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Open Source Computer Vision Library
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168 lines
6.3 KiB
168 lines
6.3 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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namespace opencv_test { namespace { |
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//#define DUMP_RESULTS |
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#ifdef DUMP_RESULTS |
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# define DUMP(image, path) imwrite(path, image) |
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#else |
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# define DUMP(image, path) |
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#endif |
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TEST(Photo_DenoisingGrayscale, regression) |
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{ |
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/"; |
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string original_path = folder + "lena_noised_gaussian_sigma=10.png"; |
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string expected_path = folder + "lena_noised_denoised_grayscale_tw=7_sw=21_h=10.png"; |
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Mat original = imread(original_path, IMREAD_GRAYSCALE); |
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Mat expected = imread(expected_path, IMREAD_GRAYSCALE); |
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ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path; |
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ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path; |
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Mat result; |
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fastNlMeansDenoising(original, result, 10); |
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DUMP(result, expected_path + ".res.png"); |
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ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2)); |
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} |
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TEST(Photo_DenoisingColored, regression) |
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{ |
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/"; |
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string original_path = folder + "lena_noised_gaussian_sigma=10.png"; |
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string expected_path = folder + "lena_noised_denoised_lab12_tw=7_sw=21_h=10_h2=10.png"; |
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Mat original = imread(original_path, IMREAD_COLOR); |
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Mat expected = imread(expected_path, IMREAD_COLOR); |
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ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path; |
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ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path; |
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Mat result; |
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fastNlMeansDenoisingColored(original, result, 10, 10); |
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DUMP(result, expected_path + ".res.png"); |
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ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2)); |
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} |
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TEST(Photo_DenoisingGrayscaleMulti, regression) |
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{ |
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const int imgs_count = 3; |
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/"; |
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string expected_path = folder + "lena_noised_denoised_multi_tw=7_sw=21_h=15.png"; |
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Mat expected = imread(expected_path, IMREAD_GRAYSCALE); |
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ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path; |
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vector<Mat> original(imgs_count); |
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for (int i = 0; i < imgs_count; i++) |
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{ |
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string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i); |
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original[i] = imread(original_path, IMREAD_GRAYSCALE); |
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ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path; |
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} |
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Mat result; |
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fastNlMeansDenoisingMulti(original, result, imgs_count / 2, imgs_count, 15); |
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DUMP(result, expected_path + ".res.png"); |
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ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2)); |
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} |
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TEST(Photo_DenoisingColoredMulti, regression) |
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{ |
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const int imgs_count = 3; |
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/"; |
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string expected_path = folder + "lena_noised_denoised_multi_lab12_tw=7_sw=21_h=10_h2=15.png"; |
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Mat expected = imread(expected_path, IMREAD_COLOR); |
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ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path; |
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vector<Mat> original(imgs_count); |
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for (int i = 0; i < imgs_count; i++) |
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{ |
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string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i); |
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original[i] = imread(original_path, IMREAD_COLOR); |
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ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path; |
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} |
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Mat result; |
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fastNlMeansDenoisingColoredMulti(original, result, imgs_count / 2, imgs_count, 10, 15); |
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DUMP(result, expected_path + ".res.png"); |
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ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2)); |
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} |
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TEST(Photo_White, issue_2646) |
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{ |
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cv::Mat img(50, 50, CV_8UC1, cv::Scalar::all(255)); |
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cv::Mat filtered; |
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cv::fastNlMeansDenoising(img, filtered); |
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int nonWhitePixelsCount = (int)img.total() - cv::countNonZero(filtered == img); |
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ASSERT_EQ(0, nonWhitePixelsCount); |
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} |
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TEST(Photo_Denoising, speed) |
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{ |
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string imgname = string(cvtest::TS::ptr()->get_data_path()) + "shared/5MP.png"; |
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Mat src = imread(imgname, 0), dst; |
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double t = (double)getTickCount(); |
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fastNlMeansDenoising(src, dst, 5, 7, 21); |
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t = (double)getTickCount() - t; |
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printf("execution time: %gms\n", t*1000./getTickFrequency()); |
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} |
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}} // namespace
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