Commit cb2f6d1b authored by Victor Poughon's avatar Victor Poughon
Browse files

Merge branch 'remove-docname' into 'develop'

Remove docname

Closes #1789

See merge request orfeotoolbox/otb!484
parents aca94bb7 0bb6f3a8
......@@ -5,7 +5,6 @@
<descr>This application helps developers to test parameters types</descr>
<longdescr>The purpose of this application is to test parameters types.</longdescr>
......@@ -68,7 +68,6 @@ private:
// \begin{description}
// \item[\code{SetName()}] Name of the application.
// \item[\code{SetDescription()}] Set the short description of the class.
// \item[\code{SetDocName()}] Set long name of the application (that can be displayed \dots).
// \item[\code{SetDocLongDescription()}] This methods is used to describe the class.
// \item[\code{SetDocLimitations()}] Set known limitations (threading, invalid pixel type \dots) or bugs.
// \item[\code{SetDocAuthors()}] Set the application Authors. Author List. Format : "John Doe, Winnie the Pooh" \dots
......@@ -81,7 +80,6 @@ private:
"Pay attention, it includes Latex snippets in order to generate "
"software guide documentation");
"The purpose of this application is "
"to present parameters types,"
......@@ -49,7 +49,6 @@ private:
SetDescription("Change detection by Multivariate Alteration Detector (MAD) algorithm");
// Documentation
SetDocName("Multivariate Alteration Detector");
SetDocLongDescription("This application performs change detection between two multispectral"
" images using the Multivariate Alteration Detector (MAD) [1]"
" algorithm.\n\n"
......@@ -62,7 +62,6 @@ private:
SetDescription("Filters the input labeled image using Majority Voting in a ball shaped neighbordhood");
SetDocName("Classification Map Regularization");
"This application filters the input labeled image (with a maximal class label = 65535) using Majority Voting in a ball shaped neighbordhood."
......@@ -109,7 +109,6 @@ private:
SetDescription("Computes the confusion matrix of a classification");
// Documentation
SetDocName("Confusion matrix Computation");
SetDocLongDescription("This application computes the confusion matrix of a classification map relative to a ground truth dataset. "
"This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different "
"from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: "
......@@ -48,7 +48,6 @@ private:
void DoInit() override
SetDocName("Compute Images second order statistics");
SetDescription("Computes global mean and standard deviation for each band "
"from a set of images and optionally saves the results in an XML file.");
SetDocLongDescription("This application computes a global mean and standard deviation "
......@@ -51,7 +51,6 @@ private:
SetDescription("Compute statistics of the features in a set of OGR Layers");
SetDocLongDescription("Compute statistics (mean and standard deviation) of the features in a set of OGR Layers, and write them in an XML file. This XML file can then be used by the training application.");
SetDocLimitations("Experimental. For now only shapefiles are supported.");
SetDocAuthors("David Youssefi during internship at CNES");
......@@ -97,7 +97,6 @@ private:
SetDescription("Fuses several classifications maps of the same image on the basis of class labels.");
SetDocName("Fusion of Classifications");
SetDocLongDescription("This application allows you to fuse several classification maps and produces a single more robust classification map. "
"Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels.\n\n"
" - MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected.\n"
......@@ -80,7 +80,6 @@ private:
SetDescription("Performs a classification of the input image according to a model file.");
// Documentation
SetDocName("Image Classification");
SetDocLongDescription("This application performs an image classification based on a model file produced by the TrainImagesClassifier application. Pixels of the output image will contain the class labels decided by the classifier (maximal class label = 65535). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be classified. By default, the remaining of pixels will be given the label 0 in the output image.");
SetDocLimitations("The input image must have the same type, order and number of bands than the images used to produce the statistics file and the SVM model file. If a statistics file was used during training by the TrainImagesClassifier, it is mandatory to use the same statistics file for classification. If an input mask is used, its size must match the input image size.");
......@@ -371,7 +371,6 @@ private:
SetDescription("Unsupervised KMeans image classification");
SetDocName("Unsupervised KMeans image classification");
SetDocLongDescription("Unsupervised KMeans image classification. "
"This is a composite application, using existing training and classification applications. "
"The SharkKMeans model is used.\n\n"
......@@ -63,7 +63,6 @@ private:
SetDescription("Compute sampling rate for an input set of images.");
// Documentation
SetDocName("Multi-image sampling rate estimation");
SetDocLongDescription("The application computes sampling rates for a set of"
" input images. Before calling this application, each pair of image and "
"training vectors has to be analysed with the application "
......@@ -60,7 +60,6 @@ private:
SetDescription("Classify an OGR layer based on a machine learning model and a list of features to consider.");
SetDocLongDescription("This application will apply a trained machine learning model on the selected feature to get a classification of each geometry contained in an OGR layer. The list of feature must match the list used for training. The predicted label is written in the user defined field for each geometry.");
SetDocLimitations("Experimental. Only shapefiles are supported for now.");
SetDocAuthors("David Youssefi during internship at CNES");
......@@ -72,7 +72,6 @@ private:
SetDescription("Computes statistics on a training polygon set.");
// Documentation
SetDocName("Polygon Class Statistics");
SetDocLongDescription("Process a set of geometries intended for training (they should have a field giving the associated "
"class). The geometries are analyzed against a support image to compute statistics:\n\n"
"* Number of samples per class\n"
......@@ -119,7 +119,6 @@ private:
SetDescription("Performs a prediction of the input image according to a regression model file.");
// Documentation
SetDocName("Predict Regression");
SetDocLongDescription("This application predict output values from an input"
" image, based on a regression model file produced by"
" the TrainRegression application. Pixels of the "
......@@ -82,7 +82,6 @@ private:
SetDescription("SOM image classification.");
// Documentation
SetDocName("SOM Classification");
SetDocLongDescription("Unsupervised Self Organizing Map image classification.");
......@@ -57,7 +57,6 @@ private:
SetDescription("Generates synthetic samples from a sample data file.");
// Documentation
SetDocName("Sample Augmentation");
SetDocLongDescription("The application takes a sample data file as "
"generated by the SampleExtraction application and "
"generates synthetic samples to increase the number of "
......@@ -59,7 +59,6 @@ private:
SetDescription("Extracts samples values from an image.");
// Documentation
SetDocName("Sample Extraction");
SetDocLongDescription("The application extracts samples values from an"
"image using positions contained in a vector data file. ");
......@@ -86,7 +86,6 @@ private:
SetDescription("Selects samples from a training vector data set.");
// Documentation
SetDocName("Sample Selection");
"The application selects a set of samples from geometries "
"intended for training (they should have a field giving the associated "
......@@ -41,7 +41,6 @@ public:
SetDescription( "Train a classifier from multiple pairs of images and training vector data." );
// Documentation
SetDocName( "Train a classifier from multiple images" );
"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 "
......@@ -105,7 +105,6 @@ void DoInit() override
"Train a classifier from multiple images to perform regression.");
// Documentation
SetDocName("Train a regression model");
"This application trains a classifier from multiple input images or a csv "
"file, in order to perform regression. Predictors are composed of pixel "
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