itkAdaptiveEqualizationHistogram.h

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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::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 };
};
 
} // namespace itk::Function
 
#endif // itkAdaptiveHistogramHistogram_h