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Updated FLANN to version 1.5

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This commit is contained in:
Marius Muja
2010-10-12 19:47:50 +00:00
parent 3230073b9b
commit 16b1f61c83
60 changed files with 2134 additions and 3685 deletions
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67
doc/cxcore_clustering_search.tex
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@@ -293,18 +293,23 @@ 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}.
\cvclass{cvflann::Index}
The FLANN nearest neighbor index class.
\cvclass{cv::flann::Index_}
The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built.
\begin{lstlisting}
namespace cvflann
namespace cv
{
class Index
namespace flann
{
template <typename T>
class Index_
{
public:
Index(const Mat& features, const IndexParams& params);
Index_(const Mat& features, const IndexParams& params);
void knnSearch(const vector<float>& query,
~Index_();
void knnSearch(const vector<T>& query,
vector<int>& indices,
vector<float>& dists,
int knn,
@@ -315,7 +320,7 @@ namespace cvflann
int knn,
const SearchParams& params);
int radiusSearch(const vector<float>& query,
int radiusSearch(const vector<T>& query,
vector<int>& indices,
vector<float>& dists,
float radius,
@@ -331,16 +336,22 @@ namespace cvflann
int veclen() const;
int size() const;
const IndexParams* getIndexParameters();
};
}
typedef Index_<float> Index;
} } // namespace cv::flann
\end{lstlisting}
\cvCppFunc{cvflann::Index::Index}
\cvCppFunc{cv::flann::Index_<T>::Index_}
Constructs a nearest neighbor search index for a given dataset.
\cvdefCpp{Index::Index(const Mat\& features, const IndexParams\& params);}
\cvdefCpp{Index_<T>::Index_(const Mat\& features, const IndexParams\& params);}
\begin{description}
\cvarg{features}{ Matrix of type CV\_32F containing the features(points) to index. The size of the matrix is num\_features x feature\_dimensionality.}
\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.}
\cvarg{params}{Structure containing the index parameters. The type of index that will be constructed depends on the type of this parameter.
The possible parameter types are:}
@@ -428,9 +439,9 @@ struct SavedIndexParams : public IndexParams
\end{description}
\end{description}
\cvCppFunc{cvflann::Index::knnSearch}
\cvCppFunc{cv::flann::Index_<T>::knnSearch}
Performs a K-nearest neighbor search for a given query point using the index.
\cvdefCpp{void Index::knnSearch(const vector<float>\& query, \par
\cvdefCpp{void Index_<T>::knnSearch(const vector<T>\& query, \par
vector<int>\& indices, \par
vector<float>\& dists, \par
int knn, \par
@@ -451,15 +462,15 @@ Performs a K-nearest neighbor search for a given query point using the index.
\end{description}
\end{description}
\cvCppFunc{cvflann::Index::knnSearch}
\cvCppFunc{cv::flann::Index_<T>::knnSearch}
Performs a K-nearest neighbor search for multiple query points.
\cvdefCpp{void Index::knnSearch(const Mat\& queries,\par
\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}
\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}
@@ -467,9 +478,9 @@ Performs a K-nearest neighbor search for multiple query points.
\end{description}
\cvCppFunc{cvflann::Index::radiusSearch}
\cvCppFunc{cv::flann::Index_<T>::radiusSearch}
Performs a radius nearest neighbor search for a given query point.
\cvdefCpp{int Index::radiusSearch(const vector<float>\& query, \par
\cvdefCpp{int Index_<T>::radiusSearch(const vector<T>\& query, \par
vector<int>\& indices, \par
vector<float>\& dists, \par
float radius, \par
@@ -483,9 +494,9 @@ Performs a radius nearest neighbor search for a given query point.
\end{description}
\cvCppFunc{cvflann::Index::radiusSearch}
\cvCppFunc{cv::flann::Index_<T>::radiusSearch}
Performs a radius nearest neighbor search for multiple query points.
\cvdefCpp{int Index::radiusSearch(const Mat\& query, \par
\cvdefCpp{int Index_<T>::radiusSearch(const Mat\& query, \par
Mat\& indices, \par
Mat\& dists, \par
float radius, \par
@@ -499,21 +510,25 @@ Performs a radius nearest neighbor search for multiple query points.
\end{description}
\cvCppFunc{cvflann::Index::save}
\cvCppFunc{cv::flann::Index_<T>::save}
Saves the index to a file.
\cvdefCpp{void Index::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}
\cvCppFunc{cv::flann::Index_<T>::getIndexParameters}
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();}
\cvCppFunc{cvflann::hierarchicalClustering}
\cvCppFunc{cv::flann::hierarchicalClustering<ET,DT>}
Clusters the given points by constructing a hierarchical k-means tree and choosing a cut in the tree that minimizes the cluster's variance.
\cvdefCpp{int hierarchicalClustering(const Mat\& features, Mat\& centers,\par
\cvdefCpp{int hierarchicalClustering<ET,DT>(const Mat\& features, Mat\& centers,\par
const KMeansIndexParams\& params);}
\begin{description}
\cvarg{features}{The points to be clustered}
\cvarg{centers}{The centers of the clusters obtained. The number of rows in this matrix represents the number of clusters desired, however, because of the way the cut in the hierarchical tree is chosen, the number of clusters computed will be the highest number of the form \texttt{(branching-1)*k+1} that's lower than the number of clusters desired, where \texttt{branching} is the tree's branching factor (see description of the KMeansIndexParams).}
\cvarg{features}{The points to be clustered. The matrix must have elements of type ET.}
\cvarg{centers}{The centers of the clusters obtained. The matrix must have type DT. The number of rows in this matrix represents the number of clusters desired, however, because of the way the cut in the hierarchical tree is chosen, the number of clusters computed will be the highest number of the form \texttt{(branching-1)*k+1} that's lower than the number of clusters desired, where \texttt{branching} is the tree's branching factor (see description of the KMeansIndexParams).}
\cvarg{params}{Parameters used in the construction of the hierarchical k-means tree}
\end{description}
The function returns the number of clusters computed.