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#include "test_precomp.hpp"

namespace opencv_test { namespace {

//#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, cvtest::norm(result, expected, NORM_L2));
}

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, cvtest::norm(result, expected, NORM_L2));
}

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, cvtest::norm(result, expected, NORM_L2));
}

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, cvtest::norm(result, expected, NORM_L2));
}

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);
}

TEST(Photo_Denoising, speed)
{
    string imgname = string(cvtest::TS::ptr()->get_data_path()) + "shared/5MP.png";
    Mat src = imread(imgname, IMREAD_GRAYSCALE), dst;

    double t = (double)getTickCount();
    fastNlMeansDenoising(src, dst, 5, 7, 21);
    t = (double)getTickCount() - t;
    printf("execution time: %gms\n", t*1000./getTickFrequency());
}

}} // namespace