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Doxygen documentation: flann, photo and stitching modules

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This commit is contained in:
Maksim Shabunin
2014-11-19 19:08:03 +03:00
parent 8e9ea0e3d1
commit 472c210687
19 changed files with 1147 additions and 61 deletions
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143
modules/flann/include/opencv2/flann.hpp
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@@ -47,6 +47,15 @@
#include "opencv2/flann/miniflann.hpp"
#include "opencv2/flann/flann_base.hpp"
/**
@defgroup flann Clustering and Search in Multi-Dimensional Spaces
This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate
Nearest Neighbors) is a library that 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 Muja2009 .
*/
namespace cvflann
{
CV_EXPORTS flann_distance_t flann_distance_type();
@@ -59,6 +68,10 @@ namespace cv
namespace flann
{
//! @addtogroup flann
//! @{
template <typename T> struct CvType {};
template <> struct CvType<unsigned char> { static int type() { return CV_8U; } };
template <> struct CvType<char> { static int type() { return CV_8S; } };
@@ -88,7 +101,9 @@ using ::cvflann::ChiSquareDistance;
using ::cvflann::KL_Divergence;
/** @brief The FLANN nearest neighbor index class. This class is templated with the type of elements for which
the index is built.
*/
template <typename Distance>
class GenericIndex
{
@@ -96,10 +111,108 @@ public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
/** @brief Constructs a nearest neighbor search index for a given dataset.
@param 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.
@param params Structure containing the index parameters. The type of index that will be
constructed depends on the type of this parameter. See the description.
@param distance
The method constructs a fast search structure from a set of features using the specified algorithm
with specified parameters, as defined by params. params is a reference to one of the following class
IndexParams descendants:
- **LinearIndexParams** When passing an object of this type, the index will perform a linear,
brute-force search. :
@code
struct LinearIndexParams : public IndexParams
{
};
@endcode
- **KDTreeIndexParams** When passing an object of this type the index constructed will consist of
a set of randomized kd-trees which will be searched in parallel. :
@code
struct KDTreeIndexParams : public IndexParams
{
KDTreeIndexParams( int trees = 4 );
};
@endcode
- **KMeansIndexParams** When passing an object of this type the index constructed will be a
hierarchical k-means tree. :
@code
struct KMeansIndexParams : public IndexParams
{
KMeansIndexParams(
int branching = 32,
int iterations = 11,
flann_centers_init_t centers_init = CENTERS_RANDOM,
float cb_index = 0.2 );
};
@endcode
- **CompositeIndexParams** When using a parameters object of this type the index created
combines the randomized kd-trees and the hierarchical k-means tree. :
@code
struct CompositeIndexParams : public IndexParams
{
CompositeIndexParams(
int trees = 4,
int branching = 32,
int iterations = 11,
flann_centers_init_t centers_init = CENTERS_RANDOM,
float cb_index = 0.2 );
};
@endcode
- **LshIndexParams** When using a parameters object of this type the index created uses
multi-probe LSH (by Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search
by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd
International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007) :
@code
struct LshIndexParams : public IndexParams
{
LshIndexParams(
unsigned int table_number,
unsigned int key_size,
unsigned int multi_probe_level );
};
@endcode
- **AutotunedIndexParams** When passing an object of this type the index created is
automatically tuned to offer the best performance, by choosing the optimal index type
(randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. :
@code
struct AutotunedIndexParams : public IndexParams
{
AutotunedIndexParams(
float target_precision = 0.9,
float build_weight = 0.01,
float memory_weight = 0,
float sample_fraction = 0.1 );
};
@endcode
- **SavedIndexParams** This object type is used for loading a previously saved index from the
disk. :
@code
struct SavedIndexParams : public IndexParams
{
SavedIndexParams( String filename );
};
@endcode
*/
GenericIndex(const Mat& features, const ::cvflann::IndexParams& params, Distance distance = Distance());
~GenericIndex();
/** @brief Performs a K-nearest neighbor search for a given query point using the index.
@param query The query point
@param indices Vector that will contain the indices of the K-nearest neighbors found. It must have
at least knn size.
@param dists Vector that will contain the distances to the K-nearest neighbors found. It must have
at least knn size.
@param knn Number of nearest neighbors to search for.
@param params SearchParams
*/
void knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices,
std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& params);
void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params);
@@ -123,6 +236,7 @@ private:
::cvflann::Index<Distance>* nnIndex;
};
//! @cond IGNORED
#define FLANN_DISTANCE_CHECK \
if ( ::cvflann::flann_distance_type() != cvflann::FLANN_DIST_L2) { \
@@ -218,6 +332,8 @@ int GenericIndex<Distance>::radiusSearch(const Mat& query, Mat& indices, Mat& di
return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
//! @endcond
/**
* @deprecated Use GenericIndex class instead
*/
@@ -283,6 +399,8 @@ template <typename T>
class FLANN_DEPRECATED Index_;
#endif
//! @cond IGNORED
template <typename T>
Index_<T>::Index_(const Mat& dataset, const ::cvflann::IndexParams& params)
{
@@ -377,7 +495,25 @@ int Index_<T>::radiusSearch(const Mat& query, Mat& indices, Mat& dists, Distance
if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
//! @endcond
/** @brief Clusters features using hierarchical k-means algorithm.
@param features The points to be clustered. The matrix must have elements of type
Distance::ElementType.
@param centers The centers of the clusters obtained. The matrix must have type
Distance::ResultType. 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 (branching-1)\*k+1 that's lower than the number of
clusters desired, where branching is the tree's branching factor (see description of the
KMeansIndexParams).
@param params Parameters used in the construction of the hierarchical k-means tree.
@param d Distance to be used for clustering.
The method clusters the given feature vectors by constructing a hierarchical k-means tree and
choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters
found.
*/
template <typename Distance>
int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params,
Distance d = Distance())
@@ -396,7 +532,8 @@ int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::K
return ::cvflann::hierarchicalClustering<Distance>(m_features, m_centers, params, d);
}
/** @deprecated
*/
template <typename ELEM_TYPE, typename DIST_TYPE>
FLANN_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params)
{
@@ -417,6 +554,8 @@ FLANN_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, c
}
}
//! @} flann
} } // namespace cv::flann
#endif