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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 itkOrthogonalSwath2DPathFilter_h
#define itkOrthogonalSwath2DPathFilter_h
#include "itkPathAndImageToPathFilter.h"
#include "itkOrthogonallyCorrected2DParametricPath.h"
#include <memory> // For unique_ptr.
namespace itk
{
/**
* \class OrthogonalSwath2DPathFilter
* \brief Filter that optimizes a 2D path relative to an image.
*
* OrthogonalSwath2DPathFilter produces an OrthogonallyCorrected2DParametricPath
* representation of a path that is optimal with respect to an image and an
* original Fourier series path (sometimes referred to as an "initial contour").
* Usage is a little complex. The input image must be preprocessed with
* ExtractOrthogonalSwath2DImageFilter (the user may want to smooth the image
* first). The user should then use the resulting swath image to produce a new
* "merit" swath image of the EXACT same size as the swath image produced in the
* preceding step. Each pixel value in the merit swath image indicates the
* local merit of having the path pass through that swath index (taking the
* absolute value of the vertical partial-derivative of the swath image is often
* a good way to do this). Both the merit swath image and the path used to
* extract the swath image should then be passed as inputs to this filter which
* will search through the merit swath image using dynamic programming to find
* the absolutely optimum (in terms of the swath image) path.
*
* The file itkOrthogonalSwath2DPathFilterTest.cxx provides a good usage example
* of itk::OrthogonalSwath2DPathFilter
*
* \ingroup PathFilters
* \ingroup ITKPath
*/
template <typename TFourierSeriesPath, typename TSwathMeritImage>
class ITK_TEMPLATE_EXPORT OrthogonalSwath2DPathFilter
: public PathAndImageToPathFilter<TFourierSeriesPath, TSwathMeritImage, OrthogonallyCorrected2DParametricPath>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(OrthogonalSwath2DPathFilter);
/** Standard class type aliases. */
using Self = OrthogonalSwath2DPathFilter;
using Superclass =
PathAndImageToPathFilter<TFourierSeriesPath, TSwathMeritImage, OrthogonallyCorrected2DParametricPath>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(OrthogonalSwath2DPathFilter);
/** Some convenient type alias. */
using InputPathType = TFourierSeriesPath;
using InputPathPointer = typename InputPathType::Pointer;
using InputPathInputType = typename InputPathType::InputType;
using ImageType = TSwathMeritImage;
using ImageConstPointer = typename ImageType::ConstPointer;
using OutputPathType = OrthogonallyCorrected2DParametricPath;
using OutputPathPointer = typename OutputPathType::Pointer;
using OutputPathInputType = typename OutputPathType::InputType;
using OrthogonalCorrectionTableType = typename OutputPathType::OrthogonalCorrectionTableType;
using OrthogonalCorrectionTablePointer = typename OutputPathType::OrthogonalCorrectionTablePointer;
using IndexType = typename InputPathType::IndexType;
using OffsetType = typename InputPathType::OffsetType;
using SizeType = typename ImageType::SizeType;
protected:
OrthogonalSwath2DPathFilter() = default;
~OrthogonalSwath2DPathFilter() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
void
GenerateData() override;
private:
// Find the "L" for the maximum merit over the range L-1 to L+1 at F & x.
// This value is both returned and stored in m_StepValues.
// The merits for F & x at L-1 to L+1 must have already been calculated.
unsigned int
FindAndStoreBestErrorStep(unsigned int x, unsigned int F, unsigned int L);
// m_StepValues & m_MeritValues are stored as datatype[x][F][L] which requires
// cols*rows*rows bytes of storage where rows and cols are the dimensions of
// the processed image.
//
// This ordering of elements is most efficient when L is incremented in the
// inner-most loop and x is incremented in the outer-most loop.
//
// m_StepValues & m_MeritValues should always be accessed using the
// StepValue()
// and MeritValue() access functions. StepValue() and MeritValue() can each
// be
// used on both the left and right hand of assignments for reads & writes, ex:
// StepValue(1,1,1) = 2+MeritValue(0,0,3);
inline int &
StepValue(int f, int l, int x)
{
int rows = m_SwathSize[1];
return m_StepValues[(x * rows * rows) + (f * rows) + (l)];
}
inline double &
MeritValue(int f, int l, int x)
{
int rows = m_SwathSize[1];
return m_MeritValues[(x * rows * rows) + (f * rows) + (l)];
}
std::unique_ptr<int[]> m_StepValues{ nullptr }; // best y=error coordinate @ x of image for (0,F) ->
// (x+1,L)
std::unique_ptr<double[]> m_MeritValues{ nullptr };
std::unique_ptr<int[]> m_OptimumStepsValues{ nullptr }; // best step (e value)
// sequence for a
// closed path
OrthogonalCorrectionTablePointer m_FinalOffsetValues{ OrthogonalCorrectionTableType::New() };
SizeType m_SwathSize{ { 0 } };
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkOrthogonalSwath2DPathFilter.hxx"
#endif
#endif
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