ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/Filtering/ZeroCrossingBasedEdgeDetectionImageFilter.cxx
/*=========================================================================
*
* Copyright Insight Software Consortium
*
* 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: {ZeroCrossingBasedEdgeDetectionImageFilter.png}
// ARGUMENTS: 1.0 0.1
// Software Guide : EndCommandLineArgs
//
// Software Guide : BeginLatex
//
// The \doxygen{ZeroCrossingBasedEdgeDetectionImageFilter} performs
// edge detection by combining a sequence of Gaussian smoothing,
// Laplacian filter, and Zero cross detections on the Laplacian.
//
// \index{itk::ZeroCrossingBasedEdgeDetectionImageFilter}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The header file corresponding to this filter should be included first.
//
// \index{itk::ZeroCrossingBasedEdgeDetectionImageFilter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int main( int argc, char * argv[] )
{
if( argc < 5 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile variance maxerror" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginCodeSnippet
using InputPixelType = double;
using OutputPixelType = double;
using CharPixelType = unsigned char;
constexpr unsigned int Dimension = 2;
// Software Guide : EndCodeSnippet
using RescaleFilterType =
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
reader->SetFileName( argv[1] );
writer->SetFileName( argv[2] );
FilterType::Pointer filter = FilterType::New();
// Software Guide : BeginLatex
//
// The filter requires two parameters. First the value of the variance to be
// used by the Gaussian smoothing stage. This value is provided in the
// method \code{SetVariance} and it is given in pixel units. Second the
// filter expects the acceptable error for computing the approximation to
// the Gaussian kernel. This error is expected to be in the range between 0
// and 1. Values outside that range will result in Exceptions being thrown.
//
//
// \index{itk::ZeroCrossingBasedEdgeDetectionImageFilter!SetVariance}
// \index{itk::ZeroCrossingBasedEdgeDetectionImageFilter!SetMaximumError}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetVariance( std::stod( argv[3] ) );
filter->SetMaximumError( std::stod( argv[4] ) );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As with most filters, we connect the input and output of this
// filter in order to create a pipeline. In this particular case the
// input is taken from a reader and the output is sent to a writer.
// Given that the zero crossing filter is producing a float image as
// output, we use a \doxygen{RescaleIntensityImageFilter} to convert
// this image to an eight bits image before sending it to the writer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetInput( reader->GetOutput() );
rescaler->SetInput( filter->GetOutput() );
writer->SetInput( rescaler->GetOutput() );
// Software Guide : EndCodeSnippet
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
try
{
writer->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << excp << std::endl;
}
return EXIT_SUCCESS;
}