Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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// If you do not agree to this license, do not download, install,
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//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
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// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
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// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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#define OUTPUT_SAVING 0
#if OUTPUT_SAVING
#define SAVE(x) std::vector<int> params;\
params.push_back(16);\
params.push_back(0);\
imwrite(folder + "output.png", x ,params);
#else
#define SAVE(x)
#endif
#include "test_precomp.hpp"
#include "opencv2/photo.hpp"
#include <string>
using namespace cv;
using namespace std;
static const double numerical_precision = 1000.;
TEST(Photo_SeamlessClone_normal, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Normal_Cloning/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "destination1.png";
string original_path3 = folder + "mask.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat destination = imread(original_path2, IMREAD_COLOR);
Mat mask = imread(original_path3, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(destination.empty()) << "Could not load destination image " << original_path2;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path3;
Mat result;
Point p;
p.x = destination.size().width/2;
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 1);
Mat reference = imread(folder + "reference.png");
SAVE(result);
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
TEST(Photo_SeamlessClone_mixed, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Mixed_Cloning/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "destination1.png";
string original_path3 = folder + "mask.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat destination = imread(original_path2, IMREAD_COLOR);
Mat mask = imread(original_path3, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(destination.empty()) << "Could not load destination image " << original_path2;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path3;
Mat result;
Point p;
p.x = destination.size().width/2;
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 2);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
TEST(Photo_SeamlessClone_featureExchange, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Monochrome_Transfer/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "destination1.png";
string original_path3 = folder + "mask.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat destination = imread(original_path2, IMREAD_COLOR);
Mat mask = imread(original_path3, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(destination.empty()) << "Could not load destination image " << original_path2;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path3;
Mat result;
Point p;
p.x = destination.size().width/2;
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 3);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
TEST(Photo_SeamlessClone_colorChange, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/color_change/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "mask.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat mask = imread(original_path2, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path2;
Mat result;
colorChange(source, mask, result, 1.5, .5, .5);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
TEST(Photo_SeamlessClone_illuminationChange, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Illumination_Change/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "mask.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat mask = imread(original_path2, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path2;
Mat result;
illuminationChange(source, mask, result, 0.2f, 0.4f);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
TEST(Photo_SeamlessClone_textureFlattening, regression)
{
string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Texture_Flattening/";
string original_path1 = folder + "source1.png";
string original_path2 = folder + "mask.png";
Mat source = imread(original_path1, IMREAD_COLOR);
Mat mask = imread(original_path2, IMREAD_COLOR);
ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path2;
Mat result;
textureFlattening(source, mask, result, 30, 45, 3);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}