Commit 48e56903 authored by Cyrille Valladeau's avatar Cyrille Valladeau

STYLE: correct some shells ;)

parent 90138f20
......@@ -83,7 +83,7 @@ private:
SetDocName("Compute Polyline Feature From Image");
SetDocLongDescription("This application computes a polyline feature descriptors from an input image which are part of the polyline pixels that verify the FeatureExpression.");
SetDocLimitations("Since it do not rely on streaming process, take care of the size of input image before launching application.");
SetDocLimitations("Since it does not rely on streaming process, take care of the size of input image before launching application.");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
AddDocTag(Tags::FeatureExtraction);
......
......@@ -47,7 +47,7 @@ private:
SetDescription("Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file.");
SetDocName("Compute Images second order statistics");
SetDocLongDescription("This application computes a global mean and standard deviation for each band of a set of images and optionally saves the results in an XML file. The output XML is intended to be used an input for the TrainImagesSVMClassifier application to normalize samples before learning.");
SetDocLimitations("The set of input images must have the same number of bands. Input images must be of the same number, type and order of bands.");
SetDocLimitations("The set of input images must have the same number of bands. Input images must be of the same number and type and bands order has to be identical.");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("Documentation of the TrainImagesSVMClassifier application.");
......
......@@ -72,7 +72,7 @@ private:
AddDocTag(Tags::Learning);
AddParameter(ParameterType_InputImage, "in", "Input Image");
SetParameterDescription( "in", "The input Image to classify.");
SetParameterDescription( "in", "The input image to classify.");
AddParameter(ParameterType_InputImage, "mask", "Input Mask");
SetParameterDescription( "mask", "The mask allows to restrict classification of the input image to the area where mask pixel values are greater than 0.");
......
......@@ -134,7 +134,7 @@ private:
// Documentation
SetDocName("Train SVM classifier from multiple image");
SetDocLongDescription("This application performs SVM 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 XML statistics file produce by the ComputeImagesStatistics application.\n The training vector data must contain polygons with a positive integer field representing the class label. Name of the 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 the two lists. One parameter allows to control the ratio between the number of samples in training and validation sets. Two parameters allow to manage the size of the training and validation sets per class and per image.\n Several SVM classifier parameters cas be set. The kernel function which defined the feature space (for example if the kernel is a Gaussian radial basis function kernel the corresponding feature space of infinite dimensions). To allow some flexibility in separating the classes, SVM models have a cost parameter, C, that controls the trade off between allowing training errors and forcing rigid margins. It creates a soft margin that permits some misclassifications. Increasing the value of C increases the cost of misclassifying points and forces the creation of a more accurate model that may not generalize well. Classifier parameters can also be optimize.");
SetDocLongDescription("This application performs SVM 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 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. Name of the 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 the two lists. One parameter allows to control the ratio between the number of samples in training and validation sets. Two parameters allow to manage the size of the training and validation sets per class and per image.\n Several SVM classifier parameters cas be set. The kernel function which defined the feature space (for example if the kernel is a Gaussian radial basis function kernel the corresponding feature space of infinite dimensions). To allow some flexibility in separating the classes, SVM models have a cost parameter, C, that controls the trade off between allowing training errors and forcing rigid margins. It creates a soft margin that permits some misclassifications. Increasing the value of C increases the cost of misclassifying points and forces the creation of a more accurate model that may not generalize well. Classifier parameters can also be optimize.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......@@ -195,7 +195,7 @@ private:
SetParameterDescription("svm.k", "SVM Kernel Type.");
AddParameter(ParameterType_Float, "svm.c", "Control trade off between training errors and forcing rigid margins.");
SetParameterFloat("svm.c", 1.0);
SetParameterDescription("svm.c", "SVM models have a cost parameter C.(1 by default).");
SetParameterDescription("svm.c", "SVM models have a cost parameter C (1 by default).");
AddParameter(ParameterType_Empty, "svm.opt", "parameters optimization");
MandatoryOff("svm.opt");
SetParameterDescription("svm.opt", "SVM parameters optimization");
......
......@@ -124,7 +124,7 @@ private:
SetDescription("Estimate the performance of the SVM model with a new set of validation samples and another image.");
SetDocName("Validate SVM Images Classifier");
SetDocLongDescription("Estimate the performance of the SVM model obtained by the ImagesSVMClassifier with a new set of validation samples and another image.\n The application asks for images statisctics as input (XML file generated with the ComputeImagesStatistics application) and a SVM model (text file) generated with the ImagesSVMClassifier application.\n It will compute the global confusion matrix and kappa index and also the precision, recall and F-score of each class.");
SetDocLongDescription("Estimate the performance of the SVM model obtained by the ImagesSVMClassifier with a new set of validation samples and another image.\n The application asks for images statisctics as input (XML file generated with the ComputeImagesStatistics application) and a SVM model (text file) generated with the ImagesSVMClassifier application.\n It will compute the global confusion matrix, kappa index and also the precision, recall and F-score of each class.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......
......@@ -150,7 +150,7 @@ private:
SetMinimumParameterIntValue("mry", 0);
AddParameter(ParameterType_InputImage, "w", "Image To Warp");
SetParameterDescription( "w", "The image to warp after disparity estimation is complete" );
SetParameterDescription( "w", "The image to warp after disparity estimation is completed" );
MandatoryOff("w");
AddParameter(ParameterType_OutputImage, "wo", "Output Warped Image");
......
......@@ -88,7 +88,6 @@ private:
// Optional parameters
AddParameter(ParameterType_RAM, "ram", "Available RAM");
SetParameterDescription("ram","Set the maximum of available memory for the pipeline execution in mega bytes (optional, 256 by default)");
SetDefaultParameterInt("ram", 256);
MandatoryOff("ram");
......
......@@ -59,7 +59,7 @@ private:
// Documentation
SetDocName("Line segment detection");
SetDocLongDescription("This application detects locally straight contours in a image. It is based on Burns, Hanson, and Riseman method and use an a contrario validation approach (Desolneux, Moisan, and Morel). The algorithm was published by Rafael Gromponevon Gioi, Jérémie Jakubowicz, Jean-Michel Morel and Gregory Randall.\n The given approach compute gradient and level lines of the image and detects aligned points in line support region. The application allows to export the detected lines in a vector data.");
SetDocLongDescription("This application detects locally straight contours in a image. It is based on Burns, Hanson, and Riseman method and use an a contrario validation approach (Desolneux, Moisan, and Morel). The algorithm was published by Rafael Gromponevon Gioi, Jérémie Jakubowicz, Jean-Michel Morel and Gregory Randall.\n The given approach computes gradient and level lines of the image and detects aligned points in line support region. The application allows to export the detected lines in a vector data.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("On Line demonstration of the LSD algorithm is available here: http://www.ipol.im/pub/algo/gjmr_line_segment_detector/\n");
......
......@@ -106,7 +106,7 @@ private:
// Documentation
SetDocName("Hyperspectral data unmixing");
SetDocLongDescription("The application applies a linear unmixing algorithm to an hyperspectral data cube. This method supposes that the mixture between materials in the scene is macroscopic and simulate a linear mixing model of spectra.\nThe Linear Mixing Model (LMM) acknowledges that reflectance spectrum associated with each pixel is a linear combination of pure materials in the recovery area, commonly known as endmembers.Endmembers can be estimated using the VertexComponentAnalysis application.\nThe application allows to estimate the abundance maps with several algorithms : Unconstrained Least Square (ucls), Fully Constrained Least Square (fcls), Image Space Reconstruction Algorithm (isra) and Non-negative constrained Least Square (ncls) and Minimum Dispertion Constrained Non Negative Matrix Factorization (MDMDNMF).\n");
SetDocLongDescription("The application applies a linear unmixing algorithm to an hyperspectral data cube. This method supposes that the mixture between materials in the scene is macroscopic and simulates a linear mixing model of spectra.\nThe Linear Mixing Model (LMM) acknowledges that reflectance spectrum associated with each pixel is a linear combination of pure materials in the recovery area, commonly known as endmembers. Endmembers can be estimated using the VertexComponentAnalysis application.\nThe application allows to estimate the abundance maps with several algorithms : Unconstrained Least Square (ucls), Fully Constrained Least Square (fcls), Image Space Reconstruction Algorithm (isra) and Non-negative constrained Least Square (ncls) and Minimum Dispertion Constrained Non Negative Matrix Factorization (MDMDNMF).\n");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("VertexComponentAnalysis");
......
......@@ -56,7 +56,7 @@ private:
// Documentation
SetDocName("Cartographic to geographic coordinates conversion");
SetDocLongDescription("This application computes the geographic coordinates from a cartographic one. user has to give the X and Y coordinate and the cartographic projection (UTM/LAMBERT/LAMBERT2/LAMBERT93/SINUS/ECKERT4/TRANSMERCATOR/MOLLWEID/SVY21).");
SetDocLongDescription("This application computes the geographic coordinates from a cartographic one. User has to give the X and Y coordinate and the cartographic projection (UTM/LAMBERT/LAMBERT2/LAMBERT93/SINUS/ECKERT4/TRANSMERCATOR/MOLLWEID/SVY21).");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......
......@@ -58,7 +58,7 @@ private:
// Documentation
SetDocName("Superimpose sensor");
SetDocLongDescription("This application performs /....");
SetDocLongDescription("This application performs the projection of an image into the geometry of another one.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......
......@@ -82,7 +82,7 @@ private:
void DoInit()
{
SetName("RadiometricVegetationIndices");
SetDescription("Compute radiometric indices based on Red and NIT channels.");
SetDescription("Compute radiometric indices based on Red and NIR channels.");
// Documentation
SetDocName("Radiometric Vegetation");
......
......@@ -53,7 +53,7 @@ private:
// Documentation
SetDocName("Images comparaison");
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 specified an ROI.");
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 specified an ROI.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("BandMath application, ImageStatistics");
......
......@@ -60,7 +60,7 @@ private:
// Documentation
SetDocName("Images Concatenation");
SetDocLongDescription("This application performs images channels concatenation. It will walk the input image list (single or multi-channel) and generate a single multi-channel image. The channel order is the one of the list.");
SetDocLongDescription("This application performs images channels concatenation. It will walk the input image list (single or multi-channel) and generates a single multi-channel image. The channel order is the one of the list.");
SetDocLimitations("All input images must have the same size.");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("Rescale application, Convert");
......
......@@ -50,7 +50,7 @@ private:
SetDescription("Concatenate VectorDatas");
SetDocName("Concatenate");
SetDocLongDescription("This application concatenate a list of VectorData to produce a unique VectorData as output"
SetDocLongDescription("This application concatenates a list of VectorData to produce a unique VectorData as output"
"Note that the VectorDatas must be of the same type (Storing polygons only, lines only, or points only)");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
......
......@@ -83,7 +83,7 @@ private:
" and/or changing the pixel type.");
// Documentation
SetDocName("Image Conversion");
SetDocLongDescription("This application performs an image pixel type conversion (short, ushort, char, uchar, int, uint, float and double types are handled). The output image is written in the specified format (ie. that corresponds to the given extension).\n The convertion can include a rescale usiong the image 2% minimum and maximum values. The rescale can be linear or log2.");
SetDocLongDescription("This application performs an image pixel type conversion (short, ushort, uchar, int, uint, float and double types are handled). The output image is written in the specified format (ie. that corresponds to the given extension).\n The convertion can include a rescale using the image 2 percent minimum and maximum values. The rescale can be linear or log2.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("Rescale");
......
......@@ -64,7 +64,7 @@ private:
// Documentation
SetDocName("Multi Resolution Pyramid");
SetDocLongDescription("This application builds a multi-resolution pyramid of the input image.");
SetDocLongDescription("This application builds a multi-resolution pyramid of the input image. USer can specified the number of levels of the pyramid and the subsampling factor. To spped ip the process, you can use the fast scheme option");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......
......@@ -56,7 +56,7 @@ private:
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 "
"This extract is subsampled using the ration OR the output image Size");
"This extract is subsampled using the ratio OR the output image Size");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......@@ -67,7 +67,7 @@ private:
SetParameterDescription( "in", "The image to read" );
AddParameter(ParameterType_OutputImage, "out", "Output Image");
SetParameterDescription( "out" , "The subsampled image." );
SetParameterDescription( "out" , "The subsampled image" );
AddParameter(ParameterType_ListView, "cl", "Channel List");
SetParameterDescription( "cl" , "Selected channels" );
......
......@@ -52,7 +52,7 @@ private:
SetDescription("Rescale the image between two given values.");
SetDocName("Rescale Image");
SetDocLongDescription("This application scale the given image pixel intensity between two given values. "
SetDocLongDescription("This application scales the given image pixel intensity between two given values. "
"By default min (resp. max) value is set to 0 (resp. 255).");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
......
......@@ -69,7 +69,7 @@ private:
AddParameter(ParameterType_InputImage, "in", "Input Image");
SetParameterDescription("in", "Input image to filter.");
AddParameter(ParameterType_OutputImage, "out", "Output Image");
SetParameterDescription("out", "filtered image.");
SetParameterDescription("out", "Filtered image.");
AddParameter(ParameterType_RAM, "ram", "Available RAM");
SetDefaultParameterInt("ram", 256);
......
......@@ -71,7 +71,7 @@ private:
SetDescription("Perform an extract ROI on the input vector data according to the input image extent");
SetDocName("VectorData Extract ROI");
SetDocLongDescription("This application extract the VectorData features belonging to a region specified by the support image envelope");
SetDocLongDescription("This application extracts the VectorData features belonging to a region specified by the support image envelope");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......
......@@ -57,7 +57,7 @@ private:
SetDescription("Apply a transform to each vertex of the input VectorData");
SetDocName("Vector Data Transformation");
SetDocLongDescription("This application performs a transformation to an input vector data transforming each vertex that composed the vector data. The applied transformation manages translation, rotation and scale, and be be centered or not.");
SetDocLongDescription("This application performs a transformation of an input vector data transforming each vertex that composed the vector data. The applied transformation manages translation, rotation and scale, and can be centered or not.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......
......@@ -91,7 +91,7 @@ protected:
RAMParameter()
{
this->SetName("RAM");
this->SetDescription("Available RAM");
this->SetDescription("Set the maximum of available memory for the pipeline execution in mega bytes (optional, 256 by default).");
this->SetKey("ram");
// Initialize the unsigned int NumericalParam
......
......@@ -115,9 +115,9 @@ private:
// Software Guide : BeginCodeSnippet
SetName("Example");
SetDescription("This application opens in image and save it. "
"(it includes Latex snippets in order to generate "
"software guide documentation)");
SetDescription("This application opens an image and save it. "
"Pay attention, it includes Latex snippets in order to generate "
"software guide documentation");
SetDocName("Example");
SetDocLongDescription("The purpose of this application is "
......
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