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Extend kdtree with k-nearest and bounding box queries

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Also add test to verify it
This commit is contained in:
tamasmeszaros 2022-05-09 12:51:58 +02:00
parent 72b82547dd
commit 12a54251c9
5 changed files with 495 additions and 186 deletions
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1
src/libslic3r/CMakeLists.txt
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@ -212,6 +212,7 @@ set(SLIC3R_SOURCES
PrintObject.cpp PrintObject.cpp
PrintObjectSlice.cpp PrintObjectSlice.cpp
PrintRegion.cpp PrintRegion.cpp
PointGrid.hpp
PNGReadWrite.hpp PNGReadWrite.hpp
PNGReadWrite.cpp PNGReadWrite.cpp
QuadricEdgeCollapse.cpp QuadricEdgeCollapse.cpp

463
src/libslic3r/KDTreeIndirect.hpp
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@ -11,231 +11,276 @@
namespace Slic3r { namespace Slic3r {
enum class VisitorReturnMask : unsigned int {
CONTINUE_LEFT = 1,
CONTINUE_RIGHT = 2,
STOP = 4,
};
// KD tree for N-dimensional closest point search. // KD tree for N-dimensional closest point search.
template<size_t ANumDimensions, typename ACoordType, typename ACoordinateFn> template<size_t ANumDimensions, typename ACoordType, typename ACoordinateFn>
class KDTreeIndirect class KDTreeIndirect
{ {
public: public:
static constexpr size_t NumDimensions = ANumDimensions; static constexpr size_t NumDimensions = ANumDimensions;
using CoordinateFn = ACoordinateFn; using CoordinateFn = ACoordinateFn;
using CoordType = ACoordType; using CoordType = ACoordType;
// Following could be static constexpr size_t, but that would not link in C++11 // Following could be static constexpr size_t, but that would not link in C++11
enum : size_t { enum : size_t {
npos = size_t(-1) npos = size_t(-1)
}; };
KDTreeIndirect(CoordinateFn coordinate) : coordinate(coordinate) {} KDTreeIndirect(CoordinateFn coordinate) : coordinate(coordinate) {}
KDTreeIndirect(CoordinateFn coordinate, std::vector<size_t> indices) : coordinate(coordinate) { this->build(std::move(indices)); } KDTreeIndirect(CoordinateFn coordinate, std::vector<size_t> indices) : coordinate(coordinate) { this->build(indices); }
KDTreeIndirect(CoordinateFn coordinate, std::vector<size_t> &&indices) : coordinate(coordinate) { this->build(std::move(indices)); } KDTreeIndirect(CoordinateFn coordinate, size_t num_indices) : coordinate(coordinate) { this->build(num_indices); }
KDTreeIndirect(CoordinateFn coordinate, size_t num_indices) : coordinate(coordinate) { this->build(num_indices); } KDTreeIndirect(KDTreeIndirect &&rhs) : m_nodes(std::move(rhs.m_nodes)), coordinate(std::move(rhs.coordinate)) {}
KDTreeIndirect(KDTreeIndirect &&rhs) : m_nodes(std::move(rhs.m_nodes)), coordinate(std::move(rhs.coordinate)) {} KDTreeIndirect& operator=(KDTreeIndirect &&rhs) { m_nodes = std::move(rhs.m_nodes); coordinate = std::move(rhs.coordinate); return *this; }
KDTreeIndirect& operator=(KDTreeIndirect &&rhs) { m_nodes = std::move(rhs.m_nodes); coordinate = std::move(rhs.coordinate); return *this; } void clear() { m_nodes.clear(); }
void clear() { m_nodes.clear(); }
void build(size_t num_indices) void build(size_t num_indices)
{ {
std::vector<size_t> indices; std::vector<size_t> indices;
indices.reserve(num_indices); indices.reserve(num_indices);
for (size_t i = 0; i < num_indices; ++ i) for (size_t i = 0; i < num_indices; ++ i)
indices.emplace_back(i); indices.emplace_back(i);
this->build(std::move(indices)); this->build(indices);
} }
void build(std::vector<size_t> &&indices) void build(std::vector<size_t> &indices)
{ {
if (indices.empty()) if (indices.empty())
clear(); clear();
else { else {
// Allocate enough memory for a full binary tree. // Allocate enough memory for a full binary tree.
m_nodes.assign(next_highest_power_of_2(indices.size() + 1), npos); m_nodes.assign(next_highest_power_of_2(indices.size() + 1), npos);
build_recursive(indices, 0, 0, 0, indices.size() - 1); build_recursive(indices, 0, 0, 0, indices.size() - 1);
} }
indices.clear(); indices.clear();
} }
enum class VisitorReturnMask : unsigned int template<typename CoordType>
{ unsigned int descent_mask(const CoordType &point_coord, const CoordType &search_radius, size_t idx, size_t dimension) const
CONTINUE_LEFT = 1, {
CONTINUE_RIGHT = 2, CoordType dist = point_coord - this->coordinate(idx, dimension);
STOP = 4, return (dist * dist < search_radius + CoordType(EPSILON)) ?
}; // The plane intersects a hypersphere centered at point_coord of search_radius.
template<typename CoordType> ((unsigned int)(VisitorReturnMask::CONTINUE_LEFT) | (unsigned int)(VisitorReturnMask::CONTINUE_RIGHT)) :
unsigned int descent_mask(const CoordType &point_coord, const CoordType &search_radius, size_t idx, size_t dimension) const // The plane does not intersect the hypersphere.
{ (dist > CoordType(0)) ? (unsigned int)(VisitorReturnMask::CONTINUE_RIGHT) : (unsigned int)(VisitorReturnMask::CONTINUE_LEFT);
CoordType dist = point_coord - this->coordinate(idx, dimension); }
return (dist * dist < search_radius + CoordType(EPSILON)) ?
// The plane intersects a hypersphere centered at point_coord of search_radius.
((unsigned int)(VisitorReturnMask::CONTINUE_LEFT) | (unsigned int)(VisitorReturnMask::CONTINUE_RIGHT)) :
// The plane does not intersect the hypersphere.
(dist > CoordType(0)) ? (unsigned int)(VisitorReturnMask::CONTINUE_RIGHT) : (unsigned int)(VisitorReturnMask::CONTINUE_LEFT);
}
// Visitor is supposed to return a bit mask of VisitorReturnMask. // Visitor is supposed to return a bit mask of VisitorReturnMask.
template<typename Visitor> template<typename Visitor>
void visit(Visitor &visitor) const void visit(Visitor &visitor) const
{ {
visit_recursive(0, 0, visitor); visit_recursive(0, 0, visitor);
} }
CoordinateFn coordinate; CoordinateFn coordinate;
private: private:
// Build a balanced tree by splitting the input sequence by an axis aligned plane at a dimension. // Build a balanced tree by splitting the input sequence by an axis aligned plane at a dimension.
void build_recursive(std::vector<size_t> &input, size_t node, const size_t dimension, const size_t left, const size_t right) void build_recursive(std::vector<size_t> &input, size_t node, const size_t dimension, const size_t left, const size_t right)
{ {
if (left > right) if (left > right)
return; return;
assert(node < m_nodes.size()); assert(node < m_nodes.size());
if (left == right) { if (left == right) {
// Insert a node into the balanced tree. // Insert a node into the balanced tree.
m_nodes[node] = input[left]; m_nodes[node] = input[left];
return; return;
} }
// Partition the input to left / right pieces of the same length to produce a balanced tree. // Partition the input to left / right pieces of the same length to produce a balanced tree.
size_t center = (left + right) / 2; size_t center = (left + right) / 2;
partition_input(input, dimension, left, right, center); partition_input(input, dimension, left, right, center);
// Insert a node into the tree. // Insert a node into the tree.
m_nodes[node] = input[center]; m_nodes[node] = input[center];
// Build up the left / right subtrees. // Build up the left / right subtrees.
size_t next_dimension = dimension; size_t next_dimension = dimension;
if (++ next_dimension == NumDimensions) if (++ next_dimension == NumDimensions)
next_dimension = 0; next_dimension = 0;
if (center > left) if (center > left)
build_recursive(input, node * 2 + 1, next_dimension, left, center - 1); build_recursive(input, node * 2 + 1, next_dimension, left, center - 1);
build_recursive(input, node * 2 + 2, next_dimension, center + 1, right); build_recursive(input, node * 2 + 2, next_dimension, center + 1, right);
} }
// Partition the input m_nodes <left, right> at "k" and "dimension" using the QuickSelect method: // Partition the input m_nodes <left, right> at "k" and "dimension" using the QuickSelect method:
// https://en.wikipedia.org/wiki/Quickselect // https://en.wikipedia.org/wiki/Quickselect
// Items left of the k'th item are lower than the k'th item in the "dimension", // Items left of the k'th item are lower than the k'th item in the "dimension",
// items right of the k'th item are higher than the k'th item in the "dimension", // items right of the k'th item are higher than the k'th item in the "dimension",
void partition_input(std::vector<size_t> &input, const size_t dimension, size_t left, size_t right, const size_t k) const void partition_input(std::vector<size_t> &input, const size_t dimension, size_t left, size_t right, const size_t k) const
{ {
while (left < right) { while (left < right) {
size_t center = (left + right) / 2; size_t center = (left + right) / 2;
CoordType pivot; CoordType pivot;
{ {
// Bubble sort the input[left], input[center], input[right], so that a median of the three values // Bubble sort the input[left], input[center], input[right], so that a median of the three values
// will end up in input[center]. // will end up in input[center].
CoordType left_value = this->coordinate(input[left], dimension); CoordType left_value = this->coordinate(input[left], dimension);
CoordType center_value = this->coordinate(input[center], dimension); CoordType center_value = this->coordinate(input[center], dimension);
CoordType right_value = this->coordinate(input[right], dimension); CoordType right_value = this->coordinate(input[right], dimension);
if (left_value > center_value) { if (left_value > center_value) {
std::swap(input[left], input[center]); std::swap(input[left], input[center]);
std::swap(left_value, center_value); std::swap(left_value, center_value);
} }
if (left_value > right_value) { if (left_value > right_value) {
std::swap(input[left], input[right]); std::swap(input[left], input[right]);
right_value = left_value; right_value = left_value;
} }
if (center_value > right_value) { if (center_value > right_value) {
std::swap(input[center], input[right]); std::swap(input[center], input[right]);
center_value = right_value; center_value = right_value;
} }
pivot = center_value; pivot = center_value;
} }
if (right <= left + 2) if (right <= left + 2)
// The <left, right> interval is already sorted. // The <left, right> interval is already sorted.
break; break;
size_t i = left; size_t i = left;
size_t j = right - 1; size_t j = right - 1;
std::swap(input[center], input[j]); std::swap(input[center], input[j]);
// Partition the set based on the pivot. // Partition the set based on the pivot.
for (;;) { for (;;) {
// Skip left points that are already at correct positions. // Skip left points that are already at correct positions.
// Search will certainly stop at position (right - 1), which stores the pivot. // Search will certainly stop at position (right - 1), which stores the pivot.
while (this->coordinate(input[++ i], dimension) < pivot) ; while (this->coordinate(input[++ i], dimension) < pivot) ;
// Skip right points that are already at correct positions. // Skip right points that are already at correct positions.
while (this->coordinate(input[-- j], dimension) > pivot && i < j) ; while (this->coordinate(input[-- j], dimension) > pivot && i < j) ;
if (i >= j) if (i >= j)
break; break;
std::swap(input[i], input[j]); std::swap(input[i], input[j]);
} }
// Restore pivot to the center of the sequence. // Restore pivot to the center of the sequence.
std::swap(input[i], input[right - 1]); std::swap(input[i], input[right - 1]);
// Which side the kth element is in? // Which side the kth element is in?
if (k < i) if (k < i)
right = i - 1; right = i - 1;
else if (k == i) else if (k == i)
// Sequence is partitioned, kth element is at its place. // Sequence is partitioned, kth element is at its place.
break; break;
else else
left = i + 1; left = i + 1;
} }
} }
template<typename Visitor> template<typename Visitor>
void visit_recursive(size_t node, size_t dimension, Visitor &visitor) const void visit_recursive(size_t node, size_t dimension, Visitor &visitor) const
{ {
assert(! m_nodes.empty()); assert(! m_nodes.empty());
if (node >= m_nodes.size() || m_nodes[node] == npos) if (node >= m_nodes.size() || m_nodes[node] == npos)
return; return;
// Left / right child node index. // Left / right child node index.
size_t left = node * 2 + 1; size_t left = node * 2 + 1;
size_t right = left + 1; size_t right = left + 1;
unsigned int mask = visitor(m_nodes[node], dimension); unsigned int mask = visitor(m_nodes[node], dimension);
if ((mask & (unsigned int)VisitorReturnMask::STOP) == 0) { if ((mask & (unsigned int)VisitorReturnMask::STOP) == 0) {
size_t next_dimension = (++ dimension == NumDimensions) ? 0 : dimension; size_t next_dimension = (++ dimension == NumDimensions) ? 0 : dimension;
if (mask & (unsigned int)VisitorReturnMask::CONTINUE_LEFT) if (mask & (unsigned int)VisitorReturnMask::CONTINUE_LEFT)
visit_recursive(left, next_dimension, visitor); visit_recursive(left, next_dimension, visitor);
if (mask & (unsigned int)VisitorReturnMask::CONTINUE_RIGHT) if (mask & (unsigned int)VisitorReturnMask::CONTINUE_RIGHT)
visit_recursive(right, next_dimension, visitor); visit_recursive(right, next_dimension, visitor);
} }
} }
std::vector<size_t> m_nodes; std::vector<size_t> m_nodes;
}; };
// Find a closest point using Euclidian metrics. // Find a closest point using Euclidian metrics.
// Returns npos if not found. // Returns npos if not found.
template<typename KDTreeIndirectType, typename PointType, typename FilterFn> template<size_t K,
size_t find_closest_point(const KDTreeIndirectType &kdtree, const PointType &point, FilterFn filter) typename PointType,
typename FilterFn,
size_t D,
typename CoordT,
typename CoordFn>
std::array<size_t, K> find_closest_points(
const KDTreeIndirect<D, CoordT, CoordFn> &kdtree,
const PointType &point,
FilterFn filter)
{ {
using CoordType = typename KDTreeIndirectType::CoordType; using Tree = KDTreeIndirect<D, CoordT, CoordFn>;
struct Visitor { struct Visitor
const KDTreeIndirectType &kdtree; {
const PointType &point; const Tree &kdtree;
const FilterFn filter; const PointType &point;
size_t min_idx = KDTreeIndirectType::npos; const FilterFn filter;
CoordType min_dist = std::numeric_limits<CoordType>::max();
Visitor(const KDTreeIndirectType &kdtree, const PointType &point, FilterFn filter) : kdtree(kdtree), point(point), filter(filter) {} std::array<std::pair<size_t, CoordT>, K> results;
unsigned int operator()(size_t idx, size_t dimension) {
if (this->filter(idx)) {
auto dist = CoordType(0);
for (size_t i = 0; i < KDTreeIndirectType::NumDimensions; ++ i) {
CoordType d = point[i] - kdtree.coordinate(idx, i);
dist += d * d;
}
if (dist < min_dist) {
min_dist = dist;
min_idx = idx;
}
}
return kdtree.descent_mask(point[dimension], min_dist, idx, dimension);
}
} visitor(kdtree, point, filter);
kdtree.visit(visitor); Visitor(const Tree &kdtree, const PointType &point, FilterFn filter)
return visitor.min_idx; : kdtree(kdtree), point(point), filter(filter)
{
results.fill(std::make_pair(Tree::npos,
std::numeric_limits<CoordT>::max()));
}
unsigned int operator()(size_t idx, size_t dimension)
{
if (this->filter(idx)) {
auto dist = CoordT(0);
for (size_t i = 0; i < D; ++i) {
CoordT d = point[i] - kdtree.coordinate(idx, i);
dist += d * d;
}
auto res = std::make_pair(idx, dist);
auto it = std::lower_bound(results.begin(), results.end(),
res, [](auto &r1, auto &r2) {
return r1.second < r2.second;
});
if (it != results.end()) {
std::rotate(it, std::prev(results.end()), results.end());
*it = res;
}
}
return kdtree.descent_mask(point[dimension],
results.front().second, idx,
dimension);
}
} visitor(kdtree, point, filter);
kdtree.visit(visitor);
std::array<size_t, K> ret;
for (size_t i = 0; i < K; i++) ret[i] = visitor.results[i].first;
return ret;
}
template<size_t K, typename PointType, size_t D, typename CoordT, typename CoordFn>
std::array<size_t, K> find_closest_points(
const KDTreeIndirect<D, CoordT, CoordFn> &kdtree, const PointType &point)
{
return find_closest_points<K>(kdtree, point, [](size_t) { return true; });
}
template<typename PointType,
typename FilterFn,
size_t D,
typename CoordT,
typename CoordFn>
size_t find_closest_point(const KDTreeIndirect<D, CoordT, CoordFn> &kdtree,
const PointType &point,
FilterFn filter)
{
return find_closest_points<1>(kdtree, point, filter)[0];
} }
template<typename KDTreeIndirectType, typename PointType> template<typename KDTreeIndirectType, typename PointType>
size_t find_closest_point(const KDTreeIndirectType& kdtree, const PointType& point) size_t find_closest_point(const KDTreeIndirectType& kdtree, const PointType& point)
{ {
return find_closest_point(kdtree, point, [](size_t) { return true; }); return find_closest_point(kdtree, point, [](size_t) { return true; });
} }
// Find nearby points (spherical neighbourhood) using Euclidian metrics. // Find nearby points (spherical neighbourhood) using Euclidian metrics.
template<typename KDTreeIndirectType, typename PointType, typename FilterFn> template<typename KDTreeIndirectType, typename PointType, typename FilterFn>
std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree, const PointType &center, std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree, const PointType &center,
const typename KDTreeIndirectType::CoordType& max_distance, FilterFn filter) const typename KDTreeIndirectType::CoordType& max_distance, FilterFn filter)
{ {
using CoordType = typename KDTreeIndirectType::CoordType; using CoordType = typename KDTreeIndirectType::CoordType;
struct Visitor { struct Visitor {
@ -247,7 +292,7 @@ std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree, const P
Visitor(const KDTreeIndirectType &kdtree, const PointType& center, const CoordType &max_distance, Visitor(const KDTreeIndirectType &kdtree, const PointType& center, const CoordType &max_distance,
FilterFn filter) : FilterFn filter) :
kdtree(kdtree), center(center), max_distance_squared(max_distance*max_distance), filter(filter) { kdtree(kdtree), center(center), max_distance_squared(max_distance*max_distance), filter(filter) {
} }
unsigned int operator()(size_t idx, size_t dimension) { unsigned int operator()(size_t idx, size_t dimension) {
if (this->filter(idx)) { if (this->filter(idx)) {
@ -260,7 +305,7 @@ std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree, const P
result.push_back(idx); result.push_back(idx);
} }
} }
return kdtree.descent_mask(center[dimension], max_distance_squared, idx, dimension); return kdtree.descent_mask(center[dimension], max_distance_squared, idx, dimension);
} }
} visitor(kdtree, center, max_distance, filter); } visitor(kdtree, center, max_distance, filter);
@ -270,13 +315,59 @@ std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree, const P
template<typename KDTreeIndirectType, typename PointType> template<typename KDTreeIndirectType, typename PointType>
std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree, const PointType &center, std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree, const PointType &center,
const typename KDTreeIndirectType::CoordType& max_distance) const typename KDTreeIndirectType::CoordType& max_distance)
{ {
return find_nearby_points(kdtree, center, max_distance, [](size_t) { return find_nearby_points(kdtree, center, max_distance, [](size_t) {
return true; return true;
}); });
} }
// Find nearby points (spherical neighbourhood) using Euclidian metrics.
template<typename KDTreeIndirectType, typename PointType, typename FilterFn>
std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree,
const PointType &bb_min,
const PointType &bb_max,
FilterFn filter)
{
struct Visitor {
const KDTreeIndirectType &kdtree;
const PointType &bb_min, &bb_max;
const FilterFn filter;
std::vector<size_t> result;
Visitor(const KDTreeIndirectType &kdtree, const PointType& bbmin, const PointType& bbmax,
FilterFn filter) :
kdtree(kdtree), bb_min{bbmin}, bb_max{bbmax}, filter(filter) {
}
unsigned int operator()(size_t idx, size_t dimension) {
unsigned int ret =
static_cast<unsigned int>(VisitorReturnMask::CONTINUE_LEFT) |
static_cast<unsigned int>(VisitorReturnMask::CONTINUE_RIGHT);
if (this->filter(idx)) {
PointType p;
bool contains = true;
for (size_t i = 0; i < KDTreeIndirectType::NumDimensions; ++i) {
p(i) = kdtree.coordinate(idx, i);
contains = contains && bb_min(i) <= p(i) && p(i) <= bb_max(i);
}
if (p(dimension) < bb_min(dimension))
ret = static_cast<unsigned int>(VisitorReturnMask::CONTINUE_RIGHT);
if (p(dimension) > bb_max(dimension))
ret = static_cast<unsigned int>(VisitorReturnMask::CONTINUE_LEFT);
if (contains)
result.emplace_back(idx);
}
return ret;
}
} visitor(kdtree, bb_min, bb_max, filter);
kdtree.visit(visitor);
return visitor.result;
}
} // namespace Slic3r } // namespace Slic3r

74
src/libslic3r/PointGrid.hpp Normal file
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@ -0,0 +1,74 @@
#ifndef POINTGRID_HPP
#define POINTGRID_HPP
#include <libslic3r/Execution/Execution.hpp>
#include <libslic3r/Point.hpp>
#include <libslic3r/BoundingBox.hpp>
namespace Slic3r {
template<class T>
class PointGrid {
Vec3i m_size;
std::vector<Vec<3, T>> m_data;
const int XY;
public:
explicit PointGrid(std::vector<Vec<3, T>> data, const Vec3i &size)
: m_data(std::move(data)), m_size{size}, XY{m_size.x() * m_size.y()}
{}
const Vec<3, T> & get(size_t idx) const { return m_data[idx]; }
const Vec<3, T> & get(const Vec3i &coord) const
{
return m_data[get_idx(coord)];
}
size_t get_idx(const Vec3i &coord) const
{
size_t ret = coord.z() * XY + coord.y() * m_size.x() + coord.x();
return ret;
}
Vec3i get_coord(size_t idx) const {
size_t iz = idx / XY;
size_t iy = (idx / m_size.x()) % m_size.y();
size_t ix = idx % m_size.x();
return {ix, iy, iz};
}
const std::vector<Vec<3, T>> & data() const { return m_data; }
size_t point_count() const { return m_data.size(); }
bool empty() const { return m_data.empty(); }
};
template<class Ex, class CoordT>
PointGrid<CoordT> point_grid(Ex policy,
const BoundingBox3Base<Vec<3, CoordT>> &bounds,
const Vec<3, CoordT> &stride)
{
Vec3i numpts = Vec3i::Zero();
for (int n = 0; n < 3; ++n)
numpts(n) = (bounds.max(n) - bounds.min(n)) / stride(n);
std::vector<Vec<3, CoordT>> out(numpts.x() * numpts.y() * numpts.z());
size_t XY = numpts[X] * numpts[Y];
execution::for_each(policy, size_t(0), out.size(), [&](size_t i) {
size_t iz = i / XY;
size_t iy = (i / numpts[X]) % numpts[Y];
size_t ix = i % numpts[X];
out[i] = Vec<3, CoordT>(ix * stride.x(), iy * stride.y(), iz * stride.z());
});
return PointGrid{std::move(out), numpts};
}
} // namespace Slic3r
#endif // POINTGRID_HPP

1
tests/libslic3r/CMakeLists.txt
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@ -4,6 +4,7 @@ add_executable(${_TEST_NAME}_tests
${_TEST_NAME}_tests.cpp ${_TEST_NAME}_tests.cpp
test_3mf.cpp test_3mf.cpp
test_aabbindirect.cpp test_aabbindirect.cpp
test_kdtreeindirect.cpp
test_clipper_offset.cpp test_clipper_offset.cpp
test_clipper_utils.cpp test_clipper_utils.cpp
test_color.cpp test_color.cpp

142
tests/libslic3r/test_kdtreeindirect.cpp Normal file
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@ -0,0 +1,142 @@
#include <catch2/catch.hpp>
#include "libslic3r/KDTreeIndirect.hpp"
#include "libslic3r/Execution/ExecutionSeq.hpp"
#include "libslic3r/BoundingBox.hpp"
#include "libslic3r/PointGrid.hpp"
using namespace Slic3r;
//template<class G>
//struct Within { // Wrapper for the `within` predicate that counts calls.
// kdtree::Within<G> pred;
// Within(G box): pred{box} {}
// // Number of times the predicate was called
// mutable size_t call_count = 0;
// std::pair<bool, unsigned int> operator() (const Vec3f &p, size_t dim)
// {
// ++call_count;
// return pred(p, dim);
// }
//};
static double volume(const BoundingBox3Base<Vec3f> &box)
{
auto sz = box.size();
return sz.x() * sz.y() * sz.z();
}
static double volume(const Eigen::AlignedBox<float, 3> &box)
{
return box.volume();
}
TEST_CASE("Test kdtree query for a Box", "[KDTreeIndirect]")
{
auto vol = BoundingBox3Base<Vec3f>{{0.f, 0.f, 0.f}, {10.f, 10.f, 10.f}};
auto pgrid = point_grid(ex_seq, vol, Vec3f{0.1f, 0.1f, 0.1f});
REQUIRE(!pgrid.empty());
auto coordfn = [&pgrid] (size_t i, size_t D) { return pgrid.get(i)(int(D)); };
KDTreeIndirect<3, float, decltype(coordfn)> tree{coordfn, pgrid.point_count()};
std::vector<size_t> out;
auto qbox = BoundingBox3Base{Vec3f{0.f, 0.f, 0.f}, Vec3f{.5f, .5f, .5f}};
size_t call_count = 0;
out = find_nearby_points(tree, qbox.min, qbox.max, [&call_count](size_t) {
call_count++;
return true;
});
// Output shall be non-empty
REQUIRE(!out.empty());
std::sort(out.begin(), out.end());
// No duplicates allowed in the output
auto it = std::unique(out.begin(), out.end());
REQUIRE(it == out.end());
// Test if inside points are in the output and outside points are not.
bool succ = true;
for (size_t i = 0; i < pgrid.point_count(); ++i) {
auto foundit = std::find(out.begin(), out.end(), i);
bool contains = qbox.contains(pgrid.get(i));
succ = succ && contains ? foundit != out.end() : foundit == out.end();
if (!succ) {
std::cout << "invalid point: " << i << " " << pgrid.get(i).transpose()
<< std::endl;
break;
}
}
REQUIRE(succ);
// Test for the expected cost of the query.
double gridvolume = volume(vol);
double queryvolume = volume(qbox);
double volratio = (queryvolume / gridvolume);
REQUIRE(call_count < 3 * volratio * pgrid.point_count());
REQUIRE(call_count < pgrid.point_count());
}
//TEST_CASE("Test kdtree query for a Sphere", "[KDTreeIndirect]") {
// auto vol = BoundingBox3Base<Vec3f>{{0.f, 0.f, 0.f}, {10.f, 10.f, 10.f}};
// auto pgrid = point_grid(ex_seq, vol, Vec3f{0.1f, 0.1f, 0.1f});
// REQUIRE(!pgrid.empty());
// auto coordfn = [&pgrid] (size_t i, size_t D) { return pgrid.get(i)(int(D)); };
// kdtree::KDTreeIndirect<3, float, decltype(coordfn)> tree{coordfn, pgrid.point_count()};
// std::vector<size_t> out;
// auto querysphere = kdtree::Sphere{Vec3f{5.f, 5.f, 5.f}, 2.f};
// auto pred = Within(querysphere);
// kdtree::query(tree, pred, std::back_inserter(out));
// // Output shall be non-empty
// REQUIRE(!out.empty());
// std::sort(out.begin(), out.end());
// // No duplicates allowed in the output
// auto it = std::unique(out.begin(), out.end());
// REQUIRE(it == out.end());
// // Test if inside points are in the output and outside points are not.
// bool succ = true;
// for (size_t i = 0; i < pgrid.point_count(); ++i) {
// auto foundit = std::find(out.begin(), out.end(), i);
// bool contains = (querysphere.center - pgrid.get(i)).squaredNorm() < pred.pred.r2;
// succ = succ && contains ? foundit != out.end() : foundit == out.end();
// if (!succ) {
// std::cout << "invalid point: " << i << " " << pgrid.get(i).transpose()
// << std::endl;
// break;
// }
// }
// REQUIRE(succ);
// // Test for the expected cost of the query.
// double gridvolume = volume(vol);
// double queryvolume = volume(querysphere);
// double volratio = (queryvolume / gridvolume);
// REQUIRE(pred.call_count < 3 * volratio * pgrid.point_count());
// REQUIRE(pred.call_count < pgrid.point_count());
//}