/*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/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, CV_LOAD_IMAGE_GRAYSCALE);
    Mat expected = imread(expected_path, CV_LOAD_IMAGE_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, CV_LOAD_IMAGE_COLOR);
    Mat expected = imread(expected_path, CV_LOAD_IMAGE_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, CV_LOAD_IMAGE_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, CV_LOAD_IMAGE_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, CV_LOAD_IMAGE_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, CV_LOAD_IMAGE_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));
}