1 /*M/////////////////////////////////////////////////////////////////////////////////////// 2 // 3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4 // 5 // By downloading, copying, installing or using the software you agree to this license. 6 // If you do not agree to this license, do not download, install, 7 // copy or use the software. 8 // 9 // 10 // License Agreement 11 // For Open Source Computer Vision Library 12 // 13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. 14 // Copyright (C) 2009, Willow Garage Inc., all rights reserved. 15 // Third party copyrights are property of their respective owners. 16 // 17 // Redistribution and use in source and binary forms, with or without modification, 18 // are permitted provided that the following conditions are met: 19 // 20 // * Redistribution's of source code must retain the above copyright notice, 21 // this list of conditions and the following disclaimer. 22 // 23 // * Redistribution's in binary form must reproduce the above copyright notice, 24 // this list of conditions and the following disclaimer in the documentation 25 // and/or other materials provided with the distribution. 26 // 27 // * The name of the copyright holders may not be used to endorse or promote products 28 // derived from this software without specific prior written permission. 29 // 30 // This software is provided by the copyright holders and contributors "as is" and 31 // any express or implied warranties, including, but not limited to, the implied 32 // warranties of merchantability and fitness for a particular purpose are disclaimed. 33 // In no event shall the Intel Corporation or contributors be liable for any direct, 34 // indirect, incidental, special, exemplary, or consequential damages 35 // (including, but not limited to, procurement of substitute goods or services; 36 // loss of use, data, or profits; or business interruption) however caused 37 // and on any theory of liability, whether in contract, strict liability, 38 // or tort (including negligence or otherwise) arising in any way out of 39 // the use of this software, even if advised of the possibility of such damage. 40 // 41 //M*/ 42 43 #include "test_precomp.hpp" 44 #include "opencv2/photo.hpp" 45 #include <string> 46 47 using namespace cv; 48 using namespace std; 49 50 //#define DUMP_RESULTS 51 52 #ifdef DUMP_RESULTS 53 # define DUMP(image, path) imwrite(path, image) 54 #else 55 # define DUMP(image, path) 56 #endif 57 58 59 TEST(Photo_DenoisingGrayscale, regression) 60 { 61 string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/"; 62 string original_path = folder + "lena_noised_gaussian_sigma=10.png"; 63 string expected_path = folder + "lena_noised_denoised_grayscale_tw=7_sw=21_h=10.png"; 64 65 Mat original = imread(original_path, IMREAD_GRAYSCALE); 66 Mat expected = imread(expected_path, IMREAD_GRAYSCALE); 67 68 ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path; 69 ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path; 70 71 Mat result; 72 fastNlMeansDenoising(original, result, 10); 73 74 DUMP(result, expected_path + ".res.png"); 75 76 ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2)); 77 } 78 79 TEST(Photo_DenoisingColored, regression) 80 { 81 string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/"; 82 string original_path = folder + "lena_noised_gaussian_sigma=10.png"; 83 string expected_path = folder + "lena_noised_denoised_lab12_tw=7_sw=21_h=10_h2=10.png"; 84 85 Mat original = imread(original_path, IMREAD_COLOR); 86 Mat expected = imread(expected_path, IMREAD_COLOR); 87 88 ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path; 89 ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path; 90 91 Mat result; 92 fastNlMeansDenoisingColored(original, result, 10, 10); 93 94 DUMP(result, expected_path + ".res.png"); 95 96 ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2)); 97 } 98 99 TEST(Photo_DenoisingGrayscaleMulti, regression) 100 { 101 const int imgs_count = 3; 102 string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/"; 103 104 string expected_path = folder + "lena_noised_denoised_multi_tw=7_sw=21_h=15.png"; 105 Mat expected = imread(expected_path, IMREAD_GRAYSCALE); 106 ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path; 107 108 vector<Mat> original(imgs_count); 109 for (int i = 0; i < imgs_count; i++) 110 { 111 string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i); 112 original[i] = imread(original_path, IMREAD_GRAYSCALE); 113 ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path; 114 } 115 116 Mat result; 117 fastNlMeansDenoisingMulti(original, result, imgs_count / 2, imgs_count, 15); 118 119 DUMP(result, expected_path + ".res.png"); 120 121 ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2)); 122 } 123 124 TEST(Photo_DenoisingColoredMulti, regression) 125 { 126 const int imgs_count = 3; 127 string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/"; 128 129 string expected_path = folder + "lena_noised_denoised_multi_lab12_tw=7_sw=21_h=10_h2=15.png"; 130 Mat expected = imread(expected_path, IMREAD_COLOR); 131 ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path; 132 133 vector<Mat> original(imgs_count); 134 for (int i = 0; i < imgs_count; i++) 135 { 136 string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i); 137 original[i] = imread(original_path, IMREAD_COLOR); 138 ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path; 139 } 140 141 Mat result; 142 fastNlMeansDenoisingColoredMulti(original, result, imgs_count / 2, imgs_count, 10, 15); 143 144 DUMP(result, expected_path + ".res.png"); 145 146 ASSERT_EQ(0, cvtest::norm(result, expected, NORM_L2)); 147 } 148 149 TEST(Photo_White, issue_2646) 150 { 151 cv::Mat img(50, 50, CV_8UC1, cv::Scalar::all(255)); 152 cv::Mat filtered; 153 cv::fastNlMeansDenoising(img, filtered); 154 155 int nonWhitePixelsCount = (int)img.total() - cv::countNonZero(filtered == img); 156 157 ASSERT_EQ(0, nonWhitePixelsCount); 158 } 159 160 TEST(Photo_Denoising, speed) 161 { 162 string imgname = string(cvtest::TS::ptr()->get_data_path()) + "shared/5MP.png"; 163 Mat src = imread(imgname, 0), dst; 164 165 double t = (double)getTickCount(); 166 fastNlMeansDenoising(src, dst, 5, 7, 21); 167 t = (double)getTickCount() - t; 168 printf("execution time: %gms\n", t*1000./getTickFrequency()); 169 } 170
