fixed PDF generation

diff --git a/doc/cxcore_clustering_search.tex b/doc/cxcore_clustering_search.tex
index 6e88502..b15d13d 100644 (file)
--- a/doc/cxcore_clustering_search.tex
+++ b/doc/cxcore_clustering_search.tex
@@ -293,7 +293,11 @@ This section documents OpenCV's interface to the FLANN\footnote{http://people.cs
 contains a collection of algorithms optimized for fast nearest neighbor search in large datasets and for high dimensional features. More 
 information about FLANN can be found in \cite{muja_flann_2009}.
 
+\ifplastex
 \cvclass{cv::flann::Index_}
+\else
+\subsubsection{cv::flann::Index\_}\label{cvflann.Index}
+\fi
 The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built.
 
 \begin{lstlisting}
@@ -345,10 +349,14 @@ namespace flann
 } } // namespace cv::flann
 \end{lstlisting}
 
+\ifplastex
 \cvCppFunc{cv::flann::Index_<T>::Index_}
+\else
+\subsubsection{cvflann::Index\_$<T>$::Index\_}\label{cvflann.Index.Index}
+\fi
 Constructs a nearest neighbor search index for a given dataset.
 
-\cvdefCpp{Index_<T>::Index_(const Mat\& features, const IndexParams\& params);}
+\cvdefCpp{Index\_<T>::Index\_(const Mat\& features, const IndexParams\& params);}
 \begin{description}
 \cvarg{features}{ Matrix of containing the features(points) to index. The size of the matrix is num\_features x feature\_dimensionality and 
 the data type of the elements in the matrix must coincide with the type of the index.}
@@ -439,13 +447,21 @@ struct SavedIndexParams : public IndexParams
 \end{description}
 \end{description}
 
+\ifplastex
 \cvCppFunc{cv::flann::Index_<T>::knnSearch}
+\else
+\subsubsection{cv::flann::Index\_$<T>$::knnSearch}\label{cvflann.Index.knnSearch}
+\fi
 Performs a K-nearest neighbor search for a given query point using the index.
-\cvdefCpp{void Index_<T>::knnSearch(const vector<T>\& query, \par
+\cvdefCpp{
+void Index\_<T>::knnSearch(const vector<T>\& query, \par
                vector<int>\& indices, \par
                vector<float>\& dists, \par
                int knn, \par
-               const SearchParams\& params);}
+               const SearchParams\& params);\newline
+void Index\_<T>::knnSearch(const Mat\& queries,\par
+           Mat\& indices, Mat\& dists,\par
+           int knn, const SearchParams\& params);}
 \begin{description}
 \cvarg{query}{The query point}
 \cvarg{indices}{Vector that will contain the indices of the K-nearest neighbors found. It must have at least knn size.}
@@ -462,64 +478,51 @@ Performs a K-nearest neighbor search for a given query point using the index.
 \end{description}
 \end{description}
 
-\cvCppFunc{cv::flann::Index_<T>::knnSearch}
-Performs a K-nearest neighbor search for multiple query points.
-
-\cvdefCpp{void Index_<T>::knnSearch(const Mat\& queries,\par
-               Mat\& indices, Mat\& dists,\par
-               int knn, const SearchParams\& params);}
-
-\begin{description}
-\cvarg{queries}{The query points, one per row. The type of queries must match the index type.}
-\cvarg{indices}{Indices of the nearest neighbors found }
-\cvarg{dists}{Distances to the nearest neighbors found}
-\cvarg{knn}{Number of nearest neighbors to search for}
-\cvarg{params}{Search parameters}
-\end{description}
-
-
+\ifplastex
 \cvCppFunc{cv::flann::Index_<T>::radiusSearch}
+\else
+\subsubsection{cv::flann::Index\_$<T>$::radiusSearch}\label{cvflann.Index.radiusSearch}
+\fi
 Performs a radius nearest neighbor search for a given query point.
-\cvdefCpp{int Index_<T>::radiusSearch(const vector<T>\& query, \par
+\cvdefCpp{
+int Index\_<T>::radiusSearch(const vector<T>\& query, \par
                  vector<int>\& indices, \par
                  vector<float>\& dists, \par
                  float radius, \par
-                 const SearchParams\& params);}
-\begin{description}
-\cvarg{query}{The query point}
-\cvarg{indices}{Vector that will contain the indices of the points found within the search radius in decreasing order of the distance to the query point. If the number of neighbors in the search radius is bigger than the size of this vector, the ones that don't fit in the vector are ignored. }
-\cvarg{dists}{Vector that will contain the distances to the points found within the search radius}
-\cvarg{radius}{The search radius}
-\cvarg{params}{Search parameters}
-\end{description}
-
-
-\cvCppFunc{cv::flann::Index_<T>::radiusSearch}
-Performs a radius nearest neighbor search for multiple query points.
-\cvdefCpp{int Index_<T>::radiusSearch(const Mat\& query, \par
+                 const SearchParams\& params);\newline
+int Index\_<T>::radiusSearch(const Mat\& query, \par
                  Mat\& indices, \par
                  Mat\& dists, \par
                  float radius, \par
                  const SearchParams\& params);}
 \begin{description}
-\cvarg{queries}{The query points, one per row}
-\cvarg{indices}{Indices of the nearest neighbors found}
-\cvarg{dists}{Distances to the nearest neighbors found}
+\cvarg{query}{The query point}
+\cvarg{indices}{Vector that will contain the indices of the points found within the search radius in decreasing order of the distance to the query point. If the number of neighbors in the search radius is bigger than the size of this vector, the ones that don't fit in the vector are ignored. }
+\cvarg{dists}{Vector that will contain the distances to the points found within the search radius}
 \cvarg{radius}{The search radius}
 \cvarg{params}{Search parameters}
 \end{description}
 
-
+\ifplastex 
 \cvCppFunc{cv::flann::Index_<T>::save}
+\else
+\subsubsection{cv::flann::Index\_$<T>$::save}\label{cvflann.Index.save}
+\fi
+
 Saves the index to a file.
-\cvdefCpp{void Index_<T>::save(std::string filename);}
+\cvdefCpp{void Index\_<T>::save(std::string filename);}
 \begin{description}
 \cvarg{filename}{The file to save the index to}
 \end{description}
 
+\ifplastex 
 \cvCppFunc{cv::flann::Index_<T>::getIndexParameters}
+\else
+\subsubsection{cv::flann::Index\_$<T>$::getIndexParameters}\label{cvflann.Index.getIndexParameters}
+\fi
+
 Returns the index paramreters. This is usefull in case of autotuned indices, when the parameters computed can be retrived using this method.
-\cvdefCpp{const IndexParams* Index_<T>::getIndexParameters();}
+\cvdefCpp{const IndexParams* Index\_<T>::getIndexParameters();}
 
 
 \cvCppFunc{cv::flann::hierarchicalClustering<ET,DT>}