[Insight-developers] Re: [Insight-users] a cast problem ?

[Lagaffe]

Well, this is how to use the
itkMahalanobisDistanceMembershipFunction with the head
version fo CVS with the code I put below:

MahalanobisDistance->SetMean(*(meanAlgorithm->GetOutput()));
instead of:
MahalanobisDistance->SetMean(meanAlgorithm->GetOutput());

So,now it works, but I am not sure it is the best
solution to invoke the filter ... (this is why a put a
copy of this mail to  the ITK developpers)

Hope it will help you,
Arnaud



--- Lagaffe <lagaffe74130 at yahoo.fr> a écrit :

> Hello Karthik,
> 
> I did a checkout this morning, for your last update
> concerning the SetMean function of the
> itkMahalanobisDistanceMembershipFunction filter ...
> But there is still a problem ...
> error: invalid conversion from 'itk::Array<double>*'
> to 'unsigned int'
> 
> So, I did a simple program below, that evaluate the
> mahalanobis distance so you can easily check if the
> filter work with the new convention (itkArray)
> Hope it will help you and I to debug the filter ;-)
> thanks
> arnaud
> 
> #if defined(_MSC_VER)
> #pragma warning ( disable : 4786 )
> #endif
> #include "itkVector.h"
> #include "itkListSample.h"
> #include "itkMeanCalculator.h"
> #include "itkCovarianceCalculator.h"
> #include
> "itkMahalanobisDistanceMembershipFunction.h"
> int main()
> {
> 
> 	const unsigned int MeasurementVectorLength = 3;
> 	typedef itk::Vector< float, MeasurementVectorLength
> >
> MeasurementVectorType;
> 	typedef itk::Statistics::ListSample<
> MeasurementVectorType > SampleType;
> 	SampleType::Pointer sample = SampleType::New();
> 	sample->SetMeasurementVectorSize(
> MeasurementVectorLength );
> 	MeasurementVectorType mv;
> 	mv[0] = 1.0;
> 	mv[1] = 2.0;
> 	mv[2] = 4.0;
> 	
> 	sample->PushBack( mv );
> 	
> 	mv[0] = 2.0;
> 	mv[1] = 4.0;
> 	mv[2] = 5.0;
> 	sample->PushBack( mv );
> 	
> 	mv[0] = 3.0;
> 	mv[1] = 8.0;
> 	mv[2] = 6.0;
> 	sample->PushBack( mv );
> 	
> 	mv[0] = 2.0;
> 	mv[1] = 7.0;
> 	mv[2] = 4.0;
> 	sample->PushBack( mv );
> 	
> 	mv[0] = 3.0;
> 	mv[1] = 2.0;
> 	mv[2] = 7.0;
> 	sample->PushBack( mv );
> 	typedef itk::Statistics::MeanCalculator< SampleType
> >
> MeanAlgorithmType;
> 	
> 	MeanAlgorithmType::Pointer meanAlgorithm =
> MeanAlgorithmType::New();
> 	
> 	meanAlgorithm->SetInputSample( sample );
> 	meanAlgorithm->Update();
> 	
> 	std::cout << "Sample mean = " <<
> *(meanAlgorithm->GetOutput()) << std::endl;
> 
> 	typedef itk::Statistics::CovarianceCalculator<
> SampleType > 
> 		CovarianceAlgorithmType;
> 	CovarianceAlgorithmType::Pointer
> covarianceAlgorithm
> = 
> 		CovarianceAlgorithmType::New();
> 	
> 	covarianceAlgorithm->SetInputSample( sample );
> 	covarianceAlgorithm->SetMean(
> meanAlgorithm->GetOutput() );
> 	covarianceAlgorithm->Update();
> 	
> 	std::cout << "Sample covariance = " << std::endl ; 
> 	std::cout << *(covarianceAlgorithm->GetOutput()) <<
> std::endl;
> 	
> 	typedef 
>
itk::Statistics::MahalanobisDistanceMembershipFunction<
> MeasurementVectorType >
> MahalanobisDistanceMembershipFunctionType;
> 	MahalanobisDistanceMembershipFunctionType::Pointer
> MahalanobisDistance =
> MahalanobisDistanceMembershipFunctionType::New();
> 
>
MahalanobisDistance->SetMean(meanAlgorithm->GetOutput());
> 
>
MahalanobisDistance->SetCovariance(covarianceAlgorithm->GetOutput()->GetVnlMatrix());
> 	std::cout << "Mahalanobis distance=" <<
> MahalanobisDistance->Evaluate(mv) << std::endl;	
> 	
> 	return 0;
> }
> 
> 
> 
> 	
> 
> 	
> 		
>
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