249 lines
8.6 KiB
C++
249 lines
8.6 KiB
C++
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#include "precomp.hpp"
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#include "opencl_kernels_core.hpp"
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#include "stat.hpp"
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#include "sum.simd.hpp"
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#include "sum.simd_declarations.hpp" // defines CV_CPU_DISPATCH_MODES_ALL=AVX2,...,BASELINE based on CMakeLists.txt content
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#undef HAVE_IPP
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#undef CV_IPP_RUN_FAST
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#define CV_IPP_RUN_FAST(f, ...)
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#undef CV_IPP_RUN
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#define CV_IPP_RUN(c, f, ...)
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namespace cv
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{
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SumFunc getSumFunc(int depth)
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{
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CV_INSTRUMENT_REGION();
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CV_CPU_DISPATCH(getSumFunc, (depth),
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CV_CPU_DISPATCH_MODES_ALL);
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}
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#ifdef HAVE_OPENCL
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bool ocl_sum( InputArray _src, Scalar & res, int sum_op, InputArray _mask,
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InputArray _src2, bool calc2, const Scalar & res2 )
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{
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CV_Assert(sum_op == OCL_OP_SUM || sum_op == OCL_OP_SUM_ABS || sum_op == OCL_OP_SUM_SQR);
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const ocl::Device & dev = ocl::Device::getDefault();
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bool doubleSupport = dev.doubleFPConfig() > 0,
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haveMask = _mask.kind() != _InputArray::NONE,
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haveSrc2 = _src2.kind() != _InputArray::NONE;
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int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type),
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kercn = cn == 1 && !haveMask ? ocl::predictOptimalVectorWidth(_src, _src2) : 1,
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mcn = std::max(cn, kercn);
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CV_Assert(!haveSrc2 || _src2.type() == type);
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int convert_cn = haveSrc2 ? mcn : cn;
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if ( (!doubleSupport && depth == CV_64F) || cn > 4 )
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return false;
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if (depth >= CV_16F)
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return false;
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int ngroups = dev.maxComputeUnits(), dbsize = ngroups * (calc2 ? 2 : 1);
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size_t wgs = dev.maxWorkGroupSize();
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int ddepth = std::max(sum_op == OCL_OP_SUM_SQR ? CV_32F : CV_32S, depth),
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dtype = CV_MAKE_TYPE(ddepth, cn);
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CV_Assert(!haveMask || _mask.type() == CV_8UC1);
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int wgs2_aligned = 1;
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while (wgs2_aligned < (int)wgs)
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wgs2_aligned <<= 1;
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wgs2_aligned >>= 1;
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static const char * const opMap[3] = { "OP_SUM", "OP_SUM_ABS", "OP_SUM_SQR" };
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char cvt[2][40];
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String opts = format("-D srcT=%s -D srcT1=%s -D dstT=%s -D dstTK=%s -D dstT1=%s -D ddepth=%d -D cn=%d"
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" -D convertToDT=%s -D %s -D WGS=%d -D WGS2_ALIGNED=%d%s%s%s%s -D kercn=%d%s%s%s -D convertFromU=%s",
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ocl::typeToStr(CV_MAKE_TYPE(depth, mcn)), ocl::typeToStr(depth),
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ocl::typeToStr(dtype), ocl::typeToStr(CV_MAKE_TYPE(ddepth, mcn)),
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ocl::typeToStr(ddepth), ddepth, cn,
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ocl::convertTypeStr(depth, ddepth, mcn, cvt[0]),
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opMap[sum_op], (int)wgs, wgs2_aligned,
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doubleSupport ? " -D DOUBLE_SUPPORT" : "",
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haveMask ? " -D HAVE_MASK" : "",
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_src.isContinuous() ? " -D HAVE_SRC_CONT" : "",
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haveMask && _mask.isContinuous() ? " -D HAVE_MASK_CONT" : "", kercn,
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haveSrc2 ? " -D HAVE_SRC2" : "", calc2 ? " -D OP_CALC2" : "",
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haveSrc2 && _src2.isContinuous() ? " -D HAVE_SRC2_CONT" : "",
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depth <= CV_32S && ddepth == CV_32S ? ocl::convertTypeStr(CV_8U, ddepth, convert_cn, cvt[1]) : "noconvert");
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ocl::Kernel k("reduce", ocl::core::reduce_oclsrc, opts);
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if (k.empty())
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return false;
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UMat src = _src.getUMat(), src2 = _src2.getUMat(),
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db(1, dbsize, dtype), mask = _mask.getUMat();
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ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
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dbarg = ocl::KernelArg::PtrWriteOnly(db),
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maskarg = ocl::KernelArg::ReadOnlyNoSize(mask),
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src2arg = ocl::KernelArg::ReadOnlyNoSize(src2);
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if (haveMask)
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{
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if (haveSrc2)
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k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg, src2arg);
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else
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k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg);
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}
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else
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{
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if (haveSrc2)
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k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, src2arg);
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else
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k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg);
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}
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size_t globalsize = ngroups * wgs;
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if (k.run(1, &globalsize, &wgs, true))
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{
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typedef Scalar (*part_sum)(Mat m);
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part_sum funcs[3] = { ocl_part_sum<int>, ocl_part_sum<float>, ocl_part_sum<double> },
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func = funcs[ddepth - CV_32S];
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Mat mres = db.getMat(ACCESS_READ);
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if (calc2)
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const_cast<Scalar &>(res2) = func(mres.colRange(ngroups, dbsize));
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res = func(mres.colRange(0, ngroups));
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return true;
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}
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return false;
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}
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#endif
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#ifdef HAVE_IPP
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static bool ipp_sum(Mat &src, Scalar &_res)
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{
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CV_INSTRUMENT_REGION_IPP();
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#if IPP_VERSION_X100 >= 700
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int cn = src.channels();
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if (cn > 4)
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return false;
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size_t total_size = src.total();
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int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0;
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if( src.dims == 2 || (src.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
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{
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IppiSize sz = { cols, rows };
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int type = src.type();
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typedef IppStatus (CV_STDCALL* ippiSumFuncHint)(const void*, int, IppiSize, double *, IppHintAlgorithm);
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typedef IppStatus (CV_STDCALL* ippiSumFuncNoHint)(const void*, int, IppiSize, double *);
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ippiSumFuncHint ippiSumHint =
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type == CV_32FC1 ? (ippiSumFuncHint)ippiSum_32f_C1R :
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type == CV_32FC3 ? (ippiSumFuncHint)ippiSum_32f_C3R :
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type == CV_32FC4 ? (ippiSumFuncHint)ippiSum_32f_C4R :
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0;
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ippiSumFuncNoHint ippiSum =
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type == CV_8UC1 ? (ippiSumFuncNoHint)ippiSum_8u_C1R :
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type == CV_8UC3 ? (ippiSumFuncNoHint)ippiSum_8u_C3R :
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type == CV_8UC4 ? (ippiSumFuncNoHint)ippiSum_8u_C4R :
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type == CV_16UC1 ? (ippiSumFuncNoHint)ippiSum_16u_C1R :
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type == CV_16UC3 ? (ippiSumFuncNoHint)ippiSum_16u_C3R :
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type == CV_16UC4 ? (ippiSumFuncNoHint)ippiSum_16u_C4R :
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type == CV_16SC1 ? (ippiSumFuncNoHint)ippiSum_16s_C1R :
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type == CV_16SC3 ? (ippiSumFuncNoHint)ippiSum_16s_C3R :
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type == CV_16SC4 ? (ippiSumFuncNoHint)ippiSum_16s_C4R :
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0;
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CV_Assert(!ippiSumHint || !ippiSum);
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if( ippiSumHint || ippiSum )
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{
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Ipp64f res[4];
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IppStatus ret = ippiSumHint ?
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CV_INSTRUMENT_FUN_IPP(ippiSumHint, src.ptr(), (int)src.step[0], sz, res, ippAlgHintAccurate) :
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CV_INSTRUMENT_FUN_IPP(ippiSum, src.ptr(), (int)src.step[0], sz, res);
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if( ret >= 0 )
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{
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for( int i = 0; i < cn; i++ )
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_res[i] = res[i];
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return true;
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}
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}
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}
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#else
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CV_UNUSED(src); CV_UNUSED(_res);
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#endif
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return false;
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}
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#endif
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Scalar sum(InputArray _src)
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{
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CV_INSTRUMENT_REGION();
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#if defined HAVE_OPENCL || defined HAVE_IPP
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Scalar _res;
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#endif
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#ifdef HAVE_OPENCL
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CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2,
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ocl_sum(_src, _res, OCL_OP_SUM),
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_res)
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#endif
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Mat src = _src.getMat();
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CV_IPP_RUN(IPP_VERSION_X100 >= 700, ipp_sum(src, _res), _res);
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int k, cn = src.channels(), depth = src.depth();
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SumFunc func = getSumFunc(depth);
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CV_Assert( cn <= 4 && func != 0 );
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const Mat* arrays[] = {&src, 0};
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uchar* ptrs[1] = {};
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NAryMatIterator it(arrays, ptrs);
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Scalar s;
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int total = (int)it.size, blockSize = total, intSumBlockSize = 0;
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int j, count = 0;
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AutoBuffer<int> _buf;
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int* buf = (int*)&s[0];
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size_t esz = 0;
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bool blockSum = depth < CV_32S;
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if( blockSum )
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{
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intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15);
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blockSize = std::min(blockSize, intSumBlockSize);
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_buf.allocate(cn);
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buf = _buf.data();
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for( k = 0; k < cn; k++ )
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buf[k] = 0;
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esz = src.elemSize();
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}
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for( size_t i = 0; i < it.nplanes; i++, ++it )
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{
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for( j = 0; j < total; j += blockSize )
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{
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int bsz = std::min(total - j, blockSize);
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func( ptrs[0], 0, (uchar*)buf, bsz, cn );
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count += bsz;
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if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
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{
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for( k = 0; k < cn; k++ )
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{
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s[k] += buf[k];
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buf[k] = 0;
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}
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count = 0;
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}
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ptrs[0] += bsz*esz;
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}
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}
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return s;
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}
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} // namespace
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