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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 itkDiscreteGaussianImageFilter_hxx
#define itkDiscreteGaussianImageFilter_hxx
#include "itkNeighborhoodOperatorImageFilter.h"
#include "itkGaussianOperator.h"
#include "itkImageRegionIterator.h"
#include "itkProgressAccumulator.h"
#include "itkImageAlgorithm.h"
namespace itk
{
template <typename TInputImage, typename TOutputImage>
void
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::GenerateKernel(const unsigned int dimension,
KernelType & oper) const
{
// Determine the size of the operator in this dimension. Note that the
// Gaussian is built as a 1D operator in each of the specified directions.
oper.SetDirection(dimension);
oper.SetMaximumError(m_MaximumError[dimension]);
oper.SetMaximumKernelWidth(m_MaximumKernelWidth);
oper.SetVariance(this->GetKernelVarianceArray()[dimension]);
oper.CreateDirectional();
}
template <typename TInputImage, typename TOutputImage>
unsigned int
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::GetKernelRadius(const unsigned int dimension) const
{
KernelType oper;
this->GenerateKernel(dimension, oper);
return oper.GetRadius(dimension);
}
template <typename TInputImage, typename TOutputImage>
auto
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::GetKernelRadius() const -> ArrayType
{
ArrayType kernelRadius;
for (unsigned int dim = 0; dim < ImageDimension; ++dim)
{
kernelRadius[dim] = this->GetKernelRadius(dim);
}
return kernelRadius;
}
template <typename TInputImage, typename TOutputImage>
auto
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::GetKernelSize() const -> ArrayType
{
ArrayType kernelSize;
for (unsigned int dim = 0; dim < ImageDimension; ++dim)
{
kernelSize[dim] = this->GetKernelRadius(dim) * 2 + 1;
}
return kernelSize;
}
template <typename TInputImage, typename TOutputImage>
auto
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::GetKernelVarianceArray() const -> ArrayType
{
if (m_UseImageSpacing)
{
if (this->GetInput() == nullptr)
{
itkExceptionMacro("Could not get kernel variance! UseImageSpacing is ON but no input image was provided");
}
const auto & spacing = this->GetInput()->GetSpacing();
ArrayType adjustedVariance;
// Adjusted variance = var / (spacing ^ 2)
for (unsigned int dim = 0; dim < ImageDimension; ++dim)
{
// convert the variance from physical units to pixels
double s = spacing[dim];
s = s * s;
adjustedVariance[dim] = m_Variance[dim] / s;
}
return adjustedVariance;
}
else
{
return this->GetVariance();
}
}
template <typename TInputImage, typename TOutputImage>
void
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::GenerateInputRequestedRegion()
{
// call the superclass' implementation of this method. this should
// copy the output requested region to the input requested region
Superclass::GenerateInputRequestedRegion();
// get pointers to the input and output
typename Superclass::InputImagePointer inputPtr = const_cast<TInputImage *>(this->GetInput());
if (!inputPtr)
{
return;
}
// Determine the kernel size in each direction
RadiusType radius;
for (unsigned int i = 0; i < TInputImage::ImageDimension; ++i)
{
if (i < m_FilterDimensionality)
{
radius[i] = GetKernelRadius(i);
}
else
{
radius[i] = 0;
}
}
// get a copy of the input requested region (should equal the output
// requested region)
typename TInputImage::RegionType inputRequestedRegion;
inputRequestedRegion = inputPtr->GetRequestedRegion();
// pad the input requested region by the operator radius
inputRequestedRegion.PadByRadius(radius);
// crop the input requested region at the input's largest possible region
inputRequestedRegion.Crop(inputPtr->GetLargestPossibleRegion());
inputPtr->SetRequestedRegion(inputRequestedRegion);
}
template <typename TInputImage, typename TOutputImage>
void
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::GenerateData()
{
TOutputImage * output = this->GetOutput();
output->SetBufferedRegion(output->GetRequestedRegion());
output->Allocate();
// Create an internal image to protect the input image's metadata
// (e.g. RequestedRegion). The StreamingImageFilter changes the
// requested region as part of its normal processing.
auto localInput = TInputImage::New();
localInput->Graft(this->GetInput());
// Determine the dimensionality to filter
unsigned int filterDimensionality = m_FilterDimensionality;
if (filterDimensionality > ImageDimension)
{
filterDimensionality = ImageDimension;
}
if (filterDimensionality == 0)
{
// no smoothing, copy input to output
ImageAlgorithm::Copy(localInput.GetPointer(),
output,
this->GetOutput()->GetRequestedRegion(),
this->GetOutput()->GetRequestedRegion());
return;
}
// Type definition for the internal neighborhood filter
//
// First filter convolves and changes type from input type to real type
// Middle filters convolves from real to real
// Last filter convolves and changes type from real type to output type
// Streaming filter forces the mini-pipeline to run in chunks
using FirstFilterType =
NeighborhoodOperatorImageFilter<InputImageType, RealOutputImageType, RealOutputPixelValueType>;
using IntermediateFilterType =
NeighborhoodOperatorImageFilter<RealOutputImageType, RealOutputImageType, RealOutputPixelValueType>;
using LastFilterType =
NeighborhoodOperatorImageFilter<RealOutputImageType, OutputImageType, RealOutputPixelValueType>;
using SingleFilterType = NeighborhoodOperatorImageFilter<InputImageType, OutputImageType, RealOutputPixelValueType>;
using FirstFilterPointer = typename FirstFilterType::Pointer;
using IntermediateFilterPointer = typename IntermediateFilterType::Pointer;
using LastFilterPointer = typename LastFilterType::Pointer;
using SingleFilterPointer = typename SingleFilterType::Pointer;
// Create a series of operators
std::vector<KernelType> oper;
oper.resize(filterDimensionality);
// Create a process accumulator for tracking the progress of minipipeline
auto progress = ProgressAccumulator::New();
progress->SetMiniPipelineFilter(this);
// Set up the operators
unsigned int i;
for (i = 0; i < filterDimensionality; ++i)
{
// we reverse the direction to minimize computation while, because
// the largest dimension will be split slice wise for streaming
unsigned int reverse_i = filterDimensionality - i - 1;
this->GenerateKernel(i, oper[reverse_i]);
}
// Create a chain of filters
//
//
if (filterDimensionality == 1)
{
// Use just a single filter
SingleFilterPointer singleFilter = SingleFilterType::New();
singleFilter->SetOperator(oper[0]);
singleFilter->SetInput(localInput);
singleFilter->OverrideBoundaryCondition(m_InputBoundaryCondition);
progress->RegisterInternalFilter(singleFilter, 1.0f / m_FilterDimensionality);
// Graft this filters output onto the mini-pipeline so the mini-pipeline
// has the correct region ivars and will write to this filters bulk data
// output.
singleFilter->GraftOutput(output);
// Update the filter
singleFilter->Update();
// Graft the last output of the mini-pipeline onto this filters output so
// the final output has the correct region ivars and a handle to the final
// bulk data
this->GraftOutput(output);
}
else
{
// Setup a full mini-pipeline and stream the data through the
// pipeline.
const unsigned int numberOfStages = filterDimensionality;
// First filter convolves and changes type from input type to real type
FirstFilterPointer firstFilter = FirstFilterType::New();
firstFilter->SetOperator(oper[0]);
firstFilter->ReleaseDataFlagOn();
firstFilter->SetInput(localInput);
firstFilter->OverrideBoundaryCondition(m_InputBoundaryCondition);
progress->RegisterInternalFilter(firstFilter, 1.0f / numberOfStages);
// Middle filters convolves from real to real
std::vector<IntermediateFilterPointer> intermediateFilters;
if (filterDimensionality > 2)
{
for (i = 1; i < filterDimensionality - 1; ++i)
{
IntermediateFilterPointer f = IntermediateFilterType::New();
f->SetOperator(oper[i]);
f->ReleaseDataFlagOn();
f->OverrideBoundaryCondition(m_RealBoundaryCondition);
progress->RegisterInternalFilter(f, 1.0f / numberOfStages);
if (i == 1)
{
f->SetInput(firstFilter->GetOutput());
}
else
{
// note: first filter in vector (zeroth element) is for i==1
f->SetInput(intermediateFilters[i - 2]->GetOutput());
}
intermediateFilters.push_back(f);
}
}
// Last filter convolves and changes type from real type to output type
LastFilterPointer lastFilter = LastFilterType::New();
lastFilter->SetOperator(oper[filterDimensionality - 1]);
lastFilter->OverrideBoundaryCondition(m_RealBoundaryCondition);
if (filterDimensionality > 2)
{
lastFilter->SetInput(intermediateFilters[filterDimensionality - 3]->GetOutput());
}
else
{
lastFilter->SetInput(firstFilter->GetOutput());
}
progress->RegisterInternalFilter(lastFilter, 1.0f / numberOfStages);
// Graft this filters output onto the mini-pipeline so the mini-pipeline
// has the correct region ivars and will write to this filters bulk data
// output.
lastFilter->GraftOutput(output);
// Update the last filter in the chain
lastFilter->Update();
// Graft the last output of the mini-pipeline onto this filters output so
// the final output has the correct region ivars and a handle to the final
// bulk data
this->GraftOutput(output);
}
}
#if !defined(ITK_LEGACY_REMOVE)
template <typename TInputImage, typename TOutputImage>
unsigned int
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::GetInternalNumberOfStreamDivisions() const
{
return 1;
}
template <typename TInputImage, typename TOutputImage>
void
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::SetInternalNumberOfStreamDivisions(unsigned int)
{
# if !defined(ITK_LEGACY_SILENT)
itkWarningMacro("SetInternalNumberOfStreamDivisions has been removed as the filter no longer internally streams!");
# endif
}
#endif
template <typename TInputImage, typename TOutputImage>
void
DiscreteGaussianImageFilter<TInputImage, TOutputImage>::PrintSelf(std::ostream & os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Variance: " << m_Variance << std::endl;
os << indent << "MaximumError: " << m_MaximumError << std::endl;
os << indent << "MaximumKernelWidth: " << m_MaximumKernelWidth << std::endl;
os << indent << "FilterDimensionality: " << m_FilterDimensionality << std::endl;
os << indent << "UseImageSpacing: " << (m_UseImageSpacing ? "On" : "Off") << std::endl;
os << indent << "RealBoundaryCondition: " << m_RealBoundaryCondition << std::endl;
}
} // end namespace itk
#endif
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