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Open Source Computer Vision Library
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818 lines
30 KiB
818 lines
30 KiB
/*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) || defined (CUDA_DISABLER) |
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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 PtrStepSzb src, PtrStepb buf, double* sum, int cn); |
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template <typename T> |
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void sumMultipassCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn); |
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template <typename T> |
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void absSumCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn); |
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template <typename T> |
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void absSumMultipassCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn); |
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template <typename T> |
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void sqrSumCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn); |
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template <typename T> |
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void sqrSumMultipassCaller(const PtrStepSzb 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 PtrStepSzb, 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 PtrStepSzb, 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 PtrStepSzb, 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 PtrStepSzb src, double* minval, double* maxval, PtrStepb buf); |
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template <typename T> |
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void minMaxMaskCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf); |
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template <typename T> |
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void minMaxMultipassCaller(const PtrStepSzb src, double* minval, double* maxval, PtrStepb buf); |
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template <typename T> |
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void minMaxMaskMultipassCaller(const PtrStepSzb 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 PtrStepSzb, double*, double*, PtrStepb); |
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typedef void (*MaskedCaller)(const PtrStepSzb, 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); |
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} |
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else |
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{ |
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MaskedCaller* callers = masked_multipass_callers; |
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if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS)) |
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callers = masked_singlepass_callers; |
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MaskedCaller caller = callers[src.type()]; |
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CV_Assert(caller != 0); |
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caller(src, mask, minVal, maxVal, buf); |
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} |
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} |
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//////////////////////////////////////////////////////////////////////// |
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// Locate min and 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 minmaxloc |
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{ |
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void getBufSizeRequired(int cols, int rows, int elem_size, int& b1cols, |
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int& b1rows, int& b2cols, int& b2rows); |
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template <typename T> |
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void minMaxLocCaller(const PtrStepSzb src, double* minval, double* maxval, |
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int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf); |
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template <typename T> |
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void minMaxLocMaskCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, |
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int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf); |
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template <typename T> |
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void minMaxLocMultipassCaller(const PtrStepSzb src, double* minval, double* maxval, |
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int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf); |
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template <typename T> |
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void minMaxLocMaskMultipassCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, |
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int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf); |
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} |
|
} |
|
}}} |
|
|
|
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 PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb); |
|
typedef void (*MaskedCaller)(const PtrStepSzb, 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 PtrStepSzb src, PtrStepb buf); |
|
|
|
template <typename T> |
|
int countNonZeroMultipassCaller(const PtrStepSzb 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 PtrStepSzb 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 PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream); |
|
template <typename T, typename S, typename D> void reduceCols_gpu(const PtrStepSzb& src, int cn, const PtrStepSzb& 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 PtrStepSzb& src, const PtrStepSzb& 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 PtrStepSzb& src, int cn, const PtrStepSzb& 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
|
|
|