opencv/modules/photo/perf/perf_cuda.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
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//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// this list of conditions and the following disclaimer.
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//M*/
#include "perf_precomp.hpp"
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#include "opencv2/photo/cuda.hpp"
#include "opencv2/ts/cuda_perf.hpp"
#include "opencv2/opencv_modules.hpp"
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#if defined (HAVE_CUDA) && defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAIMGPROC)
namespace opencv_test { namespace {
using namespace perf;
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#define CUDA_DENOISING_IMAGE_SIZES testing::Values(perf::szVGA, perf::sz720p)
//////////////////////////////////////////////////////////////////////
// nonLocalMeans
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DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int);
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PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, CUDA_NonLocalMeans,
Combine(CUDA_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
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CUDA_CHANNELS_1_3,
Values(21),
Values(5)))
{
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declare.time(600.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int search_widow_size = GET_PARAM(3);
const int block_size = GET_PARAM(4);
const float h = 10;
const int borderMode = cv::BORDER_REFLECT101;
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
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if (PERF_RUN_CUDA())
{
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const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
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TEST_CYCLE() cv::cuda::nonLocalMeans(d_src, dst, h, search_widow_size, block_size, borderMode);
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CUDA_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
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}
//////////////////////////////////////////////////////////////////////
// fastNonLocalMeans
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DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int);
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PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, CUDA_FastNonLocalMeans,
Combine(CUDA_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
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CUDA_CHANNELS_1_3,
Values(21),
Values(7)))
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{
declare.time(60.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int search_widow_size = GET_PARAM(2);
const int block_size = GET_PARAM(3);
const float h = 10;
const int type = CV_MAKE_TYPE(depth, 1);
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cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
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if (PERF_RUN_CUDA())
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{
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const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
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TEST_CYCLE() cv::cuda::fastNlMeansDenoising(d_src, dst, h, search_widow_size, block_size);
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CUDA_SANITY_CHECK(dst);
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}
else
{
cv::Mat dst;
TEST_CYCLE() cv::fastNlMeansDenoising(src, dst, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
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}
}
//////////////////////////////////////////////////////////////////////
// fastNonLocalMeans (colored)
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DEF_PARAM_TEST(Sz_Depth_WinSz_BlockSz, cv::Size, MatDepth, int, int);
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PERF_TEST_P(Sz_Depth_WinSz_BlockSz, CUDA_FastNonLocalMeansColored,
Combine(CUDA_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
Values(21),
Values(7)))
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{
declare.time(60.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int search_widow_size = GET_PARAM(2);
const int block_size = GET_PARAM(3);
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const float h = 10;
const int type = CV_MAKE_TYPE(depth, 3);
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cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
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if (PERF_RUN_CUDA())
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{
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const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
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TEST_CYCLE() cv::cuda::fastNlMeansDenoisingColored(d_src, dst, h, h, search_widow_size, block_size);
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CUDA_SANITY_CHECK(dst);
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}
else
{
cv::Mat dst;
TEST_CYCLE() cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
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}
}
}} // namespace
#endif