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
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
|
/*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {QB_Suburb.png}
// OUTPUTS: {MarkovRandomField2.png}
// 1.0 5 1
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// Using a similar structure as the previous program and the same energy
// function, we are now going to slightly alter the program to use a
// different sampler and optimizer. The proposed sample is proposed
// randomly according to the MAP probability and the optimizer is the
// ICM which accept the proposed sample if it enable a reduction of
// the energy.
//
// Software Guide : EndLatex
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "otbImage.h"
#include "otbMarkovRandomFieldFilter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
// Software Guide : BeginLatex
//
// First, we need to include header specific to these class:
//
// Software Guide : EndLatex
#include "otbMRFEnergyPotts.h"
#include "otbMRFEnergyGaussianClassification.h"
// Software Guide : BeginCodeSnippet
#include "otbMRFSamplerRandomMAP.h"
#include "otbMRFOptimizerICM.h"
// Software Guide : EndCodeSnippet
int main(int argc, char* argv[])
{
if (argc != 6)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage output lambda iterations" << std::endl;
std::cerr << " useRandomValue" << std::endl;
return 1;
}
const unsigned int Dimension = 2;
typedef double InternalPixelType;
typedef unsigned char LabelledPixelType;
typedef otb::Image<InternalPixelType, Dimension> InputImageType;
typedef otb::Image<LabelledPixelType, Dimension> LabelledImageType;
typedef otb::ImageFileReader<InputImageType> ReaderType;
typedef otb::ImageFileWriter<LabelledImageType> WriterType;
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
const char * inputFilename = argv[1];
const char * outputFilename = argv[2];
reader->SetFileName(inputFilename);
writer->SetFileName(outputFilename);
typedef otb::MarkovRandomFieldFilter
<InputImageType, LabelledImageType> MarkovRandomFieldFilterType;
// Software Guide : BeginLatex
//
// And to declare these new type:
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::MRFSamplerRandomMAP<InputImageType,
LabelledImageType> SamplerType;
// typedef otb::MRFSamplerRandom< InputImageType, LabelledImageType> SamplerType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginCodeSnippet
typedef otb::MRFOptimizerICM OptimizerType;
// Software Guide : EndCodeSnippet
typedef otb::MRFEnergyPotts
<LabelledImageType, LabelledImageType> EnergyRegularizationType;
typedef otb::MRFEnergyGaussianClassification
<InputImageType, LabelledImageType> EnergyFidelityType;
MarkovRandomFieldFilterType::Pointer markovFilter =
MarkovRandomFieldFilterType::New();
EnergyRegularizationType::Pointer energyRegularization =
EnergyRegularizationType::New();
EnergyFidelityType::Pointer energyFidelity = EnergyFidelityType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
SamplerType::Pointer sampler = SamplerType::New();
if ((bool) (atoi(argv[5])) == true)
{
// Overpass random calculation(for test only):
sampler->InitializeSeed(0);
markovFilter->InitializeSeed(1);
}
unsigned int nClass = 4;
energyFidelity->SetNumberOfParameters(2 * nClass);
EnergyFidelityType::ParametersType parameters;
parameters.SetSize(energyFidelity->GetNumberOfParameters());
parameters[0] = 10.0; //Class 0 mean
parameters[1] = 10.0; //Class 0 stdev
parameters[2] = 80.0; //Class 1 mean
parameters[3] = 10.0; //Class 1 stdev
parameters[4] = 150.0; //Class 2 mean
parameters[5] = 10.0; //Class 2 stdev
parameters[6] = 220.0; //Class 3 mean
parameters[7] = 10.0; //Class 3 stde
energyFidelity->SetParameters(parameters);
// Software Guide : BeginLatex
//
// As the \doxygen{otb}{MRFOptimizerICM} does not have any parameters,
// the call to \code{optimizer->SetParameters()} must be removed
//
// Software Guide : EndLatex
markovFilter->SetNumberOfClasses(nClass);
markovFilter->SetMaximumNumberOfIterations(atoi(argv[4]));
markovFilter->SetErrorTolerance(0.0);
markovFilter->SetLambda(atof(argv[3]));
markovFilter->SetNeighborhoodRadius(1);
markovFilter->SetEnergyRegularization(energyRegularization);
markovFilter->SetEnergyFidelity(energyFidelity);
markovFilter->SetOptimizer(optimizer);
markovFilter->SetSampler(sampler);
markovFilter->SetInput(reader->GetOutput());
typedef itk::RescaleIntensityImageFilter
<LabelledImageType, LabelledImageType> RescaleType;
RescaleType::Pointer rescaleFilter = RescaleType::New();
rescaleFilter->SetOutputMinimum(0);
rescaleFilter->SetOutputMaximum(255);
rescaleFilter->SetInput(markovFilter->GetOutput());
writer->SetInput(rescaleFilter->GetOutput());
writer->Update();
// Software Guide : BeginLatex
//
// Apart from these, no further modification is required.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// Figure~\ref{fig:MRF_CLASSIFICATION2} shows the output of the Markov Random
// Field classification after 5 iterations with a
// MAP random sampler and an ICM optimizer.
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{QB_Suburb.eps}
// \includegraphics[width=0.44\textwidth]{MarkovRandomField2.eps}
// \itkcaption[MRF restoration]{Result of applying
// the \doxygen{otb}{MarkovRandomFieldFilter} to an extract from a PAN Quickbird
// image for classification. The result is obtained after 5 iterations with a
// MAP random sampler and an ICM optimizer. From left to right : original image,
// classification.}
// \label{fig:MRF_CLASSIFICATION2}
// \end{figure}
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
|
