used new device layer for cv::cuda::norm
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Vladislav Vinogradov
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23cc31e041
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8ed47c01b7
119
modules/cudaarithm/src/cuda/norm.cu
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119
modules/cudaarithm/src/cuda/norm.cu
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "opencv2/opencv_modules.hpp"
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#ifndef HAVE_OPENCV_CUDEV
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#error "opencv_cudev is required"
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#else
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#include "opencv2/cudaarithm.hpp"
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#include "opencv2/cudev.hpp"
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using namespace cv::cudev;
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namespace
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{
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double normDiffInf(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _buf)
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{
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const GpuMat_<uchar>& src1 = (const GpuMat_<uchar>&) _src1;
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const GpuMat_<uchar>& src2 = (const GpuMat_<uchar>&) _src2;
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GpuMat_<int>& buf = (GpuMat_<int>&) _buf;
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gridFindMinMaxVal(abs_(cvt_<int>(src1) - cvt_<int>(src2)), buf);
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int data[2];
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buf.download(cv::Mat(1, 2, buf.type(), data));
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return data[1];
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}
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double normDiffL1(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _buf)
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{
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const GpuMat_<uchar>& src1 = (const GpuMat_<uchar>&) _src1;
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const GpuMat_<uchar>& src2 = (const GpuMat_<uchar>&) _src2;
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GpuMat_<int>& buf = (GpuMat_<int>&) _buf;
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gridCalcSum(abs_(cvt_<int>(src1) - cvt_<int>(src2)), buf);
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int data;
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buf.download(cv::Mat(1, 1, buf.type(), &data));
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return data;
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}
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double normDiffL2(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _buf)
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{
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const GpuMat_<uchar>& src1 = (const GpuMat_<uchar>&) _src1;
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const GpuMat_<uchar>& src2 = (const GpuMat_<uchar>&) _src2;
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GpuMat_<double>& buf = (GpuMat_<double>&) _buf;
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gridCalcSum(sqr_(cvt_<double>(src1) - cvt_<double>(src2)), buf);
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double data;
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buf.download(cv::Mat(1, 1, buf.type(), &data));
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return std::sqrt(data);
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}
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}
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double cv::cuda::norm(InputArray _src1, InputArray _src2, GpuMat& buf, int normType)
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{
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typedef double (*func_t)(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _buf);
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static const func_t funcs[] =
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{
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0, normDiffInf, normDiffL1, 0, normDiffL2
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};
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GpuMat src1 = _src1.getGpuMat();
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GpuMat src2 = _src2.getGpuMat();
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CV_Assert( src1.type() == CV_8UC1 );
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CV_Assert( src1.size() == src2.size() && src1.type() == src2.type() );
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CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
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return funcs[normType](src1, src2, buf);
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}
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#endif
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@ -133,59 +133,6 @@ double cv::cuda::norm(InputArray _src, int normType, InputArray _mask, GpuMat& b
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return std::max(std::abs(min_val), std::abs(max_val));
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return std::max(std::abs(min_val), std::abs(max_val));
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}
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}
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double cv::cuda::norm(InputArray _src1, InputArray _src2, GpuMat& buf, int normType)
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{
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#if CUDA_VERSION < 5050
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(void) buf;
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typedef NppStatus (*func_t)(const Npp8u* pSrc1, int nSrcStep1, const Npp8u* pSrc2, int nSrcStep2, NppiSize oSizeROI, Npp64f* pRetVal);
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static const func_t funcs[] = {nppiNormDiff_Inf_8u_C1R, nppiNormDiff_L1_8u_C1R, nppiNormDiff_L2_8u_C1R};
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#else
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typedef NppStatus (*func_t)(const Npp8u* pSrc1, int nSrcStep1, const Npp8u* pSrc2, int nSrcStep2,
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NppiSize oSizeROI, Npp64f* pRetVal, Npp8u * pDeviceBuffer);
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typedef NppStatus (*buf_size_func_t)(NppiSize oSizeROI, int* hpBufferSize);
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static const func_t funcs[] = {nppiNormDiff_Inf_8u_C1R, nppiNormDiff_L1_8u_C1R, nppiNormDiff_L2_8u_C1R};
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static const buf_size_func_t buf_size_funcs[] = {nppiNormDiffInfGetBufferHostSize_8u_C1R, nppiNormDiffL1GetBufferHostSize_8u_C1R, nppiNormDiffL2GetBufferHostSize_8u_C1R};
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#endif
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GpuMat src1 = _src1.getGpuMat();
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GpuMat src2 = _src2.getGpuMat();
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CV_Assert( src1.type() == CV_8UC1 );
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CV_Assert( src1.size() == src2.size() && src1.type() == src2.type() );
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CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
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NppiSize sz;
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sz.width = src1.cols;
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sz.height = src1.rows;
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const int funcIdx = normType >> 1;
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DeviceBuffer dbuf;
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#if CUDA_VERSION < 5050
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nppSafeCall( funcs[funcIdx](src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step), sz, dbuf) );
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#else
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int bufSize;
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buf_size_funcs[funcIdx](sz, &bufSize);
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ensureSizeIsEnough(1, bufSize, CV_8UC1, buf);
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nppSafeCall( funcs[funcIdx](src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step), sz, dbuf, buf.data) );
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#endif
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cudaSafeCall( cudaDeviceSynchronize() );
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double retVal;
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dbuf.download(&retVal);
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return retVal;
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}
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////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////
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// meanStdDev
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// meanStdDev
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