Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// 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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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// and on any theory of liability, whether in contract, strict liability,
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//M*/
#include "perf_precomp.hpp"
#include "opencv2/ts/gpu_perf.hpp"
using namespace std;
using namespace testing;
using namespace perf;
#define GPU_DENOISING_IMAGE_SIZES testing::Values(perf::szVGA, perf::sz720p)
//////////////////////////////////////////////////////////////////////
// BilateralFilter
DEF_PARAM_TEST(Sz_Depth_Cn_KernelSz, cv::Size, MatDepth, MatCn, int);
PERF_TEST_P(Sz_Depth_Cn_KernelSz, Denoising_BilateralFilter,
Combine(GPU_DENOISING_IMAGE_SIZES,
Values(CV_8U, CV_32F),
GPU_CHANNELS_1_3,
Values(3, 5, 9)))
{
declare.time(60.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int kernel_size = GET_PARAM(3);
const float sigma_color = 7;
const float sigma_spatial = 5;
const int borderMode = cv::BORDER_REFLECT101;
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::bilateralFilter(d_src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::bilateralFilter(src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// nonLocalMeans
DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int);
// disabled, since it takes too much time
PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, DISABLED_Denoising_NonLocalMeans,
Combine(GPU_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
GPU_CHANNELS_1_3,
Values(21),
Values(5)))
{
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);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::nonLocalMeans(d_src, dst, h, search_widow_size, block_size, borderMode);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// fastNonLocalMeans
DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int);
// disabled, since it takes too much time
PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, DISABLED_Denoising_FastNonLocalMeans,
Combine(GPU_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
GPU_CHANNELS_1_3,
Values(21),
Values(7)))
{
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);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::FastNonLocalMeansDenoising fnlmd;
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() fnlmd.simpleMethod(d_src, dst, h, search_widow_size, block_size);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::fastNlMeansDenoising(src, dst, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// fastNonLocalMeans (colored)
DEF_PARAM_TEST(Sz_Depth_WinSz_BlockSz, cv::Size, MatDepth, int, int);
PERF_TEST_P(Sz_Depth_WinSz_BlockSz, Denoising_FastNonLocalMeansColored,
Combine(GPU_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
Values(21),
Values(7)))
{
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, 3);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::FastNonLocalMeansDenoising fnlmd;
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() fnlmd.labMethod(d_src, dst, h, h, search_widow_size, block_size);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
}
}