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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 "itkImageRegionIteratorWithIndex.h"
#include "itkImageToListSampleAdaptor.h"
#include "itkStatisticsAlgorithm.h"
#include <vector>
#include <algorithm>
typedef itk::FixedArray< int, 3 > PixelType;
typedef itk::Image< PixelType, 3 > ImageType;
typedef itk::Statistics::ImageToListSampleAdaptor< 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;
unsigned long y;
unsigned long 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 itkStatisticsAlgorithmTest2(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 ImageToListSampleAdaptor object
SampleType::Pointer sample = SampleType::New();
sample->SetImage(image);
// creates a Subsample obeject using the ImageToListSampleAdaptor 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::Algorithm::InsertSort< SubsampleType >(subsample, testDimension,
0, subsample->Size());
if( !isSortedOrderCorrect(refVector, subsample) )
{
pass = false;
whereFail = "InsertSort";
}
// HeapSort algorithm test
resetData(image, refVector);
itk::Statistics::Algorithm::HeapSort< SubsampleType >(subsample, testDimension,
0, subsample->Size());
if( !isSortedOrderCorrect(refVector, subsample) )
{
pass = false;
whereFail = "HeapSort";
}
// IntospectiveSort algortihm test
resetData(image, refVector);
itk::Statistics::Algorithm::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::Algorithm::QuickSelect< SubsampleType >(subsample, testDimension,
0, subsample->Size(),
subsample->Size()/2);
if( refVector[subsample->Size()/2] != median )
{
pass = false;
whereFail = "QuickSelect";
}
if( !pass )
{
std::cerr << "Test failed in " << whereFail << "." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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