/*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 * \****************************************************************************************/ void convertAndUnrollScalar( const Mat& sc, int buftype, uchar* scbuf, size_t blocksize ) { int scn = (int)sc.total(), cn = CV_MAT_CN(buftype); size_t esz = CV_ELEM_SIZE(buftype); getConvertFunc(sc.depth(), buftype)(sc.ptr(), 1, 0, 1, scbuf, 1, Size(std::min(cn, scn), 1), 0); // unroll the scalar if( scn < cn ) { CV_Assert( scn == 1 ); size_t esz1 = CV_ELEM_SIZE1(buftype); for( size_t i = esz1; i < esz; i++ ) scbuf[i] = scbuf[i - esz1]; } for( size_t i = esz; i < blocksize*esz; i++ ) scbuf[i] = scbuf[i - esz]; } 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; sprintf(opts, "-D %s%s -D %s -D dstT=%s%s -D dstT_C1=%s -D workST=%s -D cn=%d -D rowsPerWI=%d", haveMask ? "MASK_" : "", haveScalar ? "UNARY_OP" : "BINARY_OP", oclop2str[oclop], bitwise ? ocl::memopTypeToStr(CV_MAKETYPE(srcdepth, kercn)) : ocl::typeToStr(CV_MAKETYPE(srcdepth, kercn)), doubleSupport ? " -D DOUBLE_SUPPORT" : "", bitwise ? ocl::memopTypeToStr(CV_MAKETYPE(srcdepth, 1)) : ocl::typeToStr(CV_MAKETYPE(srcdepth, 1)), bitwise ? ocl::memopTypeToStr(CV_MAKETYPE(srcdepth, scalarcn)) : ocl::typeToStr(CV_MAKETYPE(srcdepth, scalarcn)), 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(0, 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; int 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.create(sz1, 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]; Mat src1 = psrc1->getMat(), src2 = psrc2->getMat(), dst = _dst.getMat(); Size sz = getContinuousSize(src1, src2, dst); size_t len = sz.width*(size_t)cn; if( len == (size_t)(int)len ) { 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 _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]; 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; } 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; 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) { 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) { 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) { 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) { 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 ) { binary_op(src1, src2, dst, noArray(), getMaxTab(), false, OCL_OP_MAX ); } void cv::min( InputArray src1, InputArray src2, OutputArray dst ) { binary_op(src1, src2, dst, noArray(), getMinTab(), false, OCL_OP_MIN ); } void cv::max(const Mat& src1, const Mat& src2, Mat& dst) { 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) { 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) { 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) { 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][32], opts[1024]; sprintf(opts, "-D %s%s -D %s -D srcT1=%s -D srcT1_C1=%s -D srcT2=%s -D srcT2_C1=%s " "-D dstT=%s -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)), 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]), ocl::convertTypeStr(depth2, wdepth, kercn, cvtstr[1]), ocl::convertTypeStr(wdepth, ddepth, kercn, 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]) : "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( 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(0, 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(0, 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(0, 0, 0, 0, usrdata_p, usrdata_esz)); else if (n == 3) k.args(src1arg, src2arg, dstarg, ocl::KernelArg(0, 0, 0, 0, usrdata_p, usrdata_esz), ocl::KernelArg(0, 0, 0, 0, usrdata_p + usrdata_esz, usrdata_esz), ocl::KernelArg(0, 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; int 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) || (!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 = getContinuousSize(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( 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(), 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 _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)]; 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; 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; 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 ) { 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 ) { #ifdef HAVE_TEGRA_OPTIMIZATION if (tegra::useTegra()) { int kind1 = _src1.kind(), kind2 = _src2.kind(); Mat src1 = _src1.getMat(), src2 = _src2.getMat(); bool src1Scalar = checkScalar(src1, _src2.type(), kind1, kind2); bool src2Scalar = checkScalar(src2, _src1.type(), kind2, kind1); if (!src1Scalar && !src2Scalar && src1.depth() == CV_8U && src2.type() == src1.type() && src1.dims == 2 && src2.size() == src1.size() && mask.empty()) { if (dtype < 0) { if (_dst.fixedType()) { dtype = _dst.depth(); } else { dtype = src1.depth(); } } dtype = CV_MAT_DEPTH(dtype); if (!_dst.fixedType() || dtype == _dst.depth()) { _dst.create(src1.size(), CV_MAKE_TYPE(dtype, src1.channels())); if (dtype == CV_16S) { Mat dst = _dst.getMat(); if(tegra::subtract_8u8u16s(src1, src2, dst)) return; } else if (dtype == CV_32F) { Mat dst = _dst.getMat(); if(tegra::subtract_8u8u32f(src1, src2, dst)) return; } else if (dtype == CV_8S) { Mat dst = _dst.getMat(); if(tegra::subtract_8u8u8s(src1, src2, dst)) return; } } } } #endif arithm_op(_src1, _src2, _dst, mask, dtype, getSubTab(), false, 0, OCL_OP_SUB ); } void cv::absdiff( InputArray src1, InputArray src2, OutputArray dst ) { arithm_op(src1, src2, dst, noArray(), -1, getAbsDiffTab(), false, 0, OCL_OP_ABSDIFF); } /****************************************************************************************\ * 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 cv::multiply(InputArray src1, InputArray src2, OutputArray dst, double scale, int dtype) { 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 cv::divide(InputArray src1, InputArray src2, OutputArray dst, double scale, int dtype) { arithm_op(src1, src2, dst, noArray(), dtype, getDivTab(), true, &scale, OCL_OP_DIV_SCALE); } void cv::divide(double scale, InputArray src2, OutputArray dst, int dtype) { arithm_op(src2, src2, dst, noArray(), dtype, getRecipTab(), true, &scale, OCL_OP_RECIP_SCALE); } /****************************************************************************************\ * addWeighted * \****************************************************************************************/ namespace cv { 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 ) { 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[40]; String opts = format("-D %s -D srcT1=%s -D dstT=%s -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)), kercn, ocl::convertTypeStr(depth1, CV_8U, kercn, 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(0, 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_Assert( op == CMP_LT || op == CMP_LE || op == CMP_EQ || op == CMP_NE || op == CMP_GE || op == CMP_GT ); bool haveScalar = false; if ((_src1.isMatx() + _src2.isMatx()) == 1 || !_src1.sameSize(_src2) || _src1.type() != _src2.type()) { if (checkScalar(_src1, _src2.type(), _src1.kind(), _src2.kind())) { 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( !checkScalar(_src2, _src1.type(), _src2.kind(), _src1.kind()) ) 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)) int kind1 = _src1.kind(), kind2 = _src2.kind(); Mat src1 = _src1.getMat(), src2 = _src2.getMat(); if( kind1 == kind2 && src1.dims <= 2 && src2.dims <= 2 && src1.size() == src2.size() && src1.type() == src2.type() ) { int cn = src1.channels(); _dst.create(src1.size(), CV_8UC(cn)); Mat dst = _dst.getMat(); Size sz = getContinuousSize(src1, src2, dst, src1.channels()); getCmpFunc(src1.depth())(src1.ptr(), src1.step, src2.ptr(), src2.step, dst.ptr(), dst.step, sz.width, sz.height, &op); return; } int cn = src1.channels(), depth1 = src1.depth(), depth2 = src2.depth(); _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 = src1.elemSize(); size_t blocksize0 = (size_t)(BLOCK_SIZE + esz-1)/esz; BinaryFuncC func = getCmpFunc(depth1); 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 _buf(blocksize*esz); uchar *buf = _buf; if( depth1 > CV_32S ) convertAndUnrollScalar( src2, depth1, buf, blocksize ); else { double fval=0; getConvertFunc(depth2, CV_64F)(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 struct InRange_SIMD { int operator () (const T *, const T *, const T *, uchar *, int) const { return 0; } }; #if CV_SSE2 template <> struct InRange_SIMD { int operator () (const uchar * src1, const uchar * src2, const uchar * src3, uchar * dst, int len) const { int x = 0; if (USE_SSE2) { __m128i v_full = _mm_set1_epi8(-1), v_128 = _mm_set1_epi8(-128); for ( ; x <= len - 16; x += 16 ) { __m128i v_src = _mm_add_epi8(_mm_loadu_si128((const __m128i *)(src1 + x)), v_128); __m128i v_mask1 = _mm_cmpgt_epi8(_mm_add_epi8(_mm_loadu_si128((const __m128i *)(src2 + x)), v_128), v_src); __m128i v_mask2 = _mm_cmpgt_epi8(v_src, _mm_add_epi8(_mm_loadu_si128((const __m128i *)(src3 + x)), v_128)); _mm_storeu_si128((__m128i *)(dst + x), _mm_andnot_si128(_mm_or_si128(v_mask1, v_mask2), v_full)); } } return x; } }; template <> struct InRange_SIMD { int operator () (const schar * src1, const schar * src2, const schar * src3, uchar * dst, int len) const { int x = 0; if (USE_SSE2) { __m128i v_full = _mm_set1_epi8(-1); for ( ; x <= len - 16; x += 16 ) { __m128i v_src = _mm_loadu_si128((const __m128i *)(src1 + x)); __m128i v_mask1 = _mm_cmpgt_epi8(_mm_loadu_si128((const __m128i *)(src2 + x)), v_src); __m128i v_mask2 = _mm_cmpgt_epi8(v_src, _mm_loadu_si128((const __m128i *)(src3 + x))); _mm_storeu_si128((__m128i *)(dst + x), _mm_andnot_si128(_mm_or_si128(v_mask1, v_mask2), v_full)); } } return x; } }; template <> struct InRange_SIMD { int operator () (const ushort * src1, const ushort * src2, const ushort * src3, uchar * dst, int len) const { int x = 0; if (USE_SSE2) { __m128i v_zero = _mm_setzero_si128(), v_full = _mm_set1_epi16(-1), v_32768 = _mm_set1_epi16(-32768); for ( ; x <= len - 8; x += 8 ) { __m128i v_src = _mm_add_epi16(_mm_loadu_si128((const __m128i *)(src1 + x)), v_32768); __m128i v_mask1 = _mm_cmpgt_epi16(_mm_add_epi16(_mm_loadu_si128((const __m128i *)(src2 + x)), v_32768), v_src); __m128i v_mask2 = _mm_cmpgt_epi16(v_src, _mm_add_epi16(_mm_loadu_si128((const __m128i *)(src3 + x)), v_32768)); __m128i v_res = _mm_andnot_si128(_mm_or_si128(v_mask1, v_mask2), v_full); _mm_storel_epi64((__m128i *)(dst + x), _mm_packus_epi16(_mm_srli_epi16(v_res, 8), v_zero)); } } return x; } }; template <> struct InRange_SIMD { int operator () (const short * src1, const short * src2, const short * src3, uchar * dst, int len) const { int x = 0; if (USE_SSE2) { __m128i v_zero = _mm_setzero_si128(), v_full = _mm_set1_epi16(-1); for ( ; x <= len - 8; x += 8 ) { __m128i v_src = _mm_loadu_si128((const __m128i *)(src1 + x)); __m128i v_mask1 = _mm_cmpgt_epi16(_mm_loadu_si128((const __m128i *)(src2 + x)), v_src); __m128i v_mask2 = _mm_cmpgt_epi16(v_src, _mm_loadu_si128((const __m128i *)(src3 + x))); __m128i v_res = _mm_andnot_si128(_mm_or_si128(v_mask1, v_mask2), v_full); _mm_storel_epi64((__m128i *)(dst + x), _mm_packus_epi16(_mm_srli_epi16(v_res, 8), v_zero)); } } return x; } }; template <> struct InRange_SIMD { int operator () (const int * src1, const int * src2, const int * src3, uchar * dst, int len) const { int x = 0; if (USE_SSE2) { __m128i v_zero = _mm_setzero_si128(), v_full = _mm_set1_epi32(-1); for ( ; x <= len - 8; x += 8 ) { __m128i v_src = _mm_loadu_si128((const __m128i *)(src1 + x)); __m128i v_res1 = _mm_or_si128(_mm_cmpgt_epi32(_mm_loadu_si128((const __m128i *)(src2 + x)), v_src), _mm_cmpgt_epi32(v_src, _mm_loadu_si128((const __m128i *)(src3 + x)))); v_src = _mm_loadu_si128((const __m128i *)(src1 + x + 4)); __m128i v_res2 = _mm_or_si128(_mm_cmpgt_epi32(_mm_loadu_si128((const __m128i *)(src2 + x + 4)), v_src), _mm_cmpgt_epi32(v_src, _mm_loadu_si128((const __m128i *)(src3 + x + 4)))); __m128i v_res = _mm_packs_epi32(_mm_srli_epi32(_mm_andnot_si128(v_res1, v_full), 16), _mm_srli_epi32(_mm_andnot_si128(v_res2, v_full), 16)); _mm_storel_epi64((__m128i *)(dst + x), _mm_packus_epi16(v_res, v_zero)); } } return x; } }; template <> struct InRange_SIMD { int operator () (const float * src1, const float * src2, const float * src3, uchar * dst, int len) const { int x = 0; if (USE_SSE2) { __m128i v_zero = _mm_setzero_si128(); for ( ; x <= len - 8; x += 8 ) { __m128 v_src = _mm_loadu_ps(src1 + x); __m128 v_res1 = _mm_and_ps(_mm_cmple_ps(_mm_loadu_ps(src2 + x), v_src), _mm_cmple_ps(v_src, _mm_loadu_ps(src3 + x))); v_src = _mm_loadu_ps(src1 + x + 4); __m128 v_res2 = _mm_and_ps(_mm_cmple_ps(_mm_loadu_ps(src2 + x + 4), v_src), _mm_cmple_ps(v_src, _mm_loadu_ps(src3 + x + 4))); __m128i v_res1i = _mm_cvtps_epi32(v_res1), v_res2i = _mm_cvtps_epi32(v_res2); __m128i v_res = _mm_packs_epi32(_mm_srli_epi32(v_res1i, 16), _mm_srli_epi32(v_res2i, 16)); _mm_storel_epi64((__m128i *)(dst + x), _mm_packus_epi16(v_res, v_zero)); } } return x; } }; #elif CV_NEON template <> struct InRange_SIMD { int operator () (const uchar * src1, const uchar * src2, const uchar * src3, uchar * dst, int len) const { int x = 0; for ( ; x <= len - 16; x += 16 ) { uint8x16_t values = vld1q_u8(src1 + x); uint8x16_t low = vld1q_u8(src2 + x); uint8x16_t high = vld1q_u8(src3 + x); vst1q_u8(dst + x, vandq_u8(vcgeq_u8(values, low), vcgeq_u8(high, values))); } return x; } }; template <> struct InRange_SIMD { int operator () (const schar * src1, const schar * src2, const schar * src3, uchar * dst, int len) const { int x = 0; for ( ; x <= len - 16; x += 16 ) { int8x16_t values = vld1q_s8(src1 + x); int8x16_t low = vld1q_s8(src2 + x); int8x16_t high = vld1q_s8(src3 + x); vst1q_u8(dst + x, vandq_u8(vcgeq_s8(values, low), vcgeq_s8(high, values))); } return x; } }; template <> struct InRange_SIMD { int operator () (const ushort * src1, const ushort * src2, const ushort * src3, uchar * dst, int len) const { int x = 0; for ( ; x <= len - 16; x += 16 ) { uint16x8_t values = vld1q_u16((const uint16_t*)(src1 + x)); uint16x8_t low = vld1q_u16((const uint16_t*)(src2 + x)); uint16x8_t high = vld1q_u16((const uint16_t*)(src3 + x)); uint8x8_t r1 = vmovn_u16(vandq_u16(vcgeq_u16(values, low), vcgeq_u16(high, values))); values = vld1q_u16((const uint16_t*)(src1 + x + 8)); low = vld1q_u16((const uint16_t*)(src2 + x + 8)); high = vld1q_u16((const uint16_t*)(src3 + x + 8)); uint8x8_t r2 = vmovn_u16(vandq_u16(vcgeq_u16(values, low), vcgeq_u16(high, values))); vst1q_u8(dst + x, vcombine_u8(r1, r2)); } return x; } }; template <> struct InRange_SIMD { int operator () (const short * src1, const short * src2, const short * src3, uchar * dst, int len) const { int x = 0; for ( ; x <= len - 16; x += 16 ) { int16x8_t values = vld1q_s16((const int16_t*)(src1 + x)); int16x8_t low = vld1q_s16((const int16_t*)(src2 + x)); int16x8_t high = vld1q_s16((const int16_t*)(src3 + x)); uint8x8_t r1 = vmovn_u16(vandq_u16(vcgeq_s16(values, low), vcgeq_s16(high, values))); values = vld1q_s16((const int16_t*)(src1 + x + 8)); low = vld1q_s16((const int16_t*)(src2 + x + 8)); high = vld1q_s16((const int16_t*)(src3 + x + 8)); uint8x8_t r2 = vmovn_u16(vandq_u16(vcgeq_s16(values, low), vcgeq_s16(high, values))); vst1q_u8(dst + x, vcombine_u8(r1, r2)); } return x; } }; template <> struct InRange_SIMD { int operator () (const int * src1, const int * src2, const int * src3, uchar * dst, int len) const { int x = 0; for ( ; x <= len - 8; x += 8 ) { int32x4_t values = vld1q_s32((const int32_t*)(src1 + x)); int32x4_t low = vld1q_s32((const int32_t*)(src2 + x)); int32x4_t high = vld1q_s32((const int32_t*)(src3 + x)); uint16x4_t r1 = vmovn_u32(vandq_u32(vcgeq_s32(values, low), vcgeq_s32(high, values))); values = vld1q_s32((const int32_t*)(src1 + x + 4)); low = vld1q_s32((const int32_t*)(src2 + x + 4)); high = vld1q_s32((const int32_t*)(src3 + x + 4)); uint16x4_t r2 = vmovn_u32(vandq_u32(vcgeq_s32(values, low), vcgeq_s32(high, values))); uint16x8_t res_16 = vcombine_u16(r1, r2); vst1_u8(dst + x, vmovn_u16(res_16)); } return x; } }; template <> struct InRange_SIMD { int operator () (const float * src1, const float * src2, const float * src3, uchar * dst, int len) const { int x = 0; for ( ; x <= len - 8; x += 8 ) { float32x4_t values = vld1q_f32((const float32_t*)(src1 + x)); float32x4_t low = vld1q_f32((const float32_t*)(src2 + x)); float32x4_t high = vld1q_f32((const float32_t*)(src3 + x)); uint16x4_t r1 = vmovn_u32(vandq_u32(vcgeq_f32(values, low), vcgeq_f32(high, values))); values = vld1q_f32((const float32_t*)(src1 + x + 4)); low = vld1q_f32((const float32_t*)(src2 + x + 4)); high = vld1q_f32((const float32_t*)(src3 + x + 4)); uint16x4_t r2 = vmovn_u32(vandq_u32(vcgeq_f32(values, low), vcgeq_f32(high, values))); uint16x8_t res_16 = vcombine_u16(r1, r2); vst1_u8(dst + x, vmovn_u16(res_16)); } return x; } }; #endif template 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 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(); int 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 bounary 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 bounary 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 _buf(blocksize*(((int)lbScalar + (int)ubScalar)*esz + cn) + 2*cn*sizeof(int) + 128); uchar *buf = alignPtr(_buf + 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_OCL_RUN(_src.dims() <= 2 && _lowerb.dims() <= 2 && _upperb.dims() <= 2 && OCL_PERFORMANCE_CHECK(_dst.isUMat()), ocl_inRange(_src, _lowerb, _upperb, _dst)) int 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 bounary 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 bounary 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 _buf(blocksize*(((int)lbScalar + (int)ubScalar)*esz + cn) + 2*cn*sizeof(int) + 128); uchar *buf = _buf, *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; } } } /****************************************************************************************\ * 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 ); } /* End of file. */