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
98
99
100
101
102
103
104
105
106
107
108
|
/*
* Copyright (C) 2005-2020 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 otbMRFSamplerRandom_h
#define otbMRFSamplerRandom_h
#include "otbMRFSampler.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkNumericTraits.h"
namespace otb
{
/**
* \class MRFSamplerRandom
* \brief This is the base class for sampler methods used in the MRF framework.
*
* This is one sampler to be used int he MRF framework. This sampler select the
* value randomly according to a uniform probability.
*
*
* 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 MRFSamplerRandom : public MRFSampler<TInput1, TInput2>
{
public:
typedef MRFSamplerRandom Self;
typedef otb::MRFSampler<TInput1, TInput2> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
typedef typename Superclass::InputImageNeighborhoodIterator InputImageNeighborhoodIterator;
typedef typename Superclass::LabelledImageNeighborhoodIterator LabelledImageNeighborhoodIterator;
typedef typename Superclass::LabelledImagePixelType LabelledImagePixelType;
typedef typename Superclass::InputImagePixelType InputImagePixelType;
typedef typename Superclass::EnergyFidelityType EnergyFidelityType;
typedef typename Superclass::EnergyRegularizationType EnergyRegularizationType;
typedef typename Superclass::EnergyFidelityPointer EnergyFidelityPointer;
typedef typename Superclass::EnergyRegularizationPointer EnergyRegularizationPointer;
typedef itk::Statistics::MersenneTwisterRandomVariateGenerator RandomGeneratorType;
itkNewMacro(Self);
itkTypeMacro(MRFSamplerRandom, MRFSampler);
inline int Compute(const InputImageNeighborhoodIterator& itData, const LabelledImageNeighborhoodIterator& itRegul) override
{
this->m_EnergyBefore = this->m_EnergyFidelity->GetValue(itData, itRegul.GetCenterPixel());
this->m_EnergyBefore += this->m_Lambda * this->m_EnergyRegularization->GetValue(itRegul, itRegul.GetCenterPixel());
this->m_Value = static_cast<LabelledImagePixelType>(m_Generator->GetIntegerVariate() % this->m_NumberOfClasses);
this->m_EnergyAfter = this->m_EnergyFidelity->GetValue(itData, this->m_Value);
this->m_EnergyAfter += this->m_Lambda * this->m_EnergyRegularization->GetValue(itRegul, this->m_Value);
this->m_DeltaEnergy = this->m_EnergyAfter - this->m_EnergyBefore;
return 0;
}
/** Methods to cancel random effects.*/
void InitializeSeed(int seed)
{
m_Generator->SetSeed(seed);
}
void InitializeSeed()
{
m_Generator->SetSeed();
}
protected:
// The constructor and destructor.
MRFSamplerRandom()
{
m_Generator = RandomGeneratorType::GetInstance();
m_Generator->SetSeed();
}
~MRFSamplerRandom() override
{
}
private:
RandomGeneratorType::Pointer m_Generator;
};
}
#endif
|
