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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 itkHessianRecursiveGaussianImageFilter_h
#define itkHessianRecursiveGaussianImageFilter_h
#include "itkRecursiveGaussianImageFilter.h"
#include "itkNthElementImageAdaptor.h"
#include "itkImage.h"
#include "itkSymmetricSecondRankTensor.h"
#include "itkPixelTraits.h"
namespace itk
{
/**
* \class HessianRecursiveGaussianImageFilter
* \brief Computes the Hessian matrix of an image by convolution
* with the Second and Cross derivatives of a Gaussian.
*
* This filter is implemented using the recursive gaussian
* filters
*
*
* \ingroup GradientFilters
* \ingroup SingleThreaded
* \ingroup ITKImageFeature
*/
template <typename TInputImage,
typename TOutputImage =
Image<SymmetricSecondRankTensor<typename NumericTraits<typename TInputImage::PixelType>::RealType,
TInputImage::ImageDimension>,
TInputImage::ImageDimension>>
class ITK_TEMPLATE_EXPORT HessianRecursiveGaussianImageFilter : public ImageToImageFilter<TInputImage, TOutputImage>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(HessianRecursiveGaussianImageFilter);
/** Standard class type aliases. */
using Self = HessianRecursiveGaussianImageFilter;
using Superclass = ImageToImageFilter<TInputImage, TOutputImage>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Pixel Type of the input image */
using InputImageType = TInputImage;
using PixelType = typename TInputImage::PixelType;
using RealType = typename NumericTraits<PixelType>::RealType;
/** Image dimension. */
static constexpr unsigned int ImageDimension = TInputImage::ImageDimension;
/** Number of smoothing filters. */
static constexpr unsigned int NumberOfSmoothingFilters =
(TInputImage::ImageDimension > 2) ? (TInputImage::ImageDimension - 2) : (0);
/** Define the image type for internal computations
RealType is usually 'double' in NumericTraits.
Here we prefer float in order to save memory. */
using InternalRealType = float;
using RealImageType = Image<InternalRealType, TInputImage::ImageDimension>;
/** Output Image Nth Element Adaptor
* This adaptor allows to use conventional scalar
* smoothing filters to compute each one of the
* components of the gradient image pixels. */
using OutputImageAdaptorType = NthElementImageAdaptor<TOutputImage, InternalRealType>;
using OutputImageAdaptorPointer = typename OutputImageAdaptorType::Pointer;
/** Smoothing filter type */
using GaussianFilterType = RecursiveGaussianImageFilter<RealImageType, RealImageType>;
/** Derivative filter type, it will be the first in the pipeline */
using DerivativeFilterAType = RecursiveGaussianImageFilter<InputImageType, RealImageType>;
using DerivativeFilterBType = RecursiveGaussianImageFilter<RealImageType, RealImageType>;
/** Pointer to a gaussian filter. */
using GaussianFilterPointer = typename GaussianFilterType::Pointer;
using GaussianFiltersArray = std::vector<GaussianFilterPointer>;
/** Pointer to a derivative filter. */
using DerivativeFilterAPointer = typename DerivativeFilterAType::Pointer;
using DerivativeFilterBPointer = typename DerivativeFilterBType::Pointer;
/** Pointer to the Output Image */
using OutputImagePointer = typename TOutputImage::Pointer;
/** Type of the output Image */
using OutputImageType = TOutputImage;
using OutputPixelType = typename OutputImageType::PixelType;
using OutputComponentType = typename PixelTraits<OutputPixelType>::ValueType;
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(HessianRecursiveGaussianImageFilter);
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Set/Get Sigma value. Sigma is measured in the units of image spacing. */
void
SetSigma(RealType sigma);
RealType
GetSigma() const;
/** Define which normalization factor will be used for the Gaussian
* \sa RecursiveGaussianImageFilter::SetNormalizeAcrossScale
*/
void
SetNormalizeAcrossScale(bool normalize);
itkGetConstMacro(NormalizeAcrossScale, bool);
itkBooleanMacro(NormalizeAcrossScale);
/** HessianRecursiveGaussianImageFilter needs all of the input to produce an
* output. Therefore, HessianRecursiveGaussianImageFilter needs to provide
* an implementation for GenerateInputRequestedRegion in order to inform
* the pipeline execution model.
* \sa ImageToImageFilter::GenerateInputRequestedRegion() */
void
GenerateInputRequestedRegion() override;
#ifdef ITK_USE_CONCEPT_CHECKING
// Begin concept checking
itkConceptMacro(InputHasNumericTraitsCheck, (Concept::HasNumericTraits<PixelType>));
itkConceptMacro(OutputHasPixelTraitsCheck, (Concept::HasPixelTraits<OutputPixelType>));
// End concept checking
#endif
protected:
HessianRecursiveGaussianImageFilter();
~HessianRecursiveGaussianImageFilter() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
/** Generate Data */
void
GenerateData() override;
// Override since the filter produces the entire dataset
void
EnlargeOutputRequestedRegion(DataObject * output) override;
private:
GaussianFiltersArray m_SmoothingFilters{};
DerivativeFilterAPointer m_DerivativeFilterA{};
DerivativeFilterBPointer m_DerivativeFilterB{};
OutputImageAdaptorPointer m_ImageAdaptor{};
/** Normalize the image across scale space */
bool m_NormalizeAcrossScale{};
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkHessianRecursiveGaussianImageFilter.hxx"
#endif
#endif
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