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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.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#include "opencv2/photo.hpp"
#include <string>
using namespace cv;
using namespace std;
//#define DUMP_RESULTS
#ifdef DUMP_RESULTS
# define DUMP(image, path) imwrite(path, image)
#else
# define DUMP(image, path)
#endif
TEST(Photo_DenoisingGrayscale, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
string original_path = folder + "lena_noised_gaussian_sigma=10.png";
string expected_path = folder + "lena_noised_denoised_grayscale_tw=7_sw=21_h=10.png";
Mat original = imread(original_path, IMREAD_GRAYSCALE);
Mat expected = imread(expected_path, IMREAD_GRAYSCALE);
ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path;
ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
Mat result;
fastNlMeansDenoising(original, result, 10);
DUMP(result, expected_path + ".res.png");
ASSERT_EQ(0, norm(result != expected));
}
TEST(Photo_DenoisingColored, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
string original_path = folder + "lena_noised_gaussian_sigma=10.png";
string expected_path = folder + "lena_noised_denoised_lab12_tw=7_sw=21_h=10_h2=10.png";
Mat original = imread(original_path, IMREAD_COLOR);
Mat expected = imread(expected_path, IMREAD_COLOR);
ASSERT_FALSE(original.empty()) << "Could not load input image " << original_path;
ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
Mat result;
fastNlMeansDenoisingColored(original, result, 10, 10);
DUMP(result, expected_path + ".res.png");
ASSERT_EQ(0, norm(result != expected));
}
TEST(Photo_DenoisingGrayscaleMulti, regression)
{
const int imgs_count = 3;
string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
string expected_path = folder + "lena_noised_denoised_multi_tw=7_sw=21_h=15.png";
Mat expected = imread(expected_path, IMREAD_GRAYSCALE);
ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
vector<Mat> original(imgs_count);
for (int i = 0; i < imgs_count; i++)
{
string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i);
original[i] = imread(original_path, IMREAD_GRAYSCALE);
ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path;
}
Mat result;
fastNlMeansDenoisingMulti(original, result, imgs_count / 2, imgs_count, 15);
DUMP(result, expected_path + ".res.png");
ASSERT_EQ(0, norm(result != expected));
}
TEST(Photo_DenoisingColoredMulti, regression)
{
const int imgs_count = 3;
string folder = string(cvtest::TS::ptr()->get_data_path()) + "denoising/";
string expected_path = folder + "lena_noised_denoised_multi_lab12_tw=7_sw=21_h=10_h2=15.png";
Mat expected = imread(expected_path, IMREAD_COLOR);
ASSERT_FALSE(expected.empty()) << "Could not load reference image " << expected_path;
vector<Mat> original(imgs_count);
for (int i = 0; i < imgs_count; i++)
{
string original_path = format("%slena_noised_gaussian_sigma=20_multi_%d.png", folder.c_str(), i);
original[i] = imread(original_path, IMREAD_COLOR);
ASSERT_FALSE(original[i].empty()) << "Could not load input image " << original_path;
}
Mat result;
fastNlMeansDenoisingColoredMulti(original, result, imgs_count / 2, imgs_count, 10, 15);
DUMP(result, expected_path + ".res.png");
ASSERT_EQ(0, norm(result != expected));
}
TEST(Photo_White, issue_2646)
{
cv::Mat img(50, 50, CV_8UC1, cv::Scalar::all(255));
cv::Mat filtered;
cv::fastNlMeansDenoising(img, filtered);
int nonWhitePixelsCount = (int)img.total() - cv::countNonZero(filtered == img);
ASSERT_EQ(0, nonWhitePixelsCount);
}