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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 <iostream>
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkStatisticsImageFilter.h"
#include "itkRandomImageSource.h"
#include "itkFilterWatcher.h"
#include "itkMath.h"
int itkStatisticsImageFilterTest(int, char* [] )
{
std::cout << "itkStatisticsImageFilterTest Start" << std::endl;
int status = 0;
typedef itk::Image<int,3> FloatImage;
FloatImage::Pointer image = FloatImage::New();
FloatImage::RegionType region;
FloatImage::SizeType size; size.Fill(64);
FloatImage::IndexType index; index.Fill(0);
region.SetIndex (index);
region.SetSize (size);
// first try a constant image
float fillValue = -100.0;
image->SetRegions( region );
image->Allocate();
image->FillBuffer( static_cast< FloatImage::PixelType >( fillValue ) );
float sum = fillValue * static_cast<float>( region.GetNumberOfPixels() );
typedef itk::StatisticsImageFilter<FloatImage> FilterType;
FilterType::Pointer filter = FilterType::New();
FilterWatcher filterWatch(filter);
filter->SetInput (image);
filter->UpdateLargestPossibleRegion();
if ( itk::Math::NotAlmostEquals( filter->GetMinimum(), fillValue) )
{
std::cerr << "GetMinimum failed! Got " << filter->GetMinimum() << " but expected " << fillValue << std::endl;
status++;
}
if ( itk::Math::NotAlmostEquals( filter->GetMaximum(), fillValue) )
{
std::cerr << "GetMaximum failed! Got " << filter->GetMaximum() << " but expected " << fillValue << std::endl;
status++;
}
if ( itk::Math::NotAlmostEquals( filter->GetSum(), sum) )
{
std::cerr << "GetSum failed! Got " << filter->GetSum() << " but expected " << sum << std::endl;
status++;
}
if ( itk::Math::NotAlmostEquals( filter->GetMean(), fillValue) )
{
std::cerr << "GetMean failed! Got " << filter->GetMean() << " but expected " << fillValue << std::endl;
status++;
}
if ( itk::Math::NotAlmostEquals( filter->GetVariance(), 0.0) )
{
std::cerr << "GetVariance failed! Got " << filter->GetVariance() << " but expected " << 0.0 << std::endl;
status++;
}
// Now generate a real image
typedef itk::RandomImageSource<FloatImage> SourceType;
SourceType::Pointer source = SourceType::New();
FloatImage::SizeValueType randomSize[3] = {17, 8, 20};
source->SetSize(randomSize);
float minValue = -100.0;
float maxValue = 1000.0;
source->SetMin( static_cast< FloatImage::PixelType >( minValue ) );
source->SetMax( static_cast< FloatImage::PixelType >( maxValue ) );
filter->SetInput(source->GetOutput());
filter->UpdateLargestPossibleRegion();
double expectedSigma = std::sqrt((maxValue-minValue)*(maxValue-minValue)/12.0);
double epsilon = (maxValue - minValue) * .001;
if (itk::Math::abs(filter->GetSigma() - expectedSigma) > epsilon)
{
std::cerr << "GetSigma failed! Got " << filter->GetSigma() << " but expected " << expectedSigma << std::endl;
}
// Now generate an image with a known mean and variance
itk::Statistics::MersenneTwisterRandomVariateGenerator::Pointer rvgen =
itk::Statistics::MersenneTwisterRandomVariateGenerator::GetInstance();
double knownMean = 12.0;
double knownVariance = 10.0;
typedef itk::Image<double,3> DoubleImage;
DoubleImage::Pointer dImage = DoubleImage::New();
DoubleImage::SizeType dsize;
DoubleImage::IndexType dindex;
DoubleImage::RegionType dregion;
dsize.Fill(50);
dindex.Fill(0);
dregion.SetSize(dsize);
dregion.SetIndex(dindex);
dImage->SetRegions(dregion);
dImage->Allocate();
itk::ImageRegionIterator<DoubleImage> it(dImage, dregion);
while (!it.IsAtEnd())
{
it.Set(rvgen->GetNormalVariate(knownMean, knownVariance));
++it;
}
typedef itk::StatisticsImageFilter<DoubleImage> DFilterType;
DFilterType::Pointer dfilter = DFilterType::New();
dfilter->SetInput(dImage);
dfilter->UpdateLargestPossibleRegion();
double testMean = dfilter->GetMean();
double testVariance = dfilter->GetVariance();
double diff = itk::Math::abs(testMean - knownMean);
if ((diff != 0.0 && knownMean != 0.0) &&
diff / itk::Math::abs(knownMean) > .01)
{
std::cout << "Expected mean is " << knownMean << ", computed mean is " << testMean << std::endl;
return EXIT_FAILURE;
}
std::cout << "Expected mean is " << knownMean << ", computed mean is " << testMean << std::endl;
diff = itk::Math::abs(testVariance - knownVariance);
if ((diff != 0.0 && knownVariance != 0.0) &&
diff / itk::Math::abs(knownVariance) > .1)
{
std::cout << "Expected variance is " << knownVariance << ", computed variance is " << testVariance << std::endl;
return EXIT_FAILURE;
}
std::cout << "Expected variance is " << knownVariance << ", computed variance is " << testVariance << std::endl;
return status;
}
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