ITK  5.2.0
Insight Toolkit
Examples/Filtering/GradientRecursiveGaussianImageFilter.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 : BeginLatex
//
// This example illustrates the use of the
// \doxygen{GradientRecursiveGaussianImageFilter}.
//
// \index{itk::Gradient\-Recursive\-Gaussian\-Image\-Filter}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header
// file.
//
// \index{itk::Gradient\-Recursive\-Gaussian\-Image\-Filter!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 outputVectorImageFile sigma"
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be instantiated based on the pixels of the input and
// output images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr unsigned int Dimension = 3;
using InputPixelType = float;
using OutputComponentPixelType = float;
using OutputPixelType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// With them, the input and output image types can be instantiated.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputImageType = itk::Image<InputPixelType, Dimension>;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The filter type is now instantiated using both the input image and the
// output image types.
//
// \index{itk::Gradient\-Recursive\-Gaussian\-Image\-Filter!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
OutputImageType>;
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(argv[1]);
// Software Guide : BeginLatex
//
// A filter object is created by invoking the \code{New()} method and
// assigning the result to a \doxygen{SmartPointer}.
//
// \index{itk::Gradient\-Recursive\-Gaussian\-Image\-Filter!New()}
// \index{itk::Gradient\-Recursive\-Gaussian\-Image\-Filter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input image can be obtained from the output of another filter. Here,
// an image reader is used as source.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetInput(reader->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The standard deviation of the Gaussian smoothing kernel is now set.
//
// \index{itk::Gradient\-Recursive\-Gaussian\-Image\-Filter!SetSigma()}
// \index{SetSigma()!itk::Gradient\-Recursive\-Gaussian\-Image\-Filter}
//
// Software Guide : EndLatex
const double sigma = std::stod(argv[3]);
// Software Guide : BeginCodeSnippet
filter->SetSigma(sigma);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally the filter is executed by invoking the \code{Update()} method.
//
// \index{itk::Gradient\-Recursive\-Gaussian\-Image\-Filter!Update()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// If connected to other filters in a pipeline, this filter will
// automatically update when any downstream filters are updated. For
// example, we may connect this gradient magnitude filter to an image file
// writer and then update the writer.
//
// Software Guide : EndLatex
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(argv[2]);
// Software Guide : BeginCodeSnippet
writer->SetInput(filter->GetOutput());
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
//
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itkImageFileReader.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itkGradientRecursiveGaussianImageFilter.h
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::GradientRecursiveGaussianImageFilter
Computes the gradient of an image by convolution with the first derivative of a Gaussian.
Definition: itkGradientRecursiveGaussianImageFilter.h:59
itkImageFileWriter.h
itk::CovariantVector
A templated class holding a n-Dimensional covariant vector.
Definition: itkCovariantVector.h:70
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44