Commit e80f1451 authored by Guillaume Pasero's avatar Guillaume Pasero

Merge branch 'doc-train-no-output' into 'develop'

Document output of training applications

Closes #1700

See merge request orfeotoolbox/otb!228
parents 39aa809b fcd24b28
......@@ -52,8 +52,9 @@ public:
"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)." );
" are ordered according to the rows/columns of the confusion matrix.\n This application is based on LibSVM, OpenCV Machine Learning "
"(2.3.1 and later), and Shark ML. The output of this application is a text model file, whose format corresponds to the "
"ML model type chosen. There is no image nor vector data output." );
SetDocLimitations( "None" );
SetDocAuthors( "OTB-Team" );
SetDocSeeAlso( "OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html " );
......
......@@ -63,7 +63,10 @@ protected:
SetDocName( "Train Vector Classifier" );
SetDocLongDescription( "This application trains a classifier based on "
"labeled geometries and a list of features to consider for "
"classification." );
"classification.\nThis application is based on LibSVM, OpenCV Machine "
"Learning (2.3.1 and later), and Shark ML The output of this application "
"is a text model file, whose format corresponds to the ML model type "
"chosen. There is no image nor vector data output.");
SetDocLimitations( " " );
SetDocAuthors( "OTB Team" );
SetDocSeeAlso( " " );
......
......@@ -138,7 +138,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
RGBAPixelConverter<int,int>::Pointer dummyFilter =
RGBAPixelConverter<int,int>::New();
dummyFilter->SetProgress(0.0f);
this->AddProcess(dummyFilter,"Classify...");
this->AddProcess(dummyFilter,"Validation...");
dummyFilter->InvokeEvent(itk::StartEvent());
// load a machine learning model from file and predict the input sample list
......@@ -169,6 +169,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
otbAppLogINFO("Computing model file : "<<modelPath);
// Setup fake reporter
RGBAPixelConverter<int,int>::Pointer dummyFilter =
RGBAPixelConverter<int,int>::New();
......
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