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Open Source Computer Vision Library
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185 lines
6.0 KiB
185 lines
6.0 KiB
/*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 "test_precomp.hpp" |
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#ifdef HAVE_CUDA |
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using namespace cvtest; |
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//////////////////////////////////////////////////////// |
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// BilateralFilter |
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PARAM_TEST_CASE(BilateralFilter, cv::gpu::DeviceInfo, cv::Size, MatType) |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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cv::Size size; |
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int type; |
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int kernel_size; |
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float sigma_color; |
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float sigma_spatial; |
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virtual void SetUp() |
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{ |
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devInfo = GET_PARAM(0); |
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size = GET_PARAM(1); |
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type = GET_PARAM(2); |
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kernel_size = 5; |
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sigma_color = 10.f; |
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sigma_spatial = 3.5f; |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(BilateralFilter, Accuracy) |
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{ |
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cv::Mat src = randomMat(size, type); |
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src.convertTo(src, type); |
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cv::gpu::GpuMat dst; |
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cv::gpu::bilateralFilter(loadMat(src), dst, kernel_size, sigma_color, sigma_spatial); |
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cv::Mat dst_gold; |
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cv::bilateralFilter(src, dst_gold, kernel_size, sigma_color, sigma_spatial); |
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EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-3 : 1.0); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Denoising, BilateralFilter, testing::Combine( |
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ALL_DEVICES, |
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testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(639, 481)), |
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testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_32FC1), MatType(CV_32FC3)) |
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)); |
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//////////////////////////////////////////////////////// |
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// Brute Force Non local means |
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struct BruteForceNonLocalMeans: testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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virtual void SetUp() |
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{ |
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devInfo = GetParam(); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(BruteForceNonLocalMeans, Regression) |
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{ |
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using cv::gpu::GpuMat; |
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cv::Mat bgr = readImage("denoising/lena_noised_gaussian_sigma=20_multi_0.png", cv::IMREAD_COLOR); |
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ASSERT_FALSE(bgr.empty()); |
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cv::Mat gray; |
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cv::cvtColor(bgr, gray, CV_BGR2GRAY); |
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GpuMat dbgr, dgray; |
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cv::gpu::nonLocalMeans(GpuMat(bgr), dbgr, 20); |
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cv::gpu::nonLocalMeans(GpuMat(gray), dgray, 20); |
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#if 0 |
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dumpImage("denoising/nlm_denoised_lena_bgr.png", cv::Mat(dbgr)); |
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dumpImage("denoising/nlm_denoised_lena_gray.png", cv::Mat(dgray)); |
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#endif |
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cv::Mat bgr_gold = readImage("denoising/nlm_denoised_lena_bgr.png", cv::IMREAD_COLOR); |
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cv::Mat gray_gold = readImage("denoising/nlm_denoised_lena_gray.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(bgr_gold.empty() || gray_gold.empty()); |
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EXPECT_MAT_NEAR(bgr_gold, dbgr, 1e-4); |
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EXPECT_MAT_NEAR(gray_gold, dgray, 1e-4); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Denoising, BruteForceNonLocalMeans, ALL_DEVICES); |
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//////////////////////////////////////////////////////// |
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// Fast Force Non local means |
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struct FastNonLocalMeans: testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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cv::gpu::DeviceInfo devInfo; |
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virtual void SetUp() |
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{ |
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devInfo = GetParam(); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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}; |
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GPU_TEST_P(FastNonLocalMeans, Regression) |
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{ |
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using cv::gpu::GpuMat; |
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cv::Mat bgr = readImage("denoising/lena_noised_gaussian_sigma=20_multi_0.png", cv::IMREAD_COLOR); |
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ASSERT_FALSE(bgr.empty()); |
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cv::Mat gray; |
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cv::cvtColor(bgr, gray, CV_BGR2GRAY); |
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GpuMat dbgr, dgray; |
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cv::gpu::FastNonLocalMeansDenoising fnlmd; |
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fnlmd.simpleMethod(GpuMat(gray), dgray, 20); |
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fnlmd.labMethod(GpuMat(bgr), dbgr, 20, 10); |
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#if 0 |
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dumpImage("denoising/fnlm_denoised_lena_bgr.png", cv::Mat(dbgr)); |
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dumpImage("denoising/fnlm_denoised_lena_gray.png", cv::Mat(dgray)); |
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#endif |
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cv::Mat bgr_gold = readImage("denoising/fnlm_denoised_lena_bgr.png", cv::IMREAD_COLOR); |
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cv::Mat gray_gold = readImage("denoising/fnlm_denoised_lena_gray.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(bgr_gold.empty() || gray_gold.empty()); |
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EXPECT_MAT_NEAR(bgr_gold, dbgr, 1); |
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EXPECT_MAT_NEAR(gray_gold, dgray, 1); |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Denoising, FastNonLocalMeans, ALL_DEVICES); |
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#endif // HAVE_CUDA
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