diff --git a/Applications/Classification/otbSOMClassification.cxx b/Applications/Classification/otbSOMClassification.cxx
index aa2307378f0974edfdc8b1e0855c7973a0a5474b..d9ccca181d3e42f579c71cf5485391fc8bfa30d4 100644
--- a/Applications/Classification/otbSOMClassification.cxx
+++ b/Applications/Classification/otbSOMClassification.cxx
@@ -100,43 +100,55 @@ private:
AddParameter(ParameterType_InputImage, "vm", "ValidityMask");
SetParameterDescription("vm", "Validity mask");
+ MandatoryOff("vm");
AddParameter(ParameterType_Float, "tp", "TrainingProbability");
SetParameterDescription("tp", "Probability for a sample to be selected in the training set");
+ MandatoryOff("tp");
- AddParameter(ParameterType_Int, "ts", "TrainingSet");
+ AddParameter(ParameterType_Int, "ts", "TrainingSetSize");
SetParameterDescription("ts", "Maximum training set size");
+ MandatoryOff("ts");
AddParameter(ParameterType_Int, "sl", "StreamingLines");
SetParameterDescription("sl", "Number of lines in each streaming block (used during data sampling)");
+ MandatoryOff("sl");
AddParameter(ParameterType_OutputImage, "som", "SOM Map");
SetParameterDescription("som","Self-Organizing Map map");
+ MandatoryOff("som");
AddParameter(ParameterType_Int, "sx", "SizeX");
SetParameterDescription("sx", "X size of the SOM map");
+ MandatoryOff("sx");
AddParameter(ParameterType_Int, "sy", "SizeY");
SetParameterDescription("sy", "Y size of the SOM map");
+ MandatoryOff("sy");
AddParameter(ParameterType_Int, "nx", "NeighborhoodX");
SetParameterDescription("nx", "X initial neighborhood of the SOM map");
+ MandatoryOff("nx");
AddParameter(ParameterType_Int, "ny", "NeighborhoodY");
SetParameterDescription("ny", "Y initial neighborhood of the SOM map");
+ MandatoryOff("nx");
AddParameter(ParameterType_Int, "ni", "NumberIteration");
SetParameterDescription("ni", "Number of iterations of the SOM learning");
+ MandatoryOff("ni");
AddParameter(ParameterType_Float, "bi", "BetaInit");
SetParameterDescription("bi", "Initial beta value");
+ MandatoryOff("bi");
AddParameter(ParameterType_Float, "bf", "BetaFinal");
SetParameterDescription("bf", "Final beta value");
+ MandatoryOff("bf");
AddParameter(ParameterType_Float, "iv", "InitialValue");
SetParameterDescription("iv", "Initial value (max weight)");
-
+ MandatoryOff("iv");
AddParameter(ParameterType_RAM, "ram", "Available RAM");
SetDefaultParameterInt("ram", 256);
@@ -144,6 +156,7 @@ private:
// TODO : replace StreamingLines by RAM param ?
// Default parameters
+ SetDefaultParameterFloat("tp",1.0);
SetDefaultParameterInt("sx", 32);
SetDefaultParameterInt("sy", 32);
SetDefaultParameterInt("nx", 10);
@@ -177,41 +190,66 @@ private:
}
void DoExecute()
- {
+ {
// initiating random number generation
itk::Statistics::MersenneTwisterRandomVariateGenerator::Pointer
randomGen = itk::Statistics::MersenneTwisterRandomVariateGenerator::New();
FloatVectorImageType::Pointer input = GetParameterImage("in");
- LabeledImageType::Pointer mask = GetParameterImage<LabeledImageType>("vm");
+ LabeledImageType::Pointer mask;
+ m_UseMask = false;
+ if (HasValue("vm"))
+ {
+ mask = GetParameterImage<LabeledImageType>("vm");
+ if (input->GetLargestPossibleRegion()
+ != mask->GetLargestPossibleRegion())
+ {
+ otbAppLogFATAL("Mask image and input image have different sizes.");
+ }
+ m_UseMask = true;
+ }
/*******************************************/
/* Sampling data */
/*******************************************/
otbAppLogINFO("-- SAMPLING DATA --");
-
- if (input->GetLargestPossibleRegion()
- != mask->GetLargestPossibleRegion())
- {
- otbAppLogFATAL("Mask image and input image have different sizes.");
- }
RegionType largestRegion = input->GetLargestPossibleRegion();
// Setting up local streaming capabilities
SplitterType::Pointer splitter = SplitterType::New();
- unsigned int numberOfStreamDivisions = StreamingTraitsType::CalculateNumberOfStreamDivisions(input,
+ unsigned int numberOfStreamDivisions;
+ if (HasValue("sl"))
+ {
+ numberOfStreamDivisions = StreamingTraitsType::CalculateNumberOfStreamDivisions(input,
largestRegion,
splitter,
otb::SET_BUFFER_NUMBER_OF_LINES,
0, 0, GetParameterInt("sl"));
+ }
+ else
+ {
+ numberOfStreamDivisions = StreamingTraitsType::CalculateNumberOfStreamDivisions(input,
+ largestRegion,
+ splitter,
+ otb::SET_BUFFER_MEMORY_SIZE,
+ 0, 1048576*GetParameterInt("ram"),0);
+ }
otbAppLogINFO("The images will be streamed into "<<numberOfStreamDivisions<<" parts.");
// Training sample lists
ListSampleType::Pointer sampleList = ListSampleType::New();
const double trainingProb = static_cast<double>(GetParameterFloat("tp"));
- const unsigned int nbsamples = GetParameterInt("ts");
+ unsigned int nbsamples;
+ if (HasValue("ts"))
+ {
+ nbsamples = GetParameterInt("ts");
+ }
+ else
+ {
+ nbsamples = largestRegion.GetNumberOfPixels();
+ }
// Sample dimension and max dimension
unsigned int sampleSize = input->GetNumberOfComponentsPerPixel();
@@ -229,35 +267,64 @@ private:
// TODO : maybe change the approach: at the moment, the sampling process is able to pick a sample twice or more
while (totalSamples < nbsamples)
{
+ unsigned int localNbSamples=0;
+
piece = randPerm[index];
-
streamingRegion = splitter->GetSplit(piece, numberOfStreamDivisions, largestRegion);
-
//otbAppLogINFO("Processing region: "<<streamingRegion);
-
+
input->SetRequestedRegion(streamingRegion);
input->PropagateRequestedRegion();
input->UpdateOutputData();
-
- mask->SetRequestedRegion(streamingRegion);
- mask->PropagateRequestedRegion();
- mask->UpdateOutputData();
IteratorType it(input, streamingRegion);
- LabeledIteratorType maskIt(mask, streamingRegion);
-
it.GoToBegin();
- maskIt.GoToBegin();
-
- unsigned int localNbSamples=0;
-
- // Loop on the image
- while ( !it.IsAtEnd()
- && !maskIt.IsAtEnd()
- && (totalSamples<nbsamples))
+
+ if (m_UseMask)
{
- // If the current pixel is labeled
- if (maskIt.Get()>0)
+ mask->SetRequestedRegion(streamingRegion);
+ mask->PropagateRequestedRegion();
+ mask->UpdateOutputData();
+
+ LabeledIteratorType maskIt(mask, streamingRegion);
+ maskIt.GoToBegin();
+
+ // Loop on the image and the mask
+ while ( !it.IsAtEnd()
+ && !maskIt.IsAtEnd()
+ && (totalSamples<nbsamples))
+ {
+ // If the current pixel is labeled
+ if (maskIt.Get()>0)
+ {
+ if (randomGen->GetVariateWithClosedRange() < trainingProb)
+ {
+ SampleType newSample;
+ newSample.SetSize(sampleSize);
+ // build the sample
+ newSample.Fill(0);
+ for (unsigned int i = 0; i<sampleSize; ++i)
+ {
+ newSample[i]=it.Get()[i];
+ }
+ // Update the sample lists
+ sampleList->PushBack(newSample);
+ ++totalSamples;
+ ++localNbSamples;
+ }
+ }
+ ++it;
+ ++maskIt;
+ }
+ index++;
+ // we could break out of the while loop here, once the entire image has been streamed once
+ if (index == numberOfStreamDivisions) index = 0;
+ }
+ else
+ {
+ // Loop on the image
+ while ( !it.IsAtEnd()
+ && (totalSamples<nbsamples))
{
if (randomGen->GetVariateWithClosedRange() < trainingProb)
{
@@ -274,16 +341,12 @@ private:
++totalSamples;
++localNbSamples;
}
+ ++it;
}
- ++it;
- ++maskIt;
+ index++;
+ // we could break out of the while loop here, once the entire image has been streamed once
+ if (index == numberOfStreamDivisions) index = 0;
}
-
- index++;
- // we could break out of the while loop here, once the entire image has been streamed once
- if (index == numberOfStreamDivisions) index = 0;
-
- //otbAppLogINFO(""<<localNbSamples<<" samples added to the training set.");
}
otbAppLogINFO("The final training set contains "<<totalSamples<<" samples.");
@@ -313,8 +376,11 @@ private:
estimator->Update();
m_SOMMap = estimator->GetOutput();
- SetParameterOutputImage<DoubleVectorImageType>("som",m_SOMMap);
-
+ if (HasValue("som"))
+ {
+ SetParameterOutputImage<DoubleVectorImageType>("som",m_SOMMap);
+ }
+
/*******************************************/
/* Classification */
/*******************************************/
@@ -322,14 +388,15 @@ private:
m_Classifier = ClassificationFilterType::New();
m_Classifier->SetInput(input);
- m_Classifier->SetInputMask(mask);
m_Classifier->SetMap(m_SOMMap);
+ if (m_UseMask) m_Classifier->SetInputMask(mask);
AddProcess(m_Classifier,"Classification");
SetParameterOutputImage<LabeledImageType>("out",m_Classifier->GetOutput());
- }
+ }
+ bool m_UseMask;
SOMMapType::Pointer m_SOMMap;
ClassificationFilterType::Pointer m_Classifier;
};
@@ -338,8 +405,3 @@ private:
}
OTB_APPLICATION_EXPORT(otb::Wrapper::SOMClassification)
-
-int main(int argc, char * argv[])
-{
-
-}