Commit 54b718b4 authored by Victor Poughon's avatar Victor Poughon

review trainimagesclassifier

parent 57bc058b
......@@ -43,17 +43,26 @@ public:
// 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. "
"Train a classifier from multiple pairs of 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.\nThe 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 "
"the ComputeImagesStatistics application.\n\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.\n\n"
"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.\nSeveral 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. "
"validation sets per class and per image.\n\n"
"In the validation process, the confusion matrix is organized the following way:\n\n"
"* Rows: reference labels,\n"
"* Columns: produced labels.\n\n"
"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.\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 "
" are ordered according to the rows/columns of the confusion matrix.\n\n"
"This application is based on LibSVM, OpenCV Machine Learning, 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" );
......
......@@ -34,7 +34,7 @@ namespace Wrapper
::InitBoostParams()
{
AddChoice("classifier.boost", "Boost classifier");
SetParameterDescription("classifier.boost", "`Boost classifier <http://docs.opencv.org/modules/ml/doc/boosting.html>`_");
SetParameterDescription("classifier.boost", "http://docs.opencv.org/modules/ml/doc/boosting.html");
//BoostType
AddParameter(ParameterType_Choice, "classifier.boost.t", "Boost Type");
AddChoice("classifier.boost.t.discrete", "Discrete AdaBoost");
......
......@@ -35,8 +35,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
{
AddChoice("classifier.dt", "Decision Tree classifier");
SetParameterDescription("classifier.dt",
"This group of parameters allows setting Decision Tree classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/decision_trees.html}.");
"http://docs.opencv.org/modules/ml/doc/decision_trees.html");
//MaxDepth
AddParameter(ParameterType_Int, "classifier.dt.max", "Maximum depth of the tree");
#ifdef OTB_OPENCV_3
......
......@@ -38,8 +38,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
AddChoice("classifier.gbt", "Gradient Boosted Tree classifier");
SetParameterDescription(
"classifier.gbt",
"This group of parameters allows setting Gradient Boosted Tree classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html}.");
"http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html");
if (m_RegressionFlag)
{
......
......@@ -34,8 +34,7 @@ namespace Wrapper
::InitKNNParams()
{
AddChoice("classifier.knn", "KNN classifier");
SetParameterDescription("classifier.knn", "This group of parameters allows setting KNN classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html}.");
SetParameterDescription("classifier.knn", "http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html");
//K parameter
AddParameter(ParameterType_Int, "classifier.knn.k", "Number of Neighbors");
......
......@@ -35,10 +35,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
::InitNeuralNetworkParams()
{
AddChoice("classifier.ann", "Artificial Neural Network classifier");
SetParameterDescription("classifier.ann",
"This group of parameters allows setting Artificial Neural Network "
"classifier parameters. See complete documentation here "
"\\url{http://docs.opencv.org/modules/ml/doc/neural_networks.html}.");
SetParameterDescription("classifier.ann", "http://docs.opencv.org/modules/ml/doc/neural_networks.html");
//TrainMethod
AddParameter(ParameterType_Choice, "classifier.ann.t", "Train Method Type");
......
......@@ -34,8 +34,7 @@ namespace Wrapper
::InitNormalBayesParams()
{
AddChoice("classifier.bayes", "Normal Bayes classifier");
SetParameterDescription("classifier.bayes", "Use a Normal Bayes Classifier. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html}.");
SetParameterDescription("classifier.bayes", "http://docs.opencv.org/modules/ml/doc/normal_bayes_classifier.html");
}
......
......@@ -34,9 +34,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
::InitRandomForestsParams()
{
AddChoice("classifier.rf", "Random forests classifier");
SetParameterDescription("classifier.rf",
"This group of parameters allows setting Random Forests classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/random_trees.html}.");
SetParameterDescription("classifier.rf", "http://docs.opencv.org/modules/ml/doc/random_trees.html");
//MaxDepth
AddParameter(ParameterType_Int, "classifier.rf.max", "Maximum depth of the tree");
SetParameterInt("classifier.rf.max",5);
......
......@@ -34,8 +34,7 @@ namespace Wrapper
::InitSVMParams()
{
AddChoice("classifier.svm", "SVM classifier (OpenCV)");
SetParameterDescription("classifier.svm", "This group of parameters allows setting SVM classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/support_vector_machines.html}.");
SetParameterDescription("classifier.svm", "http://docs.opencv.org/modules/ml/doc/support_vector_machines.html");
AddParameter(ParameterType_Choice, "classifier.svm.m", "SVM Model Type");
SetParameterDescription("classifier.svm.m", "Type of SVM formulation.");
if (this->m_RegressionFlag)
......
......@@ -31,10 +31,7 @@ template<class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams()
{
AddChoice( "classifier.sharkkm", "Shark kmeans classifier" );
SetParameterDescription( "classifier.sharkkm",
"This group of parameters allows setting Shark kMeans classifier parameters. "
"See complete documentation here "
"\\url{http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/kmeans.html}.\n " );
SetParameterDescription("classifier.sharkkm", "http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/kmeans.html ");
//MaxNumberOfIterations
AddParameter( ParameterType_Int, "classifier.sharkkm.maxiter",
"Maximum number of iteration for the kmeans algorithm." );
......
......@@ -38,8 +38,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
AddChoice("classifier.sharkrf", "Shark Random forests classifier");
SetParameterDescription("classifier.sharkrf",
"This group of parameters allows setting Shark Random Forests classifier parameters. "
"See complete documentation here \\url{http://image.diku.dk/shark/doxygen_pages/html/classshark_1_1_r_f_trainer.html}.\n It is noteworthy that training is parallel.");
"http://image.diku.dk/shark/doxygen_pages/html/classshark_1_1_r_f_trainer.html.\n It is noteworthy that training is parallel.");
//MaxNumberOfTrees
AddParameter(ParameterType_Int, "classifier.sharkrf.nbtrees",
"Maximum number of trees in the forest");
......
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