Commit 08447a97 authored by Guillaume Pasero's avatar Guillaume Pasero

DOC: review application documentation: Classification, Calibration, Utils part2

parent 5ce768a0
......@@ -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 and type and bands order has to be identical.");
SetDocLimitations("Each image of the set must contain the same bands as the others (i.e. same types, in the same order).");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("Documentation of the TrainImagesSVMClassifier application.");
......@@ -58,7 +58,7 @@ private:
SetParameterDescription( "il", "List of input images filenames." );
AddParameter(ParameterType_Filename, "out", "Output XML file");
SetParameterDescription( "out", "XML filename where the statistics are saved for future reuse" );
SetParameterDescription( "out", "XML filename where the statistics are saved for future reuse." );
MandatoryOff("out");
// Doc example parameter settings
......
......@@ -79,14 +79,14 @@ private:
MandatoryOff("mask");
AddParameter(ParameterType_Filename, "svm", "SVM Model file");
SetParameterDescription("svm", "A SVM model file.");
SetParameterDescription("svm", "A SVM model file (*.svm extension, produced by TrainSVMImagesClassifier application).");
AddParameter(ParameterType_Filename, "imstat", "Statistics file");
SetParameterDescription("imstat", "A XML file containing mean and standard deviation to center and reduce samples before classification.");
SetParameterDescription("imstat", "A XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application).");
MandatoryOff("imstat");
AddParameter(ParameterType_OutputImage, "out", "Output Image");
SetParameterDescription( "out", "Output image labeled with class labels");
SetParameterDescription( "out", "Output image containing class labels");
SetParameterOutputImagePixelType( "out", ImagePixelType_uint8);
AddParameter(ParameterType_RAM, "ram", "Available RAM");
......
......@@ -175,7 +175,7 @@ private:
SetDescription("Unsupervised KMeans image classification");
SetDocName("Unsupervised KMeans image classification");
SetDocLongDescription("Performs Unsupervised KMeans image classification.");
SetDocLongDescription("Performs unsupervised KMeans image classification.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......@@ -183,9 +183,9 @@ private:
AddDocTag(Tags::Segmentation);
AddDocTag(Tags::Learning);
AddParameter(ParameterType_InputImage, "in", "Input Image");
SetParameterDescription("in", "Input image filename.");
SetParameterDescription("in", "Input image to classify.");
AddParameter(ParameterType_OutputImage, "out", "Output Image");
SetParameterDescription("out", "Output image filename.");
SetParameterDescription("out", "Output image containing the class indexes.");
AddParameter(ParameterType_RAM, "ram", "Available RAM");
SetDefaultParameterInt("ram", 256);
MandatoryOff("ram");
......@@ -193,14 +193,14 @@ private:
SetParameterDescription("vm", "Validity mask. Only non-zero pixels will be used to estimate KMeans modes.");
MandatoryOff("vm");
AddParameter(ParameterType_Int, "ts", "Training set size");
SetParameterDescription("ts", "Size of the training set.");
SetParameterDescription("ts", "Size of the training set (in pixels).");
SetDefaultParameterInt("ts", 100);
MandatoryOff("ts");
AddParameter(ParameterType_Int, "nc", "Number of classes");
SetParameterDescription("nc", "number of modes, which will be used to generate class membership.");
SetParameterDescription("nc", "Number of modes, which will be used to generate class membership.");
SetDefaultParameterInt("nc", 5);
AddParameter(ParameterType_Int, "maxit", "Maximum number of iterations");
SetParameterDescription("maxit", "Maximum number of iterations.");
SetParameterDescription("maxit", "Maximum number of iterations for the learning step.");
SetDefaultParameterInt("maxit", 1000);
MandatoryOff("maxit");
AddParameter(ParameterType_Float, "ct", "Convergence threshold");
......@@ -208,7 +208,7 @@ private:
SetDefaultParameterFloat("ct", 0.0001);
MandatoryOff("ct");
AddParameter(ParameterType_Filename, "outmeans", "Centroid filename");
SetParameterDescription("outmeans", "save label centroid in txt file.");
SetParameterDescription("outmeans", "Output text file containing centroid positions");
MandatoryOff("outmeans");
......
......@@ -84,13 +84,13 @@ private:
AddDocTag(Tags::Learning);
AddParameter(ParameterType_InputImage, "in", "InputImage");
SetParameterDescription("in", "Input image.");
SetParameterDescription("in", "Input image to classify.");
AddParameter(ParameterType_OutputImage, "out", "OutputImage");
SetParameterDescription("out", "Output classified image.");
SetParameterDescription("out", "Output classified image (each pixel contains the index of its corresponding vector in the SOM).");
AddParameter(ParameterType_InputImage, "vm", "ValidityMask");
SetParameterDescription("vm", "Validity mask");
SetParameterDescription("vm", "Validity mask (only pixels corresponding to a mask value greater than 0 will be used for learning)");
MandatoryOff("vm");
AddParameter(ParameterType_Float, "tp", "TrainingProbability");
......@@ -98,7 +98,7 @@ private:
MandatoryOff("tp");
AddParameter(ParameterType_Int, "ts", "TrainingSetSize");
SetParameterDescription("ts", "Maximum training set size");
SetParameterDescription("ts", "Maximum training set size (in pixels)");
MandatoryOff("ts");
AddParameter(ParameterType_Int, "sl", "StreamingLines");
......@@ -106,7 +106,7 @@ private:
MandatoryOff("sl");
AddParameter(ParameterType_OutputImage, "som", "SOM Map");
SetParameterDescription("som","Self-Organizing Map map");
SetParameterDescription("som","Output image containing the Self-Organizing Map");
MandatoryOff("som");
AddParameter(ParameterType_Int, "sx", "SizeX");
......@@ -118,27 +118,27 @@ private:
MandatoryOff("sy");
AddParameter(ParameterType_Int, "nx", "NeighborhoodX");
SetParameterDescription("nx", "X initial neighborhood of the SOM map");
SetParameterDescription("nx", "X size of the initial neighborhood in the SOM map");
MandatoryOff("nx");
AddParameter(ParameterType_Int, "ny", "NeighborhoodY");
SetParameterDescription("ny", "Y initial neighborhood of the SOM map");
SetParameterDescription("ny", "Y size of the initial neighborhood in the SOM map");
MandatoryOff("nx");
AddParameter(ParameterType_Int, "ni", "NumberIteration");
SetParameterDescription("ni", "Number of iterations of the SOM learning");
SetParameterDescription("ni", "Number of iterations for SOM learning");
MandatoryOff("ni");
AddParameter(ParameterType_Float, "bi", "BetaInit");
SetParameterDescription("bi", "Initial beta value");
SetParameterDescription("bi", "Initial learning coefficient");
MandatoryOff("bi");
AddParameter(ParameterType_Float, "bf", "BetaFinal");
SetParameterDescription("bf", "Final beta value");
SetParameterDescription("bf", "Final learning coefficient");
MandatoryOff("bf");
AddParameter(ParameterType_Float, "iv", "InitialValue");
SetParameterDescription("iv", "Initial value (max weight)");
SetParameterDescription("iv", "Maximum initial neuron weight");
MandatoryOff("iv");
AddParameter(ParameterType_RAM, "ram", "Available RAM");
......
......@@ -133,8 +133,8 @@ private:
SetDescription("Train a SVM classifier from multiple pairs of images and training vector data.");
// 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 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.");
SetDocName("Train SVM classifier from multiple images");
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 both 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 can be set, such as the kernel function which defines the feature space (for example if the kernel is a Gaussian radial basis function, the corresponding feature space is an infinite-dimensional space). To allow some flexibility in the classification, 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 optimized.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......@@ -147,12 +147,12 @@ private:
AddParameter(ParameterType_InputImageList, "io.il", "Input Image List");
SetParameterDescription("io.il", "A list of input images.");
AddParameter(ParameterType_InputVectorDataList, "io.vd", "Vector Data List");
SetParameterDescription("io.vd", "A list of vector data sample used to train the estimator.");
SetParameterDescription("io.vd", "A list of vector data to select the training samples.");
AddParameter(ParameterType_Filename, "io.imstat", "XML image statistics file");
MandatoryOff("io.imstat");
SetParameterDescription("io.imstat", "Filename of an XML file containing mean and standard deviation of input images.");
AddParameter(ParameterType_Filename, "io.out", "Output SVM model");
SetParameterDescription("io.out", "Output SVM model");
SetParameterDescription("io.out", "Output file containing the SVM model estimated");
// Elevation
ElevationParametersHandler::AddElevationParameters(this, "elev");
......@@ -164,11 +164,11 @@ private:
AddParameter(ParameterType_Int, "sample.mt", "Maximum training sample size");
//MandatoryOff("mt");
SetDefaultParameterInt("sample.mt", -1);
SetParameterDescription("sample.mt", "Maximum size of the training sample (default = -1).");
SetParameterDescription("sample.mt", "Maximum size of the training sample list (default = -1).");
AddParameter(ParameterType_Int, "sample.mv", "Maximum validation sample size");
// MandatoryOff("mv");
SetDefaultParameterInt("sample.mv", -1);
SetParameterDescription("sample.mv", "Maximum size of the validation sample (default = -1)");
SetParameterDescription("sample.mv", "Maximum size of the validation sample list (default = -1)");
// AddParameter(ParameterType_Int, "sample.b", "Balance and grow the training set");
// SetParameterDescription("sample.b", "Balance and grow the training set.");
......@@ -176,11 +176,11 @@ private:
AddParameter(ParameterType_Float, "sample.vtr", "training and validation sample ratio");
SetParameterDescription("sample.vtr",
"Ratio between training and validation sample (0.0 = all training, 1.0 = all validation) default = 0.5.");
"Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) default = 0.5.");
SetParameterFloat("sample.vtr", 0.5);
AddParameter(ParameterType_Filename, "sample.vfn", "Name of the discrimination field");
SetParameterDescription("sample.vfn", "Name of the field using to discriminate class in the vector data files.");
SetParameterDescription("sample.vfn", "Name of the field used to discriminate class in the vector data files.");
SetParameterString("sample.vfn", "Class");
//Group SVM
......@@ -193,12 +193,12 @@ private:
AddChoice("svm.k.sigmoid", "Sigmoid");
SetParameterString("svm.k", "linear");
SetParameterDescription("svm.k", "SVM Kernel Type.");
AddParameter(ParameterType_Float, "svm.c", "Control trade off between training errors and forcing rigid margins.");
AddParameter(ParameterType_Float, "svm.c", "Cost parameter C.");
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) to control the trade-off between training errors and forcing rigid margins.");
AddParameter(ParameterType_Empty, "svm.opt", "parameters optimization");
MandatoryOff("svm.opt");
SetParameterDescription("svm.opt", "SVM parameters optimization");
SetParameterDescription("svm.opt", "SVM optimization flag");
// Doc example parameter settings
SetDocExampleParameterValue("io.il", "QB_1_ortho.tif");
......
......@@ -121,10 +121,10 @@ private:
void DoInit()
{
SetName("ValidateSVMImagesClassifier");
SetDescription("Estimate the performance of the SVM model with a new set of validation samples and another image.");
SetDescription("Estimate the performance of the SVM model with a set of images and validation samples.");
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, kappa index and also the precision, recall and F-score of each class.");
SetDocLongDescription("Estimate the performance of the SVM model obtained by the TrainSVMImagesClassifier with a new set of images and validation samples.\n The application asks for images statistics as input (XML file generated with the ComputeImagesStatistics application) and a SVM model (text file) generated with the TrainSVMImagesClassifier 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(" ");
......@@ -134,7 +134,7 @@ private:
AddParameter(ParameterType_InputImageList, "il", "Input Image List");
SetParameterDescription("il", "Input image list filename.");
AddParameter(ParameterType_InputVectorDataList, "vd", "Vector Data List");
SetParameterDescription("vd", "Vector Data of samples used to validate the estimator.");
SetParameterDescription("vd", "List of vector data to select validation samples.");
AddParameter(ParameterType_Filename, "imstat", "XML image statistics file");
MandatoryOff("imstat");
SetParameterDescription("imstat", "Filename of an XML file containing mean and standard deviation of input images.");
......@@ -143,11 +143,11 @@ private:
ElevationParametersHandler::AddElevationParameters(this, "elev");
AddParameter(ParameterType_Filename, "out", "Output filename");
SetParameterDescription("out", "Filename, which contains the performances of the SVM model.");
SetParameterDescription("out", "Output file, which contains the performances of the SVM model.");
MandatoryOff("out");
AddParameter(ParameterType_Filename, "svm", "SVM validation filename");
SetParameterDescription("svm",
"SVM model to validate (given by TrainSVMImagesClassification output for instance).");
"Input SVM model to validate (given by TrainSVMImagesClassification for instance).");
// Doc example parameter settings
SetDocExampleParameterValue("il", "QB_1_ortho.tif");
......
......@@ -82,17 +82,18 @@ private:
SetDescription("Perform optical calibration TOA/TOC (Top Of Atmosphere/Top Of Canopy). Supported sensors: QuickBird, Ikonos, WorldView2, Formosat, Spot5");
// Documentation
SetDocName("Optical calibration");
SetDocLongDescription("The application allows to convert pixel values from DN (for Digital Numbers) to physically interpretable and comparable values.Calibrated values are called surface reflectivity and its values lie in the range [0, 1].\nThe first level is called Top Of Atmosphere (TOA) reflectivity. It takes into account the sensor gain, sensor spectral response and the solar illumination.\nThe second level is called Top Of Canopy (TOC) reflectivity. In addition to sensor gain and solar illumination, it takes into account the optical thickness of the atmosphere, the atmospheric pressure, the water vapor amount, the ozone amount, as well as the composition and amount of aerosol gasses.\nIt is also possible to indicate an AERONET file which contains atmospheric parameters (version 1 and version 2 of Aeronet file are supported.");
SetDocLongDescription("The application allows to convert pixel values from DN (for Digital Numbers) to physically interpretable and comparable values. Calibrated values are called surface reflectivity and its values lie in the range [0, 1].\nThe first level is called Top Of Atmosphere (TOA) reflectivity. It takes into account the sensor gain, sensor spectral response and the solar illumination.\nThe second level is called Top Of Canopy (TOC) reflectivity. In addition to sensor gain and solar illumination, it takes into account the optical thickness of the atmosphere, the atmospheric pressure, the water vapor amount, the ozone amount, as well as the composition and amount of aerosol gasses.\nIt is also possible to indicate an AERONET file which contains atmospheric parameters (version 1 and version 2 of Aeronet file are supported.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("The OTB CookBook");
AddDocTag(Tags::Calibration);
AddParameter(ParameterType_InputImage, "in", "Input Image Filename");
AddParameter(ParameterType_InputImage, "in", "Input");
SetParameterDescription("in", "Input image filename (values in DN)");
AddParameter(ParameterType_OutputImage, "out", "Output Image Filename");
SetParameterDescription("out","Calibrated Image Filename");
AddParameter(ParameterType_OutputImage, "out", "Output");
SetParameterDescription("out","Output calibrated image filename");
AddParameter(ParameterType_RAM, "ram", "Available RAM");
SetDefaultParameterInt("ram", 256);
......@@ -104,12 +105,12 @@ private:
SetParameterString("level", "toa");
AddParameter(ParameterType_Empty, "milli", "Convert to milli reflectance");
SetParameterDescription("milli", "Output milli-reflectance instead of reflectance.\n"
"This allows to put save the image in integer pixel type (in the range [0, 1000] instead of floating point in the range [0, 1]. In order to do that, use this option and set the output pixel type (-out filename uint16 for example)");
SetParameterDescription("milli", "Flag to use milli-reflectance instead of reflectance.\n"
"This allows to save the image with integer pixel type (in the range [0, 1000] instead of floating point in the range [0, 1]. In order to do that, use this option and set the output pixel type (-out filename uint16 for example)");
DisableParameter("milli");
MandatoryOff("milli");
AddParameter(ParameterType_Filename, "rsr", "Relative Spectral Response File");
AddParameter(ParameterType_Filename, "rsr", "Relative Spectral Response File");
std::ostringstream oss;
oss << "Sensor relative spectral response file"<<std::endl;
oss << "By default the application gets these informations in the metadata";
......@@ -126,9 +127,16 @@ private:
AddChoice("atmo.aerosol.desertic", "Desertic");
AddParameter(ParameterType_Float, "atmo.oz", "Ozone Amount");
SetParameterDescription("atmo.oz", "Ozone Amount");
AddParameter(ParameterType_Float, "atmo.wa", "Water Vapor Amount");
SetParameterDescription("atmo.wa", "Water Vapor Amount (in saturation fraction of water)");
AddParameter(ParameterType_Float, "atmo.pressure", "Atmospheric Pressure");
SetParameterDescription("atmo.pressure", "Atmospheric Pressure (in hPa)");
AddParameter(ParameterType_Float, "atmo.opt", "Aerosol Optical Thickness");
SetParameterDescription("atmo.opt", "Aerosol Optical Thickness");
SetDefaultParameterFloat("atmo.oz", 0.);
SetDefaultParameterFloat("atmo.wa", 2.5);
......@@ -140,7 +148,7 @@ private:
MandatoryOff("atmo.pressure");
MandatoryOff("atmo.opt");
AddParameter(ParameterType_Filename, "atmo.aeronet", "Aeronet File");
AddParameter(ParameterType_Filename, "atmo.aeronet", "Aeronet File");
SetParameterDescription("atmo.aeronet","Aeronet file containing atmospheric parameters");
MandatoryOff("atmo.aeronet");
......
......@@ -59,11 +59,17 @@ private:
AddDocTag(Tags::SAR);
AddParameter(ParameterType_ComplexInputImage, "in", "Input Complex Image");
SetParameterDescription("in", "Input complex image");
AddParameter(ParameterType_ComplexOutputImage, "out", "Output Image");
SetParameterDescription("out", "Output calibrated complex image");
AddParameter(ParameterType_RAM, "ram", "Available RAM");
SetDefaultParameterInt("ram", 256);
MandatoryOff("ram");
AddParameter(ParameterType_Empty, "noise", "Disable Noise");
SetParameterDescription("noise", "Flag to disable noise");
MandatoryOff("noise");
// Doc example parameter settings
......
......@@ -52,7 +52,7 @@ private:
// Documentation
SetDocName("Read image information");
SetDocLongDescription("Display informations about the input image like: image size, metadata, projections...");
SetDocLongDescription("Display information about the input image like: image size, metadata, projections...");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......@@ -62,76 +62,97 @@ private:
AddDocTag(Tags::Meta);
AddParameter(ParameterType_InputImage, "in", "Input Image");
SetParameterDescription("in", "Input image to analyse");
AddParameter(ParameterType_Empty, "keywordlist", "Display the OSSIM keywordlist");
SetParameterDescription("keywordlist", "Output the OSSIM keyword list.\n" "It contains metadata informations (sensor model, geometry ). Informations are store in keyword list (pairs of key/value)");
SetParameterDescription("keywordlist", "Output the OSSIM keyword list. It contains metadata information (sensor model, geometry ). Informations are stored in keyword list (pairs of key/value)");
DisableParameter("keywordlist");
MandatoryOff("keywordlist");
//Create output parameters to store image informations
AddParameter(ParameterType_Int,"sizex","Size X");
SetParameterDescription("sizex","X size (in pixels)");
SetParameterRole("sizex", Role_Output);
AddParameter(ParameterType_Int,"sizey","Size Y");
SetParameterDescription("sizey","Y size (in pixels)");
SetParameterRole("sizey", Role_Output);
AddParameter(ParameterType_Float,"spacingx","Pixel Size X");
SetParameterDescription("spacingx", "Pixel size along X (in physical units)");
SetParameterRole("spacingx", Role_Output);
AddParameter(ParameterType_Float,"spacingy","Pixel Size Y");
SetParameterDescription("spacingy", "Pixel size along Y (in physical units)");
SetParameterRole("spacingy", Role_Output);
AddParameter(ParameterType_Float,"estimatedgroundspacingx","Estimated ground spacing X (in meters)");
AddParameter(ParameterType_Float,"estimatedgroundspacingx","Estimated ground spacing X");
SetParameterDescription("estimatedgroundspacingx", "Estimated ground spacing along X (in meters).");
SetParameterRole("estimatedgroundspacingx", Role_Output);
AddParameter(ParameterType_Float,"estimatedgroundspacingy","Estimated ground spacing Y (in meters)");
AddParameter(ParameterType_Float,"estimatedgroundspacingy","Estimated ground spacing Y");
SetParameterDescription("estimatedgroundspacingy", "Estimated ground spacing along Y (in meters).");
SetParameterRole("estimatedgroundspacingy", Role_Output);
AddParameter(ParameterType_Int,"numberbands","Number Of Bands");
SetParameterDescription("numberbands", "Number of bands");
SetParameterRole("numberbands", Role_Output);
AddParameter(ParameterType_String,"sensor","Sensor id");
SetParameterDescription("sensor", "Sensor identifier");
SetParameterRole("sensor", Role_Output);
AddParameter(ParameterType_String,"id","Image id");
SetParameterDescription("id", "Image identifier");
SetParameterRole("id", Role_Output);
AddParameter(ParameterType_String,"time","Acquisition time");
SetParameterDescription("time", "Acquisition time.");
SetParameterRole("time", Role_Output);
AddParameter(ParameterType_Float,"ullat","Upper left lattitude");
SetParameterDescription("ullat", "Lattitude of the upper left corner.");
SetParameterRole("ullat", Role_Output);
SetDefaultParameterFloat("ullat", 0);
AddParameter(ParameterType_Float,"ullon","Upper left longitude");
SetParameterDescription("ullon", "Longitude of the upper left corner.");
SetParameterRole("ullon", Role_Output);
SetDefaultParameterFloat("ullon", 0);
AddParameter(ParameterType_Float,"urlat","Upper right lattitude");
SetParameterDescription("urlat", "Lattitude of the upper right corner.");
SetParameterRole("urlat", Role_Output);
SetDefaultParameterFloat("urlat", 0);
AddParameter(ParameterType_Float,"urlon","Upper right longitude");
SetParameterDescription("urlon", "Longitude of the upper right corner.");
SetParameterRole("urlon", Role_Output);
SetDefaultParameterFloat("urlon", 0);
AddParameter(ParameterType_Float,"lrlat","Lower right lattitude");
SetParameterDescription("lrlat", "Lattitude of the lower right corner.");
SetParameterRole("lrlat", Role_Output);
SetDefaultParameterFloat("lrlat", 0);
AddParameter(ParameterType_Float,"lrlon","Lower right longitude");
SetParameterDescription("lrlon", "Longitude of the lower right corner.");
SetParameterRole("lrlon", Role_Output);
SetDefaultParameterFloat("lrlon", 0);
AddParameter(ParameterType_Float,"lllat","Lower left lattitude");
SetParameterDescription("lllat", "Lattitude of the lower left corner.");
SetParameterRole("lllat", Role_Output);
SetDefaultParameterFloat("lllat", 0);
AddParameter(ParameterType_Float,"lllon","Lower left longitude");
SetParameterDescription("lllon", "Longitude of the lower left corner.");
SetParameterRole("lllon", Role_Output);
SetDefaultParameterFloat("lllon", 0);
AddParameter(ParameterType_String,"town","Main town near center of image");
AddParameter(ParameterType_String,"town", "Nearest town");
SetParameterDescription("town", "Main town near center of image");
SetParameterRole("town", Role_Output);
AddParameter(ParameterType_String,"country","Country of the image");
AddParameter(ParameterType_String,"country","Country");
SetParameterDescription("country", "Country of the image");
SetParameterRole("country", Role_Output);
AddParameter(ParameterType_Group, "rgb", "Default RGB Display");
......@@ -152,10 +173,12 @@ private:
SetDefaultParameterInt("rgb.b", 3);
SetParameterRole("rgb.b", Role_Output);
AddParameter(ParameterType_String,"projectionref","Projection Coordinate System");
AddParameter(ParameterType_String,"projectionref","Projection");
SetParameterDescription("projectionref", "Projection Coordinate System");
SetParameterRole("projectionref", Role_Output);
AddParameter(ParameterType_String,"keyword","Image Keywordlist");
AddParameter(ParameterType_String,"keyword","Keywordlist");
SetParameterDescription("keyword","Image keyword list");
SetParameterRole("keyword", Role_Output);
AddParameter(ParameterType_Group, "gcp", "Ground Control Points informations");
......@@ -163,20 +186,28 @@ private:
SetParameterRole("gcp", Role_Output);
AddParameter(ParameterType_Int, "gcp.count", "GCPs Number");
SetParameterDescription("gcp.count","Number of GCPs");
SetParameterDescription("gcp.count", "Number of GCPs");
SetDefaultParameterInt("gcp.count", 0);
SetParameterRole("gcp.count", Role_Output);
AddParameter(ParameterType_String,"gcp.proj","GCP Projection System");
AddParameter(ParameterType_String,"gcp.proj", "GCP Projection");
SetParameterDescription("gcp.proj", "Projection Coordinate System for GCPs");
SetParameterRole("gcp.proj", Role_Output);
AddParameter(ParameterType_StringList,"gcp.ids","GCPs Id");
AddParameter(ParameterType_StringList,"gcp.ids", "GCPs Id");
SetParameterDescription("gcp.ids", "GCPs identifier");
SetParameterRole("gcp.ids", Role_Output);
AddParameter(ParameterType_StringList,"gcp.info","GCPs Info");
AddParameter(ParameterType_StringList,"gcp.info", "GCPs Info");
SetParameterDescription("gcp.info", "GCPs Information");
SetParameterRole("gcp.info", Role_Output);
AddParameter(ParameterType_StringList,"gcp.imcoord","GCPs Image Coordinates");
AddParameter(ParameterType_StringList,"gcp.imcoord", "GCPs Image Coordinates");
SetParameterDescription("gcp.imcoord", "GCPs Image coordinates");
SetParameterRole("gcp.imcoord", Role_Output);
AddParameter(ParameterType_StringList,"gcp.geocoord","GCPs Geographic Coordinates");
AddParameter(ParameterType_StringList, "gcp.geocoord", "GCPs Geographic Coordinates");
SetParameterDescription("gcp.geocoord", "GCPs Geographic Coordinates");
SetParameterRole("gcp.geocoord", Role_Output);
// Doc example parameter settings
......
......@@ -67,28 +67,37 @@ private:
AddDocTag(Tags::Filter);
AddParameter(ParameterType_InputImage, "in", "Input Image");
SetParameterDescription("in", "Input image to filter.");
SetParameterDescription("in", "Input image to smooth.");
AddParameter(ParameterType_OutputImage, "out", "Output Image");
SetParameterDescription("out", "Filtered image.");
SetParameterDescription("out", "Output smoothed image.");
AddParameter(ParameterType_RAM, "ram", "Available RAM");
SetDefaultParameterInt("ram", 256);
MandatoryOff("ram");
AddParameter(ParameterType_Choice, "type", "Smoothing Type");
SetParameterDescription("type", "smoothing kernel to apply : mean, gaussian, anisotropric diffusion.");
SetParameterDescription("type", "Smoothing kernel to apply");
AddChoice("type.mean", "Mean");
AddParameter(ParameterType_Radius, "type.mean.radius", "Radius");
SetParameterDescription("type.mean.radius", "Mean radius (in pixels)");
SetDefaultParameterInt("type.mean.radius", 2);
AddChoice("type.gaussian", "Gaussian");
AddParameter(ParameterType_Radius, "type.gaussian.radius", "Radius");
SetParameterDescription("type.gaussian.radius", "Gaussian radius (in pixels)");
SetDefaultParameterInt("type.gaussian.radius", 2);
AddChoice("type.anidif", "Anisotropic Diffusion");
AddParameter(ParameterType_Float, "type.anidif.timestep", "Time Step");
SetParameterDescription("type.anidif.timestep", "Diffusion equation time step");
AddParameter(ParameterType_Int, "type.anidif.nbiter", "Nb Iterations");
SetParameterDescription("type.anidif.nbiter", "Number of iterations");
SetDefaultParameterFloat("type.anidif.timestep", 0.125);
SetDefaultParameterInt("type.anidif.nbiter", 10);
......
......@@ -54,7 +54,7 @@ private:
SetDescription("Split a N multiband image into N images");
SetDocName("Split Image");
SetDocLongDescription("This application splits a N multiband image into N images. The output images filename will be generated from the output one. Thus if the input image has 2 channels, and the user has set ad output outimage.tif, the generated images will be outimage_0.tif and outimage_1.tif");
SetDocLongDescription("This application splits a N-bands image into N mono-band images. The output images filename will be generated from the output parameter. Thus if the input image has 2 channels, and the user has set an output outimage.tif, the generated images will be outimage_0.tif and outimage_1.tif");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......@@ -62,11 +62,11 @@ private:
AddDocTag(Tags::Manip);
AddParameter(ParameterType_InputImage, "in", "Input Image");
SetParameterDescription("in","Input image filename.");
SetParameterDescription("in","Input multiband image filename.");
AddParameter(ParameterType_Filename, "out", "Output Image");
SetParameterDescription("out",
"Will be used to get the prefix and the extension of the output images to write");
"Output filename that will be used to get the prefix and the extension of the output images to write");
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 extracts the VectorData features belonging to a region specified by the support image envelope");
SetDocLongDescription("This application extracts the vector data features belonging to a region specified by the support image envelope");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......@@ -79,9 +79,16 @@ private:
AddDocTag(Tags::Vector);
AddParameter(ParameterType_Group,"io","Input and output data");
SetParameterDescription("io", "Group containing input and output parameters");
AddParameter(ParameterType_InputVectorData, "io.vd", "Input Vector data");
SetParameterDescription("io.vd", "Input vector data");
AddParameter(ParameterType_InputImage, "io.in", "Support image");
SetParameterDescription("io.in", "Support image that specifies the extracted region");
AddParameter(ParameterType_OutputVectorData,"io.out","Output Vector data");
SetParameterDescription("io.out", "Output extracted vector data");
// Elevation
ElevationParametersHandler::AddElevationParameters(this, "elev");
......
......@@ -47,8 +47,8 @@ private:
// Documentation
SetDocName("Vector data set field");
SetDocLongDescription("Set a specified field to a specified value on all features of a vector data (Note: doesn't work with KML files yet)");
SetDocLimitations("None");
SetDocLongDescription("Set a specified field to a specified value on all features of a vector data.");
SetDocLimitations("Doesn't work with KML files yet");
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 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.");
SetDocLongDescription("This application performs a transformation of an input vector data transforming each vertex in the vector data. The applied transformation manages translation, rotation and scale, and can be centered or not.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......@@ -65,18 +65,22 @@ private:
AddDocTag(Tags::Vector);
AddParameter(ParameterType_InputVectorData, "vd", "Input Vector data");
SetParameterDescription("vd", "Input vector data to transform");
AddParameter(ParameterType_OutputVectorData,"out","Output Vector data");
SetParameterDescription("out", "Output transformed vector data");
AddParameter(ParameterType_InputImage, "in", "Support image");
SetParameterDescription("in","Image needed as a support of the VectorData");
SetParameterDescription("in","Image needed as a support to the vector data");
// Transform Group
AddParameter(ParameterType_Group, "transform", "Transform parameters");
SetParameterDescription("transform", "Group of parameters to define the transform");
AddParameter(ParameterType_Float, "transform.tx", "Translation X");
SetParameterDescription("transform.tx","Translation in the X direction");
SetParameterDescription("transform.tx","Translation in the X direction (in pixels)");
AddParameter(ParameterType_Float, "transform.ty", "Translation Y");
SetParameterDescription("transform.ty","Translation in the Y direction");
SetParameterDescription("transform.ty","Translation in the Y direction (in pixels)");
SetDefaultParameterFloat("transform.tx", 0.);
SetDefaultParameterFloat("transform.ty", 0.);
......@@ -85,9 +89,9 @@ private:
SetDefaultParameterFloat("transform.ro", 0.);
AddParameter(ParameterType_Float, "transform.centerx", "Center X");
SetParameterDescription("transform.centerx","The first coordinate of the rotation center");
SetParameterDescription("transform.centerx","X coordinate of the rotation center (in physical units)");
AddParameter(ParameterType_Float, "transform.centery", "Center Y");
SetParameterDescription("transform.centery","The second coordinate of the rotation center");
SetParameterDescription("transform.centery","Y coordinate of the rotation center (in physical units)");
SetDefaultParameterFloat("transform.centerx", 0.);
SetDefaultParameterFloat("transform.centery", 0.);
......
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