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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 itkAdaptiveEqualizationHistogram_h
#define itkAdaptiveEqualizationHistogram_h
#include <unordered_map>
#include "itkStructHashFunction.h"
#include "itkMath.h"
#include <cmath>
namespace itk
{
namespace Function
{
/* \class AdaptiveEqualizationHistogram
*
* Implements the function class for a moving histogram algorithm for
* adaptive histogram equalization.
*
* \sa AdaptiveHistogramEqualizationImageFilter
* \sa MovingHistogramImageFilter
* \ingroup ITKImageStatistics
*/
template <class TInputPixel, class TOutputPixel>
class AdaptiveEqualizationHistogram
{
public:
using RealType = float;
AdaptiveEqualizationHistogram() = default;
// ~AdaptiveEqualizationHistogram() {} default is ok
void
AddPixel(const TInputPixel & p)
{
m_Map[p]++;
}
void
RemovePixel(const TInputPixel & p)
{
// insert new item if one doesn't exist
auto it = m_Map.find(p);
itkAssertInDebugAndIgnoreInReleaseMacro(it != m_Map.end());
if (--(it->second) == 0)
{
m_Map.erase(it);
}
}
TOutputPixel
GetValue(const TInputPixel & pixel)
{
// Normalize input pixels to [-0.5 0.5] gray level.
// AdaptiveHistogramEqualization compute kernel components with
// float, but use double for accumulate and temporaries.
const double iscale = static_cast<double>(m_Maximum) - m_Minimum;
double sum = 0.0;
auto itMap = m_Map.begin();
const RealType u = (static_cast<double>(pixel) - m_Minimum) / iscale - 0.5;
while (itMap != m_Map.end())
{
const RealType v = (static_cast<double>(itMap->first) - m_Minimum) / iscale - 0.5;
const double ikernel = m_KernelSize - m_BoundaryCount;
sum += itMap->second * CumulativeFunction(u, v) / ikernel;
++itMap;
}
return (TOutputPixel)(iscale * (sum + 0.5) + m_Minimum);
}
void
AddBoundary()
{
++m_BoundaryCount;
}
void
RemoveBoundary()
{
--m_BoundaryCount;
}
void
SetAlpha(RealType alpha)
{
m_Alpha = alpha;
}
void
SetBeta(RealType beta)
{
m_Beta = beta;
}
void
SetKernelSize(RealType kernelSize)
{
m_KernelSize = kernelSize;
}
void
SetMinimum(TInputPixel minimum)
{
m_Minimum = minimum;
}
void
SetMaximum(TInputPixel maximum)
{
m_Maximum = maximum;
}
private:
RealType m_Alpha{};
RealType m_Beta{};
RealType m_KernelSize{};
TInputPixel m_Minimum;
TInputPixel m_Maximum;
RealType
CumulativeFunction(RealType u, RealType v)
{
// Calculate cumulative function
const RealType s = itk::Math::sgn(u - v);
const RealType ad = itk::Math::abs(2.0 * (u - v));
return 0.5 * s * std::pow(ad, m_Alpha) - m_Beta * 0.5 * s * ad + m_Beta * u;
}
private:
using MapType = typename std::unordered_map<TInputPixel, size_t, StructHashFunction<TInputPixel>>;
MapType m_Map;
size_t m_BoundaryCount{ 0 };
};
} // end namespace Function
} // end namespace itk
#endif // itkAdaptiveHistogramHistogram_h
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