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
|
/*=========================================================================
*
* 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.
*
*=========================================================================*/
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkEuler2DTransform.h"
#include "itkMeanSquaresImageToImageMetricv4.h"
#include "itkTestingMacros.h"
#include "itkImageRegistrationMethodv4.h"
#include "itkConjugateGradientLineSearchOptimizerv4.h"
#include <iomanip>
namespace
{
template<typename TOptimizer>
class CommandIterationUpdate : public itk::Command
{
public:
typedef CommandIterationUpdate Self;
typedef itk::Command Superclass;
typedef itk::SmartPointer<Self> Pointer;
itkNewMacro( Self );
protected:
CommandIterationUpdate()
{
// mark used to avoid warnings
(void) &Self::Clone;
};
public:
virtual void Execute(itk::Object *caller, const itk::EventObject & event) ITK_OVERRIDE
{
Execute( (const itk::Object *) caller, event);
}
virtual void Execute(const itk::Object * object, const itk::EventObject & event) ITK_OVERRIDE
{
const TOptimizer *optimizer = dynamic_cast< const TOptimizer * > (object);
if( typeid( event ) != typeid( itk::IterationEvent ) || !optimizer )
{ return; }
// stash the stream state
std::ios state(NULL);
state.copyfmt(std::cout);
std::cout << std::fixed << std::setfill(' ') << std::setprecision( 5 );
std::cout << std::setw(3) << optimizer->GetCurrentIteration();
std::cout << " = " << std::setw(10) << optimizer->GetCurrentMetricValue();
std::cout << " : " << optimizer->GetCurrentPosition() << std::endl;
std::cout << "\nScales: " << optimizer->GetScales() << std::endl;
}
};
template<unsigned int TDimension>
int ImageRegistration( int itkNotUsed( argc ), char *argv[] )
{
const unsigned int ImageDimension = TDimension;
typedef float PixelType;
typedef itk::Image<PixelType, ImageDimension> FixedImageType;
typedef itk::Image<PixelType, ImageDimension> MovingImageType;
typedef itk::ImageFileReader<FixedImageType> ImageReaderType;
typename ImageReaderType::Pointer fixedImageReader = ImageReaderType::New();
fixedImageReader->SetFileName( argv[2] );
fixedImageReader->Update();
typename FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
fixedImage->Update();
fixedImage->DisconnectPipeline();
typename ImageReaderType::Pointer movingImageReader = ImageReaderType::New();
movingImageReader->SetFileName( argv[3] );
movingImageReader->Update();
typename MovingImageType::Pointer movingImage = movingImageReader->GetOutput();
movingImage->Update();
movingImage->DisconnectPipeline();
// Set up the centered transform initializer
typedef itk::Euler2DTransform<double> TransformType;
typename TransformType::Pointer initialTransform = TransformType::New();
typedef itk::MeanSquaresImageToImageMetricv4<FixedImageType, MovingImageType> MetricType;
typename MetricType::Pointer metric = MetricType::New();
typedef itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType> RegistrationType;
typename RegistrationType::Pointer registration = RegistrationType::New();
registration->SetFixedImage( fixedImage );
registration->SetMovingImage( movingImage );
registration->SetMetric( metric );
registration->SetMovingInitialTransform( initialTransform );
registration->SetNumberOfLevels(1);
typedef itk::ConjugateGradientLineSearchOptimizerv4 Optimizerv4Type;
typename Optimizerv4Type::Pointer optimizer = Optimizerv4Type::New();
optimizer->SetLearningRate( 1.0 );
optimizer->SetNumberOfIterations( 100 );
optimizer->SetMinimumConvergenceValue(1e-5);
optimizer->SetConvergenceWindowSize(2);
double scaleData[] = {200000,1.0,1.0};
typename Optimizerv4Type::ScalesType::Superclass scales( scaleData, 3);
optimizer->SetScales( scales );
registration->SetOptimizer(optimizer);
typedef CommandIterationUpdate<Optimizerv4Type> CommandType;
typename CommandType::Pointer observer = CommandType::New();
optimizer->AddObserver( itk::IterationEvent(), observer );
try
{
registration->Update();
}
catch( itk::ExceptionObject &e )
{
std::cerr << "Exception caught: " << e << std::endl;
return EXIT_FAILURE;
}
registration->GetTransform()->Print(std::cout);
std::cout << optimizer->GetStopConditionDescription() << std::endl;
typename TransformType::ParametersType results = registration->GetTransform()->GetParameters();
std::cout << "Expecting close (+/- 0.3) to: ( 0.0, -2.8, 9.5 )" << std::endl;
std::cout << "Parameters: " << results << std::endl;
std::cout << "Number Of Iterations: " << optimizer->GetCurrentIteration();
TEST_EXPECT_TRUE( optimizer->GetCurrentIteration() > 5 );
return EXIT_SUCCESS;
}
}
int itkSimpleImageRegistrationTest4( int argc, char *argv[] )
{
if ( argc < 4 )
{
std::cout << argv[0] << " imageDimension fixedImage movingImage" << std::endl;
exit( 1 );
}
switch( atoi( argv[1] ) )
{
case 2:
return ImageRegistration<2>( argc, argv );
default:
std::cerr << "Unsupported dimension" << std::endl;
exit( EXIT_FAILURE );
}
return EXIT_SUCCESS;
}
|
