diff --git a/Applications/Classification/otbValidateSVMImagesClassifier.cxx b/Applications/Classification/otbValidateSVMImagesClassifier.cxx
index b54663c53c18431bf8830b7015ff2b032d0c3f63..f91d7fd1677e866b661c82a85a62aae9bd6ae3fe 100644
--- a/Applications/Classification/otbValidateSVMImagesClassifier.cxx
+++ b/Applications/Classification/otbValidateSVMImagesClassifier.cxx
@@ -15,20 +15,14 @@
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
-#include "otbValidateImagesClassifier.h"
+#include "otbWrapperApplication.h"
+#include "otbWrapperApplicationFactory.h"
-#include <iostream>
#include <fstream>
-#include "otbCommandLineArgumentParser.h"
//Image
-#include "otbImage.h"
-#include "otbVectorImage.h"
-#include "otbImageFileReader.h"
-#include "otbVectorData.h"
-#include "otbVectorDataFileReader.h"
#include "otbListSampleGenerator.h"
-//#include "otbTypeManager.h"
+
// ListSample
#include "itkListSample.h"
@@ -45,9 +39,6 @@
#include "otbSVMClassifier.h"
#include "otbConfusionMatrixCalculator.h"
-#include "itkTimeProbe.h"
-#include "otbStandardFilterWatcher.h"
-
// Normalize the samples
#include "otbShiftScaleSampleListFilter.h"
@@ -62,236 +53,252 @@
namespace otb
{
-
-int ValidateImagesClassifier::Describe(ApplicationDescriptor* descriptor)
+namespace Wrapper
{
- descriptor->SetName("ValidateImagesClassifier");
- descriptor->SetDescription("Perform SVM validation from multiple input images and multiple vector data.");
- descriptor->AddOptionNParams("InputImages", "A list of images",
- "in", true, ApplicationDescriptor::InputImage);
- descriptor->AddOptionNParams("VectorDataSamples", "Vector Data of sample used to validate the estimator",
- "vd", true, ApplicationDescriptor::FileName);
- descriptor->AddOption("ImagesStatistics", "XML file containing mean and standard deviation of input images which have been used to train the svm estimator.",
- "is", 1, false, ApplicationDescriptor::FileName);
- descriptor->AddOption("SVMmodel", "SVM model to validate its performances",
- "svm", 1, true, ApplicationDescriptor::FileName);
- descriptor->AddOption("OutputPerfEstimation", "File which will contain the performance of the svm model",
- "out", 1, false, ApplicationDescriptor::FileName);
- return EXIT_SUCCESS;
-}
-
-int ValidateImagesClassifier::Execute(otb::ApplicationOptionsResult* parseResult)
+
+class ValidateSVMImagesClassifier: public Application
{
- // Input Image
- typedef float PixelType;
- typedef otb::VectorImage<PixelType, 2> VectorImageType;
- typedef otb::Image<PixelType, 2> ImageType;
- typedef otb::ImageFileReader<VectorImageType> ReaderType;
- typedef int LabeledPixelType;
- typedef otb::Image<LabeledPixelType, 2> LabeledImageType;
+
+public:
+ /** Standard class typedefs. */
+ typedef ValidateSVMImagesClassifier Self;
+ typedef Application Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Standard macro */
+ itkNewMacro(Self)
+ ;
+
+ itkTypeMacro(ValidateSVMImagesClassifier, otb::Application)
+ ;
+
+ typedef otb::Image<FloatVectorImageType::InternalPixelType, 2> ImageReaderType;
+
+ typedef FloatVectorImageType::PixelType PixelType;
+ typedef FloatVectorImageType VectorImageType;
+ typedef FloatImageType ImageType;
+ typedef Int32ImageType LabeledImageType;
// Training vectordata
- typedef otb::VectorData<> VectorDataType;
- typedef otb::VectorDataFileReader<VectorDataType> VectorDataReaderType;
- typedef itk::VariableLengthVector<PixelType> MeasurementType;
+ typedef itk::VariableLengthVector<ImageType::PixelType> MeasurementType;
// SampleList manipulation
typedef otb::ListSampleGenerator<VectorImageType, VectorDataType> ListSampleGeneratorType;
- typedef ListSampleGeneratorType::ListSampleType ListSampleType;
- typedef ListSampleGeneratorType::LabelType LabelType;
- typedef ListSampleGeneratorType::ListLabelType LabelListSampleType;
- typedef otb::Statistics::ConcatenateSampleListFilter
- <ListSampleType> ConcatenateListSampleFilterType;
- typedef otb::Statistics::ConcatenateSampleListFilter
- <LabelListSampleType> ConcatenateLabelListSampleFilterType;
+ typedef ListSampleGeneratorType::ListSampleType ListSampleType;
+ typedef ListSampleGeneratorType::LabelType LabelType;
+ typedef ListSampleGeneratorType::ListLabelType LabelListSampleType;
+ typedef otb::Statistics::ConcatenateSampleListFilter<ListSampleType> ConcatenateListSampleFilterType;
+ typedef otb::Statistics::ConcatenateSampleListFilter<LabelListSampleType> ConcatenateLabelListSampleFilterType;
// Statistic XML file Reader
- typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
-
+ typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
// Enhance List Sample
- typedef otb::Statistics::ShiftScaleSampleListFilter<
- ListSampleType, ListSampleType> ShiftScaleFilterType;
+ typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
/// Classification typedefs
- typedef otb::SVMImageClassificationFilter<VectorImageType, LabeledImageType> ClassificationFilterType;
- typedef ClassificationFilterType::Pointer ClassificationFilterPointerType;
- typedef ClassificationFilterType::ModelType ModelType;
- typedef ModelType::Pointer ModelPointerType;
-
- typedef otb::Functor::VariableLengthVectorToMeasurementVectorFunctor<
- MeasurementType> MeasurementVectorFunctorType;
- typedef otb::SVMSampleListModelEstimator<
- ListSampleType,
- LabelListSampleType,
- MeasurementVectorFunctorType> SVMEstimatorType;
-
+ typedef otb::SVMImageClassificationFilter<VectorImageType, LabeledImageType> ClassificationFilterType;
+ typedef ClassificationFilterType::Pointer ClassificationFilterPointerType;
+ typedef ClassificationFilterType::ModelType ModelType;
+ typedef ModelType::Pointer ModelPointerType;
+
+ typedef otb::Functor::VariableLengthVectorToMeasurementVectorFunctor<MeasurementType> MeasurementVectorFunctorType;
+ typedef otb::SVMSampleListModelEstimator<ListSampleType, LabelListSampleType, MeasurementVectorFunctorType>
+ SVMEstimatorType;
// Estimate performance on validation sample
- typedef otb::SVMClassifier<ListSampleType, LabelType::ValueType> ClassifierType;
- typedef otb::ConfusionMatrixCalculator<LabelListSampleType,
- LabelListSampleType> ConfusionMatrixCalculatorType;
+ typedef otb::SVMClassifier<ListSampleType, LabelType::ValueType> ClassifierType;
+ typedef otb::ConfusionMatrixCalculator<LabelListSampleType, LabelListSampleType> ConfusionMatrixCalculatorType;
typedef ClassifierType::OutputType ClassifierOutputType;
// Projection of a vectorData
- typedef otb::VectorDataProjectionFilter<VectorDataType, VectorDataType> VectorDataProjectionFilterType;
-
- //Create validation list samples and validation label list samples
- ConcatenateLabelListSampleFilterType::Pointer concatenateValidationLabels =
- ConcatenateLabelListSampleFilterType::New();
- ConcatenateListSampleFilterType::Pointer concatenateValidationSamples =
- ConcatenateListSampleFilterType::New();
- //--------------------------
- // Load measuremnts from input images
- unsigned int nbBands = 0;
- //Iterate over all input images
- for(int imgIndex = 0; imgIndex < parseResult->GetNumberOfParameters("InputImages"); ++imgIndex)
- {
- // Read the image
- ReaderType::Pointer reader = ReaderType::New();
- reader->SetFileName(parseResult->GetParameterString("InputImages", imgIndex));
- reader->UpdateOutputInformation();
-
- std::cout<<"Processing image ("<<imgIndex<<"): "<<reader->GetFileName()<<std::endl;
-
- if (imgIndex == 0)
+ typedef otb::VectorDataProjectionFilter<VectorDataType, VectorDataType> VectorDataProjectionFilterType;
+
+private:
+ ValidateSVMImagesClassifier()
+ {
+ SetName("ValidateSVMImagesClassifier");
+ SetDescription("Perform SVM validation from multiple input images and multiple vector data.");
+ }
+
+ virtual ~ValidateSVMImagesClassifier()
+ {
+ }
+
+ void DoCreateParameters()
+ {
+ AddParameter(ParameterType_InputImageList, "il", "Input Image List");
+ AddParameter(ParameterType_InputVectorDataList, "vd", "Vector Data of sample used to validate the estimator");
+ AddParameter(ParameterType_Filename, "dem", "A DEM repository");
+ MandatoryOff("dem");
+ AddParameter(ParameterType_Filename, "imstat", "XML file containing mean and standard deviation of input images.");
+ MandatoryOff("imstat");
+ AddParameter(ParameterType_Filename, "svm", "SVM model to validate its performances.");
+ AddParameter(ParameterType_Filename, "out", "File which will contain the performance of the SVM model.");
+
+ }
+
+ void DoUpdateParameters()
+ {
+ // Nothing to do here : all parameters are independent
+ }
+
+ void DoExecute()
+ {
+ GetLogger()->Debug("Entering DoExecute\n");
+ //Create training and validation for list samples and label list samples
+ ConcatenateLabelListSampleFilterType::Pointer
+ concatenateTrainingLabels = ConcatenateLabelListSampleFilterType::New();
+ ConcatenateListSampleFilterType::Pointer concatenateTrainingSamples = ConcatenateListSampleFilterType::New();
+ ConcatenateLabelListSampleFilterType::Pointer
+ concatenateValidationLabels = ConcatenateLabelListSampleFilterType::New();
+ ConcatenateListSampleFilterType::Pointer concatenateValidationSamples = ConcatenateListSampleFilterType::New();
+
+ MeasurementType meanMeasurementVector;
+ MeasurementType stddevMeasurementVector;
+
+ //--------------------------
+ // Load measurements from images
+ unsigned int nbBands = 0;
+ //Iterate over all input images
+
+ FloatVectorImageListType* imageList = GetParameterImageList("il");
+ VectorDataListType* vectorDataList = GetParameterVectorDataList("vd");
+
+ //Iterate over all input images
+ for (unsigned int imgIndex = 0; imgIndex < imageList->Size(); ++imgIndex)
+ {
+ FloatVectorImageType::Pointer image = imageList->GetNthElement(imgIndex);
+ image->UpdateOutputInformation();
+
+ if (imgIndex == 0)
+ {
+ nbBands = image->GetNumberOfComponentsPerPixel();
+ }
+
+ // read the Vectordata
+ VectorDataType::Pointer vectorData = vectorDataList->GetNthElement(imgIndex);
+ vectorData->Update();
+
+ // Project the vectorData in the Image Coodinate system
+ VectorDataProjectionFilterType::Pointer vproj = VectorDataProjectionFilterType::New();
+ vproj->SetInput(vectorData);
+ vproj->SetInputProjectionRef(vectorData->GetProjectionRef());
+ vproj->SetOutputKeywordList(image->GetImageKeywordlist());
+ vproj->SetOutputProjectionRef(image->GetProjectionRef());
+ // TODO add DEM support
+ vproj->Update();
+ //Sample list generator
+ ListSampleGeneratorType::Pointer sampleGenerator = ListSampleGeneratorType::New();
+
+ //Set inputs of the sample generator
+ //TODO the ListSampleGenerator perform UpdateOutputData over the input image (need a persistent implementation)
+ sampleGenerator->SetInput(image);
+ sampleGenerator->SetInputVectorData(vproj->GetOutput());
+ sampleGenerator->SetValidationTrainingProportion(1.0); // All in validation
+
+ sampleGenerator->SetClassKey("Class");
+
+ sampleGenerator->Update();
+
+ //Concatenate training and validation samples from the image
+ concatenateValidationLabels->AddInput(sampleGenerator->GetValidationListLabel());
+ concatenateValidationSamples->AddInput(sampleGenerator->GetValidationListSample());
+ }
+
+ // Update
+ concatenateValidationSamples->Update();
+ concatenateValidationLabels->Update();
+
+ if (HasValue("imstat"))
+ {
+ StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
+ statisticsReader->SetFileName(GetParameterString("imstat"));
+ meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
+ stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
+ }
+ else
{
- nbBands = reader->GetOutput()->GetNumberOfComponentsPerPixel();
+ meanMeasurementVector.SetSize(nbBands);
+ meanMeasurementVector.Fill(0.);
+ stddevMeasurementVector.SetSize(nbBands);
+ stddevMeasurementVector.Fill(1.);
}
- // read the Vectordata
- VectorDataReaderType::Pointer vdreader = VectorDataReaderType::New();
- vdreader->SetFileName(parseResult->GetParameterString("VectorDataSamples", imgIndex));
- vdreader->Update();
- std::cout<<"Set VectorData filename: "<< parseResult->GetParameterString("VectorDataSamples", imgIndex) <<std::endl;
-
- // Project the vectorData in the Image Coodinate system
- VectorDataProjectionFilterType::Pointer vproj = VectorDataProjectionFilterType::New();
- vproj->SetInput(vdreader->GetOutput());
- vproj->SetInputProjectionRef(vdreader->GetOutput()->GetProjectionRef());
- vproj->SetOutputKeywordList(reader->GetOutput()->GetImageKeywordlist());
- vproj->SetOutputProjectionRef(reader->GetOutput()->GetProjectionRef());
- // TODO add DEM support
- vproj->Update();
-
- //Sample list generator
- ListSampleGeneratorType::Pointer sampleGenerator = ListSampleGeneratorType::New();
-
- //Set inputs of the sample generator
- //TODO the ListSampleGenerator perform UpdateOutputData over the input image (need a persistent implementation)
- sampleGenerator->SetInput(reader->GetOutput());
- sampleGenerator->SetInputVectorData(vproj->GetOutput());
- sampleGenerator->SetValidationTrainingProportion(1.0); // All in validation
-
- sampleGenerator->SetClassKey("Class");
-
- sampleGenerator->Update();
-
- //Concatenate training and validation samples from the image
- concatenateValidationLabels->AddInput(sampleGenerator->GetValidationListLabel());
- concatenateValidationSamples->AddInput(sampleGenerator->GetValidationListSample());
- }
-
- // Update
- concatenateValidationSamples->Update();
- concatenateValidationLabels->Update();
-
- //--------------------------
- // Normalize the samples
- // Read the mean and standard deviation form the XML file (estimate with the otbEstimateImagesStatistics application)
- MeasurementType meanMeasurentVector;
- MeasurementType stddevMeasurentVector;
- if(parseResult->IsOptionPresent("ImagesStatistics"))
- {
- StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
- statisticsReader->SetFileName(parseResult->GetParameterString("ImagesStatistics").c_str());
- meanMeasurentVector = statisticsReader->GetStatisticVectorByName("mean");
- stddevMeasurentVector = statisticsReader->GetStatisticVectorByName("stddev");
- }
- else
- {
- meanMeasurentVector.SetSize(nbBands);
- meanMeasurentVector.Fill(0.);
- stddevMeasurentVector.SetSize(nbBands);
- stddevMeasurentVector.Fill(1.);
- }
-
- std::cout << "Mean vector loaded and used: " << meanMeasurentVector << std::endl;
- std::cout << "Standard deviation vector loaded and used: " << stddevMeasurentVector << std::endl;
-
- // Shift scale the samples
- ShiftScaleFilterType::Pointer validationShiftScaleFilter = ShiftScaleFilterType::New();
- validationShiftScaleFilter->SetInput(concatenateValidationSamples->GetOutput());
- validationShiftScaleFilter->SetShifts(meanMeasurentVector);
- validationShiftScaleFilter->SetScales(stddevMeasurentVector);
- validationShiftScaleFilter->Update();
-
- //--------------------------
- // split the data set into training/validation set
- ListSampleType::Pointer validationListSample = validationShiftScaleFilter->GetOutputSampleList();
- LabelListSampleType::Pointer validationLabeledListSample = concatenateValidationLabels->GetOutputSampleList();
-
- std::cout<<"Size of validation set: "<<validationListSample->Size()<<std::endl;
- std::cout<<"Size of labeled validation set: "<<validationLabeledListSample->Size()<<std::endl;
-
- //--------------------------
- // Load svm model
- ModelPointerType modelSVM = ModelType::New();
- modelSVM->LoadModel(parseResult->GetParameterString("SVMmodel").c_str());
-
- //--------------------------
- // Performances estimation
- ClassifierType::Pointer validationClassifier = ClassifierType::New();
- validationClassifier->SetSample(validationListSample);
- validationClassifier->SetNumberOfClasses(modelSVM->GetNumberOfClasses());
- validationClassifier->SetModel(modelSVM);
- validationClassifier->Update();
-
- // Estimate performances
- ClassifierOutputType::ConstIterator it = validationClassifier->GetOutput()->Begin();
- ClassifierOutputType::ConstIterator itEnd = validationClassifier->GetOutput()->End();
-
- LabelListSampleType::Pointer classifierListLabel = LabelListSampleType::New();
-
- while (it != itEnd)
- {
- // Due to a bug in SVMClassifier, outlier in one-class SVM are labeled with unsigned int max
- classifierListLabel->PushBack(
- it.GetClassLabel() == itk::NumericTraits<unsigned int>::max() ? 2 : it.GetClassLabel()
- );
- ++it;
- }
-
- ConfusionMatrixCalculatorType::Pointer confMatCalc = ConfusionMatrixCalculatorType::New();
-
- confMatCalc->SetReferenceLabels(validationLabeledListSample);
- confMatCalc->SetProducedLabels(classifierListLabel);
-
- confMatCalc->Update();
-
- std::cout << "*** SVM training performances ***\n" <<"Confusion matrix:\n" << confMatCalc->GetConfusionMatrix() << std::endl;
+ ShiftScaleFilterType::Pointer validationShiftScaleFilter = ShiftScaleFilterType::New();
+ validationShiftScaleFilter->SetInput(concatenateValidationSamples->GetOutput());
+ validationShiftScaleFilter->SetShifts(meanMeasurementVector);
+ validationShiftScaleFilter->SetScales(stddevMeasurementVector);
+ validationShiftScaleFilter->Update();
+ //--------------------------
+ // split the data set into training/validation set
+ ListSampleType::Pointer validationListSample = validationShiftScaleFilter->GetOutputSampleList();
+ LabelListSampleType::Pointer validationLabeledListSample = concatenateValidationLabels->GetOutputSampleList();
+
+ otbAppLogINFO("Size of validation set: " << validationListSample->Size());
+ otbAppLogINFO("Size of labeled validation set: " << validationLabeledListSample->Size());
+
+ //--------------------------
+ // Load svm model
+ ModelPointerType modelSVM = ModelType::New();
+ modelSVM->LoadModel(GetParameterString("svm").c_str());
+
+ //--------------------------
+ // Performances estimation
+ ClassifierType::Pointer validationClassifier = ClassifierType::New();
+ validationClassifier->SetSample(validationListSample);
+ validationClassifier->SetNumberOfClasses(modelSVM->GetNumberOfClasses());
+ validationClassifier->SetModel(modelSVM);
+ validationClassifier->Update();
+
+ // Estimate performances
+ ClassifierOutputType::ConstIterator it = validationClassifier->GetOutput()->Begin();
+ ClassifierOutputType::ConstIterator itEnd = validationClassifier->GetOutput()->End();
+
+ LabelListSampleType::Pointer classifierListLabel = LabelListSampleType::New();
+
+ while (it != itEnd)
+ {
+ // Due to a bug in SVMClassifier, outlier in one-class SVM are labeled with unsigned int max
+ classifierListLabel->PushBack(
+ it.GetClassLabel() == itk::NumericTraits<unsigned int>::max() ? 2
+ : it.GetClassLabel());
+ ++it;
+ }
+
+ ConfusionMatrixCalculatorType::Pointer confMatCalc = ConfusionMatrixCalculatorType::New();
+
+ confMatCalc->SetReferenceLabels(validationLabeledListSample);
+ confMatCalc->SetProducedLabels(classifierListLabel);
+
+ confMatCalc->Update();
+
+ otbAppLogINFO("*** SVM training performances ***\n" <<"Confusion matrix:\n" << confMatCalc->GetConfusionMatrix() << std::endl);
for (unsigned int itClasses = 0; itClasses < modelSVM->GetNumberOfClasses(); itClasses++)
{
- std::cout << "Precision of class [" << itClasses << "] vs all: " << confMatCalc->GetPrecisions()[itClasses] << std::endl;
- std::cout << "Recall of class [" << itClasses << "] vs all: " << confMatCalc->GetRecalls()[itClasses] << std::endl;
- std::cout << "F-score of class [" << itClasses << "] vs all: " << confMatCalc->GetFScores()[itClasses] << "\n" << std::endl;
+ otbAppLogINFO("Precision of class [" << itClasses << "] vs all: " << confMatCalc->GetPrecisions()[itClasses] << std::endl);
+ otbAppLogINFO("Recall of class [" << itClasses << "] vs all: " << confMatCalc->GetRecalls()[itClasses] << std::endl);
+ otbAppLogINFO("F-score of class [" << itClasses << "] vs all: " << confMatCalc->GetFScores()[itClasses] << "\n" << std::endl);
}
- std::cout << "Global performance, Kappa index: " << confMatCalc->GetKappaIndex() << std::endl;
-
- //--------------------------
- // Save output in a ascii file (if needed)
- if(parseResult->IsOptionPresent("OutputPerfEstimation"))
- {
- std::ofstream file;
- file.open(parseResult->GetParameterString("OutputPerfEstimation").c_str());
- file << "Precision of the different class: " << confMatCalc->GetPrecisions() << std::endl;
- file << "Recall of the different class: " << confMatCalc->GetRecalls() << std::endl;
- file << "F-score of the different class: " << confMatCalc->GetFScores() << std::endl;
- file << "Kappa index: " << confMatCalc->GetKappaIndex() << std::endl;
- file.close();
- }
-
- return EXIT_SUCCESS;
-}
-
-}
+ otbAppLogINFO("Global performance, Kappa index: " << confMatCalc->GetKappaIndex() << std::endl);
+
+ //--------------------------
+ // Save output in a ascii file (if needed)
+ if (IsParameterEnabled("out"))
+ {
+ std::ofstream file;
+ file.open(GetParameterString("out").c_str());
+ file << "Precision of the different class: " << confMatCalc->GetPrecisions() << std::endl;
+ file << "Recall of the different class: " << confMatCalc->GetRecalls() << std::endl;
+ file << "F-score of the different class: " << confMatCalc->GetFScores() << std::endl;
+ file << "Kappa index: " << confMatCalc->GetKappaIndex() << std::endl;
+ file.close();
+ }
+ }
+};
+
+} // end of namespace Wrapper
+} // end of namespace otb
+
+OTB_APPLICATION_EXPORT(otb::Wrapper::ValidateSVMImagesClassifier)