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819 lines
30 KiB
C++
819 lines
30 KiB
C++
/*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 GpuMaterials 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 bpied warranties, including, but not limited to, the bpied
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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 "precomp.hpp"
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using namespace cv;
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using namespace cv::gpu;
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#if !defined (HAVE_CUDA)
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void cv::gpu::meanStdDev(const GpuMat&, Scalar&, Scalar&) { throw_nogpu(); }
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void cv::gpu::meanStdDev(const GpuMat&, Scalar&, Scalar&, GpuMat&) { throw_nogpu(); }
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double cv::gpu::norm(const GpuMat&, int) { throw_nogpu(); return 0.0; }
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double cv::gpu::norm(const GpuMat&, int, GpuMat&) { throw_nogpu(); return 0.0; }
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double cv::gpu::norm(const GpuMat&, const GpuMat&, int) { throw_nogpu(); return 0.0; }
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Scalar cv::gpu::sum(const GpuMat&) { throw_nogpu(); return Scalar(); }
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Scalar cv::gpu::sum(const GpuMat&, GpuMat&) { throw_nogpu(); return Scalar(); }
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Scalar cv::gpu::absSum(const GpuMat&) { throw_nogpu(); return Scalar(); }
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Scalar cv::gpu::absSum(const GpuMat&, GpuMat&) { throw_nogpu(); return Scalar(); }
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Scalar cv::gpu::sqrSum(const GpuMat&) { throw_nogpu(); return Scalar(); }
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Scalar cv::gpu::sqrSum(const GpuMat&, GpuMat&) { throw_nogpu(); return Scalar(); }
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void cv::gpu::minMax(const GpuMat&, double*, double*, const GpuMat&) { throw_nogpu(); }
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void cv::gpu::minMax(const GpuMat&, double*, double*, const GpuMat&, GpuMat&) { throw_nogpu(); }
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void cv::gpu::minMaxLoc(const GpuMat&, double*, double*, Point*, Point*, const GpuMat&) { throw_nogpu(); }
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void cv::gpu::minMaxLoc(const GpuMat&, double*, double*, Point*, Point*, const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
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int cv::gpu::countNonZero(const GpuMat&) { throw_nogpu(); return 0; }
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int cv::gpu::countNonZero(const GpuMat&, GpuMat&) { throw_nogpu(); return 0; }
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void cv::gpu::reduce(const GpuMat&, GpuMat&, int, int, int, Stream&) { throw_nogpu(); }
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#else
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namespace
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{
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class DeviceBuffer
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{
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public:
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explicit DeviceBuffer(int count_ = 1) : count(count_)
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{
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cudaSafeCall( cudaMalloc(&pdev, count * sizeof(double)) );
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}
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~DeviceBuffer()
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{
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cudaSafeCall( cudaFree(pdev) );
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}
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operator double*() {return pdev;}
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void download(double* hptr)
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{
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double hbuf;
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cudaSafeCall( cudaMemcpy(&hbuf, pdev, sizeof(double), cudaMemcpyDeviceToHost) );
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*hptr = hbuf;
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}
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void download(double** hptrs)
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{
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AutoBuffer<double, 2 * sizeof(double)> hbuf(count);
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cudaSafeCall( cudaMemcpy((void*)hbuf, pdev, count * sizeof(double), cudaMemcpyDeviceToHost) );
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for (int i = 0; i < count; ++i)
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*hptrs[i] = hbuf[i];
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}
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private:
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double* pdev;
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int count;
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};
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}
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////////////////////////////////////////////////////////////////////////
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// meanStdDev
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void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev)
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{
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GpuMat buf;
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meanStdDev(src, mean, stddev, buf);
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}
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void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev, GpuMat& buf)
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{
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CV_Assert(src.type() == CV_8UC1);
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if (!TargetArchs::builtWith(FEATURE_SET_COMPUTE_13) || !DeviceInfo().supports(FEATURE_SET_COMPUTE_13))
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CV_Error(CV_StsNotImplemented, "Not sufficient compute capebility");
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NppiSize sz;
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sz.width = src.cols;
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sz.height = src.rows;
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DeviceBuffer dbuf(2);
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int bufSize;
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#if (CUDA_VERSION <= 4020)
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nppSafeCall( nppiMeanStdDev8uC1RGetBufferHostSize(sz, &bufSize) );
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#else
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nppSafeCall( nppiMeanStdDevGetBufferHostSize_8u_C1R(sz, &bufSize) );
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#endif
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ensureSizeIsEnough(1, bufSize, CV_8UC1, buf);
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nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), sz, buf.ptr<Npp8u>(), dbuf, (double*)dbuf + 1) );
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cudaSafeCall( cudaDeviceSynchronize() );
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double* ptrs[2] = {mean.val, stddev.val};
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dbuf.download(ptrs);
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}
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////////////////////////////////////////////////////////////////////////
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// norm
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double cv::gpu::norm(const GpuMat& src, int normType)
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{
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GpuMat buf;
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return norm(src, normType, buf);
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}
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double cv::gpu::norm(const GpuMat& src, int normType, GpuMat& buf)
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{
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CV_Assert(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2);
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GpuMat src_single_channel = src.reshape(1);
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if (normType == NORM_L1)
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return absSum(src_single_channel, buf)[0];
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if (normType == NORM_L2)
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return std::sqrt(sqrSum(src_single_channel, buf)[0]);
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// NORM_INF
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double min_val, max_val;
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minMax(src_single_channel, &min_val, &max_val, GpuMat(), buf);
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return std::max(std::abs(min_val), std::abs(max_val));
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}
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double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
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{
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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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typedef NppStatus (*npp_norm_diff_func_t)(const Npp8u* pSrc1, int nSrcStep1, const Npp8u* pSrc2, int nSrcStep2,
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NppiSize oSizeROI, Npp64f* pRetVal);
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static const npp_norm_diff_func_t npp_norm_diff_func[] = {nppiNormDiff_Inf_8u_C1R, nppiNormDiff_L1_8u_C1R, nppiNormDiff_L2_8u_C1R};
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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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int funcIdx = normType >> 1;
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double retVal;
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DeviceBuffer dbuf;
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nppSafeCall( npp_norm_diff_func[funcIdx](src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step), sz, dbuf) );
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cudaSafeCall( cudaDeviceSynchronize() );
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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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// Sum
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namespace cv { namespace gpu { namespace device
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{
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namespace matrix_reductions
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{
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namespace sum
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{
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template <typename T>
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void sumCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
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template <typename T>
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void sumMultipassCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
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template <typename T>
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void absSumCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
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template <typename T>
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void absSumMultipassCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
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template <typename T>
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void sqrSumCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
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template <typename T>
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void sqrSumMultipassCaller(const DevMem2Db src, PtrStepb buf, double* sum, int cn);
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void getBufSizeRequired(int cols, int rows, int cn, int& bufcols, int& bufrows);
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}
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}
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}}}
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Scalar cv::gpu::sum(const GpuMat& src)
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{
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GpuMat buf;
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return sum(src, buf);
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}
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Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
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{
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using namespace cv::gpu::device::matrix_reductions::sum;
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typedef void (*Caller)(const DevMem2Db, PtrStepb, double*, int);
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static Caller multipass_callers[] =
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{
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sumMultipassCaller<unsigned char>, sumMultipassCaller<char>,
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sumMultipassCaller<unsigned short>, sumMultipassCaller<short>,
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sumMultipassCaller<int>, sumMultipassCaller<float>
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};
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static Caller singlepass_callers[] = {
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sumCaller<unsigned char>, sumCaller<char>,
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sumCaller<unsigned short>, sumCaller<short>,
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sumCaller<int>, sumCaller<float>
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};
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CV_Assert(src.depth() <= CV_32F);
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Size buf_size;
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getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
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ensureSizeIsEnough(buf_size, CV_8U, buf);
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Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
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Caller caller = callers[src.depth()];
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double result[4];
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caller(src, buf, result, src.channels());
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return Scalar(result[0], result[1], result[2], result[3]);
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}
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Scalar cv::gpu::absSum(const GpuMat& src)
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{
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GpuMat buf;
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return absSum(src, buf);
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}
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Scalar cv::gpu::absSum(const GpuMat& src, GpuMat& buf)
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{
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using namespace cv::gpu::device::matrix_reductions::sum;
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typedef void (*Caller)(const DevMem2Db, PtrStepb, double*, int);
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static Caller multipass_callers[] =
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{
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absSumMultipassCaller<unsigned char>, absSumMultipassCaller<char>,
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absSumMultipassCaller<unsigned short>, absSumMultipassCaller<short>,
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absSumMultipassCaller<int>, absSumMultipassCaller<float>
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};
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static Caller singlepass_callers[] =
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{
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absSumCaller<unsigned char>, absSumCaller<char>,
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absSumCaller<unsigned short>, absSumCaller<short>,
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absSumCaller<int>, absSumCaller<float>
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};
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CV_Assert(src.depth() <= CV_32F);
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Size buf_size;
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getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
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ensureSizeIsEnough(buf_size, CV_8U, buf);
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Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
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Caller caller = callers[src.depth()];
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double result[4];
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caller(src, buf, result, src.channels());
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return Scalar(result[0], result[1], result[2], result[3]);
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}
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Scalar cv::gpu::sqrSum(const GpuMat& src)
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{
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GpuMat buf;
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return sqrSum(src, buf);
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}
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Scalar cv::gpu::sqrSum(const GpuMat& src, GpuMat& buf)
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{
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using namespace cv::gpu::device::matrix_reductions::sum;
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typedef void (*Caller)(const DevMem2Db, PtrStepb, double*, int);
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static Caller multipass_callers[] =
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{
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sqrSumMultipassCaller<unsigned char>, sqrSumMultipassCaller<char>,
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sqrSumMultipassCaller<unsigned short>, sqrSumMultipassCaller<short>,
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sqrSumMultipassCaller<int>, sqrSumMultipassCaller<float>
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};
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static Caller singlepass_callers[7] =
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{
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sqrSumCaller<unsigned char>, sqrSumCaller<char>,
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sqrSumCaller<unsigned short>, sqrSumCaller<short>,
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sqrSumCaller<int>, sqrSumCaller<float>
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};
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CV_Assert(src.depth() <= CV_32F);
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Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
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Size buf_size;
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getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
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ensureSizeIsEnough(buf_size, CV_8U, buf);
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Caller caller = callers[src.depth()];
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double result[4];
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caller(src, buf, result, src.channels());
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return Scalar(result[0], result[1], result[2], result[3]);
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}
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////////////////////////////////////////////////////////////////////////
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// Find min or max
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namespace cv { namespace gpu { namespace device
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{
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namespace matrix_reductions
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{
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namespace minmax
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{
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void getBufSizeRequired(int cols, int rows, int elem_size, int& bufcols, int& bufrows);
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template <typename T>
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void minMaxCaller(const DevMem2Db src, double* minval, double* maxval, PtrStepb buf);
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template <typename T>
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void minMaxMaskCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
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template <typename T>
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void minMaxMultipassCaller(const DevMem2Db src, double* minval, double* maxval, PtrStepb buf);
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template <typename T>
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void minMaxMaskMultipassCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
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}
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}
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}}}
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void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask)
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{
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GpuMat buf;
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minMax(src, minVal, maxVal, mask, buf);
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}
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void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf)
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{
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using namespace ::cv::gpu::device::matrix_reductions::minmax;
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typedef void (*Caller)(const DevMem2Db, double*, double*, PtrStepb);
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typedef void (*MaskedCaller)(const DevMem2Db, const PtrStepb, double*, double*, PtrStepb);
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static Caller multipass_callers[] =
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{
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minMaxMultipassCaller<unsigned char>, minMaxMultipassCaller<char>,
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minMaxMultipassCaller<unsigned short>, minMaxMultipassCaller<short>,
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minMaxMultipassCaller<int>, minMaxMultipassCaller<float>, 0
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};
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static Caller singlepass_callers[] =
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{
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minMaxCaller<unsigned char>, minMaxCaller<char>,
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minMaxCaller<unsigned short>, minMaxCaller<short>,
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minMaxCaller<int>, minMaxCaller<float>, minMaxCaller<double>
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};
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static MaskedCaller masked_multipass_callers[] =
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{
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minMaxMaskMultipassCaller<unsigned char>, minMaxMaskMultipassCaller<char>,
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minMaxMaskMultipassCaller<unsigned short>, minMaxMaskMultipassCaller<short>,
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minMaxMaskMultipassCaller<int>, minMaxMaskMultipassCaller<float>, 0
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};
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static MaskedCaller masked_singlepass_callers[] =
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{
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minMaxMaskCaller<unsigned char>, minMaxMaskCaller<char>,
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minMaxMaskCaller<unsigned short>, minMaxMaskCaller<short>,
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minMaxMaskCaller<int>, minMaxMaskCaller<float>, minMaxMaskCaller<double>
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};
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CV_Assert(src.depth() <= CV_64F);
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CV_Assert(src.channels() == 1);
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CV_Assert(mask.empty() || (mask.type() == CV_8U && src.size() == mask.size()));
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if (src.depth() == CV_64F)
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{
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if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
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CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
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}
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double minVal_; if (!minVal) minVal = &minVal_;
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double maxVal_; if (!maxVal) maxVal = &maxVal_;
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Size buf_size;
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getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), buf_size.width, buf_size.height);
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ensureSizeIsEnough(buf_size, CV_8U, buf);
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if (mask.empty())
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{
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Caller* callers = multipass_callers;
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
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callers = singlepass_callers;
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Caller caller = callers[src.type()];
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CV_Assert(caller != 0);
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caller(src, minVal, maxVal, buf);
|
|
}
|
|
else
|
|
{
|
|
MaskedCaller* callers = masked_multipass_callers;
|
|
if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
|
|
callers = masked_singlepass_callers;
|
|
|
|
MaskedCaller caller = callers[src.type()];
|
|
CV_Assert(caller != 0);
|
|
caller(src, mask, minVal, maxVal, buf);
|
|
}
|
|
}
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// Locate min and max
|
|
|
|
namespace cv { namespace gpu { namespace device
|
|
{
|
|
namespace matrix_reductions
|
|
{
|
|
namespace minmaxloc
|
|
{
|
|
void getBufSizeRequired(int cols, int rows, int elem_size, int& b1cols,
|
|
int& b1rows, int& b2cols, int& b2rows);
|
|
|
|
template <typename T>
|
|
void minMaxLocCaller(const DevMem2Db src, double* minval, double* maxval,
|
|
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
|
|
|
|
template <typename T>
|
|
void minMaxLocMaskCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval,
|
|
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
|
|
|
|
template <typename T>
|
|
void minMaxLocMultipassCaller(const DevMem2Db src, double* minval, double* maxval,
|
|
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
|
|
|
|
template <typename T>
|
|
void minMaxLocMaskMultipassCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval,
|
|
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
|
|
}
|
|
}
|
|
}}}
|
|
|
|
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, const GpuMat& mask)
|
|
{
|
|
GpuMat valBuf, locBuf;
|
|
minMaxLoc(src, minVal, maxVal, minLoc, maxLoc, mask, valBuf, locBuf);
|
|
}
|
|
|
|
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
|
|
const GpuMat& mask, GpuMat& valBuf, GpuMat& locBuf)
|
|
{
|
|
using namespace ::cv::gpu::device::matrix_reductions::minmaxloc;
|
|
|
|
typedef void (*Caller)(const DevMem2Db, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
|
|
typedef void (*MaskedCaller)(const DevMem2Db, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
|
|
|
|
static Caller multipass_callers[] =
|
|
{
|
|
minMaxLocMultipassCaller<unsigned char>, minMaxLocMultipassCaller<char>,
|
|
minMaxLocMultipassCaller<unsigned short>, minMaxLocMultipassCaller<short>,
|
|
minMaxLocMultipassCaller<int>, minMaxLocMultipassCaller<float>, 0
|
|
};
|
|
|
|
static Caller singlepass_callers[] =
|
|
{
|
|
minMaxLocCaller<unsigned char>, minMaxLocCaller<char>,
|
|
minMaxLocCaller<unsigned short>, minMaxLocCaller<short>,
|
|
minMaxLocCaller<int>, minMaxLocCaller<float>, minMaxLocCaller<double>
|
|
};
|
|
|
|
static MaskedCaller masked_multipass_callers[] =
|
|
{
|
|
minMaxLocMaskMultipassCaller<unsigned char>, minMaxLocMaskMultipassCaller<char>,
|
|
minMaxLocMaskMultipassCaller<unsigned short>, minMaxLocMaskMultipassCaller<short>,
|
|
minMaxLocMaskMultipassCaller<int>, minMaxLocMaskMultipassCaller<float>, 0
|
|
};
|
|
|
|
static MaskedCaller masked_singlepass_callers[] =
|
|
{
|
|
minMaxLocMaskCaller<unsigned char>, minMaxLocMaskCaller<char>,
|
|
minMaxLocMaskCaller<unsigned short>, minMaxLocMaskCaller<short>,
|
|
minMaxLocMaskCaller<int>, minMaxLocMaskCaller<float>, minMaxLocMaskCaller<double>
|
|
};
|
|
|
|
CV_Assert(src.depth() <= CV_64F);
|
|
CV_Assert(src.channels() == 1);
|
|
CV_Assert(mask.empty() || (mask.type() == CV_8U && src.size() == mask.size()));
|
|
|
|
if (src.depth() == CV_64F)
|
|
{
|
|
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
|
|
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
|
|
}
|
|
|
|
double minVal_; if (!minVal) minVal = &minVal_;
|
|
double maxVal_; if (!maxVal) maxVal = &maxVal_;
|
|
int minLoc_[2];
|
|
int maxLoc_[2];
|
|
|
|
Size valbuf_size, locbuf_size;
|
|
getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), valbuf_size.width,
|
|
valbuf_size.height, locbuf_size.width, locbuf_size.height);
|
|
ensureSizeIsEnough(valbuf_size, CV_8U, valBuf);
|
|
ensureSizeIsEnough(locbuf_size, CV_8U, locBuf);
|
|
|
|
if (mask.empty())
|
|
{
|
|
Caller* callers = multipass_callers;
|
|
if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
|
|
callers = singlepass_callers;
|
|
|
|
Caller caller = callers[src.type()];
|
|
CV_Assert(caller != 0);
|
|
caller(src, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
|
|
}
|
|
else
|
|
{
|
|
MaskedCaller* callers = masked_multipass_callers;
|
|
if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
|
|
callers = masked_singlepass_callers;
|
|
|
|
MaskedCaller caller = callers[src.type()];
|
|
CV_Assert(caller != 0);
|
|
caller(src, mask, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
|
|
}
|
|
|
|
if (minLoc) { minLoc->x = minLoc_[0]; minLoc->y = minLoc_[1]; }
|
|
if (maxLoc) { maxLoc->x = maxLoc_[0]; maxLoc->y = maxLoc_[1]; }
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
// Count non-zero elements
|
|
|
|
namespace cv { namespace gpu { namespace device
|
|
{
|
|
namespace matrix_reductions
|
|
{
|
|
namespace countnonzero
|
|
{
|
|
void getBufSizeRequired(int cols, int rows, int& bufcols, int& bufrows);
|
|
|
|
template <typename T>
|
|
int countNonZeroCaller(const DevMem2Db src, PtrStepb buf);
|
|
|
|
template <typename T>
|
|
int countNonZeroMultipassCaller(const DevMem2Db src, PtrStepb buf);
|
|
}
|
|
}
|
|
}}}
|
|
|
|
int cv::gpu::countNonZero(const GpuMat& src)
|
|
{
|
|
GpuMat buf;
|
|
return countNonZero(src, buf);
|
|
}
|
|
|
|
|
|
int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
|
|
{
|
|
using namespace ::cv::gpu::device::matrix_reductions::countnonzero;
|
|
|
|
typedef int (*Caller)(const DevMem2Db src, PtrStepb buf);
|
|
|
|
static Caller multipass_callers[7] =
|
|
{
|
|
countNonZeroMultipassCaller<unsigned char>, countNonZeroMultipassCaller<char>,
|
|
countNonZeroMultipassCaller<unsigned short>, countNonZeroMultipassCaller<short>,
|
|
countNonZeroMultipassCaller<int>, countNonZeroMultipassCaller<float>, 0
|
|
};
|
|
|
|
static Caller singlepass_callers[7] =
|
|
{
|
|
countNonZeroCaller<unsigned char>, countNonZeroCaller<char>,
|
|
countNonZeroCaller<unsigned short>, countNonZeroCaller<short>,
|
|
countNonZeroCaller<int>, countNonZeroCaller<float>, countNonZeroCaller<double> };
|
|
|
|
CV_Assert(src.depth() <= CV_64F);
|
|
CV_Assert(src.channels() == 1);
|
|
|
|
if (src.depth() == CV_64F)
|
|
{
|
|
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
|
|
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
|
|
}
|
|
|
|
Size buf_size;
|
|
getBufSizeRequired(src.cols, src.rows, buf_size.width, buf_size.height);
|
|
ensureSizeIsEnough(buf_size, CV_8U, buf);
|
|
|
|
Caller* callers = multipass_callers;
|
|
if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
|
|
callers = singlepass_callers;
|
|
|
|
Caller caller = callers[src.type()];
|
|
CV_Assert(caller != 0);
|
|
return caller(src, buf);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
// reduce
|
|
|
|
namespace cv { namespace gpu { namespace device
|
|
{
|
|
namespace matrix_reductions
|
|
{
|
|
template <typename T, typename S, typename D> void reduceRows_gpu(const DevMem2Db& src, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
|
|
template <typename T, typename S, typename D> void reduceCols_gpu(const DevMem2Db& src, int cn, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
|
|
}
|
|
}}}
|
|
|
|
void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int dtype, Stream& stream)
|
|
{
|
|
using namespace ::cv::gpu::device::matrix_reductions;
|
|
|
|
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4 && dtype <= CV_32F);
|
|
CV_Assert(dim == 0 || dim == 1);
|
|
CV_Assert(reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG || reduceOp == CV_REDUCE_MAX || reduceOp == CV_REDUCE_MIN);
|
|
|
|
if (dtype < 0)
|
|
dtype = src.depth();
|
|
|
|
dst.create(1, dim == 0 ? src.cols : src.rows, CV_MAKETYPE(dtype, src.channels()));
|
|
|
|
if (dim == 0)
|
|
{
|
|
typedef void (*caller_t)(const DevMem2Db& src, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
|
|
|
|
static const caller_t callers[6][6] =
|
|
{
|
|
{
|
|
reduceRows_gpu<unsigned char, int, unsigned char>,
|
|
0/*reduceRows_gpu<unsigned char, int, signed char>*/,
|
|
0/*reduceRows_gpu<unsigned char, int, unsigned short>*/,
|
|
0/*reduceRows_gpu<unsigned char, int, short>*/,
|
|
reduceRows_gpu<unsigned char, int, int>,
|
|
reduceRows_gpu<unsigned char, int, float>
|
|
},
|
|
{
|
|
0/*reduceRows_gpu<signed char, int, unsigned char>*/,
|
|
0/*reduceRows_gpu<signed char, int, signed char>*/,
|
|
0/*reduceRows_gpu<signed char, int, unsigned short>*/,
|
|
0/*reduceRows_gpu<signed char, int, short>*/,
|
|
0/*reduceRows_gpu<signed char, int, int>*/,
|
|
0/*reduceRows_gpu<signed char, int, float>*/
|
|
},
|
|
{
|
|
0/*reduceRows_gpu<unsigned short, int, unsigned char>*/,
|
|
0/*reduceRows_gpu<unsigned short, int, signed char>*/,
|
|
reduceRows_gpu<unsigned short, int, unsigned short>,
|
|
0/*reduceRows_gpu<unsigned short, int, short>*/,
|
|
reduceRows_gpu<unsigned short, int, int>,
|
|
reduceRows_gpu<unsigned short, int, float>
|
|
},
|
|
{
|
|
0/*reduceRows_gpu<short, int, unsigned char>*/,
|
|
0/*reduceRows_gpu<short, int, signed char>*/,
|
|
0/*reduceRows_gpu<short, int, unsigned short>*/,
|
|
reduceRows_gpu<short, int, short>,
|
|
reduceRows_gpu<short, int, int>,
|
|
reduceRows_gpu<short, int, float>
|
|
},
|
|
{
|
|
0/*reduceRows_gpu<int, int, unsigned char>*/,
|
|
0/*reduceRows_gpu<int, int, signed char>*/,
|
|
0/*reduceRows_gpu<int, int, unsigned short>*/,
|
|
0/*reduceRows_gpu<int, int, short>*/,
|
|
reduceRows_gpu<int, int, int>,
|
|
reduceRows_gpu<int, int, float>
|
|
},
|
|
{
|
|
0/*reduceRows_gpu<float, float, unsigned char>*/,
|
|
0/*reduceRows_gpu<float, float, signed char>*/,
|
|
0/*reduceRows_gpu<float, float, unsigned short>*/,
|
|
0/*reduceRows_gpu<float, float, short>*/,
|
|
0/*reduceRows_gpu<float, float, int>*/,
|
|
reduceRows_gpu<float, float, float>
|
|
}
|
|
};
|
|
|
|
const caller_t func = callers[src.depth()][dst.depth()];
|
|
|
|
if (!func)
|
|
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
|
|
|
|
func(src.reshape(1), dst.reshape(1), reduceOp, StreamAccessor::getStream(stream));
|
|
}
|
|
else
|
|
{
|
|
typedef void (*caller_t)(const DevMem2Db& src, int cn, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
|
|
|
|
static const caller_t callers[6][6] =
|
|
{
|
|
{
|
|
reduceCols_gpu<unsigned char, int, unsigned char>,
|
|
0/*reduceCols_gpu<unsigned char, int, signed char>*/,
|
|
0/*reduceCols_gpu<unsigned char, int, unsigned short>*/,
|
|
0/*reduceCols_gpu<unsigned char, int, short>*/,
|
|
reduceCols_gpu<unsigned char, int, int>,
|
|
reduceCols_gpu<unsigned char, int, float>
|
|
},
|
|
{
|
|
0/*reduceCols_gpu<signed char, int, unsigned char>*/,
|
|
0/*reduceCols_gpu<signed char, int, signed char>*/,
|
|
0/*reduceCols_gpu<signed char, int, unsigned short>*/,
|
|
0/*reduceCols_gpu<signed char, int, short>*/,
|
|
0/*reduceCols_gpu<signed char, int, int>*/,
|
|
0/*reduceCols_gpu<signed char, int, float>*/
|
|
},
|
|
{
|
|
0/*reduceCols_gpu<unsigned short, int, unsigned char>*/,
|
|
0/*reduceCols_gpu<unsigned short, int, signed char>*/,
|
|
reduceCols_gpu<unsigned short, int, unsigned short>,
|
|
0/*reduceCols_gpu<unsigned short, int, short>*/,
|
|
reduceCols_gpu<unsigned short, int, int>,
|
|
reduceCols_gpu<unsigned short, int, float>
|
|
},
|
|
{
|
|
0/*reduceCols_gpu<short, int, unsigned char>*/,
|
|
0/*reduceCols_gpu<short, int, signed char>*/,
|
|
0/*reduceCols_gpu<short, int, unsigned short>*/,
|
|
reduceCols_gpu<short, int, short>,
|
|
reduceCols_gpu<short, int, int>,
|
|
reduceCols_gpu<short, int, float>
|
|
},
|
|
{
|
|
0/*reduceCols_gpu<int, int, unsigned char>*/,
|
|
0/*reduceCols_gpu<int, int, signed char>*/,
|
|
0/*reduceCols_gpu<int, int, unsigned short>*/,
|
|
0/*reduceCols_gpu<int, int, short>*/,
|
|
reduceCols_gpu<int, int, int>,
|
|
reduceCols_gpu<int, int, float>
|
|
},
|
|
{
|
|
0/*reduceCols_gpu<float, unsigned char>*/,
|
|
0/*reduceCols_gpu<float, signed char>*/,
|
|
0/*reduceCols_gpu<float, unsigned short>*/,
|
|
0/*reduceCols_gpu<float, short>*/,
|
|
0/*reduceCols_gpu<float, int>*/,
|
|
reduceCols_gpu<float, float, float>
|
|
}
|
|
};
|
|
|
|
const caller_t func = callers[src.depth()][dst.depth()];
|
|
|
|
if (!func)
|
|
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
|
|
|
|
func(src, src.channels(), dst, reduceOp, StreamAccessor::getStream(stream));
|
|
}
|
|
}
|
|
|
|
#endif
|