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Commit a9837cda authored by Rashad Kanavath's avatar Rashad Kanavath
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Merge remote-tracking branch 'origin/develop' into uninstall

parents 5e64ec25 205922a5
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Modules/Applications/AppClassification/app/CMakeLists.txt
View file @ a9837cda
......@@ -45,6 +45,12 @@ otb_create_application(
SOURCES otbTrainOGRLayersClassifier.cxx
LINK_LIBRARIES ${${otb-module}_LIBRARIES})
otb_create_application(
NAME TrainVectorClassifier
SOURCES otbTrainVectorClassifier.cxx
LINK_LIBRARIES ${${otb-module}_LIBRARIES})
otb_create_application(
NAME ComputeConfusionMatrix
SOURCES otbComputeConfusionMatrix.cxx
......
Modules/Applications/AppClassification/app/otbTrainOGRLayersClassifier.cxx
View file @ a9837cda
......@@ -56,8 +56,11 @@ private:
SetName("TrainOGRLayersClassifier");
SetDescription("Train a SVM classifier based on labeled geometries and a list of features to consider.");
SetDocName("TrainOGRLayersClassifier");
SetDocLongDescription("This application trains a SVM classifier based on labeled geometries and a list of features to consider for classification.");
SetDocName("TrainOGRLayersClassifier (DEPRECATED)");
SetDocLongDescription("This application trains a SVM classifier based on "
"labeled geometries and a list of features to consider for classification."
" This application is deprecated, prefer using TrainVectorClassifier which"
" offers access to all the classifiers.");
SetDocLimitations("Experimental. For now only shapefiles are supported. Tuning of SVM classifier is not available.");
SetDocAuthors("David Youssefi during internship at CNES");
SetDocSeeAlso("OGRLayerClassifier,ComputeOGRLayersFeaturesStatistics");
......
Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx 0 → 100644
View file @ a9837cda
/*=========================================================================
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 "otbWrapperApplication.h"
#include "otbWrapperApplicationFactory.h"
#include "otbLearningApplicationBase.h"
#include "otbOGRDataSourceWrapper.h"
#include "otbOGRFeatureWrapper.h"
#include "otbStatisticsXMLFileWriter.h"
#include "itkVariableLengthVector.h"
#include "otbStatisticsXMLFileReader.h"
#include "itkListSample.h"
#include "otbShiftScaleSampleListFilter.h"
// Validation
#include "otbConfusionMatrixCalculator.h"
#include <algorithm>
#include <locale>
namespace otb
{
namespace Wrapper
{
/** Utility function to negate std::isalnum */
bool IsNotAlphaNum(char c)
{
return !std::isalnum(c);
}
class TrainVectorClassifier : public LearningApplicationBase<float,int>
{
public:
typedef TrainVectorClassifier Self;
typedef LearningApplicationBase<float, int> Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
itkNewMacro(Self)
itkTypeMacro(Self, Superclass)
typedef Superclass::SampleType SampleType;
typedef Superclass::ListSampleType ListSampleType;
typedef Superclass::TargetListSampleType TargetListSampleType;
typedef Superclass::SampleImageType SampleImageType;
typedef double ValueType;
typedef itk::VariableLengthVector<ValueType> MeasurementType;
typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
// Estimate performance on validation sample
typedef otb::ConfusionMatrixCalculator<TargetListSampleType, TargetListSampleType> ConfusionMatrixCalculatorType;
typedef ConfusionMatrixCalculatorType::ConfusionMatrixType ConfusionMatrixType;
typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
private:
void DoInit()
{
SetName("TrainVectorClassifier");
SetDescription("Train a classifier based on labeled geometries and a list of features to consider.");
SetDocName("Train Vector Classifier");
SetDocLongDescription("This application trains a classifier based on "
"labeled geometries and a list of features to consider for classification.");
SetDocLimitations(" ");
SetDocAuthors("OTB Team");
SetDocSeeAlso(" ");
//Group IO
AddParameter(ParameterType_Group, "io", "Input and output data");
SetParameterDescription("io", "This group of parameters allows setting input and output data.");
AddParameter(ParameterType_InputVectorData, "io.vd", "Input Vector Data");
SetParameterDescription("io.vd", "Input geometries used for training (note : all geometries from the layer will be used)");
AddParameter(ParameterType_InputFilename, "io.stats", "Input XML image statistics file");
MandatoryOff("io.stats");
SetParameterDescription("io.stats", "XML file containing mean and variance of each feature.");
AddParameter(ParameterType_OutputFilename, "io.confmatout", "Output confusion matrix");
SetParameterDescription("io.confmatout", "Output file containing the confusion matrix (.csv format).");
MandatoryOff("io.confmatout");
AddParameter(ParameterType_OutputFilename, "io.out", "Output model");
SetParameterDescription("io.out", "Output file containing the model estimated (.txt format).");
AddParameter(ParameterType_ListView, "feat", "Field names for training features.");
SetParameterDescription("feat","List of field names in the input vector data to be used as features for training.");
AddParameter(ParameterType_String,"cfield","Field containing the class id for supervision");
SetParameterDescription("cfield","Field containing the class id for supervision. "
"Only geometries with this field available will be taken into account.");
SetParameterString("cfield","class");
AddParameter(ParameterType_Int, "layer", "Layer Index");
SetParameterDescription("layer", "Index of the layer to use in the input vector file.");
MandatoryOff("layer");
SetDefaultParameterInt("layer",0);
AddParameter(ParameterType_Group, "valid", "Validation data");
SetParameterDescription("valid", "This group of parameters defines validation data.");
AddParameter(ParameterType_InputVectorData, "valid.vd", "Validation Vector Data");
SetParameterDescription("valid.vd", "Geometries used for validation "
"(must contain the same fields used for training, all geometries from the layer will be used)");
MandatoryOff("valid.vd");
AddParameter(ParameterType_Int, "valid.layer", "Layer Index");
SetParameterDescription("valid.layer", "Index of the layer to use in the validation vector file.");
MandatoryOff("valid.layer");
SetDefaultParameterInt("valid.layer",0);
// Add parameters for the classifier choice
Superclass::DoInit();
AddRANDParameter();
// Doc example parameter settings
SetDocExampleParameterValue("io.vd", "vectorData.shp");
SetDocExampleParameterValue("io.stats", "meanVar.xml");
SetDocExampleParameterValue("io.out", "svmModel.svm");
SetDocExampleParameterValue("feat", "perimeter area width");
SetDocExampleParameterValue("cfield", "predicted");
}
void DoUpdateParameters()
{
if ( HasValue("io.vd") )
{
std::string vectorFile = GetParameterString("io.vd");
ogr::DataSource::Pointer ogrDS =
ogr::DataSource::New(vectorFile, ogr::DataSource::Modes::Read);
ogr::Layer layer = ogrDS->GetLayer(this->GetParameterInt("layer"));
ogr::Feature feature = layer.ogr().GetNextFeature();
ClearChoices("feat");
for(int iField=0; iField<feature.ogr().GetFieldCount(); iField++)
{
std::string key, item = feature.ogr().GetFieldDefnRef(iField)->GetNameRef();
key = item;
std::string::iterator end = std::remove_if(key.begin(),key.end(),IsNotAlphaNum);
std::transform(key.begin(), end, key.begin(), tolower);
key="feat."+key.substr(0, end - key.begin());
AddChoice(key,item);
}
}
}
void LogConfusionMatrix(ConfusionMatrixCalculatorType* confMatCalc)
{
ConfusionMatrixCalculatorType::ConfusionMatrixType matrix = confMatCalc->GetConfusionMatrix();
// Compute minimal width
size_t minwidth = 0;
for (unsigned int i = 0; i < matrix.Rows(); i++)
{
for (unsigned int j = 0; j < matrix.Cols(); j++)
{
std::ostringstream os;
os << matrix(i, j);
size_t size = os.str().size();
if (size > minwidth)
{
minwidth = size;
}
}
}
MapOfIndicesType mapOfIndices = confMatCalc->GetMapOfIndices();
MapOfIndicesType::const_iterator it = mapOfIndices.begin();
MapOfIndicesType::const_iterator end = mapOfIndices.end();
for (; it != end; ++it)
{
std::ostringstream os;
os << "[" << it->second << "]";
size_t size = os.str().size();
if (size > minwidth)
{
minwidth = size;
}
}
// Generate matrix string, with 'minwidth' as size specifier
std::ostringstream os;
// Header line
for (size_t i = 0; i < minwidth; ++i)
os << " ";
os << " ";
it = mapOfIndices.begin();
end = mapOfIndices.end();
for (; it != end; ++it)
{
os << "[" << it->second << "]" << " ";
}
os << std::endl;
// Each line of confusion matrix
for (unsigned int i = 0; i < matrix.Rows(); i++)
{
ConfusionMatrixCalculatorType::ClassLabelType label = mapOfIndices[i];
os << "[" << std::setw(minwidth - 2) << label << "]" << " ";
for (unsigned int j = 0; j < matrix.Cols(); j++)
{
os << std::setw(minwidth) << matrix(i, j) << " ";
}
os << std::endl;
}
otbAppLogINFO("Confusion matrix (rows = reference labels, columns = produced labels):\n" << os.str());
}
void DoExecute()
{
std::string shapefile = GetParameterString("io.vd");
std::string modelfile = GetParameterString("io.out");
typedef int LabelPixelType;
typedef itk::FixedArray<LabelPixelType,1> LabelSampleType;
typedef itk::Statistics::ListSample <LabelSampleType> LabelListSampleType;
const int nbFeatures = GetSelectedItems("feat").size();
// Statistics for shift/scale
MeasurementType meanMeasurementVector;
MeasurementType stddevMeasurementVector;
if (HasValue("io.stats") && IsParameterEnabled("io.stats"))
{
StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
std::string XMLfile = GetParameterString("io.stats");
statisticsReader->SetFileName(XMLfile);
meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
}
else
{
meanMeasurementVector.SetSize(nbFeatures);
meanMeasurementVector.Fill(0.);
stddevMeasurementVector.SetSize(nbFeatures);
stddevMeasurementVector.Fill(1.);
}
ogr::DataSource::Pointer source = ogr::DataSource::New(shapefile, ogr::DataSource::Modes::Read);
ogr::Layer layer = source->GetLayer(this->GetParameterInt("layer"));
ogr::Feature feature = layer.ogr().GetNextFeature();
bool goesOn = feature.addr() != 0;
ListSampleType::Pointer input = ListSampleType::New();
LabelListSampleType::Pointer target = LabelListSampleType::New();
input->SetMeasurementVectorSize(nbFeatures);
int cFieldIndex=-1;
std::vector<int> featureFieldIndex = GetSelectedItems("feat");
if (feature.addr())
{
cFieldIndex = feature.ogr().GetFieldIndex(GetParameterString("cfield").c_str());
}
// Check that the class field exists
if (cFieldIndex < 0)
{
std::ostringstream oss;
std::vector<std::string> names = GetChoiceNames("feat");
for (unsigned int i=0 ; i<names.size() ; i++)
{
if (i) oss << ", ";
oss << names[i];
}
otbAppLogFATAL("The field name for class label ("<<GetParameterString("cfield")
<<") has not been found in the input vector file! Choices are "<< oss.str());
}
while(goesOn)
{
if(feature.ogr().IsFieldSet(cFieldIndex))
{
MeasurementType mv;
mv.SetSize(nbFeatures);
for(int idx=0; idx < nbFeatures; ++idx)
mv[idx] = feature.ogr().GetFieldAsDouble(featureFieldIndex[idx]);
input->PushBack(mv);
target->PushBack(feature.ogr().GetFieldAsInteger(cFieldIndex));
}
feature = layer.ogr().GetNextFeature();
goesOn = feature.addr() != 0;
}
ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
trainingShiftScaleFilter->SetInput(input);
trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
trainingShiftScaleFilter->Update();
ListSampleType::Pointer listSample;
LabelListSampleType::Pointer labelListSample;
listSample = trainingShiftScaleFilter->GetOutput();
labelListSample = target;
ListSampleType::Pointer trainingListSample = listSample;
LabelListSampleType::Pointer trainingLabeledListSample = labelListSample;
//--------------------------
// Estimate model
//--------------------------
this->Train(trainingListSample,trainingLabeledListSample,GetParameterString("io.out"));
//--------------------------
// Performances estimation
//--------------------------
ListSampleType::Pointer validationListSample=ListSampleType::New();
TargetListSampleType::Pointer validationLabeledListSample = TargetListSampleType::New();
// Import validation data
if (HasValue("valid.vd") && IsParameterEnabled("valid.vd"))
{
std::string validFile = this->GetParameterString("valid.vd");
source = ogr::DataSource::New(validFile, ogr::DataSource::Modes::Read);
layer = source->GetLayer(this->GetParameterInt("valid.layer"));
feature = layer.ogr().GetNextFeature();
goesOn = feature.addr() != 0;
// find usefull field indexes
// TODO : detect corresponding indexes in validation data set, for the moment
// Assume they have the same fields, in the same order.
input = ListSampleType::New();
target = LabelListSampleType::New();
input->SetMeasurementVectorSize(nbFeatures);
while(goesOn)
{
if(feature.ogr().IsFieldSet(cFieldIndex))
{
MeasurementType mv;
mv.SetSize(nbFeatures);
for(int idx=0; idx < nbFeatures; ++idx)
mv[idx] = feature.ogr().GetFieldAsDouble(featureFieldIndex[idx]);
input->PushBack(mv);
target->PushBack(feature.ogr().GetFieldAsInteger(cFieldIndex));
}
feature = layer.ogr().GetNextFeature();
goesOn = feature.addr() != 0;
}
ShiftScaleFilterType::Pointer validShiftScaleFilter = ShiftScaleFilterType::New();
validShiftScaleFilter->SetInput(input);
validShiftScaleFilter->SetShifts(meanMeasurementVector);
validShiftScaleFilter->SetScales(stddevMeasurementVector);
validShiftScaleFilter->Update();
validationListSample = validShiftScaleFilter->GetOutput();
validationLabeledListSample = target;
}
//Test the input validation set size
TargetListSampleType::Pointer predictedList = TargetListSampleType::New();
ListSampleType::Pointer performanceListSample;
TargetListSampleType::Pointer performanceLabeledListSample;
if(validationLabeledListSample->Size() != 0)
{
performanceListSample = validationListSample;
performanceLabeledListSample = validationLabeledListSample;
}
else
{
otbAppLogWARNING("The validation set is empty. The performance estimation is done using the input training set in this case.");
performanceListSample = trainingListSample;
performanceLabeledListSample = trainingLabeledListSample;
}
this->Classify(performanceListSample, predictedList, GetParameterString("io.out"));
ConfusionMatrixCalculatorType::Pointer confMatCalc = ConfusionMatrixCalculatorType::New();
otbAppLogINFO("Predicted list size : " << predictedList->Size());
otbAppLogINFO("ValidationLabeledListSample size : " << performanceLabeledListSample->Size());
confMatCalc->SetReferenceLabels(performanceLabeledListSample);
confMatCalc->SetProducedLabels(predictedList);
confMatCalc->Compute();
otbAppLogINFO("training performances");
LogConfusionMatrix(confMatCalc);
for (unsigned int itClasses = 0; itClasses < confMatCalc->GetNumberOfClasses(); itClasses++)
{
ConfusionMatrixCalculatorType::ClassLabelType classLabel = confMatCalc->GetMapOfIndices()[itClasses];
otbAppLogINFO("Precision of class [" << classLabel << "] vs all: " << confMatCalc->GetPrecisions()[itClasses]);
otbAppLogINFO("Recall of class [" << classLabel << "] vs all: " << confMatCalc->GetRecalls()[itClasses]);
otbAppLogINFO(
"F-score of class [" << classLabel << "] vs all: " << confMatCalc->GetFScores()[itClasses] << "\n");
}
otbAppLogINFO("Global performance, Kappa index: " << confMatCalc->GetKappaIndex());
if (this->HasValue("io.confmatout"))
{
// Writing the confusion matrix in the output .CSV file
MapOfIndicesType::iterator itMapOfIndicesValid, itMapOfIndicesPred;
ClassLabelType labelValid = 0;
ConfusionMatrixType confusionMatrix = confMatCalc->GetConfusionMatrix();
MapOfIndicesType mapOfIndicesValid = confMatCalc->GetMapOfIndices();
unsigned int nbClassesPred = mapOfIndicesValid.size();
/////////////////////////////////////////////
// Filling the 2 headers for the output file
const std::string commentValidStr = "#Reference labels (rows):";
const std::string commentPredStr = "#Produced labels (columns):";
const char separatorChar = ',';
std::ostringstream ossHeaderValidLabels, ossHeaderPredLabels;
// Filling ossHeaderValidLabels and ossHeaderPredLabels for the output file
ossHeaderValidLabels << commentValidStr;
ossHeaderPredLabels << commentPredStr;
itMapOfIndicesValid = mapOfIndicesValid.begin();
while (itMapOfIndicesValid != mapOfIndicesValid.end())
{
// labels labelValid of mapOfIndicesValid are already sorted in otbConfusionMatrixCalculator
labelValid = itMapOfIndicesValid->second;
otbAppLogINFO("mapOfIndicesValid[" << itMapOfIndicesValid->first << "] = " << labelValid);
ossHeaderValidLabels << labelValid;
ossHeaderPredLabels << labelValid;
++itMapOfIndicesValid;
if (itMapOfIndicesValid != mapOfIndicesValid.end())
{
ossHeaderValidLabels << separatorChar;
ossHeaderPredLabels << separatorChar;
}
else
{
ossHeaderValidLabels << std::endl;
ossHeaderPredLabels << std::endl;
}
}
std::ofstream outFile;
outFile.open(this->GetParameterString("io.confmatout").c_str());
outFile << std::fixed;
outFile.precision(10);
/////////////////////////////////////
// Writing the 2 headers
outFile << ossHeaderValidLabels.str();
outFile << ossHeaderPredLabels.str();
/////////////////////////////////////
unsigned int indexLabelValid = 0, indexLabelPred = 0;
for (itMapOfIndicesValid = mapOfIndicesValid.begin(); itMapOfIndicesValid != mapOfIndicesValid.end(); ++itMapOfIndicesValid)
{
indexLabelPred = 0;
for (itMapOfIndicesPred = mapOfIndicesValid.begin(); itMapOfIndicesPred != mapOfIndicesValid.end(); ++itMapOfIndicesPred)
{
// Writing the confusion matrix (sorted in otbConfusionMatrixCalculator) in the output file
outFile << confusionMatrix(indexLabelValid, indexLabelPred);
if (indexLabelPred < (nbClassesPred - 1))
{
outFile << separatorChar;
}
else
{
outFile << std::endl;
}
++indexLabelPred;
}
++indexLabelValid;
}
outFile.close();
} // END if (this->HasValue("io.confmatout"))
}
};
}
}
OTB_APPLICATION_EXPORT(otb::Wrapper::TrainVectorClassifier)
Modules/Applications/AppClassification/test/CMakeLists.txt
View file @ a9837cda
......@@ -8,7 +8,7 @@ otb_test_application(NAME apTvClComputeOGRLayersFeaturesStatistics
VALID --compare-ascii ${NOTOL}
${OTBAPP_BASELINE_FILES}/apTvClComputeOGRLayersFeaturesStatistics.xml
${TEMP}/apTvClComputeOGRLayersFeaturesStatistics.xml)
#----------- SOMClassification TESTS ----------------
otb_test_application(NAME apTvClSOMClassificationSmall
APP SOMClassification
......@@ -323,7 +323,7 @@ otb_test_application(NAME apTvClTrainOGRLayersClassifier
${OTBAPP_BASELINE_FILES}/apTvClModel.svm
${TEMP}/apTvClModel.svm)
set_tests_properties(apTvClTrainOGRLayersClassifier PROPERTIES DEPENDS apTvClComputeOGRLayersFeaturesStatistics)
set_tests_properties(apTvClTrainOGRLayersClassifier PROPERTIES DEPENDS apTvClComputeOGRLayersFeaturesStatistics)
#----------- ComputeConfusionMatrix TESTS ----------------
otb_test_application(NAME apTvComputeConfusionMatrixV
......@@ -580,7 +580,7 @@ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt
${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp
${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt.shp)
set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt)
set_tests_properties(apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_gt PROPERTIES DEPENDS apTvCdbComputePolylineFeatureFromImage_LI_ROADSA_gt)
otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication
APP ComputePolylineFeatureFromImage
......@@ -593,7 +593,7 @@ otb_test_application(NAME apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadE
${OTBAPP_BASELINE_FILES}/cdbTvComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp
${TEMP}/apTvCdbComputePolylineFeatureFromImage_LI_NOBUIL_RoadExtractionApplication.shp)