248 lines
9.9 KiB
C++
248 lines
9.9 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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#define OUTPUT_SAVING 0
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#if OUTPUT_SAVING
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#define SAVE(x) std::vector<int> params;\
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params.push_back(16);\
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params.push_back(0);\
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imwrite(folder + "output.png", x ,params);
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#else
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#define SAVE(x)
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#endif
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static const double numerical_precision = 0.05; // 95% of pixels should have exact values
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TEST(Photo_SeamlessClone_normal, regression)
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{
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Normal_Cloning/";
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string original_path1 = folder + "source1.png";
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string original_path2 = folder + "destination1.png";
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string original_path3 = folder + "mask.png";
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string reference_path = folder + "reference.png";
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Mat source = imread(original_path1, IMREAD_COLOR);
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Mat destination = imread(original_path2, IMREAD_COLOR);
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Mat mask = imread(original_path3, IMREAD_COLOR);
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ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
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ASSERT_FALSE(destination.empty()) << "Could not load destination image " << original_path2;
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ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path3;
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Mat result;
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Point p;
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p.x = destination.size().width/2;
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p.y = destination.size().height/2;
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seamlessClone(source, destination, mask, p, result, 1);
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Mat reference = imread(reference_path);
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ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
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SAVE(result);
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double errorINF = cvtest::norm(reference, result, NORM_INF);
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EXPECT_LE(errorINF, 1);
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double errorL1 = cvtest::norm(reference, result, NORM_L1);
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EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
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mask = Scalar(0, 0, 0);
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seamlessClone(source, destination, mask, p, result, 1);
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reference = destination;
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errorINF = cvtest::norm(reference, result, NORM_INF);
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EXPECT_LE(errorINF, 1);
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errorL1 = cvtest::norm(reference, result, NORM_L1);
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EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
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}
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TEST(Photo_SeamlessClone_mixed, regression)
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{
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Mixed_Cloning/";
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string original_path1 = folder + "source1.png";
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string original_path2 = folder + "destination1.png";
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string original_path3 = folder + "mask.png";
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string reference_path = folder + "reference.png";
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Mat source = imread(original_path1, IMREAD_COLOR);
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Mat destination = imread(original_path2, IMREAD_COLOR);
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Mat mask = imread(original_path3, IMREAD_COLOR);
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ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
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ASSERT_FALSE(destination.empty()) << "Could not load destination image " << original_path2;
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ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path3;
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Mat result;
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Point p;
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p.x = destination.size().width/2;
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p.y = destination.size().height/2;
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seamlessClone(source, destination, mask, p, result, 2);
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SAVE(result);
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Mat reference = imread(reference_path);
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ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
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double errorINF = cvtest::norm(reference, result, NORM_INF);
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EXPECT_LE(errorINF, 1);
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double errorL1 = cvtest::norm(reference, result, NORM_L1);
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EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
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}
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TEST(Photo_SeamlessClone_featureExchange, regression)
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{
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Monochrome_Transfer/";
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string original_path1 = folder + "source1.png";
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string original_path2 = folder + "destination1.png";
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string original_path3 = folder + "mask.png";
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string reference_path = folder + "reference.png";
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Mat source = imread(original_path1, IMREAD_COLOR);
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Mat destination = imread(original_path2, IMREAD_COLOR);
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Mat mask = imread(original_path3, IMREAD_COLOR);
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ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
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ASSERT_FALSE(destination.empty()) << "Could not load destination image " << original_path2;
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ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path3;
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Mat result;
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Point p;
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p.x = destination.size().width/2;
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p.y = destination.size().height/2;
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seamlessClone(source, destination, mask, p, result, 3);
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SAVE(result);
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Mat reference = imread(reference_path);
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ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
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double errorINF = cvtest::norm(reference, result, NORM_INF);
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EXPECT_LE(errorINF, 1);
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double errorL1 = cvtest::norm(reference, result, NORM_L1);
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EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
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}
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TEST(Photo_SeamlessClone_colorChange, regression)
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{
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/color_change/";
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string original_path1 = folder + "source1.png";
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string original_path2 = folder + "mask.png";
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string reference_path = folder + "reference.png";
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Mat source = imread(original_path1, IMREAD_COLOR);
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Mat mask = imread(original_path2, IMREAD_COLOR);
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ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
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ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path2;
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Mat result;
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colorChange(source, mask, result, 1.5, .5, .5);
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SAVE(result);
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Mat reference = imread(reference_path);
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ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
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double errorINF = cvtest::norm(reference, result, NORM_INF);
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EXPECT_LE(errorINF, 1);
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double errorL1 = cvtest::norm(reference, result, NORM_L1);
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EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
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}
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TEST(Photo_SeamlessClone_illuminationChange, regression)
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{
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Illumination_Change/";
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string original_path1 = folder + "source1.png";
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string original_path2 = folder + "mask.png";
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string reference_path = folder + "reference.png";
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Mat source = imread(original_path1, IMREAD_COLOR);
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Mat mask = imread(original_path2, IMREAD_COLOR);
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ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
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ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path2;
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Mat result;
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illuminationChange(source, mask, result, 0.2f, 0.4f);
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SAVE(result);
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Mat reference = imread(reference_path);
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ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
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double errorINF = cvtest::norm(reference, result, NORM_INF);
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EXPECT_LE(errorINF, 1);
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double errorL1 = cvtest::norm(reference, result, NORM_L1);
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EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
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}
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TEST(Photo_SeamlessClone_textureFlattening, regression)
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{
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "cloning/Texture_Flattening/";
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string original_path1 = folder + "source1.png";
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string original_path2 = folder + "mask.png";
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string reference_path = folder + "reference.png";
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Mat source = imread(original_path1, IMREAD_COLOR);
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Mat mask = imread(original_path2, IMREAD_COLOR);
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ASSERT_FALSE(source.empty()) << "Could not load source image " << original_path1;
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ASSERT_FALSE(mask.empty()) << "Could not load mask image " << original_path2;
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Mat result;
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textureFlattening(source, mask, result, 30, 45, 3);
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SAVE(result);
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Mat reference = imread(reference_path);
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ASSERT_FALSE(reference.empty()) << "Could not load reference image " << reference_path;
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double errorINF = cvtest::norm(reference, result, NORM_INF);
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EXPECT_LE(errorINF, 1);
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double errorL1 = cvtest::norm(reference, result, NORM_L1);
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EXPECT_LE(errorL1, reference.total() * numerical_precision) << "size=" << reference.size();
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}
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}} // namespace
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