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/*=========================================================================
*
* 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 "itkImageRegistrationMethod.h"
#include "itkLinearInterpolateImageFunction.h"
#include "itkGradientDescentOptimizer.h"
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkImageRegistrationMethodImageSource.h"
/**
* This program tests one instantiation of the itk::ImageRegistrationMethod class
*
*
*/
template<typename DataType>
bool DoRegistration ()
{
bool pass = true;
const unsigned int dimension = 2;
// Fixed Image Type
typedef itk::Image<DataType,dimension> FixedImageType;
// Moving Image Type
typedef itk::Image<DataType,dimension> MovingImageType;
// Size Type
typedef typename MovingImageType::SizeType SizeType;
// Transform Type
typedef itk::AffineTransform< double, dimension > TransformType;
typedef typename TransformType::ParametersType ParametersType;
typedef typename FixedImageType::PixelType FixedImagePixelType;
typedef typename MovingImageType::PixelType MovingImagePixelType;
// ImageSource
typedef itk::testhelper::ImageRegistrationMethodImageSource<
FixedImagePixelType,
MovingImagePixelType,
dimension > ImageSourceType;
// Transform Type
typedef itk::AffineTransform< double, dimension > TransformType;
typedef typename TransformType::ParametersType ParametersType;
// Optimizer Type
typedef itk::GradientDescentOptimizer OptimizerType;
// Metric Type
typedef itk::MeanSquaresImageToImageMetric<
FixedImageType,
MovingImageType > MetricType;
// Interpolation technique
typedef itk:: LinearInterpolateImageFunction<
MovingImageType,
double > InterpolatorType;
// Registration Method
typedef itk::ImageRegistrationMethod<
FixedImageType,
MovingImageType > RegistrationType;
typedef itk::CommandIterationUpdate<
OptimizerType > CommandIterationType;
typename MetricType::Pointer metric = MetricType::New();
typename TransformType::Pointer transform = TransformType::New();
typename OptimizerType::Pointer optimizer = OptimizerType::New();
typename InterpolatorType::Pointer interpolator = InterpolatorType::New();
typename RegistrationType::Pointer registration = RegistrationType::New();
typename ImageSourceType::Pointer imageSource = ImageSourceType::New();
SizeType size;
size[0] = 100;
size[1] = 100;
imageSource->GenerateImages( size );
typename FixedImageType::ConstPointer fixedImage = imageSource->GetFixedImage();
typename MovingImageType::ConstPointer movingImage = imageSource->GetMovingImage();
//
// Connect all the components required for Registratio
//
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetTransform( transform );
registration->SetFixedImage( fixedImage );
registration->SetMovingImage( movingImage );
registration->SetInterpolator( interpolator );
// Select the Region of Interest over which the Metric will be computed
// Registration time will be proportional to the number of pixels in this region.
metric->SetFixedImageRegion( fixedImage->GetBufferedRegion() );
// Instantiate an Observer to report the progress of the Optimization
CommandIterationType::Pointer iterationCommand = CommandIterationType::New();
iterationCommand->SetOptimizer( optimizer.GetPointer() );
// Scale the translation components of the Transform in the Optimizer
OptimizerType::ScalesType scales( transform->GetNumberOfParameters() );
scales.Fill( 1.0 );
unsigned long numberOfIterations = 100;
double translationScale = 1e-6;
double learningRate = 1e-8;
for( unsigned int i=0; i<dimension; i++)
{
scales[ i + dimension * dimension ] = translationScale;
}
optimizer->SetScales( scales );
optimizer->SetLearningRate( learningRate );
optimizer->SetNumberOfIterations( numberOfIterations );
optimizer->MinimizeOn();
// Start from an Identity transform (in a normal case, the user
// can probably provide a better guess than the identity...
transform->SetIdentity();
registration->SetInitialTransformParameters( transform->GetParameters() );
// Initialize the internal connections of the registration method.
// This can potentially throw an exception
try
{
registration->Update();
}
catch( itk::ExceptionObject & e )
{
std::cerr << e << std::endl;
pass = false;
}
ParametersType actualParameters = imageSource->GetActualParameters();
ParametersType finalParameters = registration->GetLastTransformParameters();
const unsigned int numbeOfParameters = actualParameters.Size();
// We know that for the Affine transform the Translation parameters are at
// the end of the list of parameters.
const unsigned int offsetOrder = finalParameters.Size()-actualParameters.Size();
const double tolerance = 1.0; // equivalent to 1 pixel.
for(unsigned int i=0; i<numbeOfParameters; i++)
{
// the parameters are negated in order to get the inverse transformation.
// this only works for comparing translation parameters....
std::cout << finalParameters[i+offsetOrder] << " == " << -actualParameters[i] << std::endl;
if( itk::Math::abs ( finalParameters[i+offsetOrder] - (-actualParameters[i]) ) > tolerance )
{
std::cout << "Tolerance exceeded at component " << i << std::endl;
pass = false;
}
}
return pass;
}
int itkImageRegistrationMethodTest_16(int itkNotUsed(argc), char*[] itkNotUsed(argv) )
{
bool result_uc, result_c, result_us, result_s,
result_ui, result_i, result_ul, result_l,
result_f, result_d;
result_uc = DoRegistration<unsigned char>();
result_c = DoRegistration<char>();
result_us = DoRegistration<unsigned short>();
result_s = DoRegistration<short>();
result_ui = DoRegistration<unsigned int>();
result_i = DoRegistration<int>();
result_ul = DoRegistration<unsigned long>();
result_l = DoRegistration<long>();
result_f = DoRegistration<float>();
result_d = DoRegistration<double>();
std::cout << "<unsigned char>: " << result_uc << std::endl;
std::cout << "<char>: " << result_c << std::endl;
std::cout << "<unsigned short>: " << result_us << std::endl;
std::cout << "<short>: " << result_s << std::endl;
std::cout << "<unsigned int>: " << result_ui << std::endl;
std::cout << "<int>: " << result_i << std::endl;
std::cout << "<unsigned long>: " << result_ul << std::endl;
std::cout << "<long>: " << result_l << std::endl;
std::cout << "<float>: " << result_f << std::endl;
std::cout << "<double>: " << result_d << std::endl;
return EXIT_SUCCESS;
}
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