Diff: include/opencv2/flann/result_set.h

Revision:

0:0e0631af0305

diff -r 000000000000 -r 0e0631af0305 include/opencv2/flann/result_set.h
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+++ b/include/opencv2/flann/result_set.h	Fri Jan 29 04:53:38 2021 +0000
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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_RESULTSET_H
+#define OPENCV_FLANN_RESULTSET_H
+
+#include <algorithm>
+#include <cstring>
+#include <iostream>
+#include <limits>
+#include <set>
+#include <vector>
+
+namespace cvflann
+{
+
+/* This record represents a branch point when finding neighbors in
+    the tree.  It contains a record of the minimum distance to the query
+    point, as well as the node at which the search resumes.
+ */
+
+template <typename T, typename DistanceType>
+struct BranchStruct
+{
+    T node;           /* Tree node at which search resumes */
+    DistanceType mindist;     /* Minimum distance to query for all nodes below. */
+
+    BranchStruct() {}
+    BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {}
+
+    bool operator<(const BranchStruct<T, DistanceType>& rhs) const
+    {
+        return mindist<rhs.mindist;
+    }
+};
+
+
+template <typename DistanceType>
+class ResultSet
+{
+public:
+    virtual ~ResultSet() {}
+
+    virtual bool full() const = 0;
+
+    virtual void addPoint(DistanceType dist, int index) = 0;
+
+    virtual DistanceType worstDist() const = 0;
+
+};
+
+/**
+ * KNNSimpleResultSet does not ensure that the element it holds are unique.
+ * Is used in those cases where the nearest neighbour algorithm used does not
+ * attempt to insert the same element multiple times.
+ */
+template <typename DistanceType>
+class KNNSimpleResultSet : public ResultSet<DistanceType>
+{
+    int* indices;
+    DistanceType* dists;
+    int capacity;
+    int count;
+    DistanceType worst_distance_;
+
+public:
+    KNNSimpleResultSet(int capacity_) : capacity(capacity_), count(0)
+    {
+    }
+
+    void init(int* indices_, DistanceType* dists_)
+    {
+        indices = indices_;
+        dists = dists_;
+        count = 0;
+        worst_distance_ = (std::numeric_limits<DistanceType>::max)();
+        dists[capacity-1] = worst_distance_;
+    }
+
+    size_t size() const
+    {
+        return count;
+    }
+
+    bool full() const
+    {
+        return count == capacity;
+    }
+
+
+    void addPoint(DistanceType dist, int index)
+    {
+        if (dist >= worst_distance_) return;
+        int i;
+        for (i=count; i>0; --i) {
+#ifdef FLANN_FIRST_MATCH
+            if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) )
+#else
+            if (dists[i-1]>dist)
+#endif
+            {
+                if (i<capacity) {
+                    dists[i] = dists[i-1];
+                    indices[i] = indices[i-1];
+                }
+            }
+            else break;
+        }
+        if (count < capacity) ++count;
+        dists[i] = dist;
+        indices[i] = index;
+        worst_distance_ = dists[capacity-1];
+    }
+
+    DistanceType worstDist() const
+    {
+        return worst_distance_;
+    }
+};
+
+/**
+ * K-Nearest neighbour result set. Ensures that the elements inserted are unique
+ */
+template <typename DistanceType>
+class KNNResultSet : public ResultSet<DistanceType>
+{
+    int* indices;
+    DistanceType* dists;
+    int capacity;
+    int count;
+    DistanceType worst_distance_;
+
+public:
+    KNNResultSet(int capacity_) : capacity(capacity_), count(0)
+    {
+    }
+
+    void init(int* indices_, DistanceType* dists_)
+    {
+        indices = indices_;
+        dists = dists_;
+        count = 0;
+        worst_distance_ = (std::numeric_limits<DistanceType>::max)();
+        dists[capacity-1] = worst_distance_;
+    }
+
+    size_t size() const
+    {
+        return count;
+    }
+
+    bool full() const
+    {
+        return count == capacity;
+    }
+
+
+    void addPoint(DistanceType dist, int index)
+    {
+        if (dist >= worst_distance_) return;
+        int i;
+        for (i = count; i > 0; --i) {
+#ifdef FLANN_FIRST_MATCH
+            if ( (dists[i-1]<=dist) && ((dist!=dists[i-1])||(indices[i-1]<=index)) )
+#else
+            if (dists[i-1]<=dist)
+#endif
+            {
+                // Check for duplicate indices
+                int j = i - 1;
+                while ((j >= 0) && (dists[j] == dist)) {
+                    if (indices[j] == index) {
+                        return;
+                    }
+                    --j;
+                }
+                break;
+            }
+        }
+
+        if (count < capacity) ++count;
+        for (int j = count-1; j > i; --j) {
+            dists[j] = dists[j-1];
+            indices[j] = indices[j-1];
+        }
+        dists[i] = dist;
+        indices[i] = index;
+        worst_distance_ = dists[capacity-1];
+    }
+
+    DistanceType worstDist() const
+    {
+        return worst_distance_;
+    }
+};
+
+
+/**
+ * A result-set class used when performing a radius based search.
+ */
+template <typename DistanceType>
+class RadiusResultSet : public ResultSet<DistanceType>
+{
+    DistanceType radius;
+    int* indices;
+    DistanceType* dists;
+    size_t capacity;
+    size_t count;
+
+public:
+    RadiusResultSet(DistanceType radius_, int* indices_, DistanceType* dists_, int capacity_) :
+        radius(radius_), indices(indices_), dists(dists_), capacity(capacity_)
+    {
+        init();
+    }
+
+    ~RadiusResultSet()
+    {
+    }
+
+    void init()
+    {
+        count = 0;
+    }
+
+    size_t size() const
+    {
+        return count;
+    }
+
+    bool full() const
+    {
+        return true;
+    }
+
+    void addPoint(DistanceType dist, int index)
+    {
+        if (dist<radius) {
+            if ((capacity>0)&&(count < capacity)) {
+                dists[count] = dist;
+                indices[count] = index;
+            }
+            count++;
+        }
+    }
+
+    DistanceType worstDist() const
+    {
+        return radius;
+    }
+
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the k NN neighbors
+ * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays
+ */
+template<typename DistanceType>
+class UniqueResultSet : public ResultSet<DistanceType>
+{
+public:
+    struct DistIndex
+    {
+        DistIndex(DistanceType dist, unsigned int index) :
+            dist_(dist), index_(index)
+        {
+        }
+        bool operator<(const DistIndex dist_index) const
+        {
+            return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_);
+        }
+        DistanceType dist_;
+        unsigned int index_;
+    };
+
+    /** Default cosntructor */
+    UniqueResultSet() :
+        worst_distance_(std::numeric_limits<DistanceType>::max())
+    {
+    }
+
+    /** Check the status of the set
+     * @return true if we have k NN
+     */
+    inline bool full() const
+    {
+        return is_full_;
+    }
+
+    /** Remove all elements in the set
+     */
+    virtual void clear() = 0;
+
+    /** Copy the set to two C arrays
+     * @param indices pointer to a C array of indices
+     * @param dist pointer to a C array of distances
+     * @param n_neighbors the number of neighbors to copy
+     */
+    virtual void copy(int* indices, DistanceType* dist, int n_neighbors = -1) const
+    {
+        if (n_neighbors < 0) {
+            for (typename std::set<DistIndex>::const_iterator dist_index = dist_indices_.begin(), dist_index_end =
+                     dist_indices_.end(); dist_index != dist_index_end; ++dist_index, ++indices, ++dist) {
+                *indices = dist_index->index_;
+                *dist = dist_index->dist_;
+            }
+        }
+        else {
+            int i = 0;
+            for (typename std::set<DistIndex>::const_iterator dist_index = dist_indices_.begin(), dist_index_end =
+                     dist_indices_.end(); (dist_index != dist_index_end) && (i < n_neighbors); ++dist_index, ++indices, ++dist, ++i) {
+                *indices = dist_index->index_;
+                *dist = dist_index->dist_;
+            }
+        }
+    }
+
+    /** Copy the set to two C arrays but sort it according to the distance first
+     * @param indices pointer to a C array of indices
+     * @param dist pointer to a C array of distances
+     * @param n_neighbors the number of neighbors to copy
+     */
+    virtual void sortAndCopy(int* indices, DistanceType* dist, int n_neighbors = -1) const
+    {
+        copy(indices, dist, n_neighbors);
+    }
+
+    /** The number of neighbors in the set
+     * @return
+     */
+    size_t size() const
+    {
+        return dist_indices_.size();
+    }
+
+    /** The distance of the furthest neighbor
+     * If we don't have enough neighbors, it returns the max possible value
+     * @return
+     */
+    inline DistanceType worstDist() const
+    {
+        return worst_distance_;
+    }
+protected:
+    /** Flag to say if the set is full */
+    bool is_full_;
+
+    /** The worst distance found so far */
+    DistanceType worst_distance_;
+
+    /** The best candidates so far */
+    std::set<DistIndex> dist_indices_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the k NN neighbors
+ * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays
+ */
+template<typename DistanceType>
+class KNNUniqueResultSet : public UniqueResultSet<DistanceType>
+{
+public:
+    /** Constructor
+     * @param capacity the number of neighbors to store at max
+     */
+    KNNUniqueResultSet(unsigned int capacity) : capacity_(capacity)
+    {
+        this->is_full_ = false;
+        this->clear();
+    }
+
+    /** Add a possible candidate to the best neighbors
+     * @param dist distance for that neighbor
+     * @param index index of that neighbor
+     */
+    inline void addPoint(DistanceType dist, int index)
+    {
+        // Don't do anything if we are worse than the worst
+        if (dist >= worst_distance_) return;
+        dist_indices_.insert(DistIndex(dist, index));
+
+        if (is_full_) {
+            if (dist_indices_.size() > capacity_) {
+                dist_indices_.erase(*dist_indices_.rbegin());
+                worst_distance_ = dist_indices_.rbegin()->dist_;
+            }
+        }
+        else if (dist_indices_.size() == capacity_) {
+            is_full_ = true;
+            worst_distance_ = dist_indices_.rbegin()->dist_;
+        }
+    }
+
+    /** Remove all elements in the set
+     */
+    void clear()
+    {
+        dist_indices_.clear();
+        worst_distance_ = std::numeric_limits<DistanceType>::max();
+        is_full_ = false;
+    }
+
+protected:
+    typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
+    using UniqueResultSet<DistanceType>::is_full_;
+    using UniqueResultSet<DistanceType>::worst_distance_;
+    using UniqueResultSet<DistanceType>::dist_indices_;
+
+    /** The number of neighbors to keep */
+    unsigned int capacity_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the radius nearest neighbors
+ * It is more accurate than RadiusResult as it is not limited in the number of neighbors
+ */
+template<typename DistanceType>
+class RadiusUniqueResultSet : public UniqueResultSet<DistanceType>
+{
+public:
+    /** Constructor
+     * @param radius the maximum distance of a neighbor
+     */
+    RadiusUniqueResultSet(DistanceType radius) :
+        radius_(radius)
+    {
+        is_full_ = true;
+    }
+
+    /** Add a possible candidate to the best neighbors
+     * @param dist distance for that neighbor
+     * @param index index of that neighbor
+     */
+    void addPoint(DistanceType dist, int index)
+    {
+        if (dist <= radius_) dist_indices_.insert(DistIndex(dist, index));
+    }
+
+    /** Remove all elements in the set
+     */
+    inline void clear()
+    {
+        dist_indices_.clear();
+    }
+
+
+    /** Check the status of the set
+     * @return alwys false
+     */
+    inline bool full() const
+    {
+        return true;
+    }
+
+    /** The distance of the furthest neighbor
+     * If we don't have enough neighbors, it returns the max possible value
+     * @return
+     */
+    inline DistanceType worstDist() const
+    {
+        return radius_;
+    }
+private:
+    typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
+    using UniqueResultSet<DistanceType>::dist_indices_;
+    using UniqueResultSet<DistanceType>::is_full_;
+
+    /** The furthest distance a neighbor can be */
+    DistanceType radius_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the k NN neighbors within a radius distance
+ */
+template<typename DistanceType>
+class KNNRadiusUniqueResultSet : public KNNUniqueResultSet<DistanceType>
+{
+public:
+    /** Constructor
+     * @param capacity the number of neighbors to store at max
+     * @param radius the maximum distance of a neighbor
+     */
+    KNNRadiusUniqueResultSet(unsigned int capacity, DistanceType radius)
+    {
+        this->capacity_ = capacity;
+        this->radius_ = radius;
+        this->dist_indices_.reserve(capacity_);
+        this->clear();
+    }
+
+    /** Remove all elements in the set
+     */
+    void clear()
+    {
+        dist_indices_.clear();
+        worst_distance_ = radius_;
+        is_full_ = false;
+    }
+private:
+    using KNNUniqueResultSet<DistanceType>::dist_indices_;
+    using KNNUniqueResultSet<DistanceType>::is_full_;
+    using KNNUniqueResultSet<DistanceType>::worst_distance_;
+
+    /** The maximum number of neighbors to consider */
+    unsigned int capacity_;
+
+    /** The maximum distance of a neighbor */
+    DistanceType radius_;
+};
+}
+
+#endif //OPENCV_FLANN_RESULTSET_H