ITK  4.9.0
Insight Segmentation and Registration Toolkit
Examples/Filtering/CannyEdgeDetectionImageFilter.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 : BeginLatex
//
// This example introduces the use of the
// \doxygen{CannyEdgeDetectionImageFilter}. Canny edge detection is widely used for
// edge detection since it is the optimal solution satisfying the constraints
// of good sensitivity, localization and noise robustness. To achieve this
// end, Canny edge detection is implemented internally as a multi-stage
// algorithm, which involves Gaussian smoothing to remove noise, calculation
// of gradient magnitudes to localize edge features, non-maximum suppression
// to remove suprious features, and finally thresholding to yield a binary image.
// Though the specifics of this internal pipeline are largely abstracted from
// the user of the class, it is nonetheless beneficial to have a general
// understanding of these components so that parameters can be appropriately
// adjusted.
//
// \index{itk::CannyEdgeDetectionImageFilter|textbf}
//
// The first step required for using this filter is to include its header file.
//
// \index{itk::CannyEdgeDetectionImageFilter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int main(int argc, char* argv[])
{
if( argc < 3 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0]
<< " inputImage outputImage"
<< " [variance upperThreshold lowerThreshold]" << std::endl;
return EXIT_FAILURE;
}
const char * inputFilename = argv[1];
const char * outputFilename = argv[2];
float variance = 2.0;
float upperThreshold = 0.0;
float lowerThreshold = 0.0;
if( argc > 3 )
{
variance = atof( argv[3] );
}
if( argc > 4 )
{
upperThreshold = atof( argv[4] );
}
if( argc > 5 )
{
lowerThreshold = atof( argv[5] );
}
std::cout << "Variance = " << variance << std::endl;
std::cout << "UpperThreshold = " << upperThreshold << std::endl;
std::cout << "LowerThreshold = " << lowerThreshold << std::endl;
// Software Guide : BeginLatex
//
// In this example, images are read and written with \code{unsigned char}
// pixel type. However, Canny edge detection requires floating point
// pixel types in order to avoid numerical errors. For this reason,
// a separate internal image type with pixel type \code{double} is defined
// for edge detection.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int Dimension = 2;
typedef unsigned char CharPixelType; // IO
typedef double RealPixelType; // Operations
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The \code{CharImageType} image is cast to and from \code{RealImageType}
// using \doxygen{CastImageFilter} and \code{RescaleIntensityImageFilter},
// respectively; both the input and output of \code{CannyEdgeDetectionImageFilter}
// are \code{RealImageType}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
CastToRealFilterType;
CannyFilterType;
RescaleFilterType;
// Software Guide : EndCodeSnippet
//Setting the IO
ReaderType::Pointer reader = ReaderType::New();
CastToRealFilterType::Pointer toReal = CastToRealFilterType::New();
CannyFilterType::Pointer cannyFilter = CannyFilterType::New();
RescaleFilterType::Pointer rescale = RescaleFilterType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName( inputFilename );
writer->SetFileName( outputFilename );
toReal->SetInput( reader->GetOutput() );
cannyFilter->SetInput( toReal->GetOutput() );
rescale->SetInput( cannyFilter->GetOutput() );
writer->SetInput( rescale->GetOutput() );
// Software Guide : BeginLatex
//
// In this example, three parameters of the Canny edge detection
// filter may be set via the \code{SetVariance()}, \code{SetUpperThreshold()},
// and \code{SetLowerThreshold()} methods. Based on the previous discussion
// of the steps in the internal pipeline, we understand that
// \code{variance} adjusts the amount of Gaussian smoothing and
// \code{upperThreshold} and \code{lowerThreshold} control which edges are
// selected in the final step.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
cannyFilter->SetVariance( variance );
cannyFilter->SetUpperThreshold( upperThreshold );
cannyFilter->SetLowerThreshold( lowerThreshold );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, \code{Update()} is called on \code{writer} to trigger excecution
// of the pipeline. As usual, the call is wrapped in a \code{try/catch}
// block.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
writer->Update();
}
catch( itk::ExceptionObject & err )
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
return EXIT_SUCCESS;
}