SimpleITK  1.0.1
ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx
/*=========================================================================
*
* 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.
*
*=========================================================================*/
// This one header will include all SimpleITK filters and external
// objects.
#include <SimpleITK.h>
#include <iostream>
#include <stdlib.h>
#include <iomanip>
namespace sitk = itk::simple;
class IterationUpdate
: public sitk::Command
{
public:
IterationUpdate( const sitk::ImageRegistrationMethod &m)
: m_Method(m)
{}
virtual void Execute( )
{
// use sitk's output operator for std::vector etc..
using sitk::operator<<;
// stash the stream state
std::ios state(NULL);
state.copyfmt(std::cout);
std::cout << std::fixed << std::setfill(' ') << std::setprecision( 5 );
if ( m_Method.GetOptimizerIteration() == 0 )
{
std::cout << "\tLevel: " << std::setw(3) << m_Method.GetCurrentLevel() << std::endl;
std::cout << "\tScales: " << m_Method.GetOptimizerScales() << std::endl;
}
std::cout << '#' << m_Method.GetOptimizerIteration() << std::endl;
std::cout << "\tMetric Value: " << m_Method.GetMetricValue() << std::endl;
std::cout << "\tLearning Rate: " << m_Method.GetOptimizerLearningRate() << std::endl;
if (m_Method.GetOptimizerConvergenceValue() != std::numeric_limits<double>::max())
{
std::cout << "\tConvergence Value: " << std::scientific << m_Method.GetOptimizerConvergenceValue() << std::endl;
}
std::cout.copyfmt(state);
}
private:
};
class MultiResolutionIterationUpdate
: public sitk::Command
{
public:
MultiResolutionIterationUpdate( const sitk::ImageRegistrationMethod &m)
: m_Method(m)
{}
virtual void Execute( )
{
// use sitk's output operator for std::vector etc..
using sitk::operator<<;
// stash the stream state
std::ios state(NULL);
state.copyfmt(std::cout);
std::cout << std::fixed << std::setfill(' ') << std::setprecision( 5 );
std::cout << "\tStop Condition: " << m_Method.GetOptimizerStopConditionDescription() << std::endl;
std::cout << "============= Resolution Change =============" << std::endl;
std::cout.copyfmt(state);
}
private:
};
int main(int argc, char *argv[])
{
if ( argc < 4 )
{
std::cerr << "Usage: " << argv[0] << " <fixedImageFilter> <movingImageFile> <outputTransformFile>" << std::endl;
return 1;
}
{
std::vector<unsigned int> shrinkFactors;
shrinkFactors.push_back(3);
shrinkFactors.push_back(2);
shrinkFactors.push_back(1);
std::vector<double> smoothingSigmas;
smoothingSigmas.push_back(2.0);
smoothingSigmas.push_back(1.0);
smoothingSigmas.push_back(1.0);
R.SetShrinkFactorsPerLevel(shrinkFactors);
R.SetSmoothingSigmasPerLevel(smoothingSigmas);
}
{
double learningRate=1.0;
unsigned int numberOfIterations=100;
double convergenceMinimumValue = 1e-6;
unsigned int convergenceWindowSize = 10;
numberOfIterations,
convergenceMinimumValue,
convergenceWindowSize,
estimateLearningRate
);
}
R.SetInitialTransform(initialTx, true);
IterationUpdate cmd(R);
MultiResolutionIterationUpdate cmd2(R);
sitk::Transform outTx = R.Execute( fixed, moving );
std::cout << "-------" << std::endl;
std::cout << outTx.ToString() << std::endl;
std::cout << "Optimizer stop condition: " << R.GetOptimizerStopConditionDescription() << std::endl;
std::cout << " Iteration: " << R.GetOptimizerIteration() << std::endl;
std::cout << " Metric value: " << R.GetMetricValue() << std::endl;
sitk::Image displacementField = sitk::Image(fixed.GetSize(), sitk::sitkVectorFloat64);
displacementField.CopyInformation(fixed);
sitk::DisplacementFieldTransform displacementTx(displacementField);
const double varianceForUpdateField=0.0;
const double varianceForTotalField=1.5;
displacementTx.SetSmoothingGaussianOnUpdate(varianceForUpdateField,
varianceForTotalField);
R.SetInitialTransform(displacementTx, true);
{
std::vector<unsigned int> shrinkFactors;
shrinkFactors.push_back(3);
shrinkFactors.push_back(2);
shrinkFactors.push_back(1);
std::vector<double> smoothingSigmas;
smoothingSigmas.push_back(2.0);
smoothingSigmas.push_back(1.0);
smoothingSigmas.push_back(1.0);
R.SetShrinkFactorsPerLevel(shrinkFactors);
R.SetSmoothingSigmasPerLevel(smoothingSigmas);
}
{
double learningRate=1.0;
unsigned int numberOfIterations=300;
double convergenceMinimumValue = 1e-6;
unsigned int convergenceWindowSize = 10;
numberOfIterations,
convergenceMinimumValue,
convergenceWindowSize,
estimateLearningRate
);
}
outTx.AddTransform( R.Execute(fixed, moving) );
std::cout << "-------" << std::endl;
std::cout << outTx.ToString() << std::endl;
std::cout << "Optimizer stop condition: " << R.GetOptimizerStopConditionDescription() << std::endl;
std::cout << " Iteration: " << R.GetOptimizerIteration() << std::endl;
std::cout << " Metric value: " << R.GetMetricValue() << std::endl;
sitk::WriteTransform(outTx, argv[3]);
return 0;
}