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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* 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
*
* http://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 };
typedef float MeasurementType;
typedef itk::FixedArray< MeasurementType, MeasurementVectorSize > MeasurementVectorType;
typedef itk::Image< MeasurementVectorType, 3 > ImageType;
typedef itk::Image< unsigned char, 3 > MaskImageType;
ImageType::Pointer 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();
typedef itk::ImageRegionIterator< ImageType > ImageIterator;
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
typedef itk::Statistics::ImageToListSampleFilter< ImageType, MaskImageType > ImageToListSampleFilterType;
ImageToListSampleFilterType::Pointer sampleGeneratingFilter = ImageToListSampleFilterType::New();
sampleGeneratingFilter->SetInput( image );
try
{
sampleGeneratingFilter->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr<< excp << std::endl;
return EXIT_FAILURE;
}
typedef ImageToListSampleFilterType::ListSampleType ListSampleType;
typedef itk::Statistics::CovarianceSampleFilter< ListSampleType > CovarianceSampleFilterType;
CovarianceSampleFilterType::Pointer 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 ( itk::ExceptionObject & excp )
{
std::cerr << "Exception caught: " << excp << std::endl;
}
covarianceFilter->ResetPipeline();
if ( covarianceFilter->GetInput() != ITK_NULLPTR )
{
std::cerr << "GetInput() should return ITK_NULLPTR if the input \
has not been set" << std::endl;
return EXIT_FAILURE;
}
covarianceFilter->SetInput( sampleGeneratingFilter->GetOutput() );
try
{
covarianceFilter->Update();
}
catch( 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 ( ( std::fabs( mean[0] - mean2[0]) > epsilon ) ||
( std::fabs( mean[1] - mean2[1]) > epsilon) ||
( std::fabs( 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;
typedef itk::Statistics::MeanSampleFilter< ListSampleType > MeanSampleFilterType;
MeanSampleFilterType::Pointer meanFilter = MeanSampleFilterType::New();
meanFilter->SetInput( sampleGeneratingFilter->GetOutput());
try
{
meanFilter->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Exception caught: " << excp << std::endl;
}
MeanSampleFilterType::MeasurementVectorRealType meanCalculatedUsingMeanSampleFilter = meanFilter->GetMean();
if ( ( std::fabs( meanCalculatedUsingMeanSampleFilter[0] - mean[0]) > epsilon ) ||
( std::fabs( meanCalculatedUsingMeanSampleFilter[1] - mean[1]) > epsilon) ||
( std::fabs( 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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