Commit 4cf3f1bf authored by Rashad Kanavath's avatar Rashad Kanavath

Merge remote-tracking branch 'origin/develop' into update_pkg

parents 2a5a6904 fa157c55
......@@ -86,6 +86,7 @@ set(CTEST_CUSTOM_WARNING_EXCEPTION
# Ignore clang's summary warning, assuming prior text has matched some
# other warning expression:
"[0-9,]+ warnings? generated."
"cl...Command.line.warning.D9025"
".*include.opencv2.*warning.*"
".*include.opencv2.*note.*"
".*include.kml.*warning.*"
......
......@@ -428,7 +428,7 @@ with:
\end{verbatim}
This simple example shows that the classical dot product is already
implemented
into \subdoxygen{otb}{GenericKernelFunctorBase}{dot()} as a protected
into \code{otb::GenericKernelFunctorBase::dot()} as a protected
function.
\item The \code{Update()} function which synchronizes local variables and
their
......
......@@ -502,9 +502,9 @@ describe the main characteristics of such transforms.
%% The first case can be solved with a closed form solution when we are dealing
%% with a Rigid or an Affine Transform~\cite{Horn1987}. This is done in ITK with
%% the class \doxygen{LandmarkBasedTransformInitializer}. If we are interested in
%% the class \doxygen{itk}{LandmarkBasedTransformInitializer}. If we are interested in
%% a deformable Transformation then the problem can be solved with the
%% \doxygen{KernelTransform} family of classes, which includes Thin Plate Splines
%% \doxygen{itk}{KernelTransform} family of classes, which includes Thin Plate Splines
%% among others~\cite{Rohr2001}. In both circumstances, the availability o f
%% correspondences between the points make possible to apply a straight forward
%% solution to the problem.
......
......@@ -118,7 +118,7 @@ optimizer in \code{netlib}. Details on this optimizer can be found
in~\cite{Byrd1995,Zhu1997} (\doxygen{itk}{LBFGSBOptimizer}).
\item \textbf{One Plus One Evolutionary}: Strategy that simulates the
biological evolution of a set of samples in the search space (\doxygen{itk}{OnePlusOneEvolutionaryOptimizer.}). Details on this optimizer can be
biological evolution of a set of samples in the search space (\doxygen{itk}{OnePlusOneEvolutionaryOptimizer}). Details on this optimizer can be
found in~\cite{Styner2000}.
\item \textbf{Regular Step Gradient Descent}: Advances parameters in the
......
......@@ -30,7 +30,7 @@ divisions (see section \ref{sec:appParam}).
\section{Architecture of the class}
\label{sec:appArchitecture}
Every application derive from the class \doxygen{otb}{Wrapper::Application}. An
Every application derive from the class \subdoxygen{otb}{Wrapper}{Application}. An
application can't be templated. It must contain the standard class typedefs and
a call to the \code{OTB\_APPLICATION\_EXPORT} macro.
......@@ -53,8 +53,8 @@ contain the following actions:
\item Fill the documentation and give an example
\item Declare all the parameters
\item Define the documentation link:
\item for contrib application use SetDocLink("\textit{docLink}") function defined in \doxygen{otb}{Wrapper::Application}
\item for official application use SetOfficialDocLink() function defined in \doxygen{otb}{Wrapper::Application}
\item for contrib application use SetDocLink("\textit{docLink}") function defined in \subdoxygen{otb}{Wrapper}{Application}
\item for official application use SetOfficialDocLink() function defined in \subdoxygen{otb}{Wrapper}{Application}
\end{itemize}
......@@ -97,7 +97,7 @@ created and updated.
\subsection{Parameters selection}
\label{sec:appParam}
In the new application framework, every input, output or parameter derive from
\doxygen{otb}{Wrapper::Parameter}. The application engine supplies the following
\subdoxygen{otb}{Wrapper}{Parameter}. The application engine supplies the following
types of parameters:
\begin{itemize}
\item \code{ParameterType\_Empty} : parameter without value (can be used to represent
......@@ -136,7 +136,7 @@ can be used to set a parameter optional or test if the user has modified the par
are created in the \code{DoInit()} method, then the framework will set their value (either by parsing the
command line or reading the graphical user interface). The \code{DoExecute()} method is called when all
mandatory parameters have been given a value, which can be obtained with "Get" methods defined in
\doxygen{otb}{Wrapper::Application}. Parameters are set mandatory (or not) using \code{MandatoryOn(key)} method (\code{MandatoryOff(key)}).
\subdoxygen{otb}{Wrapper}{Application}. Parameters are set mandatory (or not) using \code{MandatoryOn(key)} method (\code{MandatoryOff(key)}).
Some functions are specific to numeric parameters, such as \code{SetMinimumParameterIntValue(key,value)}
or \code{SetMaximumParameterFloatValue(key,value)}. By default, numeric parameters are treated as inputs.
......@@ -158,7 +158,7 @@ that you want to chain in order to build a third application C. Rather than writ
the code of A and B, you would like to re-use applications A and B. This plain example will be
re-used in this section for explanations.
A dedicated class \doxygen{otb}{Wrapper::CompositeApplication} has been added to create such applications.
A dedicated class \subdoxygen{otb}{Wrapper}{CompositeApplication} has been added to create such applications.
If you derive this class to implement application C, you will be able to create a composite application.
\subsection{Creating internal applications}
......
......@@ -11,12 +11,17 @@
}{
\href{http://www.orfeo-toolbox.org/doxygen/class#1_1_1#2.html}{\code{#1::#2}}
}{
\href{http://www.itk.org/Doxygen46/html/class#1_1_1#2.html}{\code{#1::#2}}
\href{http://www.itk.org/Doxygen/html/class#1_1_1#2.html}{\code{#1::#2}}
}}
% Define command to make reference to on-line Doxygen documentation
\newcommand{\subdoxygen}[3]{
\href{http://www.orfeo-toolbox.org/doxygen/class#1_1_1#2_1_1#3.html}{\code{#1::#2::#3}}}
\ifthenelse{ \equal{#1}{otb}{
\href{http://www.orfeo-toolbox.org/doxygen/class#1_1_1#2_1_1#3.html}{\code{#1::#2::#3}}
}{
\href{http://www.itk.org/Doxygen/html/class#1_1_1#2_1_1#3.html}{\code{#1::#2::#3}}
}
}
% Define command for the standard comment introducing classes with similar functionalities
\newcommand{\relatedClasses}{
......
......@@ -28,7 +28,7 @@
// Software Guide : BeginLatex
// This example illustrates the creation of an application.
// A new application is a class, which derives from \doxygen{otb}{Wrapper::Application} class.
// A new application is a class, which derives from \subdoxygen{otb}{Wrapper}{Application} class.
// We start by including the needed header files.
//
// Software Guide : EndLatex
......@@ -147,7 +147,7 @@ private:
// Software Guide : BeginLatex
// Application parameters declaration is done using \code{AddParameter()} method.
// \code{AddParameter()} requires Parameter type, its name and description.
// \doxygen{otb}{Wrapper::Application} class contains methods to set parameters characteristics.
// \subdoxygen{otb}{Wrapper}{Application} class contains methods to set parameters characteristics.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
......
......@@ -32,7 +32,7 @@
// all the components of the classifier system and the data flow. This system
// differs with the previous k-means clustering algorithms in several
// ways. The biggest difference is that this classifier uses the
// \subdoxygen{itk}{Statistics}{itkGaussianMembershipFunction} as membership functions
// \subdoxygen{itk}{Statistics}{GaussianMembershipFunction} as membership functions
// instead of the \subdoxygen{itk}{Statistics}{EuclideanDistanceMetric}. Since the
// membership function is different, the membership function requires a
// different set of parameters, mean vectors and covariance matrices. We
......
......@@ -74,7 +74,7 @@
// class assumes that such type of components.
//
// In the following example we use the \subdoxygen{itk}{Statistics}{ListSample} as
// the sample (test and training). The \subdoxygen{itk}{Vector} is our measurement
// the sample (test and training). The \doxygen{itk}{Vector} is our measurement
// vector class. To store measurement vectors into two separate sample
// container, we use the \subdoxygen{itk}{Statistics}{Subsample} objects.
//
......
......@@ -21,7 +21,7 @@
// Software Guide : BeginLatex
//
// This example illustrates the use of the \subdoxygen{otb}{ImageList}
// This example illustrates the use of the \doxygen{otb}{ImageList}
// class. This class provides the functionnalities needed in order to
// integrate image lists as data objects into the OTB
// pipeline. Indeed, if a \code{std::list< ImageType >} was used, the
......@@ -29,10 +29,10 @@
// effects.
//
// In this example, we will only present the basic operations which
// can be applied on an \subdoxygen{otb}{ImageList} object.
// can be applied on an \doxygen{otb}{ImageList} object.
//
// The first thing required to read an image from a file is to include
// the header file of the \subdoxygen{otb}{ImageFileReader} class.
// the header file of the \doxygen{otb}{ImageFileReader} class.
//
// Software Guide : EndLatex
......@@ -70,7 +70,7 @@ int main(int itkNotUsed(argc), char * argv[])
// Software Guide : BeginLatex
//
// We can now define the type for the image list. The
// \subdoxygen{otb}{ImageList} class is templated over the type of image
// \doxygen{otb}{ImageList} class is templated over the type of image
// contained in it. This means that all images in a list must have the
// same type.
//
......@@ -146,7 +146,7 @@ int main(int itkNotUsed(argc), char * argv[])
//
// Also, iterator classes are defined in order to have an efficient
// mean of moving through the list. Finally, the
// \subdoxygen{otb}{ImageListToImageListFilter} is provided in order
// \doxygen{otb}{ImageListToImageListFilter} is provided in order
// to implement filter which operate on image lists and produce image lists.
// Software Guide : EndLatex
......
......@@ -125,7 +125,7 @@ int main(int argc, char** argv)
// Software Guide : BeginLatex
//
// Now, we declare and instantiate the \doxygen{otb}{FineCorrelationImageFilter} which is going to perform the registration:
// Now, we declare and instantiate the \doxygen{otb}{FineRegistrationImageFilter} which is going to perform the registration:
//
// Software Guide : EndLatex
......@@ -188,7 +188,7 @@ int main(int argc, char** argv)
// Software Guide : BeginLatex
//
// The default matching metric used by the \doxygen{FineRegistrationImageFilter} is standard correlation.
// The default matching metric used by the \doxygen{otb}{FineRegistrationImageFilter} is standard correlation.
// However, we may also use any other image-to-image metric provided by ITK. For instance, here is how we
// would use the \doxygen{itk}{MutualInformationImageToImageMetric} (do not forget to include the proper header).
//
......
......@@ -99,7 +99,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// An \doxygen{ImageFileReader} class is also instantiated in order to read
// An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file.
//
// Software Guide : EndLatex
......@@ -110,7 +110,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// An \doxygen{ImageFileWriter} is instantiated in order to write the
// An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.
//
// Software Guide : EndLatex
......
......@@ -156,7 +156,7 @@ int main(int argc, char * argv[])
//
// Before calling the \code{Update()} method of the writer in order to
// trigger the pipeline execution, we call the
// \doxygen{GenerateOutputInformation()} of the reader, so the LSD
// \code{GenerateOutputInformation()} of the reader, so the LSD
// filter gets the information about image size and spacing.
//
// Software Guide : EndLatex
......
......@@ -194,7 +194,7 @@ int main(int argc, char * argv[])
//
// Before calling the \code{Update()} method of the writer in order to
// trigger the pipeline execution, we call the
// \doxygen{GenerateOutputInformation()} of the reader, so the
// \code{GenerateOutputInformation()} of the reader, so the
// filter gets the information about image size and spacing.
//
// Software Guide : EndLatex
......
......@@ -105,7 +105,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// An \doxygen{ImageFileReader} class is also instantiated in order to read
// An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file.
//
// Software Guide : EndLatex
......@@ -116,7 +116,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// An \doxygen{ImageFileWriter} is instantiated in order to write the
// An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.
//
// Software Guide : EndLatex
......
......@@ -24,8 +24,8 @@
//
// \index{Iterators!and image slices}
//
// The \doxygen{ImageSliceIteratorWithIndex} class is an extension of
// \doxygen{ImageLinearIteratorWithIndex} from iteration along lines to
// The \doxygen{itk}{ImageSliceIteratorWithIndex} class is an extension of
// \doxygen{itk}{ImageLinearIteratorWithIndex} from iteration along lines to
// iteration along both lines \emph{and planes} in an image.
// A \emph{slice} is a 2D
// plane spanned by two vectors pointing along orthogonal coordinate axes. The
......
......@@ -46,9 +46,9 @@
// 2, stride = 3, end = 8)}, that represents the neighborhood offsets $(1,
// -1)$, $(1, 0)$, $(1, 1)$ (see Figure~\ref{fig:NeighborhoodIteratorFig2}). If we
// pass this slice as an extra argument to the
// \doxygen{NeighborhoodInnerProduct} function, then the inner product is taken
// \doxygen{itk}{NeighborhoodInnerProduct} function, then the inner product is taken
// only along that slice. This ``sliced'' inner product with a 1D
// \doxygen{DerivativeOperator} gives the desired derivative.
// \doxygen{itk}{DerivativeOperator} gives the desired derivative.
//
// The previous separable Gaussian filtering example can be rewritten using
// slices and slice-based inner products. In general, slice-based processing
......@@ -57,7 +57,7 @@
// Section~\ref{sec:NeighborhoodExample4} becomes impractical or inefficient.
// Good examples of slice-based neighborhood processing can be found in any of
// the ND anisotropic diffusion function objects, such as
// \doxygen{CurvatureNDAnisotropicDiffusionFunction}.
// \doxygen{itk}{CurvatureNDAnisotropicDiffusionFunction}.
//
// Software Guide : EndLatex
......
......@@ -32,11 +32,11 @@
// \doxygen{otb}{SEMClassifier}. This class performs a stochastic version
// of the EM algorithm, but instead of inheriting from
// \doxygen{itk}{ExpectationMaximizationMixtureModelEstimator}, we chose to
// inherit from \subdoxygen{itk}{Statistics}{ListSample< TSample >},
// inherit from \subdoxygen{itk}{Statistics}{ListSample},
// in the same way as \doxygen{otb}{SVMClassifier}.
//
// The program begins with \doxygen{otb}{VectorImage} and outputs
// \doxygen{itb}{Image}. Then appropriate header files have to be included:
// \doxygen{otb}{Image}. Then appropriate header files have to be included:
//
// Software Guide : EndLatex
......@@ -144,7 +144,7 @@ int main(int argc, char * argv[])
// When an initial segmentation is available, the classifier may use it
// as image (of type \code{OutputImageType}) or as a
// \doxygen{itk}{SampleClassifier} result (of type
// \subdoxygen{itk}{Statistics}{MembershipSample< SampleType >}).
// \subdoxygen{itk}{Statistics}{MembershipSample}).
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
......@@ -205,7 +205,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// The segmentation may outputs a result of type
// \subdoxygen{itk}{Statistics}{MembershipSample< SampleType >} as it is the
// \subdoxygen{itk}{Statistics}{MembershipSample} as it is the
// case for the \doxygen{otb}{SVMClassifier}. But when using
// \code{GetOutputImage} the output is directly an Image.
//
......
......@@ -77,7 +77,7 @@ int main(int argc, char* argv[])
// As for the SOM learning step, we must define the types for the
// \code{otb::SOMMap}, and therefore, also for the distance to be
// used. We will also define the type for the SOM reader, which is
// actually an \subdoxygen{otb}{ImageFileReader} which the appropriate
// actually an \doxygen{otb}{ImageFileReader} which the appropriate
// image type.
//
// Software Guide : EndLatex
......@@ -94,7 +94,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
// The classification will be performed by the
// \subdoxygen{otb}{SOMClassifier}, which, as most of the
// \doxygen{otb}{SOMClassifier}, which, as most of the
// classifiers, works on
// \subdoxygen{itk}{Statistics}{ListSample}s. In order to be able
// to perform an image classification, we will need to use the
......@@ -218,7 +218,7 @@ int main(int argc, char* argv[])
//
// Software Guide : BeginLatex
//
// We also declare an \subdoxygen{itk}{ImageRegionIterator} in order
// We also declare an \doxygen{itk}{ImageRegionIterator} in order
// to fill the output image with the class labels.
//
// Software Guide : EndLatex
......
......@@ -118,7 +118,7 @@ int main(int itkNotUsed(argc), char* argv[])
//
// Software Guide : BeginLatex
//
// We can now define the type for the map. The \subdoxygen{otb}{SOMMap}
// We can now define the type for the map. The \doxygen{otb}{SOMMap}
// class is templated over the neuron type -- \code{PixelType} here
// --, the distance type and the number of dimensions. Note that the
// number of dimensions of the map could be different from the one of
......
......@@ -42,12 +42,13 @@
// This example illustrates the use of the confidence connected concept
// applied to images with vector pixel types. The confidence connected
// algorithm is implemented for vector images in the class
// \doxygen{VectorConfidenceConnected}. The basic difference between the
// \doxygen{itk}{VectorConfidenceConnectedImageFilter}. The basic difference
// between the
// scalar and vector version is that the vector version uses the covariance
// matrix instead of a variance, and a vector mean instead of a scalar mean.
// The membership of a vector pixel value to the region is measured using the
// Mahalanobis distance as implemented in the class
// \subdoxygen{Statistics}{MahalanobisDistanceThresholdImageFunction}.
// \subdoxygen{itk}{Statistics}{MahalanobisDistanceThresholdImageFunction}.
//
// Software Guide : EndLatex
......@@ -108,7 +109,7 @@ int main( int argc, char *argv[] )
// Software Guide : BeginLatex
//
// We now declare the type of the region growing filter. In this case it
// is the \doxygen{VectorConfidenceConnectedImageFilter}.
// is the \doxygen{itk}{VectorConfidenceConnectedImageFilter}.
//
// Software Guide : EndLatex
......@@ -211,7 +212,7 @@ int main( int argc, char *argv[] )
// anatomical structure to be segmented. A small neighborhood around the
// seed point will be used to compute the initial mean and standard
// deviation for the inclusion criterion. The seed is passed in the form
// of a \doxygen{Index} to the \code{SetSeed()} method.
// of a \doxygen{itk}{Index} to the \code{SetSeed()} method.
//
// \index{itk::Vector\-Confidence\-Connected\-Image\-Filter!SetSeed()}
// \index{itk::Vector\-Confidence\-Connected\-Image\-Filter!SetInitialNeighborhoodRadius()}
......
......@@ -422,7 +422,7 @@ protected:
// Software Guide : BeginLatex
//
// \code{TernaryFunctorImageFilterWithNBands} class is defined here.
// This class inherits form \doxygen{itk::TernaryFunctorImageFilter} with additional nuber of band parameters.
// This class inherits form \doxygen{itk}{TernaryFunctorImageFilter} with additional nuber of band parameters.
// It's implementation is done to process Label, LAI, and mask image with Simulation functor.
// Software Guide : EndLatex
......@@ -595,7 +595,7 @@ int main(int argc, char *argv[])
// Software Guide : BeginLatex
//
// Acquisition parameters are loaded using text file. A detailed definition of acquisition parameters can
// be found in class \doxygen{SailModel}.
// be found in class \doxygen{otb}{SailModel}.
//
// Software Guide : EndLatex
......
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