fixed PDF generation

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}
+\ifplastex
-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}
-
-
 \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);}
+		  const SearchParams\& params);\newline
-\begin{description}
+int Index\_<T>::radiusSearch(const Mat\& query, \par
-\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
 		  Mat\& indices, \par
 		  Mat\& dists, \par
 		  float radius, \par
 		  const SearchParams\& params);}
 \begin{description}
-\cvarg{queries}{The query points, one per row}
+\cvarg{query}{The query point}
-\cvarg{indices}{Indices of the nearest neighbors found}
+\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}{Distances to the nearest neighbors found}
+\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>}