/usr/include/OTB-6.4/otbMRFEnergyGaussianClassification.h is in libotb-dev 6.4.0+dfsg-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | /*
* Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* 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
*
* 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 otbMRFEnergyGaussianClassification_h
#define otbMRFEnergyGaussianClassification_h
#include "otbMRFEnergy.h"
#include "otbMath.h"
namespace otb
{
/**
* \class MRFEnergyGaussianClassification
* \brief This is the implementation of the Gaussian model for Markov classification.
*
* This is the implementation of the Gaussian Energy model for Markov classification, to be used for
* the fidelity term for classification. Energy is:
* \f[ U(x_s / y_s) = \frac{(y_s+\mu_{x_s})^2}{2\sigma^2_{x_s}}+\log{\sqrt{2\pi}\sigma_{x_s}} \f]
* with
* - \f$ x_s \f$ the label on site s
* - \f$ y_s \f$ the value on the reference image
* - \f$ \mu_{x_s} \f$ and \f$ \sigma^2_{x_s} \f$ the mean and variance of label \f$ x_s \f$
*
* This class is meant to be used in the MRF framework with the otb::MarkovRandomFieldFilter
*
* \ingroup Markov
*
* \ingroup OTBMarkov
*/
template<class TInput1, class TInput2>
class ITK_EXPORT MRFEnergyGaussianClassification : public MRFEnergy<TInput1, TInput2>
{
public:
typedef MRFEnergyGaussianClassification Self;
typedef MRFEnergy<TInput1, TInput2> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
typedef TInput1 InputImageType;
typedef TInput2 LabelledImageType;
typedef typename InputImageType::PixelType InputImagePixelType;
typedef typename LabelledImageType::PixelType LabelledImagePixelType;
typedef itk::Array <double> ParametersType;
itkNewMacro(Self);
itkTypeMacro(MRFEnergyGaussianClassification, MRFEnergy);
void SetNumberOfParameters(const unsigned int nParameters) ITK_OVERRIDE
{
Superclass::SetNumberOfParameters(nParameters);
this->m_Parameters.SetSize(nParameters);
this->Modified();
}
double GetSingleValue(const InputImagePixelType& value1, const LabelledImagePixelType& value2) ITK_OVERRIDE
{
if ((unsigned int) value2 >= this->GetNumberOfParameters() / 2)
{
itkExceptionMacro(<< "Number of parameters does not correspond to number of classes");
}
double val1 = static_cast<double>(value1);
double result = vnl_math_sqr(val1 - this->m_Parameters[2 * static_cast<int>(value2)])
/ (2 * vnl_math_sqr(this->m_Parameters[2 * static_cast<int>(value2) + 1]))
+ vcl_log(vcl_sqrt(CONST_2PI) * this->m_Parameters[2 * static_cast<int>(value2) + 1]);
return static_cast<double>(result);
}
protected:
// The constructor and destructor.
MRFEnergyGaussianClassification() {};
~MRFEnergyGaussianClassification() ITK_OVERRIDE {}
};
}
#endif
|