SetParameterDescription("method.dempstershafer.cmfl","A list of confusion matrix files (*.CSV format) to define the masses of belief and the class labels. Each file should be formatted the following way: "
SetParameterDescription("method.dempstershafer.cmfl","A list of confusion matrix files (.csv format) to define the masses of belief and the class labels. Each file should be formatted the following way: "
"the first line, beginning with a '#' symbol, should be a list of the class labels present in the corresponding input classification image, organized in the same order as the confusion matrix rows/columns.");
AddParameter(ParameterType_Choice,"method.dempstershafer.mob","Mass of belief measurement");
SetParameterDescription("confmap","Confidence map of the produced classification. The confidence index depends on the model : \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"
" - OpenCV\n"
" * Boost : sum of votes\n"
" * DecisionTree : (not supported)\n"
" * GradientBoostedTree : (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");
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"
"* GradientBoostedTree: (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");
SetDocName("Train a classifier from multiple images");
SetDocLongDescription(
"This application performs a classifier training from multiple pairs of input 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.\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. 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.\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, 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.");
"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.\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.\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.\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");
SetDocSeeAlso("OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html ");
SetParameterDescription("confmap","Confidence map of the produced classification. "
"The confidence index depends on the model : \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"
" - OpenCV\n"
" * Boost : sum of votes\n"
" * DecisionTree : (not supported)\n"
" * GradientBoostedTree : (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");
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"
"* GradientBoostedTree: (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)."
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.");
SetDocLongDescription("This application computes MSE (Mean Squared Error), MAE (Mean Absolute Error) and PSNR (Peak Signal to Noise Ratio) between the channel of two images (reference and measurement). The user has to set the used channel and can specify a ROI.");
SetDocLongDescription(
"Compute MSE (Mean Squared Error), MAE (Mean Absolute Error) and PSNR (Peak Signal to Noise Ratio) between two image bands (reference and measurement). "
"The user has to set the used channel and can specify a ROI."
SetDescription("Generates a subsampled version of an image extract");
SetDocName("Quick Look");
SetDocLongDescription("Generates a subsampled version of an extract of an image defined by ROIStart and ROISize.\n"
SetDocLongDescription("Generates a subsampled version of an extract of an image defined by ROIStart and ROISize.\n"
"This extract is subsampled using the ratio OR the output image Size.");
SetDocLimitations("This application does not provide yet the optimal way to decode coarser level of resolution from JPEG2000 images (like in Monteverdi).\n"
SetDocLimitations("This application does not provide yet the optimal way to decode coarser level of resolution from JPEG2000 images (like in Monteverdi).\n"
"Trying to subsampled huge JPEG200 image with the application will lead to poor performances for now.");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
...
...
@@ -100,12 +100,12 @@ private:
MandatoryOff("sr");
AddParameter(ParameterType_Int,"sx","Size X");
SetParameterDescription("sx","quicklook size in x-direction (used if no sampling ration is given)");
SetParameterDescription("sx","quicklook size in x-direction (used if no sampling ratio is given)");
MandatoryOff("sx");
DisableParameter("sx");
AddParameter(ParameterType_Int,"sy","Size Y");
SetParameterDescription("sy","quicklook size in y-direction (used if no sampling ration is given)");
SetParameterDescription("sy","quicklook size in y-direction (used if no sampling ratio is given)");
