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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 "itkRegistrationParameterScalesEstimator.h"
#include "itkImageToImageMetricv4.h"
#include "itkAffineTransform.h"
#include <algorithm> // For max.
/**
* \class RegistrationParameterScalesEstimatorTestMetric for test.
* Create a simple metric to use for testing here.
*/
template <
typename TFixedImage,
typename TMovingImage,
typename TVirtualImage = TFixedImage,
typename TInternalComputationValueType = double,
typename TMetricTraits =
itk::DefaultImageToImageMetricTraitsv4<TFixedImage, TMovingImage, TVirtualImage, TInternalComputationValueType>>
class RegistrationParameterScalesEstimatorTestMetric
: public itk::
ImageToImageMetricv4<TFixedImage, TMovingImage, TVirtualImage, TInternalComputationValueType, TMetricTraits>
{
public:
/** Standard class type aliases. */
using Self = RegistrationParameterScalesEstimatorTestMetric;
using Superclass =
itk::ImageToImageMetricv4<TFixedImage, TMovingImage, TVirtualImage, TInternalComputationValueType, TMetricTraits>;
using Pointer = itk::SmartPointer<Self>;
using ConstPointer = itk::SmartPointer<const Self>;
using typename Superclass::MeasureType;
using typename Superclass::DerivativeType;
using typename Superclass::ParametersType;
using typename Superclass::ParametersValueType;
itkOverrideGetNameOfClassMacro(RegistrationParameterScalesEstimatorTestMetric);
itkNewMacro(Self);
// Pure virtual functions that all Metrics must provide
unsigned int
GetNumberOfParameters() const override
{
return 5;
}
MeasureType
GetValue() const override
{
return 1.0;
}
void
GetValueAndDerivative(MeasureType & value, DerivativeType & derivative) const override
{
value = 1.0;
derivative.Fill(0.0);
}
unsigned int
GetNumberOfLocalParameters() const override
{
return 0;
}
void
UpdateTransformParameters(const DerivativeType &, ParametersValueType) override
{}
const ParametersType &
GetParameters() const override
{
return m_Parameters;
}
void
Initialize() override
{}
void
PrintSelf(std::ostream & os, itk::Indent indent) const override
{
Superclass::PrintSelf(os, indent);
}
ParametersType m_Parameters;
// Image related types
using FixedImageType = TFixedImage;
using MovingImageType = TMovingImage;
using VirtualImageType = TVirtualImage;
using FixedImageConstPointer = typename FixedImageType::ConstPointer;
using MovingImageConstPointer = typename MovingImageType::ConstPointer;
using VirtualImagePointer = typename VirtualImageType::Pointer;
using VirtualRegionType = typename VirtualImageType::RegionType;
/* Image dimension accessors */
static constexpr itk::SizeValueType FixedImageDimension = FixedImageType::ImageDimension;
static constexpr itk::SizeValueType MovingImageDimension = MovingImageType::ImageDimension;
static constexpr itk::SizeValueType VirtualImageDimension = VirtualImageType::ImageDimension;
private:
RegistrationParameterScalesEstimatorTestMetric() = default;
~RegistrationParameterScalesEstimatorTestMetric() override = default;
};
/**
* \class RegistrationParameterScalesEstimatorTest for test.
* Create a simple scales estimator class to use for testing here.
*/
template <typename TMetric>
class RegistrationParameterScalesEstimatorTest : public itk::RegistrationParameterScalesEstimator<TMetric>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(RegistrationParameterScalesEstimatorTest);
/** Standard class type aliases. */
using Self = RegistrationParameterScalesEstimatorTest;
using Superclass = itk::RegistrationParameterScalesEstimator<TMetric>;
using Pointer = itk::SmartPointer<Self>;
using ConstPointer = itk::SmartPointer<const Self>;
itkNewMacro(Self);
itkOverrideGetNameOfClassMacro(RegistrationParameterScalesEstimatorTest);
/** Type of scales */
using typename Superclass::ScalesType;
/** Type of parameters of the optimizer */
using typename Superclass::ParametersType;
/** Type of float */
using typename Superclass::FloatType;
using typename Superclass::VirtualPointType;
using typename Superclass::VirtualIndexType;
using typename Superclass::MovingTransformType;
using typename Superclass::FixedTransformType;
using typename Superclass::JacobianType;
using typename Superclass::VirtualImageConstPointer;
/** Estimate parameter scales with maximum squared norms of Jacobians. */
void
EstimateScales(ScalesType & parameterScales) override
{
this->CheckAndSetInputs();
this->SetSamplingStrategy(itk::SamplingStrategyEnum::RandomSampling);
this->SetNumberOfRandomSamples(1000);
this->SampleVirtualDomain();
itk::SizeValueType numPara = this->GetTransform()->GetNumberOfParameters();
parameterScales.SetSize(numPara);
ParametersType norms(numPara);
auto numSamples = static_cast<itk::SizeValueType>(this->m_SamplePoints.size());
norms.Fill(0.0);
parameterScales.Fill(1.0);
// checking each sample point
for (itk::SizeValueType c = 0; c < numSamples; ++c)
{
VirtualPointType point = this->m_SamplePoints[c];
ParametersType squaredNorms(numPara);
this->ComputeSquaredJacobianNorms(point, squaredNorms);
for (itk::SizeValueType p = 0; p < numPara; ++p)
{
norms[p] = std::max(norms[p], squaredNorms[p]);
}
} // for numSamples
if (numSamples > 0)
{
for (itk::SizeValueType p = 0; p < numPara; ++p)
{
parameterScales[p] = norms[p];
}
}
}
double
EstimateStepScale(const ParametersType & step) override
{
double norm = step.two_norm();
return norm;
}
/** Estimate the scales of local steps. */
void
EstimateLocalStepScales(const ParametersType & step, ScalesType & localStepScales) override
{
localStepScales.SetSize(step.size());
}
protected:
RegistrationParameterScalesEstimatorTest() = default;
~RegistrationParameterScalesEstimatorTest() override = default;
};
/**
*/
int
itkRegistrationParameterScalesEstimatorTest(int, char *[])
{
// Image begins
constexpr itk::SizeValueType ImageDimension = 2;
using PixelType = double;
// Image Types
using FixedImageType = itk::Image<PixelType, ImageDimension>;
using MovingImageType = itk::Image<PixelType, ImageDimension>;
using VirtualImageType = itk::Image<PixelType, ImageDimension>;
auto fixedImage = FixedImageType::New();
auto movingImage = MovingImageType::New();
VirtualImageType::Pointer virtualImage = fixedImage;
MovingImageType::SizeType size;
size.Fill(100);
movingImage->SetRegions(size);
fixedImage->SetRegions(size);
// Image done
// Transform begins
using MovingTransformType = itk::AffineTransform<double, ImageDimension>;
auto movingTransform = MovingTransformType::New();
movingTransform->SetIdentity();
using FixedTransformType = itk::TranslationTransform<double, ImageDimension>;
auto fixedTransform = FixedTransformType::New();
fixedTransform->SetIdentity();
// Transform done
// Metric begins
using MetricType = RegistrationParameterScalesEstimatorTestMetric<FixedImageType, MovingImageType>;
auto metric = MetricType::New();
metric->SetVirtualDomainFromImage(virtualImage);
metric->SetFixedImage(fixedImage);
metric->SetMovingImage(movingImage);
metric->SetFixedTransform(fixedTransform);
metric->SetMovingTransform(movingTransform);
// Metric done
// Scales for the affine transform from max squared norm of transform jacobians
using RegistrationParameterScalesEstimatorTestType = RegistrationParameterScalesEstimatorTest<MetricType>;
RegistrationParameterScalesEstimatorTestType::Pointer jacobianScaleEstimator =
RegistrationParameterScalesEstimatorTestType::New();
jacobianScaleEstimator->SetMetric(metric);
jacobianScaleEstimator->SetTransformForward(true);
jacobianScaleEstimator->Print(std::cout);
RegistrationParameterScalesEstimatorTestType::ScalesType jacobianScales(movingTransform->GetNumberOfParameters());
jacobianScaleEstimator->EstimateScales(jacobianScales);
std::cout << "Scales from max squared Jacobian norm for the affine transform = " << jacobianScales << std::endl;
// Check the correctness
RegistrationParameterScalesEstimatorTestType::ScalesType theoreticalJacobianScales(
movingTransform->GetNumberOfParameters());
VirtualImageType::PointType upperPoint;
virtualImage->TransformIndexToPhysicalPoint(virtualImage->GetLargestPossibleRegion().GetUpperIndex(), upperPoint);
itk::SizeValueType param = 0;
for (itk::SizeValueType row = 0; row < ImageDimension; ++row)
{
for (itk::SizeValueType col = 0; col < ImageDimension; ++col)
{
// max squared jacobian norms
theoreticalJacobianScales[param++] = upperPoint[col] * upperPoint[col];
}
}
for (itk::SizeValueType row = 0; row < ImageDimension; ++row)
{
theoreticalJacobianScales[param++] = 1;
}
bool jacobianPass = true;
for (itk::SizeValueType p = 0; p < jacobianScales.GetSize(); ++p)
{
if (itk::Math::NotAlmostEquals(jacobianScales[p], theoreticalJacobianScales[p]))
{
jacobianPass = false;
break;
}
}
bool nonUniformForJacobian = false;
for (itk::SizeValueType p = 1; p < jacobianScales.GetSize(); ++p)
{
if (itk::Math::NotAlmostEquals(jacobianScales[p], jacobianScales[0]))
{
nonUniformForJacobian = true;
break;
}
}
// Check done
jacobianScaleEstimator->EstimateScales(jacobianScales);
bool randomPass = true;
for (itk::SizeValueType p = 0; p < jacobianScales.GetSize(); ++p)
{
if (itk::Math::abs((jacobianScales[p] - theoreticalJacobianScales[p]) / theoreticalJacobianScales[p]) > 0.3)
{
randomPass = false;
break;
}
}
jacobianScaleEstimator->EstimateScales(jacobianScales);
bool fullDomainPass = true;
for (itk::SizeValueType p = 0; p < jacobianScales.GetSize(); ++p)
{
if (itk::Math::NotAlmostEquals(jacobianScales[p], theoreticalJacobianScales[p]))
{
fullDomainPass = false;
break;
}
}
// Testing RegistrationParameterScalesEstimatorTest done
std::cout << std::endl;
if (!jacobianPass)
{
std::cout << "Failed: the jacobian scales for the affine transform are not correct." << std::endl;
}
else
{
std::cout << "Passed: the jacobian scales for the affine transform are correct." << std::endl;
}
if (!randomPass)
{
std::cout << "Failed: the jacobian scales with random sampling are not correct." << std::endl;
}
else
{
std::cout << "Passed: the jacobian scales with random sampling are correct." << std::endl;
}
if (!fullDomainPass)
{
std::cout << "Failed: the jacobian scales from checking the full domain are not correct." << std::endl;
}
else
{
std::cout << "Passed: the jacobian scales from checking the full domain are correct." << std::endl;
}
if (!nonUniformForJacobian)
{
std::cout << "Error: the jacobian scales for an affine transform are equal for all parameters." << std::endl;
}
// Test streaming enumeration for RegistrationParameterScalesEstimatorEnums::SamplingStrategy elements
const std::set<itk::RegistrationParameterScalesEstimatorEnums::SamplingStrategy> allSamplingStrategy{
itk::RegistrationParameterScalesEstimatorEnums::SamplingStrategy::FullDomainSampling,
itk::RegistrationParameterScalesEstimatorEnums::SamplingStrategy::CornerSampling,
itk::RegistrationParameterScalesEstimatorEnums::SamplingStrategy::RandomSampling,
itk::RegistrationParameterScalesEstimatorEnums::SamplingStrategy::CentralRegionSampling,
itk::RegistrationParameterScalesEstimatorEnums::SamplingStrategy::VirtualDomainPointSetSampling
};
for (const auto & ee : allSamplingStrategy)
{
std::cout << "STREAMED ENUM VALUE RegistrationParameterScalesEstimatorEnums::SamplingStrategy: " << ee << std::endl;
}
if (jacobianPass && nonUniformForJacobian && randomPass && fullDomainPass)
{
std::cout << "Test passed" << std::endl;
return EXIT_SUCCESS;
}
else
{
std::cout << "Test failed" << std::endl;
return EXIT_FAILURE;
}
}
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