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424 lines
17 KiB
C++
424 lines
17 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&, 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::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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////////////////////////////////////////////////////////////////////////
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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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nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), src.step, sz, mean.val, stddev.val) );
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}
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////////////////////////////////////////////////////////////////////////
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// norm
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double cv::gpu::norm(const GpuMat& src1, int normType)
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{
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return norm(src1, GpuMat(src1.size(), src1.type(), Scalar::all(0.0)), normType);
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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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nppSafeCall( npp_norm_diff_func[funcIdx](src1.ptr<Npp8u>(), src1.step,
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src2.ptr<Npp8u>(), src2.step,
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sz, &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 sum_caller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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template <typename T>
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void sum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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template <typename T>
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void sqsum_caller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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template <typename T>
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void sqsum_multipass_caller(const DevMem2D src, PtrStep buf, double* sum, int cn);
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namespace sum
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{
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void get_buf_size_required(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 const Caller callers[2][7] =
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{ { sum_multipass_caller<unsigned char>, sum_multipass_caller<char>,
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sum_multipass_caller<unsigned short>, sum_multipass_caller<short>,
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sum_multipass_caller<int>, sum_multipass_caller<float>, 0 },
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{ sum_caller<unsigned char>, sum_caller<char>,
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sum_caller<unsigned short>, sum_caller<short>,
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sum_caller<int>, sum_caller<float>, 0 } };
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Size bufSize;
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sum::get_buf_size_required(src.cols, src.rows, src.channels(), bufSize.width, bufSize.height);
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buf.create(bufSize, CV_8U);
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Caller caller = callers[hasAtomicsSupport(getDevice())][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::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 const Caller callers[2][7] =
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{ { sqsum_multipass_caller<unsigned char>, sqsum_multipass_caller<char>,
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sqsum_multipass_caller<unsigned short>, sqsum_multipass_caller<short>,
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sqsum_multipass_caller<int>, sqsum_multipass_caller<float>, 0 },
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{ sqsum_caller<unsigned char>, sqsum_caller<char>,
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sqsum_caller<unsigned short>, sqsum_caller<short>,
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sqsum_caller<int>, sqsum_caller<float>, 0 } };
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Size bufSize;
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sum::get_buf_size_required(src.cols, src.rows, src.channels(), bufSize.width, bufSize.height);
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buf.create(bufSize, CV_8U);
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Caller caller = callers[hasAtomicsSupport(getDevice())][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 get_buf_size_required(int cols, int rows, int elem_size, int& bufcols, int& bufrows);
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template <typename T>
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void min_max_caller(const DevMem2D src, double* minval, double* maxval, PtrStep buf);
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template <typename T>
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void min_max_mask_caller(const DevMem2D src, const PtrStep mask, double* minval, double* maxval, PtrStep buf);
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template <typename T>
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void min_max_multipass_caller(const DevMem2D src, double* minval, double* maxval, PtrStep buf);
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template <typename T>
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void min_max_mask_multipass_caller(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 const Caller callers[2][7] =
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{ { min_max_multipass_caller<unsigned char>, min_max_multipass_caller<char>,
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min_max_multipass_caller<unsigned short>, min_max_multipass_caller<short>,
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min_max_multipass_caller<int>, min_max_multipass_caller<float>, 0 },
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{ min_max_caller<unsigned char>, min_max_caller<char>,
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min_max_caller<unsigned short>, min_max_caller<short>,
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min_max_caller<int>, min_max_caller<float>, min_max_caller<double> } };
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static const MaskedCaller masked_callers[2][7] =
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{ { min_max_mask_multipass_caller<unsigned char>, min_max_mask_multipass_caller<char>,
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min_max_mask_multipass_caller<unsigned short>, min_max_mask_multipass_caller<short>,
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min_max_mask_multipass_caller<int>, min_max_mask_multipass_caller<float>, 0 },
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{ min_max_mask_caller<unsigned char>, min_max_mask_caller<char>,
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min_max_mask_caller<unsigned short>, min_max_mask_caller<short>,
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min_max_mask_caller<int>, min_max_mask_caller<float>,
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min_max_mask_caller<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 || hasNativeDoubleSupport(getDevice()));
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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 bufSize;
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get_buf_size_required(src.cols, src.rows, src.elemSize(), bufSize.width, bufSize.height);
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buf.create(bufSize, CV_8U);
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if (mask.empty())
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{
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Caller caller = callers[hasAtomicsSupport(getDevice())][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 caller = masked_callers[hasAtomicsSupport(getDevice())][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 get_buf_size_required(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 min_max_loc_caller(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 min_max_loc_mask_caller(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 min_max_loc_multipass_caller(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 min_max_loc_mask_multipass_caller(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 const Caller callers[2][7] =
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{ { min_max_loc_multipass_caller<unsigned char>, min_max_loc_multipass_caller<char>,
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min_max_loc_multipass_caller<unsigned short>, min_max_loc_multipass_caller<short>,
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min_max_loc_multipass_caller<int>, min_max_loc_multipass_caller<float>, 0 },
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{ min_max_loc_caller<unsigned char>, min_max_loc_caller<char>,
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min_max_loc_caller<unsigned short>, min_max_loc_caller<short>,
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min_max_loc_caller<int>, min_max_loc_caller<float>, min_max_loc_caller<double> } };
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static const MaskedCaller masked_callers[2][7] =
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{ { min_max_loc_mask_multipass_caller<unsigned char>, min_max_loc_mask_multipass_caller<char>,
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min_max_loc_mask_multipass_caller<unsigned short>, min_max_loc_mask_multipass_caller<short>,
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min_max_loc_mask_multipass_caller<int>, min_max_loc_mask_multipass_caller<float>, 0 },
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{ min_max_loc_mask_caller<unsigned char>, min_max_loc_mask_caller<char>,
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min_max_loc_mask_caller<unsigned short>, min_max_loc_mask_caller<short>,
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min_max_loc_mask_caller<int>, min_max_loc_mask_caller<float>, min_max_loc_mask_caller<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 || hasNativeDoubleSupport(getDevice()));
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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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get_buf_size_required(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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valbuf.create(valbuf_size, CV_8U);
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locbuf.create(locbuf_size, CV_8U);
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if (mask.empty())
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{
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Caller caller = callers[hasAtomicsSupport(getDevice())][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 caller = masked_callers[hasAtomicsSupport(getDevice())][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 get_buf_size_required(int cols, int rows, int& bufcols, int& bufrows);
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template <typename T>
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int count_non_zero_caller(const DevMem2D src, PtrStep buf);
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template <typename T>
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int count_non_zero_multipass_caller(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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int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
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{
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using namespace mathfunc::countnonzero;
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typedef int (*Caller)(const DevMem2D src, PtrStep buf);
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static const Caller callers[2][7] =
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{ { count_non_zero_multipass_caller<unsigned char>, count_non_zero_multipass_caller<char>,
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count_non_zero_multipass_caller<unsigned short>, count_non_zero_multipass_caller<short>,
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count_non_zero_multipass_caller<int>, count_non_zero_multipass_caller<float>, 0},
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{ count_non_zero_caller<unsigned char>, count_non_zero_caller<char>,
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count_non_zero_caller<unsigned short>, count_non_zero_caller<short>,
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count_non_zero_caller<int>, count_non_zero_caller<float>, count_non_zero_caller<double> } };
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CV_Assert(src.channels() == 1);
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CV_Assert(src.type() != CV_64F || hasNativeDoubleSupport(getDevice()));
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|
Size buf_size;
|
|
get_buf_size_required(src.cols, src.rows, buf_size.width, buf_size.height);
|
|
buf.create(buf_size, CV_8U);
|
|
|
|
Caller caller = callers[hasAtomicsSupport(getDevice())][src.type()];
|
|
if (!caller) CV_Error(CV_StsBadArg, "countNonZero: unsupported type");
|
|
return caller(src, buf);
|
|
}
|
|
|
|
#endif
|