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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.
*
*=========================================================================*/
#ifndef itkShotNoiseImageFilter_hxx
#define itkShotNoiseImageFilter_hxx
#include "itkBitCast.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkImageScanlineIterator.h"
#include "itkTotalProgressReporter.h"
#include "itkNormalVariateGenerator.h"
namespace itk
{
template <class TInputImage, class TOutputImage>
ShotNoiseImageFilter<TInputImage, TOutputImage>::ShotNoiseImageFilter()
{
this->DynamicMultiThreadingOff();
this->ThreaderUpdateProgressOff();
}
template <class TInputImage, class TOutputImage>
void
ShotNoiseImageFilter<TInputImage, TOutputImage>::ThreadedGenerateData(
const OutputImageRegionType & outputRegionForThread,
ThreadIdType)
{
const InputImageType * inputPtr = this->GetInput();
OutputImageType * outputPtr = this->GetOutput(0);
// Create a random generator per thread
IndexValueType indSeed = 0;
for (unsigned int d = 0; d < TOutputImage::ImageDimension; ++d)
{
indSeed += outputRegionForThread.GetIndex(d);
}
typename Statistics::MersenneTwisterRandomVariateGenerator::Pointer rand =
Statistics::MersenneTwisterRandomVariateGenerator::New();
const uint32_t seed = Self::Hash(this->GetSeed(), uint32_t(indSeed));
rand->Initialize(seed);
typename Statistics::NormalVariateGenerator::Pointer randn = Statistics::NormalVariateGenerator::New();
randn->Initialize(bit_cast<int32_t>(seed));
// Define the portion of the input to walk for this thread, using
// the CallCopyOutputRegionToInputRegion method allows for the input
// and output images to be different dimensions
InputImageRegionType inputRegionForThread;
this->CallCopyOutputRegionToInputRegion(inputRegionForThread, outputRegionForThread);
// Define the iterators
ImageScanlineConstIterator inputIt(inputPtr, inputRegionForThread);
ImageScanlineIterator outputIt(outputPtr, outputRegionForThread);
TotalProgressReporter progress(this, outputPtr->GetRequestedRegion().GetNumberOfPixels());
while (!inputIt.IsAtEnd())
{
while (!inputIt.IsAtEndOfLine())
{
const double in = m_Scale * inputIt.Get();
// The value of >=50, is the lambda value in a Poisson
// distribution where a Gaussian distribution is a "good"
// approximation of the Poisson. This could be considered to be
// exposed as an advanced parameter in the future.
if (in < 50)
{
const double L = std::exp(-in);
long k = 0;
double p = 1.0;
do
{
k += 1;
p *= rand->GetVariate();
} while (p > L);
// Clip the output to the actual supported range
outputIt.Set(Self::ClampCast((k - 1) / m_Scale));
}
else
{
const double out = in + std::sqrt(in) * randn->GetVariate();
outputIt.Set(Self::ClampCast(out / m_Scale));
}
++inputIt;
++outputIt;
}
inputIt.NextLine();
outputIt.NextLine();
progress.Completed(outputRegionForThread.GetSize()[0]);
}
}
template <class TInputImage, class TOutputImage>
void
ShotNoiseImageFilter<TInputImage, TOutputImage>::PrintSelf(std::ostream & os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Scale: " << static_cast<typename NumericTraits<double>::PrintType>(m_Scale) << std::endl;
}
} // end namespace itk
#endif
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