169 lines
6.3 KiB
C++
169 lines
6.3 KiB
C++
/*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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