602 lines
22 KiB
C++
602 lines
22 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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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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#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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CV_Assert(src.type() == CV_8UC1);
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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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nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), src.step, sz, 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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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 sqrt(sqrSum(src_single_channel, buf)[0]);
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if (normType == NORM_INF)
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{
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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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CV_Error(CV_StsBadArg, "norm: unsupported norm type");
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return 0;
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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_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
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CV_Assert(src1.type() == CV_8UC1);
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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>(), src1.step, src2.ptr<Npp8u>(), 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 mathfunc
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{
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template <typename T>
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void sumCaller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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template <typename T>
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void sumMultipassCaller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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template <typename T>
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void absSumCaller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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template <typename T>
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void absSumMultipassCaller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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template <typename T>
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void sqrSumCaller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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template <typename T>
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void sqrSumMultipassCaller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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namespace sums
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{
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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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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 mathfunc;
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typedef void (*Caller)(const DevMem2D, PtrStep, double*, int);
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static Caller multipass_callers[7] = {
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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>, 0 };
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static Caller singlepass_callers[7] = {
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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>, 0 };
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Size buf_size;
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sums::getBufSizeRequired(src.cols, src.rows, src.channels(),
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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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if (!caller) CV_Error(CV_StsBadArg, "sum: unsupported type");
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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 mathfunc;
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typedef void (*Caller)(const DevMem2D, PtrStep, double*, int);
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static Caller multipass_callers[7] = {
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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>, 0 };
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static Caller singlepass_callers[7] = {
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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>, 0 };
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Size buf_size;
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sums::getBufSizeRequired(src.cols, src.rows, src.channels(),
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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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if (!caller) CV_Error(CV_StsBadArg, "absSum: unsupported type");
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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 mathfunc;
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typedef void (*Caller)(const DevMem2D, PtrStep, double*, int);
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static Caller multipass_callers[7] = {
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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>, 0 };
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static Caller singlepass_callers[7] = {
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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>, 0 };
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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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sums::getBufSizeRequired(src.cols, src.rows, src.channels(),
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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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if (!caller) CV_Error(CV_StsBadArg, "sqrSum: unsupported type");
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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 mathfunc { namespace minmax {
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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 DevMem2D src, double* minval, double* maxval, PtrStep buf);
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template <typename T>
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void minMaxMaskCaller(const DevMem2D src, const PtrStep mask, double* minval, double* maxval, PtrStep buf);
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template <typename T>
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void minMaxMultipassCaller(const DevMem2D src, double* minval, double* maxval, PtrStep buf);
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template <typename T>
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void minMaxMaskMultipassCaller(const DevMem2D src, const PtrStep mask, double* minval, double* maxval, PtrStep buf);
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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 mathfunc::minmax;
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typedef void (*Caller)(const DevMem2D, double*, double*, PtrStep);
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typedef void (*MaskedCaller)(const DevMem2D, const PtrStep, double*, double*, PtrStep);
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static Caller multipass_callers[7] = {
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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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static Caller singlepass_callers[7] = {
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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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static MaskedCaller masked_multipass_callers[7] = {
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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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static MaskedCaller masked_singlepass_callers[7] = {
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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>,
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minMaxMaskCaller<double> };
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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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CV_Assert(src.type() != CV_64F || (TargetArchs::builtWith(NATIVE_DOUBLE) &&
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DeviceInfo().supports(NATIVE_DOUBLE)));
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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, 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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if (!caller) CV_Error(CV_StsBadArg, "minMax: unsupported type");
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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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if (!caller) CV_Error(CV_StsBadArg, "minMax: unsupported type");
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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 mathfunc { namespace minmaxloc {
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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 DevMem2D src, double* minval, double* maxval,
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int minloc[2], int maxloc[2], PtrStep valBuf, PtrStep locBuf);
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template <typename T>
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void minMaxLocMaskCaller(const DevMem2D src, const PtrStep mask, double* minval, double* maxval,
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int minloc[2], int maxloc[2], PtrStep valBuf, PtrStep locBuf);
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template <typename T>
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void minMaxLocMultipassCaller(const DevMem2D src, double* minval, double* maxval,
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int minloc[2], int maxloc[2], PtrStep valBuf, PtrStep locBuf);
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template <typename T>
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void minMaxLocMaskMultipassCaller(const DevMem2D src, const PtrStep mask, double* minval, double* maxval,
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int minloc[2], int maxloc[2], PtrStep valBuf, PtrStep locBuf);
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}}}}
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void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, const GpuMat& mask)
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{
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GpuMat valBuf, locBuf;
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minMaxLoc(src, minVal, maxVal, minLoc, maxLoc, mask, valBuf, locBuf);
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}
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void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
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const GpuMat& mask, GpuMat& valBuf, GpuMat& locBuf)
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{
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using namespace mathfunc::minmaxloc;
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typedef void (*Caller)(const DevMem2D, double*, double*, int[2], int[2], PtrStep, PtrStep);
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typedef void (*MaskedCaller)(const DevMem2D, const PtrStep, double*, double*, int[2], int[2], PtrStep, PtrStep);
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static Caller multipass_callers[7] = {
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minMaxLocMultipassCaller<unsigned char>, minMaxLocMultipassCaller<char>,
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minMaxLocMultipassCaller<unsigned short>, minMaxLocMultipassCaller<short>,
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minMaxLocMultipassCaller<int>, minMaxLocMultipassCaller<float>, 0 };
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static Caller singlepass_callers[7] = {
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minMaxLocCaller<unsigned char>, minMaxLocCaller<char>,
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minMaxLocCaller<unsigned short>, minMaxLocCaller<short>,
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minMaxLocCaller<int>, minMaxLocCaller<float>, minMaxLocCaller<double> };
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static MaskedCaller masked_multipass_callers[7] = {
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minMaxLocMaskMultipassCaller<unsigned char>, minMaxLocMaskMultipassCaller<char>,
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minMaxLocMaskMultipassCaller<unsigned short>, minMaxLocMaskMultipassCaller<short>,
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minMaxLocMaskMultipassCaller<int>, minMaxLocMaskMultipassCaller<float>, 0 };
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static MaskedCaller masked_singlepass_callers[7] = {
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minMaxLocMaskCaller<unsigned char>, minMaxLocMaskCaller<char>,
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minMaxLocMaskCaller<unsigned short>, minMaxLocMaskCaller<short>,
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minMaxLocMaskCaller<int>, minMaxLocMaskCaller<float>,
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minMaxLocMaskCaller<double> };
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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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CV_Assert(src.type() != CV_64F || (TargetArchs::builtWith(NATIVE_DOUBLE) &&
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DeviceInfo().supports(NATIVE_DOUBLE)));
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double minVal_; if (!minVal) minVal = &minVal_;
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double maxVal_; if (!maxVal) maxVal = &maxVal_;
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int minLoc_[2];
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int maxLoc_[2];
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Size valbuf_size, locbuf_size;
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getBufSizeRequired(src.cols, src.rows, src.elemSize(), valbuf_size.width,
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valbuf_size.height, locbuf_size.width, locbuf_size.height);
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ensureSizeIsEnough(valbuf_size, CV_8U, valBuf);
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ensureSizeIsEnough(locbuf_size, CV_8U, locBuf);
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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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if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
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caller(src, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
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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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if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
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caller(src, mask, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
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}
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if (minLoc) { minLoc->x = minLoc_[0]; minLoc->y = minLoc_[1]; }
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if (maxLoc) { maxLoc->x = maxLoc_[0]; maxLoc->y = maxLoc_[1]; }
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}
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//////////////////////////////////////////////////////////////////////////////
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// Count non-zero elements
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namespace cv { namespace gpu { namespace mathfunc { namespace countnonzero {
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void getBufSizeRequired(int cols, int rows, int& bufcols, int& bufrows);
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template <typename T>
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int countNonZeroCaller(const DevMem2D src, PtrStep buf);
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template <typename T>
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int countNonZeroMultipassCaller(const DevMem2D src, PtrStep buf);
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|
|
}}}}
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int cv::gpu::countNonZero(const GpuMat& src)
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|
{
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GpuMat buf;
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return countNonZero(src, buf);
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|
}
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|
|
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int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
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|
{
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|
using namespace mathfunc::countnonzero;
|
|
|
|
typedef int (*Caller)(const DevMem2D src, PtrStep 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.channels() == 1);
|
|
|
|
CV_Assert(src.type() != CV_64F || (TargetArchs::builtWith(NATIVE_DOUBLE) &&
|
|
DeviceInfo().supports(NATIVE_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()];
|
|
if (!caller) CV_Error(CV_StsBadArg, "countNonZero: unsupported type");
|
|
return caller(src, buf);
|
|
}
|
|
|
|
#endif
|