#ifndef STATIC_RTREE_HPP
#define STATIC_RTREE_HPP
#include "storage/tar_fwd.hpp"
#include "util/bearing.hpp"
#include "util/coordinate_calculation.hpp"
#include "util/deallocating_vector.hpp"
#include "util/exception.hpp"
#include "util/hilbert_value.hpp"
#include "util/integer_range.hpp"
#include "util/mmap_file.hpp"
#include "util/rectangle.hpp"
#include "util/timing_util.hpp"
#include "util/typedefs.hpp"
#include "util/vector_view.hpp"
#include "util/web_mercator.hpp"
#include "osrm/coordinate.hpp"
#include "storage/shared_memory_ownership.hpp"
#include <boost/assert.hpp>
#include <boost/filesystem.hpp>
#include <boost/format.hpp>
#include <boost/iostreams/device/mapped_file.hpp>
#include <tbb/blocked_range.h>
#include <tbb/parallel_for.h>
#include <tbb/parallel_sort.h>
#include <algorithm>
#include <array>
#include <limits>
#include <memory>
#include <queue>
#include <string>
#include <vector>
namespace osrm::util
{
template <class EdgeDataT,
storage::Ownership Ownership = storage::Ownership::Container,
std::uint32_t BRANCHING_FACTOR = 64,
std::uint32_t LEAF_PAGE_SIZE = 4096>
class StaticRTree;
namespace serialization
{
template <class EdgeDataT,
storage::Ownership Ownership,
std::uint32_t BRANCHING_FACTOR,
std::uint32_t LEAF_PAGE_SIZE>
inline void read(storage::tar::FileReader &reader,
const std::string &name,
util::StaticRTree<EdgeDataT, Ownership, BRANCHING_FACTOR, LEAF_PAGE_SIZE> &rtree);
template <class EdgeDataT,
storage::Ownership Ownership,
std::uint32_t BRANCHING_FACTOR,
std::uint32_t LEAF_PAGE_SIZE>
inline void
write(storage::tar::FileWriter &writer,
const std::string &name,
const util::StaticRTree<EdgeDataT, Ownership, BRANCHING_FACTOR, LEAF_PAGE_SIZE> &rtree);
} // namespace serialization
/***
* Static RTree for serving nearest neighbour queries
* // All coordinates are projected first to Web Mercator before the bounding boxes
* // are computed, this means the internal distance metric doesn not represent meters!
*/
template <class EdgeDataT,
storage::Ownership Ownership,
std::uint32_t BRANCHING_FACTOR,
std::uint32_t LEAF_PAGE_SIZE>
class StaticRTree
{
/**********************************************************
* Example RTree construction:
*
* 30 elements (EdgeDataT objects)
* LEAF_NODE_SIZE = 3
* BRANCHING_FACTOR = 2
*
* 012 345 678 901 234 567 890 123 456 789 <- EdgeDataT objects in .fileIndex data, sorted by
* \|/ \|/ \|/ \|/ \|/ \|/ \|/ \|/ \|/ \|/ Hilbert Code of the centroid coordinate
* A B C D E F G H I J <- Everything from here down is a Rectangle in
* \ / \ / \ / \ / \ / .ramIndex
* K L M N O
* \ / \ / /
* \ / \ / /
* \ / \ / /
* P Q R
* \ / /
* \ / /
* \ / /
* \ / /
* \ / /
* \ / /
* \ / /
* U V
* \ /
* \ /
* \ /
* W
*
* Step 1 - objects 01234567... are sorted by Hilbert code (these are the line
* segments of the OSM roads)
* Step 2 - we grab LEAF_NODE_SIZE of them at a time and create TreeNode A with a
* bounding-box that surrounds the first LEAF_NODE_SIZE objects
* Step 2a- continue grabbing LEAF_NODE_SIZE objects, creating TreeNodes B,C,D,E...J
* until we run out of objects. The last TreeNode J may not have
* LEAF_NODE_SIZE entries. Our math later on caters for this.
* Step 3 - Now start grabbing nodes from A..J in groups of BRANCHING_FACTOR,
* and create K..O with bounding boxes surrounding the groups of
* BRANCHING_FACTOR. Again, O, the last entry, may have fewer than
* BRANCHING_FACTOR entries.
* Step 3a- Repeat this process for each level, until you only create 1 TreeNode
* to contain its children (in this case, W).
*
* As we create TreeNodes, we append them to the m_search_tree vector.
*
* After this part of the building process, m_search_tree will contain TreeNode
* objects in this order:
*
* ABCDEFGHIJ KLMNO PQR UV W
* 10 5 3 2 1 <- number of nodes in the level
*
* In order to make our math easy later on, we reverse the whole array,
* then reverse the nodes within each level:
*
* Reversed: W VU RQP ONMKL JIHGFEDCBA
* Levels reversed: W UV PQR KLMNO ABCDEFGHIJ
*
* We also now have the following information:
*
* level sizes = {1,2,3,5,10}
*
* and we can calculate the array position the nodes for each level
* start (based on the sum of the previous level sizes):
*
* level starts = {0,1,3,6,11}
*
* Now, some basic math can be used to navigate around the tree. See
* the body of the `child_indexes` function for the details.
*
***********************************************/
template <typename T> using Vector = ViewOrVector<T, Ownership>;
public:
using Rectangle = RectangleInt2D;
using EdgeData = EdgeDataT;
using CoordinateList = Vector<util::Coordinate>;
static_assert(LEAF_PAGE_SIZE >= sizeof(EdgeDataT), "page size is too small");
static_assert(((LEAF_PAGE_SIZE - 1) & LEAF_PAGE_SIZE) == 0, "page size is not a power of 2");
static constexpr std::uint32_t LEAF_NODE_SIZE = (LEAF_PAGE_SIZE / sizeof(EdgeDataT));
struct CandidateSegment
{
Coordinate fixed_projected_coordinate;
EdgeDataT data;
};
/**
* Represents a node position somewhere in our tree. This is purely a navigation
* class used to find children of each node - the actual data for each node
* is in the m_search_tree vector of TreeNode objects.
*/
struct TreeIndex
{
TreeIndex() : level(0), offset(0) {}
TreeIndex(std::uint32_t level_, std::uint32_t offset_) : level(level_), offset(offset_) {}
std::uint32_t level; // Which level of the tree is this node in
std::uint32_t offset; // Which node on this level is this (0=leftmost)
};
/**
* An actual node in the tree. It's pretty minimal, we use the TreeIndex
* classes to navigate around. The TreeNode is packed into m_search_tree
* in a specific order so we can calculate positions of children
* (see the children_indexes function)
*/
struct TreeNode
{
Rectangle minimum_bounding_rectangle;
};
private:
/**
* A lightweight wrapper for the Hilbert Code for each EdgeDataT object
* A vector of these is used to sort the EdgeDataT input onto the
* Hilbert Curve.
* The sorting doesn't modify the original array, so this struct
* maintains a pointer to the original index position (m_original_index)
* so we can fetch the original data from the sorted position.
*/
struct WrappedInputElement
{
explicit WrappedInputElement(const uint64_t _hilbert_value,
const std::uint32_t _original_index)
: m_hilbert_value(_hilbert_value), m_original_index(_original_index)
{
}
WrappedInputElement() : m_hilbert_value(0), m_original_index(UINT_MAX) {}
uint64_t m_hilbert_value;
std::uint32_t m_original_index;
inline bool operator<(const WrappedInputElement &other) const
{
return m_hilbert_value < other.m_hilbert_value;
}
};
struct QueryCandidate
{
QueryCandidate(std::uint64_t squared_min_dist, TreeIndex tree_index)
: squared_min_dist(squared_min_dist), tree_index(tree_index),
segment_index(std::numeric_limits<std::uint32_t>::max())
{
}
QueryCandidate(std::uint64_t squared_min_dist,
TreeIndex tree_index,
std::uint32_t segment_index,
const Coordinate &coordinate)
: squared_min_dist(squared_min_dist), tree_index(tree_index),
fixed_projected_coordinate(coordinate), segment_index(segment_index)
{
}
inline bool is_segment() const
{
return segment_index != std::numeric_limits<std::uint32_t>::max();
}
inline bool operator<(const QueryCandidate &other) const
{
// Attn: this is reversed order. std::priority_queue is a
// max pq (biggest item at the front)!
return other.squared_min_dist < squared_min_dist;
}
std::uint64_t squared_min_dist;
TreeIndex tree_index;
Coordinate fixed_projected_coordinate;
std::uint32_t segment_index;
};
// Representation of the in-memory search tree
Vector<TreeNode> m_search_tree;
// Reference to the actual lon/lat data we need for doing math
util::vector_view<const Coordinate> m_coordinate_list;
// Holds the start indexes of each level in m_search_tree
Vector<std::uint64_t> m_tree_level_starts;
// mmap'd .fileIndex file
boost::iostreams::mapped_file_source m_objects_region;
// This is a view of the EdgeDataT data mmap'd from the .fileIndex file
util::vector_view<const EdgeDataT> m_objects;
public:
StaticRTree() = default;
StaticRTree(const StaticRTree &) = delete;
StaticRTree &operator=(const StaticRTree &) = delete;
StaticRTree(StaticRTree &&) = default;
StaticRTree &operator=(StaticRTree &&) = default;
// Construct a packed Hilbert-R-Tree with Kamel-Faloutsos algorithm [1]
explicit StaticRTree(const std::vector<EdgeDataT> &input_data_vector,
const Vector<Coordinate> &coordinate_list,
const boost::filesystem::path &on_disk_file_name)
: m_coordinate_list(coordinate_list.data(), coordinate_list.size())
{
const auto element_count = input_data_vector.size();
std::vector<WrappedInputElement> input_wrapper_vector(element_count);
// Step 1 - create a vector of Hilbert Code/original position pairs
tbb::parallel_for(
tbb::blocked_range<uint64_t>(0, element_count),
[&input_data_vector, &input_wrapper_vector, this](
const tbb::blocked_range<uint64_t> &range)
{
for (uint64_t element_counter = range.begin(), end = range.end();
element_counter != end;
++element_counter)
{
WrappedInputElement ¤t_wrapper = input_wrapper_vector[element_counter];
current_wrapper.m_original_index = element_counter;
EdgeDataT const ¤t_element = input_data_vector[element_counter];
// Get Hilbert-Value for centroid in mercartor projection
BOOST_ASSERT(current_element.u < m_coordinate_list.size());
BOOST_ASSERT(current_element.v < m_coordinate_list.size());
Coordinate current_centroid = coordinate_calculation::centroid(
m_coordinate_list[current_element.u], m_coordinate_list[current_element.v]);
current_centroid.lat = FixedLatitude{static_cast<std::int32_t>(
COORDINATE_PRECISION *
web_mercator::latToY(toFloating(current_centroid.lat)))};
current_wrapper.m_hilbert_value = GetHilbertCode(current_centroid);
}
});
// sort the hilbert-value representatives
tbb::parallel_sort(input_wrapper_vector.begin(), input_wrapper_vector.end());
{
boost::iostreams::mapped_file out_objects_region;
auto out_objects = mmapFile<EdgeDataT>(on_disk_file_name,
out_objects_region,
input_data_vector.size() * sizeof(EdgeDataT));
// Note, we can't just write everything in one go, because the input_data_vector
// is not sorted by hilbert code, only the input_wrapper_vector is in the correct
// order. Instead, we iterate over input_wrapper_vector, copy the hilbert-indexed
// entries from input_data_vector into a temporary contiguous array, then write
// that array to disk.
// Create the first level of TreeNodes - each bounding LEAF_NODE_COUNT EdgeDataT
// objects.
std::size_t wrapped_element_index = 0;
auto objects_iter = out_objects.begin();
while (wrapped_element_index < element_count)
{
TreeNode current_node;
// Loop over the next block of EdgeDataT, calculate the bounding box
// for the block, and save the data to write to disk in the correct
// order.
for (std::uint32_t object_index = 0;
object_index < LEAF_NODE_SIZE && wrapped_element_index < element_count;
++object_index, ++wrapped_element_index)
{
const std::uint32_t input_object_index =
input_wrapper_vector[wrapped_element_index].m_original_index;
const EdgeDataT &object = input_data_vector[input_object_index];
*objects_iter++ = object;
Coordinate projected_u{
web_mercator::fromWGS84(Coordinate{m_coordinate_list[object.u]})};
Coordinate projected_v{
web_mercator::fromWGS84(Coordinate{m_coordinate_list[object.v]})};
BOOST_ASSERT(std::abs(toFloating(projected_u.lon).operator double()) <= 180.);
BOOST_ASSERT(std::abs(toFloating(projected_u.lat).operator double()) <= 180.);
BOOST_ASSERT(std::abs(toFloating(projected_v.lon).operator double()) <= 180.);
BOOST_ASSERT(std::abs(toFloating(projected_v.lat).operator double()) <= 180.);
Rectangle rectangle;
rectangle.min_lon =
std::min(rectangle.min_lon, std::min(projected_u.lon, projected_v.lon));
rectangle.max_lon =
std::max(rectangle.max_lon, std::max(projected_u.lon, projected_v.lon));
rectangle.min_lat =
std::min(rectangle.min_lat, std::min(projected_u.lat, projected_v.lat));
rectangle.max_lat =
std::max(rectangle.max_lat, std::max(projected_u.lat, projected_v.lat));
BOOST_ASSERT(rectangle.IsValid());
current_node.minimum_bounding_rectangle.MergeBoundingBoxes(rectangle);
}
m_search_tree.emplace_back(current_node);
}
}
// mmap as read-only now
m_objects = mmapFile<EdgeDataT>(on_disk_file_name, m_objects_region);
// Should hold the number of nodes at the lowest level of the graph (closest
// to the data)
std::uint32_t nodes_in_previous_level = m_search_tree.size();
// Holds the number of TreeNodes in each level.
// We always start with the root node, so
// m_tree_level_sizes[0] should always be 1
std::vector<std::uint64_t> tree_level_sizes;
tree_level_sizes.push_back(nodes_in_previous_level);
// Now, repeatedly create levels of nodes that contain BRANCHING_FACTOR
// nodes from the previous level.
while (nodes_in_previous_level > 1)
{
auto previous_level_start_pos = m_search_tree.size() - nodes_in_previous_level;
// We can calculate how many nodes will be in this level, we divide by
// BRANCHING_FACTOR
// and round up
std::uint32_t nodes_in_current_level =
std::ceil(static_cast<double>(nodes_in_previous_level) / BRANCHING_FACTOR);
for (auto current_node_idx : irange<std::size_t>(0, nodes_in_current_level))
{
TreeNode parent_node;
auto first_child_index =
current_node_idx * BRANCHING_FACTOR + previous_level_start_pos;
auto last_child_index =
first_child_index +
std::min<std::size_t>(BRANCHING_FACTOR,
nodes_in_previous_level -
current_node_idx * BRANCHING_FACTOR);
// Calculate the bounding box for BRANCHING_FACTOR nodes in the previous
// level, then save that box as a new TreeNode in the new level.
for (auto child_node_idx : irange<std::size_t>(first_child_index, last_child_index))
{
parent_node.minimum_bounding_rectangle.MergeBoundingBoxes(
m_search_tree[child_node_idx].minimum_bounding_rectangle);
}
m_search_tree.emplace_back(parent_node);
}
nodes_in_previous_level = nodes_in_current_level;
tree_level_sizes.push_back(nodes_in_previous_level);
}
// At this point, we've got our tree built, but the nodes are in a weird order.
// Next thing we'll do is flip it around so that we don't end up with a lot of
// `size - n` math later on.
// Flip the tree so that the root node is at 0.
// This just makes our math during search a bit more intuitive
std::reverse(m_search_tree.begin(), m_search_tree.end());
// Same for the level sizes - root node / base level is at 0
std::reverse(tree_level_sizes.begin(), tree_level_sizes.end());
// The first level starts at 0
m_tree_level_starts = {0};
// The remaining levels start at the partial sum of the preceeding level sizes
std::partial_sum(tree_level_sizes.begin(),
tree_level_sizes.end(),
std::back_inserter(m_tree_level_starts));
BOOST_ASSERT(m_tree_level_starts.size() >= 2);
// Now we have to flip the coordinates within each level so that math is easier
// later on. The workflow here is:
// The initial order of tree nodes in the m_search_tree array is roughly:
// 6789 345 12 0 (each block here is a level of the tree)
// Then we reverse it and get:
// 0 21 543 9876
// Now the loop below reverses each level to give us the final result
// 0 12 345 6789
// This ordering keeps the position math easy to understand during later
// searches
for (auto i : irange<std::size_t>(0, tree_level_sizes.size()))
{
std::reverse(m_search_tree.begin() + m_tree_level_starts[i],
m_search_tree.begin() + m_tree_level_starts[i] + tree_level_sizes[i]);
}
}
/**
* Constructs an empty RTree for de-serialization.
*/
template <typename = std::enable_if<Ownership == storage::Ownership::Container>>
explicit StaticRTree(const boost::filesystem::path &on_disk_file_name,
const Vector<Coordinate> &coordinate_list)
: m_coordinate_list(coordinate_list.data(), coordinate_list.size()),
m_objects(mmapFile<EdgeDataT>(on_disk_file_name, m_objects_region))
{
}
/**
* Constructs an r-tree from blocks of memory loaded by someone else
* (usually a shared memory block created by osrm-datastore)
* These memory blocks basically just contain the files read into RAM,
* excep the .fileIndex file always stays on disk, and we mmap() it as usual
*/
explicit StaticRTree(Vector<TreeNode> search_tree_,
Vector<std::uint64_t> tree_level_starts,
const boost::filesystem::path &on_disk_file_name,
const Vector<Coordinate> &coordinate_list)
: m_search_tree(std::move(search_tree_)),
m_coordinate_list(coordinate_list.data(), coordinate_list.size()),
m_tree_level_starts(std::move(tree_level_starts))
{
BOOST_ASSERT(m_tree_level_starts.size() >= 2);
m_objects = mmapFile<EdgeDataT>(on_disk_file_name, m_objects_region);
}
/* Returns all features inside the bounding box.
Rectangle needs to be projected!*/
std::vector<EdgeDataT> SearchInBox(const Rectangle &search_rectangle) const
{
std::vector<EdgeDataT> results;
SearchInBox(search_rectangle,
[&results](const auto &edge_data) { results.push_back(edge_data); });
return results;
}
// Override filter and terminator for the desired behaviour.
std::vector<CandidateSegment> Nearest(const Coordinate input_coordinate,
const std::size_t max_results) const
{
return Nearest(
input_coordinate,
[](const CandidateSegment &) { return std::make_pair(true, true); },
[max_results](const std::size_t num_results, const CandidateSegment &)
{ return num_results >= max_results; });
}
// NB 1: results are not guaranteed to be sorted by distance
// NB 2: maxDistanceMeters is not a hard limit, it's just a way to reduce the number of edges
// returned
template <typename FilterT>
std::vector<CandidateSegment> SearchInRange(const Coordinate input_coordinate,
double maxDistanceMeters,
const FilterT filter) const
{
auto projected_coordinate = web_mercator::fromWGS84(input_coordinate);
Coordinate fixed_projected_coordinate{projected_coordinate};
auto bbox = Rectangle::ExpandMeters(input_coordinate, maxDistanceMeters);
std::vector<CandidateSegment> results;
SearchInBox(
bbox,
[&results, &filter, fixed_projected_coordinate, this](const EdgeDataT ¤t_edge)
{
const auto projected_u = web_mercator::fromWGS84(m_coordinate_list[current_edge.u]);
const auto projected_v = web_mercator::fromWGS84(m_coordinate_list[current_edge.v]);
auto [_, projected_nearest] = coordinate_calculation::projectPointOnSegment(
projected_u, projected_v, fixed_projected_coordinate);
CandidateSegment current_candidate{projected_nearest, current_edge};
auto use_segment = filter(current_candidate);
if (!use_segment.first && !use_segment.second)
{
return;
}
current_candidate.data.forward_segment_id.enabled &= use_segment.first;
current_candidate.data.reverse_segment_id.enabled &= use_segment.second;
results.push_back(current_candidate);
});
return results;
}
// Return edges in distance order with the coordinate of the closest point on the edge.
template <typename FilterT, typename TerminationT>
std::vector<CandidateSegment> Nearest(const Coordinate input_coordinate,
const FilterT filter,
const TerminationT terminate) const
{
std::vector<CandidateSegment> results;
auto projected_coordinate = web_mercator::fromWGS84(input_coordinate);
Coordinate fixed_projected_coordinate{projected_coordinate};
// initialize queue with root element
std::priority_queue<QueryCandidate> traversal_queue;
traversal_queue.push(QueryCandidate{0, TreeIndex{}});
while (!traversal_queue.empty())
{
QueryCandidate current_query_node = traversal_queue.top();
traversal_queue.pop();
const TreeIndex ¤t_tree_index = current_query_node.tree_index;
if (!current_query_node.is_segment())
{ // current object is a tree node
if (is_leaf(current_tree_index))
{
ExploreLeafNode(current_tree_index,
fixed_projected_coordinate,
projected_coordinate,
traversal_queue);
}
else
{
ExploreTreeNode(
current_tree_index, fixed_projected_coordinate, traversal_queue);
}
}
else
{ // current candidate is an actual road segment
const auto &edge_data = m_objects[current_query_node.segment_index];
// We deliberately make an edge data copy here, we mutate the value below
CandidateSegment current_candidate{current_query_node.fixed_projected_coordinate,
edge_data};
// to allow returns of no-results if too restrictive filtering, this needs to be
// done here even though performance would indicate that we want to stop after
// adding the first candidate
if (terminate(results.size(), current_candidate))
{
break;
}
auto use_segment = filter(current_candidate);
if (!use_segment.first && !use_segment.second)
{
continue;
}
current_candidate.data.forward_segment_id.enabled &= use_segment.first;
current_candidate.data.reverse_segment_id.enabled &= use_segment.second;
// store phantom node in result vector
results.push_back(std::move(current_candidate));
}
}
return results;
}
private:
template <typename Callback>
void SearchInBox(const Rectangle &search_rectangle, Callback &&callback) const
{
const Rectangle projected_rectangle{
search_rectangle.min_lon,
search_rectangle.max_lon,
toFixed(FloatLatitude{
web_mercator::latToY(toFloating(FixedLatitude(search_rectangle.min_lat)))}),
toFixed(FloatLatitude{
web_mercator::latToY(toFloating(FixedLatitude(search_rectangle.max_lat)))})};
std::queue<TreeIndex> traversal_queue;
traversal_queue.push(TreeIndex{});
while (!traversal_queue.empty())
{
auto const current_tree_index = traversal_queue.front();
traversal_queue.pop();
// If we're at the bottom of the tree, we need to explore the
// element array
if (is_leaf(current_tree_index))
{
// Note: irange is [start,finish), so we need to +1 to make sure we visit the
// last
for (const auto current_child_index : child_indexes(current_tree_index))
{
const auto ¤t_edge = m_objects[current_child_index];
// we don't need to project the coordinates here,
// because we use the unprojected rectangle to test against
const Rectangle bbox{std::min(m_coordinate_list[current_edge.u].lon,
m_coordinate_list[current_edge.v].lon),
std::max(m_coordinate_list[current_edge.u].lon,
m_coordinate_list[current_edge.v].lon),
std::min(m_coordinate_list[current_edge.u].lat,
m_coordinate_list[current_edge.v].lat),
std::max(m_coordinate_list[current_edge.u].lat,
m_coordinate_list[current_edge.v].lat)};
// use the _unprojected_ input rectangle here
if (bbox.Intersects(search_rectangle))
{
callback(current_edge);
}
}
}
else
{
BOOST_ASSERT(current_tree_index.level + 1 < m_tree_level_starts.size());
for (const auto child_index : child_indexes(current_tree_index))
{
const auto &child_rectangle =
m_search_tree[child_index].minimum_bounding_rectangle;
if (child_rectangle.Intersects(projected_rectangle))
{
traversal_queue.push(TreeIndex(
current_tree_index.level + 1,
child_index - m_tree_level_starts[current_tree_index.level + 1]));
}
}
}
}
}
/**
* Iterates over all the objects in a leaf node and inserts them into our
* search priority queue. The speed of this function is very much governed
* by the value of LEAF_NODE_SIZE, as we'll calculate the euclidean distance
* for every child of each leaf node visited.
*/
template <typename QueueT>
void ExploreLeafNode(const TreeIndex &leaf_id,
const Coordinate &projected_input_coordinate_fixed,
const FloatCoordinate &projected_input_coordinate,
QueueT &traversal_queue) const
{
// Check that we're actually looking at the bottom level of the tree
BOOST_ASSERT(is_leaf(leaf_id));
for (const auto i : child_indexes(leaf_id))
{
const auto ¤t_edge = m_objects[i];
const auto projected_u = web_mercator::fromWGS84(m_coordinate_list[current_edge.u]);
const auto projected_v = web_mercator::fromWGS84(m_coordinate_list[current_edge.v]);
FloatCoordinate projected_nearest;
std::tie(std::ignore, projected_nearest) =
coordinate_calculation::projectPointOnSegment(
projected_u, projected_v, projected_input_coordinate);
const auto squared_distance = coordinate_calculation::squaredEuclideanDistance(
projected_input_coordinate_fixed, projected_nearest);
// distance must be non-negative
BOOST_ASSERT(0. <= squared_distance);
BOOST_ASSERT(i < std::numeric_limits<std::uint32_t>::max());
traversal_queue.push(QueryCandidate{squared_distance,
leaf_id,
static_cast<std::uint32_t>(i),
Coordinate{projected_nearest}});
}
}
/**
* Iterates over all the children of a TreeNode and inserts them into the search
* priority queue using their distance from the search coordinate as the
* priority metric.
* The closest distance to a box from our point is also the closest distance
* to the closest line in that box (assuming the boxes hug their contents).
*/
template <class QueueT>
void ExploreTreeNode(const TreeIndex &parent,
const Coordinate &fixed_projected_input_coordinate,
QueueT &traversal_queue) const
{
// Figure out which_id level the parent is on, and it's offset
// in that level.
// Check that we're actually looking at the bottom level of the tree
BOOST_ASSERT(!is_leaf(parent));
for (const auto child_index : child_indexes(parent))
{
const auto &child = m_search_tree[child_index];
const auto squared_lower_bound_to_element =
child.minimum_bounding_rectangle.GetMinSquaredDist(
fixed_projected_input_coordinate);
traversal_queue.push(QueryCandidate{
squared_lower_bound_to_element,
TreeIndex(parent.level + 1, child_index - m_tree_level_starts[parent.level + 1])});
}
}
std::uint64_t GetLevelSize(const std::size_t level) const
{
BOOST_ASSERT(m_tree_level_starts.size() > level + 1);
BOOST_ASSERT(m_tree_level_starts[level + 1] >= m_tree_level_starts[level]);
return m_tree_level_starts[level + 1] - m_tree_level_starts[level];
}
/**
* Calculates the absolute position of child data in our packed data
* vectors.
*
* when given a TreeIndex that is a leaf node (i.e. at the bottom of the tree),
* this function returns indexes valid for `m_objects`
*
* otherwise, the indexes are to be used with m_search_tree to iterate over
* the children of `parent`
*
* This function assumes we pack nodes as described in the big comment
* at the top of this class. All nodes are fully filled except for the last
* one in each level.
*/
range<std::size_t> child_indexes(const TreeIndex &parent) const
{
// If we're looking at a leaf node, the index is from 0 to m_objects.size(),
// there is only 1 level of object data in the m_objects array
if (is_leaf(parent))
{
const std::uint64_t first_child_index = parent.offset * LEAF_NODE_SIZE;
const std::uint64_t end_child_index = std::min(
first_child_index + LEAF_NODE_SIZE, static_cast<std::uint64_t>(m_objects.size()));
BOOST_ASSERT(first_child_index < std::numeric_limits<std::uint32_t>::max());
BOOST_ASSERT(end_child_index < std::numeric_limits<std::uint32_t>::max());
BOOST_ASSERT(end_child_index <= m_objects.size());
return irange<std::size_t>(first_child_index, end_child_index);
}
else
{
const std::uint64_t first_child_index =
m_tree_level_starts[parent.level + 1] + parent.offset * BRANCHING_FACTOR;
const std::uint64_t end_child_index =
std::min(first_child_index + BRANCHING_FACTOR,
m_tree_level_starts[parent.level + 1] + GetLevelSize(parent.level + 1));
BOOST_ASSERT(first_child_index < std::numeric_limits<std::uint32_t>::max());
BOOST_ASSERT(end_child_index < std::numeric_limits<std::uint32_t>::max());
BOOST_ASSERT(end_child_index <= m_search_tree.size());
BOOST_ASSERT(end_child_index <=
m_tree_level_starts[parent.level + 1] + GetLevelSize(parent.level + 1));
return irange<std::size_t>(first_child_index, end_child_index);
}
}
bool is_leaf(const TreeIndex &treeindex) const
{
BOOST_ASSERT(m_tree_level_starts.size() >= 2);
return treeindex.level == m_tree_level_starts.size() - 2;
}
friend void serialization::read<EdgeDataT, Ownership, BRANCHING_FACTOR, LEAF_PAGE_SIZE>(
storage::tar::FileReader &reader, const std::string &name, StaticRTree &rtree);
friend void serialization::write<EdgeDataT, Ownership, BRANCHING_FACTOR, LEAF_PAGE_SIZE>(
storage::tar::FileWriter &writer, const std::string &name, const StaticRTree &rtree);
};
//[1] "On Packing R-Trees"; I. Kamel, C. Faloutsos; 1993; DOI: 10.1145/170088.170403
//[2] "Nearest Neighbor Queries", N. Roussopulos et al; 1995; DOI: 10.1145/223784.223794
//[3] "Distance Browsing in Spatial Databases"; G. Hjaltason, H. Samet; 1999; ACM Trans. DB Sys
// Vol.24 No.2, pp.265-318
} // namespace osrm::util
#endif // STATIC_RTREE_HPP