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

CMake/CTestCustom.cmake.in

@@ -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.*"

Documentation/SoftwareGuide/Latex/Classification.tex

@@ -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

Documentation/SoftwareGuide/Latex/ImageRegistration.tex

@@ -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.

Documentation/SoftwareGuide/Latex/Optimizers.tex

@@ -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

Documentation/SoftwareGuide/Latex/WriteAnApplication.tex

@@ -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}

Documentation/SoftwareGuide/SoftwareGuideConfiguration.tex.in

@@ -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}{

Examples/Application/ApplicationExample.cxx

@@ -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

Examples/Classification/BayesianPluginClassifier.cxx

@@ -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

Examples/Classification/ExpectationMaximizationMixtureModelEstimator.cxx

@@ -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.
 //

Examples/DataRepresentation/Image/ImageListExample.cxx

@@ -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
 

Examples/DisparityMap/FineRegistrationImageFilterExample.cxx

@@ -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).
   //

Examples/FeatureExtraction/ExtractSegmentsExample.cxx

@@ -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

Examples/FeatureExtraction/LineSegmentDetectorExample.cxx

@@ -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

Examples/FeatureExtraction/RightAngleDetectionExample.cxx

@@ -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

Examples/FeatureExtraction/TouziEdgeDetectorExample.cxx

@@ -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

Examples/Iterators/ImageSliceIteratorWithIndex.cxx

@@ -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

Examples/Iterators/NeighborhoodIterators5.cxx

@@ -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
 

Examples/Learning/SEMModelEstimatorExample.cxx

@@ -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.
 //

Examples/Learning/SOMClassifierExample.cxx

@@ -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

Examples/Learning/SOMExample.cxx

@@ -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

Examples/Segmentation/VectorConfidenceConnected.cxx

@@ -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()}

Examples/Simulation/LAIAndPROSAILToSensorResponse.cxx

@@ -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