OTB  6.7.0
Orfeo Toolbox
Patented/SIFTFastExample.cxx
/*
* Copyright (C) 2005-2019 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:
./SIFTFastExample Input/ROISpot5.png Output/ROISpot5SIFTFast.png 3
*/
// This example illustrates the use of the \doxygen{otb}{SiftFastImageFilter}.
// The Scale-Invariant Feature Transform (or SIFT) is an algorithm in
// computer vision to detect and describe local features in
// images. The algorithm was published by David Lowe
// \cite{LoweSIFT}. The detection and description of local image
// features can help in object recognition and image registration. The
// SIFT features are local and based on the appearance of the object
// at particular interest points, and are invariant to image scale and
// rotation. They are also robust to changes in illumination, noise,
// occlusion and minor changes in viewpoint.
//
// The first step required to use this filter is to include its header file.
#include "itkPointSet.h"
#include "itkRGBPixel.h"
#include <iostream>
#include <fstream>
int main(int argc, char* argv[])
{
if (argc != 4)
{
std::cerr << "Usage: " << argv[0];
std::cerr << " InputImage OutputImage scales" << std::endl;
return 1;
}
const char* infname = argv[1];
const char* outputImageFilename = argv[2];
const unsigned int scales = atoi(argv[3]);
const unsigned int Dimension = 2;
// We will start by defining the required types. We will work with a
// scalar image of float pixels. We also define the corresponding
// image reader.
using RealType = float;
using ReaderType = otb::ImageFileReader<ImageType>;
// The SIFT descriptors will be stored in a point set containing the
// vector of features.
using RealVectorType = itk::VariableLengthVector<RealType>;
// The SIFT filter itself is templated over the input image and the
// generated point set.
using ImageToFastSIFTKeyPointSetFilterType = otb::SiftFastImageFilter<ImageType, PointSetType>;
// We instantiate the reader.
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(infname);
// We instantiate the filter.
ImageToFastSIFTKeyPointSetFilterType::Pointer filter = ImageToFastSIFTKeyPointSetFilterType::New();
// We plug the filter and set the number of scales for the SIFT
// computation. We can afterwards run the processing with the
// \code{Update()} method.
filter->SetInput(reader->GetOutput());
filter->SetScalesNumber(scales);
filter->Update();
// Once the SIFT are computed, we may want to draw them on top of the
// input image. In order to do this, we will create the following RGB
// image and the corresponding writer:
using PixelType = unsigned char;
using RGBPixelType = itk::RGBPixel<PixelType>;
using OutputImageType = otb::Image<RGBPixelType, 2>;
OutputImageType::Pointer outputImage = OutputImageType::New();
// We set the regions of the image by copying the information from the
// input image and we allocate the memory for the output image.
outputImage->SetRegions(reader->GetOutput()->GetLargestPossibleRegion());
outputImage->Allocate();
// We can now proceed to copy the input image into the output one
// using region iterators. The input image is a grey level one. The
// output image will be made of color crosses for each SIFT on top of
// the grey level input image. So we start by copying the grey level
// values on each of the 3 channels of the color image.
itk::ImageRegionIterator<OutputImageType> iterOutput(outputImage, outputImage->GetLargestPossibleRegion());
itk::ImageRegionIterator<ImageType> iterInput(reader->GetOutput(), reader->GetOutput()->GetLargestPossibleRegion());
for (iterOutput.GoToBegin(), iterInput.GoToBegin(); !iterOutput.IsAtEnd(); ++iterOutput, ++iterInput)
{
OutputImageType::PixelType rgbPixel;
rgbPixel.SetRed(static_cast<PixelType>(iterInput.Get()));
rgbPixel.SetGreen(static_cast<PixelType>(iterInput.Get()));
rgbPixel.SetBlue(static_cast<PixelType>(iterInput.Get()));
iterOutput.Set(rgbPixel);
}
// We are now going to plot color crosses on the output image. We will
// need to define offsets (top, bottom, left and right) with respect
// to the SIFT position in order to draw the cross segments.
ImageType::OffsetType t = {{0, 1}};
ImageType::OffsetType b = {{0, -1}};
ImageType::OffsetType l = {{1, 0}};
ImageType::OffsetType r = {{-1, 0}};
// Now, we are going to access the point set generated by the SIFT
// filter. The points are stored into a points container that we are
// going to walk through using an iterator. These are the types needed
// for this task:
using PointsContainerType = PointSetType::PointsContainer;
using PointsIteratorType = PointsContainerType::Iterator;
// We set the iterator to the beginning of the point set.
PointsIteratorType pIt = filter->GetOutput()->GetPoints()->Begin();
// We get the information about image size and spacing before drawing
// the crosses.
ImageType::SpacingType spacing = reader->GetOutput()->GetSignedSpacing();
ImageType::PointType origin = reader->GetOutput()->GetOrigin();
// And we iterate through the SIFT set:
while (pIt != filter->GetOutput()->GetPoints()->End())
{
// We get the pixel coordinates for each SIFT by using the
// \code{Value()} method on the point set iterator. We use the
// information about size and spacing in order to convert the physical
// coordinates of the point into pixel coordinates.
index[0] = static_cast<unsigned int>(std::floor(static_cast<double>((pIt.Value()[0] - origin[0]) / spacing[0] + 0.5)));
index[1] = static_cast<unsigned int>(std::floor(static_cast<double>((pIt.Value()[1] - origin[1]) / spacing[1] + 0.5)));
// We create a green pixel.
OutputImageType::PixelType keyPixel;
keyPixel.SetRed(0);
keyPixel.SetGreen(255);
keyPixel.SetBlue(0);
// We draw the crosses using the offsets and checking that we are
// inside the image, since SIFTs on the image borders would cause an
// out of bounds pixel access.
if (outputImage->GetLargestPossibleRegion().IsInside(index))
{
outputImage->SetPixel(index, keyPixel);
if (outputImage->GetLargestPossibleRegion().IsInside(index + t))
outputImage->SetPixel(index + t, keyPixel);
if (outputImage->GetLargestPossibleRegion().IsInside(index + b))
outputImage->SetPixel(index + b, keyPixel);
if (outputImage->GetLargestPossibleRegion().IsInside(index + l))
outputImage->SetPixel(index + l, keyPixel);
if (outputImage->GetLargestPossibleRegion().IsInside(index + r))
outputImage->SetPixel(index + r, keyPixel);
}
++pIt;
}
// Finally, we write the image.
WriterType::Pointer writer = WriterType::New();
writer->SetFileName(outputImageFilename);
writer->SetInput(outputImage);
writer->Update();
// Figure~\ref{fig:SIFTFast} shows the result of applying the SIFT
// point detector to a small patch extracted from a Spot 5 image.
// \begin{figure}
// \center
// \includegraphics[width=0.40\textwidth]{ROISpot5.eps}
// \includegraphics[width=0.40\textwidth]{ROISpot5SIFTFast.eps}
// \itkcaption[SIFT Application]{Result of applying the
// \doxygen{otb}{SiftFastImageFilter} to a Spot 5
// image.}
// \label{fig:SIFTFast}
// \end{figure}
return EXIT_SUCCESS;
}