mirror of
https://github.com/SoftFever/OrcaSlicer.git
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236 lines
8.2 KiB
C++
236 lines
8.2 KiB
C++
// KD tree built upon external data set, referencing the external data by integer indices.
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#ifndef slic3r_KDTreeIndirect_hpp_
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#define slic3r_KDTreeIndirect_hpp_
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#include <algorithm>
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#include <limits>
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#include <vector>
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#include "Utils.hpp" // for next_highest_power_of_2()
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namespace Slic3r {
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// KD tree for N-dimensional closest point search.
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template<size_t ANumDimensions, typename ACoordType, typename ACoordinateFn>
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class KDTreeIndirect
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{
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public:
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static constexpr size_t NumDimensions = ANumDimensions;
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using CoordinateFn = ACoordinateFn;
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using CoordType = ACoordType;
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// Following could be static constexpr size_t, but that would not link in C++11
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enum : size_t {
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npos = size_t(-1)
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};
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KDTreeIndirect(CoordinateFn coordinate) : coordinate(coordinate) {}
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KDTreeIndirect(CoordinateFn coordinate, std::vector<size_t> indices) : coordinate(coordinate) { this->build(std::move(indices)); }
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KDTreeIndirect(CoordinateFn coordinate, std::vector<size_t> &&indices) : coordinate(coordinate) { this->build(std::move(indices)); }
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KDTreeIndirect(CoordinateFn coordinate, size_t num_indices) : coordinate(coordinate) { this->build(num_indices); }
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KDTreeIndirect(KDTreeIndirect &&rhs) : m_nodes(std::move(rhs.m_nodes)), coordinate(std::move(rhs.coordinate)) {}
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KDTreeIndirect& operator=(KDTreeIndirect &&rhs) { m_nodes = std::move(rhs.m_nodes); coordinate = std::move(rhs.coordinate); return *this; }
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void clear() { m_nodes.clear(); }
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void build(size_t num_indices)
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{
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std::vector<size_t> indices;
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indices.reserve(num_indices);
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for (size_t i = 0; i < num_indices; ++ i)
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indices.emplace_back(i);
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this->build(std::move(indices));
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}
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void build(std::vector<size_t> &&indices)
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{
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if (indices.empty())
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clear();
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else {
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// Allocate enough memory for a full binary tree.
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m_nodes.assign(next_highest_power_of_2(indices.size() + 1), npos);
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build_recursive(indices, 0, 0, 0, indices.size() - 1);
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}
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indices.clear();
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}
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enum class VisitorReturnMask : unsigned int
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{
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CONTINUE_LEFT = 1,
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CONTINUE_RIGHT = 2,
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STOP = 4,
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};
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template<typename CoordType>
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unsigned int descent_mask(const CoordType &point_coord, const CoordType &search_radius, size_t idx, size_t dimension) const
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{
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CoordType dist = point_coord - this->coordinate(idx, dimension);
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return (dist * dist < search_radius + CoordType(EPSILON)) ?
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// The plane intersects a hypersphere centered at point_coord of search_radius.
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((unsigned int)(VisitorReturnMask::CONTINUE_LEFT) | (unsigned int)(VisitorReturnMask::CONTINUE_RIGHT)) :
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// The plane does not intersect the hypersphere.
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(dist > CoordType(0)) ? (unsigned int)(VisitorReturnMask::CONTINUE_RIGHT) : (unsigned int)(VisitorReturnMask::CONTINUE_LEFT);
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}
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// Visitor is supposed to return a bit mask of VisitorReturnMask.
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template<typename Visitor>
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void visit(Visitor &visitor) const
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{
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visit_recursive(0, 0, visitor);
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}
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CoordinateFn coordinate;
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private:
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// Build a balanced tree by splitting the input sequence by an axis aligned plane at a dimension.
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void build_recursive(std::vector<size_t> &input, size_t node, const size_t dimension, const size_t left, const size_t right)
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{
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if (left > right)
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return;
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assert(node < m_nodes.size());
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if (left == right) {
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// Insert a node into the balanced tree.
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m_nodes[node] = input[left];
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return;
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}
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// Partition the input to left / right pieces of the same length to produce a balanced tree.
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size_t center = (left + right) / 2;
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partition_input(input, dimension, left, right, center);
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// Insert a node into the tree.
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m_nodes[node] = input[center];
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// Build up the left / right subtrees.
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size_t next_dimension = dimension;
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if (++ next_dimension == NumDimensions)
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next_dimension = 0;
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if (center > left)
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build_recursive(input, node * 2 + 1, next_dimension, left, center - 1);
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build_recursive(input, node * 2 + 2, next_dimension, center + 1, right);
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}
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// Partition the input m_nodes <left, right> at "k" and "dimension" using the QuickSelect method:
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// https://en.wikipedia.org/wiki/Quickselect
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// Items left of the k'th item are lower than the k'th item in the "dimension",
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// items right of the k'th item are higher than the k'th item in the "dimension",
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void partition_input(std::vector<size_t> &input, const size_t dimension, size_t left, size_t right, const size_t k) const
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{
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while (left < right) {
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size_t center = (left + right) / 2;
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CoordType pivot;
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{
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// Bubble sort the input[left], input[center], input[right], so that a median of the three values
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// will end up in input[center].
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CoordType left_value = this->coordinate(input[left], dimension);
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CoordType center_value = this->coordinate(input[center], dimension);
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CoordType right_value = this->coordinate(input[right], dimension);
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if (left_value > center_value) {
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std::swap(input[left], input[center]);
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std::swap(left_value, center_value);
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}
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if (left_value > right_value) {
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std::swap(input[left], input[right]);
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right_value = left_value;
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}
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if (center_value > right_value) {
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std::swap(input[center], input[right]);
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center_value = right_value;
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}
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pivot = center_value;
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}
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if (right <= left + 2)
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// The <left, right> interval is already sorted.
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break;
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size_t i = left;
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size_t j = right - 1;
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std::swap(input[center], input[j]);
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// Partition the set based on the pivot.
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for (;;) {
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// Skip left points that are already at correct positions.
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// Search will certainly stop at position (right - 1), which stores the pivot.
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while (this->coordinate(input[++ i], dimension) < pivot) ;
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// Skip right points that are already at correct positions.
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while (this->coordinate(input[-- j], dimension) > pivot && i < j) ;
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if (i >= j)
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break;
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std::swap(input[i], input[j]);
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}
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// Restore pivot to the center of the sequence.
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std::swap(input[i], input[right - 1]);
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// Which side the kth element is in?
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if (k < i)
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right = i - 1;
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else if (k == i)
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// Sequence is partitioned, kth element is at its place.
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break;
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else
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left = i + 1;
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}
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}
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template<typename Visitor>
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void visit_recursive(size_t node, size_t dimension, Visitor &visitor) const
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{
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assert(! m_nodes.empty());
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if (node >= m_nodes.size() || m_nodes[node] == npos)
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return;
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// Left / right child node index.
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size_t left = node * 2 + 1;
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size_t right = left + 1;
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unsigned int mask = visitor(m_nodes[node], dimension);
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if ((mask & (unsigned int)VisitorReturnMask::STOP) == 0) {
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size_t next_dimension = (++ dimension == NumDimensions) ? 0 : dimension;
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if (mask & (unsigned int)VisitorReturnMask::CONTINUE_LEFT)
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visit_recursive(left, next_dimension, visitor);
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if (mask & (unsigned int)VisitorReturnMask::CONTINUE_RIGHT)
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visit_recursive(right, next_dimension, visitor);
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}
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}
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std::vector<size_t> m_nodes;
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};
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// Find a closest point using Euclidian metrics.
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// Returns npos if not found.
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template<typename KDTreeIndirectType, typename PointType, typename FilterFn>
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size_t find_closest_point(const KDTreeIndirectType &kdtree, const PointType &point, FilterFn filter)
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{
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using CoordType = typename KDTreeIndirectType::CoordType;
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struct Visitor {
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const KDTreeIndirectType &kdtree;
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const PointType &point;
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const FilterFn filter;
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size_t min_idx = KDTreeIndirectType::npos;
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CoordType min_dist = std::numeric_limits<CoordType>::max();
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Visitor(const KDTreeIndirectType &kdtree, const PointType &point, FilterFn filter) : kdtree(kdtree), point(point), filter(filter) {}
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unsigned int operator()(size_t idx, size_t dimension) {
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if (this->filter(idx)) {
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auto dist = CoordType(0);
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for (size_t i = 0; i < KDTreeIndirectType::NumDimensions; ++ i) {
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CoordType d = point[i] - kdtree.coordinate(idx, i);
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dist += d * d;
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}
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if (dist < min_dist) {
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min_dist = dist;
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min_idx = idx;
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}
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}
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return kdtree.descent_mask(point[dimension], min_dist, idx, dimension);
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}
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} visitor(kdtree, point, filter);
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kdtree.visit(visitor);
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return visitor.min_idx;
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}
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template<typename KDTreeIndirectType, typename PointType>
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size_t find_closest_point(const KDTreeIndirectType& kdtree, const PointType& point)
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{
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return find_closest_point(kdtree, point, [](size_t) { return true; });
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}
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} // namespace Slic3r
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#endif /* slic3r_KDTreeIndirect_hpp_ */
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