214 lines
7.2 KiB
C++
214 lines
7.2 KiB
C++
/*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;
|
|
|
|
class CV_DenoisingGrayscaleTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_DenoisingGrayscaleTest();
|
|
~CV_DenoisingGrayscaleTest();
|
|
protected:
|
|
void run(int);
|
|
};
|
|
|
|
CV_DenoisingGrayscaleTest::CV_DenoisingGrayscaleTest() {}
|
|
CV_DenoisingGrayscaleTest::~CV_DenoisingGrayscaleTest() {}
|
|
|
|
void CV_DenoisingGrayscaleTest::run( int )
|
|
{
|
|
string folder = string(ts->get_data_path()) + "denoising/";
|
|
Mat orig = imread(folder + "lena_noised_gaussian_sigma=10.png", 0);
|
|
Mat exp = imread(folder + "lena_noised_denoised_grayscale_tw=7_sw=21_h=10.png", 0);
|
|
|
|
if (orig.empty() || exp.empty())
|
|
{
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
|
return;
|
|
}
|
|
|
|
Mat res;
|
|
fastNlMeansDenoising(orig, res, 7, 21, 10);
|
|
|
|
if (norm(res - exp) > 0) {
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
|
|
} else {
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
}
|
|
|
|
class CV_DenoisingColoredTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_DenoisingColoredTest();
|
|
~CV_DenoisingColoredTest();
|
|
protected:
|
|
void run(int);
|
|
};
|
|
|
|
CV_DenoisingColoredTest::CV_DenoisingColoredTest() {}
|
|
CV_DenoisingColoredTest::~CV_DenoisingColoredTest() {}
|
|
|
|
void CV_DenoisingColoredTest::run( int )
|
|
{
|
|
string folder = string(ts->get_data_path()) + "denoising/";
|
|
Mat orig = imread(folder + "lena_noised_gaussian_sigma=10.png", 1);
|
|
Mat exp = imread(folder + "lena_noised_denoised_lab12_tw=7_sw=21_h=10_h2=10.png", 1);
|
|
|
|
if (orig.empty() || exp.empty())
|
|
{
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
|
return;
|
|
}
|
|
|
|
Mat res;
|
|
fastNlMeansDenoisingColored(orig, res, 7, 21, 10, 10);
|
|
|
|
if (norm(res - exp) > 0) {
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
|
|
} else {
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
}
|
|
|
|
class CV_DenoisingGrayscaleMultiTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_DenoisingGrayscaleMultiTest();
|
|
~CV_DenoisingGrayscaleMultiTest();
|
|
protected:
|
|
void run(int);
|
|
};
|
|
|
|
CV_DenoisingGrayscaleMultiTest::CV_DenoisingGrayscaleMultiTest() {}
|
|
CV_DenoisingGrayscaleMultiTest::~CV_DenoisingGrayscaleMultiTest() {}
|
|
|
|
void CV_DenoisingGrayscaleMultiTest::run( int )
|
|
{
|
|
string folder = string(ts->get_data_path()) + "denoising/";
|
|
|
|
const int imgs_count = 3;
|
|
vector<Mat> src_imgs(imgs_count);
|
|
src_imgs[0] = imread(folder + "lena_noised_gaussian_sigma=20_multi_0.png", 0);
|
|
src_imgs[1] = imread(folder + "lena_noised_gaussian_sigma=20_multi_1.png", 0);
|
|
src_imgs[2] = imread(folder + "lena_noised_gaussian_sigma=20_multi_2.png", 0);
|
|
|
|
Mat exp = imread(folder + "lena_noised_denoised_multi_tw=7_sw=21_h=15.png", 0);
|
|
|
|
bool have_empty_src = false;
|
|
for (int i = 0; i < imgs_count; i++) {
|
|
have_empty_src |= src_imgs[i].empty();
|
|
}
|
|
|
|
if (have_empty_src || exp.empty())
|
|
{
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
|
return;
|
|
}
|
|
|
|
Mat res;
|
|
fastNlMeansDenoisingMulti(src_imgs, imgs_count / 2, imgs_count, res, 7, 21, 15);
|
|
|
|
if (norm(res - exp) > 0) {
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
|
|
} else {
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
}
|
|
|
|
class CV_DenoisingColoredMultiTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_DenoisingColoredMultiTest();
|
|
~CV_DenoisingColoredMultiTest();
|
|
protected:
|
|
void run(int);
|
|
};
|
|
|
|
CV_DenoisingColoredMultiTest::CV_DenoisingColoredMultiTest() {}
|
|
CV_DenoisingColoredMultiTest::~CV_DenoisingColoredMultiTest() {}
|
|
|
|
void CV_DenoisingColoredMultiTest::run( int )
|
|
{
|
|
string folder = string(ts->get_data_path()) + "denoising/";
|
|
|
|
const int imgs_count = 3;
|
|
vector<Mat> src_imgs(imgs_count);
|
|
src_imgs[0] = imread(folder + "lena_noised_gaussian_sigma=20_multi_0.png", 1);
|
|
src_imgs[1] = imread(folder + "lena_noised_gaussian_sigma=20_multi_1.png", 1);
|
|
src_imgs[2] = imread(folder + "lena_noised_gaussian_sigma=20_multi_2.png", 1);
|
|
|
|
Mat exp = imread(folder + "lena_noised_denoised_multi_lab12_tw=7_sw=21_h=10_h2=15.png", 1);
|
|
|
|
bool have_empty_src = false;
|
|
for (int i = 0; i < imgs_count; i++) {
|
|
have_empty_src |= src_imgs[i].empty();
|
|
}
|
|
|
|
if (have_empty_src || exp.empty())
|
|
{
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
|
return;
|
|
}
|
|
|
|
Mat res;
|
|
fastNlMeansDenoisingColoredMulti(src_imgs, imgs_count / 2, imgs_count, res, 7, 21, 10, 15);
|
|
|
|
if (norm(res - exp) > 0) {
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
|
|
} else {
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
}
|
|
|
|
|
|
TEST(Imgproc_DenoisingGrayscale, regression) { CV_DenoisingGrayscaleTest test; test.safe_run(); }
|
|
TEST(Imgproc_DenoisingColored, regression) { CV_DenoisingColoredTest test; test.safe_run(); }
|
|
TEST(Imgproc_DenoisingGrayscaleMulti, regression) { CV_DenoisingGrayscaleMultiTest test; test.safe_run(); }
|
|
TEST(Imgproc_DenoisingColoredMulti, regression) { CV_DenoisingColoredMultiTest test; test.safe_run(); }
|
|
|