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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 "itkPointSet.h"
#include "itkVariableLengthVector.h"
#include "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
/* Example usage:
./SIFTDensityExample Input/suburb2.jpeg Output/SIFTDensityOutput.tif Output/PrettySIFTDensityOutput.png 3 3 7
*/
// This example illustrates the use of the
// \doxygen{otb}{KeyPointDensityImageFilter}.
// This filter computes a local density of keypoints (SIFT or SURF,
// for instance) 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 keypoints are detected can be chosen by the user.
//
// The first step required to use this filter is to include its header file.
#include "otbKeyPointDensityImageFilter.h"
#include "otbImageToSIFTKeyPointSetFilter.h"
int main(int itkNotUsed(argc), char* argv[])
{
const char* infname = argv[1];
const char* outfname = argv[2];
const char* prettyfname = argv[3];
const unsigned int scales = atoi(argv[4]);
const unsigned int octaves = atoi(argv[5]);
const unsigned int radius = atoi(argv[6]);
const unsigned int Dimension = 2;
using PixelType = float;
// 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 keypoint detection. The keypoints
// will be stored in vector form (they may contain many descriptors)
// into a point set. The filter for detecting the SIFT is templated
// over the input image type and the output pointset type.
using RealVectorType = itk::VariableLengthVector<PixelType>;
using PointSetType = itk::PointSet<RealVectorType, Dimension>;
using DetectorType = otb::ImageToSIFTKeyPointSetFilter<ImageType, PointSetType>;
// We can now define the filter which will compute the SIFT
// density. It will be templated over the input and output image
// types and the SIFT detector.
using FilterType = otb::KeyPointDensityImageFilter<ImageType, ImageType, DetectorType>;
// We can instantiate the reader and the writer as wella s the
// filter and the detector. The detector needs to be instantiated in
// order to set its parameters.
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
FilterType::Pointer filter = FilterType::New();
DetectorType::Pointer detector = DetectorType::New();
// We set the file names for the input and the output images.
reader->SetFileName(infname);
writer->SetFileName(outfname);
// We set the parameters for the SIFT detector (the number of
// octaves and the number of scales per octave).
detector->SetOctavesNumber(octaves);
detector->SetScalesNumber(scales);
// And we pass the detector to the filter and we set the radius for
// the density estimation.
filter->SetDetector(detector);
filter->SetNeighborhoodRadius(radius);
// We plug the pipeline.
filter->SetInput(reader->GetOutput());
writer->SetInput(filter->GetOutput());
// We trigger the execution by calling th \code{Update()} method on
// the writer, but before that we run the
// \code{GenerateOutputInformation()} on the reader so the filter
// gets the information about the image size (needed for the SIFT
// computation).
reader->GenerateOutputInformation();
writer->Update();
// Figure~\ref{fig:SIFTDENSITY_FILTER} shows the result of applying
// the key point density filter to an image using the SIFT
// detector. The main difference with respect to figure
// \ref{fig:EDGEDENSITY_FILTER} is that for SIFTS, individual trees
// contribute to the density.
// \begin{figure}
// \center
// \includegraphics[width=0.25\textwidth]{suburb2.eps}
// \includegraphics[width=0.25\textwidth]{PrettySIFTDensityOutput.eps}
// \itkcaption[SIFT Density Filter]{Result of applying the
// \doxygen{otb}{KeypointDensityImageFilter} to an image. From left
// to right :
// original image, SIF density.}
// \label{fig:SIFTDENSITY_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(prettyfname);
outwriter->SetInput(rescaler->GetOutput());
outwriter->Update();
return EXIT_SUCCESS;
}
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