Skip to content
Snippets Groups Projects
Commit 7456ffd0 authored by Guillaume Pasero's avatar Guillaume Pasero
Browse files

ENH: improve application flexibility

parent 78ef69b9
Branches
Tags
No related merge requests found
Hide whitespace changes
Inline Side-by-side
Applications/Classification/otbSOMClassification.cxx
+ 110
− 48
View file @ 7456ffd0
......@@ -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[])
{
}
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment