ITK  5.2.0
Insight Toolkit
Examples/Filtering/BinomialBlurImageFilter.cxx
/*=========================================================================
*
* 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
*
* http://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.
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {BinomialBlurImageFilterOutput.png}
// ARGUMENTS: 5
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The \doxygen{BinomialBlurImageFilter} computes a nearest neighbor average
// along each dimension. The process is repeated a number of times, as
// specified by the user. In principle, after a large number of iterations
// the result will approach the convolution with a Gaussian.
//
// \index{itk::Binomial\-Blur\-Image\-Filter}
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
//
// \index{itk::BinomialBlurImageFilter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int
main(int argc, char * argv[])
{
if (argc < 4)
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0]
<< " inputImageFile outputImageFile numberOfRepetitions"
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be chosen for the pixels of the input and output images.
// Image types can be instantiated using the pixel type and dimension.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputPixelType = float;
using OutputPixelType = float;
using InputImageType = itk::Image<InputPixelType, 2>;
using OutputImageType = itk::Image<OutputPixelType, 2>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filter type is now instantiated using both the input image and the
// output image types. Then a filter object is created.
//
// \index{itk::BinomialBlurImageFilter!instantiation}
// \index{itk::BinomialBlurImageFilter!New()}
// \index{itk::BinomialBlurImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(argv[1]);
const unsigned int repetitions = std::stoi(argv[3]);
// Software Guide : BeginLatex
//
// The input image can be obtained from the output of another filter. Here,
// an image reader is used as the source. The number of repetitions is set
// with the \code{SetRepetitions()} method. Computation time will increase
// linearly with the number of repetitions selected. Finally, the filter
// can be executed by calling the \code{Update()} method.
//
// \index{itk::BinomialBlurImageFilter!Update()}
// \index{itk::BinomialBlurImageFilter!SetInput()}
// \index{itk::BinomialBlurImageFilter!SetRepetitions()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetInput(reader->GetOutput());
filter->SetRepetitions(repetitions);
filter->Update();
// Software Guide : EndCodeSnippet
// This section connects the filter output to a writer
//
using WritePixelType = unsigned char;
using WriteImageType = itk::Image<WritePixelType, 2>;
using RescaleFilterType =
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv[2]);
rescaler->SetInput(filter->GetOutput());
writer->SetInput(rescaler->GetOutput());
writer->Update();
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.44\textwidth]{BinomialBlurImageFilterOutput}
// \itkcaption[BinomialBlurImageFilter output.]{Effect of the
// BinomialBlurImageFilter on a slice from a MRI proton density image of the
// brain.}
// \label{fig:BinomialBlurImageFilterInputOutput}
// \end{figure}
//
// Figure \ref{fig:BinomialBlurImageFilterInputOutput} illustrates the
// effect of this filter on a MRI proton density image of the brain.
//
// Note that the standard deviation $\sigma$ of the equivalent Gaussian is
// fixed. In the spatial spectrum, the effect of every iteration of this
// filter is like a multiplication with a sinus cardinal function.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itk::BinomialBlurImageFilter
Performs a separable blur on each dimension of an image.
Definition: itkBinomialBlurImageFilter.h:44
itkImageFileReader.h
itkImage.h
itkBinomialBlurImageFilter.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkRescaleIntensityImageFilter.h
itkImageFileWriter.h
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86