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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 itkGaussianImageSource_hxx
#define itkGaussianImageSource_hxx
#include "itkGaussianSpatialFunction.h"
#include "itkImageRegionIterator.h"
#include "itkProgressReporter.h"
#include "itkObjectFactory.h"
namespace itk
{
template <typename TOutputImage>
GaussianImageSource<TOutputImage>::GaussianImageSource()
{
// Gaussian parameters, defined so that the Gaussian
// is centered in the default image
m_Mean.Fill(32.0);
m_Sigma.Fill(16.0);
}
template <typename TOutputImage>
void
GaussianImageSource<TOutputImage>::SetParameters(const ParametersType & parameters)
{
ArrayType sigma;
ArrayType mean;
for (unsigned int i = 0; i < ArrayType::Length; ++i)
{
sigma[i] = parameters[i];
mean[i] = parameters[i + ArrayType::Length];
}
this->SetSigma(sigma);
this->SetMean(mean);
const double scale = parameters[2 * ArrayType::Length];
this->SetScale(scale);
}
template <typename TOutputImage>
auto
GaussianImageSource<TOutputImage>::GetParameters() const -> ParametersType
{
ParametersType parameters(2 * ArrayType::Length + 1);
for (unsigned int i = 0; i < ArrayType::Length; ++i)
{
parameters[i] = m_Sigma[i];
parameters[i + ArrayType::Length] = m_Mean[i];
}
parameters[2 * ArrayType::Length] = m_Scale;
return parameters;
}
template <typename TOutputImage>
unsigned int
GaussianImageSource<TOutputImage>::GetNumberOfParameters() const
{
return 2 * ArrayType::Length + 1;
}
template <typename TOutputImage>
void
GaussianImageSource<TOutputImage>::GenerateData()
{
TOutputImage * outputPtr = this->GetOutput();
// Allocate the output buffer
outputPtr->SetBufferedRegion(outputPtr->GetRequestedRegion());
outputPtr->Allocate();
// Create and initialize a new Gaussian function
using FunctionType = GaussianSpatialFunction<double, NDimensions>;
auto gaussian = FunctionType::New();
gaussian->SetSigma(m_Sigma);
gaussian->SetMean(m_Mean);
gaussian->SetScale(m_Scale);
gaussian->SetNormalized(m_Normalized);
// Create an iterator that will walk the output region
using OutputIterator = ImageRegionIterator<TOutputImage>;
ProgressReporter progress(this, 0, outputPtr->GetRequestedRegion().GetNumberOfPixels());
// Walk the output image, evaluating the spatial function at each pixel
for (OutputIterator outIt(outputPtr, outputPtr->GetRequestedRegion()); !outIt.IsAtEnd(); ++outIt)
{
const typename TOutputImage::IndexType index = outIt.GetIndex();
// The position at which the function is evaluated
typename FunctionType::InputType evalPoint;
outputPtr->TransformIndexToPhysicalPoint(index, evalPoint);
const double value = gaussian->Evaluate(evalPoint);
// Set the pixel value to the function value
outIt.Set(static_cast<typename TOutputImage::PixelType>(value));
progress.CompletedPixel();
}
}
template <typename TOutputImage>
void
GaussianImageSource<TOutputImage>::PrintSelf(std::ostream & os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Mean: " << m_Mean << std::endl;
os << indent << "Sigma: " << m_Sigma << std::endl;
os << indent << "Scale: " << m_Scale << std::endl;
os << indent << "Normalized: " << (m_Normalized ? "On" : "Off") << std::endl;
}
} // end namespace itk
#endif
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