Renesas
openCV library for Renesas RZ/A
Dependents: RZ_A2M_Mbed_samples
Diff: include/opencv2/flann/kdtree_single_index.h
- Revision:
- 0:0e0631af0305
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/include/opencv2/flann/kdtree_single_index.h Fri Jan 29 04:53:38 2021 +0000
@@ -0,0 +1,634 @@
+/***********************************************************************
+ * 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_KDTREE_SINGLE_INDEX_H_
+#define OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_
+
+#include <algorithm>
+#include <map>
+#include <cassert>
+#include <cstring>
+
+#include "general.h"
+#include "nn_index.h"
+#include "matrix.h"
+#include "result_set.h"
+#include "heap.h"
+#include "allocator.h"
+#include "random.h"
+#include "saving.h"
+
+namespace cvflann
+{
+
+struct KDTreeSingleIndexParams : public IndexParams
+{
+ KDTreeSingleIndexParams(int leaf_max_size = 10, bool reorder = true, int dim = -1)
+ {
+ (*this)["algorithm"] = FLANN_INDEX_KDTREE_SINGLE;
+ (*this)["leaf_max_size"] = leaf_max_size;
+ (*this)["reorder"] = reorder;
+ (*this)["dim"] = dim;
+ }
+};
+
+
+/**
+ * Randomized kd-tree index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ */
+template <typename Distance>
+class KDTreeSingleIndex : public NNIndex<Distance>
+{
+public:
+ typedef typename Distance::ElementType ElementType;
+ typedef typename Distance::ResultType DistanceType;
+
+
+ /**
+ * KDTree constructor
+ *
+ * Params:
+ * inputData = dataset with the input features
+ * params = parameters passed to the kdtree algorithm
+ */
+ KDTreeSingleIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KDTreeSingleIndexParams(),
+ Distance d = Distance() ) :
+ dataset_(inputData), index_params_(params), distance_(d)
+ {
+ size_ = dataset_.rows;
+ dim_ = dataset_.cols;
+ int dim_param = get_param(params,"dim",-1);
+ if (dim_param>0) dim_ = dim_param;
+ leaf_max_size_ = get_param(params,"leaf_max_size",10);
+ reorder_ = get_param(params,"reorder",true);
+
+ // Create a permutable array of indices to the input vectors.
+ vind_.resize(size_);
+ for (size_t i = 0; i < size_; i++) {
+ vind_[i] = (int)i;
+ }
+ }
+
+ KDTreeSingleIndex(const KDTreeSingleIndex&);
+ KDTreeSingleIndex& operator=(const KDTreeSingleIndex&);
+
+ /**
+ * Standard destructor
+ */
+ ~KDTreeSingleIndex()
+ {
+ if (reorder_) delete[] data_.data;
+ }
+
+ /**
+ * Builds the index
+ */
+ void buildIndex()
+ {
+ computeBoundingBox(root_bbox_);
+ root_node_ = divideTree(0, (int)size_, root_bbox_ ); // construct the tree
+
+ if (reorder_) {
+ delete[] data_.data;
+ data_ = cvflann::Matrix<ElementType>(new ElementType[size_*dim_], size_, dim_);
+ for (size_t i=0; i<size_; ++i) {
+ for (size_t j=0; j<dim_; ++j) {
+ data_[i][j] = dataset_[vind_[i]][j];
+ }
+ }
+ }
+ else {
+ data_ = dataset_;
+ }
+ }
+
+ flann_algorithm_t getType() const
+ {
+ return FLANN_INDEX_KDTREE_SINGLE;
+ }
+
+
+ void saveIndex(FILE* stream)
+ {
+ save_value(stream, size_);
+ save_value(stream, dim_);
+ save_value(stream, root_bbox_);
+ save_value(stream, reorder_);
+ save_value(stream, leaf_max_size_);
+ save_value(stream, vind_);
+ if (reorder_) {
+ save_value(stream, data_);
+ }
+ save_tree(stream, root_node_);
+ }
+
+
+ void loadIndex(FILE* stream)
+ {
+ load_value(stream, size_);
+ load_value(stream, dim_);
+ load_value(stream, root_bbox_);
+ load_value(stream, reorder_);
+ load_value(stream, leaf_max_size_);
+ load_value(stream, vind_);
+ if (reorder_) {
+ load_value(stream, data_);
+ }
+ else {
+ data_ = dataset_;
+ }
+ load_tree(stream, root_node_);
+
+
+ index_params_["algorithm"] = getType();
+ index_params_["leaf_max_size"] = leaf_max_size_;
+ index_params_["reorder"] = reorder_;
+ }
+
+ /**
+ * Returns size of index.
+ */
+ size_t size() const
+ {
+ return size_;
+ }
+
+ /**
+ * Returns the length of an index feature.
+ */
+ size_t veclen() const
+ {
+ return dim_;
+ }
+
+ /**
+ * Computes the inde memory usage
+ * Returns: memory used by the index
+ */
+ int usedMemory() const
+ {
+ return (int)(pool_.usedMemory+pool_.wastedMemory+dataset_.rows*sizeof(int)); // pool memory and vind array memory
+ }
+
+
+ /**
+ * \brief Perform k-nearest neighbor search
+ * \param[in] queries The query points for which to find the nearest neighbors
+ * \param[out] indices The indices of the nearest neighbors found
+ * \param[out] dists Distances to the nearest neighbors found
+ * \param[in] knn Number of nearest neighbors to return
+ * \param[in] params Search parameters
+ */
+ void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
+ {
+ assert(queries.cols == veclen());
+ assert(indices.rows >= queries.rows);
+ assert(dists.rows >= queries.rows);
+ assert(int(indices.cols) >= knn);
+ assert(int(dists.cols) >= knn);
+
+ KNNSimpleResultSet<DistanceType> resultSet(knn);
+ for (size_t i = 0; i < queries.rows; i++) {
+ resultSet.init(indices[i], dists[i]);
+ findNeighbors(resultSet, queries[i], params);
+ }
+ }
+
+ IndexParams getParameters() const
+ {
+ return index_params_;
+ }
+
+ /**
+ * Find set of nearest neighbors to vec. Their indices are stored inside
+ * the result object.
+ *
+ * Params:
+ * result = the result object in which the indices of the nearest-neighbors are stored
+ * vec = the vector for which to search the nearest neighbors
+ * maxCheck = the maximum number of restarts (in a best-bin-first manner)
+ */
+ void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
+ {
+ float epsError = 1+get_param(searchParams,"eps",0.0f);
+
+ std::vector<DistanceType> dists(dim_,0);
+ DistanceType distsq = computeInitialDistances(vec, dists);
+ searchLevel(result, vec, root_node_, distsq, dists, epsError);
+ }
+
+private:
+
+
+ /*--------------------- Internal Data Structures --------------------------*/
+ struct Node
+ {
+ /**
+ * Indices of points in leaf node
+ */
+ int left, right;
+ /**
+ * Dimension used for subdivision.
+ */
+ int divfeat;
+ /**
+ * The values used for subdivision.
+ */
+ DistanceType divlow, divhigh;
+ /**
+ * The child nodes.
+ */
+ Node* child1, * child2;
+ };
+ typedef Node* NodePtr;
+
+
+ struct Interval
+ {
+ DistanceType low, high;
+ };
+
+ typedef std::vector<Interval> BoundingBox;
+
+ typedef BranchStruct<NodePtr, DistanceType> BranchSt;
+ typedef BranchSt* Branch;
+
+
+
+
+ void save_tree(FILE* stream, NodePtr tree)
+ {
+ save_value(stream, *tree);
+ if (tree->child1!=NULL) {
+ save_tree(stream, tree->child1);
+ }
+ if (tree->child2!=NULL) {
+ save_tree(stream, tree->child2);
+ }
+ }
+
+
+ void load_tree(FILE* stream, NodePtr& tree)
+ {
+ tree = pool_.allocate<Node>();
+ load_value(stream, *tree);
+ if (tree->child1!=NULL) {
+ load_tree(stream, tree->child1);
+ }
+ if (tree->child2!=NULL) {
+ load_tree(stream, tree->child2);
+ }
+ }
+
+
+ void computeBoundingBox(BoundingBox& bbox)
+ {
+ bbox.resize(dim_);
+ for (size_t i=0; i<dim_; ++i) {
+ bbox[i].low = (DistanceType)dataset_[0][i];
+ bbox[i].high = (DistanceType)dataset_[0][i];
+ }
+ for (size_t k=1; k<dataset_.rows; ++k) {
+ for (size_t i=0; i<dim_; ++i) {
+ if (dataset_[k][i]<bbox[i].low) bbox[i].low = (DistanceType)dataset_[k][i];
+ if (dataset_[k][i]>bbox[i].high) bbox[i].high = (DistanceType)dataset_[k][i];
+ }
+ }
+ }
+
+
+ /**
+ * Create a tree node that subdivides the list of vecs from vind[first]
+ * to vind[last]. The routine is called recursively on each sublist.
+ * Place a pointer to this new tree node in the location pTree.
+ *
+ * Params: pTree = the new node to create
+ * first = index of the first vector
+ * last = index of the last vector
+ */
+ NodePtr divideTree(int left, int right, BoundingBox& bbox)
+ {
+ NodePtr node = pool_.allocate<Node>(); // allocate memory
+
+ /* If too few exemplars remain, then make this a leaf node. */
+ if ( (right-left) <= leaf_max_size_) {
+ node->child1 = node->child2 = NULL; /* Mark as leaf node. */
+ node->left = left;
+ node->right = right;
+
+ // compute bounding-box of leaf points
+ for (size_t i=0; i<dim_; ++i) {
+ bbox[i].low = (DistanceType)dataset_[vind_[left]][i];
+ bbox[i].high = (DistanceType)dataset_[vind_[left]][i];
+ }
+ for (int k=left+1; k<right; ++k) {
+ for (size_t i=0; i<dim_; ++i) {
+ if (bbox[i].low>dataset_[vind_[k]][i]) bbox[i].low=(DistanceType)dataset_[vind_[k]][i];
+ if (bbox[i].high<dataset_[vind_[k]][i]) bbox[i].high=(DistanceType)dataset_[vind_[k]][i];
+ }
+ }
+ }
+ else {
+ int idx;
+ int cutfeat;
+ DistanceType cutval;
+ middleSplit_(&vind_[0]+left, right-left, idx, cutfeat, cutval, bbox);
+
+ node->divfeat = cutfeat;
+
+ BoundingBox left_bbox(bbox);
+ left_bbox[cutfeat].high = cutval;
+ node->child1 = divideTree(left, left+idx, left_bbox);
+
+ BoundingBox right_bbox(bbox);
+ right_bbox[cutfeat].low = cutval;
+ node->child2 = divideTree(left+idx, right, right_bbox);
+
+ node->divlow = left_bbox[cutfeat].high;
+ node->divhigh = right_bbox[cutfeat].low;
+
+ for (size_t i=0; i<dim_; ++i) {
+ bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
+ bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
+ }
+ }
+
+ return node;
+ }
+
+ void computeMinMax(int* ind, int count, int dim, ElementType& min_elem, ElementType& max_elem)
+ {
+ min_elem = dataset_[ind[0]][dim];
+ max_elem = dataset_[ind[0]][dim];
+ for (int i=1; i<count; ++i) {
+ ElementType val = dataset_[ind[i]][dim];
+ if (val<min_elem) min_elem = val;
+ if (val>max_elem) max_elem = val;
+ }
+ }
+
+ void middleSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
+ {
+ // find the largest span from the approximate bounding box
+ ElementType max_span = bbox[0].high-bbox[0].low;
+ cutfeat = 0;
+ cutval = (bbox[0].high+bbox[0].low)/2;
+ for (size_t i=1; i<dim_; ++i) {
+ ElementType span = bbox[i].high-bbox[i].low;
+ if (span>max_span) {
+ max_span = span;
+ cutfeat = i;
+ cutval = (bbox[i].high+bbox[i].low)/2;
+ }
+ }
+
+ // compute exact span on the found dimension
+ ElementType min_elem, max_elem;
+ computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+ cutval = (min_elem+max_elem)/2;
+ max_span = max_elem - min_elem;
+
+ // check if a dimension of a largest span exists
+ size_t k = cutfeat;
+ for (size_t i=0; i<dim_; ++i) {
+ if (i==k) continue;
+ ElementType span = bbox[i].high-bbox[i].low;
+ if (span>max_span) {
+ computeMinMax(ind, count, i, min_elem, max_elem);
+ span = max_elem - min_elem;
+ if (span>max_span) {
+ max_span = span;
+ cutfeat = i;
+ cutval = (min_elem+max_elem)/2;
+ }
+ }
+ }
+ int lim1, lim2;
+ planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
+
+ if (lim1>count/2) index = lim1;
+ else if (lim2<count/2) index = lim2;
+ else index = count/2;
+ }
+
+
+ void middleSplit_(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
+ {
+ const float EPS=0.00001f;
+ DistanceType max_span = bbox[0].high-bbox[0].low;
+ for (size_t i=1; i<dim_; ++i) {
+ DistanceType span = bbox[i].high-bbox[i].low;
+ if (span>max_span) {
+ max_span = span;
+ }
+ }
+ DistanceType max_spread = -1;
+ cutfeat = 0;
+ for (size_t i=0; i<dim_; ++i) {
+ DistanceType span = bbox[i].high-bbox[i].low;
+ if (span>(DistanceType)((1-EPS)*max_span)) {
+ ElementType min_elem, max_elem;
+ computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+ DistanceType spread = (DistanceType)(max_elem-min_elem);
+ if (spread>max_spread) {
+ cutfeat = (int)i;
+ max_spread = spread;
+ }
+ }
+ }
+ // split in the middle
+ DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2;
+ ElementType min_elem, max_elem;
+ computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+
+ if (split_val<min_elem) cutval = (DistanceType)min_elem;
+ else if (split_val>max_elem) cutval = (DistanceType)max_elem;
+ else cutval = split_val;
+
+ int lim1, lim2;
+ planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
+
+ if (lim1>count/2) index = lim1;
+ else if (lim2<count/2) index = lim2;
+ else index = count/2;
+ }
+
+
+ /**
+ * Subdivide the list of points by a plane perpendicular on axe corresponding
+ * to the 'cutfeat' dimension at 'cutval' position.
+ *
+ * On return:
+ * dataset[ind[0..lim1-1]][cutfeat]<cutval
+ * dataset[ind[lim1..lim2-1]][cutfeat]==cutval
+ * dataset[ind[lim2..count]][cutfeat]>cutval
+ */
+ void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2)
+ {
+ /* Move vector indices for left subtree to front of list. */
+ int left = 0;
+ int right = count-1;
+ for (;; ) {
+ while (left<=right && dataset_[ind[left]][cutfeat]<cutval) ++left;
+ while (left<=right && dataset_[ind[right]][cutfeat]>=cutval) --right;
+ if (left>right) break;
+ std::swap(ind[left], ind[right]); ++left; --right;
+ }
+ /* If either list is empty, it means that all remaining features
+ * are identical. Split in the middle to maintain a balanced tree.
+ */
+ lim1 = left;
+ right = count-1;
+ for (;; ) {
+ while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left;
+ while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right;
+ if (left>right) break;
+ std::swap(ind[left], ind[right]); ++left; --right;
+ }
+ lim2 = left;
+ }
+
+ DistanceType computeInitialDistances(const ElementType* vec, std::vector<DistanceType>& dists)
+ {
+ DistanceType distsq = 0.0;
+
+ for (size_t i = 0; i < dim_; ++i) {
+ if (vec[i] < root_bbox_[i].low) {
+ dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].low, (int)i);
+ distsq += dists[i];
+ }
+ if (vec[i] > root_bbox_[i].high) {
+ dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].high, (int)i);
+ distsq += dists[i];
+ }
+ }
+
+ return distsq;
+ }
+
+ /**
+ * Performs an exact search in the tree starting from a node.
+ */
+ void searchLevel(ResultSet<DistanceType>& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq,
+ std::vector<DistanceType>& dists, const float epsError)
+ {
+ /* If this is a leaf node, then do check and return. */
+ if ((node->child1 == NULL)&&(node->child2 == NULL)) {
+ DistanceType worst_dist = result_set.worstDist();
+ for (int i=node->left; i<node->right; ++i) {
+ int index = reorder_ ? i : vind_[i];
+ DistanceType dist = distance_(vec, data_[index], dim_, worst_dist);
+ if (dist<worst_dist) {
+ result_set.addPoint(dist,vind_[i]);
+ }
+ }
+ return;
+ }
+
+ /* Which child branch should be taken first? */
+ int idx = node->divfeat;
+ ElementType val = vec[idx];
+ DistanceType diff1 = val - node->divlow;
+ DistanceType diff2 = val - node->divhigh;
+
+ NodePtr bestChild;
+ NodePtr otherChild;
+ DistanceType cut_dist;
+ if ((diff1+diff2)<0) {
+ bestChild = node->child1;
+ otherChild = node->child2;
+ cut_dist = distance_.accum_dist(val, node->divhigh, idx);
+ }
+ else {
+ bestChild = node->child2;
+ otherChild = node->child1;
+ cut_dist = distance_.accum_dist( val, node->divlow, idx);
+ }
+
+ /* Call recursively to search next level down. */
+ searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);
+
+ DistanceType dst = dists[idx];
+ mindistsq = mindistsq + cut_dist - dst;
+ dists[idx] = cut_dist;
+ if (mindistsq*epsError<=result_set.worstDist()) {
+ searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
+ }
+ dists[idx] = dst;
+ }
+
+private:
+
+ /**
+ * The dataset used by this index
+ */
+ const Matrix<ElementType> dataset_;
+
+ IndexParams index_params_;
+
+ int leaf_max_size_;
+ bool reorder_;
+
+
+ /**
+ * Array of indices to vectors in the dataset.
+ */
+ std::vector<int> vind_;
+
+ Matrix<ElementType> data_;
+
+ size_t size_;
+ size_t dim_;
+
+ /**
+ * Array of k-d trees used to find neighbours.
+ */
+ NodePtr root_node_;
+
+ BoundingBox root_bbox_;
+
+ /**
+ * Pooled memory allocator.
+ *
+ * Using a pooled memory allocator is more efficient
+ * than allocating memory directly when there is a large
+ * number small of memory allocations.
+ */
+ PooledAllocator pool_;
+
+ Distance distance_;
+}; // class KDTree
+
+}
+
+#endif //OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_