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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 "itkSimpleFilterWatcher.h"
#include "itkMath.h"
#include "itkTestingMacros.h"
int
itkStatisticsImageFilterTest(int argc, char * argv[])
{
std::cout << "itkStatisticsImageFilterTest [numberOfStreamDivisions]" << std::endl;
int status = 0;
unsigned int numberOfStreamDivisions = 1;
if (argc > 1)
{
numberOfStreamDivisions = std::max(std::stoi(argv[1]), 1);
}
using FloatImage = itk::Image<int, 3>;
itk::Statistics::MersenneTwisterRandomVariateGenerator::GetInstance()->SetSeed(987);
auto 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());
float sumOfSquares = std::pow(fillValue, 2.0) * static_cast<float>(region.GetNumberOfPixels());
using FilterType = itk::StatisticsImageFilter<FloatImage>;
auto filter = FilterType::New();
ITK_EXERCISE_BASIC_OBJECT_METHODS(filter, StatisticsImageFilter, ImageSink);
itk::SimpleFilterWatcher filterWatch(filter);
filter->SetNumberOfStreamDivisions(numberOfStreamDivisions);
ITK_TEST_SET_GET_VALUE(numberOfStreamDivisions, filter->GetNumberOfStreamDivisions());
filter->SetInput(image);
ITK_TRY_EXPECT_NO_EXCEPTION(filter->Update());
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->GetSumOfSquares(), sumOfSquares))
{
std::cerr << "GetSumOfSquares failed! Got " << filter->GetSumOfSquares() << " but expected " << sumOfSquares
<< 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
using SourceType = itk::RandomImageSource<FloatImage>;
auto source = SourceType::New();
FloatImage::SizeValueType randomSize[3] = { 17, 8, 241 };
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->SetNumberOfStreamDivisions(numberOfStreamDivisions);
ITK_TRY_EXPECT_NO_EXCEPTION(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;
using DoubleImage = itk::Image<double, 3>;
auto 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;
}
using DFilterType = itk::StatisticsImageFilter<DoubleImage>;
auto dfilter = DFilterType::New();
dfilter->SetInput(dImage);
dfilter->SetNumberOfStreamDivisions(numberOfStreamDivisions);
ITK_TRY_EXPECT_NO_EXCEPTION(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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