diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
index 5433dcf170b2d8110b488267033bf87bd2ef9628..4a3b98b20610383fb47fe420274093fc599f97c7 100644
--- a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
@@ -1,518 +1,22 @@
-/*=========================================================================
- Program: ORFEO Toolbox
- Language: C++
- Date: $Date$
- Version: $Revision$
-
-
- Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
- See OTBCopyright.txt for details.
-
-
- This software is distributed WITHOUT ANY WARRANTY; without even
- the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
- PURPOSE. See the above copyright notices for more information.
-
- =========================================================================*/
-#include "otbWrapperCompositeApplication.h"
-#include "otbWrapperApplicationFactory.h"
-
-#include "otbOGRDataToSamplePositionFilter.h"
-#include "otbSamplingRateCalculator.h"
+#include "otbTrainImagesBase.h"
namespace otb
{
namespace Wrapper
{
-class TrainImagesClassifier: public CompositeApplication
+class TrainImagesClassifier : public TrainImagesBase<true>
{
public:
- /** Standard class typedefs. */
- typedef TrainImagesClassifier Self;
- typedef CompositeApplication Superclass;
- typedef itk::SmartPointer<Self> Pointer;
+ typedef TrainImagesClassifier Self;
+ typedef TrainImagesBase<true> Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
+ itkNewMacro( Self )
+ itkTypeMacro( Self, Superclass )
+};
- /** Standard macro */
- itkNewMacro(Self)
-
- itkTypeMacro(TrainImagesClassifier, otb::Wrapper::CompositeApplication)
-
- /** filters typedefs*/
- typedef otb::OGRDataToSamplePositionFilter<
- FloatVectorImageType,
- UInt8ImageType,
- otb::PeriodicSampler> PeriodicSamplerType;
-
- typedef otb::SamplingRateCalculator::MapRateType MapRateType;
-
-protected:
-
-private:
-
-bool RemoveFile(std::string &filePath)
-{
- bool res = true;
- if(itksys::SystemTools::FileExists(filePath.c_str()))
- {
- size_t posExt = filePath.rfind('.');
- if (posExt != std::string::npos &&
- filePath.compare(posExt,std::string::npos,".shp") == 0)
- {
- std::string shxPath = filePath.substr(0,posExt) + std::string(".shx");
- std::string dbfPath = filePath.substr(0,posExt) + std::string(".dbf");
- std::string prjPath = filePath.substr(0,posExt) + std::string(".prj");
- RemoveFile(shxPath);
- RemoveFile(dbfPath);
- RemoveFile(prjPath);
- }
- res = itksys::SystemTools::RemoveFile(filePath.c_str());
- if (!res)
- {
- otbAppLogINFO(<<"Unable to remove file "<<filePath);
- }
- }
- return res;
-}
-
-void DoInit() ITK_OVERRIDE
-{
- SetName("TrainImagesClassifier");
- SetDescription(
- "Train a classifier from multiple pairs of images and training vector data.");
-
- // Documentation
- SetDocName("Train a classifier from multiple images");
- SetDocLongDescription(
- "This application performs a classifier training from multiple pairs of input images and training vector data. "
- "Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by "
- "the ComputeImagesStatistics application.\n The training vector data must contain polygons with a positive integer field "
- "representing the class label. The name of this field can be set using the \"Class label field\" parameter. Training and validation "
- "sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio "
- "between the number of samples in training and validation sets. Two parameters allow managing the size of the training and "
- "validation sets per class and per image.\n Several classifier parameters can be set depending on the chosen classifier. In the "
- "validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. "
- "In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels"
- " are ordered according to the rows/columns of the confusion matrix.\n This application is based on LibSVM and OpenCV Machine Learning "
- "(2.3.1 and later).");
- SetDocLimitations("None");
- SetDocAuthors("OTB-Team");
- SetDocSeeAlso("OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html ");
-
- AddDocTag(Tags::Learning);
-
- ClearApplications();
- AddApplication("PolygonClassStatistics", "polystat","Polygon analysis");
- AddApplication("MultiImageSamplingRate", "rates", "Sampling rates");
- AddApplication("SampleSelection", "select", "Sample selection");
- AddApplication("SampleExtraction","extraction", "Sample extraction");
- AddApplication("TrainVectorClassifier", "training", "Model training");
-
- //Group IO
- AddParameter(ParameterType_Group, "io", "Input and output data");
- SetParameterDescription("io", "This group of parameters allows setting input and output data.");
-
- AddParameter(ParameterType_InputImageList, "io.il", "Input Image List");
- SetParameterDescription("io.il", "A list of input images.");
- AddParameter(ParameterType_InputVectorDataList, "io.vd", "Input Vector Data List");
- SetParameterDescription("io.vd", "A list of vector data to select the training samples.");
-
- AddParameter(ParameterType_InputVectorDataList, "io.valid", "Validation Vector Data List");
- SetParameterDescription("io.valid", "A list of vector data to select the training samples.");
- MandatoryOff("io.valid");
-
- ShareParameter("io.imstat","training.io.stats");
- ShareParameter("io.confmatout","training.io.confmatout");
- ShareParameter("io.out","training.io.out");
-
- ShareParameter("elev","polystat.elev");
-
- // Sampling settings
- AddParameter(ParameterType_Group, "sample", "Training and validation samples parameters");
- SetParameterDescription("sample",
- "This group of parameters allows you to set training and validation sample lists parameters.");
- AddParameter(ParameterType_Int, "sample.mt", "Maximum training sample size per class");
- SetDefaultParameterInt("sample.mt", 1000);
- SetParameterDescription("sample.mt", "Maximum size per class (in pixels) of "
- "the training sample list (default = 1000) (no limit = -1). If equal to -1,"
- " then the maximal size of the available training sample list per class "
- "will be equal to the surface area of the smallest class multiplied by the"
- " training sample ratio.");
- AddParameter(ParameterType_Int, "sample.mv", "Maximum validation sample size per class");
- SetDefaultParameterInt("sample.mv", 1000);
- SetParameterDescription("sample.mv", "Maximum size per class (in pixels) of "
- "the validation sample list (default = 1000) (no limit = -1). If equal to -1,"
- " then the maximal size of the available validation sample list per class "
- "will be equal to the surface area of the smallest class multiplied by the "
- "validation sample ratio.");
- AddParameter(ParameterType_Int, "sample.bm", "Bound sample number by minimum");
- SetDefaultParameterInt("sample.bm", 1);
- SetParameterDescription("sample.bm", "Bound the number of samples for each "
- "class by the number of available samples by the smaller class. Proportions "
- "between training and validation are respected. Default is true (=1).");
- AddParameter(ParameterType_Float, "sample.vtr", "Training and validation sample ratio");
- SetParameterDescription("sample.vtr",
- "Ratio between training and validation samples (0.0 = all training, 1.0 = "
- "all validation) (default = 0.5).");
- SetParameterFloat("sample.vtr",0.5, false);
- SetMaximumParameterFloatValue("sample.vtr",1.0);
- SetMinimumParameterFloatValue("sample.vtr",0.0);
-
- ShareParameter("sample.vfn","polystat.field");
-
- // hide sampling parameters
- //ShareParameter("sample.strategy","rates.strategy");
- //ShareParameter("sample.mim","rates.mim");
-
- // Classifier settings
- ShareParameter("classifier","training.classifier");
-
- ShareParameter("rand","training.rand");
-
- // Synchronization between applications
- Connect("select.field", "polystat.field");
- Connect("select.layer", "polystat.layer");
- Connect("select.elev", "polystat.elev");
-
- Connect("extraction.in", "select.in");
- Connect("extraction.vec", "select.out");
- Connect("extraction.field", "polystat.field");
- Connect("extraction.layer", "polystat.layer");
-
- Connect("training.cfield", "polystat.field");
-
- ShareParameter("ram","polystat.ram");
- Connect("select.ram", "polystat.ram");
- Connect("extraction.ram", "polystat.ram");
-
- Connect("select.rand", "training.rand");
-
- AddParameter(ParameterType_Empty,"cleanup","Temporary files cleaning");
- EnableParameter("cleanup");
- SetParameterDescription("cleanup","If activated, the application will try to clean all temporary files it created");
- MandatoryOff("cleanup");
-
- // Doc example parameter settings
- SetDocExampleParameterValue("io.il", "QB_1_ortho.tif");
- SetDocExampleParameterValue("io.vd", "VectorData_QB1.shp");
- SetDocExampleParameterValue("io.imstat", "EstimateImageStatisticsQB1.xml");
- SetDocExampleParameterValue("sample.mv", "100");
- SetDocExampleParameterValue("sample.mt", "100");
- SetDocExampleParameterValue("sample.vtr", "0.5");
- SetDocExampleParameterValue("sample.vfn", "Class");
- SetDocExampleParameterValue("classifier", "libsvm");
- SetDocExampleParameterValue("classifier.libsvm.k", "linear");
- SetDocExampleParameterValue("classifier.libsvm.c", "1");
- SetDocExampleParameterValue("classifier.libsvm.opt", "false");
- SetDocExampleParameterValue("io.out", "svmModelQB1.txt");
- SetDocExampleParameterValue("io.confmatout", "svmConfusionMatrixQB1.csv");
}
-
-void DoUpdateParameters() ITK_OVERRIDE
-{
- if ( HasValue("io.vd") )
- {
- std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
- GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[0], false);
- UpdateInternalParameters("polystat");
- }
}
-void DoExecute() ITK_OVERRIDE
-{
- FloatVectorImageListType* imageList = GetParameterImageList("io.il");
- std::vector<std::string> vectorFileList = GetParameterStringList("io.vd");
- unsigned int nbInputs = imageList->Size();
- if (nbInputs > vectorFileList.size())
- {
- otbAppLogFATAL("Missing input vector data files to match number of images ("<<nbInputs<<").");
- }
-
- // check if validation vectors are given
- std::vector<std::string> validationVectorFileList;
- bool dedicatedValidation = false;
- if (IsParameterEnabled("io.valid") && HasValue("io.valid"))
- {
- dedicatedValidation = true;
- validationVectorFileList = GetParameterStringList("io.valid");
- if (nbInputs > validationVectorFileList.size())
- {
- otbAppLogFATAL("Missing validation vector data files to match number of images ("<<nbInputs<<").");
- }
- }
-
- // Prepare temporary file names
- std::string outModel(GetParameterString("io.out"));
- std::vector<std::string> polyStatTrainOutputs;
- std::vector<std::string> polyStatValidOutputs;
- std::vector<std::string> ratesTrainOutputs;
- std::vector<std::string> ratesValidOutputs;
- std::vector<std::string> sampleOutputs;
- std::vector<std::string> sampleTrainOutputs;
- std::vector<std::string> sampleValidOutputs;
- std::string rateTrainOut;
- if (dedicatedValidation)
- {
- rateTrainOut = outModel + "_ratesTrain.csv";
- }
- else
- {
- rateTrainOut = outModel + "_rates.csv";
- }
- std::string rateValidOut(outModel + "_ratesValid.csv");
- for (unsigned int i=0 ; i<nbInputs ; i++)
- {
- std::ostringstream oss;
- oss <<i+1;
- std::string strIndex(oss.str());
- if (dedicatedValidation)
- {
- polyStatTrainOutputs.push_back(outModel + "_statsTrain_" + strIndex + ".xml");
- polyStatValidOutputs.push_back(outModel + "_statsValid_" + strIndex + ".xml");
- ratesTrainOutputs.push_back(outModel + "_ratesTrain_" + strIndex + ".csv");
- ratesValidOutputs.push_back(outModel + "_ratesValid_" + strIndex + ".csv");
- sampleOutputs.push_back(outModel + "_samplesTrain_" + strIndex + ".shp");
- }
- else
- {
- polyStatTrainOutputs.push_back(outModel + "_stats_" + strIndex + ".xml");
- ratesTrainOutputs.push_back(outModel + "_rates_" + strIndex + ".csv");
- sampleOutputs.push_back(outModel + "_samples_" + strIndex + ".shp");
- }
- sampleTrainOutputs.push_back(outModel + "_samplesTrain_" + strIndex + ".shp");
- sampleValidOutputs.push_back(outModel + "_samplesValid_" + strIndex + ".shp");
- }
-
- // ---------------------------------------------------------------------------
- // Polygons stats
- for (unsigned int i=0 ; i<nbInputs ; i++)
- {
- GetInternalApplication("polystat")->SetParameterInputImage("in",imageList->GetNthElement(i));
- GetInternalApplication("polystat")->SetParameterString("vec",vectorFileList[i], false);
- GetInternalApplication("polystat")->SetParameterString("out",polyStatTrainOutputs[i], false);
- ExecuteInternal("polystat");
- // analyse polygons given for validation
- if (dedicatedValidation)
- {
- GetInternalApplication("polystat")->SetParameterString("vec",validationVectorFileList[i], false);
- GetInternalApplication("polystat")->SetParameterString("out",polyStatValidOutputs[i], false);
- ExecuteInternal("polystat");
- }
- }
-
- // ---------------------------------------------------------------------------
- // Compute sampling rates
- GetInternalApplication("rates")->SetParameterString("mim","proportional", false);
- double vtr = GetParameterFloat("sample.vtr");
- long mt = GetParameterInt("sample.mt");
- long mv = GetParameterInt("sample.mv");
- // compute final maximum training and final maximum validation
- // By default take all samples (-1 means all samples)
- long fmt = -1;
- long fmv = -1;
- if (GetParameterInt("sample.bm") == 0)
- {
- if (dedicatedValidation)
- {
- // fmt and fmv will be used separately
- fmt = mt;
- fmv = mv;
- if (mt > -1 && mv <= -1 && vtr < 0.99999)
- {
- fmv = static_cast<long>((double) mt * vtr / (1.0 - vtr));
- }
- if (mt <= -1 && mv > -1 && vtr > 0.00001)
- {
- fmt = static_cast<long>((double) mv * (1.0 - vtr) / vtr);
- }
- }
- else
- {
- // only fmt will be used for both training and validation samples
- // So we try to compute the total number of samples given input
- // parameters mt, mv and vtr.
- if (mt > -1 && mv > -1)
- {
- fmt = mt + mv;
- }
- if (mt > -1 && mv <= -1 && vtr < 0.99999)
- {
- fmt = static_cast<long>((double) mt / (1.0 - vtr));
- }
- if (mt <= -1 && mv > -1 && vtr > 0.00001)
- {
- fmt = static_cast<long>((double) mv / vtr);
- }
- }
- }
-
- // Sampling rates for training
- GetInternalApplication("rates")->SetParameterStringList("il",polyStatTrainOutputs, false);
- GetInternalApplication("rates")->SetParameterString("out",rateTrainOut, false);
- if (GetParameterInt("sample.bm") != 0)
- {
- GetInternalApplication("rates")->SetParameterString("strategy","smallest", false);
- }
- else
- {
- if (fmt > -1)
- {
- GetInternalApplication("rates")->SetParameterString("strategy","constant", false);
- GetInternalApplication("rates")->SetParameterInt("strategy.constant.nb",fmt);
- }
- else
- {
- GetInternalApplication("rates")->SetParameterString("strategy","all", false);
- }
- }
- ExecuteInternal("rates");
- // Sampling rates for validation
- if (dedicatedValidation)
- {
- GetInternalApplication("rates")->SetParameterStringList("il",polyStatValidOutputs, false);
- GetInternalApplication("rates")->SetParameterString("out",rateValidOut, false);
- if (GetParameterInt("sample.bm") != 0)
- {
- GetInternalApplication("rates")->SetParameterString("strategy","smallest", false);
- }
- else
- {
- if (fmv > -1)
- {
- GetInternalApplication("rates")->SetParameterString("strategy","constant", false);
- GetInternalApplication("rates")->SetParameterInt("strategy.constant.nb",fmv);
- }
- else
- {
- GetInternalApplication("rates")->SetParameterString("strategy","all", false);
- }
- }
- ExecuteInternal("rates");
- }
-
- // ---------------------------------------------------------------------------
- // Select & extract samples
- GetInternalApplication("select")->SetParameterString("sampler", "periodic", false);
- GetInternalApplication("select")->SetParameterInt("sampler.periodic.jitter",50);
- GetInternalApplication("select")->SetParameterString("strategy","byclass", false);
- GetInternalApplication("extraction")->SetParameterString("outfield", "prefix", false);
- GetInternalApplication("extraction")->SetParameterString("outfield.prefix.name","value_", false);
- for (unsigned int i=0 ; i<nbInputs ; i++)
- {
- GetInternalApplication("select")->SetParameterInputImage("in",imageList->GetNthElement(i));
- GetInternalApplication("select")->SetParameterString("vec",vectorFileList[i], false);
- GetInternalApplication("select")->SetParameterString("out",sampleOutputs[i], false);
- GetInternalApplication("select")->SetParameterString("instats",polyStatTrainOutputs[i], false);
- GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesTrainOutputs[i], false);
- // select sample positions
- ExecuteInternal("select");
- // extract sample descriptors
- ExecuteInternal("extraction");
-
- if (dedicatedValidation)
- {
- GetInternalApplication("select")->SetParameterString("vec",validationVectorFileList[i], false);
- GetInternalApplication("select")->SetParameterString("out",sampleValidOutputs[i], false);
- GetInternalApplication("select")->SetParameterString("instats",polyStatValidOutputs[i], false);
- GetInternalApplication("select")->SetParameterString("strategy.byclass.in",ratesValidOutputs[i], false);
- // select sample positions
- ExecuteInternal("select");
- // extract sample descriptors
- ExecuteInternal("extraction");
- }
- else
- {
- // Split between training and validation
- ogr::DataSource::Pointer source = ogr::DataSource::New(sampleOutputs[i], ogr::DataSource::Modes::Read);
- ogr::DataSource::Pointer destTrain = ogr::DataSource::New(sampleTrainOutputs[i], ogr::DataSource::Modes::Overwrite);
- ogr::DataSource::Pointer destValid = ogr::DataSource::New(sampleValidOutputs[i], ogr::DataSource::Modes::Overwrite);
- // read sampling rates from ratesTrainOutputs[i]
- SamplingRateCalculator::Pointer rateCalculator = SamplingRateCalculator::New();
- rateCalculator->Read(ratesTrainOutputs[i]);
- // Compute sampling rates for train and valid
- const MapRateType &inputRates = rateCalculator->GetRatesByClass();
- MapRateType trainRates;
- MapRateType validRates;
- otb::SamplingRateCalculator::TripletType tpt;
- for (MapRateType::const_iterator it = inputRates.begin() ;
- it != inputRates.end() ;
- ++it)
- {
- unsigned long total = std::min(it->second.Required,it->second.Tot );
- unsigned long neededValid = static_cast<unsigned long>((double) total * vtr );
- unsigned long neededTrain = total - neededValid;
- tpt.Tot = total;
- tpt.Required = neededTrain;
- tpt.Rate = (1.0 - vtr);
- trainRates[it->first] = tpt;
- tpt.Tot = neededValid;
- tpt.Required = neededValid;
- tpt.Rate = 1.0;
- validRates[it->first] = tpt;
- }
-
- // Use an otb::OGRDataToSamplePositionFilter with 2 outputs
- PeriodicSamplerType::SamplerParameterType param;
- param.Offset = 0;
- param.MaxJitter = 0;
- PeriodicSamplerType::Pointer splitter = PeriodicSamplerType::New();
- splitter->SetInput(imageList->GetNthElement(i));
- splitter->SetOGRData(source);
- splitter->SetOutputPositionContainerAndRates(destTrain, trainRates, 0);
- splitter->SetOutputPositionContainerAndRates(destValid, validRates, 1);
- splitter->SetFieldName(this->GetParameterStringList("sample.vfn")[0]);
- splitter->SetLayerIndex(0);
- splitter->SetOriginFieldName(std::string(""));
- splitter->SetSamplerParameters(param);
- splitter->GetStreamer()->SetAutomaticTiledStreaming(this->GetParameterInt("ram"));
- AddProcess(splitter->GetStreamer(),"Split samples between training and validation...");
- splitter->Update();
- }
- }
-
- // ---------------------------------------------------------------------------
- // Train model
- GetInternalApplication("training")->SetParameterStringList("io.vd",sampleTrainOutputs, false);
- GetInternalApplication("training")->SetParameterStringList("valid.vd",sampleValidOutputs, false);
- UpdateInternalParameters("training");
- // set field names
- FloatVectorImageType::Pointer image = imageList->GetNthElement(0);
- unsigned int nbBands = image->GetNumberOfComponentsPerPixel();
- std::vector<std::string> selectedNames;
- for (unsigned int i=0 ; i<nbBands ; i++)
- {
- std::ostringstream oss;
- oss << i;
- selectedNames.push_back("value_"+oss.str());
- }
- GetInternalApplication("training")->SetParameterStringList("feat",selectedNames, false);
- ExecuteInternal("training");
-
- // cleanup
- if(IsParameterEnabled("cleanup"))
- {
- otbAppLogINFO(<<"Final clean-up ...");
- for(unsigned int i=0 ; i<polyStatTrainOutputs.size() ; i++)
- RemoveFile(polyStatTrainOutputs[i]);
- for(unsigned int i=0 ; i<polyStatValidOutputs.size() ; i++)
- RemoveFile(polyStatValidOutputs[i]);
- for(unsigned int i=0 ; i<ratesTrainOutputs.size() ; i++)
- RemoveFile(ratesTrainOutputs[i]);
- for(unsigned int i=0 ; i<ratesValidOutputs.size() ; i++)
- RemoveFile(ratesValidOutputs[i]);
- for(unsigned int i=0 ; i<sampleOutputs.size() ; i++)
- RemoveFile(sampleOutputs[i]);
- for(unsigned int i=0 ; i<sampleTrainOutputs.size() ; i++)
- RemoveFile(sampleTrainOutputs[i]);
- for(unsigned int i=0 ; i<sampleValidOutputs.size() ; i++)
- RemoveFile(sampleValidOutputs[i]);
- }
-}
-
-};
-
-} // end namespace Wrapper
-} // end namespace otb
-
-OTB_APPLICATION_EXPORT(otb::Wrapper::TrainImagesClassifier)
+OTB_APPLICATION_EXPORT( otb::Wrapper::TrainImagesClassifier )
\ No newline at end of file