int minusStep, plusStep;
};
-
-
-/*!
- Fast Nearest Neighbor Search Class.
-
- The class implements D. Lowe BBF (Best-Bin-First) algorithm for the last
- approximate (or accurate) nearest neighbor search in multi-dimensional spaces.
-
- First, a set of vectors is passed to KDTree::KDTree() constructor
- or KDTree::build() method, where it is reordered.
-
- Then arbitrary vectors can be passed to KDTree::findNearest() methods, which
- find the K nearest neighbors among the vectors from the initial set.
- The user can balance between the speed and accuracy of the search by varying Emax
- parameter, which is the number of leaves that the algorithm checks.
- The larger parameter values yield more accurate results at the expense of lower processing speed.
-
- \code
- KDTree T(points, false);
- const int K = 3, Emax = INT_MAX;
- int idx[K];
- float dist[K];
- T.findNearest(query_vec, K, Emax, idx, 0, dist);
- CV_Assert(dist[0] <= dist[1] && dist[1] <= dist[2]);
- \endcode
-*/
-class CV_EXPORTS_W KDTree
-{
-public:
- /*!
- The node of the search tree.
- */
- struct Node
- {
- Node() : idx(-1), left(-1), right(-1), boundary(0.f) {}
- Node(int _idx, int _left, int _right, float _boundary)
- : idx(_idx), left(_left), right(_right), boundary(_boundary) {}
-
- //! split dimension; >=0 for nodes (dim), < 0 for leaves (index of the point)
- int idx;
- //! node indices of the left and the right branches
- int left, right;
- //! go to the left if query_vec[node.idx]<=node.boundary, otherwise go to the right
- float boundary;
- };
-
- //! the default constructor
- CV_WRAP KDTree();
- //! the full constructor that builds the search tree
- CV_WRAP KDTree(InputArray points, bool copyAndReorderPoints = false);
- //! the full constructor that builds the search tree
- CV_WRAP KDTree(InputArray points, InputArray _labels,
- bool copyAndReorderPoints = false);
- //! builds the search tree
- CV_WRAP void build(InputArray points, bool copyAndReorderPoints = false);
- //! builds the search tree
- CV_WRAP void build(InputArray points, InputArray labels,
- bool copyAndReorderPoints = false);
- //! finds the K nearest neighbors of "vec" while looking at Emax (at most) leaves
- CV_WRAP int findNearest(InputArray vec, int K, int Emax,
- OutputArray neighborsIdx,
- OutputArray neighbors = noArray(),
- OutputArray dist = noArray(),
- OutputArray labels = noArray()) const;
- //! finds all the points from the initial set that belong to the specified box
- CV_WRAP void findOrthoRange(InputArray minBounds,
- InputArray maxBounds,
- OutputArray neighborsIdx,
- OutputArray neighbors = noArray(),
- OutputArray labels = noArray()) const;
- //! returns vectors with the specified indices
- CV_WRAP void getPoints(InputArray idx, OutputArray pts,
- OutputArray labels = noArray()) const;
- //! return a vector with the specified index
- const float* getPoint(int ptidx, int* label = 0) const;
- //! returns the search space dimensionality
- CV_WRAP int dims() const;
-
- std::vector<Node> nodes; //!< all the tree nodes
- CV_PROP Mat points; //!< all the points. It can be a reordered copy of the input vector set or the original vector set.
- CV_PROP std::vector<int> labels; //!< the parallel array of labels.
- CV_PROP int maxDepth; //!< maximum depth of the search tree. Do not modify it
- CV_PROP_RW int normType; //!< type of the distance (cv::NORM_L1 or cv::NORM_L2) used for search. Initially set to cv::NORM_L2, but you can modify it
-};
-
-
-
/*!
Random Number Generator
set(the_description "2D Features Framework")
-ocv_define_module(features2d opencv_imgproc opencv_flann OPTIONAL opencv_highgui)
+ocv_define_module(features2d opencv_imgproc opencv_ml opencv_flann OPTIONAL opencv_highgui)
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2014, Itseez Inc, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
virtual int findNeighbors( Mat& points, Mat& neighbors );
virtual int checkFindBoxed();
virtual void releaseModel();
- KDTree* tr;
+ ml::KDTree* tr;
};
void CV_KDTreeTest_CPP::createModel( const Mat& data )
{
- tr = new KDTree( data, false );
+ tr = new ml::KDTree( data, false );
}
int CV_KDTreeTest_CPP::checkGetPoins( const Mat& data )
#include "opencv2/imgproc.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/imgcodecs.hpp"
+#include "opencv2/ml.hpp"
#include <iostream>
#endif
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2014, Itseez Inc, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
static Ptr<ANN_MLP> create(const Params& params=Params());
};
+/*!
+ Fast Nearest Neighbor Search Class.
+
+ The class implements D. Lowe BBF (Best-Bin-First) algorithm for the last
+ approximate (or accurate) nearest neighbor search in multi-dimensional spaces.
+
+ First, a set of vectors is passed to KDTree::KDTree() constructor
+ or KDTree::build() method, where it is reordered.
+
+ Then arbitrary vectors can be passed to KDTree::findNearest() methods, which
+ find the K nearest neighbors among the vectors from the initial set.
+ The user can balance between the speed and accuracy of the search by varying Emax
+ parameter, which is the number of leaves that the algorithm checks.
+ The larger parameter values yield more accurate results at the expense of lower processing speed.
+
+ \code
+ KDTree T(points, false);
+ const int K = 3, Emax = INT_MAX;
+ int idx[K];
+ float dist[K];
+ T.findNearest(query_vec, K, Emax, idx, 0, dist);
+ CV_Assert(dist[0] <= dist[1] && dist[1] <= dist[2]);
+ \endcode
+*/
+class CV_EXPORTS_W KDTree
+{
+public:
+ /*!
+ The node of the search tree.
+ */
+ struct Node
+ {
+ Node() : idx(-1), left(-1), right(-1), boundary(0.f) {}
+ Node(int _idx, int _left, int _right, float _boundary)
+ : idx(_idx), left(_left), right(_right), boundary(_boundary) {}
+
+ //! split dimension; >=0 for nodes (dim), < 0 for leaves (index of the point)
+ int idx;
+ //! node indices of the left and the right branches
+ int left, right;
+ //! go to the left if query_vec[node.idx]<=node.boundary, otherwise go to the right
+ float boundary;
+ };
+
+ //! the default constructor
+ CV_WRAP KDTree();
+ //! the full constructor that builds the search tree
+ CV_WRAP KDTree(InputArray points, bool copyAndReorderPoints = false);
+ //! the full constructor that builds the search tree
+ CV_WRAP KDTree(InputArray points, InputArray _labels,
+ bool copyAndReorderPoints = false);
+ //! builds the search tree
+ CV_WRAP void build(InputArray points, bool copyAndReorderPoints = false);
+ //! builds the search tree
+ CV_WRAP void build(InputArray points, InputArray labels,
+ bool copyAndReorderPoints = false);
+ //! finds the K nearest neighbors of "vec" while looking at Emax (at most) leaves
+ CV_WRAP int findNearest(InputArray vec, int K, int Emax,
+ OutputArray neighborsIdx,
+ OutputArray neighbors = noArray(),
+ OutputArray dist = noArray(),
+ OutputArray labels = noArray()) const;
+ //! finds all the points from the initial set that belong to the specified box
+ CV_WRAP void findOrthoRange(InputArray minBounds,
+ InputArray maxBounds,
+ OutputArray neighborsIdx,
+ OutputArray neighbors = noArray(),
+ OutputArray labels = noArray()) const;
+ //! returns vectors with the specified indices
+ CV_WRAP void getPoints(InputArray idx, OutputArray pts,
+ OutputArray labels = noArray()) const;
+ //! return a vector with the specified index
+ const float* getPoint(int ptidx, int* label = 0) const;
+ //! returns the search space dimensionality
+ CV_WRAP int dims() const;
+
+ std::vector<Node> nodes; //!< all the tree nodes
+ CV_PROP Mat points; //!< all the points. It can be a reordered copy of the input vector set or the original vector set.
+ CV_PROP std::vector<int> labels; //!< the parallel array of labels.
+ CV_PROP int maxDepth; //!< maximum depth of the search tree. Do not modify it
+ CV_PROP_RW int normType; //!< type of the distance (cv::NORM_L1 or cv::NORM_L2) used for search. Initially set to cv::NORM_L2, but you can modify it
+};
+
/****************************************************************************************\
* Auxilary functions declarations *
\****************************************************************************************/
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2014, Itseez Inc, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
namespace cv
{
-
+namespace ml
+{
// This is reimplementation of kd-trees from cvkdtree*.* by Xavier Delacour, cleaned-up and
-// adopted to work with the new OpenCV data structures. It's in cxcore to be shared by
-// both cv (CvFeatureTree) and ml (kNN).
+// adopted to work with the new OpenCV data structures.
// The algorithm is taken from:
// J.S. Beis and D.G. Lowe. Shape indexing using approximate nearest-neighbor search
}
}
+}
projects / profile / ivi / opencv.git / commitdiff