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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.
*
*=========================================================================*/
#ifndef itkShotNoiseImageFilter_hxx
#define itkShotNoiseImageFilter_hxx
#include "itkShotNoiseImageFilter.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkImageScanlineIterator.h"
#include "itkProgressReporter.h"
#include "itkNormalVariateGenerator.h"
namespace itk
{
template <class TInputImage, class TOutputImage>
ShotNoiseImageFilter<TInputImage, TOutputImage>
::ShotNoiseImageFilter() :
m_Scale( 1.0 )
{
}
template <class TInputImage, class TOutputImage>
void
ShotNoiseImageFilter<TInputImage, TOutputImage>
::ThreadedGenerateData( const OutputImageRegionType &outputRegionForThread, ThreadIdType threadId)
{
const InputImageType* inputPtr = this->GetInput();
OutputImageType* outputPtr = this->GetOutput(0);
// Create a random generator per thread
typename Statistics::MersenneTwisterRandomVariateGenerator::Pointer rand =
Statistics::MersenneTwisterRandomVariateGenerator::New();
const uint32_t seed = Self::Hash( this->GetSeed(), threadId );
rand->Initialize(seed);
typename Statistics::NormalVariateGenerator::Pointer randn = Statistics::NormalVariateGenerator::New();
randn->Initialize(*reinterpret_cast<const 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<TInputImage> inputIt(inputPtr, inputRegionForThread);
ImageScanlineIterator<TOutputImage> outputIt(outputPtr, outputRegionForThread);
ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels() );
inputIt.GoToBegin();
outputIt.GoToBegin();
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.CompletedPixel(); // potential exception thrown here
}
}
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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