Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
13980 Commits
6 Branches
128 Tags
3.0 GiB
 
 
 
 
 
 
Tag: Branch: Tree: c9876c3cd8
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'c9876c3cd8'
${ noResults }
opencv/modules/core/src/convert.cpp

2268 lines
74 KiB
Raw Blame History

/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencl_kernels.hpp"
namespace cv
{
/****************************************************************************************\
* split & merge *
\****************************************************************************************/
template<typename T> static void
split_( const T* src, T** dst, int len, int cn )
{
int k = cn % 4 ? cn % 4 : 4;
int i, j;
if( k == 1 )
{
T* dst0 = dst[0];
for( i = j = 0; i < len; i++, j += cn )
dst0[i] = src[j];
}
else if( k == 2 )
{
T *dst0 = dst[0], *dst1 = dst[1];
for( i = j = 0; i < len; i++, j += cn )
{
dst0[i] = src[j];
dst1[i] = src[j+1];
}
}
else if( k == 3 )
{
T *dst0 = dst[0], *dst1 = dst[1], *dst2 = dst[2];
for( i = j = 0; i < len; i++, j += cn )
{
dst0[i] = src[j];
dst1[i] = src[j+1];
dst2[i] = src[j+2];
}
}
else
{
T *dst0 = dst[0], *dst1 = dst[1], *dst2 = dst[2], *dst3 = dst[3];
for( i = j = 0; i < len; i++, j += cn )
{
dst0[i] = src[j]; dst1[i] = src[j+1];
dst2[i] = src[j+2]; dst3[i] = src[j+3];
}
}
for( ; k < cn; k += 4 )
{
T *dst0 = dst[k], *dst1 = dst[k+1], *dst2 = dst[k+2], *dst3 = dst[k+3];
for( i = 0, j = k; i < len; i++, j += cn )
{
dst0[i] = src[j]; dst1[i] = src[j+1];
dst2[i] = src[j+2]; dst3[i] = src[j+3];
}
}
}
template<typename T> static void
merge_( const T** src, T* dst, int len, int cn )
{
int k = cn % 4 ? cn % 4 : 4;
int i, j;
if( k == 1 )
{
const T* src0 = src[0];
for( i = j = 0; i < len; i++, j += cn )
dst[j] = src0[i];
}
else if( k == 2 )
{
const T *src0 = src[0], *src1 = src[1];
for( i = j = 0; i < len; i++, j += cn )
{
dst[j] = src0[i];
dst[j+1] = src1[i];
}
}
else if( k == 3 )
{
const T *src0 = src[0], *src1 = src[1], *src2 = src[2];
for( i = j = 0; i < len; i++, j += cn )
{
dst[j] = src0[i];
dst[j+1] = src1[i];
dst[j+2] = src2[i];
}
}
else
{
const T *src0 = src[0], *src1 = src[1], *src2 = src[2], *src3 = src[3];
for( i = j = 0; i < len; i++, j += cn )
{
dst[j] = src0[i]; dst[j+1] = src1[i];
dst[j+2] = src2[i]; dst[j+3] = src3[i];
}
}
for( ; k < cn; k += 4 )
{
const T *src0 = src[k], *src1 = src[k+1], *src2 = src[k+2], *src3 = src[k+3];
for( i = 0, j = k; i < len; i++, j += cn )
{
dst[j] = src0[i]; dst[j+1] = src1[i];
dst[j+2] = src2[i]; dst[j+3] = src3[i];
}
}
}
static void split8u(const uchar* src, uchar** dst, int len, int cn )
{
split_(src, dst, len, cn);
}
static void split16u(const ushort* src, ushort** dst, int len, int cn )
{
split_(src, dst, len, cn);
}
static void split32s(const int* src, int** dst, int len, int cn )
{
split_(src, dst, len, cn);
}
static void split64s(const int64* src, int64** dst, int len, int cn )
{
split_(src, dst, len, cn);
}
static void merge8u(const uchar** src, uchar* dst, int len, int cn )
{
merge_(src, dst, len, cn);
}
static void merge16u(const ushort** src, ushort* dst, int len, int cn )
{
merge_(src, dst, len, cn);
}
static void merge32s(const int** src, int* dst, int len, int cn )
{
merge_(src, dst, len, cn);
}
static void merge64s(const int64** src, int64* dst, int len, int cn )
{
merge_(src, dst, len, cn);
}
typedef void (*SplitFunc)(const uchar* src, uchar** dst, int len, int cn);
typedef void (*MergeFunc)(const uchar** src, uchar* dst, int len, int cn);
static SplitFunc getSplitFunc(int depth)
{
static SplitFunc splitTab[] =
{
(SplitFunc)GET_OPTIMIZED(split8u), (SplitFunc)GET_OPTIMIZED(split8u), (SplitFunc)GET_OPTIMIZED(split16u), (SplitFunc)GET_OPTIMIZED(split16u),
(SplitFunc)GET_OPTIMIZED(split32s), (SplitFunc)GET_OPTIMIZED(split32s), (SplitFunc)GET_OPTIMIZED(split64s), 0
};
return splitTab[depth];
}
static MergeFunc getMergeFunc(int depth)
{
static MergeFunc mergeTab[] =
{
(MergeFunc)GET_OPTIMIZED(merge8u), (MergeFunc)GET_OPTIMIZED(merge8u), (MergeFunc)GET_OPTIMIZED(merge16u), (MergeFunc)GET_OPTIMIZED(merge16u),
(MergeFunc)GET_OPTIMIZED(merge32s), (MergeFunc)GET_OPTIMIZED(merge32s), (MergeFunc)GET_OPTIMIZED(merge64s), 0
};
return mergeTab[depth];
}
}
void cv::split(const Mat& src, Mat* mv)
{
int k, depth = src.depth(), cn = src.channels();
if( cn == 1 )
{
src.copyTo(mv[0]);
return;
}
SplitFunc func = getSplitFunc(depth);
CV_Assert( func != 0 );
int esz = (int)src.elemSize(), esz1 = (int)src.elemSize1();
int blocksize0 = (BLOCK_SIZE + esz-1)/esz;
AutoBuffer<uchar> _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16);
const Mat** arrays = (const Mat**)(uchar*)_buf;
uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16);
arrays[0] = &src;
for( k = 0; k < cn; k++ )
{
mv[k].create(src.dims, src.size, depth);
arrays[k+1] = &mv[k];
}
NAryMatIterator it(arrays, ptrs, cn+1);
int total = (int)it.size, blocksize = cn <= 4 ? total : std::min(total, blocksize0);
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( int j = 0; j < total; j += blocksize )
{
int bsz = std::min(total - j, blocksize);
func( ptrs[0], &ptrs[1], bsz, cn );
if( j + blocksize < total )
{
ptrs[0] += bsz*esz;
for( k = 0; k < cn; k++ )
ptrs[k+1] += bsz*esz1;
}
}
}
}
#ifdef HAVE_OPENCL
namespace cv {
static bool ocl_split( InputArray _m, OutputArrayOfArrays _mv )
{
int type = _m.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type),
rowsPerWI = ocl::Device::getDefault().isIntel() ? 4 : 1;
String dstargs, processelem, indexdecl;
for (int i = 0; i < cn; ++i)
{
dstargs += format("DECLARE_DST_PARAM(%d)", i);
indexdecl += format("DECLARE_INDEX(%d)", i);
processelem += format("PROCESS_ELEM(%d)", i);
}
ocl::Kernel k("split", ocl::core::split_merge_oclsrc,
format("-D T=%s -D OP_SPLIT -D cn=%d -D DECLARE_DST_PARAMS=%s"
" -D PROCESS_ELEMS_N=%s -D DECLARE_INDEX_N=%s",
ocl::memopTypeToStr(depth), cn, dstargs.c_str(),
processelem.c_str(), indexdecl.c_str()));
if (k.empty())
return false;
Size size = _m.size();
_mv.create(cn, 1, depth);
for (int i = 0; i < cn; ++i)
_mv.create(size, depth, i);
std::vector<UMat> dst;
_mv.getUMatVector(dst);
int argidx = k.set(0, ocl::KernelArg::ReadOnly(_m.getUMat()));
for (int i = 0; i < cn; ++i)
argidx = k.set(argidx, ocl::KernelArg::WriteOnlyNoSize(dst[i]));
k.set(argidx, rowsPerWI);
size_t globalsize[2] = { size.width, (size.height + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
}
#endif
void cv::split(InputArray _m, OutputArrayOfArrays _mv)
{
CV_OCL_RUN(_m.dims() <= 2 && _mv.isUMatVector(),
ocl_split(_m, _mv))
Mat m = _m.getMat();
if( m.empty() )
{
_mv.release();
return;
}
CV_Assert( !_mv.fixedType() || _mv.empty() || _mv.type() == m.depth() );
Size size = m.size();
int depth = m.depth(), cn = m.channels();
_mv.create(cn, 1, depth);
for (int i = 0; i < cn; ++i)
_mv.create(size, depth, i);
std::vector<Mat> dst;
_mv.getMatVector(dst);
split(m, &dst[0]);
}
void cv::merge(const Mat* mv, size_t n, OutputArray _dst)
{
CV_Assert( mv && n > 0 );
int depth = mv[0].depth();
bool allch1 = true;
int k, cn = 0;
size_t i;
for( i = 0; i < n; i++ )
{
CV_Assert(mv[i].size == mv[0].size && mv[i].depth() == depth);
allch1 = allch1 && mv[i].channels() == 1;
cn += mv[i].channels();
}
CV_Assert( 0 < cn && cn <= CV_CN_MAX );
_dst.create(mv[0].dims, mv[0].size, CV_MAKETYPE(depth, cn));
Mat dst = _dst.getMat();
if( n == 1 )
{
mv[0].copyTo(dst);
return;
}
if( !allch1 )
{
AutoBuffer<int> pairs(cn*2);
int j, ni=0;
for( i = 0, j = 0; i < n; i++, j += ni )
{
ni = mv[i].channels();
for( k = 0; k < ni; k++ )
{
pairs[(j+k)*2] = j + k;
pairs[(j+k)*2+1] = j + k;
}
}
mixChannels( mv, n, &dst, 1, &pairs[0], cn );
return;
}
size_t esz = dst.elemSize(), esz1 = dst.elemSize1();
int blocksize0 = (int)((BLOCK_SIZE + esz-1)/esz);
AutoBuffer<uchar> _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16);
const Mat** arrays = (const Mat**)(uchar*)_buf;
uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16);
arrays[0] = &dst;
for( k = 0; k < cn; k++ )
arrays[k+1] = &mv[k];
NAryMatIterator it(arrays, ptrs, cn+1);
int total = (int)it.size, blocksize = cn <= 4 ? total : std::min(total, blocksize0);
MergeFunc func = getMergeFunc(depth);
for( i = 0; i < it.nplanes; i++, ++it )
{
for( int j = 0; j < total; j += blocksize )
{
int bsz = std::min(total - j, blocksize);
func( (const uchar**)&ptrs[1], ptrs[0], bsz, cn );
if( j + blocksize < total )
{
ptrs[0] += bsz*esz;
for( int t = 0; t < cn; t++ )
ptrs[t+1] += bsz*esz1;
}
}
}
}
#ifdef HAVE_OPENCL
namespace cv {
static bool ocl_merge( InputArrayOfArrays _mv, OutputArray _dst )
{
std::vector<UMat> src, ksrc;
_mv.getUMatVector(src);
CV_Assert(!src.empty());
int type = src[0].type(), depth = CV_MAT_DEPTH(type),
rowsPerWI = ocl::Device::getDefault().isIntel() ? 4 : 1;
Size size = src[0].size();
for (size_t i = 0, srcsize = src.size(); i < srcsize; ++i)
{
int itype = src[i].type(), icn = CV_MAT_CN(itype), idepth = CV_MAT_DEPTH(itype),
esz1 = CV_ELEM_SIZE1(idepth);
if (src[i].dims > 2)
return false;
CV_Assert(size == src[i].size() && depth == idepth);
for (int cn = 0; cn < icn; ++cn)
{
UMat tsrc = src[i];
tsrc.offset += cn * esz1;
ksrc.push_back(tsrc);
}
}
int dcn = (int)ksrc.size();
String srcargs, processelem, cndecl, indexdecl;
for (int i = 0; i < dcn; ++i)
{
srcargs += format("DECLARE_SRC_PARAM(%d)", i);
processelem += format("PROCESS_ELEM(%d)", i);
indexdecl += format("DECLARE_INDEX(%d)", i);
cndecl += format(" -D scn%d=%d", i, ksrc[i].channels());
}
ocl::Kernel k("merge", ocl::core::split_merge_oclsrc,
format("-D OP_MERGE -D cn=%d -D T=%s -D DECLARE_SRC_PARAMS_N=%s"
" -D DECLARE_INDEX_N=%s -D PROCESS_ELEMS_N=%s%s",
dcn, ocl::memopTypeToStr(depth), srcargs.c_str(),
indexdecl.c_str(), processelem.c_str(), cndecl.c_str()));
if (k.empty())
return false;
_dst.create(size, CV_MAKE_TYPE(depth, dcn));
UMat dst = _dst.getUMat();
int argidx = 0;
for (int i = 0; i < dcn; ++i)
argidx = k.set(argidx, ocl::KernelArg::ReadOnlyNoSize(ksrc[i]));
argidx = k.set(argidx, ocl::KernelArg::WriteOnly(dst));
k.set(argidx, rowsPerWI);
size_t globalsize[2] = { dst.cols, (dst.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
}
#endif
void cv::merge(InputArrayOfArrays _mv, OutputArray _dst)
{
CV_OCL_RUN(_mv.isUMatVector() && _dst.isUMat(),
ocl_merge(_mv, _dst))
std::vector<Mat> mv;
_mv.getMatVector(mv);
merge(!mv.empty() ? &mv[0] : 0, mv.size(), _dst);
}
/****************************************************************************************\
* Generalized split/merge: mixing channels *
\****************************************************************************************/
namespace cv
{
template<typename T> static void
mixChannels_( const T** src, const int* sdelta,
T** dst, const int* ddelta,
int len, int npairs )
{
int i, k;
for( k = 0; k < npairs; k++ )
{
const T* s = src[k];
T* d = dst[k];
int ds = sdelta[k], dd = ddelta[k];
if( s )
{
for( i = 0; i <= len - 2; i += 2, s += ds*2, d += dd*2 )
{
T t0 = s[0], t1 = s[ds];
d[0] = t0; d[dd] = t1;
}
if( i < len )
d[0] = s[0];
}
else
{
for( i = 0; i <= len - 2; i += 2, d += dd*2 )
d[0] = d[dd] = 0;
if( i < len )
d[0] = 0;
}
}
}
static void mixChannels8u( const uchar** src, const int* sdelta,
uchar** dst, const int* ddelta,
int len, int npairs )
{
mixChannels_(src, sdelta, dst, ddelta, len, npairs);
}
static void mixChannels16u( const ushort** src, const int* sdelta,
ushort** dst, const int* ddelta,
int len, int npairs )
{
mixChannels_(src, sdelta, dst, ddelta, len, npairs);
}
static void mixChannels32s( const int** src, const int* sdelta,
int** dst, const int* ddelta,
int len, int npairs )
{
mixChannels_(src, sdelta, dst, ddelta, len, npairs);
}
static void mixChannels64s( const int64** src, const int* sdelta,
int64** dst, const int* ddelta,
int len, int npairs )
{
mixChannels_(src, sdelta, dst, ddelta, len, npairs);
}
typedef void (*MixChannelsFunc)( const uchar** src, const int* sdelta,
uchar** dst, const int* ddelta, int len, int npairs );
static MixChannelsFunc getMixchFunc(int depth)
{
static MixChannelsFunc mixchTab[] =
{
(MixChannelsFunc)mixChannels8u, (MixChannelsFunc)mixChannels8u, (MixChannelsFunc)mixChannels16u,
(MixChannelsFunc)mixChannels16u, (MixChannelsFunc)mixChannels32s, (MixChannelsFunc)mixChannels32s,
(MixChannelsFunc)mixChannels64s, 0
};
return mixchTab[depth];
}
}
void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, const int* fromTo, size_t npairs )
{
if( npairs == 0 )
return;
CV_Assert( src && nsrcs > 0 && dst && ndsts > 0 && fromTo && npairs > 0 );
size_t i, j, k, esz1 = dst[0].elemSize1();
int depth = dst[0].depth();
AutoBuffer<uchar> buf((nsrcs + ndsts + 1)*(sizeof(Mat*) + sizeof(uchar*)) + npairs*(sizeof(uchar*)*2 + sizeof(int)*6));
const Mat** arrays = (const Mat**)(uchar*)buf;
uchar** ptrs = (uchar**)(arrays + nsrcs + ndsts);
const uchar** srcs = (const uchar**)(ptrs + nsrcs + ndsts + 1);
uchar** dsts = (uchar**)(srcs + npairs);
int* tab = (int*)(dsts + npairs);
int *sdelta = (int*)(tab + npairs*4), *ddelta = sdelta + npairs;
for( i = 0; i < nsrcs; i++ )
arrays[i] = &src[i];
for( i = 0; i < ndsts; i++ )
arrays[i + nsrcs] = &dst[i];
ptrs[nsrcs + ndsts] = 0;
for( i = 0; i < npairs; i++ )
{
int i0 = fromTo[i*2], i1 = fromTo[i*2+1];
if( i0 >= 0 )
{
for( j = 0; j < nsrcs; i0 -= src[j].channels(), j++ )
if( i0 < src[j].channels() )
break;
CV_Assert(j < nsrcs && src[j].depth() == depth);
tab[i*4] = (int)j; tab[i*4+1] = (int)(i0*esz1);
sdelta[i] = src[j].channels();
}
else
{
tab[i*4] = (int)(nsrcs + ndsts); tab[i*4+1] = 0;
sdelta[i] = 0;
}
for( j = 0; j < ndsts; i1 -= dst[j].channels(), j++ )
if( i1 < dst[j].channels() )
break;
CV_Assert(i1 >= 0 && j < ndsts && dst[j].depth() == depth);
tab[i*4+2] = (int)(j + nsrcs); tab[i*4+3] = (int)(i1*esz1);
ddelta[i] = dst[j].channels();
}
NAryMatIterator it(arrays, ptrs, (int)(nsrcs + ndsts));
int total = (int)it.size, blocksize = std::min(total, (int)((BLOCK_SIZE + esz1-1)/esz1));
MixChannelsFunc func = getMixchFunc(depth);
for( i = 0; i < it.nplanes; i++, ++it )
{
for( k = 0; k < npairs; k++ )
{
srcs[k] = ptrs[tab[k*4]] + tab[k*4+1];
dsts[k] = ptrs[tab[k*4+2]] + tab[k*4+3];
}
for( int t = 0; t < total; t += blocksize )
{
int bsz = std::min(total - t, blocksize);
func( srcs, sdelta, dsts, ddelta, bsz, (int)npairs );
if( t + blocksize < total )
for( k = 0; k < npairs; k++ )
{
srcs[k] += blocksize*sdelta[k]*esz1;
dsts[k] += blocksize*ddelta[k]*esz1;
}
}
}
}
#ifdef HAVE_OPENCL
namespace cv {
static void getUMatIndex(const std::vector<UMat> & um, int cn, int & idx, int & cnidx)
{
int totalChannels = 0;
for (size_t i = 0, size = um.size(); i < size; ++i)
{
int ccn = um[i].channels();
totalChannels += ccn;
if (totalChannels == cn)
{
idx = (int)(i + 1);
cnidx = 0;
return;
}
else if (totalChannels > cn)
{
idx = (int)i;
cnidx = i == 0 ? cn : (cn - totalChannels + ccn);
return;
}
}
idx = cnidx = -1;
}
static bool ocl_mixChannels(InputArrayOfArrays _src, InputOutputArrayOfArrays _dst,
const int* fromTo, size_t npairs)
{
std::vector<UMat> src, dst;
_src.getUMatVector(src);
_dst.getUMatVector(dst);
size_t nsrc = src.size(), ndst = dst.size();
CV_Assert(nsrc > 0 && ndst > 0);
Size size = src[0].size();
int depth = src[0].depth(), esz = CV_ELEM_SIZE(depth),
rowsPerWI = ocl::Device::getDefault().isIntel() ? 4 : 1;
for (size_t i = 1, ssize = src.size(); i < ssize; ++i)
CV_Assert(src[i].size() == size && src[i].depth() == depth);
for (size_t i = 0, dsize = dst.size(); i < dsize; ++i)
CV_Assert(dst[i].size() == size && dst[i].depth() == depth);
String declsrc, decldst, declproc, declcn, indexdecl;
std::vector<UMat> srcargs(npairs), dstargs(npairs);
for (size_t i = 0; i < npairs; ++i)
{
int scn = fromTo[i<<1], dcn = fromTo[(i<<1) + 1];
int src_idx, src_cnidx, dst_idx, dst_cnidx;
getUMatIndex(src, scn, src_idx, src_cnidx);
getUMatIndex(dst, dcn, dst_idx, dst_cnidx);
CV_Assert(dst_idx >= 0 && src_idx >= 0);
srcargs[i] = src[src_idx];
srcargs[i].offset += src_cnidx * esz;
dstargs[i] = dst[dst_idx];
dstargs[i].offset += dst_cnidx * esz;
declsrc += format("DECLARE_INPUT_MAT(%d)", i);
decldst += format("DECLARE_OUTPUT_MAT(%d)", i);
indexdecl += format("DECLARE_INDEX(%d)", i);
declproc += format("PROCESS_ELEM(%d)", i);
declcn += format(" -D scn%d=%d -D dcn%d=%d", i, src[src_idx].channels(), i, dst[dst_idx].channels());
}
ocl::Kernel k("mixChannels", ocl::core::mixchannels_oclsrc,
format("-D T=%s -D DECLARE_INPUT_MAT_N=%s -D DECLARE_OUTPUT_MAT_N=%s"
" -D PROCESS_ELEM_N=%s -D DECLARE_INDEX_N=%s%s",
ocl::memopTypeToStr(depth), declsrc.c_str(), decldst.c_str(),
declproc.c_str(), indexdecl.c_str(), declcn.c_str()));
if (k.empty())
return false;
int argindex = 0;
for (size_t i = 0; i < npairs; ++i)
argindex = k.set(argindex, ocl::KernelArg::ReadOnlyNoSize(srcargs[i]));
for (size_t i = 0; i < npairs; ++i)
argindex = k.set(argindex, ocl::KernelArg::WriteOnlyNoSize(dstargs[i]));
argindex = k.set(argindex, size.height);
argindex = k.set(argindex, size.width);
k.set(argindex, rowsPerWI);
size_t globalsize[2] = { size.width, (size.height + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
}
#endif
void cv::mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
const int* fromTo, size_t npairs)
{
if (npairs == 0 || fromTo == NULL)
return;
CV_OCL_RUN(dst.isUMatVector(),
ocl_mixChannels(src, dst, fromTo, npairs))
bool src_is_mat = src.kind() != _InputArray::STD_VECTOR_MAT &&
src.kind() != _InputArray::STD_VECTOR_VECTOR &&
src.kind() != _InputArray::STD_VECTOR_UMAT;
bool dst_is_mat = dst.kind() != _InputArray::STD_VECTOR_MAT &&
dst.kind() != _InputArray::STD_VECTOR_VECTOR &&
dst.kind() != _InputArray::STD_VECTOR_UMAT;
int i;
int nsrc = src_is_mat ? 1 : (int)src.total();
int ndst = dst_is_mat ? 1 : (int)dst.total();
CV_Assert(nsrc > 0 && ndst > 0);
cv::AutoBuffer<Mat> _buf(nsrc + ndst);
Mat* buf = _buf;
for( i = 0; i < nsrc; i++ )
buf[i] = src.getMat(src_is_mat ? -1 : i);
for( i = 0; i < ndst; i++ )
buf[nsrc + i] = dst.getMat(dst_is_mat ? -1 : i);
mixChannels(&buf[0], nsrc, &buf[nsrc], ndst, fromTo, npairs);
}
void cv::mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
const std::vector<int>& fromTo)
{
if (fromTo.empty())
return;
CV_OCL_RUN(dst.isUMatVector(),
ocl_mixChannels(src, dst, &fromTo[0], fromTo.size()>>1))
bool src_is_mat = src.kind() != _InputArray::STD_VECTOR_MAT &&
src.kind() != _InputArray::STD_VECTOR_VECTOR &&
src.kind() != _InputArray::STD_VECTOR_UMAT;
bool dst_is_mat = dst.kind() != _InputArray::STD_VECTOR_MAT &&
dst.kind() != _InputArray::STD_VECTOR_VECTOR &&
dst.kind() != _InputArray::STD_VECTOR_UMAT;
int i;
int nsrc = src_is_mat ? 1 : (int)src.total();
int ndst = dst_is_mat ? 1 : (int)dst.total();
CV_Assert(fromTo.size()%2 == 0 && nsrc > 0 && ndst > 0);
cv::AutoBuffer<Mat> _buf(nsrc + ndst);
Mat* buf = _buf;
for( i = 0; i < nsrc; i++ )
buf[i] = src.getMat(src_is_mat ? -1 : i);
for( i = 0; i < ndst; i++ )
buf[nsrc + i] = dst.getMat(dst_is_mat ? -1 : i);
mixChannels(&buf[0], nsrc, &buf[nsrc], ndst, &fromTo[0], fromTo.size()/2);
}
void cv::extractChannel(InputArray _src, OutputArray _dst, int coi)
{
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
CV_Assert( 0 <= coi && coi < cn );
int ch[] = { coi, 0 };
if (ocl::useOpenCL() && _src.dims() <= 2 && _dst.isUMat())
{
UMat src = _src.getUMat();
_dst.create(src.dims, &src.size[0], depth);
UMat dst = _dst.getUMat();
mixChannels(std::vector<UMat>(1, src), std::vector<UMat>(1, dst), ch, 1);
return;
}
Mat src = _src.getMat();
_dst.create(src.dims, &src.size[0], depth);
Mat dst = _dst.getMat();
mixChannels(&src, 1, &dst, 1, ch, 1);
}
void cv::insertChannel(InputArray _src, InputOutputArray _dst, int coi)
{
int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), scn = CV_MAT_CN(stype);
int dtype = _dst.type(), ddepth = CV_MAT_DEPTH(dtype), dcn = CV_MAT_CN(dtype);
CV_Assert( _src.sameSize(_dst) && sdepth == ddepth );
CV_Assert( 0 <= coi && coi < dcn && scn == 1 );
int ch[] = { 0, coi };
if (ocl::useOpenCL() && _src.dims() <= 2 && _dst.isUMat())
{
UMat src = _src.getUMat(), dst = _dst.getUMat();
mixChannels(std::vector<UMat>(1, src), std::vector<UMat>(1, dst), ch, 1);
return;
}
Mat src = _src.getMat(), dst = _dst.getMat();
mixChannels(&src, 1, &dst, 1, ch, 1);
}
/****************************************************************************************\
* convertScale[Abs] *
\****************************************************************************************/
namespace cv
{
template<typename T, typename DT, typename WT>
struct cvtScaleAbs_SSE2
{
int operator () (const T *, DT *, int, WT, WT) const
{
return 0;
}
};
#if CV_SSE2
template <>
struct cvtScaleAbs_SSE2<uchar, uchar, float>
{
int operator () (const uchar * src, uchar * dst, int width,
float scale, float shift) const
{
int x = 0;
if (USE_SSE2)
{
__m128 v_scale = _mm_set1_ps(scale), v_shift = _mm_set1_ps(shift),
v_zero_f = _mm_setzero_ps();
__m128i v_zero_i = _mm_setzero_si128();
for ( ; x <= width - 16; x += 16)
{
__m128i v_src = _mm_loadu_si128((const __m128i *)(src + x));
__m128i v_src12 = _mm_unpacklo_epi8(v_src, v_zero_i), v_src_34 = _mm_unpackhi_epi8(v_src, v_zero_i);
__m128 v_dst1 = _mm_add_ps(_mm_mul_ps(_mm_cvtepi32_ps(_mm_unpacklo_epi16(v_src12, v_zero_i)), v_scale), v_shift);
v_dst1 = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst1), v_dst1);
__m128 v_dst2 = _mm_add_ps(_mm_mul_ps(_mm_cvtepi32_ps(_mm_unpackhi_epi16(v_src12, v_zero_i)), v_scale), v_shift);
v_dst2 = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst2), v_dst2);
__m128 v_dst3 = _mm_add_ps(_mm_mul_ps(_mm_cvtepi32_ps(_mm_unpacklo_epi16(v_src_34, v_zero_i)), v_scale), v_shift);
v_dst3 = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst3), v_dst3);
__m128 v_dst4 = _mm_add_ps(_mm_mul_ps(_mm_cvtepi32_ps(_mm_unpackhi_epi16(v_src_34, v_zero_i)), v_scale), v_shift);
v_dst4 = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst4), v_dst4);
__m128i v_dst_i = _mm_packus_epi16(_mm_packs_epi32(_mm_cvtps_epi32(v_dst1), _mm_cvtps_epi32(v_dst2)),
_mm_packs_epi32(_mm_cvtps_epi32(v_dst3), _mm_cvtps_epi32(v_dst4)));
_mm_storeu_si128((__m128i *)(dst + x), v_dst_i);
}
}
return x;
}
};
template <>
struct cvtScaleAbs_SSE2<ushort, uchar, float>
{
int operator () (const ushort * src, uchar * dst, int width,
float scale, float shift) const
{
int x = 0;
if (USE_SSE2)
{
__m128 v_scale = _mm_set1_ps(scale), v_shift = _mm_set1_ps(shift),
v_zero_f = _mm_setzero_ps();
__m128i v_zero_i = _mm_setzero_si128();
for ( ; x <= width - 8; x += 8)
{
__m128i v_src = _mm_loadu_si128((const __m128i *)(src + x));
__m128 v_dst1 = _mm_add_ps(_mm_mul_ps(_mm_cvtepi32_ps(_mm_unpacklo_epi16(v_src, v_zero_i)), v_scale), v_shift);
v_dst1 = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst1), v_dst1);
__m128 v_dst2 = _mm_add_ps(_mm_mul_ps(_mm_cvtepi32_ps(_mm_unpackhi_epi16(v_src, v_zero_i)), v_scale), v_shift);
v_dst2 = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst2), v_dst2);
__m128i v_dst_i = _mm_packus_epi16(_mm_packs_epi32(_mm_cvtps_epi32(v_dst1), _mm_cvtps_epi32(v_dst2)), v_zero_i);
_mm_storel_epi64((__m128i *)(dst + x), v_dst_i);
}
}
return x;
}
};
template <>
struct cvtScaleAbs_SSE2<short, uchar, float>
{
int operator () (const short * src, uchar * dst, int width,
float scale, float shift) const
{
int x = 0;
if (USE_SSE2)
{
__m128 v_scale = _mm_set1_ps(scale), v_shift = _mm_set1_ps(shift),
v_zero_f = _mm_setzero_ps();
__m128i v_zero_i = _mm_setzero_si128();
for ( ; x <= width - 8; x += 8)
{
__m128i v_src = _mm_loadu_si128((const __m128i *)(src + x));
__m128 v_dst1 = _mm_add_ps(_mm_mul_ps(_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(v_src, v_src), 16)), v_scale), v_shift);
v_dst1 = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst1), v_dst1);
__m128 v_dst2 = _mm_add_ps(_mm_mul_ps(_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpackhi_epi16(v_src, v_src), 16)), v_scale), v_shift);
v_dst2 = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst2), v_dst2);
__m128i v_dst_i = _mm_packus_epi16(_mm_packs_epi32(_mm_cvtps_epi32(v_dst1), _mm_cvtps_epi32(v_dst2)), v_zero_i);
_mm_storel_epi64((__m128i *)(dst + x), v_dst_i);
}
}
return x;
}
};
template <>
struct cvtScaleAbs_SSE2<int, uchar, float>
{
int operator () (const int * src, uchar * dst, int width,
float scale, float shift) const
{
int x = 0;
if (USE_SSE2)
{
__m128 v_scale = _mm_set1_ps(scale), v_shift = _mm_set1_ps(shift),
v_zero_f = _mm_setzero_ps();
__m128i v_zero_i = _mm_setzero_si128();
for ( ; x <= width - 8; x += 4)
{
__m128i v_src = _mm_loadu_si128((const __m128i *)(src + x));
__m128 v_dst1 = _mm_add_ps(_mm_mul_ps(_mm_cvtepi32_ps(v_src), v_scale), v_shift);
v_dst1 = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst1), v_dst1);
__m128i v_dst_i = _mm_packus_epi16(_mm_packs_epi32(_mm_cvtps_epi32(v_dst1), v_zero_i), v_zero_i);
_mm_storel_epi64((__m128i *)(dst + x), v_dst_i);
}
}
return x;
}
};
template <>
struct cvtScaleAbs_SSE2<float, uchar, float>
{
int operator () (const float * src, uchar * dst, int width,
float scale, float shift) const
{
int x = 0;
if (USE_SSE2)
{
__m128 v_scale = _mm_set1_ps(scale), v_shift = _mm_set1_ps(shift),
v_zero_f = _mm_setzero_ps();
__m128i v_zero_i = _mm_setzero_si128();
for ( ; x <= width - 8; x += 4)
{
__m128 v_dst = _mm_add_ps(_mm_mul_ps(_mm_loadu_ps(src + x), v_scale), v_shift);
v_dst = _mm_max_ps(_mm_sub_ps(v_zero_f, v_dst), v_dst);
__m128i v_dst_i = _mm_packs_epi32(_mm_cvtps_epi32(v_dst), v_zero_i);
_mm_storel_epi64((__m128i *)(dst + x), _mm_packus_epi16(v_dst_i, v_zero_i));
}
}
return x;
}
};
#endif
template<typename T, typename DT, typename WT> static void
cvtScaleAbs_( const T* src, size_t sstep,
DT* dst, size_t dstep, Size size,
WT scale, WT shift )
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
cvtScaleAbs_SSE2<T, DT, WT> vop;
for( ; size.height--; src += sstep, dst += dstep )
{
int x = vop(src, dst, size.width, scale, shift);
#if CV_ENABLE_UNROLLED
for( ; x <= size.width - 4; x += 4 )
{
DT t0, t1;
t0 = saturate_cast<DT>(std::abs(src[x]*scale + shift));
t1 = saturate_cast<DT>(std::abs(src[x+1]*scale + shift));
dst[x] = t0; dst[x+1] = t1;
t0 = saturate_cast<DT>(std::abs(src[x+2]*scale + shift));
t1 = saturate_cast<DT>(std::abs(src[x+3]*scale + shift));
dst[x+2] = t0; dst[x+3] = t1;
}
#endif
for( ; x < size.width; x++ )
dst[x] = saturate_cast<DT>(std::abs(src[x]*scale + shift));
}
}
template<typename T, typename DT, typename WT> static void
cvtScale_( const T* src, size_t sstep,
DT* dst, size_t dstep, Size size,
WT scale, WT shift )
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
#if CV_ENABLE_UNROLLED
for( ; x <= size.width - 4; x += 4 )
{
DT t0, t1;
t0 = saturate_cast<DT>(src[x]*scale + shift);
t1 = saturate_cast<DT>(src[x+1]*scale + shift);
dst[x] = t0; dst[x+1] = t1;
t0 = saturate_cast<DT>(src[x+2]*scale + shift);
t1 = saturate_cast<DT>(src[x+3]*scale + shift);
dst[x+2] = t0; dst[x+3] = t1;
}
#endif
for( ; x < size.width; x++ )
dst[x] = saturate_cast<DT>(src[x]*scale + shift);
}
}
//vz optimized template specialization
template<> void
cvtScale_<short, short, float>( const short* src, size_t sstep,
short* dst, size_t dstep, Size size,
float scale, float shift )
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
#if CV_SSE2
if(USE_SSE2)
{
__m128 scale128 = _mm_set1_ps (scale);
__m128 shift128 = _mm_set1_ps (shift);
for(; x <= size.width - 8; x += 8 )
{
__m128i r0 = _mm_loadl_epi64((const __m128i*)(src + x));
__m128i r1 = _mm_loadl_epi64((const __m128i*)(src + x + 4));
__m128 rf0 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r0, r0), 16));
__m128 rf1 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r1, r1), 16));
rf0 = _mm_add_ps(_mm_mul_ps(rf0, scale128), shift128);
rf1 = _mm_add_ps(_mm_mul_ps(rf1, scale128), shift128);
r0 = _mm_cvtps_epi32(rf0);
r1 = _mm_cvtps_epi32(rf1);
r0 = _mm_packs_epi32(r0, r1);
_mm_storeu_si128((__m128i*)(dst + x), r0);
}
}
#endif
for(; x < size.width; x++ )
dst[x] = saturate_cast<short>(src[x]*scale + shift);
}
}
template<> void
cvtScale_<short, int, float>( const short* src, size_t sstep,
int* dst, size_t dstep, Size size,
float scale, float shift )
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
#if CV_SSE2
if(USE_SSE2)//~5X
{
__m128 scale128 = _mm_set1_ps (scale);
__m128 shift128 = _mm_set1_ps (shift);
for(; x <= size.width - 8; x += 8 )
{
__m128i r0 = _mm_loadl_epi64((const __m128i*)(src + x));
__m128i r1 = _mm_loadl_epi64((const __m128i*)(src + x + 4));
__m128 rf0 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r0, r0), 16));
__m128 rf1 =_mm_cvtepi32_ps(_mm_srai_epi32(_mm_unpacklo_epi16(r1, r1), 16));
rf0 = _mm_add_ps(_mm_mul_ps(rf0, scale128), shift128);
rf1 = _mm_add_ps(_mm_mul_ps(rf1, scale128), shift128);
r0 = _mm_cvtps_epi32(rf0);
r1 = _mm_cvtps_epi32(rf1);
_mm_storeu_si128((__m128i*)(dst + x), r0);
_mm_storeu_si128((__m128i*)(dst + x + 4), r1);
}
}
#endif
//We will wait Haswell
/*
#if CV_AVX
if(USE_AVX)//2X - bad variant
{
////TODO:AVX implementation (optimization?) required
__m256 scale256 = _mm256_set1_ps (scale);
__m256 shift256 = _mm256_set1_ps (shift);
for(; x <= size.width - 8; x += 8 )
{
__m256i buf = _mm256_set_epi32((int)(*(src+x+7)),(int)(*(src+x+6)),(int)(*(src+x+5)),(int)(*(src+x+4)),(int)(*(src+x+3)),(int)(*(src+x+2)),(int)(*(src+x+1)),(int)(*(src+x)));
__m256 r0 = _mm256_add_ps( _mm256_mul_ps(_mm256_cvtepi32_ps (buf), scale256), shift256);
__m256i res = _mm256_cvtps_epi32(r0);
_mm256_storeu_si256 ((__m256i*)(dst+x), res);
}
}
#endif*/
for(; x < size.width; x++ )
dst[x] = saturate_cast<int>(src[x]*scale + shift);
}
}
template<typename T, typename DT> static void
cvt_( const T* src, size_t sstep,
DT* dst, size_t dstep, Size size )
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
#if CV_ENABLE_UNROLLED
for( ; x <= size.width - 4; x += 4 )
{
DT t0, t1;
t0 = saturate_cast<DT>(src[x]);
t1 = saturate_cast<DT>(src[x+1]);
dst[x] = t0; dst[x+1] = t1;
t0 = saturate_cast<DT>(src[x+2]);
t1 = saturate_cast<DT>(src[x+3]);
dst[x+2] = t0; dst[x+3] = t1;
}
#endif
for( ; x < size.width; x++ )
dst[x] = saturate_cast<DT>(src[x]);
}
}
//vz optimized template specialization, test Core_ConvertScale/ElemWiseTest
template<> void
cvt_<float, short>( const float* src, size_t sstep,
short* dst, size_t dstep, Size size )
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
{
int x = 0;
#if CV_SSE2
if(USE_SSE2){
for( ; x <= size.width - 8; x += 8 )
{
__m128 src128 = _mm_loadu_ps (src + x);
__m128i src_int128 = _mm_cvtps_epi32 (src128);
src128 = _mm_loadu_ps (src + x + 4);
__m128i src1_int128 = _mm_cvtps_epi32 (src128);
src1_int128 = _mm_packs_epi32(src_int128, src1_int128);
_mm_storeu_si128((__m128i*)(dst + x),src1_int128);
}
}
#endif
for( ; x < size.width; x++ )
dst[x] = saturate_cast<short>(src[x]);
}
}
template<typename T> static void
cpy_( const T* src, size_t sstep, T* dst, size_t dstep, Size size )
{
sstep /= sizeof(src[0]);
dstep /= sizeof(dst[0]);
for( ; size.height--; src += sstep, dst += dstep )
memcpy(dst, src, size.width*sizeof(src[0]));
}
#define DEF_CVT_SCALE_ABS_FUNC(suffix, tfunc, stype, dtype, wtype) \
static void cvtScaleAbs##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
dtype* dst, size_t dstep, Size size, double* scale) \
{ \
tfunc(src, sstep, dst, dstep, size, (wtype)scale[0], (wtype)scale[1]); \
}
#define DEF_CVT_SCALE_FUNC(suffix, stype, dtype, wtype) \
static void cvtScale##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
dtype* dst, size_t dstep, Size size, double* scale) \
{ \
cvtScale_(src, sstep, dst, dstep, size, (wtype)scale[0], (wtype)scale[1]); \
}
#if defined(HAVE_IPP)
#define DEF_CVT_FUNC_F(suffix, stype, dtype, ippFavor) \
static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
dtype* dst, size_t dstep, Size size, double*) \
{ \
if (src && dst)\
{\
if (ippiConvert_##ippFavor(src, (int)sstep, dst, (int)dstep, ippiSize(size.width, size.height)) >= 0) \
return; \
setIppErrorStatus(); \
}\
cvt_(src, sstep, dst, dstep, size); \
}
#define DEF_CVT_FUNC_F2(suffix, stype, dtype, ippFavor) \
static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
dtype* dst, size_t dstep, Size size, double*) \
{ \
if (src && dst)\
{\
if (ippiConvert_##ippFavor(src, (int)sstep, dst, (int)dstep, ippiSize(size.width, size.height), ippRndFinancial, 0) >= 0) \
return; \
setIppErrorStatus(); \
}\
cvt_(src, sstep, dst, dstep, size); \
}
#else
#define DEF_CVT_FUNC_F(suffix, stype, dtype, ippFavor) \
static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
dtype* dst, size_t dstep, Size size, double*) \
{ \
cvt_(src, sstep, dst, dstep, size); \
}
#define DEF_CVT_FUNC_F2 DEF_CVT_FUNC_F
#endif
#define DEF_CVT_FUNC(suffix, stype, dtype) \
static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
dtype* dst, size_t dstep, Size size, double*) \
{ \
cvt_(src, sstep, dst, dstep, size); \
}
#define DEF_CPY_FUNC(suffix, stype) \
static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
stype* dst, size_t dstep, Size size, double*) \
{ \
cpy_(src, sstep, dst, dstep, size); \
}
DEF_CVT_SCALE_ABS_FUNC(8u, cvtScaleAbs_, uchar, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(8s8u, cvtScaleAbs_, schar, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(16u8u, cvtScaleAbs_, ushort, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(16s8u, cvtScaleAbs_, short, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(32s8u, cvtScaleAbs_, int, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(32f8u, cvtScaleAbs_, float, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float)
DEF_CVT_SCALE_FUNC(8u, uchar, uchar, float)
DEF_CVT_SCALE_FUNC(8s8u, schar, uchar, float)
DEF_CVT_SCALE_FUNC(16u8u, ushort, uchar, float)
DEF_CVT_SCALE_FUNC(16s8u, short, uchar, float)
DEF_CVT_SCALE_FUNC(32s8u, int, uchar, float)
DEF_CVT_SCALE_FUNC(32f8u, float, uchar, float)
DEF_CVT_SCALE_FUNC(64f8u, double, uchar, float)
DEF_CVT_SCALE_FUNC(8u8s, uchar, schar, float)
DEF_CVT_SCALE_FUNC(8s, schar, schar, float)
DEF_CVT_SCALE_FUNC(16u8s, ushort, schar, float)
DEF_CVT_SCALE_FUNC(16s8s, short, schar, float)
DEF_CVT_SCALE_FUNC(32s8s, int, schar, float)
DEF_CVT_SCALE_FUNC(32f8s, float, schar, float)
DEF_CVT_SCALE_FUNC(64f8s, double, schar, float)
DEF_CVT_SCALE_FUNC(8u16u, uchar, ushort, float)
DEF_CVT_SCALE_FUNC(8s16u, schar, ushort, float)
DEF_CVT_SCALE_FUNC(16u, ushort, ushort, float)
DEF_CVT_SCALE_FUNC(16s16u, short, ushort, float)
DEF_CVT_SCALE_FUNC(32s16u, int, ushort, float)
DEF_CVT_SCALE_FUNC(32f16u, float, ushort, float)
DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float)
DEF_CVT_SCALE_FUNC(8u16s, uchar, short, float)
DEF_CVT_SCALE_FUNC(8s16s, schar, short, float)
DEF_CVT_SCALE_FUNC(16u16s, ushort, short, float)
DEF_CVT_SCALE_FUNC(16s, short, short, float)
DEF_CVT_SCALE_FUNC(32s16s, int, short, float)
DEF_CVT_SCALE_FUNC(32f16s, float, short, float)
DEF_CVT_SCALE_FUNC(64f16s, double, short, float)
DEF_CVT_SCALE_FUNC(8u32s, uchar, int, float)
DEF_CVT_SCALE_FUNC(8s32s, schar, int, float)
DEF_CVT_SCALE_FUNC(16u32s, ushort, int, float)
DEF_CVT_SCALE_FUNC(16s32s, short, int, float)
DEF_CVT_SCALE_FUNC(32s, int, int, double)
DEF_CVT_SCALE_FUNC(32f32s, float, int, float)
DEF_CVT_SCALE_FUNC(64f32s, double, int, double)
DEF_CVT_SCALE_FUNC(8u32f, uchar, float, float)
DEF_CVT_SCALE_FUNC(8s32f, schar, float, float)
DEF_CVT_SCALE_FUNC(16u32f, ushort, float, float)
DEF_CVT_SCALE_FUNC(16s32f, short, float, float)
DEF_CVT_SCALE_FUNC(32s32f, int, float, double)
DEF_CVT_SCALE_FUNC(32f, float, float, float)
DEF_CVT_SCALE_FUNC(64f32f, double, float, double)
DEF_CVT_SCALE_FUNC(8u64f, uchar, double, double)
DEF_CVT_SCALE_FUNC(8s64f, schar, double, double)
DEF_CVT_SCALE_FUNC(16u64f, ushort, double, double)
DEF_CVT_SCALE_FUNC(16s64f, short, double, double)
DEF_CVT_SCALE_FUNC(32s64f, int, double, double)
DEF_CVT_SCALE_FUNC(32f64f, float, double, double)
DEF_CVT_SCALE_FUNC(64f, double, double, double)
DEF_CPY_FUNC(8u, uchar)
DEF_CVT_FUNC_F(8s8u, schar, uchar, 8s8u_C1Rs)
DEF_CVT_FUNC_F(16u8u, ushort, uchar, 16u8u_C1R)
DEF_CVT_FUNC_F(16s8u, short, uchar, 16s8u_C1R)
DEF_CVT_FUNC_F(32s8u, int, uchar, 32s8u_C1R)
DEF_CVT_FUNC_F2(32f8u, float, uchar, 32f8u_C1RSfs)
DEF_CVT_FUNC(64f8u, double, uchar)
DEF_CVT_FUNC_F2(8u8s, uchar, schar, 8u8s_C1RSfs)
DEF_CVT_FUNC_F2(16u8s, ushort, schar, 16u8s_C1RSfs)
DEF_CVT_FUNC_F2(16s8s, short, schar, 16s8s_C1RSfs)
DEF_CVT_FUNC_F(32s8s, int, schar, 32s8s_C1R)
DEF_CVT_FUNC_F2(32f8s, float, schar, 32f8s_C1RSfs)
DEF_CVT_FUNC(64f8s, double, schar)
DEF_CVT_FUNC_F(8u16u, uchar, ushort, 8u16u_C1R)
DEF_CVT_FUNC_F(8s16u, schar, ushort, 8s16u_C1Rs)
DEF_CPY_FUNC(16u, ushort)
DEF_CVT_FUNC_F(16s16u, short, ushort, 16s16u_C1Rs)
DEF_CVT_FUNC_F2(32s16u, int, ushort, 32s16u_C1RSfs)
DEF_CVT_FUNC_F2(32f16u, float, ushort, 32f16u_C1RSfs)
DEF_CVT_FUNC(64f16u, double, ushort)
DEF_CVT_FUNC_F(8u16s, uchar, short, 8u16s_C1R)
DEF_CVT_FUNC_F(8s16s, schar, short, 8s16s_C1R)
DEF_CVT_FUNC_F2(16u16s, ushort, short, 16u16s_C1RSfs)
DEF_CVT_FUNC_F2(32s16s, int, short, 32s16s_C1RSfs)
DEF_CVT_FUNC_F2(32f16s, float, short, 32f16s_C1RSfs)
DEF_CVT_FUNC(64f16s, double, short)
DEF_CVT_FUNC_F(8u32s, uchar, int, 8u32s_C1R)
DEF_CVT_FUNC_F(8s32s, schar, int, 8s32s_C1R)
DEF_CVT_FUNC_F(16u32s, ushort, int, 16u32s_C1R)
DEF_CVT_FUNC_F(16s32s, short, int, 16s32s_C1R)
DEF_CPY_FUNC(32s, int)
DEF_CVT_FUNC_F2(32f32s, float, int, 32f32s_C1RSfs)
DEF_CVT_FUNC(64f32s, double, int)
DEF_CVT_FUNC_F(8u32f, uchar, float, 8u32f_C1R)
DEF_CVT_FUNC_F(8s32f, schar, float, 8s32f_C1R)
DEF_CVT_FUNC_F(16u32f, ushort, float, 16u32f_C1R)
DEF_CVT_FUNC_F(16s32f, short, float, 16s32f_C1R)
DEF_CVT_FUNC_F(32s32f, int, float, 32s32f_C1R)
DEF_CVT_FUNC(64f32f, double, float)
DEF_CVT_FUNC(8u64f, uchar, double)
DEF_CVT_FUNC(8s64f, schar, double)
DEF_CVT_FUNC(16u64f, ushort, double)
DEF_CVT_FUNC(16s64f, short, double)
DEF_CVT_FUNC(32s64f, int, double)
DEF_CVT_FUNC(32f64f, float, double)
DEF_CPY_FUNC(64s, int64)
static BinaryFunc getCvtScaleAbsFunc(int depth)
{
static BinaryFunc cvtScaleAbsTab[] =
{
(BinaryFunc)cvtScaleAbs8u, (BinaryFunc)cvtScaleAbs8s8u, (BinaryFunc)cvtScaleAbs16u8u,
(BinaryFunc)cvtScaleAbs16s8u, (BinaryFunc)cvtScaleAbs32s8u, (BinaryFunc)cvtScaleAbs32f8u,
(BinaryFunc)cvtScaleAbs64f8u, 0
};
return cvtScaleAbsTab[depth];
}
BinaryFunc getConvertFunc(int sdepth, int ddepth)
{
static BinaryFunc cvtTab[][8] =
{
{
(BinaryFunc)(cvt8u), (BinaryFunc)GET_OPTIMIZED(cvt8s8u), (BinaryFunc)GET_OPTIMIZED(cvt16u8u),
(BinaryFunc)GET_OPTIMIZED(cvt16s8u), (BinaryFunc)GET_OPTIMIZED(cvt32s8u), (BinaryFunc)GET_OPTIMIZED(cvt32f8u),
(BinaryFunc)GET_OPTIMIZED(cvt64f8u), 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvt8u8s), (BinaryFunc)cvt8u, (BinaryFunc)GET_OPTIMIZED(cvt16u8s),
(BinaryFunc)GET_OPTIMIZED(cvt16s8s), (BinaryFunc)GET_OPTIMIZED(cvt32s8s), (BinaryFunc)GET_OPTIMIZED(cvt32f8s),
(BinaryFunc)GET_OPTIMIZED(cvt64f8s), 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvt8u16u), (BinaryFunc)GET_OPTIMIZED(cvt8s16u), (BinaryFunc)cvt16u,
(BinaryFunc)GET_OPTIMIZED(cvt16s16u), (BinaryFunc)GET_OPTIMIZED(cvt32s16u), (BinaryFunc)GET_OPTIMIZED(cvt32f16u),
(BinaryFunc)GET_OPTIMIZED(cvt64f16u), 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvt8u16s), (BinaryFunc)GET_OPTIMIZED(cvt8s16s), (BinaryFunc)GET_OPTIMIZED(cvt16u16s),
(BinaryFunc)cvt16u, (BinaryFunc)GET_OPTIMIZED(cvt32s16s), (BinaryFunc)GET_OPTIMIZED(cvt32f16s),
(BinaryFunc)GET_OPTIMIZED(cvt64f16s), 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvt8u32s), (BinaryFunc)GET_OPTIMIZED(cvt8s32s), (BinaryFunc)GET_OPTIMIZED(cvt16u32s),
(BinaryFunc)GET_OPTIMIZED(cvt16s32s), (BinaryFunc)cvt32s, (BinaryFunc)GET_OPTIMIZED(cvt32f32s),
(BinaryFunc)GET_OPTIMIZED(cvt64f32s), 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvt8u32f), (BinaryFunc)GET_OPTIMIZED(cvt8s32f), (BinaryFunc)GET_OPTIMIZED(cvt16u32f),
(BinaryFunc)GET_OPTIMIZED(cvt16s32f), (BinaryFunc)GET_OPTIMIZED(cvt32s32f), (BinaryFunc)cvt32s,
(BinaryFunc)GET_OPTIMIZED(cvt64f32f), 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvt8u64f), (BinaryFunc)GET_OPTIMIZED(cvt8s64f), (BinaryFunc)GET_OPTIMIZED(cvt16u64f),
(BinaryFunc)GET_OPTIMIZED(cvt16s64f), (BinaryFunc)GET_OPTIMIZED(cvt32s64f), (BinaryFunc)GET_OPTIMIZED(cvt32f64f),
(BinaryFunc)(cvt64s), 0
},
{
0, 0, 0, 0, 0, 0, 0, 0
}
};
return cvtTab[CV_MAT_DEPTH(ddepth)][CV_MAT_DEPTH(sdepth)];
}
static BinaryFunc getConvertScaleFunc(int sdepth, int ddepth)
{
static BinaryFunc cvtScaleTab[][8] =
{
{
(BinaryFunc)GET_OPTIMIZED(cvtScale8u), (BinaryFunc)GET_OPTIMIZED(cvtScale8s8u), (BinaryFunc)GET_OPTIMIZED(cvtScale16u8u),
(BinaryFunc)GET_OPTIMIZED(cvtScale16s8u), (BinaryFunc)GET_OPTIMIZED(cvtScale32s8u), (BinaryFunc)GET_OPTIMIZED(cvtScale32f8u),
(BinaryFunc)cvtScale64f8u, 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvtScale8u8s), (BinaryFunc)GET_OPTIMIZED(cvtScale8s), (BinaryFunc)GET_OPTIMIZED(cvtScale16u8s),
(BinaryFunc)GET_OPTIMIZED(cvtScale16s8s), (BinaryFunc)GET_OPTIMIZED(cvtScale32s8s), (BinaryFunc)GET_OPTIMIZED(cvtScale32f8s),
(BinaryFunc)cvtScale64f8s, 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvtScale8u16u), (BinaryFunc)GET_OPTIMIZED(cvtScale8s16u), (BinaryFunc)GET_OPTIMIZED(cvtScale16u),
(BinaryFunc)GET_OPTIMIZED(cvtScale16s16u), (BinaryFunc)GET_OPTIMIZED(cvtScale32s16u), (BinaryFunc)GET_OPTIMIZED(cvtScale32f16u),
(BinaryFunc)cvtScale64f16u, 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvtScale8u16s), (BinaryFunc)GET_OPTIMIZED(cvtScale8s16s), (BinaryFunc)GET_OPTIMIZED(cvtScale16u16s),
(BinaryFunc)GET_OPTIMIZED(cvtScale16s), (BinaryFunc)GET_OPTIMIZED(cvtScale32s16s), (BinaryFunc)GET_OPTIMIZED(cvtScale32f16s),
(BinaryFunc)cvtScale64f16s, 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvtScale8u32s), (BinaryFunc)GET_OPTIMIZED(cvtScale8s32s), (BinaryFunc)GET_OPTIMIZED(cvtScale16u32s),
(BinaryFunc)GET_OPTIMIZED(cvtScale16s32s), (BinaryFunc)GET_OPTIMIZED(cvtScale32s), (BinaryFunc)GET_OPTIMIZED(cvtScale32f32s),
(BinaryFunc)cvtScale64f32s, 0
},
{
(BinaryFunc)GET_OPTIMIZED(cvtScale8u32f), (BinaryFunc)GET_OPTIMIZED(cvtScale8s32f), (BinaryFunc)GET_OPTIMIZED(cvtScale16u32f),
(BinaryFunc)GET_OPTIMIZED(cvtScale16s32f), (BinaryFunc)GET_OPTIMIZED(cvtScale32s32f), (BinaryFunc)GET_OPTIMIZED(cvtScale32f),
(BinaryFunc)cvtScale64f32f, 0
},
{
(BinaryFunc)cvtScale8u64f, (BinaryFunc)cvtScale8s64f, (BinaryFunc)cvtScale16u64f,
(BinaryFunc)cvtScale16s64f, (BinaryFunc)cvtScale32s64f, (BinaryFunc)cvtScale32f64f,
(BinaryFunc)cvtScale64f, 0
},
{
0, 0, 0, 0, 0, 0, 0, 0
}
};
return cvtScaleTab[CV_MAT_DEPTH(ddepth)][CV_MAT_DEPTH(sdepth)];
}
#ifdef HAVE_OPENCL
static bool ocl_convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, double beta )
{
const ocl::Device & d = ocl::Device::getDefault();
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type),
kercn = ocl::predictOptimalVectorWidth(_src, _dst), rowsPerWI = d.isIntel() ? 4 : 1;
bool doubleSupport = d.doubleFPConfig() > 0;
if (!doubleSupport && depth == CV_64F)
return false;
char cvt[2][50];
int wdepth = std::max(depth, CV_32F);
ocl::Kernel k("KF", ocl::core::arithm_oclsrc,
format("-D OP_CONVERT_SCALE_ABS -D UNARY_OP -D dstT=%s -D srcT1=%s"
" -D workT=%s -D wdepth=%d -D convertToWT1=%s -D convertToDT=%s"
" -D workT1=%s -D rowsPerWI=%d%s",
ocl::typeToStr(CV_8UC(kercn)),
ocl::typeToStr(CV_MAKE_TYPE(depth, kercn)),
ocl::typeToStr(CV_MAKE_TYPE(wdepth, kercn)), wdepth,
ocl::convertTypeStr(depth, wdepth, kercn, cvt[0]),
ocl::convertTypeStr(wdepth, CV_8U, kercn, cvt[1]),
ocl::typeToStr(wdepth), rowsPerWI,
doubleSupport ? " -D DOUBLE_SUPPORT" : ""));
if (k.empty())
return false;
UMat src = _src.getUMat();
_dst.create(src.size(), CV_8UC(cn));
UMat dst = _dst.getUMat();
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
dstarg = ocl::KernelArg::WriteOnly(dst, cn, kercn);
if (wdepth == CV_32F)
k.args(srcarg, dstarg, (float)alpha, (float)beta);
else if (wdepth == CV_64F)
k.args(srcarg, dstarg, alpha, beta);
size_t globalsize[2] = { src.cols * cn / kercn, (src.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
#endif
}
void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, double beta )
{
CV_OCL_RUN(_src.dims() <= 2 && _dst.isUMat(),
ocl_convertScaleAbs(_src, _dst, alpha, beta))
Mat src = _src.getMat();
int cn = src.channels();
double scale[] = {alpha, beta};
_dst.create( src.dims, src.size, CV_8UC(cn) );
Mat dst = _dst.getMat();
BinaryFunc func = getCvtScaleAbsFunc(src.depth());
CV_Assert( func != 0 );
if( src.dims <= 2 )
{
Size sz = getContinuousSize(src, dst, cn);
func( src.data, src.step, 0, 0, dst.data, dst.step, sz, scale );
}
else
{
const Mat* arrays[] = {&src, &dst, 0};
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs);
Size sz((int)it.size*cn, 1);
for( size_t i = 0; i < it.nplanes; i++, ++it )
func( ptrs[0], 0, 0, 0, ptrs[1], 0, sz, scale );
}
}
void cv::Mat::convertTo(OutputArray _dst, int _type, double alpha, double beta) const
{
bool noScale = fabs(alpha-1) < DBL_EPSILON && fabs(beta) < DBL_EPSILON;
if( _type < 0 )
_type = _dst.fixedType() ? _dst.type() : type();
else
_type = CV_MAKETYPE(CV_MAT_DEPTH(_type), channels());
int sdepth = depth(), ddepth = CV_MAT_DEPTH(_type);
if( sdepth == ddepth && noScale )
{
copyTo(_dst);
return;
}
Mat src = *this;
BinaryFunc func = noScale ? getConvertFunc(sdepth, ddepth) : getConvertScaleFunc(sdepth, ddepth);
double scale[] = {alpha, beta};
int cn = channels();
CV_Assert( func != 0 );
if( dims <= 2 )
{
_dst.create( size(), _type );
Mat dst = _dst.getMat();
Size sz = getContinuousSize(src, dst, cn);
func( src.data, src.step, 0, 0, dst.data, dst.step, sz, scale );
}
else
{
_dst.create( dims, size, _type );
Mat dst = _dst.getMat();
const Mat* arrays[] = {&src, &dst, 0};
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs);
Size sz((int)(it.size*cn), 1);
for( size_t i = 0; i < it.nplanes; i++, ++it )
func(ptrs[0], 1, 0, 0, ptrs[1], 1, sz, scale);
}
}
/****************************************************************************************\
* LUT Transform *
\****************************************************************************************/
namespace cv
{
template<typename T> static void
LUT8u_( const uchar* src, const T* lut, T* dst, int len, int cn, int lutcn )
{
if( lutcn == 1 )
{
for( int i = 0; i < len*cn; i++ )
dst[i] = lut[src[i]];
}
else
{
for( int i = 0; i < len*cn; i += cn )
for( int k = 0; k < cn; k++ )
dst[i+k] = lut[src[i+k]*cn+k];
}
}
static void LUT8u_8u( const uchar* src, const uchar* lut, uchar* dst, int len, int cn, int lutcn )
{
LUT8u_( src, lut, dst, len, cn, lutcn );
}
static void LUT8u_8s( const uchar* src, const schar* lut, schar* dst, int len, int cn, int lutcn )
{
LUT8u_( src, lut, dst, len, cn, lutcn );
}
static void LUT8u_16u( const uchar* src, const ushort* lut, ushort* dst, int len, int cn, int lutcn )
{
LUT8u_( src, lut, dst, len, cn, lutcn );
}
static void LUT8u_16s( const uchar* src, const short* lut, short* dst, int len, int cn, int lutcn )
{
LUT8u_( src, lut, dst, len, cn, lutcn );
}
static void LUT8u_32s( const uchar* src, const int* lut, int* dst, int len, int cn, int lutcn )
{
LUT8u_( src, lut, dst, len, cn, lutcn );
}
static void LUT8u_32f( const uchar* src, const float* lut, float* dst, int len, int cn, int lutcn )
{
LUT8u_( src, lut, dst, len, cn, lutcn );
}
static void LUT8u_64f( const uchar* src, const double* lut, double* dst, int len, int cn, int lutcn )
{
LUT8u_( src, lut, dst, len, cn, lutcn );
}
typedef void (*LUTFunc)( const uchar* src, const uchar* lut, uchar* dst, int len, int cn, int lutcn );
static LUTFunc lutTab[] =
{
(LUTFunc)LUT8u_8u, (LUTFunc)LUT8u_8s, (LUTFunc)LUT8u_16u, (LUTFunc)LUT8u_16s,
(LUTFunc)LUT8u_32s, (LUTFunc)LUT8u_32f, (LUTFunc)LUT8u_64f, 0
};
#ifdef HAVE_OPENCL
static bool ocl_LUT(InputArray _src, InputArray _lut, OutputArray _dst)
{
int lcn = _lut.channels(), dcn = _src.channels(), ddepth = _lut.depth();
UMat src = _src.getUMat(), lut = _lut.getUMat();
_dst.create(src.size(), CV_MAKETYPE(ddepth, dcn));
UMat dst = _dst.getUMat();
int kercn = lcn == 1 ? std::min(4, ocl::predictOptimalVectorWidth(_dst)) : dcn;
ocl::Kernel k("LUT", ocl::core::lut_oclsrc,
format("-D dcn=%d -D lcn=%d -D srcT=%s -D dstT=%s", kercn, lcn,
ocl::typeToStr(src.depth()), ocl::memopTypeToStr(ddepth)));
if (k.empty())
return false;
k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::ReadOnlyNoSize(lut),
ocl::KernelArg::WriteOnly(dst, dcn, kercn));
size_t globalSize[2] = { dst.cols * dcn / kercn, (dst.rows + 3) / 4 };
return k.run(2, globalSize, NULL, false);
}
#endif
#if defined(HAVE_IPP)
namespace ipp {
#if 0 // there are no performance benefits (PR #2653)
class IppLUTParallelBody_LUTC1 : public ParallelLoopBody
{
public:
bool* ok;
const Mat& src_;
const Mat& lut_;
Mat& dst_;
typedef IppStatus (*IppFn)(const Ipp8u* pSrc, int srcStep, void* pDst, int dstStep,
IppiSize roiSize, const void* pTable, int nBitSize);
IppFn fn;
int width;
IppLUTParallelBody_LUTC1(const Mat& src, const Mat& lut, Mat& dst, bool* _ok)
: ok(_ok), src_(src), lut_(lut), dst_(dst)
{
width = dst.cols * dst.channels();
size_t elemSize1 = CV_ELEM_SIZE1(dst.depth());
fn =
elemSize1 == 1 ? (IppFn)ippiLUTPalette_8u_C1R :
elemSize1 == 4 ? (IppFn)ippiLUTPalette_8u32u_C1R :
NULL;
*ok = (fn != NULL);
}
void operator()( const cv::Range& range ) const
{
if (!*ok)
return;
const int row0 = range.start;
const int row1 = range.end;
Mat src = src_.rowRange(row0, row1);
Mat dst = dst_.rowRange(row0, row1);
IppiSize sz = { width, dst.rows };
CV_DbgAssert(fn != NULL);
if (fn(src.data, (int)src.step[0], dst.data, (int)dst.step[0], sz, lut_.data, 8) < 0)
{
setIppErrorStatus();
*ok = false;
}
}
private:
IppLUTParallelBody_LUTC1(const IppLUTParallelBody_LUTC1&);
IppLUTParallelBody_LUTC1& operator=(const IppLUTParallelBody_LUTC1&);
};
#endif
class IppLUTParallelBody_LUTCN : public ParallelLoopBody
{
public:
bool *ok;
const Mat& src_;
const Mat& lut_;
Mat& dst_;
int lutcn;
uchar* lutBuffer;
uchar* lutTable[4];
IppLUTParallelBody_LUTCN(const Mat& src, const Mat& lut, Mat& dst, bool* _ok)
: ok(_ok), src_(src), lut_(lut), dst_(dst), lutBuffer(NULL)
{
lutcn = lut.channels();
IppiSize sz256 = {256, 1};
size_t elemSize1 = dst.elemSize1();
CV_DbgAssert(elemSize1 == 1);
lutBuffer = (uchar*)ippMalloc(256 * (int)elemSize1 * 4);
lutTable[0] = lutBuffer + 0;
lutTable[1] = lutBuffer + 1 * 256 * elemSize1;
lutTable[2] = lutBuffer + 2 * 256 * elemSize1;
lutTable[3] = lutBuffer + 3 * 256 * elemSize1;
CV_DbgAssert(lutcn == 3 || lutcn == 4);
if (lutcn == 3)
{
IppStatus status = ippiCopy_8u_C3P3R(lut.data, (int)lut.step[0], lutTable, (int)lut.step[0], sz256);
if (status < 0)
{
setIppErrorStatus();
return;
}
}
else if (lutcn == 4)
{
IppStatus status = ippiCopy_8u_C4P4R(lut.data, (int)lut.step[0], lutTable, (int)lut.step[0], sz256);
if (status < 0)
{
setIppErrorStatus();
return;
}
}
*ok = true;
}
~IppLUTParallelBody_LUTCN()
{
if (lutBuffer != NULL)
ippFree(lutBuffer);
lutBuffer = NULL;
lutTable[0] = NULL;
}
void operator()( const cv::Range& range ) const
{
if (!*ok)
return;
const int row0 = range.start;
const int row1 = range.end;
Mat src = src_.rowRange(row0, row1);
Mat dst = dst_.rowRange(row0, row1);
if (lutcn == 3)
{
if (ippiLUTPalette_8u_C3R(
src.data, (int)src.step[0], dst.data, (int)dst.step[0],
ippiSize(dst.size()), lutTable, 8) >= 0)
return;
}
else if (lutcn == 4)
{
if (ippiLUTPalette_8u_C4R(
src.data, (int)src.step[0], dst.data, (int)dst.step[0],
ippiSize(dst.size()), lutTable, 8) >= 0)
return;
}
setIppErrorStatus();
*ok = false;
}
private:
IppLUTParallelBody_LUTCN(const IppLUTParallelBody_LUTCN&);
IppLUTParallelBody_LUTCN& operator=(const IppLUTParallelBody_LUTCN&);
};
} // namespace ipp
#endif // IPP
class LUTParallelBody : public ParallelLoopBody
{
public:
bool* ok;
const Mat& src_;
const Mat& lut_;
Mat& dst_;
LUTFunc func;
LUTParallelBody(const Mat& src, const Mat& lut, Mat& dst, bool* _ok)
: ok(_ok), src_(src), lut_(lut), dst_(dst)
{
func = lutTab[lut.depth()];
*ok = (func != NULL);
}
void operator()( const cv::Range& range ) const
{
CV_DbgAssert(*ok);
const int row0 = range.start;
const int row1 = range.end;
Mat src = src_.rowRange(row0, row1);
Mat dst = dst_.rowRange(row0, row1);
int cn = src.channels();
int lutcn = lut_.channels();
const Mat* arrays[] = {&src, &dst, 0};
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs);
int len = (int)it.size;
for( size_t i = 0; i < it.nplanes; i++, ++it )
func(ptrs[0], lut_.data, ptrs[1], len, cn, lutcn);
}
private:
LUTParallelBody(const LUTParallelBody&);
LUTParallelBody& operator=(const LUTParallelBody&);
};
}
void cv::LUT( InputArray _src, InputArray _lut, OutputArray _dst )
{
int cn = _src.channels(), depth = _src.depth();
int lutcn = _lut.channels();
CV_Assert( (lutcn == cn || lutcn == 1) &&
_lut.total() == 256 && _lut.isContinuous() &&
(depth == CV_8U || depth == CV_8S) );
CV_OCL_RUN(_dst.isUMat() && _src.dims() <= 2,
ocl_LUT(_src, _lut, _dst))
Mat src = _src.getMat(), lut = _lut.getMat();
_dst.create(src.dims, src.size, CV_MAKETYPE(_lut.depth(), cn));
Mat dst = _dst.getMat();
if (_src.dims() <= 2)
{
bool ok = false;
Ptr<ParallelLoopBody> body;
#if defined(HAVE_IPP)
size_t elemSize1 = CV_ELEM_SIZE1(dst.depth());
#if 0 // there are no performance benefits (PR #2653)
if (lutcn == 1)
{
ParallelLoopBody* p = new ipp::IppLUTParallelBody_LUTC1(src, lut, dst, &ok);
body.reset(p);
}
else
#endif
if ((lutcn == 3 || lutcn == 4) && elemSize1 == 1)
{
ParallelLoopBody* p = new ipp::IppLUTParallelBody_LUTCN(src, lut, dst, &ok);
body.reset(p);
}
#endif
if (body == NULL || ok == false)
{
ok = false;
ParallelLoopBody* p = new LUTParallelBody(src, lut, dst, &ok);
body.reset(p);
}
if (body != NULL && ok)
{
Range all(0, dst.rows);
if (dst.total()>>18)
parallel_for_(all, *body, (double)std::max((size_t)1, dst.total()>>16));
else
(*body)(all);
if (ok)
return;
}
}
LUTFunc func = lutTab[lut.depth()];
CV_Assert( func != 0 );
const Mat* arrays[] = {&src, &dst, 0};
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs);
int len = (int)it.size;
for( size_t i = 0; i < it.nplanes; i++, ++it )
func(ptrs[0], lut.data, ptrs[1], len, cn, lutcn);
}
namespace cv {
#ifdef HAVE_OPENCL
static bool ocl_normalize( InputArray _src, InputOutputArray _dst, InputArray _mask, int dtype,
double scale, double delta )
{
UMat src = _src.getUMat();
if( _mask.empty() )
src.convertTo( _dst, dtype, scale, delta );
else if (src.channels() <= 4)
{
const ocl::Device & dev = ocl::Device::getDefault();
int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype),
ddepth = CV_MAT_DEPTH(dtype), wdepth = std::max(CV_32F, std::max(sdepth, ddepth)),
rowsPerWI = dev.isIntel() ? 4 : 1;
float fscale = static_cast<float>(scale), fdelta = static_cast<float>(delta);
bool haveScale = std::fabs(scale - 1) > DBL_EPSILON,
haveZeroScale = !(std::fabs(scale) > DBL_EPSILON),
haveDelta = std::fabs(delta) > DBL_EPSILON,
doubleSupport = dev.doubleFPConfig() > 0;
if (!haveScale && !haveDelta && stype == dtype)
{
_src.copyTo(_dst, _mask);
return true;
}
if (haveZeroScale)
{
_dst.setTo(Scalar(delta), _mask);
return true;
}
if ((sdepth == CV_64F || ddepth == CV_64F) && !doubleSupport)
return false;
char cvt[2][40];
String opts = format("-D srcT=%s -D dstT=%s -D convertToWT=%s -D cn=%d -D rowsPerWI=%d"
" -D convertToDT=%s -D workT=%s%s%s%s -D srcT1=%s -D dstT1=%s",
ocl::typeToStr(stype), ocl::typeToStr(dtype),
ocl::convertTypeStr(sdepth, wdepth, cn, cvt[0]), cn,
rowsPerWI, ocl::convertTypeStr(wdepth, ddepth, cn, cvt[1]),
ocl::typeToStr(CV_MAKE_TYPE(wdepth, cn)),
doubleSupport ? " -D DOUBLE_SUPPORT" : "",
haveScale ? " -D HAVE_SCALE" : "",
haveDelta ? " -D HAVE_DELTA" : "",
ocl::typeToStr(sdepth), ocl::typeToStr(ddepth));
ocl::Kernel k("normalizek", ocl::core::normalize_oclsrc, opts);
if (k.empty())
return false;
UMat mask = _mask.getUMat(), dst = _dst.getUMat();
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
maskarg = ocl::KernelArg::ReadOnlyNoSize(mask),
dstarg = ocl::KernelArg::ReadWrite(dst);
if (haveScale)
{
if (haveDelta)
k.args(srcarg, maskarg, dstarg, fscale, fdelta);
else
k.args(srcarg, maskarg, dstarg, fscale);
}
else
{
if (haveDelta)
k.args(srcarg, maskarg, dstarg, fdelta);
else
k.args(srcarg, maskarg, dstarg);
}
size_t globalsize[2] = { src.cols, (src.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
else
{
UMat temp;
src.convertTo( temp, dtype, scale, delta );
temp.copyTo( _dst, _mask );
}
return true;
}
#endif
}
void cv::normalize( InputArray _src, InputOutputArray _dst, double a, double b,
int norm_type, int rtype, InputArray _mask )
{
double scale = 1, shift = 0;
if( norm_type == CV_MINMAX )
{
double smin = 0, smax = 0;
double dmin = MIN( a, b ), dmax = MAX( a, b );
minMaxLoc( _src, &smin, &smax, 0, 0, _mask );
scale = (dmax - dmin)*(smax - smin > DBL_EPSILON ? 1./(smax - smin) : 0);
shift = dmin - smin*scale;
}
else if( norm_type == CV_L2 || norm_type == CV_L1 || norm_type == CV_C )
{
scale = norm( _src, norm_type, _mask );
scale = scale > DBL_EPSILON ? a/scale : 0.;
shift = 0;
}
else
CV_Error( CV_StsBadArg, "Unknown/unsupported norm type" );
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
if( rtype < 0 )
rtype = _dst.fixedType() ? _dst.depth() : depth;
_dst.createSameSize(_src, CV_MAKETYPE(rtype, cn));
CV_OCL_RUN(_dst.isUMat(),
ocl_normalize(_src, _dst, _mask, rtype, scale, shift))
Mat src = _src.getMat(), dst = _dst.getMat();
if( _mask.empty() )
src.convertTo( dst, rtype, scale, shift );
else
{
Mat temp;
src.convertTo( temp, rtype, scale, shift );
temp.copyTo( dst, _mask );
}
}
CV_IMPL void
cvSplit( const void* srcarr, void* dstarr0, void* dstarr1, void* dstarr2, void* dstarr3 )
{
void* dptrs[] = { dstarr0, dstarr1, dstarr2, dstarr3 };
cv::Mat src = cv::cvarrToMat(srcarr);
int i, j, nz = 0;
for( i = 0; i < 4; i++ )
nz += dptrs[i] != 0;
CV_Assert( nz > 0 );
std::vector<cv::Mat> dvec(nz);
std::vector<int> pairs(nz*2);
for( i = j = 0; i < 4; i++ )
{
if( dptrs[i] != 0 )
{
dvec[j] = cv::cvarrToMat(dptrs[i]);
CV_Assert( dvec[j].size() == src.size() );
CV_Assert( dvec[j].depth() == src.depth() );
CV_Assert( dvec[j].channels() == 1 );
CV_Assert( i < src.channels() );
pairs[j*2] = i;
pairs[j*2+1] = j;
j++;
}
}
if( nz == src.channels() )
cv::split( src, dvec );
else
{
cv::mixChannels( &src, 1, &dvec[0], nz, &pairs[0], nz );
}
}
CV_IMPL void
cvMerge( const void* srcarr0, const void* srcarr1, const void* srcarr2,
const void* srcarr3, void* dstarr )
{
const void* sptrs[] = { srcarr0, srcarr1, srcarr2, srcarr3 };
cv::Mat dst = cv::cvarrToMat(dstarr);
int i, j, nz = 0;
for( i = 0; i < 4; i++ )
nz += sptrs[i] != 0;
CV_Assert( nz > 0 );
std::vector<cv::Mat> svec(nz);
std::vector<int> pairs(nz*2);
for( i = j = 0; i < 4; i++ )
{
if( sptrs[i] != 0 )
{
svec[j] = cv::cvarrToMat(sptrs[i]);
CV_Assert( svec[j].size == dst.size &&
svec[j].depth() == dst.depth() &&
svec[j].channels() == 1 && i < dst.channels() );
pairs[j*2] = j;
pairs[j*2+1] = i;
j++;
}
}
if( nz == dst.channels() )
cv::merge( svec, dst );
else
{
cv::mixChannels( &svec[0], nz, &dst, 1, &pairs[0], nz );
}
}
CV_IMPL void
cvMixChannels( const CvArr** src, int src_count,
CvArr** dst, int dst_count,
const int* from_to, int pair_count )
{
cv::AutoBuffer<cv::Mat> buf(src_count + dst_count);
int i;
for( i = 0; i < src_count; i++ )
buf[i] = cv::cvarrToMat(src[i]);
for( i = 0; i < dst_count; i++ )
buf[i+src_count] = cv::cvarrToMat(dst[i]);
cv::mixChannels(&buf[0], src_count, &buf[src_count], dst_count, from_to, pair_count);
}
CV_IMPL void
cvConvertScaleAbs( const void* srcarr, void* dstarr,
double scale, double shift )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size == dst.size && dst.type() == CV_8UC(src.channels()));
cv::convertScaleAbs( src, dst, scale, shift );
}
CV_IMPL void
cvConvertScale( const void* srcarr, void* dstarr,
double scale, double shift )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size == dst.size && src.channels() == dst.channels() );
src.convertTo(dst, dst.type(), scale, shift);
}
CV_IMPL void cvLUT( const void* srcarr, void* dstarr, const void* lutarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), lut = cv::cvarrToMat(lutarr);
CV_Assert( dst.size() == src.size() && dst.type() == CV_MAKETYPE(lut.depth(), src.channels()) );
cv::LUT( src, lut, dst );
}
CV_IMPL void cvNormalize( const CvArr* srcarr, CvArr* dstarr,
double a, double b, int norm_type, const CvArr* maskarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), mask;
if( maskarr )
mask = cv::cvarrToMat(maskarr);
CV_Assert( dst.size() == src.size() && src.channels() == dst.channels() );
cv::normalize( src, dst, a, b, norm_type, dst.type(), mask );
}
/* End of file. */