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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
CommandIterationUpdate()
{
// mark used to avoid warnings
(void)&Self::Clone;
};
public:
void
Execute(itk::Object * caller, const itk::EventObject & event) override
{
Execute((const itk::Object *)caller, event);
}
void
Execute(const itk::Object * object, const itk::EventObject & event) override
{
const auto * optimizer = dynamic_cast<const TOptimizer *>(object);
if (typeid(event) != typeid(itk::IterationEvent) || !optimizer)
{
return;
}
// stash the stream state
std::ios state(nullptr);
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;
using PixelType = float;
using FixedImageType = itk::Image<PixelType, ImageDimension>;
using MovingImageType = itk::Image<PixelType, ImageDimension>;
using ImageReaderType = itk::ImageFileReader<FixedImageType>;
auto fixedImageReader = ImageReaderType::New();
fixedImageReader->SetFileName(argv[2]);
fixedImageReader->Update();
typename FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
fixedImage->Update();
fixedImage->DisconnectPipeline();
auto 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
using TransformType = itk::Euler2DTransform<double>;
auto initialTransform = TransformType::New();
using MetricType = itk::MeanSquaresImageToImageMetricv4<FixedImageType, MovingImageType>;
auto metric = MetricType::New();
using RegistrationType = itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType>;
auto registration = RegistrationType::New();
registration->SetFixedImage(fixedImage);
registration->SetMovingImage(movingImage);
registration->SetMetric(metric);
registration->SetMovingInitialTransform(initialTransform);
registration->SetNumberOfLevels(1);
using Optimizerv4Type = itk::ConjugateGradientLineSearchOptimizerv4;
auto 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);
using CommandType = CommandIterationUpdate<Optimizerv4Type>;
auto observer = CommandType::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
ITK_TRY_EXPECT_NO_EXCEPTION(registration->Update());
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();
ITK_TEST_EXPECT_TRUE(optimizer->GetCurrentIteration() > 5);
return EXIT_SUCCESS;
}
} // namespace
int
itkSimpleImageRegistrationTest4(int argc, char * argv[])
{
if (argc < 4)
{
std::cerr << "Missing parameters." << std::endl;
std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv);
std::cerr << " imageDimension fixedImage movingImage" << std::endl;
return EXIT_FAILURE;
}
switch (std::stoi(argv[1]))
{
case 2:
return ImageRegistration<2>(argc, argv);
default:
std::cerr << "Unsupported dimension" << std::endl;
return EXIT_FAILURE;
}
}
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