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opencv/modules/core/src/arithm.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
// Copyright (C) 2014-2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* ////////////////////////////////////////////////////////////////////
//
// Arithmetic and logical operations: +, -, *, /, &, |, ^, ~, abs ...
//
// */
#include "precomp.hpp"
#include "opencl_kernels_core.hpp"
namespace cv
{
/****************************************************************************************\
* logical operations *
\****************************************************************************************/
enum { OCL_OP_ADD=0, OCL_OP_SUB=1, OCL_OP_RSUB=2, OCL_OP_ABSDIFF=3, OCL_OP_MUL=4,
OCL_OP_MUL_SCALE=5, OCL_OP_DIV_SCALE=6, OCL_OP_RECIP_SCALE=7, OCL_OP_ADDW=8,
OCL_OP_AND=9, OCL_OP_OR=10, OCL_OP_XOR=11, OCL_OP_NOT=12, OCL_OP_MIN=13, OCL_OP_MAX=14,
OCL_OP_RDIV_SCALE=15 };
#ifdef HAVE_OPENCL
static const char* oclop2str[] = { "OP_ADD", "OP_SUB", "OP_RSUB", "OP_ABSDIFF",
"OP_MUL", "OP_MUL_SCALE", "OP_DIV_SCALE", "OP_RECIP_SCALE",
"OP_ADDW", "OP_AND", "OP_OR", "OP_XOR", "OP_NOT", "OP_MIN", "OP_MAX", "OP_RDIV_SCALE", 0 };
static bool ocl_binary_op(InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray _mask, bool bitwise, int oclop, bool haveScalar )
{
bool haveMask = !_mask.empty();
int srctype = _src1.type();
int srcdepth = CV_MAT_DEPTH(srctype);
int cn = CV_MAT_CN(srctype);
const ocl::Device d = ocl::Device::getDefault();
bool doubleSupport = d.doubleFPConfig() > 0;
if( oclop < 0 || ((haveMask || haveScalar) && cn > 4) ||
(!doubleSupport && srcdepth == CV_64F && !bitwise))
return false;
char opts[1024];
int kercn = haveMask || haveScalar ? cn : ocl::predictOptimalVectorWidth(_src1, _src2, _dst);
int scalarcn = kercn == 3 ? 4 : kercn;
int rowsPerWI = d.isIntel() ? 4 : 1;
const int dstDepth = srcdepth;
const int dstType = CV_MAKETYPE(dstDepth, kercn);
const int dstType1 = CV_MAKETYPE(dstDepth, 1);
const int scalarType = CV_MAKETYPE(srcdepth, scalarcn);
snprintf(opts, sizeof(opts), "-D %s%s -D %s%s -D dstT=%s -D DEPTH_dst=%d -D dstT_C1=%s -D workST=%s -D cn=%d -D rowsPerWI=%d",
haveMask ? "MASK_" : "", haveScalar ? "UNARY_OP" : "BINARY_OP", oclop2str[oclop],
doubleSupport ? " -D DOUBLE_SUPPORT" : "",
bitwise ? ocl::memopTypeToStr(dstType) : ocl::typeToStr(dstType),
dstDepth,
bitwise ? ocl::memopTypeToStr(dstType1) : ocl::typeToStr(dstType1),
bitwise ? ocl::memopTypeToStr(scalarType) : ocl::typeToStr(scalarType),
kercn, rowsPerWI);
ocl::Kernel k("KF", ocl::core::arithm_oclsrc, opts);
if (k.empty())
return false;
UMat src1 = _src1.getUMat(), src2;
UMat dst = _dst.getUMat(), mask = _mask.getUMat();
ocl::KernelArg src1arg = ocl::KernelArg::ReadOnlyNoSize(src1, cn, kercn);
ocl::KernelArg dstarg = haveMask ? ocl::KernelArg::ReadWrite(dst, cn, kercn) :
ocl::KernelArg::WriteOnly(dst, cn, kercn);
ocl::KernelArg maskarg = ocl::KernelArg::ReadOnlyNoSize(mask, 1);
if( haveScalar )
{
size_t esz = CV_ELEM_SIZE1(srctype)*scalarcn;
double buf[4] = {0,0,0,0};
if( oclop != OCL_OP_NOT )
{
Mat src2sc = _src2.getMat();
convertAndUnrollScalar(src2sc, srctype, (uchar*)buf, 1);
}
ocl::KernelArg scalararg = ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, buf, esz);
if( !haveMask )
k.args(src1arg, dstarg, scalararg);
else
k.args(src1arg, maskarg, dstarg, scalararg);
}
else
{
src2 = _src2.getUMat();
ocl::KernelArg src2arg = ocl::KernelArg::ReadOnlyNoSize(src2, cn, kercn);
if( !haveMask )
k.args(src1arg, src2arg, dstarg);
else
k.args(src1arg, src2arg, maskarg, dstarg);
}
size_t globalsize[] = { (size_t)src1.cols * cn / kercn, ((size_t)src1.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, 0, false);
}
#endif
static void binary_op( InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray _mask, const BinaryFuncC* tab,
bool bitwise, int oclop )
{
const _InputArray *psrc1 = &_src1, *psrc2 = &_src2;
_InputArray::KindFlag kind1 = psrc1->kind(), kind2 = psrc2->kind();
int type1 = psrc1->type(), depth1 = CV_MAT_DEPTH(type1), cn = CV_MAT_CN(type1);
int type2 = psrc2->type(), depth2 = CV_MAT_DEPTH(type2), cn2 = CV_MAT_CN(type2);
int dims1 = psrc1->dims(), dims2 = psrc2->dims();
Size sz1 = dims1 <= 2 ? psrc1->size() : Size();
Size sz2 = dims2 <= 2 ? psrc2->size() : Size();
#ifdef HAVE_OPENCL
bool use_opencl = (kind1 == _InputArray::UMAT || kind2 == _InputArray::UMAT) &&
dims1 <= 2 && dims2 <= 2;
#endif
bool haveMask = !_mask.empty(), haveScalar = false;
BinaryFuncC func;
if( dims1 <= 2 && dims2 <= 2 && kind1 == kind2 && sz1 == sz2 && type1 == type2 && !haveMask )
{
_dst.createSameSize(*psrc1, type1);
CV_OCL_RUN(use_opencl,
ocl_binary_op(*psrc1, *psrc2, _dst, _mask, bitwise, oclop, false))
if( bitwise )
{
func = *tab;
cn = (int)CV_ELEM_SIZE(type1);
}
else
{
func = tab[depth1];
}
CV_Assert(func);
Mat src1 = psrc1->getMat(), src2 = psrc2->getMat(), dst = _dst.getMat();
Size sz = getContinuousSize2D(src1, src2, dst);
size_t len = sz.width*(size_t)cn;
if (len < INT_MAX) // FIXIT similar code below doesn't have that check
{
sz.width = (int)len;
func(src1.ptr(), src1.step, src2.ptr(), src2.step, dst.ptr(), dst.step, sz.width, sz.height, 0);
return;
}
}
if( oclop == OCL_OP_NOT )
haveScalar = true;
else if( (kind1 == _InputArray::MATX) + (kind2 == _InputArray::MATX) == 1 ||
!psrc1->sameSize(*psrc2) || type1 != type2 )
{
if( checkScalar(*psrc1, type2, kind1, kind2) )
{
// src1 is a scalar; swap it with src2
swap(psrc1, psrc2);
swap(type1, type2);
swap(depth1, depth2);
swap(cn, cn2);
swap(sz1, sz2);
}
else if( !checkScalar(*psrc2, type1, kind2, kind1) )
CV_Error( CV_StsUnmatchedSizes,
"The operation is neither 'array op array' (where arrays have the same size and type), "
"nor 'array op scalar', nor 'scalar op array'" );
haveScalar = true;
}
else
{
CV_Assert( psrc1->sameSize(*psrc2) && type1 == type2 );
}
size_t esz = CV_ELEM_SIZE(type1);
size_t blocksize0 = (BLOCK_SIZE + esz-1)/esz;
BinaryFunc copymask = 0;
bool reallocate = false;
if( haveMask )
{
int mtype = _mask.type();
CV_Assert( (mtype == CV_8U || mtype == CV_8S) && _mask.sameSize(*psrc1));
copymask = getCopyMaskFunc(esz);
reallocate = !_dst.sameSize(*psrc1) || _dst.type() != type1;
}
AutoBuffer<uchar> _buf;
uchar *scbuf = 0, *maskbuf = 0;
_dst.createSameSize(*psrc1, type1);
// if this is mask operation and dst has been reallocated,
// we have to clear the destination
if( haveMask && reallocate )
_dst.setTo(0.);
CV_OCL_RUN(use_opencl,
ocl_binary_op(*psrc1, *psrc2, _dst, _mask, bitwise, oclop, haveScalar))
Mat src1 = psrc1->getMat(), src2 = psrc2->getMat();
Mat dst = _dst.getMat(), mask = _mask.getMat();
if( bitwise )
{
func = *tab;
cn = (int)esz;
}
else
func = tab[depth1];
CV_Assert(func);
if( !haveScalar )
{
const Mat* arrays[] = { &src1, &src2, &dst, &mask, 0 };
uchar* ptrs[4] = {};
NAryMatIterator it(arrays, ptrs);
size_t total = it.size, blocksize = total;
if( blocksize*cn > INT_MAX )
blocksize = INT_MAX/cn;
if( haveMask )
{
blocksize = std::min(blocksize, blocksize0);
_buf.allocate(blocksize*esz);
maskbuf = _buf.data();
}
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
func( ptrs[0], 0, ptrs[1], 0, haveMask ? maskbuf : ptrs[2], 0, bsz*cn, 1, 0 );
if( haveMask )
{
copymask( maskbuf, 0, ptrs[3], 0, ptrs[2], 0, Size(bsz, 1), &esz );
ptrs[3] += bsz;
}
bsz *= (int)esz;
ptrs[0] += bsz; ptrs[1] += bsz; ptrs[2] += bsz;
}
}
}
else
{
const Mat* arrays[] = { &src1, &dst, &mask, 0 };
uchar* ptrs[3] = {};
NAryMatIterator it(arrays, ptrs);
size_t total = it.size, blocksize = std::min(total, blocksize0);
_buf.allocate(blocksize*(haveMask ? 2 : 1)*esz + 32);
scbuf = _buf.data();
maskbuf = alignPtr(scbuf + blocksize*esz, 16);
convertAndUnrollScalar( src2, src1.type(), scbuf, blocksize);
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
func( ptrs[0], 0, scbuf, 0, haveMask ? maskbuf : ptrs[1], 0, bsz*cn, 1, 0 );
if( haveMask )
{
copymask( maskbuf, 0, ptrs[2], 0, ptrs[1], 0, Size(bsz, 1), &esz );
ptrs[2] += bsz;
}
bsz *= (int)esz;
ptrs[0] += bsz; ptrs[1] += bsz;
}
}
}
}
static BinaryFuncC* getMaxTab()
{
static BinaryFuncC maxTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::max8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::max8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::max16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::max16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::max32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::max32f), (BinaryFuncC)cv::hal::max64f,
0
};
return maxTab;
}
static BinaryFuncC* getMinTab()
{
static BinaryFuncC minTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::min8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::min8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::min16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::min16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::min32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::min32f), (BinaryFuncC)cv::hal::min64f,
0
};
return minTab;
}
}
void cv::bitwise_and(InputArray a, InputArray b, OutputArray c, InputArray mask)
{
CV_INSTRUMENT_REGION();
BinaryFuncC f = (BinaryFuncC)GET_OPTIMIZED(cv::hal::and8u);
binary_op(a, b, c, mask, &f, true, OCL_OP_AND);
}
void cv::bitwise_or(InputArray a, InputArray b, OutputArray c, InputArray mask)
{
CV_INSTRUMENT_REGION();
BinaryFuncC f = (BinaryFuncC)GET_OPTIMIZED(cv::hal::or8u);
binary_op(a, b, c, mask, &f, true, OCL_OP_OR);
}
void cv::bitwise_xor(InputArray a, InputArray b, OutputArray c, InputArray mask)
{
CV_INSTRUMENT_REGION();
BinaryFuncC f = (BinaryFuncC)GET_OPTIMIZED(cv::hal::xor8u);
binary_op(a, b, c, mask, &f, true, OCL_OP_XOR);
}
void cv::bitwise_not(InputArray a, OutputArray c, InputArray mask)
{
CV_INSTRUMENT_REGION();
BinaryFuncC f = (BinaryFuncC)GET_OPTIMIZED(cv::hal::not8u);
binary_op(a, a, c, mask, &f, true, OCL_OP_NOT);
}
void cv::max( InputArray src1, InputArray src2, OutputArray dst )
{
CV_INSTRUMENT_REGION();
binary_op(src1, src2, dst, noArray(), getMaxTab(), false, OCL_OP_MAX );
}
void cv::min( InputArray src1, InputArray src2, OutputArray dst )
{
CV_INSTRUMENT_REGION();
binary_op(src1, src2, dst, noArray(), getMinTab(), false, OCL_OP_MIN );
}
void cv::max(const Mat& src1, const Mat& src2, Mat& dst)
{
CV_INSTRUMENT_REGION();
OutputArray _dst(dst);
binary_op(src1, src2, _dst, noArray(), getMaxTab(), false, OCL_OP_MAX );
}
void cv::min(const Mat& src1, const Mat& src2, Mat& dst)
{
CV_INSTRUMENT_REGION();
OutputArray _dst(dst);
binary_op(src1, src2, _dst, noArray(), getMinTab(), false, OCL_OP_MIN );
}
void cv::max(const UMat& src1, const UMat& src2, UMat& dst)
{
CV_INSTRUMENT_REGION();
OutputArray _dst(dst);
binary_op(src1, src2, _dst, noArray(), getMaxTab(), false, OCL_OP_MAX );
}
void cv::min(const UMat& src1, const UMat& src2, UMat& dst)
{
CV_INSTRUMENT_REGION();
OutputArray _dst(dst);
binary_op(src1, src2, _dst, noArray(), getMinTab(), false, OCL_OP_MIN );
}
/****************************************************************************************\
* add/subtract *
\****************************************************************************************/
namespace cv
{
static int actualScalarDepth(const double* data, int len)
{
int i = 0, minval = INT_MAX, maxval = INT_MIN;
for(; i < len; ++i)
{
int ival = cvRound(data[i]);
if( ival != data[i] )
break;
minval = MIN(minval, ival);
maxval = MAX(maxval, ival);
}
return i < len ? CV_64F :
minval >= 0 && maxval <= (int)UCHAR_MAX ? CV_8U :
minval >= (int)SCHAR_MIN && maxval <= (int)SCHAR_MAX ? CV_8S :
minval >= 0 && maxval <= (int)USHRT_MAX ? CV_16U :
minval >= (int)SHRT_MIN && maxval <= (int)SHRT_MAX ? CV_16S :
CV_32S;
}
#ifdef HAVE_OPENCL
static bool ocl_arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray _mask, int wtype,
void* usrdata, int oclop,
bool haveScalar )
{
const ocl::Device d = ocl::Device::getDefault();
bool doubleSupport = d.doubleFPConfig() > 0;
int type1 = _src1.type(), depth1 = CV_MAT_DEPTH(type1), cn = CV_MAT_CN(type1);
bool haveMask = !_mask.empty();
if ( (haveMask || haveScalar) && cn > 4 )
return false;
int dtype = _dst.type(), ddepth = CV_MAT_DEPTH(dtype), wdepth = std::max(CV_32S, CV_MAT_DEPTH(wtype));
if (!doubleSupport)
wdepth = std::min(wdepth, CV_32F);
wtype = CV_MAKETYPE(wdepth, cn);
int type2 = haveScalar ? wtype : _src2.type(), depth2 = CV_MAT_DEPTH(type2);
if (!doubleSupport && (depth2 == CV_64F || depth1 == CV_64F))
return false;
int kercn = haveMask || haveScalar ? cn : ocl::predictOptimalVectorWidth(_src1, _src2, _dst);
int scalarcn = kercn == 3 ? 4 : kercn, rowsPerWI = d.isIntel() ? 4 : 1;
char cvtstr[4][50], opts[1024];
snprintf(opts, sizeof(opts), "-D %s%s -D %s -D srcT1=%s -D srcT1_C1=%s -D srcT2=%s -D srcT2_C1=%s "
"-D dstT=%s -D DEPTH_dst=%d -D dstT_C1=%s -D workT=%s -D workST=%s -D scaleT=%s -D wdepth=%d -D convertToWT1=%s "
"-D convertToWT2=%s -D convertToDT=%s%s -D cn=%d -D rowsPerWI=%d -D convertFromU=%s",
(haveMask ? "MASK_" : ""), (haveScalar ? "UNARY_OP" : "BINARY_OP"),
oclop2str[oclop], ocl::typeToStr(CV_MAKETYPE(depth1, kercn)),
ocl::typeToStr(depth1), ocl::typeToStr(CV_MAKETYPE(depth2, kercn)),
ocl::typeToStr(depth2), ocl::typeToStr(CV_MAKETYPE(ddepth, kercn)), ddepth,
ocl::typeToStr(ddepth), ocl::typeToStr(CV_MAKETYPE(wdepth, kercn)),
ocl::typeToStr(CV_MAKETYPE(wdepth, scalarcn)),
ocl::typeToStr(wdepth), wdepth,
ocl::convertTypeStr(depth1, wdepth, kercn, cvtstr[0], sizeof(cvtstr[0])),
ocl::convertTypeStr(depth2, wdepth, kercn, cvtstr[1], sizeof(cvtstr[1])),
ocl::convertTypeStr(wdepth, ddepth, kercn, cvtstr[2], sizeof(cvtstr[2])),
doubleSupport ? " -D DOUBLE_SUPPORT" : "", kercn, rowsPerWI,
oclop == OCL_OP_ABSDIFF && wdepth == CV_32S && ddepth == wdepth ?
ocl::convertTypeStr(CV_8U, ddepth, kercn, cvtstr[3], sizeof(cvtstr[3])) : "noconvert");
size_t usrdata_esz = CV_ELEM_SIZE(wdepth);
const uchar* usrdata_p = (const uchar*)usrdata;
const double* usrdata_d = (const double*)usrdata;
float usrdata_f[3];
int i, n = oclop == OCL_OP_MUL_SCALE || oclop == OCL_OP_DIV_SCALE ||
oclop == OCL_OP_RDIV_SCALE || oclop == OCL_OP_RECIP_SCALE ? 1 : oclop == OCL_OP_ADDW ? 3 : 0;
if( usrdata && n > 0 && wdepth == CV_32F )
{
for( i = 0; i < n; i++ )
usrdata_f[i] = (float)usrdata_d[i];
usrdata_p = (const uchar*)usrdata_f;
}
ocl::Kernel k("KF", ocl::core::arithm_oclsrc, opts);
if (k.empty())
return false;
UMat src1 = _src1.getUMat(), src2;
UMat dst = _dst.getUMat(), mask = _mask.getUMat();
ocl::KernelArg src1arg = ocl::KernelArg::ReadOnlyNoSize(src1, cn, kercn);
ocl::KernelArg dstarg = haveMask ? ocl::KernelArg::ReadWrite(dst, cn, kercn) :
ocl::KernelArg::WriteOnly(dst, cn, kercn);
ocl::KernelArg maskarg = ocl::KernelArg::ReadOnlyNoSize(mask, 1);
if( haveScalar )
{
size_t esz = CV_ELEM_SIZE1(wtype)*scalarcn;
double buf[4]={0,0,0,0};
Mat src2sc = _src2.getMat();
if( !src2sc.empty() )
convertAndUnrollScalar(src2sc, wtype, (uchar*)buf, 1);
ocl::KernelArg scalararg = ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, buf, esz);
if( !haveMask )
{
if(n == 0)
k.args(src1arg, dstarg, scalararg);
else if(n == 1)
k.args(src1arg, dstarg, scalararg,
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p, usrdata_esz));
else
CV_Error(Error::StsNotImplemented, "unsupported number of extra parameters");
}
else
k.args(src1arg, maskarg, dstarg, scalararg);
}
else
{
src2 = _src2.getUMat();
ocl::KernelArg src2arg = ocl::KernelArg::ReadOnlyNoSize(src2, cn, kercn);
if( !haveMask )
{
if (n == 0)
k.args(src1arg, src2arg, dstarg);
else if (n == 1)
k.args(src1arg, src2arg, dstarg,
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p, usrdata_esz));
else if (n == 3)
k.args(src1arg, src2arg, dstarg,
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p, usrdata_esz),
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p + usrdata_esz, usrdata_esz),
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p + usrdata_esz*2, usrdata_esz));
else
CV_Error(Error::StsNotImplemented, "unsupported number of extra parameters");
}
else
k.args(src1arg, src2arg, maskarg, dstarg);
}
size_t globalsize[] = { (size_t)src1.cols * cn / kercn, ((size_t)src1.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
#endif
static void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray _mask, int dtype, BinaryFuncC* tab, bool muldiv=false,
void* usrdata=0, int oclop=-1 )
{
const _InputArray *psrc1 = &_src1, *psrc2 = &_src2;
_InputArray::KindFlag kind1 = psrc1->kind(), kind2 = psrc2->kind();
bool haveMask = !_mask.empty();
bool reallocate = false;
int type1 = psrc1->type(), depth1 = CV_MAT_DEPTH(type1), cn = CV_MAT_CN(type1);
int type2 = psrc2->type(), depth2 = CV_MAT_DEPTH(type2), cn2 = CV_MAT_CN(type2);
int wtype, dims1 = psrc1->dims(), dims2 = psrc2->dims();
Size sz1 = dims1 <= 2 ? psrc1->size() : Size();
Size sz2 = dims2 <= 2 ? psrc2->size() : Size();
#ifdef HAVE_OPENCL
bool use_opencl = OCL_PERFORMANCE_CHECK(_dst.isUMat()) && dims1 <= 2 && dims2 <= 2;
#endif
bool src1Scalar = checkScalar(*psrc1, type2, kind1, kind2);
bool src2Scalar = checkScalar(*psrc2, type1, kind2, kind1);
if( (kind1 == kind2 || cn == 1) && sz1 == sz2 && dims1 <= 2 && dims2 <= 2 && type1 == type2 &&
!haveMask && ((!_dst.fixedType() && (dtype < 0 || CV_MAT_DEPTH(dtype) == depth1)) ||
(_dst.fixedType() && _dst.type() == type1)) &&
(src1Scalar == src2Scalar) )
{
_dst.createSameSize(*psrc1, type1);
CV_OCL_RUN(use_opencl,
ocl_arithm_op(*psrc1, *psrc2, _dst, _mask,
(!usrdata ? type1 : std::max(depth1, CV_32F)),
usrdata, oclop, false))
Mat src1 = psrc1->getMat(), src2 = psrc2->getMat(), dst = _dst.getMat();
Size sz = getContinuousSize2D(src1, src2, dst, src1.channels());
tab[depth1](src1.ptr(), src1.step, src2.ptr(), src2.step, dst.ptr(), dst.step, sz.width, sz.height, usrdata);
return;
}
bool haveScalar = false, swapped12 = false;
if( dims1 != dims2 || sz1 != sz2 || cn != cn2 ||
(kind1 == _InputArray::MATX && (sz1 == Size(1,4) || sz1 == Size(1,1))) ||
(kind2 == _InputArray::MATX && (sz2 == Size(1,4) || sz2 == Size(1,1))) )
{
if ((type1 == CV_64F && (sz1.height == 1 || sz1.height == 4)) &&
checkScalar(*psrc1, type2, kind1, kind2))
{
// src1 is a scalar; swap it with src2
swap(psrc1, psrc2);
swap(sz1, sz2);
swap(type1, type2);
swap(depth1, depth2);
swap(cn, cn2);
swap(dims1, dims2);
swapped12 = true;
if( oclop == OCL_OP_SUB )
oclop = OCL_OP_RSUB;
if ( oclop == OCL_OP_DIV_SCALE )
oclop = OCL_OP_RDIV_SCALE;
}
else if( !checkScalar(*psrc2, type1, kind2, kind1) )
CV_Error( CV_StsUnmatchedSizes,
"The operation is neither 'array op array' "
"(where arrays have the same size and the same number of channels), "
"nor 'array op scalar', nor 'scalar op array'" );
haveScalar = true;
CV_Assert(type2 == CV_64F && (sz2.height == 1 || sz2.height == 4));
if (!muldiv)
{
Mat sc = psrc2->getMat();
depth2 = actualScalarDepth(sc.ptr<double>(), sz2 == Size(1, 1) ? cn2 : cn);
if( depth2 == CV_64F && (depth1 < CV_32S || depth1 == CV_32F) )
depth2 = CV_32F;
}
else
depth2 = CV_64F;
}
if( dtype < 0 )
{
if( _dst.fixedType() )
dtype = _dst.type();
else
{
if( !haveScalar && type1 != type2 )
CV_Error(CV_StsBadArg,
"When the input arrays in add/subtract/multiply/divide functions have different types, "
"the output array type must be explicitly specified");
dtype = type1;
}
}
dtype = CV_MAT_DEPTH(dtype);
if( depth1 == depth2 && dtype == depth1 )
wtype = dtype;
else if( !muldiv )
{
wtype = depth1 <= CV_8S && depth2 <= CV_8S ? CV_16S :
depth1 <= CV_32S && depth2 <= CV_32S ? CV_32S : std::max(depth1, depth2);
wtype = std::max(wtype, dtype);
// when the result of addition should be converted to an integer type,
// and just one of the input arrays is floating-point, it makes sense to convert that input to integer type before the operation,
// instead of converting the other input to floating-point and then converting the operation result back to integers.
if( dtype < CV_32F && (depth1 < CV_32F || depth2 < CV_32F) )
wtype = CV_32S;
}
else
{
wtype = std::max(depth1, std::max(depth2, CV_32F));
wtype = std::max(wtype, dtype);
}
dtype = CV_MAKETYPE(dtype, cn);
wtype = CV_MAKETYPE(wtype, cn);
if( haveMask )
{
int mtype = _mask.type();
CV_Assert( (mtype == CV_8UC1 || mtype == CV_8SC1) && _mask.sameSize(*psrc1) );
reallocate = !_dst.sameSize(*psrc1) || _dst.type() != dtype;
}
_dst.createSameSize(*psrc1, dtype);
if( reallocate )
_dst.setTo(0.);
CV_OCL_RUN(use_opencl,
ocl_arithm_op(*psrc1, *psrc2, _dst, _mask, wtype,
usrdata, oclop, haveScalar))
BinaryFunc cvtsrc1 = type1 == wtype ? 0 : getConvertFunc(type1, wtype);
BinaryFunc cvtsrc2 = type2 == type1 ? cvtsrc1 : type2 == wtype ? 0 : getConvertFunc(type2, wtype);
BinaryFunc cvtdst = dtype == wtype ? 0 : getConvertFunc(wtype, dtype);
size_t esz1 = CV_ELEM_SIZE(type1), esz2 = CV_ELEM_SIZE(type2);
size_t dsz = CV_ELEM_SIZE(dtype), wsz = CV_ELEM_SIZE(wtype);
size_t blocksize0 = (size_t)(BLOCK_SIZE + wsz-1)/wsz;
BinaryFunc copymask = getCopyMaskFunc(dsz);
Mat src1 = psrc1->getMat(), src2 = psrc2->getMat(), dst = _dst.getMat(), mask = _mask.getMat();
AutoBuffer<uchar> _buf;
uchar *buf, *maskbuf = 0, *buf1 = 0, *buf2 = 0, *wbuf = 0;
size_t bufesz = (cvtsrc1 ? wsz : 0) +
(cvtsrc2 || haveScalar ? wsz : 0) +
(cvtdst ? wsz : 0) +
(haveMask ? dsz : 0);
BinaryFuncC func = tab[CV_MAT_DEPTH(wtype)];
CV_Assert(func);
if( !haveScalar )
{
const Mat* arrays[] = { &src1, &src2, &dst, &mask, 0 };
uchar* ptrs[4] = {};
NAryMatIterator it(arrays, ptrs);
size_t total = it.size, blocksize = total;
if( haveMask || cvtsrc1 || cvtsrc2 || cvtdst )
blocksize = std::min(blocksize, blocksize0);
_buf.allocate(bufesz*blocksize + 64);
buf = _buf.data();
if( cvtsrc1 )
buf1 = buf, buf = alignPtr(buf + blocksize*wsz, 16);
if( cvtsrc2 )
buf2 = buf, buf = alignPtr(buf + blocksize*wsz, 16);
wbuf = maskbuf = buf;
if( cvtdst )
buf = alignPtr(buf + blocksize*wsz, 16);
if( haveMask )
maskbuf = buf;
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
Size bszn(bsz*cn, 1);
const uchar *sptr1 = ptrs[0], *sptr2 = ptrs[1];
uchar* dptr = ptrs[2];
if( cvtsrc1 )
{
cvtsrc1( sptr1, 1, 0, 1, buf1, 1, bszn, 0 );
sptr1 = buf1;
}
if( ptrs[0] == ptrs[1] )
sptr2 = sptr1;
else if( cvtsrc2 )
{
cvtsrc2( sptr2, 1, 0, 1, buf2, 1, bszn, 0 );
sptr2 = buf2;
}
if( !haveMask && !cvtdst )
func( sptr1, 1, sptr2, 1, dptr, 1, bszn.width, bszn.height, usrdata );
else
{
func( sptr1, 1, sptr2, 1, wbuf, 0, bszn.width, bszn.height, usrdata );
if( !haveMask )
cvtdst( wbuf, 1, 0, 1, dptr, 1, bszn, 0 );
else if( !cvtdst )
{
copymask( wbuf, 1, ptrs[3], 1, dptr, 1, Size(bsz, 1), &dsz );
ptrs[3] += bsz;
}
else
{
cvtdst( wbuf, 1, 0, 1, maskbuf, 1, bszn, 0 );
copymask( maskbuf, 1, ptrs[3], 1, dptr, 1, Size(bsz, 1), &dsz );
ptrs[3] += bsz;
}
}
ptrs[0] += bsz*esz1; ptrs[1] += bsz*esz2; ptrs[2] += bsz*dsz;
}
}
}
else
{
const Mat* arrays[] = { &src1, &dst, &mask, 0 };
uchar* ptrs[3] = {};
NAryMatIterator it(arrays, ptrs);
size_t total = it.size, blocksize = std::min(total, blocksize0);
_buf.allocate(bufesz*blocksize + 64);
buf = _buf.data();
if( cvtsrc1 )
buf1 = buf, buf = alignPtr(buf + blocksize*wsz, 16);
buf2 = buf; buf = alignPtr(buf + blocksize*wsz, 16);
wbuf = maskbuf = buf;
if( cvtdst )
buf = alignPtr(buf + blocksize*wsz, 16);
if( haveMask )
maskbuf = buf;
convertAndUnrollScalar( src2, wtype, buf2, blocksize);
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
Size bszn(bsz*cn, 1);
const uchar *sptr1 = ptrs[0];
const uchar* sptr2 = buf2;
uchar* dptr = ptrs[1];
if( cvtsrc1 )
{
cvtsrc1( sptr1, 1, 0, 1, buf1, 1, bszn, 0 );
sptr1 = buf1;
}
if( swapped12 )
std::swap(sptr1, sptr2);
if( !haveMask && !cvtdst )
func( sptr1, 1, sptr2, 1, dptr, 1, bszn.width, bszn.height, usrdata );
else
{
func( sptr1, 1, sptr2, 1, wbuf, 1, bszn.width, bszn.height, usrdata );
if( !haveMask )
cvtdst( wbuf, 1, 0, 1, dptr, 1, bszn, 0 );
else if( !cvtdst )
{
copymask( wbuf, 1, ptrs[2], 1, dptr, 1, Size(bsz, 1), &dsz );
ptrs[2] += bsz;
}
else
{
cvtdst( wbuf, 1, 0, 1, maskbuf, 1, bszn, 0 );
copymask( maskbuf, 1, ptrs[2], 1, dptr, 1, Size(bsz, 1), &dsz );
ptrs[2] += bsz;
}
}
ptrs[0] += bsz*esz1; ptrs[1] += bsz*dsz;
}
}
}
}
static BinaryFuncC* getAddTab()
{
static BinaryFuncC addTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::add8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::add8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::add16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::add16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::add32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::add32f), (BinaryFuncC)cv::hal::add64f,
0
};
return addTab;
}
static BinaryFuncC* getSubTab()
{
static BinaryFuncC subTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::sub8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::sub8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::sub16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::sub16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::sub32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::sub32f), (BinaryFuncC)cv::hal::sub64f,
0
};
return subTab;
}
static BinaryFuncC* getAbsDiffTab()
{
static BinaryFuncC absDiffTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff32f), (BinaryFuncC)cv::hal::absdiff64f,
0
};
return absDiffTab;
}
}
void cv::add( InputArray src1, InputArray src2, OutputArray dst,
InputArray mask, int dtype )
{
CV_INSTRUMENT_REGION();
arithm_op(src1, src2, dst, mask, dtype, getAddTab(), false, 0, OCL_OP_ADD );
}
void cv::subtract( InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray mask, int dtype )
{
CV_INSTRUMENT_REGION();
arithm_op(_src1, _src2, _dst, mask, dtype, getSubTab(), false, 0, OCL_OP_SUB );
}
void cv::absdiff( InputArray src1, InputArray src2, OutputArray dst )
{
CV_INSTRUMENT_REGION();
arithm_op(src1, src2, dst, noArray(), -1, getAbsDiffTab(), false, 0, OCL_OP_ABSDIFF);
}
void cv::copyTo(InputArray _src, OutputArray _dst, InputArray _mask)
{
CV_INSTRUMENT_REGION();
_src.copyTo(_dst, _mask);
}
/****************************************************************************************\
* multiply/divide *
\****************************************************************************************/
namespace cv
{
static BinaryFuncC* getMulTab()
{
static BinaryFuncC mulTab[] =
{
(BinaryFuncC)cv::hal::mul8u, (BinaryFuncC)cv::hal::mul8s, (BinaryFuncC)cv::hal::mul16u,
(BinaryFuncC)cv::hal::mul16s, (BinaryFuncC)cv::hal::mul32s, (BinaryFuncC)cv::hal::mul32f,
(BinaryFuncC)cv::hal::mul64f, 0
};
return mulTab;
}
static BinaryFuncC* getDivTab()
{
static BinaryFuncC divTab[] =
{
(BinaryFuncC)cv::hal::div8u, (BinaryFuncC)cv::hal::div8s, (BinaryFuncC)cv::hal::div16u,
(BinaryFuncC)cv::hal::div16s, (BinaryFuncC)cv::hal::div32s, (BinaryFuncC)cv::hal::div32f,
(BinaryFuncC)cv::hal::div64f, 0
};
return divTab;
}
static BinaryFuncC* getRecipTab()
{
static BinaryFuncC recipTab[] =
{
(BinaryFuncC)cv::hal::recip8u, (BinaryFuncC)cv::hal::recip8s, (BinaryFuncC)cv::hal::recip16u,
(BinaryFuncC)cv::hal::recip16s, (BinaryFuncC)cv::hal::recip32s, (BinaryFuncC)cv::hal::recip32f,
(BinaryFuncC)cv::hal::recip64f, 0
};
return recipTab;
}
void multiply(InputArray src1, InputArray src2,
OutputArray dst, double scale, int dtype)
{
CV_INSTRUMENT_REGION();
arithm_op(src1, src2, dst, noArray(), dtype, getMulTab(),
true, &scale, std::abs(scale - 1.0) < DBL_EPSILON ? OCL_OP_MUL : OCL_OP_MUL_SCALE);
}
void divide(InputArray src1, InputArray src2,
OutputArray dst, double scale, int dtype)
{
CV_INSTRUMENT_REGION();
arithm_op(src1, src2, dst, noArray(), dtype, getDivTab(), true, &scale, OCL_OP_DIV_SCALE);
}
void divide(double scale, InputArray src2,
OutputArray dst, int dtype)
{
CV_INSTRUMENT_REGION();
arithm_op(src2, src2, dst, noArray(), dtype, getRecipTab(), true, &scale, OCL_OP_RECIP_SCALE);
}
UMat UMat::mul(InputArray m, double scale) const
{
UMat dst;
multiply(*this, m, dst, scale);
return dst;
}
/****************************************************************************************\
* addWeighted *
\****************************************************************************************/
static BinaryFuncC* getAddWeightedTab()
{
static BinaryFuncC addWeightedTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::addWeighted8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::addWeighted8s), (BinaryFuncC)GET_OPTIMIZED(cv::hal::addWeighted16u),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::addWeighted16s), (BinaryFuncC)GET_OPTIMIZED(cv::hal::addWeighted32s), (BinaryFuncC)cv::hal::addWeighted32f,
(BinaryFuncC)cv::hal::addWeighted64f, 0
};
return addWeightedTab;
}
}
void cv::addWeighted( InputArray src1, double alpha, InputArray src2,
double beta, double gamma, OutputArray dst, int dtype )
{
CV_INSTRUMENT_REGION();
double scalars[] = {alpha, beta, gamma};
arithm_op(src1, src2, dst, noArray(), dtype, getAddWeightedTab(), true, scalars, OCL_OP_ADDW);
}
/****************************************************************************************\
* compare *
\****************************************************************************************/
namespace cv
{
static BinaryFuncC getCmpFunc(int depth)
{
static BinaryFuncC cmpTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::cmp8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::cmp8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::cmp16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::cmp16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::cmp32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::cmp32f), (BinaryFuncC)cv::hal::cmp64f,
0
};
return cmpTab[depth];
}
static double getMinVal(int depth)
{
static const double tab[] = {0, -128, 0, -32768, INT_MIN, -FLT_MAX, -DBL_MAX, 0};
return tab[depth];
}
static double getMaxVal(int depth)
{
static const double tab[] = {255, 127, 65535, 32767, INT_MAX, FLT_MAX, DBL_MAX, 0};
return tab[depth];
}
#ifdef HAVE_OPENCL
static bool ocl_compare(InputArray _src1, InputArray _src2, OutputArray _dst, int op, bool haveScalar)
{
const ocl::Device& dev = ocl::Device::getDefault();
bool doubleSupport = dev.doubleFPConfig() > 0;
int type1 = _src1.type(), depth1 = CV_MAT_DEPTH(type1), cn = CV_MAT_CN(type1),
type2 = _src2.type(), depth2 = CV_MAT_DEPTH(type2);
if (!doubleSupport && depth1 == CV_64F)
return false;
if (!haveScalar && (!_src1.sameSize(_src2) || type1 != type2))
return false;
int kercn = haveScalar ? cn : ocl::predictOptimalVectorWidth(_src1, _src2, _dst), rowsPerWI = dev.isIntel() ? 4 : 1;
// Workaround for bug with "?:" operator in AMD OpenCL compiler
if (depth1 >= CV_16U)
kercn = 1;
int scalarcn = kercn == 3 ? 4 : kercn;
const char * const operationMap[] = { "==", ">", ">=", "<", "<=", "!=" };
char cvt[50];
String opts = format("-D %s -D srcT1=%s -D dstT=%s -D DEPTH_dst=%d -D workT=srcT1 -D cn=%d"
" -D convertToDT=%s -D OP_CMP -D CMP_OPERATOR=%s -D srcT1_C1=%s"
" -D srcT2_C1=%s -D dstT_C1=%s -D workST=%s -D rowsPerWI=%d%s",
haveScalar ? "UNARY_OP" : "BINARY_OP",
ocl::typeToStr(CV_MAKE_TYPE(depth1, kercn)),
ocl::typeToStr(CV_8UC(kercn)), CV_8U, kercn,
ocl::convertTypeStr(depth1, CV_8U, kercn, cvt, sizeof(cvt)),
operationMap[op], ocl::typeToStr(depth1),
ocl::typeToStr(depth1), ocl::typeToStr(CV_8U),
ocl::typeToStr(CV_MAKE_TYPE(depth1, scalarcn)), rowsPerWI,
doubleSupport ? " -D DOUBLE_SUPPORT" : "");
ocl::Kernel k("KF", ocl::core::arithm_oclsrc, opts);
if (k.empty())
return false;
UMat src1 = _src1.getUMat();
Size size = src1.size();
_dst.create(size, CV_8UC(cn));
UMat dst = _dst.getUMat();
if (haveScalar)
{
size_t esz = CV_ELEM_SIZE1(type1) * scalarcn;
double buf[4] = { 0, 0, 0, 0 };
Mat src2 = _src2.getMat();
if( depth1 > CV_32S )
convertAndUnrollScalar( src2, depth1, (uchar *)buf, kercn );
else
{
double fval = 0;
getConvertFunc(depth2, CV_64F)(src2.ptr(), 1, 0, 1, (uchar *)&fval, 1, Size(1, 1), 0);
if( fval < getMinVal(depth1) )
return dst.setTo(Scalar::all(op == CMP_GT || op == CMP_GE || op == CMP_NE ? 255 : 0)), true;
if( fval > getMaxVal(depth1) )
return dst.setTo(Scalar::all(op == CMP_LT || op == CMP_LE || op == CMP_NE ? 255 : 0)), true;
int ival = cvRound(fval);
if( fval != ival )
{
if( op == CMP_LT || op == CMP_GE )
ival = cvCeil(fval);
else if( op == CMP_LE || op == CMP_GT )
ival = cvFloor(fval);
else
return dst.setTo(Scalar::all(op == CMP_NE ? 255 : 0)), true;
}
convertAndUnrollScalar(Mat(1, 1, CV_32S, &ival), depth1, (uchar *)buf, kercn);
}
ocl::KernelArg scalararg = ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, buf, esz);
k.args(ocl::KernelArg::ReadOnlyNoSize(src1, cn, kercn),
ocl::KernelArg::WriteOnly(dst, cn, kercn), scalararg);
}
else
{
UMat src2 = _src2.getUMat();
k.args(ocl::KernelArg::ReadOnlyNoSize(src1),
ocl::KernelArg::ReadOnlyNoSize(src2),
ocl::KernelArg::WriteOnly(dst, cn, kercn));
}
size_t globalsize[2] = { (size_t)dst.cols * cn / kercn, ((size_t)dst.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
#endif
}
void cv::compare(InputArray _src1, InputArray _src2, OutputArray _dst, int op)
{
CV_INSTRUMENT_REGION();
CV_Assert( op == CMP_LT || op == CMP_LE || op == CMP_EQ ||
op == CMP_NE || op == CMP_GE || op == CMP_GT );
CV_Assert(_src1.empty() == _src2.empty());
if (_src1.empty() && _src2.empty())
{
_dst.release();
return;
}
bool haveScalar = false;
if ((_src1.isMatx() + _src2.isMatx()) == 1
|| !_src1.sameSize(_src2)
|| _src1.type() != _src2.type())
{
bool is_src1_scalar = checkScalar(_src1, _src2.type(), _src1.kind(), _src2.kind());
bool is_src2_scalar = checkScalar(_src2, _src1.type(), _src2.kind(), _src1.kind());
if (is_src1_scalar && !is_src2_scalar)
{
op = op == CMP_LT ? CMP_GT : op == CMP_LE ? CMP_GE :
op == CMP_GE ? CMP_LE : op == CMP_GT ? CMP_LT : op;
// src1 is a scalar; swap it with src2
compare(_src2, _src1, _dst, op);
return;
}
else if(is_src1_scalar == is_src2_scalar)
CV_Error( CV_StsUnmatchedSizes,
"The operation is neither 'array op array' (where arrays have the same size and the same type), "
"nor 'array op scalar', nor 'scalar op array'" );
haveScalar = true;
}
CV_OCL_RUN(_src1.dims() <= 2 && _src2.dims() <= 2 && OCL_PERFORMANCE_CHECK(_dst.isUMat()),
ocl_compare(_src1, _src2, _dst, op, haveScalar))
_InputArray::KindFlag kind1 = _src1.kind(), kind2 = _src2.kind();
Mat src1 = _src1.getMat(), src2 = _src2.getMat();
int depth1 = src1.depth(), depth2 = src2.depth();
if (depth1 == CV_16F || depth2 == CV_16F)
CV_Error(Error::StsNotImplemented, "Unsupported depth value CV_16F");
if( kind1 == kind2 && src1.dims <= 2 && src2.dims <= 2 && src1.size() == src2.size() && src1.type() == src2.type() )
{
int cn = src1.channels();
_dst.createSameSize(src1, CV_8UC(cn));
Mat dst = _dst.getMat();
Size sz = getContinuousSize2D(src1, src2, dst, src1.channels());
BinaryFuncC cmpFn = getCmpFunc(depth1);
CV_Assert(cmpFn);
cmpFn(src1.ptr(), src1.step, src2.ptr(), src2.step, dst.ptr(), dst.step, sz.width, sz.height, &op);
return;
}
int cn = src1.channels();
_dst.create(src1.dims, src1.size, CV_8UC(cn));
src1 = src1.reshape(1); src2 = src2.reshape(1);
Mat dst = _dst.getMat().reshape(1);
size_t esz = std::max(src1.elemSize(), (size_t)1);
size_t blocksize0 = (size_t)(BLOCK_SIZE + esz-1)/esz;
BinaryFuncC func = getCmpFunc(depth1);
CV_Assert(func);
if( !haveScalar )
{
const Mat* arrays[] = { &src1, &src2, &dst, 0 };
uchar* ptrs[3] = {};
NAryMatIterator it(arrays, ptrs);
size_t total = it.size;
for( size_t i = 0; i < it.nplanes; i++, ++it )
func( ptrs[0], 0, ptrs[1], 0, ptrs[2], 0, (int)total, 1, &op );
}
else
{
const Mat* arrays[] = { &src1, &dst, 0 };
uchar* ptrs[2] = {};
NAryMatIterator it(arrays, ptrs);
size_t total = it.size, blocksize = std::min(total, blocksize0);
AutoBuffer<uchar> _buf(blocksize*esz);
uchar *buf = _buf.data();
if( depth1 > CV_32S )
convertAndUnrollScalar( src2, depth1, buf, blocksize );
else
{
double fval=0;
BinaryFunc cvtFn = getConvertFunc(depth2, CV_64F);
CV_Assert(cvtFn);
cvtFn(src2.ptr(), 1, 0, 1, (uchar*)&fval, 1, Size(1,1), 0);
if( fval < getMinVal(depth1) )
{
dst = Scalar::all(op == CMP_GT || op == CMP_GE || op == CMP_NE ? 255 : 0);
return;
}
if( fval > getMaxVal(depth1) )
{
dst = Scalar::all(op == CMP_LT || op == CMP_LE || op == CMP_NE ? 255 : 0);
return;
}
int ival = cvRound(fval);
if( fval != ival )
{
if( op == CMP_LT || op == CMP_GE )
ival = cvCeil(fval);
else if( op == CMP_LE || op == CMP_GT )
ival = cvFloor(fval);
else
{
dst = Scalar::all(op == CMP_NE ? 255 : 0);
return;
}
}
convertAndUnrollScalar(Mat(1, 1, CV_32S, &ival), depth1, buf, blocksize);
}
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
func( ptrs[0], 0, buf, 0, ptrs[1], 0, bsz, 1, &op);
ptrs[0] += bsz*esz;
ptrs[1] += bsz;
}
}
}
}
/****************************************************************************************\
* inRange *
\****************************************************************************************/
namespace cv
{
template <typename T>
struct InRange_SIMD
{
int operator () (const T *, const T *, const T *, uchar *, int) const
{
return 0;
}
};
#if CV_SIMD
template <>
struct InRange_SIMD<uchar>
{
int operator () (const uchar * src1, const uchar * src2, const uchar * src3,
uchar * dst, int len) const
{
int x = 0;
const int width = v_uint8::nlanes;
for (; x <= len - width; x += width)
{
v_uint8 values = vx_load(src1 + x);
v_uint8 low = vx_load(src2 + x);
v_uint8 high = vx_load(src3 + x);
v_store(dst + x, (values >= low) & (high >= values));
}
vx_cleanup();
return x;
}
};
template <>
struct InRange_SIMD<schar>
{
int operator () (const schar * src1, const schar * src2, const schar * src3,
uchar * dst, int len) const
{
int x = 0;
const int width = v_int8::nlanes;
for (; x <= len - width; x += width)
{
v_int8 values = vx_load(src1 + x);
v_int8 low = vx_load(src2 + x);
v_int8 high = vx_load(src3 + x);
v_store((schar*)(dst + x), (values >= low) & (high >= values));
}
vx_cleanup();
return x;
}
};
template <>
struct InRange_SIMD<ushort>
{
int operator () (const ushort * src1, const ushort * src2, const ushort * src3,
uchar * dst, int len) const
{
int x = 0;
const int width = v_uint16::nlanes * 2;
for (; x <= len - width; x += width)
{
v_uint16 values1 = vx_load(src1 + x);
v_uint16 low1 = vx_load(src2 + x);
v_uint16 high1 = vx_load(src3 + x);
v_uint16 values2 = vx_load(src1 + x + v_uint16::nlanes);
v_uint16 low2 = vx_load(src2 + x + v_uint16::nlanes);
v_uint16 high2 = vx_load(src3 + x + v_uint16::nlanes);
v_store(dst + x, v_pack((values1 >= low1) & (high1 >= values1), (values2 >= low2) & (high2 >= values2)));
}
vx_cleanup();
return x;
}
};
template <>
struct InRange_SIMD<short>
{
int operator () (const short * src1, const short * src2, const short * src3,
uchar * dst, int len) const
{
int x = 0;
const int width = (int)v_int16::nlanes * 2;
for (; x <= len - width; x += width)
{
v_int16 values1 = vx_load(src1 + x);
v_int16 low1 = vx_load(src2 + x);
v_int16 high1 = vx_load(src3 + x);
v_int16 values2 = vx_load(src1 + x + v_int16::nlanes);
v_int16 low2 = vx_load(src2 + x + v_int16::nlanes);
v_int16 high2 = vx_load(src3 + x + v_int16::nlanes);
v_store((schar*)(dst + x), v_pack((values1 >= low1) & (high1 >= values1), (values2 >= low2) & (high2 >= values2)));
}
vx_cleanup();
return x;
}
};
template <>
struct InRange_SIMD<int>
{
int operator () (const int * src1, const int * src2, const int * src3,
uchar * dst, int len) const
{
int x = 0;
const int width = (int)v_int32::nlanes * 2;
for (; x <= len - width; x += width)
{
v_int32 values1 = vx_load(src1 + x);
v_int32 low1 = vx_load(src2 + x);
v_int32 high1 = vx_load(src3 + x);
v_int32 values2 = vx_load(src1 + x + v_int32::nlanes);
v_int32 low2 = vx_load(src2 + x + v_int32::nlanes);
v_int32 high2 = vx_load(src3 + x + v_int32::nlanes);
v_pack_store(dst + x, v_reinterpret_as_u16(v_pack((values1 >= low1) & (high1 >= values1), (values2 >= low2) & (high2 >= values2))));
}
vx_cleanup();
return x;
}
};
template <>
struct InRange_SIMD<float>
{
int operator () (const float * src1, const float * src2, const float * src3,
uchar * dst, int len) const
{
int x = 0;
const int width = (int)v_float32::nlanes * 2;
for (; x <= len - width; x += width)
{
v_float32 values1 = vx_load(src1 + x);
v_float32 low1 = vx_load(src2 + x);
v_float32 high1 = vx_load(src3 + x);
v_float32 values2 = vx_load(src1 + x + v_float32::nlanes);
v_float32 low2 = vx_load(src2 + x + v_float32::nlanes);
v_float32 high2 = vx_load(src3 + x + v_float32::nlanes);
v_pack_store(dst + x, v_pack(v_reinterpret_as_u32(values1 >= low1) & v_reinterpret_as_u32(high1 >= values1),
v_reinterpret_as_u32(values2 >= low2) & v_reinterpret_as_u32(high2 >= values2)));
}
vx_cleanup();
return x;
}
};
#endif
template <typename T>
static void inRange_(const T* src1, size_t step1, const T* src2, size_t step2,
const T* src3, size_t step3, uchar* dst, size_t step,
Size size)
{
step1 /= sizeof(src1[0]);
step2 /= sizeof(src2[0]);
step3 /= sizeof(src3[0]);
InRange_SIMD<T> vop;
for( ; size.height--; src1 += step1, src2 += step2, src3 += step3, dst += step )
{
int x = vop(src1, src2, src3, dst, size.width);
#if CV_ENABLE_UNROLLED
for( ; x <= size.width - 4; x += 4 )
{
int t0, t1;
t0 = src2[x] <= src1[x] && src1[x] <= src3[x];
t1 = src2[x+1] <= src1[x+1] && src1[x+1] <= src3[x+1];
dst[x] = (uchar)-t0; dst[x+1] = (uchar)-t1;
t0 = src2[x+2] <= src1[x+2] && src1[x+2] <= src3[x+2];
t1 = src2[x+3] <= src1[x+3] && src1[x+3] <= src3[x+3];
dst[x+2] = (uchar)-t0; dst[x+3] = (uchar)-t1;
}
#endif
for( ; x < size.width; x++ )
dst[x] = (uchar)-(src2[x] <= src1[x] && src1[x] <= src3[x]);
}
}
static void inRange8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2,
const uchar* src3, size_t step3, uchar* dst, size_t step, Size size)
{
inRange_(src1, step1, src2, step2, src3, step3, dst, step, size);
}
static void inRange8s(const schar* src1, size_t step1, const schar* src2, size_t step2,
const schar* src3, size_t step3, uchar* dst, size_t step, Size size)
{
inRange_(src1, step1, src2, step2, src3, step3, dst, step, size);
}
static void inRange16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2,
const ushort* src3, size_t step3, uchar* dst, size_t step, Size size)
{
inRange_(src1, step1, src2, step2, src3, step3, dst, step, size);
}
static void inRange16s(const short* src1, size_t step1, const short* src2, size_t step2,
const short* src3, size_t step3, uchar* dst, size_t step, Size size)
{
inRange_(src1, step1, src2, step2, src3, step3, dst, step, size);
}
static void inRange32s(const int* src1, size_t step1, const int* src2, size_t step2,
const int* src3, size_t step3, uchar* dst, size_t step, Size size)
{
inRange_(src1, step1, src2, step2, src3, step3, dst, step, size);
}
static void inRange32f(const float* src1, size_t step1, const float* src2, size_t step2,
const float* src3, size_t step3, uchar* dst, size_t step, Size size)
{
inRange_(src1, step1, src2, step2, src3, step3, dst, step, size);
}
static void inRange64f(const double* src1, size_t step1, const double* src2, size_t step2,
const double* src3, size_t step3, uchar* dst, size_t step, Size size)
{
inRange_(src1, step1, src2, step2, src3, step3, dst, step, size);
}
static void inRangeReduce(const uchar* src, uchar* dst, size_t len, int cn)
{
int k = cn % 4 ? cn % 4 : 4;
size_t i, j;
if( k == 1 )
for( i = j = 0; i < len; i++, j += cn )
dst[i] = src[j];
else if( k == 2 )
for( i = j = 0; i < len; i++, j += cn )
dst[i] = src[j] & src[j+1];
else if( k == 3 )
for( i = j = 0; i < len; i++, j += cn )
dst[i] = src[j] & src[j+1] & src[j+2];
else
for( i = j = 0; i < len; i++, j += cn )
dst[i] = src[j] & src[j+1] & src[j+2] & src[j+3];
for( ; k < cn; k += 4 )
{
for( i = 0, j = k; i < len; i++, j += cn )
dst[i] &= src[j] & src[j+1] & src[j+2] & src[j+3];
}
}
typedef void (*InRangeFunc)( const uchar* src1, size_t step1, const uchar* src2, size_t step2,
const uchar* src3, size_t step3, uchar* dst, size_t step, Size sz );
static InRangeFunc getInRangeFunc(int depth)
{
static InRangeFunc inRangeTab[] =
{
(InRangeFunc)GET_OPTIMIZED(inRange8u), (InRangeFunc)GET_OPTIMIZED(inRange8s), (InRangeFunc)GET_OPTIMIZED(inRange16u),
(InRangeFunc)GET_OPTIMIZED(inRange16s), (InRangeFunc)GET_OPTIMIZED(inRange32s), (InRangeFunc)GET_OPTIMIZED(inRange32f),
(InRangeFunc)inRange64f, 0
};
return inRangeTab[depth];
}
#ifdef HAVE_OPENCL
static bool ocl_inRange( InputArray _src, InputArray _lowerb,
InputArray _upperb, OutputArray _dst )
{
const ocl::Device & d = ocl::Device::getDefault();
_InputArray::KindFlag skind = _src.kind(), lkind = _lowerb.kind(), ukind = _upperb.kind();
Size ssize = _src.size(), lsize = _lowerb.size(), usize = _upperb.size();
int stype = _src.type(), ltype = _lowerb.type(), utype = _upperb.type();
int sdepth = CV_MAT_DEPTH(stype), ldepth = CV_MAT_DEPTH(ltype), udepth = CV_MAT_DEPTH(utype);
int cn = CV_MAT_CN(stype), rowsPerWI = d.isIntel() ? 4 : 1;
bool lbScalar = false, ubScalar = false;
if( (lkind == _InputArray::MATX && skind != _InputArray::MATX) ||
ssize != lsize || stype != ltype )
{
if( !checkScalar(_lowerb, stype, lkind, skind) )
CV_Error( CV_StsUnmatchedSizes,
"The lower boundary is neither an array of the same size and same type as src, nor a scalar");
lbScalar = true;
}
if( (ukind == _InputArray::MATX && skind != _InputArray::MATX) ||
ssize != usize || stype != utype )
{
if( !checkScalar(_upperb, stype, ukind, skind) )
CV_Error( CV_StsUnmatchedSizes,
"The upper boundary is neither an array of the same size and same type as src, nor a scalar");
ubScalar = true;
}
if (lbScalar != ubScalar)
return false;
bool doubleSupport = d.doubleFPConfig() > 0,
haveScalar = lbScalar && ubScalar;
if ( (!doubleSupport && sdepth == CV_64F) ||
(!haveScalar && (sdepth != ldepth || sdepth != udepth)) )
return false;
int kercn = haveScalar ? cn : std::max(std::min(ocl::predictOptimalVectorWidth(_src, _lowerb, _upperb, _dst), 4), cn);
if (kercn % cn != 0)
kercn = cn;
int colsPerWI = kercn / cn;
String opts = format("%s-D cn=%d -D srcT=%s -D srcT1=%s -D dstT=%s -D kercn=%d -D depth=%d%s -D colsPerWI=%d",
haveScalar ? "-D HAVE_SCALAR " : "", cn, ocl::typeToStr(CV_MAKE_TYPE(sdepth, kercn)),
ocl::typeToStr(sdepth), ocl::typeToStr(CV_8UC(colsPerWI)), kercn, sdepth,
doubleSupport ? " -D DOUBLE_SUPPORT" : "", colsPerWI);
ocl::Kernel ker("inrange", ocl::core::inrange_oclsrc, opts);
if (ker.empty())
return false;
_dst.create(ssize, CV_8UC1);
UMat src = _src.getUMat(), dst = _dst.getUMat(), lscalaru, uscalaru;
Mat lscalar, uscalar;
if (lbScalar && ubScalar)
{
lscalar = _lowerb.getMat();
uscalar = _upperb.getMat();
size_t esz = src.elemSize();
size_t blocksize = 36;
AutoBuffer<uchar> _buf(blocksize*(((int)lbScalar + (int)ubScalar)*esz + cn) + 2*cn*sizeof(int) + 128);
uchar *buf = alignPtr(_buf.data() + blocksize*cn, 16);
if( ldepth != sdepth && sdepth < CV_32S )
{
int* ilbuf = (int*)alignPtr(buf + blocksize*esz, 16);
int* iubuf = ilbuf + cn;
BinaryFunc sccvtfunc = getConvertFunc(ldepth, CV_32S);
sccvtfunc(lscalar.ptr(), 1, 0, 1, (uchar*)ilbuf, 1, Size(cn, 1), 0);
sccvtfunc(uscalar.ptr(), 1, 0, 1, (uchar*)iubuf, 1, Size(cn, 1), 0);
int minval = cvRound(getMinVal(sdepth)), maxval = cvRound(getMaxVal(sdepth));
for( int k = 0; k < cn; k++ )
{
if( ilbuf[k] > iubuf[k] || ilbuf[k] > maxval || iubuf[k] < minval )
ilbuf[k] = minval+1, iubuf[k] = minval;
}
lscalar = Mat(cn, 1, CV_32S, ilbuf);
uscalar = Mat(cn, 1, CV_32S, iubuf);
}
lscalar.convertTo(lscalar, stype);
uscalar.convertTo(uscalar, stype);
}
else
{
lscalaru = _lowerb.getUMat();
uscalaru = _upperb.getUMat();
}
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
dstarg = ocl::KernelArg::WriteOnly(dst, 1, colsPerWI);
if (haveScalar)
{
lscalar.copyTo(lscalaru);
uscalar.copyTo(uscalaru);
ker.args(srcarg, dstarg, ocl::KernelArg::PtrReadOnly(lscalaru),
ocl::KernelArg::PtrReadOnly(uscalaru), rowsPerWI);
}
else
ker.args(srcarg, dstarg, ocl::KernelArg::ReadOnlyNoSize(lscalaru),
ocl::KernelArg::ReadOnlyNoSize(uscalaru), rowsPerWI);
size_t globalsize[2] = { (size_t)ssize.width / colsPerWI, ((size_t)ssize.height + rowsPerWI - 1) / rowsPerWI };
return ker.run(2, globalsize, NULL, false);
}
#endif
}
void cv::inRange(InputArray _src, InputArray _lowerb,
InputArray _upperb, OutputArray _dst)
{
CV_INSTRUMENT_REGION();
CV_Assert(! _src.empty());
CV_OCL_RUN(_src.dims() <= 2 && _lowerb.dims() <= 2 &&
_upperb.dims() <= 2 && OCL_PERFORMANCE_CHECK(_dst.isUMat()),
ocl_inRange(_src, _lowerb, _upperb, _dst))
_InputArray::KindFlag skind = _src.kind(), lkind = _lowerb.kind(), ukind = _upperb.kind();
Mat src = _src.getMat(), lb = _lowerb.getMat(), ub = _upperb.getMat();
bool lbScalar = false, ubScalar = false;
if( (lkind == _InputArray::MATX && skind != _InputArray::MATX) ||
src.size != lb.size || src.type() != lb.type() )
{
if( !checkScalar(lb, src.type(), lkind, skind) )
CV_Error( CV_StsUnmatchedSizes,
"The lower boundary is neither an array of the same size and same type as src, nor a scalar");
lbScalar = true;
}
if( (ukind == _InputArray::MATX && skind != _InputArray::MATX) ||
src.size != ub.size || src.type() != ub.type() )
{
if( !checkScalar(ub, src.type(), ukind, skind) )
CV_Error( CV_StsUnmatchedSizes,
"The upper boundary is neither an array of the same size and same type as src, nor a scalar");
ubScalar = true;
}
CV_Assert(lbScalar == ubScalar);
int cn = src.channels(), depth = src.depth();
size_t esz = src.elemSize();
size_t blocksize0 = (size_t)(BLOCK_SIZE + esz-1)/esz;
_dst.create(src.dims, src.size, CV_8UC1);
Mat dst = _dst.getMat();
InRangeFunc func = getInRangeFunc(depth);
const Mat* arrays_sc[] = { &src, &dst, 0 };
const Mat* arrays_nosc[] = { &src, &dst, &lb, &ub, 0 };
uchar* ptrs[4] = {};
NAryMatIterator it(lbScalar && ubScalar ? arrays_sc : arrays_nosc, ptrs);
size_t total = it.size, blocksize = std::min(total, blocksize0);
AutoBuffer<uchar> _buf(blocksize*(((int)lbScalar + (int)ubScalar)*esz + cn) + 2*cn*sizeof(int) + 128);
uchar *buf = _buf.data(), *mbuf = buf, *lbuf = 0, *ubuf = 0;
buf = alignPtr(buf + blocksize*cn, 16);
if( lbScalar && ubScalar )
{
lbuf = buf;
ubuf = buf = alignPtr(buf + blocksize*esz, 16);
CV_Assert( lb.type() == ub.type() );
int scdepth = lb.depth();
if( scdepth != depth && depth < CV_32S )
{
int* ilbuf = (int*)alignPtr(buf + blocksize*esz, 16);
int* iubuf = ilbuf + cn;
BinaryFunc sccvtfunc = getConvertFunc(scdepth, CV_32S);
sccvtfunc(lb.ptr(), 1, 0, 1, (uchar*)ilbuf, 1, Size(cn, 1), 0);
sccvtfunc(ub.ptr(), 1, 0, 1, (uchar*)iubuf, 1, Size(cn, 1), 0);
int minval = cvRound(getMinVal(depth)), maxval = cvRound(getMaxVal(depth));
for( int k = 0; k < cn; k++ )
{
if( ilbuf[k] > iubuf[k] || ilbuf[k] > maxval || iubuf[k] < minval )
ilbuf[k] = minval+1, iubuf[k] = minval;
}
lb = Mat(cn, 1, CV_32S, ilbuf);
ub = Mat(cn, 1, CV_32S, iubuf);
}
convertAndUnrollScalar( lb, src.type(), lbuf, blocksize );
convertAndUnrollScalar( ub, src.type(), ubuf, blocksize );
}
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
size_t delta = bsz*esz;
uchar *lptr = lbuf, *uptr = ubuf;
if( !lbScalar )
{
lptr = ptrs[2];
ptrs[2] += delta;
}
if( !ubScalar )
{
int idx = !lbScalar ? 3 : 2;
uptr = ptrs[idx];
ptrs[idx] += delta;
}
func( ptrs[0], 0, lptr, 0, uptr, 0, cn == 1 ? ptrs[1] : mbuf, 0, Size(bsz*cn, 1));
if( cn > 1 )
inRangeReduce(mbuf, ptrs[1], bsz, cn);
ptrs[0] += delta;
ptrs[1] += bsz;
}
}
}
#ifndef OPENCV_EXCLUDE_C_API
/****************************************************************************************\
* Earlier API: cvAdd etc. *
\****************************************************************************************/
CV_IMPL void
cvNot( const CvArr* srcarr, CvArr* dstarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size == dst.size && src.type() == dst.type() );
cv::bitwise_not( src, dst );
}
CV_IMPL void
cvAnd( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_and( src1, src2, dst, mask );
}
CV_IMPL void
cvOr( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_or( src1, src2, dst, mask );
}
CV_IMPL void
cvXor( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_xor( src1, src2, dst, mask );
}
CV_IMPL void
cvAndS( const CvArr* srcarr, CvScalar s, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src.size == dst.size && src.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_and( src, (const cv::Scalar&)s, dst, mask );
}
CV_IMPL void
cvOrS( const CvArr* srcarr, CvScalar s, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src.size == dst.size && src.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_or( src, (const cv::Scalar&)s, dst, mask );
}
CV_IMPL void
cvXorS( const CvArr* srcarr, CvScalar s, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src.size == dst.size && src.type() == dst.type() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::bitwise_xor( src, (const cv::Scalar&)s, dst, mask );
}
CV_IMPL void cvAdd( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::add( src1, src2, dst, mask, dst.type() );
}
CV_IMPL void cvSub( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::subtract( src1, src2, dst, mask, dst.type() );
}
CV_IMPL void cvAddS( const CvArr* srcarr1, CvScalar value, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::add( src1, (const cv::Scalar&)value, dst, mask, dst.type() );
}
CV_IMPL void cvSubRS( const CvArr* srcarr1, CvScalar value, CvArr* dstarr, const CvArr* maskarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
if( maskarr )
mask = cv::cvarrToMat(maskarr);
cv::subtract( (const cv::Scalar&)value, src1, dst, mask, dst.type() );
}
CV_IMPL void cvMul( const CvArr* srcarr1, const CvArr* srcarr2,
CvArr* dstarr, double scale )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
cv::multiply( src1, src2, dst, scale, dst.type() );
}
CV_IMPL void cvDiv( const CvArr* srcarr1, const CvArr* srcarr2,
CvArr* dstarr, double scale )
{
cv::Mat src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr), mask;
CV_Assert( src2.size == dst.size && src2.channels() == dst.channels() );
if( srcarr1 )
cv::divide( cv::cvarrToMat(srcarr1), src2, dst, scale, dst.type() );
else
cv::divide( scale, src2, dst, dst.type() );
}
CV_IMPL void
cvAddWeighted( const CvArr* srcarr1, double alpha,
const CvArr* srcarr2, double beta,
double gamma, CvArr* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), src2 = cv::cvarrToMat(srcarr2),
dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && src1.channels() == dst.channels() );
cv::addWeighted( src1, alpha, src2, beta, gamma, dst, dst.type() );
}
CV_IMPL void
cvAbsDiff( const CvArr* srcarr1, const CvArr* srcarr2, CvArr* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
cv::absdiff( src1, cv::cvarrToMat(srcarr2), dst );
}
CV_IMPL void
cvAbsDiffS( const CvArr* srcarr1, CvArr* dstarr, CvScalar scalar )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
cv::absdiff( src1, (const cv::Scalar&)scalar, dst );
}
CV_IMPL void
cvInRange( const void* srcarr1, const void* srcarr2,
const void* srcarr3, void* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && dst.type() == CV_8U );
cv::inRange( src1, cv::cvarrToMat(srcarr2), cv::cvarrToMat(srcarr3), dst );
}
CV_IMPL void
cvInRangeS( const void* srcarr1, CvScalar lowerb, CvScalar upperb, void* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && dst.type() == CV_8U );
cv::inRange( src1, (const cv::Scalar&)lowerb, (const cv::Scalar&)upperb, dst );
}
CV_IMPL void
cvCmp( const void* srcarr1, const void* srcarr2, void* dstarr, int cmp_op )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && dst.type() == CV_8U );
cv::compare( src1, cv::cvarrToMat(srcarr2), dst, cmp_op );
}
CV_IMPL void
cvCmpS( const void* srcarr1, double value, void* dstarr, int cmp_op )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && dst.type() == CV_8U );
cv::compare( src1, value, dst, cmp_op );
}
CV_IMPL void
cvMin( const void* srcarr1, const void* srcarr2, void* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
cv::min( src1, cv::cvarrToMat(srcarr2), dst );
}
CV_IMPL void
cvMax( const void* srcarr1, const void* srcarr2, void* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
cv::max( src1, cv::cvarrToMat(srcarr2), dst );
}
CV_IMPL void
cvMinS( const void* srcarr1, double value, void* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
cv::min( src1, value, dst );
}
CV_IMPL void
cvMaxS( const void* srcarr1, double value, void* dstarr )
{
cv::Mat src1 = cv::cvarrToMat(srcarr1), dst = cv::cvarrToMat(dstarr);
CV_Assert( src1.size == dst.size && src1.type() == dst.type() );
cv::max( src1, value, dst );
}
#endif // OPENCV_EXCLUDE_C_API
/* End of file. */