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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 "itkSmoothingRecursiveGaussianImageFilter.h"
#include "itkImageFileReader.h"
#include "itkSimpleFilterWatcher.h"
#include "itkImageFileWriter.h"
#include "itkImageRegionConstIterator.h"
#include "itkTestingMacros.h"
namespace
{
template <typename TFilter>
int
InPlaceTest(char * inputFilename, bool normalizeAcrossScale, typename TFilter::SigmaArrayType::ValueType sigmaValue)
{
// Read the input image
using ReaderType = itk::ImageFileReader<typename TFilter::InputImageType>;
auto reader = ReaderType::New();
reader->SetFileName(inputFilename);
// Create the filter
auto filter = TFilter::New();
filter->SetNormalizeAcrossScale(normalizeAcrossScale);
filter->SetSigma(sigmaValue);
filter->SetInput(reader->GetOutput());
if (!filter->CanRunInPlace())
{
std::cerr << "Test failed!" << std::endl;
std::cerr << "Expected the filter to be able to run in-place!" << std::endl;
std::cerr << "Expected itk:SmoothingRecursiveGaussianImageFilter::CanRunInPlace to be true, but got: "
<< filter->CanRunInPlace() << std::endl;
return EXIT_FAILURE;
}
ITK_TRY_EXPECT_NO_EXCEPTION(filter->Update());
typename TFilter::OutputImageType::Pointer outputImage1 = filter->GetOutput();
outputImage1->DisconnectPipeline();
// Set the InPlace flag to On
filter->InPlaceOn();
ITK_TRY_EXPECT_NO_EXCEPTION(filter->Update());
typename TFilter::OutputImageType::Pointer outputImage2 = filter->GetOutput();
outputImage2->DisconnectPipeline();
using IteratorType = itk::ImageRegionConstIterator<typename TFilter::OutputImageType>;
IteratorType it1(outputImage1, outputImage1->GetBufferedRegion());
IteratorType it2(outputImage2, outputImage2->GetBufferedRegion());
// Check whether the values of the in-place and not in-place executions are the same
it1.GoToBegin();
it2.GoToBegin();
double epsilon = itk::NumericTraits<double>::epsilon();
while (!it1.IsAtEnd())
{
if (!itk::Math::FloatAlmostEqual(static_cast<double>(it1.Get()), static_cast<double>(it2.Get()), 10, epsilon))
{
std::cerr.precision(static_cast<int>(itk::Math::abs(std::log10(epsilon))));
std::cerr << "Test failed!" << std::endl;
std::cerr << "Error in pixel value at index [" << std::endl;
std::cerr << "Error in pixel value at index [" << it1.GetIndex() << ']' << std::endl;
std::cerr << "Expected value " << it1.Get() << std::endl;
std::cerr << " differs from " << it2.Get();
std::cerr << " by more than " << epsilon << std::endl;
return EXIT_FAILURE;
}
++it1;
++it2;
}
return EXIT_SUCCESS;
}
} // namespace
int
itkSmoothingRecursiveGaussianImageFilterTest(int argc, char * argv[])
{
if (argc != 5)
{
std::cerr << "Missing parameters." << std::endl;
std::cerr << "Usage: " << std::endl;
std::cerr << itkNameOfTestExecutableMacro(argv) << " inputImageFile outputImageFile normalizeAcrossScale sigma"
<< std::endl;
return EXIT_FAILURE;
}
int testStatus = EXIT_SUCCESS;
// Define the dimension of the images
constexpr unsigned int Dimension = 2;
// Declare the types of the images
using PixelType = unsigned char;
using ImageType = itk::Image<PixelType, Dimension>;
// Read the input image
using ReaderType = itk::ImageFileReader<ImageType>;
auto reader = ReaderType::New();
reader->SetFileName(argv[1]);
// Declare the type for the itk::SmoothingRecursiveGaussianImageFilter
using SmoothingRecursiveGaussianImageFilterType = itk::SmoothingRecursiveGaussianImageFilter<ImageType>;
// Create the filter
auto filter = SmoothingRecursiveGaussianImageFilterType::New();
ITK_EXERCISE_BASIC_OBJECT_METHODS(filter, SmoothingRecursiveGaussianImageFilter, InPlaceImageFilter);
itk::SimpleFilterWatcher watcher(filter);
// Set the scale normalization flag
bool normalizeAcrossScale = std::stoi(argv[3]);
ITK_TEST_SET_GET_BOOLEAN(filter, NormalizeAcrossScale, normalizeAcrossScale);
// Set the value of the standard deviation of the Gaussian used for smoothing
SmoothingRecursiveGaussianImageFilterType::SigmaArrayType::ValueType sigmaValue = std::stod(argv[4]);
SmoothingRecursiveGaussianImageFilterType::SigmaArrayType sigma;
sigma.Fill(sigmaValue);
filter->SetSigma(sigmaValue);
ITK_TEST_SET_GET_VALUE(sigmaValue, filter->GetSigma());
filter->SetSigmaArray(sigma);
ITK_TEST_SET_GET_VALUE(sigma, filter->GetSigmaArray());
// Set the input image
filter->SetInput(reader->GetOutput());
// Run the filter
ITK_TRY_EXPECT_NO_EXCEPTION(filter->Update());
// Write the output
using WriterType = itk::ImageFileWriter<ImageType>;
auto writer = WriterType::New();
writer->SetFileName(argv[2]);
writer->SetInput(filter->GetOutput());
ITK_TRY_EXPECT_NO_EXCEPTION(writer->Update());
// Test the InPlaceOn option output
if (InPlaceTest<SmoothingRecursiveGaussianImageFilterType>(argv[1], normalizeAcrossScale, sigmaValue) == EXIT_FAILURE)
{
testStatus = EXIT_FAILURE;
}
// All objects should be automatically destroyed at this point
std::cout << "Test finished." << std::endl;
return testStatus;
}
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