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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 "itkFFTConvolutionImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRichardsonLucyDeconvolutionImageFilter.h"
#include "itkDeconvolutionIterationCommand.h"
#include "itkSimpleFilterWatcher.h"
#include "itkTestingMacros.h"
int
itkRichardsonLucyDeconvolutionImageFilterTest(int argc, char * argv[])
{
if (argc < 5)
{
std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv)
<< " <input image> <kernel image> <output image> <iterations> [convolution image]" << std::endl;
return EXIT_FAILURE;
}
using PixelType = float;
constexpr unsigned int Dimension = 2;
using ImageType = itk::Image<PixelType, Dimension>;
using ReaderType = itk::ImageFileReader<ImageType>;
using WriterType = itk::ImageFileWriter<ImageType>;
auto inputReader = ReaderType::New();
inputReader->SetFileName(argv[1]);
inputReader->Update();
auto kernelReader = ReaderType::New();
kernelReader->SetFileName(argv[2]);
kernelReader->Update();
// Generate a convolution of the input image with the kernel image
using ConvolutionFilterType = itk::FFTConvolutionImageFilter<ImageType>;
auto convolutionFilter = ConvolutionFilterType::New();
convolutionFilter->SetInput(inputReader->GetOutput());
convolutionFilter->NormalizeOn();
convolutionFilter->SetKernelImage(kernelReader->GetOutput());
// Optionally write the convolution result
if (argc > 5)
{
try
{
auto writer = WriterType::New();
writer->SetFileName(argv[5]);
writer->SetInput(convolutionFilter->GetOutput());
writer->Update();
}
catch (const itk::ExceptionObject & e)
{
std::cerr << "Unexpected exception caught when writing convolution image: " << e << std::endl;
return EXIT_FAILURE;
}
}
// Test the deconvolution algorithm
using DeconvolutionFilterType = itk::RichardsonLucyDeconvolutionImageFilter<ImageType>;
auto deconvolutionFilter = DeconvolutionFilterType::New();
deconvolutionFilter->SetInput(convolutionFilter->GetOutput());
deconvolutionFilter->SetKernelImage(kernelReader->GetOutput());
deconvolutionFilter->NormalizeOn();
auto iterations = static_cast<unsigned int>(std::stoi(argv[4]));
deconvolutionFilter->SetNumberOfIterations(iterations);
// Add an observer to report on filter iteration progress
using IterationCommandType = itk::DeconvolutionIterationCommand<DeconvolutionFilterType>;
auto observer = IterationCommandType::New();
deconvolutionFilter->AddObserver(itk::IterationEvent(), observer);
itk::SimpleFilterWatcher watcher(deconvolutionFilter);
// Write the deconvolution result
try
{
auto writer = WriterType::New();
writer->SetFileName(argv[3]);
writer->SetInput(deconvolutionFilter->GetOutput());
writer->Update();
}
catch (const itk::ExceptionObject & e)
{
std::cerr << "Unexpected exception caught when writing deconvolution image: " << e << std::endl;
return EXIT_FAILURE;
}
if (!observer->GetInvoked())
{
std::cerr << "Iteration command observer was never invoked, but should have been." << std::endl;
return EXIT_FAILURE;
}
// Tests to increase coverage
deconvolutionFilter->Print(std::cout);
const DeconvolutionFilterType::InternalImageType * estimate = deconvolutionFilter->GetCurrentEstimate();
if (estimate != nullptr)
{
std::cerr << "Estimate should be nullptr after the last iteration." << std::endl;
return EXIT_FAILURE;
}
unsigned int numIterations = 5;
deconvolutionFilter->SetNumberOfIterations(numIterations);
if (deconvolutionFilter->GetNumberOfIterations() != numIterations)
{
std::cerr << "Set/GetNumberOfIterations() test failed." << std::endl;
return EXIT_FAILURE;
}
deconvolutionFilter->SetStopIteration(false);
deconvolutionFilter->SetStopIteration(true);
if (deconvolutionFilter->GetStopIteration() != true)
{
std::cerr << "Set/GetStopIteration() test failed." << std::endl;
return EXIT_FAILURE;
}
unsigned int iteration = deconvolutionFilter->GetIteration();
std::cout << "Iteration: " << iteration << std::endl;
std::cout << deconvolutionFilter->DeconvolutionFilterType::Superclass::GetNameOfClass() << std::endl;
// Instantiate types with non-default template parameters
using FloatImageType = itk::Image<float, Dimension>;
using DoubleImageType = itk::Image<double, Dimension>;
using IntImageType = itk::Image<int, Dimension>;
using FilterType = itk::RichardsonLucyDeconvolutionImageFilter<FloatImageType, DoubleImageType, IntImageType, float>;
auto filter = FilterType::New();
filter->Print(std::cout);
return EXIT_SUCCESS;
}
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