itkstatisticsalgorithmtest.cxx

InsightToolkit-1.4.0(有大量的优化算法程序)

/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: itkStatisticsAlgorithmTest.cxx,v $
  Language:  C++
  Date:      $Date: 2003/09/10 14:30:11 $
  Version:   $Revision: 1.6 $

  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.

=========================================================================*/
#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 activeDimension = 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()[activeDimension] ;
      ++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()[activeDimension])
        {
          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, activeDimension,
                                    0, subsample->Size()) ;
  if (!isSortedOrderCorrect(refVector, subsample))
    {
      pass = false ;
      whereFail = "InsertSort" ;
    }

  // HeapSort algorithm test
  resetData(image, refVector) ;
  itk::Statistics::HeapSort< SubsampleType >(subsample, activeDimension,
                                  0, subsample->Size()) ;
  if (!isSortedOrderCorrect(refVector, subsample))
    {
      pass = false ;
      whereFail = "HeapSort" ;
    }

  // IntospectiveSort algortihm test
  resetData(image, refVector) ;
  itk::Statistics::IntrospectiveSort< SubsampleType >
    (subsample, activeDimension, 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, activeDimension, 
                                                  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;
}