From 85fc4c8c973a840c76613cf918a9de555edf6025 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?C=C3=A9dric=20Traizet?= <cedric.traizet@c-s.fr>
Date: Tue, 30 Jul 2019 16:45:20 +0200
Subject: [PATCH] ENH: partial specialization for the DoInit method
---
.../app/otbVectorClassifier.cxx | 73 ++++++++++++++++++
.../app/otbVectorRegression.cxx | 63 ++++++++++++++++
.../include/otbVectorPrediction.h | 75 ++-----------------
3 files changed, 142 insertions(+), 69 deletions(-)
diff --git a/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
index da553cfae0..62d6800210 100644
--- a/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
@@ -27,6 +27,79 @@ namespace Wrapper
using VectorClassifier = VectorPrediction<false, float, unsigned int>;
+template<>
+void
+VectorClassifier
+::DoInitSpecialization()
+{
+ SetName("VectorClassifier");
+ SetDescription("Performs a classification of the input vector data according to a model file.");
+
+ SetDocAuthors("OTB-Team");
+ SetDocLongDescription("This application performs a vector data classification "
+ "based on a model file produced by the TrainVectorClassifier application."
+ "Features of the vector data output will contain the class labels decided by the classifier "
+ "(maximal class label = 65535). \n"
+ "There are two modes: \n"
+ "1) Update mode: add of the 'cfield' field containing the predicted class in the input file. \n"
+ "2) Write mode: copies the existing fields of the input file to the output file "
+ " and add the 'cfield' field containing the predicted class. \n"
+ "If you have declared the output file, the write mode applies. "
+ "Otherwise, the input file update mode will be applied.");
+
+ SetDocLimitations("Shapefiles are supported, but the SQLite format is only supported in update mode.");
+ SetDocSeeAlso("TrainVectorClassifier");
+ AddDocTag(Tags::Learning);
+
+ AddParameter(ParameterType_InputVectorData, "in", "Name of the input vector data");
+ SetParameterDescription("in","The input vector data file to classify.");
+
+ AddParameter(ParameterType_InputFilename, "instat", "Statistics file");
+ SetParameterDescription("instat", "A XML file containing mean and standard deviation to center"
+ "and reduce samples before classification, produced by ComputeImagesStatistics application.");
+ MandatoryOff("instat");
+
+ AddParameter(ParameterType_InputFilename, "model", "Model file");
+ SetParameterDescription("model", "Model file produced by TrainVectorClassifier application.");
+
+ AddParameter(ParameterType_String,"cfield","Output field");
+ SetParameterDescription("cfield","Field containing the predicted class."
+ "Only geometries with this field available will be taken into account.\n"
+ "The field is added either in the input file (if 'out' off) or in the output file.\n"
+ "Caution, the 'cfield' must not exist in the input file if you are updating the file.");
+ SetParameterString("cfield","predicted");
+
+ AddParameter(ParameterType_ListView, "feat", "Field names to be calculated");
+ SetParameterDescription("feat","List of field names in the input vector data used as features for training. "
+ "Put the same field names as the TrainVectorClassifier application.");
+
+ AddParameter(ParameterType_Bool, "confmap", "Confidence map");
+ SetParameterDescription( "confmap", "Confidence map of the produced classification. The confidence index depends on the model: \n\n"
+ "* LibSVM: difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample)\n"
+ "* Boost: sum of votes\n"
+ "* DecisionTree: (not supported)\n"
+ "* KNearestNeighbors: number of neighbors with the same label\n"
+ "* NeuralNetwork: difference between the two highest responses\n"
+ "* NormalBayes: (not supported)\n"
+ "* RandomForest: Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now.\n"
+ "* SVM: distance to margin (only works for 2-class models)\n");
+
+ AddParameter(ParameterType_OutputFilename, "out", "Output vector data file");
+ MandatoryOff("out");
+ SetParameterDescription("out","Output vector data file storing sample values (OGR format)."
+ "If not given, the input vector data file is updated.");
+
+ // Doc example parameter settings
+ SetDocExampleParameterValue("in", "vectorData.shp");
+ SetDocExampleParameterValue("instat", "meanVar.xml");
+ SetDocExampleParameterValue("model", "svmModel.svm");
+ SetDocExampleParameterValue("out", "vectorDataLabeledVector.shp");
+ SetDocExampleParameterValue("feat", "perimeter area width");
+ SetDocExampleParameterValue("cfield", "predicted");
+
+ SetOfficialDocLink();
+}
+
template<>
void
VectorClassifier
diff --git a/Modules/Applications/AppClassification/app/otbVectorRegression.cxx b/Modules/Applications/AppClassification/app/otbVectorRegression.cxx
index bba74a3152..02dd347096 100644
--- a/Modules/Applications/AppClassification/app/otbVectorRegression.cxx
+++ b/Modules/Applications/AppClassification/app/otbVectorRegression.cxx
@@ -27,6 +27,69 @@ namespace Wrapper
using VectorRegression = VectorPrediction<true, float, float>;
+template<>
+void
+VectorRegression
+::DoInitSpecialization()
+{
+ SetName("VectorRegression");
+ SetDescription("Performs regression on the input vector data according to a model file.");
+
+ SetDocAuthors("OTB-Team");
+ SetDocLongDescription("This application performs a vector data regression "
+ "based on a model file produced by the TrainVectorRegression application."
+ "Features of the vector data output will contain the values predicted by the classifier. \n"
+ "There are two modes: \n"
+ "1) Update mode: add of the 'cfield' field containing the predicted value in the input file. \n"
+ "2) Write mode: copies the existing fields of the input file to the output file "
+ " and add the 'cfield' field containing the predicted value. \n"
+ "If you have declared the output file, the write mode applies. "
+ "Otherwise, the input file update mode will be applied.");
+
+ SetDocLimitations("Shapefiles are supported, but the SQLite format is only supported in update mode.");
+ SetDocSeeAlso("TrainVectorRegression");
+ AddDocTag(Tags::Learning);
+
+ AddParameter(ParameterType_InputVectorData, "in", "Name of the input vector data");
+ SetParameterDescription("in","The input vector data file to classify.");
+
+ AddParameter(ParameterType_InputFilename, "instat", "Statistics file");
+ SetParameterDescription("instat", "A XML file containing mean and standard deviation to center"
+ "and reduce samples before classification, produced by ComputeImagesStatistics application.");
+ MandatoryOff("instat");
+
+ AddParameter(ParameterType_InputFilename, "model", "Model file");
+ SetParameterDescription("model", "Model file produced by TrainVectorRegression application.");
+
+ AddParameter(ParameterType_String,"cfield","Output field");
+ SetParameterDescription("cfield","Field containing the predicted value."
+ "Only geometries with this field available will be taken into account.\n"
+ "The field is added either in the input file (if 'out' off) or in the output file.\n"
+ "Caution, the 'cfield' must not exist in the input file if you are updating the file.");
+ SetParameterString("cfield","predicted");
+
+ AddParameter(ParameterType_ListView, "feat", "Field names to be calculated");
+ SetParameterDescription("feat","List of field names in the input vector data used as features for training. "
+ "Put the same field names as the TrainVectorRegression application.");
+
+ AddParameter(ParameterType_OutputFilename, "out", "Output vector data file");
+ MandatoryOff("out");
+
+ SetParameterDescription("out","Output vector data file storing sample values (OGR format)."
+ "If not given, the input vector data file is updated.");
+ MandatoryOff("out");
+
+ // Doc example parameter settings
+ SetDocExampleParameterValue("in", "vectorData.shp");
+ SetDocExampleParameterValue("instat", "meanVar.xml");
+ SetDocExampleParameterValue("model", "rfModel.rf");
+ SetDocExampleParameterValue("out", "vectorDataLabeledVector.shp");
+ SetDocExampleParameterValue("feat", "perimeter area width");
+ SetDocExampleParameterValue("cfield", "predicted");
+
+ SetOfficialDocLink();
+}
+
template<>
void
VectorRegression
diff --git a/Modules/Applications/AppClassification/include/otbVectorPrediction.h b/Modules/Applications/AppClassification/include/otbVectorPrediction.h
index dfa537be48..247a2e4ace 100644
--- a/Modules/Applications/AppClassification/include/otbVectorPrediction.h
+++ b/Modules/Applications/AppClassification/include/otbVectorPrediction.h
@@ -92,74 +92,12 @@ private:
void DoInit() override
{
- SetName("VectorClassifier");
- SetDescription("Performs a classification of the input vector data according to a model file.");
-
- SetDocAuthors("OTB-Team");
- SetDocLongDescription("This application performs a vector data classification "
- "based on a model file produced by the TrainVectorClassifier application."
- "Features of the vector data output will contain the class labels decided by the classifier "
- "(maximal class label = 65535). \n"
- "There are two modes: \n"
- "1) Update mode: add of the 'cfield' field containing the predicted class in the input file. \n"
- "2) Write mode: copies the existing fields of the input file to the output file "
- " and add the 'cfield' field containing the predicted class. \n"
- "If you have declared the output file, the write mode applies. "
- "Otherwise, the input file update mode will be applied.");
-
- SetDocLimitations("Shapefiles are supported, but the SQLite format is only supported in update mode.");
- SetDocSeeAlso("TrainVectorClassifier");
- AddDocTag(Tags::Learning);
-
- AddParameter(ParameterType_InputVectorData, "in", "Name of the input vector data");
- SetParameterDescription("in","The input vector data file to classify.");
-
- AddParameter(ParameterType_InputFilename, "instat", "Statistics file");
- SetParameterDescription("instat", "A XML file containing mean and standard deviation to center"
- "and reduce samples before classification, produced by ComputeImagesStatistics application.");
- MandatoryOff("instat");
-
- AddParameter(ParameterType_InputFilename, "model", "Model file");
- SetParameterDescription("model", "Model file produced by TrainVectorClassifier application.");
-
- AddParameter(ParameterType_String,"cfield","Field class");
- SetParameterDescription("cfield","Field containing the predicted class."
- "Only geometries with this field available will be taken into account.\n"
- "The field is added either in the input file (if 'out' off) or in the output file.\n"
- "Caution, the 'cfield' must not exist in the input file if you are updating the file.");
- SetParameterString("cfield","predicted");
-
- AddParameter(ParameterType_ListView, "feat", "Field names to be calculated");
- SetParameterDescription("feat","List of field names in the input vector data used as features for training. "
- "Put the same field names as the TrainVectorClassifier application.");
-
- AddParameter(ParameterType_Bool, "confmap", "Confidence map");
- SetParameterDescription( "confmap", "Confidence map of the produced classification. The confidence index depends on the model: \n\n"
- "* LibSVM: difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample)\n"
- "* Boost: sum of votes\n"
- "* DecisionTree: (not supported)\n"
- "* KNearestNeighbors: number of neighbors with the same label\n"
- "* NeuralNetwork: difference between the two highest responses\n"
- "* NormalBayes: (not supported)\n"
- "* RandomForest: Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now.\n"
- "* SVM: distance to margin (only works for 2-class models)\n");
-
- AddParameter(ParameterType_OutputFilename, "out", "Output vector data file containing class labels");
- SetParameterDescription("out","Output vector data file storing sample values (OGR format)."
- "If not given, the input vector data file is updated.");
- MandatoryOff("out");
-
- // Doc example parameter settings
- SetDocExampleParameterValue("in", "vectorData.shp");
- SetDocExampleParameterValue("instat", "meanVar.xml");
- SetDocExampleParameterValue("model", "svmModel.svm");
- SetDocExampleParameterValue("out", "vectorDataLabeledVector.shp");
- SetDocExampleParameterValue("feat", "perimeter area width");
- SetDocExampleParameterValue("cfield", "predicted");
-
- SetOfficialDocLink();
+ DoInitSpecialization();
+ //TODO add assert to check that parameters has been correctly defined
}
+ void DoInitSpecialization();
+
void DoUpdateParameters() override
{
if ( HasValue("in") )
@@ -288,10 +226,9 @@ private:
typename ConfidenceListSampleType::Pointer quality;
- bool computeConfidenceMap(GetParameterInt("confmap") && m_Model->HasConfidenceIndex()
- && !m_Model->GetRegressionMode());
+ bool computeConfidenceMap(!m_Model->GetRegressionMode() && GetParameterInt("confmap") && m_Model->HasConfidenceIndex() );
- if (!m_Model->HasConfidenceIndex() && GetParameterInt("confmap"))
+ if (!m_Model->GetRegressionMode() && !m_Model->HasConfidenceIndex() && GetParameterInt("confmap"))
{
otbAppLogWARNING("Confidence map requested but the classifier doesn't support it!");
}
--
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