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/*
* Copyright (C) 2005-2020 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* 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
*
* 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.
*/
/* Example usage:
./DanielssonDistanceMapImageFilter Input/FivePoints.png Output/DanielssonDistanceMapImageFilterOutput1.png Output/DanielssonDistanceMapImageFilterOutput2.png
*/
// This example illustrates the use of the
// \doxygen{itk}{DanielssonDistanceMapImageFilter}. This filter generates a
// distance map from the input image using the algorithm developed by
// Danielsson \cite{Danielsson1980}. As secondary outputs, a Voronoi
// partition of the input elements is produced, as well as a vector image
// with the components of the distance vector to the closest point. The input
// to the map is assumed to be a set of points on the input image. Each
// point/pixel is considered to be a separate entity even if they share the
// same gray level value.
//
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!Instantiation}
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!Header}
//
// The first step required to use this filter is to include its header file.
#include "itkConnectedComponentImageFilter.h"
#include "itkDanielssonDistanceMapImageFilter.h"
#include "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
int main(int argc, char* argv[])
{
if (argc < 4)
{
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImageFile outputDistanceMapImageFile ";
std::cerr << " outputVoronoiMapImageFilter ";
std::cerr << std::endl;
return EXIT_FAILURE;
}
// Then we must decide what pixel types to use for the input and output
// images. Since the output will contain distances measured in pixels, the
// pixel type should be able to represent at least the width of the image,
// or said in $N-D$ terms, the maximum extension along all the dimensions.
// The input and output image types are now defined using their respective
// pixel type and dimension.
using InputPixelType = unsigned char;
using OutputPixelType = unsigned short;
using InputImageType = otb::Image<InputPixelType, 2>;
using OutputImageType = otb::Image<OutputPixelType, 2>;
// The filter type can be instantiated using the input and output image
// types defined above. A filter object is created with the \code{New()}
// method.
//
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!instantiation}
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!New()}
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!Pointer}
using ConnectedType = itk::ConnectedComponentImageFilter<InputImageType, InputImageType>;
ConnectedType::Pointer connectedComponents = ConnectedType::New();
using FilterType = itk::DanielssonDistanceMapImageFilter<InputImageType, OutputImageType, OutputImageType>;
FilterType::Pointer filter = FilterType::New();
using RescalerType = itk::RescaleIntensityImageFilter<OutputImageType, OutputImageType>;
RescalerType::Pointer scaler = RescalerType::New();
//
// Reader and Writer types are instantiated.
//
using ReaderType = otb::ImageFileReader<InputImageType>;
using WriterType = otb::ImageFileWriter<OutputImageType>;
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName(argv[1]);
writer->SetFileName(argv[2]);
// The input to the filter is taken from a reader and its output is passed
// to a \doxygen{itk}{RescaleIntensityImageFilter} and then to a writer.
//
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!SetInput()}
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!GetOutput()}
connectedComponents->SetInput(reader->GetOutput());
filter->SetInput(connectedComponents->GetOutput());
scaler->SetInput(filter->GetOutput());
writer->SetInput(scaler->GetOutput());
scaler->SetOutputMaximum(65535L);
scaler->SetOutputMinimum(0L);
// The type of input image has to be specified. In this case, a binary
// image is selected.
//
// \index{itk::Danielsson\-Distance\-MapImage\-Filter!InputIsBinaryOn()}
filter->InputIsBinaryOff();
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{FivePoints.eps}
// \includegraphics[width=0.32\textwidth]{DanielssonDistanceMapImageFilterOutput1.eps}
// \includegraphics[width=0.32\textwidth]{DanielssonDistanceMapImageFilterOutput2.eps}
// \itkcaption[DanielssonDistanceMapImageFilter
// output]{DanielssonDistanceMapImageFilter output. Set of pixels, distance
// map and Voronoi partition.}
// \label{fig:DanielssonDistanceMapImageFilterInputOutput}
// \end{figure}
//
// Figure \ref{fig:DanielssonDistanceMapImageFilterInputOutput} illustrates
// the effect of this filter on a binary image with a set of points. The
// input image is shown at left, the distance map at the center and the
// Voronoi partition at right. This filter computes distance maps in
// N-dimensions and is therefore capable of producing $N-D$ Voronoi
// partitions.
//
// \index{Voronoi partitions}
// \index{Voronoi partitions!itk::Danielsson\-Distance\-Map\-Image\-Filter}
writer->Update();
const char* voronoiMapFileName = argv[3];
// The Voronoi map is obtained with the \code{GetVoronoiMap()} method. In
// the lines below we connect this output to the intensity rescaler and
// save the result in a file.
//
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!GetVoronoiMap()}
scaler->SetInput(filter->GetVoronoiMap());
writer->SetFileName(voronoiMapFileName);
writer->Update();
// Execution of the writer is triggered by the invocation of the
// \code{Update()} method. Since this method can potentially throw
// exceptions it must be placed in a \code{try/catch} block.
return EXIT_SUCCESS;
}
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