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245 lines
8.4 KiB
C++
245 lines
8.4 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 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, GpuMat&) { throw_no_cuda(); return 0.0; }
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double cv::cuda::norm(InputArray, InputArray, GpuMat&, int) { throw_no_cuda(); return 0.0; }
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Scalar cv::cuda::sum(InputArray, InputArray, GpuMat&) { throw_no_cuda(); return Scalar(); }
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Scalar cv::cuda::absSum(InputArray, InputArray, GpuMat&) { throw_no_cuda(); return Scalar(); }
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Scalar cv::cuda::sqrSum(InputArray, InputArray, GpuMat&) { throw_no_cuda(); return Scalar(); }
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void cv::cuda::minMax(InputArray, double*, double*, InputArray, GpuMat&) { throw_no_cuda(); }
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void cv::cuda::minMaxLoc(InputArray, double*, double*, Point*, Point*, InputArray, GpuMat&, GpuMat&) { throw_no_cuda(); }
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int cv::cuda::countNonZero(InputArray, GpuMat&) { throw_no_cuda(); return 0; }
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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&, GpuMat&) { 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, GpuMat&, GpuMat&) { throw_no_cuda(); }
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void cv::cuda::integral(InputArray, OutputArray, GpuMat&, Stream&) { throw_no_cuda(); }
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void cv::cuda::sqrIntegral(InputArray, OutputArray, GpuMat&, Stream&) { throw_no_cuda(); }
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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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// norm
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double cv::cuda::norm(InputArray _src, int normType, InputArray _mask, GpuMat& buf)
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{
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GpuMat src = _src.getGpuMat();
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GpuMat mask = _mask.getGpuMat();
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CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
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CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size() && src.channels() == 1) );
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GpuMat src_single_channel = src.reshape(1);
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if (normType == NORM_L1)
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return cuda::absSum(src_single_channel, mask, buf)[0];
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if (normType == NORM_L2)
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return std::sqrt(cuda::sqrSum(src_single_channel, mask, buf)[0]);
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// NORM_INF
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double min_val, max_val;
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cuda::minMax(src_single_channel, &min_val, &max_val, mask, buf);
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return std::max(std::abs(min_val), std::abs(max_val));
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}
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////////////////////////////////////////////////////////////////////////
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// meanStdDev
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void cv::cuda::meanStdDev(InputArray _src, Scalar& mean, Scalar& stddev, GpuMat& buf)
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{
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GpuMat src = _src.getGpuMat();
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CV_Assert( src.type() == CV_8UC1 );
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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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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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// 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 = _src.getGpuMat();
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GpuMat sqr = _sqr.getGpuMat();
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CV_Assert( src.type() == CV_32SC1 && sqr.type() == CV_64FC1 );
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_dst.create(src.size(), CV_32FC1);
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GpuMat dst = _dst.getGpuMat();
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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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}
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////////////////////////////////////////////////////////////////////////
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// normalize
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void cv::cuda::normalize(InputArray _src, OutputArray dst, double a, double b, int norm_type, int dtype, InputArray mask, GpuMat& norm_buf, GpuMat& cvt_buf)
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{
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GpuMat src = _src.getGpuMat();
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double scale = 1, shift = 0;
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if (norm_type == NORM_MINMAX)
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{
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double smin = 0, smax = 0;
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double dmin = std::min(a, b), dmax = std::max(a, b);
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cuda::minMax(src, &smin, &smax, mask, norm_buf);
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scale = (dmax - dmin) * (smax - smin > std::numeric_limits<double>::epsilon() ? 1.0 / (smax - smin) : 0.0);
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shift = dmin - smin * scale;
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}
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else if (norm_type == NORM_L2 || norm_type == NORM_L1 || norm_type == NORM_INF)
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{
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scale = cuda::norm(src, norm_type, mask, norm_buf);
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scale = scale > std::numeric_limits<double>::epsilon() ? a / scale : 0.0;
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shift = 0;
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}
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else
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{
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CV_Error(cv::Error::StsBadArg, "Unknown/unsupported norm type");
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}
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if (mask.empty())
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{
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src.convertTo(dst, dtype, scale, shift);
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}
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else
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{
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src.convertTo(cvt_buf, dtype, scale, shift);
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cvt_buf.copyTo(dst, mask);
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}
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}
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#endif
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