32s)src.step[0];
for(int i = 0; i < src.cols; i++)
{
subRect.x = i;
dstSub = Mat(dst, subRect);
if(CV_INSTRUMENT_FUN_IPP(ippsSortRadixIndex, (const void*)src.ptr(0, i), srcStep, (Ipp32s*)dstRow.ptr(), src.rows, buffer.data()) < 0)
return false;
dstRow = dstRow.reshape(1, dstSub.rows);
dstRow.copyTo(dstSub);
}
}
return true;
}
#endif
typedef void (*SortFunc)(const Mat& src, Mat& dst, int flags);
}
void cv::sort( InputArray _src, OutputArray _dst, int flags )
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
CV_Assert( src.dims <= 2 && src.channels() == 1 );
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
CV_IPP_RUN_FAST(ipp_sort(src, dst, flags));
static SortFunc tab[] =
{
sort_<uchar>, sort_<schar>, sort_<ushort>, sort_<short>,
sort_<int>, sort_<float>, sort_<double>, 0
};
SortFunc func = tab[src.depth()];
CV_Assert( func != 0 );
func( src, dst, flags );
}
void cv::sortIdx( InputArray _src, OutputArray _dst, int flags )
{
CV_INSTRUMENT_REGION();
Mat src = _src.getMat();
CV_Assert( src.dims <= 2 && src.channels() == 1 );
Mat dst = _dst.getMat();
if( dst.data == src.data )
_dst.release();
_dst.create( src.size(), CV_32S );
dst = _dst.getMat();
CV_IPP_RUN_FAST(ipp_sortIdx(src, dst, flags));
static SortFunc tab[] =
{
sortIdx_<uchar>, sortIdx_<schar>, sortIdx_<ushort>, sortIdx_<short>,
sortIdx_<int>, sortIdx_<float>, sortIdx_<double>, 0
};
SortFunc func = tab[src.depth()];
CV_Assert( func != 0 );
func( src, dst, flags );
}
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