BandMathXImageFilterExample.cxxΒΆ
Example usage:
./BandMathXImageFilterExample Input/qb_RoadExtract.tif Output/qb_BandMath-res1.tif Output/qb_BandMath-res2.tif Output/context.txt
Example source code (BandMathXImageFilterExample.cxx):
// This filter is based on the mathematical parser library muParserX.
// The built in functions and operators list is available at:
// \url{http://articles.beltoforion.de/article.php?a=muparserx}.
//
// In order to use this filter, at least one input image is to be
// set. An associated variable name can be specified or not by using
// the corresponding SetNthInput method. For the jth (j=1..T) input image, if
// no associated variable name has been specified, a default variable
// name is given by concatenating the prefix "im" with the
// corresponding input index plus one (for instance, im1 is related to the first input).
// If the jth input image is multidimensional, then the variable imj represents a vector whose components are related to its bands.
// In order to access the kth band, the variable observes the following pattern : imjbk.
#include "itkMacro.h"
#include <iostream>
#include "otbVectorImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
// We start by including the needed header files.
#include "otbVectorImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "otbBandMathXImageFilter.h"
int main(int argc, char* argv[])
{
if (argc != 5)
{
std::cerr << "Usage: " << argv[0] << " inputImageFile ";
std::cerr << " outputImageFile ";
std::cerr << " outputImageFile2";
std::cerr << " context.txt" << std::endl;
return EXIT_FAILURE;
}
// Then, we set the classical \code{typedef}s needed for reading and
// writing the images. The \doxygen{otb}{BandMathXImageFilter} class
// works with \doxygen{otb}{VectorImage}.
using PixelType = double;
using ImageType = otb::VectorImage<PixelType, 2>;
using ReaderType = otb::ImageFileReader<ImageType>;
using WriterType = otb::ImageFileWriter<ImageType>;
// We can now define the type for the filter:
using FilterType = otb::BandMathXImageFilter<ImageType>;
// We instantiate the filter, the reader, and the writer:
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
FilterType::Pointer filter = FilterType::New();
// The reader and the writer are parametrized with usual settings:
reader->SetFileName(argv[1]);
writer->SetFileName(argv[2]);
// The aim of this example is to compute a simple high-pass filter.
// For that purpose, we are going to perform the difference between the original signal
// and its averaged version. The definition of the expression that follows is only suitable for a 4-band image.
// So first, we must check this requirement:
reader->UpdateOutputInformation();
if (reader->GetOutput()->GetNumberOfComponentsPerPixel() != 4)
itkGenericExceptionMacro(<< "Input image must have 4 bands." << std::endl);
// Now, we can define the expression. The variable im1 represents a pixel (made of 4 components) of the input image.
// The variable im1b1N5x5 represents a neighborhood of size 5x5 around this pixel (and so on for each band).
// The last element we need is the operator 'mean'. By setting its inputs with four neigborhoods, we tell this operator to process the four related bands.
// As output, it will produce a vector of four components; this is consistent with the fact that we wish to perform a difference with im1.
//
// Thus, the expression is as follows:
filter->SetExpression("im1-mean(im1b1N5x5,im1b2N5x5,im1b3N5x5,im1b4N5x5)");
// Note that the importance of the averaging is driven by the names of the neighborhood variables.
// Last thing we have to do, is to set the pipeline:
filter->SetNthInput(0, reader->GetOutput());
writer->SetInput(filter->GetOutput());
writer->Update();
// Figure~\ref{fig:BandMathXImageFilter} shows the result of our high-pass filter.
// \begin{figure}
// \center
// \includegraphics[width=0.45\textwidth]{qb_ExtractRoad_pretty.eps}
// \includegraphics[width=0.45\textwidth]{qb_BandMath-res1.eps}
// \itkcaption[Band Math X]{From left to right:
// Original image, high-pass filter output.}
// \label{fig:BandMathXImageFilter}
// \end{figure}
// Now let's see a little bit more complex example.
// The aim now is to give the central pixel a higher weight. Moreover :
// - we wish to use smaller neighborhoods
// - we wish to drop the 4th band
// - we wish to add a given number to each band.
//
// First, we instantiate new filters to later make a proper pipeline:
ReaderType::Pointer reader2 = ReaderType::New();
WriterType::Pointer writer2 = WriterType::New();
FilterType::Pointer filter2 = FilterType::New();
reader2->SetFileName(argv[1]);
writer2->SetFileName(argv[3]);
// We define a new kernel (rows are separated by semi-colons, whereas their elements are separated by commas):
filter2->SetMatrix("kernel", "{ 0.1 , 0.1 , 0.1; 0.1 , 0.2 , 0.1; 0.1 , 0.1 , 0.1 }");
// We then define a new constant:
filter2->SetConstant("cst", 1.0);
// We now set the expression (note the use of 'dotpr' operator, as well as the 'bands' operator which is used as a band selector):
filter2->SetExpression("bands(im1,{1,2,3})-dotpr(kernel,im1b1N3x3,im1b2N3x3,im1b3N3x3) + {cst,cst,cst}");
// It is possible to export these definitions to a txt file (they will be reusable later thanks to the method ImportContext):
filter2->ExportContext(argv[4]);
// And finally, we set the pipeline:
filter2->SetNthInput(0, reader2->GetOutput());
writer2->SetInput(filter2->GetOutput());
writer2->Update();
// Figure~\ref{fig:BandMathXImageFilter2} shows the result of the second filter.
// \begin{figure}
// \center
// \includegraphics[width=0.45\textwidth]{qb_ExtractRoad_pretty.eps}
// \includegraphics[width=0.45\textwidth]{qb_BandMath-res2.eps}
// \itkcaption[Band Math X]{From left to right:
// Original image, second filter output.}
// \label{fig:BandMathXImageFilter2}
// \end{figure}
// Finally, it is strongly recommended to take a look at the cookbook, where additional information and examples can be found
// (http://www.orfeo-toolbox.org/packages/OTBCookBook.pdf).
return EXIT_SUCCESS;
}