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/*=========================================================================
*
* Copyright UMC Utrecht and contributors
*
* 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 _itkBSplineInterpolateVectorImageFunction_hxx
#define _itkBSplineInterpolateVectorImageFunction_hxx
#include "itkBSplineInterpolateVectorImageFunction.h"
#include <itkVectorIndexSelectionCastImageFilter.h>
/**
* ******************* SetInputImage ***********************
*/
template <typename TImage, typename TInterpolator>
void
BSplineInterpolateVectorImageFunction<TImage, TInterpolator>::SetInputImage(typename TImage::Pointer vectorImage)
{
// Loop over each feature (channel) in the vector image
// Create a separate scalar image and corresponding interpolator for it
for (unsigned int i = 0; i < vectorImage->GetVectorLength(); ++i)
{
auto selector = itk::VectorIndexSelectionCastImageFilter<TImage, itk::Image<float, TImage::ImageDimension>>::New();
selector->SetInput(vectorImage);
selector->SetIndex(i);
selector->Update();
auto interpolator = TInterpolator::New();
interpolator->SetInputImage(selector->GetOutput());
interpolator->SetSplineOrder(3);
m_Interpolators.push_back(interpolator);
}
} // end SetInputImage
/**
* ******************* Evaluate ***********************
*/
template <typename TImage, typename TInterpolator>
typename torch::Tensor
BSplineInterpolateVectorImageFunction<TImage, TInterpolator>::Evaluate(typename TImage::PointType point,
std::vector<unsigned int> subsetOfFeatures) const
{
std::vector<float> result;
for (const unsigned int feature : subsetOfFeatures)
{
result.push_back(m_Interpolators[feature]->Evaluate(point));
}
return torch::from_blob(result.data(), { static_cast<int64_t>(result.size()) }, torch::kFloat32).clone();
} // end Evaluate
/**
* ******************* EvaluateDerivative ***********************
*/
template <typename TImage, typename TInterpolator>
typename torch::Tensor
BSplineInterpolateVectorImageFunction<TImage, TInterpolator>::EvaluateDerivative(
typename ImageType::PointType point,
std::vector<unsigned int> subsetOfFeatures) const
{
using CovariantVectorType = itk::CovariantVector<float, TImage::ImageDimension>;
std::vector<float> derivative(subsetOfFeatures.size() * TImage::ImageDimension, 0.0f);
CovariantVectorType dev;
// Fill the derivative tensor with directional gradients for each selected feature
for (int i = 0; i < subsetOfFeatures.size(); ++i)
{
dev = m_Interpolators[subsetOfFeatures[i]]->EvaluateDerivative(point);
for (unsigned int it = 0; it < TImage::ImageDimension; ++it)
{
derivative[i * TImage::ImageDimension + it] = static_cast<float>(dev[it]);
}
}
return torch::from_blob(derivative.data(),
{ static_cast<int64_t>(subsetOfFeatures.size()), TImage::ImageDimension },
torch::kFloat32)
.clone();
} // end EvaluateDerivative
#endif // end #ifndef _itkBSplineInterpolateVectorImageFunction_hxx
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