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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkStatisticsAlgorithmTest.cxx,v $
Language: C++
Date: $Date: 2008-01-07 21:28:03 $
Version: $Revision: 1.8 $
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#include "itkImage.h"
#include "itkImageRegionIteratorWithIndex.h"
#include "itkImageToListAdaptor.h"
#include "itkSubsample.h"
#include "itkStatisticsAlgorithm.h"
#include <vector>
#include <algorithm>
typedef itk::FixedArray< int, 3 > PixelType ;
typedef itk::Image< PixelType, 3 > ImageType ;
typedef itk::Statistics::ImageToListAdaptor< ImageType > SampleType ;
typedef itk::Statistics::Subsample< SampleType > SubsampleType ;
const unsigned int testDimension = 1 ;
void resetData(::itk::Image<PixelType, 3>::Pointer image, std::vector<int> &refVector)
{
ImageType::IndexType index ;
ImageType::SizeType size ;
size = image->GetLargestPossibleRegion().GetSize() ;
unsigned long x, y, z ;
PixelType temp ;
// fill the image with random values
for (z = 0 ; z < size[2] ; z++)
{
index[2] = z ;
temp[2] = rand() ;
for (y = 0 ; y < size[1] ; y++)
{
index[1] = y ;
temp[1] = rand() ;
for (x = 0 ; x < size[0] ; x++)
{
index[0] = x ;
temp[0] = rand() ;
image->SetPixel(index, temp) ;
}
}
}
// fill the vector
itk::ImageRegionIteratorWithIndex< ImageType >
i_iter(image, image->GetLargestPossibleRegion()) ;
i_iter.GoToBegin() ;
std::vector< int >::iterator viter ;
refVector.resize(size[0] * size[1] * size[2]) ;
viter = refVector.begin() ;
while (viter != refVector.end())
{
*viter = i_iter.Get()[testDimension] ;
++viter ;
++i_iter ;
}
// sort result using stl vector for reference
std::sort(refVector.begin(), refVector.end()) ;
}
bool isSortedOrderCorrect(std::vector<int> &ref,
::itk::Statistics::Subsample<SampleType>::Pointer subsample)
{
bool ret = true ;
std::vector<int>::iterator viter = ref.begin() ;
SubsampleType::Iterator siter = subsample->Begin() ;
while (siter != subsample->End())
{
if (*viter != siter.GetMeasurementVector()[testDimension])
{
ret = false ;
}
++siter ;
++viter ;
}
return ret ;
}
int itkStatisticsAlgorithmTest(int, char* [] )
{
std::cout << "Statistics Algorithm Test \n \n";
bool pass = true;
std::string whereFail = "" ;
// creats an image and allocate memory
ImageType::Pointer image = ImageType::New() ;
ImageType::SizeType size ;
size.Fill(5) ;
ImageType::IndexType index ;
index.Fill(0) ;
ImageType::RegionType region ;
region.SetSize(size) ;
region.SetIndex(index) ;
image->SetLargestPossibleRegion(region) ;
image->SetBufferedRegion(region) ;
image->Allocate() ;
// creates an ImageToListAdaptor object
SampleType::Pointer sample = SampleType::New() ;
sample->SetImage(image) ;
// creates a Subsample obeject using the ImageToListAdaptor object
SubsampleType::Pointer subsample = SubsampleType::New() ;
subsample->SetSample(sample) ;
PixelType temp ;
// each algorithm test will be compared with the sorted
// refVector
std::vector< int > refVector ;
// creats a subsample with all instances in the image
subsample->InitializeWithAllInstances() ;
// InsertSort algorithm test
// fill the image with random values and fill and sort the
// refVector
resetData(image, refVector) ;
itk::Statistics::InsertSort< SubsampleType >(subsample, testDimension,
0, subsample->Size()) ;
if (!isSortedOrderCorrect(refVector, subsample))
{
pass = false ;
whereFail = "InsertSort" ;
}
// HeapSort algorithm test
resetData(image, refVector) ;
itk::Statistics::HeapSort< SubsampleType >(subsample, testDimension,
0, subsample->Size()) ;
if (!isSortedOrderCorrect(refVector, subsample))
{
pass = false ;
whereFail = "HeapSort" ;
}
// IntospectiveSort algortihm test
resetData(image, refVector) ;
itk::Statistics::IntrospectiveSort< SubsampleType >
(subsample, testDimension, 0, subsample->Size(), 16) ;
if (!isSortedOrderCorrect(refVector, subsample))
{
pass = false ;
whereFail = "IntrospectiveSort" ;
}
// QuickSelect algorithm test
resetData(image, refVector) ;
SubsampleType::MeasurementType median =
itk::Statistics::QuickSelect< SubsampleType >(subsample, testDimension,
0, subsample->Size(),
subsample->Size()/2) ;
if (refVector[subsample->Size()/2] != median)
{
pass = false ;
whereFail = "QuickSelect" ;
}
if( !pass )
{
std::cout << "Test failed in " << whereFail << "." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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