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/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#include "itkLevelSetContainer.h"
#include "itkLevelSetEquationLaplacianTerm.h"
#include "itkSinRegularizedHeavisideStepFunction.h"
#include "itkBinaryImageToLevelSetImageAdaptor.h"
#include "itkTestingMacros.h"
int
itkLevelSetEquationLaplacianTermTest(int argc, char * argv[])
{
if (argc < 2)
{
std::cerr << "Missing Arguments" << std::endl;
std::cerr << "Program " << itkNameOfTestExecutableMacro(argv) << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int Dimension = 2;
using InputPixelType = unsigned short;
using InputImageType = itk::Image<InputPixelType, Dimension>;
using IdentifierType = itk::IdentifierType;
using InputPixelType = unsigned short;
using InputImageType = itk::Image<InputPixelType, Dimension>;
using PixelType = float;
using SparseLevelSetType = itk::WhitakerSparseLevelSetImage<PixelType, Dimension>;
using BinaryToSparseAdaptorType = itk::BinaryImageToLevelSetImageAdaptor<InputImageType, SparseLevelSetType>;
using LevelSetContainerType = itk::LevelSetContainer<IdentifierType, SparseLevelSetType>;
using LaplacianTermType = itk::LevelSetEquationLaplacianTerm<InputImageType, LevelSetContainerType>;
using IdListType = std::list<IdentifierType>;
using IdListImageType = itk::Image<IdListType, Dimension>;
using CacheImageType = itk::Image<short, Dimension>;
using DomainMapImageFilterType = itk::LevelSetDomainMapImageFilter<IdListImageType, CacheImageType>;
using LevelSetOutputRealType = SparseLevelSetType::OutputRealType;
using HeavisideFunctionBaseType =
itk::SinRegularizedHeavisideStepFunction<LevelSetOutputRealType, LevelSetOutputRealType>;
using InputImageIteratorType = itk::ImageRegionIteratorWithIndex<InputImageType>;
// load binary mask
InputImageType::SizeType size;
size.Fill(50);
InputImageType::PointType origin;
origin[0] = 0.0;
origin[1] = 0.0;
InputImageType::SpacingType spacing;
spacing[0] = 1.0;
spacing[1] = 1.0;
InputImageType::IndexType index;
index.Fill(0);
InputImageType::RegionType region{ index, size };
// Binary initialization
auto binary = InputImageType::New();
binary->SetRegions(region);
binary->SetSpacing(spacing);
binary->SetOrigin(origin);
binary->Allocate();
binary->FillBuffer(InputPixelType{});
index.Fill(10);
size.Fill(30);
region.SetIndex(index);
region.SetSize(size);
InputImageIteratorType iIt(binary, region);
iIt.GoToBegin();
while (!iIt.IsAtEnd())
{
iIt.Set(itk::NumericTraits<InputPixelType>::OneValue());
++iIt;
}
// Convert binary mask to sparse level set
auto adaptor = BinaryToSparseAdaptorType::New();
adaptor->SetInputImage(binary);
adaptor->Initialize();
std::cout << "Finished converting to sparse format" << std::endl;
SparseLevelSetType::Pointer level_set = adaptor->GetModifiableLevelSet();
IdListType list_ids;
list_ids.push_back(1);
auto id_image = IdListImageType::New();
id_image->SetRegions(binary->GetLargestPossibleRegion());
id_image->Allocate();
id_image->FillBuffer(list_ids);
auto domainMapFilter = DomainMapImageFilterType::New();
domainMapFilter->SetInput(id_image);
domainMapFilter->Update();
std::cout << "Domain map computed" << std::endl;
// Define the Heaviside function
auto heaviside = HeavisideFunctionBaseType::New();
heaviside->SetEpsilon(1.0);
// Insert the levelsets in a levelset container
auto lscontainer = LevelSetContainerType::New();
lscontainer->SetHeaviside(heaviside);
lscontainer->SetDomainMapFilter(domainMapFilter);
bool LevelSetNotYetAdded = lscontainer->AddLevelSet(0, level_set, false);
if (!LevelSetNotYetAdded)
{
return EXIT_FAILURE;
}
// Create ChanAndVese External term for phi_{1}
auto term = LaplacianTermType::New();
ITK_EXERCISE_BASIC_OBJECT_METHODS(term, LevelSetEquationLaplacianTerm, LevelSetEquationTermBase);
term->SetInput(binary);
ITK_TEST_SET_GET_VALUE(binary, term->GetInput());
typename LaplacianTermType::LevelSetOutputRealType coefficient = 1.0;
term->SetCoefficient(coefficient);
ITK_TEST_SET_GET_VALUE(coefficient, term->GetCoefficient());
typename LaplacianTermType::LevelSetIdentifierType currentLevelSetId = 0;
term->SetCurrentLevelSetId(currentLevelSetId);
ITK_TEST_SET_GET_VALUE(currentLevelSetId, term->GetCurrentLevelSetId());
term->SetLevelSetContainer(lscontainer);
ITK_TEST_SET_GET_VALUE(lscontainer, term->GetLevelSetContainer());
std::string termName = "Laplacia term";
term->SetTermName(termName);
ITK_TEST_SET_GET_VALUE(termName, term->GetTermName());
std::cout << "Laplacian term created" << std::endl;
// Initialize the ChanAndVese term here
term->InitializeParameters();
InputImageIteratorType it(binary, binary->GetLargestPossibleRegion());
it.GoToBegin();
while (!it.IsAtEnd())
{
term->Initialize(it.GetIndex());
++it;
}
term->Update();
index[0] = 10;
index[1] = 20;
if (itk::Math::abs(term->Evaluate(index)) > 5e-2)
{
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
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