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+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_COMPOSITE_INDEX_H_
+#define OPENCV_FLANN_COMPOSITE_INDEX_H_
+
+#include "general.h"
+#include "nn_index.h"
+#include "kdtree_index.h"
+#include "kmeans_index.h"
+
+namespace cvflann
+{
+
+/**
+ * Index parameters for the CompositeIndex.
+ */
+struct CompositeIndexParams : public IndexParams
+{
+    CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
+                         flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KMEANS;
+        // number of randomized trees to use (for kdtree)
+        (*this)["trees"] = trees;
+        // branching factor
+        (*this)["branching"] = branching;
+        // max iterations to perform in one kmeans clustering (kmeans tree)
+        (*this)["iterations"] = iterations;
+        // algorithm used for picking the initial cluster centers for kmeans tree
+        (*this)["centers_init"] = centers_init;
+        // cluster boundary index. Used when searching the kmeans tree
+        (*this)["cb_index"] = cb_index;
+    }
+};
+
+
+/**
+ * This index builds a kd-tree index and a k-means index and performs nearest
+ * neighbour search both indexes. This gives a slight boost in search performance
+ * as some of the neighbours that are missed by one index are found by the other.
+ */
+template <typename Distance>
+class CompositeIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    /**
+     * Index constructor
+     * @param inputData dataset containing the points to index
+     * @param params Index parameters
+     * @param d Distance functor
+     * @return
+     */
+    CompositeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = CompositeIndexParams(),
+                   Distance d = Distance()) : index_params_(params)
+    {
+        kdtree_index_ = new KDTreeIndex<Distance>(inputData, params, d);
+        kmeans_index_ = new KMeansIndex<Distance>(inputData, params, d);
+
+    }
+
+    CompositeIndex(const CompositeIndex&);
+    CompositeIndex& operator=(const CompositeIndex&);
+
+    virtual ~CompositeIndex()
+    {
+        delete kdtree_index_;
+        delete kmeans_index_;
+    }
+
+    /**
+     * @return The index type
+     */
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_COMPOSITE;
+    }
+
+    /**
+     * @return Size of the index
+     */
+    size_t size() const
+    {
+        return kdtree_index_->size();
+    }
+
+    /**
+     * \returns The dimensionality of the features in this index.
+     */
+    size_t veclen() const
+    {
+        return kdtree_index_->veclen();
+    }
+
+    /**
+     * \returns The amount of memory (in bytes) used by the index.
+     */
+    int usedMemory() const
+    {
+        return kmeans_index_->usedMemory() + kdtree_index_->usedMemory();
+    }
+
+    /**
+     * \brief Builds the index
+     */
+    void buildIndex()
+    {
+        Logger::info("Building kmeans tree...\n");
+        kmeans_index_->buildIndex();
+        Logger::info("Building kdtree tree...\n");
+        kdtree_index_->buildIndex();
+    }
+
+    /**
+     * \brief Saves the index to a stream
+     * \param stream The stream to save the index to
+     */
+    void saveIndex(FILE* stream)
+    {
+        kmeans_index_->saveIndex(stream);
+        kdtree_index_->saveIndex(stream);
+    }
+
+    /**
+     * \brief Loads the index from a stream
+     * \param stream The stream from which the index is loaded
+     */
+    void loadIndex(FILE* stream)
+    {
+        kmeans_index_->loadIndex(stream);
+        kdtree_index_->loadIndex(stream);
+    }
+
+    /**
+     * \returns The index parameters
+     */
+    IndexParams getParameters() const
+    {
+        return index_params_;
+    }
+
+    /**
+     * \brief Method that searches for nearest-neighbours
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+    {
+        kmeans_index_->findNeighbors(result, vec, searchParams);
+        kdtree_index_->findNeighbors(result, vec, searchParams);
+    }
+
+private:
+    /** The k-means index */
+    KMeansIndex<Distance>* kmeans_index_;
+
+    /** The kd-tree index */
+    KDTreeIndex<Distance>* kdtree_index_;
+
+    /** The index parameters */
+    const IndexParams index_params_;
+};
+
+}
+
+#endif //OPENCV_FLANN_COMPOSITE_INDEX_H_