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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* 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.
*
*=========================================================================*/
// histogram from the moving histogram operations
#ifndef __itkTextureHistogram_h
#define __itkTextureHistogram_h
#include "itkNumericTraits.h"
namespace itk
{
namespace Function
{
/*
*
*
*/
template< class TInputPixel, class TOutputPixel >
class TextureHistogram
{
public:
TextureHistogram()
{
m_Count = 0;
}
// ~TextureHistogram() {} default is ok
void AddPixel( const TInputPixel & p )
{
m_Map[p]++;
++m_Count;
}
void RemovePixel(const TInputPixel & p)
{
// insert new item if one doesn't exist
typename MapType::iterator it = m_Map.find( p );
assert( it != m_Map.end() );
if ( --(it->second) == 0 )
{
m_Map.erase( it );
}
--m_Count;
}
TOutputPixel GetValue(const TInputPixel &)
{
TOutputPixel out;
NumericTraits<TOutputPixel>::SetLength( out, 8 );
double sum = 0.0;
double sum2 = 0.0;
double sum3 = 0.0;
double sum4 = 0.0;
const size_t count = m_Count;
//double median = 0.0;
double entropy = 0.0;
size_t curCount = 0;
for ( typename MapType::iterator i = m_Map.begin(); i != m_Map.end(); ++i )
{
double t = double(i->first)*double(i->second);
sum += t;
sum2 += ( t *= double(i->first) );
sum3 += ( t *= double(i->first) );
sum4 += ( t *= double(i->first) );
curCount += i->second;
const double p_x = double( i->second ) / count;
entropy += -p_x*vcl_log( p_x );
// // this is wrong!
// if ( curCount == count / 2 )
// {
// median += i->first;
// medianIt = il
// // we have an even number so take the average
// if ( !(count % 2) )
// {
// median *= 0.5;
// }
// }
}
// curCount = 0;
// typename MapType::iterator fmedianIt = medianIt;
// typename MapType::iterator rmedianIt = medianIt;
// double mad = 0.0;
// while (curCount < count/2 )
// {
// if ( vcl_fabs( fmedianIt->first - median ) < vcl_fabs( rmedianIt->first - median ) )
// {
// curCount += fmedianIt->second;
// ++fmedianIt;
// }
// else
// {
// curCount += rmedianIt->second;
// --rmedianIt;
// }
// }
const double icount = 1.0 / count;
const double mean = sum * icount;
// unbiased estimate
const double variance = ( sum2 - ( sum * sum * icount ) ) / ( count - 1 );
const double sigma = vcl_sqrt(variance);
double skewness = 0.0;
double kurtosis = 0.0;
if(vcl_abs(variance * sigma) > itk::NumericTraits<double>::min())
{
skewness = ( ( sum3 - 3.0 * mean * sum2 ) * icount + 2.0 * mean * mean*mean ) / ( variance * sigma );
}
if(vcl_abs(variance) > itk::NumericTraits<double>::min())
{
kurtosis = ( sum4 * icount + mean *( -4.0 * sum3 * icount + mean * ( 6.0 *sum2 * icount - 3.0 * mean * mean ))) /
( variance * variance ) - 3.0;
}
unsigned int i = 0;
out[i++] = mean;
out[i++] = m_Map.begin()->first;
out[i++] = m_Map.rbegin()->first;
out[i++] = variance;
out[i++] = sigma;
out[i++] = skewness;
out[i++] = kurtosis;
out[i++] = entropy;
return out;
}
void AddBoundary(){}
void RemoveBoundary(){}
private:
typedef typename std::map< TInputPixel, size_t > MapType;
MapType m_Map;
size_t m_Count;
};
} // end namespace Function
} // end namespace itk
#endif
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