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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 "itkImageToListSampleFilter.h"
#include "itkCovarianceSampleFilter.h"
#include "itkImageRegionIterator.h"
int
itkCovarianceSampleFilterTest(int, char *[])
{
std::cout << "CovarianceSampleFilter Test \n \n";
// Now generate an image
enum
{
MeasurementVectorSize = 3
};
using MeasurementType = float;
using MeasurementVectorType = itk::FixedArray<MeasurementType, MeasurementVectorSize>;
using ImageType = itk::Image<MeasurementVectorType, 3>;
using MaskImageType = itk::Image<unsigned char, 3>;
auto image = ImageType::New();
ImageType::RegionType region;
ImageType::SizeType size;
ImageType::IndexType index;
index.Fill(0);
size.Fill(5);
region.SetIndex(index);
region.SetSize(size);
image->SetBufferedRegion(region);
image->Allocate();
using ImageIterator = itk::ImageRegionIterator<ImageType>;
ImageIterator iter(image, region);
unsigned int count = 0;
MeasurementVectorType temp;
temp.Fill(0);
// fill the image
while (!iter.IsAtEnd())
{
temp[0] = count;
iter.Set(temp);
++iter;
++count;
}
// creates an ImageToListSampleAdaptor object
using ImageToListSampleFilterType = itk::Statistics::ImageToListSampleFilter<ImageType, MaskImageType>;
auto sampleGeneratingFilter = ImageToListSampleFilterType::New();
sampleGeneratingFilter->SetInput(image);
try
{
sampleGeneratingFilter->Update();
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
using ListSampleType = ImageToListSampleFilterType::ListSampleType;
using CovarianceSampleFilterType = itk::Statistics::CovarianceSampleFilter<ListSampleType>;
auto covarianceFilter = CovarianceSampleFilterType::New();
std::cout << "GetNameOfClass() = " << covarianceFilter->GetNameOfClass() << std::endl;
// Invoke update before adding an input. An exception should be
try
{
covarianceFilter->Update();
std::cerr << "Exception should have been thrown since Update() is invoked without setting an input " << std::endl;
return EXIT_FAILURE;
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << "Exception caught: " << excp << std::endl;
}
covarianceFilter->ResetPipeline();
if (covarianceFilter->GetInput() != nullptr)
{
std::cerr << "GetInput() should return nullptr if the input has not been set" << std::endl;
return EXIT_FAILURE;
}
covarianceFilter->SetInput(sampleGeneratingFilter->GetOutput());
try
{
covarianceFilter->Update();
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
covarianceFilter->Print(std::cout);
const double epsilon = 1e-6;
// CHECK THE RESULTS
const CovarianceSampleFilterType::MeasurementVectorDecoratedType * meanDecorator = covarianceFilter->GetMeanOutput();
CovarianceSampleFilterType::MeasurementVectorRealType mean = meanDecorator->Get();
std::cout << "Mean: " << mean << std::endl;
CovarianceSampleFilterType::MeasurementVectorRealType mean2 = covarianceFilter->GetMean();
if ((itk::Math::abs(mean[0] - mean2[0]) > epsilon) || (itk::Math::abs(mean[1] - mean2[1]) > epsilon) ||
(itk::Math::abs(mean[2] - mean2[2]) > epsilon))
{
std::cerr << "Mean parameter value retrieved using GetMean() and the decorator are not the same:: " << mean << ','
<< mean2 << std::endl;
return EXIT_FAILURE;
}
const CovarianceSampleFilterType::MatrixDecoratedType * decorator = covarianceFilter->GetCovarianceMatrixOutput();
CovarianceSampleFilterType::MatrixType covarianceMatrix = decorator->Get();
std::cout << "Covariance matrix: " << covarianceMatrix << std::endl;
using MeanSampleFilterType = itk::Statistics::MeanSampleFilter<ListSampleType>;
auto meanFilter = MeanSampleFilterType::New();
meanFilter->SetInput(sampleGeneratingFilter->GetOutput());
try
{
meanFilter->Update();
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << "Exception caught: " << excp << std::endl;
}
MeanSampleFilterType::MeasurementVectorRealType meanCalculatedUsingMeanSampleFilter = meanFilter->GetMean();
if ((itk::Math::abs(meanCalculatedUsingMeanSampleFilter[0] - mean[0]) > epsilon) ||
(itk::Math::abs(meanCalculatedUsingMeanSampleFilter[1] - mean[1]) > epsilon) ||
(itk::Math::abs(meanCalculatedUsingMeanSampleFilter[2] - mean[2]) > epsilon))
{
std::cerr
<< "Mean calculated using the MeanSampleFilter is different from the one calculated using the covariance filter "
<< std::endl;
std::cerr << "Mean computed with covariance filter = " << mean << std::endl;
std::cerr << "Mean computed with mean filter = " << meanCalculatedUsingMeanSampleFilter << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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