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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 "itkImage.h"
#include "itkVectorImage.h"
#include "itkMath.h"
#include "itkMinimumMaximumImageCalculator.h"
#include "itkRGBPixel.h"
#include "itkRGBAPixel.h"
#include "itkDiffusionTensor3D.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkMeasurementVectorTraits.h"
#include "itkStdStreamLogOutput.h"
#include "itkGaussianRandomSpatialNeighborSubsampler.h"
#include "itkPatchBasedDenoisingImageFilter.h"
#include "itkTestingMacros.h"
template <typename TFilter>
typename TFilter::RealArrayType
ParseKernelBandwithSigma(char * kernelBandwithSigmaIn, unsigned int numIndependentComponents)
{
typename TFilter::RealArrayType kernelBandwithSigmaOut;
kernelBandwithSigmaOut.SetSize(numIndependentComponents);
// Get the individual components
char * endPtr;
unsigned int i = 0;
typename TFilter::RealArrayType::ValueType value;
while (*kernelBandwithSigmaIn && i < numIndependentComponents)
{
value = strtod(kernelBandwithSigmaIn, &endPtr);
if (kernelBandwithSigmaIn == endPtr)
{
(*kernelBandwithSigmaIn)++;
}
else if (endPtr == nullptr || *endPtr == 0)
{
kernelBandwithSigmaOut[i] = value;
break;
}
else
{
kernelBandwithSigmaOut[i] = value;
kernelBandwithSigmaIn = endPtr + 1;
}
++i;
}
return kernelBandwithSigmaOut;
}
template <typename ImageT>
int
doDenoising(const std::string & inputFileName,
const std::string & outputFileName,
const unsigned int numIterations,
const int numThreads,
char * kernelBandwithSigma,
bool alwaysTreatComponentsAsEuclidean,
bool manualReinitialization,
const int numToSample,
bool computeConditionalDerivatives,
const double kernelBandwidthMultiplicationFactor,
const std::string & noiseModelStr,
const double noiseModelFidelityWeight)
{
using ReaderType = itk::ImageFileReader<ImageT>;
using FilterType = itk::PatchBasedDenoisingImageFilter<ImageT, ImageT>;
using SamplerType = itk::Statistics::GaussianRandomSpatialNeighborSubsampler<typename FilterType::PatchSampleType,
typename ImageT::RegionType>;
using OutputImageType = typename FilterType::OutputImageType;
using WriterType = itk::ImageFileWriter<OutputImageType>;
// Read the noisy image to be denoised
auto reader = ReaderType::New();
reader->SetFileName(inputFileName);
ITK_TRY_EXPECT_NO_EXCEPTION(reader->Update());
// Create filter and initialize
auto filter = FilterType::New();
typename FilterType::InputImageType::Pointer inputImage = reader->GetOutput();
filter->SetInput(inputImage);
// Set whether conditional derivatives should be used estimating sigma
ITK_TEST_SET_GET_BOOLEAN(filter, ComputeConditionalDerivatives, computeConditionalDerivatives);
// Patch radius is same for all dimensions of the image
constexpr unsigned int patchRadius = 4;
filter->SetPatchRadius(patchRadius);
ITK_TEST_SET_GET_VALUE(patchRadius, filter->GetPatchRadius());
// Instead of directly setting the weights, could also specify type
bool useSmoothDiscPatchWeights = true;
ITK_TEST_SET_GET_BOOLEAN(filter, UseSmoothDiscPatchWeights, useSmoothDiscPatchWeights);
bool useFastTensorComputations = true;
ITK_TEST_SET_GET_BOOLEAN(filter, UseFastTensorComputations, useFastTensorComputations);
// Noise model to use
typename FilterType::NoiseModelEnum noiseModel;
if (noiseModelStr == "GAUSSIAN")
{
noiseModel = FilterType::NoiseModelEnum::GAUSSIAN;
}
else if (noiseModelStr == "RICIAN")
{
noiseModel = FilterType::NoiseModelEnum::RICIAN;
}
else if (noiseModelStr == "POISSON")
{
noiseModel = FilterType::NoiseModelEnum::POISSON;
}
else
{
noiseModel = FilterType::NoiseModelEnum::NOMODEL;
}
filter->SetNoiseModel(noiseModel);
ITK_TEST_SET_GET_VALUE(noiseModel, filter->GetNoiseModel());
// Stepsize or weight for smoothing term
double smoothingWeight = 1.0;
filter->SetSmoothingWeight(smoothingWeight);
ITK_TEST_SET_GET_VALUE(smoothingWeight, filter->GetSmoothingWeight());
// Stepsize or weight for fidelity term
filter->SetNoiseModelFidelityWeight(noiseModelFidelityWeight);
ITK_TEST_SET_GET_VALUE(noiseModelFidelityWeight, filter->GetNoiseModelFidelityWeight());
// Number of iterations over the image of denoising
filter->SetNumberOfIterations(numIterations);
ITK_TEST_SET_GET_VALUE(numIterations, filter->GetNumberOfIterations());
ITK_TEST_SET_GET_BOOLEAN(filter, AlwaysTreatComponentsAsEuclidean, alwaysTreatComponentsAsEuclidean);
ITK_TEST_SET_GET_BOOLEAN(filter, ManualReinitialization, manualReinitialization);
// Number of threads to use in parallel
filter->SetNumberOfWorkUnits(numThreads);
// Sampling the image to find similar patches
auto sampler = SamplerType::New();
// Variance (in physical units) for semi-local Gaussian sampling
sampler->SetVariance(400);
// Rectangular window restricting the Gaussian sampling
sampler->SetRadius(50); // 2.5 * standard deviation
// Number of random sample "patches" to use for computations
sampler->SetNumberOfResultsRequested(numToSample);
// Sampler can be complete neighborhood sampler, random neighborhood sampler,
// Gaussian sampler, etc.
filter->SetSampler(sampler);
ITK_TEST_SET_GET_VALUE(sampler, filter->GetSampler());
// Automatic estimation of the kernel bandwidth
bool kernelBandwidthEstimation = true;
ITK_TEST_SET_GET_BOOLEAN(filter, KernelBandwidthEstimation, kernelBandwidthEstimation);
// Update bandwidth every 'n' iterations
unsigned int kernelBandwidthUpdateFrequency = 3;
filter->SetKernelBandwidthUpdateFrequency(kernelBandwidthUpdateFrequency);
ITK_TEST_SET_GET_VALUE(kernelBandwidthUpdateFrequency, filter->GetKernelBandwidthUpdateFrequency());
// Use 20% of the pixels for the sigma update calculation
double kernelBandwidthFractionPixelsForEstimation = 0.20;
filter->SetKernelBandwidthFractionPixelsForEstimation(kernelBandwidthFractionPixelsForEstimation);
ITK_TEST_SET_GET_VALUE(kernelBandwidthFractionPixelsForEstimation,
filter->GetKernelBandwidthFractionPixelsForEstimation());
// Multiplication factor modifying the automatically-estimated kernel sigma
filter->SetKernelBandwidthMultiplicationFactor(kernelBandwidthMultiplicationFactor);
ITK_TEST_SET_GET_VALUE(kernelBandwidthMultiplicationFactor, filter->GetKernelBandwidthMultiplicationFactor());
// Test the filter exceptions
//
// Test the nonpositive pixel component exception for the RICIAN and POISSON
// noise models.
// Temporarily modify the value of an arbitrary pixel of the input image to
// get a nonpositive value.
if (filter->GetNoiseModel() == FilterType::NoiseModelEnum::RICIAN ||
filter->GetNoiseModel() == FilterType::NoiseModelEnum::POISSON)
{
typename ImageT::IndexType::IndexValueType indexValue = 0;
typename ImageT::IndexType pixelIndex;
pixelIndex.Fill(indexValue);
typename ImageT::PixelType originalPixelValue = inputImage->GetPixel(pixelIndex);
typename ImageT::PixelType nonpositivePixelValue = itk::NumericTraits<typename ImageT::PixelType>::NonpositiveMin();
inputImage->SetPixel(pixelIndex, nonpositivePixelValue);
ITK_TRY_EXPECT_EXCEPTION(filter->Update());
std::cout << "NumIndependentComponents: " << filter->GetNumIndependentComponents() << std::endl;
// Restore the original pixel value
inputImage->SetPixel(pixelIndex, originalPixelValue);
}
// Denoise the image
ITK_TRY_EXPECT_NO_EXCEPTION(filter->Update());
// Exercise the PrintSelf method to know the patch radius in voxels once
// the filter has been updated
std::cout << filter << std::endl;
// Regression test
typename FilterType::RealArrayType expectedKernelBandwidthSigma =
ParseKernelBandwithSigma<FilterType>(kernelBandwithSigma, filter->GetNumIndependentComponents());
typename FilterType::RealArrayType resultKernelBandwidthSigma = filter->GetKernelBandwidthSigma();
if (expectedKernelBandwidthSigma.Size() != resultKernelBandwidthSigma.Size())
{
std::cout << "Error in GetKernelBandwidthSigma() " << std::endl;
std::cout << "Expected value: " << expectedKernelBandwidthSigma << ", but got: " << resultKernelBandwidthSigma
<< std::endl;
std::cout << "Array size mismatch." << std::endl;
std::cout << "Test failed!" << std::endl;
return EXIT_FAILURE;
}
else
{
typename FilterType::RealArrayType::iterator expectedKernelBandwidthSigmaIt = expectedKernelBandwidthSigma.begin();
typename FilterType::RealArrayType::iterator resultKernelBandwidthSigmaIt = resultKernelBandwidthSigma.begin();
unsigned int i = 0;
// Although some cases converge to a higher degree of accuracy, the
// tolerance set for the test is relatively loose due to the convergence
// value of the algorithm set by
// itk::PatchBasedDenoisingImageFilter::m_SigmaUpdateConvergenceTolerance
// Hence, some tests require such a low degree of accuracy.
//
while (expectedKernelBandwidthSigmaIt != expectedKernelBandwidthSigma.end() &&
resultKernelBandwidthSigmaIt != resultKernelBandwidthSigma.end())
{
typename FilterType::RealArrayType::ValueType expectedValue = *expectedKernelBandwidthSigmaIt;
typename FilterType::RealArrayType::ValueType resultValue = *resultKernelBandwidthSigmaIt;
double tolerance = 1e-2 * expectedValue;
if (!itk::Math::FloatAlmostEqual(expectedValue, resultValue, 10, tolerance))
{
std::cout.precision(static_cast<unsigned int>(itk::Math::abs(std::log10(tolerance))));
std::cout << "Error in GetKernelBandwidthSigma() "
<< "at index: [" << i << ']' << std::endl;
std::cout << "Expected value: " << expectedValue << ", but got: " << resultValue << std::endl;
std::cout << "Test failed!" << std::endl;
return EXIT_FAILURE;
}
++expectedKernelBandwidthSigmaIt;
++resultKernelBandwidthSigmaIt;
++i;
}
}
// Write the denoised image to file
auto writer = WriterType::New();
writer->SetFileName(outputFileName);
writer->SetInput(filter->GetOutput());
ITK_TRY_EXPECT_NO_EXCEPTION(writer->Update());
std::cout << "Test finished" << std::endl;
return EXIT_SUCCESS;
}
int
itkPatchBasedDenoisingImageFilterTest(int argc, char * argv[])
{
if (argc < 8)
{
std::cerr << "Missing command line arguments" << std::endl;
std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv) << " inputImageFileName outputImageFileName"
<< " numDimensions numComponents"
<< " kernelBandwithSigma"
<< " alwaysTreatComponentsAsEuclidean"
<< " manualReinitialization"
<< " [numIterations] [numThreads]"
<< " [numPatchesToSample]"
<< " [computeConditionalDerivatives]"
<< " [kernelBandwidthMultiplicationFactor]"
<< " [noiseModel] [noiseModelFidelityWeight]" << std::endl;
return EXIT_FAILURE;
}
// Exercise basic object methods
// Done outside the helper function in the test because GCC is limited
// when calling overloaded base class functions.
using PixelType = float;
using ImageType = itk::Image<PixelType, 3>;
using FilterType = itk::PatchBasedDenoisingImageFilter<ImageType, ImageType>;
auto filter = FilterType::New();
ITK_EXERCISE_BASIC_OBJECT_METHODS(filter, PatchBasedDenoisingImageFilter, PatchBasedDenoisingBaseImageFilter);
const std::string inFileName(argv[1]);
const std::string outFileName(argv[2]);
const unsigned int numDimensions = std::stoi(argv[3]);
const unsigned int numComponents = std::stoi(argv[4]);
char * kernelBandwithSigma = argv[5];
const bool alwaysTreatComponentsAsEuclidean = std::stoi(argv[6]);
const bool manualReinitialization = std::stoi(argv[7]);
unsigned int numIterations = 1;
if (argc > 8)
{
numIterations = std::stoi(argv[8]);
}
unsigned int numThreads = 1;
if (argc > 9)
{
numThreads = std::stoi(argv[9]);
}
unsigned int numToSample = 1000;
if (argc > 10)
{
numToSample = std::stoi(argv[10]);
}
bool computeConditionalDerivatives = false;
if (argc > 11)
{
computeConditionalDerivatives = static_cast<bool>(std::stoi(argv[11]));
}
double kernelBandwidthMultFactor = 1.0;
if (argc > 12)
{
kernelBandwidthMultFactor = std::stod(argv[12]);
}
const std::vector<std::string> modelChoices{ "GAUSSIAN", "RICIAN", "POISSON", "NOMODEL" };
std::string noiseModel = modelChoices[0];
double noiseModelFidelityWeight = 0.0;
if (argc > 13)
{
noiseModel = argv[13];
bool validChoice = false;
for (const auto & modelChoice : modelChoices)
{
if (noiseModel == modelChoice)
{
validChoice = true;
}
}
if (!validChoice)
{
std::cerr << "Test failed!" << std::endl;
std::cerr << noiseModel << " is not a valid noise model choice. Please choose one of: ";
for (const auto & modelChoice : modelChoices)
{
std::cerr << modelChoice << ' ' << std::endl;
}
return EXIT_FAILURE;
}
if (argc > 14)
{
noiseModelFidelityWeight = std::stod(argv[14]);
}
else
{
std::cerr << "Test failed!" << std::endl;
std::cerr << "Must also specify a noise model fidelity weight when a noise model is specified." << std::endl;
return EXIT_FAILURE;
}
}
using PixelComponentType = float;
using OneComponentType = PixelComponentType;
using ThreeComponentType = itk::RGBPixel<PixelComponentType>;
using FourComponentType = itk::RGBAPixel<PixelComponentType>;
using SixComponentType = itk::DiffusionTensor3D<PixelComponentType>;
using OneComponent2DImage = itk::Image<OneComponentType, 2>;
using OneComponent3DImage = itk::Image<OneComponentType, 3>;
// using TwoComponent2DImage = itk::VectorImage< PixelComponentType, 2 >;
// using TwoComponent3DImage = itk::VectorImage< PixelComponentType, 3 >;
using ThreeComponent2DImage = itk::Image<ThreeComponentType, 2>;
using ThreeComponent3DImage = itk::Image<ThreeComponentType, 3>;
using FourComponent2DImage = itk::Image<FourComponentType, 2>;
using FourComponent3DImage = itk::Image<FourComponentType, 3>;
using SixComponent2DImage = itk::Image<SixComponentType, 2>;
using SixComponent3DImage = itk::Image<SixComponentType, 3>;
// Test streaming enumeration for PatchBasedDenoisingBaseImageFilterEnums::NoiseModel elements
const std::set<itk::PatchBasedDenoisingBaseImageFilterEnums::NoiseModel> allNoiseModel{
itk::PatchBasedDenoisingBaseImageFilterEnums::NoiseModel::NOMODEL,
itk::PatchBasedDenoisingBaseImageFilterEnums::NoiseModel::GAUSSIAN,
itk::PatchBasedDenoisingBaseImageFilterEnums::NoiseModel::RICIAN,
itk::PatchBasedDenoisingBaseImageFilterEnums::NoiseModel::POISSON
};
for (const auto & ee : allNoiseModel)
{
std::cout << "STREAMED ENUM VALUE PatchBasedDenoisingBaseImageFilterEnums::NoiseModel: " << ee << std::endl;
}
// Test streaming enumeration for PatchBasedDenoisingBaseImageFilterEnums::ComponentSpace elements
const std::set<itk::PatchBasedDenoisingBaseImageFilterEnums::ComponentSpace> allComponentSpace{
itk::PatchBasedDenoisingBaseImageFilterEnums::ComponentSpace::EUCLIDEAN,
itk::PatchBasedDenoisingBaseImageFilterEnums::ComponentSpace::RIEMANNIAN
};
for (const auto & ee : allComponentSpace)
{
std::cout << "STREAMED ENUM VALUE PatchBasedDenoisingBaseImageFilterEnums::ComponentSpace: " << ee << std::endl;
}
// Test streaming enumeration for PatchBasedDenoisingBaseImageFilterEnums::FilterState elements
const std::set<itk::PatchBasedDenoisingBaseImageFilterEnums::FilterState> allFilterState{
itk::PatchBasedDenoisingBaseImageFilterEnums::FilterState::UNINITIALIZED,
itk::PatchBasedDenoisingBaseImageFilterEnums::FilterState::INITIALIZED
};
for (const auto & ee : allFilterState)
{
std::cout << "STREAMED ENUM VALUE PatchBasedDenoisingBaseImageFilterEnums::FilterStateEnum: " << ee << std::endl;
}
if (numComponents == 1 && numDimensions == 2)
{
return doDenoising<OneComponent2DImage>(inFileName,
outFileName,
numIterations,
numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel,
noiseModelFidelityWeight);
}
/*else if( numComponents == 2 && numDimensions == 2 )
{
return doDenoising< TwoComponent2DImage >( inFileName, outFileName,
numIterations, numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel, noiseModelFidelityWeight );
}*/
else if (numComponents == 3 && numDimensions == 2)
{
return doDenoising<ThreeComponent2DImage>(inFileName,
outFileName,
numIterations,
numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel,
noiseModelFidelityWeight);
}
else if (numComponents == 4 && numDimensions == 2)
{
return doDenoising<FourComponent2DImage>(inFileName,
outFileName,
numIterations,
numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel,
noiseModelFidelityWeight);
}
else if (numComponents == 6 && numDimensions == 2)
{
return doDenoising<SixComponent2DImage>(inFileName,
outFileName,
numIterations,
numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel,
noiseModelFidelityWeight);
}
else if (numComponents == 1 && numDimensions == 3)
{
return doDenoising<OneComponent3DImage>(inFileName,
outFileName,
numIterations,
numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel,
noiseModelFidelityWeight);
}
/*else if( numComponents == 2 && numDimensions == 3 )
{
return doDenoising< TwoComponent3DImage >( inFileName, outFileName,
numIterations, numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel, noiseModelFidelityWeight );
}*/
else if (numComponents == 3 && numDimensions == 3)
{
return doDenoising<ThreeComponent3DImage>(inFileName,
outFileName,
numIterations,
numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel,
noiseModelFidelityWeight);
}
else if (numComponents == 4 && numDimensions == 3)
{
return doDenoising<FourComponent3DImage>(inFileName,
outFileName,
numIterations,
numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel,
noiseModelFidelityWeight);
}
else if (numComponents == 6 && numDimensions == 3)
{
return doDenoising<SixComponent3DImage>(inFileName,
outFileName,
numIterations,
numThreads,
kernelBandwithSigma,
alwaysTreatComponentsAsEuclidean,
manualReinitialization,
numToSample,
computeConditionalDerivatives,
kernelBandwidthMultFactor,
noiseModel,
noiseModelFidelityWeight);
}
else
{
std::cout << "Test failed!" << std::endl;
std::cout << "Combination of " << numComponents << " components and " << numDimensions << " dimensions "
<< "isn't supported in this test driver." << std::endl;
return EXIT_FAILURE;
}
}
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