Commit c8c098a1 authored by Victor Poughon's avatar Victor Poughon

DOC: review example CorrChDet

parent df935279
......@@ -24,31 +24,12 @@
#include "otbImage.h"
#include "itkShiftScaleImageFilter.h"
#include "otbCommandProgressUpdate.h"
#include "otbCorrelationChangeDetector.h"
/* Example usage:
./CorrelChDet Input/ERSBefore.png Input/ERSAfter.png Output/CorrChDet.tif 15
*/
// This example illustrates the class
// \doxygen{otb}{CorrelationChangeDetector} for detecting changes
// between pairs of images. This filter computes the correlation coefficient in
// the neighborhood of each pixel of the pair of images to be compared. This
// example will use the images shown in
// figure ~\ref{fig:CORRCHDETINIM}. These correspond to two ERS acquisitions before and during a flood.
// \begin{figure}
// \center
// \includegraphics[width=0.35\textwidth]{ERSBefore.eps}
// \includegraphics[width=0.35\textwidth]{ERSAfter.eps}
// \itkcaption[ERS Images for Change Detection]{Images used for the
// change detection. Left: Before the flood. Right: during the flood.}
// \label{fig:CORRCHDETINIM}
// \end{figure}
//
// We start by including the corresponding header file.
#include "otbCorrelationChangeDetector.h"
int main(int argc, char* argv[])
{
......@@ -65,7 +46,6 @@ int main(int argc, char* argv[])
// We start by declaring the types for the two input images, the
// change image and the image to be stored in a file for visualization.
using InternalPixelType = float;
using OutputPixelType = unsigned char;
using InputImageType1 = otb::Image<InternalPixelType, Dimension>;
......@@ -73,32 +53,27 @@ int main(int argc, char* argv[])
using ChangeImageType = otb::Image<InternalPixelType, Dimension>;
using OutputImageType = otb::Image<OutputPixelType, Dimension>;
// We can now declare the types for the readers. Since the images
// can be vey large, we will force the pipeline to use
// streaming. For this purpose, the file writer will be
// streamed. This is achieved by using the
// \doxygen{otb}{ImageFileWriter} class.
// We can now declare the types for the readers. Since the images
// can be very large, we will force the pipeline to use
// streaming. For this purpose, the file writer will be
// streamed.
using ReaderType1 = otb::ImageFileReader<InputImageType1>;
using ReaderType2 = otb::ImageFileReader<InputImageType2>;
using WriterType = otb::ImageFileWriter<OutputImageType>;
// The change detector will give a response which is normalized
// between 0 and 1. Before
// saving the image to a file in, for instance, PNG format, we will
// rescale the results of the change detection in order to use all
// the output pixel type range of values.
// The change detector will give a response which is normalized
// between 0 and 1.
// Before saving the image to a file in, for instance, PNG format, we will
// rescale the results of the change detection in order to use all
// the output pixel type range of values.
using RescalerType = itk::ShiftScaleImageFilter<ChangeImageType, OutputImageType>;
// The \doxygen{otb}{CorrelationChangeDetector} is templated over
// the types of the two input images and the type of the generated change
// image.
// The CorrelationChangeDetector is templated over
// the types of the two input images and the type of the generated change
// image.
using FilterType = otb::CorrelationChangeDetector<InputImageType1, InputImageType2, ChangeImageType>;
// The different elements of the pipeline can now be instantiated.
// The different elements of the pipeline can now be instantiated.
ReaderType1::Pointer reader1 = ReaderType1::New();
ReaderType2::Pointer reader2 = ReaderType2::New();
WriterType::Pointer writer = WriterType::New();
......@@ -107,8 +82,8 @@ int main(int argc, char* argv[])
const char* inputFilename1 = argv[1];
const char* inputFilename2 = argv[2];
const char* outputFilename = argv[3];
// We set the parameters of the different elements of the pipeline.
// We set the parameters of the different elements of the pipeline.
reader1->SetFileName(inputFilename1);
reader2->SetFileName(inputFilename2);
writer->SetFileName(outputFilename);
......@@ -116,49 +91,25 @@ int main(int argc, char* argv[])
float scale = itk::NumericTraits<OutputPixelType>::max();
rescaler->SetScale(scale);
// The only parameter for this change detector is the radius of
// the window used for computing the correlation coefficient.
// The only parameter for this change detector is the radius of
// the window used for computing the correlation coefficient.
filter->SetRadius(atoi(argv[4]));
// We build the pipeline by plugging all the elements together.
// We build the pipeline by plugging all the elements together.
filter->SetInput1(reader1->GetOutput());
filter->SetInput2(reader2->GetOutput());
rescaler->SetInput(filter->GetOutput());
writer->SetInput(rescaler->GetOutput());
// Since the processing time of large images can be long, it is
// interesting to monitor the evolution of the computation. In
// order to do so, the change detectors can use the
// command/observer design pattern. This is easily done by
// attaching an observer to the filter.
// Since the processing time of large images can be long, it is
// interesting to monitor the evolution of the computation. In
// order to do so, the change detectors can use the
// command/observer design pattern. This is easily done by
// attaching an observer to the filter.
using CommandType = otb::CommandProgressUpdate<FilterType>;
CommandType::Pointer observer = CommandType::New();
filter->AddObserver(itk::ProgressEvent(), observer);
try
{
writer->Update();
}
catch (itk::ExceptionObject& err)
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return -1;
}
// Figure \ref{fig:RESCORRCHDET} shows the result of the change
// detection by local correlation.
// \begin{figure}
// \center
// \includegraphics[width=0.35\textwidth]{CorrChDet.eps}
// \itkcaption[Correlation Change Detection Results]{Result of the
// correlation change detector}
// \label{fig:RESCORRCHDET}
// \end{figure}
return EXIT_SUCCESS;
writer->Update();
}
This example illustrates the class :doxygen:`CorrelationChangeDetector` for detecting changes
between pairs of images. This filter computes the correlation coefficient in
the neighborhood of each pixel of the pair of images to be compared.
.. |image1| image:: /Input/ERSBefore.png
.. |image2| image:: /Input/ERSAfter.png
.. |image3| image:: /Output/CorrChDet.png
.. _Figure1:
+--------------------------+-------------------------+-------------------------+
| |image1| | |image2| | |image3| |
+--------------------------+-------------------------+-------------------------+
ERS Images for Change Detection. Left: Before the flood. Middle: during the
flood. Right: result of the correlation change detector
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment