Open Source Computer Vision Library
https://opencv.org/
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219 lines
8.0 KiB
219 lines
8.0 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 materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "precomp.hpp" |
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using namespace cv; |
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using namespace cv::cuda; |
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#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) |
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double cv::cuda::norm(InputArray, int, InputArray) { throw_no_cuda(); return 0.0; } |
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void cv::cuda::calcNorm(InputArray, OutputArray, int, InputArray, Stream&) { throw_no_cuda(); } |
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double cv::cuda::norm(InputArray, InputArray, int) { throw_no_cuda(); return 0.0; } |
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void cv::cuda::calcNormDiff(InputArray, InputArray, OutputArray, int, Stream&) { throw_no_cuda(); } |
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Scalar cv::cuda::sum(InputArray, InputArray) { throw_no_cuda(); return Scalar(); } |
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void cv::cuda::calcSum(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); } |
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Scalar cv::cuda::absSum(InputArray, InputArray) { throw_no_cuda(); return Scalar(); } |
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void cv::cuda::calcAbsSum(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); } |
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Scalar cv::cuda::sqrSum(InputArray, InputArray) { throw_no_cuda(); return Scalar(); } |
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void cv::cuda::calcSqrSum(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); } |
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void cv::cuda::minMax(InputArray, double*, double*, InputArray) { throw_no_cuda(); } |
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void cv::cuda::findMinMax(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); } |
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void cv::cuda::minMaxLoc(InputArray, double*, double*, Point*, Point*, InputArray) { throw_no_cuda(); } |
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void cv::cuda::findMinMaxLoc(InputArray, OutputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); } |
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int cv::cuda::countNonZero(InputArray) { throw_no_cuda(); return 0; } |
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void cv::cuda::countNonZero(InputArray, OutputArray, Stream&) { throw_no_cuda(); } |
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void cv::cuda::reduce(InputArray, OutputArray, int, int, int, Stream&) { throw_no_cuda(); } |
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void cv::cuda::meanStdDev(InputArray, Scalar&, Scalar&) { throw_no_cuda(); } |
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void cv::cuda::meanStdDev(InputArray, OutputArray, Stream&) { throw_no_cuda(); } |
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void cv::cuda::rectStdDev(InputArray, InputArray, OutputArray, Rect, Stream&) { throw_no_cuda(); } |
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void cv::cuda::normalize(InputArray, OutputArray, double, double, int, int, InputArray, Stream&) { throw_no_cuda(); } |
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void cv::cuda::integral(InputArray, OutputArray, Stream&) { throw_no_cuda(); } |
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void cv::cuda::sqrIntegral(InputArray, OutputArray, Stream&) { throw_no_cuda(); } |
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#else |
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//////////////////////////////////////////////////////////////////////// |
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// norm |
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namespace cv { namespace cuda { namespace device { |
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void normL2(cv::InputArray _src, cv::OutputArray _dst, cv::InputArray _mask, Stream& stream); |
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void findMaxAbs(cv::InputArray _src, cv::OutputArray _dst, cv::InputArray _mask, Stream& stream); |
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}}} |
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void cv::cuda::calcNorm(InputArray _src, OutputArray dst, int normType, InputArray mask, Stream& stream) |
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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 = getInputMat(_src, stream); |
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GpuMat src_single_channel = src.reshape(1); |
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if (normType == NORM_L1) |
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{ |
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calcAbsSum(src_single_channel, dst, mask, stream); |
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} |
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else if (normType == NORM_L2) |
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{ |
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cv::cuda::device::normL2(src_single_channel, dst, mask, stream); |
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} |
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else // NORM_INF |
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{ |
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cv::cuda::device::findMaxAbs(src_single_channel, dst, mask, stream); |
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} |
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} |
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double cv::cuda::norm(InputArray _src, int normType, InputArray _mask) |
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{ |
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Stream& stream = Stream::Null(); |
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HostMem dst; |
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calcNorm(_src, dst, normType, _mask, stream); |
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stream.waitForCompletion(); |
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double val; |
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dst.createMatHeader().convertTo(Mat(1, 1, CV_64FC1, &val), CV_64F); |
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return val; |
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} |
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//////////////////////////////////////////////////////////////////////// |
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// meanStdDev |
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void cv::cuda::meanStdDev(InputArray _src, OutputArray _dst, Stream& stream) |
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{ |
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if (!deviceSupports(FEATURE_SET_COMPUTE_13)) |
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CV_Error(cv::Error::StsNotImplemented, "Not sufficient compute capebility"); |
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const GpuMat src = getInputMat(_src, stream); |
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CV_Assert( src.type() == CV_8UC1 ); |
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GpuMat dst = getOutputMat(_dst, 1, 2, CV_64FC1, stream); |
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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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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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BufferPool pool(stream); |
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GpuMat buf = pool.getBuffer(1, bufSize, CV_8UC1); |
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// detail: https://github.com/opencv/opencv/issues/11063 |
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//NppStreamHandler h(StreamAccessor::getStream(stream)); |
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nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), sz, buf.ptr<Npp8u>(), dst.ptr<Npp64f>(), dst.ptr<Npp64f>() + 1) ); |
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syncOutput(dst, _dst, stream); |
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} |
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void cv::cuda::meanStdDev(InputArray _src, Scalar& mean, Scalar& stddev) |
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{ |
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Stream& stream = Stream::Null(); |
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HostMem dst; |
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meanStdDev(_src, dst, stream); |
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stream.waitForCompletion(); |
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double vals[2]; |
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dst.createMatHeader().copyTo(Mat(1, 2, CV_64FC1, &vals[0])); |
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mean = Scalar(vals[0]); |
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stddev = Scalar(vals[1]); |
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} |
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////////////////////////////////////////////////////////////////////////////// |
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// rectStdDev |
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void cv::cuda::rectStdDev(InputArray _src, InputArray _sqr, OutputArray _dst, Rect rect, Stream& _stream) |
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{ |
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GpuMat src = getInputMat(_src, _stream); |
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GpuMat sqr = getInputMat(_sqr, _stream); |
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CV_Assert( src.type() == CV_32SC1 && sqr.type() == CV_64FC1 ); |
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GpuMat dst = getOutputMat(_dst, src.size(), CV_32FC1, _stream); |
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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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NppiRect nppRect; |
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nppRect.height = rect.height; |
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nppRect.width = rect.width; |
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nppRect.x = rect.x; |
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nppRect.y = rect.y; |
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cudaStream_t stream = StreamAccessor::getStream(_stream); |
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NppStreamHandler h(stream); |
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nppSafeCall( nppiRectStdDev_32s32f_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), sqr.ptr<Npp64f>(), static_cast<int>(sqr.step), |
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dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, nppRect) ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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syncOutput(dst, _dst, _stream); |
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} |
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#endif
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