ITK  4.13.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv4/IterativeClosestPoint2.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.
*
*=========================================================================*/
// Software Guide : BeginLatex
//
// This example illustrates how to perform Iterative Closest Point (ICP)
// registration in ITK using sets of 3D points.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The first step is to include the relevant headers.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include <iostream>
#include <fstream>
// Software Guide : EndCodeSnippet
class CommandIterationUpdate : public itk::Command
{
public:
typedef CommandIterationUpdate Self;
itkNewMacro( Self );
protected:
CommandIterationUpdate() {};
public:
typedef itk::LevenbergMarquardtOptimizer OptimizerType;
typedef const OptimizerType * OptimizerPointer;
void Execute(itk::Object *caller, const itk::EventObject & event) ITK_OVERRIDE
{
Execute( (const itk::Object *)caller, event);
}
void Execute(const itk::Object * object, const itk::EventObject & event) ITK_OVERRIDE
{
OptimizerPointer optimizer = dynamic_cast< OptimizerPointer >( object );
if( optimizer == ITK_NULLPTR )
{
itkExceptionMacro( "Could not cast optimizer." );
}
if( ! itk::IterationEvent().CheckEvent( &event ) )
{
return;
}
std::cout << "Value = " << optimizer->GetCachedValue() << std::endl;
std::cout << "Position = " << optimizer->GetCachedCurrentPosition();
std::cout << std::endl << std::endl;
}
};
int main(int argc, char * argv[] )
{
if( argc < 3 )
{
std::cerr << "Arguments Missing. " << std::endl;
std::cerr <<
"Usage: IterativeClosestPoint2 fixedPointsFile movingPointsFile "
<< std::endl;
return EXIT_FAILURE;
}
const unsigned int Dimension = 3;
// Software Guide : BeginLatex
//
// First, define the necessary types for the moving and fixed point sets.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::PointSet< float, Dimension > PointSetType;
PointSetType::Pointer fixedPointSet = PointSetType::New();
PointSetType::Pointer movingPointSet = PointSetType::New();
typedef PointSetType::PointsContainer PointsContainer;
PointsContainer::Pointer fixedPointContainer = PointsContainer::New();
PointsContainer::Pointer movingPointContainer = PointsContainer::New();
PointType fixedPoint;
PointType movingPoint;
// Software Guide : EndCodeSnippet
// Read the file containing coordinates of fixed points.
std::ifstream fixedFile;
fixedFile.open( argv[1] );
if( fixedFile.fail() )
{
std::cerr << "Error opening points file with name : " << std::endl;
std::cerr << argv[1] << std::endl;
return EXIT_FAILURE;
}
unsigned int pointId = 0;
fixedFile >> fixedPoint;
while( !fixedFile.eof() )
{
fixedPointContainer->InsertElement( pointId, fixedPoint );
fixedFile >> fixedPoint;
pointId++;
}
fixedPointSet->SetPoints( fixedPointContainer );
std::cout <<
"Number of fixed Points = " << fixedPointSet->GetNumberOfPoints()
<< std::endl;
// Read the file containing coordinates of moving points.
std::ifstream movingFile;
movingFile.open( argv[2] );
if( movingFile.fail() )
{
std::cerr << "Error opening points file with name : " << std::endl;
std::cerr << argv[2] << std::endl;
return EXIT_FAILURE;
}
pointId = 0;
movingFile >> movingPoint;
while( !movingFile.eof() )
{
movingPointContainer->InsertElement( pointId, movingPoint );
movingFile >> movingPoint;
pointId++;
}
movingPointSet->SetPoints( movingPointContainer );
std::cout <<
"Number of moving Points = "
<< movingPointSet->GetNumberOfPoints() << std::endl;
// Software Guide : BeginLatex
//
// After the points are read in from files, setup the metric to be used
// later by the registration.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
PointSetType,
PointSetType>
MetricType;
MetricType::Pointer metric = MetricType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next, setup the tranform, optimizers, and registration.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::Euler3DTransform< double > TransformType;
TransformType::Pointer transform = TransformType::New();
// Optimizer Type
typedef itk::LevenbergMarquardtOptimizer OptimizerType;
OptimizerType::Pointer optimizer = OptimizerType::New();
optimizer->SetUseCostFunctionGradient(false);
// Registration Method
PointSetType,
PointSetType >
RegistrationType;
RegistrationType::Pointer registration = RegistrationType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Scale the translation components of the Transform in the Optimizer
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
OptimizerType::ScalesType scales( transform->GetNumberOfParameters() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next, set the scales and ranges for translations and rotations in the
// transform. Also, set the convergence criteria and number of iterations
// to be used by the optimizer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const double translationScale = 1000.0; // dynamic range of translations
const double rotationScale = 1.0; // dynamic range of rotations
scales[0] = 1.0 / rotationScale;
scales[1] = 1.0 / rotationScale;
scales[2] = 1.0 / rotationScale;
scales[3] = 1.0 / translationScale;
scales[4] = 1.0 / translationScale;
scales[5] = 1.0 / translationScale;
unsigned long numberOfIterations = 2000;
double gradientTolerance = 1e-4; // convergence criterion
double valueTolerance = 1e-4; // convergence criterion
double epsilonFunction = 1e-5; // convergence criterion
optimizer->SetScales( scales );
optimizer->SetNumberOfIterations( numberOfIterations );
optimizer->SetValueTolerance( valueTolerance );
optimizer->SetGradientTolerance( gradientTolerance );
optimizer->SetEpsilonFunction( epsilonFunction );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Here we start with an identity transform, although the user will usually
// be able to provide a better guess than this.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
transform->SetIdentity();
// Software Guide : EndCodeSnippet
registration->SetInitialTransformParameters( transform->GetParameters() );
// Software Guide : BeginLatex
//
// Connect all the components required for the registration.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetTransform( transform );
registration->SetFixedPointSet( fixedPointSet );
registration->SetMovingPointSet( movingPointSet );
// Software Guide : EndCodeSnippet
//
// Connect an observer
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver( itk::IterationEvent(), observer );
try
{
registration->Update();
}
{
std::cerr << e << std::endl;
return EXIT_FAILURE;
}
std::cout << "Solution = " << transform->GetParameters() << std::endl;
std::cout << "Stopping condition: " << optimizer->GetStopConditionDescription() << std::endl;
return EXIT_SUCCESS;
}