first version of moving KDTree from core to ml

modules/core/include/opencv2/core.hpp

@@ -747,93 +747,6 @@ public:
     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
 

modules/features2d/CMakeLists.txt

 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)

modules/features2d/test/test_nearestneighbors.cpp

@@ -12,6 +12,7 @@
 //
 // 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,
@@ -167,13 +168,13 @@ protected:
     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 )

modules/features2d/test/test_precomp.hpp

@@ -13,6 +13,7 @@
 #include "opencv2/imgproc.hpp"
 #include "opencv2/features2d.hpp"
 #include "opencv2/imgcodecs.hpp"
+#include "opencv2/ml.hpp"
 #include <iostream>
 
 #endif

modules/ml/include/opencv2/ml.hpp

@@ -12,6 +12,7 @@
 //
 // 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,
@@ -566,6 +567,89 @@ public:
     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                              *
 \****************************************************************************************/

modules/core/src/kdtree.cpp → modules/ml/src/kdtree.cpp

@@ -13,6 +13,7 @@
 // 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,
@@ -45,10 +46,10 @@
 
 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
@@ -529,3 +530,4 @@ int KDTree::dims() const
 }
 
 }
+}