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/*
* Copyright (C) 2005-2022 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.
*/
#include "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
/* Example usage:
./EdgeDensityExample Input/suburb2.jpeg Output/EdgeDensityOutput.tif Output/PrettyEdgeDensityOutput.png 7 50 10 1.0 0.01
*/
// This example illustrates the use of the
// \doxygen{otb}{EdgeDensityImageFilter}.
// This filter computes a local density of edges on an image and can
// be useful to detect man made objects or urban areas, for
// instance. The filter has been implemented in a generic way, so that
// the way the edges are detected and the way their density is
// computed can be chosen by the user.
//
// The first step required to use this filter is to include its header file.
#include "otbEdgeDensityImageFilter.h"
// We will also include the header files for the edge detector (a
// Canny filter) and the density estimation (a simple count on a
// binary image).
//
// The first step required to use this filter is to include its header file.
#include "itkCannyEdgeDetectionImageFilter.h"
#include "otbBinaryImageDensityFunction.h"
int main(int itkNotUsed(argc), char* argv[])
{
const char* infname = argv[1];
const char* outfname = argv[2];
const char* prettyfilename = argv[3];
const unsigned int radius = atoi(argv[4]);
/*--*/
const unsigned int Dimension = 2;
using PixelType = float;
/** Variables for the canny detector*/
const PixelType upperThreshold = static_cast<PixelType>(atof(argv[5]));
const PixelType lowerThreshold = static_cast<PixelType>(atof(argv[6]));
const double variance = atof(argv[7]);
const double maximumError = atof(argv[8]);
// As usual, we start by defining the types for the images, the reader
// and the writer.
using ImageType = otb::Image<PixelType, Dimension>;
using ReaderType = otb::ImageFileReader<ImageType>;
using WriterType = otb::ImageFileWriter<ImageType>;
// We define now the type for the function which will be used by the
// edge density filter to estimate this density. Here we choose a
// function which counts the number of non null pixels per area. The
// function takes as template the type of the image to be processed.
using CountFunctionType = otb::BinaryImageDensityFunction<ImageType>;
// These {\em non null pixels} will be the result of an edge
// detector. We use here the classical Canny edge detector, which is
// templated over the input and output image types.
using CannyDetectorType = itk::CannyEdgeDetectionImageFilter<ImageType, ImageType>;
// Finally, we can define the type for the edge density filter which
// takes as template the input and output image types, the edge
// detector type, and the count function type..
using EdgeDensityFilterType = otb::EdgeDensityImageFilter<ImageType, ImageType, CannyDetectorType, CountFunctionType>;
// We can now instantiate the different processing objects of the
// pipeline using the \code{New()} method.
ReaderType::Pointer reader = ReaderType::New();
EdgeDensityFilterType::Pointer filter = EdgeDensityFilterType::New();
CannyDetectorType::Pointer cannyFilter = CannyDetectorType::New();
WriterType::Pointer writer = WriterType::New();
// The edge detection filter needs to be instantiated because we
// need to set its parameters. This is what we do here for the Canny
// filter.
cannyFilter->SetUpperThreshold(upperThreshold);
cannyFilter->SetLowerThreshold(lowerThreshold);
cannyFilter->SetVariance(variance);
cannyFilter->SetMaximumError(maximumError);
// After that, we can pass the edge detector to the filter which
// will be used it internally.
filter->SetDetector(cannyFilter);
filter->SetNeighborhoodRadius(radius);
// Finally, we set the file names for the input and the output
// images and we plug the pipeline. The \code{Update()} method of
// the writer will trigger the processing.
reader->SetFileName(infname);
writer->SetFileName(outfname);
filter->SetInput(reader->GetOutput());
writer->SetInput(filter->GetOutput());
writer->Update();
// Figure~\ref{fig:EDGEDENSITY_FILTER} shows the result of applying
// the edge density filter to an image.
// \begin{figure}
// \center
// \includegraphics[width=0.25\textwidth]{suburb2.eps}
// \includegraphics[width=0.25\textwidth]{PrettyEdgeDensityOutput.eps}
// \itkcaption[Edge Density Filter]{Result of applying the
// \doxygen{otb}{EdgeDensityImageFilter} to an image. From left to right :
// original image, edge density.}
// \label{fig:EDGEDENSITY_FILTER}
// \end{figure}
/************* Image for printing **************/
using OutputImageType = otb::Image<unsigned char, 2>;
using RescalerType = itk::RescaleIntensityImageFilter<ImageType, OutputImageType>;
RescalerType::Pointer rescaler = RescalerType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
rescaler->SetInput(filter->GetOutput());
using OutputWriterType = otb::ImageFileWriter<OutputImageType>;
OutputWriterType::Pointer outwriter = OutputWriterType::New();
outwriter->SetFileName(prettyfilename);
outwriter->SetInput(rescaler->GetOutput());
outwriter->Update();
return EXIT_SUCCESS;
}
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