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#include "test_precomp.hpp"
#include "opencv2/photo.hpp"
#include <string>

using namespace cv;
using namespace std;

static const double numerical_precision = 1.;

TEST(Photo_NPR_EdgePreserveSmoothing_RecursiveFilter, regression)
{
    string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
    string original_path = folder + "test1.png";

    Mat source = imread(original_path, IMREAD_COLOR);

    ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;

    Mat result;
    edgePreservingFilter(source,result,1);

    Mat reference = imread(folder + "smoothened_RF_reference.png");
    double error = cvtest::norm(reference, result, NORM_L1);
    EXPECT_LE(error, numerical_precision);
}

TEST(Photo_NPR_EdgePreserveSmoothing_NormConvFilter, regression)
{
    string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
    string original_path = folder + "test1.png";

    Mat source = imread(original_path, IMREAD_COLOR);

    ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;

    Mat result;
    edgePreservingFilter(source,result,2);

    Mat reference = imread(folder + "smoothened_NCF_reference.png");
    double error = cvtest::norm(reference, result, NORM_L1);
    EXPECT_LE(error, numerical_precision);

}

TEST(Photo_NPR_DetailEnhance, regression)
{
    string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
    string original_path = folder + "test1.png";

    Mat source = imread(original_path, IMREAD_COLOR);

    ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;

    Mat result;
    detailEnhance(source,result);

    Mat reference = imread(folder + "detail_enhanced_reference.png");
    double error = cvtest::norm(reference, result, NORM_L1);
    EXPECT_LE(error, numerical_precision);
}

TEST(Photo_NPR_PencilSketch, regression)
{
    string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
    string original_path = folder + "test1.png";

    Mat source = imread(original_path, IMREAD_COLOR);

    ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;

    Mat pencil_result, color_pencil_result;
    pencilSketch(source,pencil_result, color_pencil_result, 10, 0.1f, 0.03f);

    Mat pencil_reference = imread(folder + "pencil_sketch_reference.png", 0 /* == grayscale*/);
    double pencil_error = norm(pencil_reference, pencil_result, NORM_L1);
    EXPECT_LE(pencil_error, numerical_precision);

    Mat color_pencil_reference = imread(folder + "color_pencil_sketch_reference.png");
    double color_pencil_error = cvtest::norm(color_pencil_reference, color_pencil_result, NORM_L1);
    EXPECT_LE(color_pencil_error, numerical_precision);
}

TEST(Photo_NPR_Stylization, regression)
{
    string folder = string(cvtest::TS::ptr()->get_data_path()) + "npr/";
    string original_path = folder + "test1.png";

    Mat source = imread(original_path, IMREAD_COLOR);

    ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;

    Mat result;
    stylization(source,result);

    Mat stylized_reference = imread(folder + "stylized_reference.png");
    double stylized_error = cvtest::norm(stylized_reference, result, NORM_L1);
    EXPECT_LE(stylized_error, numerical_precision);

}