3DPrinting/PrusaSlicer-NonPlainar
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'tm_branching_tree_backports'

Browse Source
This commit is contained in:
tamasmeszaros 2022-05-11 10:55:35 +02:00
commit b2890b983b
16 changed files with 527 additions and 212 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
src/libslic3r/AABBTreeIndirect.hpp
View File

@ -771,8 +771,8 @@ inline bool is_any_triangle_in_radius(
auto distancer = detail::IndexedTriangleSetDistancer<VertexType, IndexedFaceType, TreeType, VectorType>
{ vertices, faces, tree, point };
size_t hit_idx;
VectorType hit_point = VectorType::Ones() * (std::nan(""));
size_t hit_idx;
VectorType hit_point = VectorType::Ones() * (NaN<typename VectorType::Scalar>);
if(tree.empty())
{
@ -828,22 +828,22 @@ struct Intersecting<Eigen::AlignedBox<CoordType, NumD>> {
template<class G> auto intersecting(const G &g) { return Intersecting<G>{g}; }
template<class G> struct Containing {};
template<class G> struct Within {};
// Intersection predicate specialization for box-box intersections
template<class CoordType, int NumD>
struct Containing<Eigen::AlignedBox<CoordType, NumD>> {
struct Within<Eigen::AlignedBox<CoordType, NumD>> {
Eigen::AlignedBox<CoordType, NumD> box;
Containing(const Eigen::AlignedBox<CoordType, NumD> &bb): box{bb} {}
Within(const Eigen::AlignedBox<CoordType, NumD> &bb): box{bb} {}
bool operator() (const typename Tree<NumD, CoordType>::Node &node) const
{
return box.contains(node.bbox);
return node.is_leaf() ? box.contains(node.bbox) : box.intersects(node.bbox);
}
};
template<class G> auto containing(const G &g) { return Containing<G>{g}; }
template<class G> auto within(const G &g) { return Within<G>{g}; }
namespace detail {
@ -858,7 +858,7 @@ void traverse_recurse(const Tree<Dims, T> &tree,
if (!pred(tree.node(idx))) return;
if (tree.node(idx).is_leaf()) {
callback(tree.node(idx).idx);
callback(tree.node(idx));
} else {
// call this with left and right node idx:

2
src/libslic3r/Arrange.hpp
View File

@ -15,7 +15,7 @@ class CircleBed {
double radius_;
public:
inline CircleBed(): center_(0, 0), radius_(std::nan("")) {}
inline CircleBed(): center_(0, 0), radius_(NaNd) {}
explicit inline CircleBed(const Point& c, double r): center_(c), radius_(r) {}
inline double radius() const { return radius_; }

1
src/libslic3r/CMakeLists.txt
View File

@ -212,6 +212,7 @@ set(SLIC3R_SOURCES
PrintObject.cpp
PrintObjectSlice.cpp
PrintRegion.cpp
PointGrid.hpp
PNGReadWrite.hpp
PNGReadWrite.cpp
QuadricEdgeCollapse.cpp

4
src/libslic3r/Execution/Execution.hpp
View File

@ -30,8 +30,8 @@ template<class EP> using AsTraits = Traits<remove_cvref_t<EP>>;
// Each execution policy should declare two types of mutexes. A a spin lock and
// a blocking mutex. These types should satisfy the BasicLockable concept.
template<class EP> using SpinningMutex = typename Traits<EP>::SpinningMutex;
template<class EP> using BlockingMutex = typename Traits<EP>::BlockingMutex;
template<class EP> using SpinningMutex = typename AsTraits<EP>::SpinningMutex;
template<class EP> using BlockingMutex = typename AsTraits<EP>::BlockingMutex;
// Query the available threads for concurrency.
template<class EP, class = ExecutionPolicyOnly<EP> >

463
src/libslic3r/KDTreeIndirect.hpp
View File

@ -11,231 +11,276 @@
namespace Slic3r {
enum class VisitorReturnMask : unsigned int {
CONTINUE_LEFT = 1,
CONTINUE_RIGHT = 2,
STOP = 4,
};
// KD tree for N-dimensional closest point search.
template<size_t ANumDimensions, typename ACoordType, typename ACoordinateFn>
class KDTreeIndirect
{
public:
static constexpr size_t NumDimensions = ANumDimensions;
using CoordinateFn = ACoordinateFn;
using CoordType = ACoordType;
static constexpr size_t NumDimensions = ANumDimensions;
using CoordinateFn = ACoordinateFn;
using CoordType = ACoordType;
// Following could be static constexpr size_t, but that would not link in C++11
enum : size_t {
npos = size_t(-1)
};
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(std::move(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& operator=(KDTreeIndirect &&rhs) { m_nodes = std::move(rhs.m_nodes); coordinate = std::move(rhs.coordinate); return *this; }
void clear() { m_nodes.clear(); }
KDTreeIndirect(CoordinateFn coordinate) : coordinate(coordinate) {}
KDTreeIndirect(CoordinateFn coordinate, std::vector<size_t> indices) : coordinate(coordinate) { this->build(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& operator=(KDTreeIndirect &&rhs) { m_nodes = std::move(rhs.m_nodes); coordinate = std::move(rhs.coordinate); return *this; }
void clear() { m_nodes.clear(); }
void build(size_t num_indices)
{
std::vector<size_t> indices;
indices.reserve(num_indices);
for (size_t i = 0; i < num_indices; ++ i)
indices.emplace_back(i);
this->build(std::move(indices));
}
void build(size_t num_indices)
{
std::vector<size_t> indices;
indices.reserve(num_indices);
for (size_t i = 0; i < num_indices; ++ i)
indices.emplace_back(i);
this->build(indices);
}
void build(std::vector<size_t> &&indices)
{
if (indices.empty())
clear();
else {
// Allocate enough memory for a full binary tree.
m_nodes.assign(next_highest_power_of_2(indices.size() + 1), npos);
build_recursive(indices, 0, 0, 0, indices.size() - 1);
}
indices.clear();
}
void build(std::vector<size_t> &indices)
{
if (indices.empty())
clear();
else {
// Allocate enough memory for a full binary tree.
m_nodes.assign(next_highest_power_of_2(indices.size() + 1), npos);
build_recursive(indices, 0, 0, 0, indices.size() - 1);
}
indices.clear();
}
enum class VisitorReturnMask : unsigned int
{
CONTINUE_LEFT = 1,
CONTINUE_RIGHT = 2,
STOP = 4,
};
template<typename CoordType>
unsigned int descent_mask(const CoordType &point_coord, const CoordType &search_radius, size_t idx, size_t dimension) const
{
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);
}
template<typename CoordType>
unsigned int descent_mask(const CoordType &point_coord, const CoordType &search_radius, size_t idx, size_t dimension) const
{
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.
template<typename Visitor>
void visit(Visitor &visitor) const
{
// Visitor is supposed to return a bit mask of VisitorReturnMask.
template<typename Visitor>
void visit(Visitor &visitor) const
{
visit_recursive(0, 0, visitor);
}
}
CoordinateFn coordinate;
CoordinateFn coordinate;
private:
// 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)
{
if (left > right)
return;
// 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)
{
if (left > right)
return;
assert(node < m_nodes.size());
assert(node < m_nodes.size());
if (left == right) {
// Insert a node into the balanced tree.
m_nodes[node] = input[left];
return;
}
if (left == right) {
// Insert a node into the balanced tree.
m_nodes[node] = input[left];
return;
}
// Partition the input to left / right pieces of the same length to produce a balanced tree.
size_t center = (left + right) / 2;
partition_input(input, dimension, left, right, center);
// Insert a node into the tree.
m_nodes[node] = input[center];
// Build up the left / right subtrees.
size_t next_dimension = dimension;
if (++ next_dimension == NumDimensions)
next_dimension = 0;
if (center > left)
build_recursive(input, node * 2 + 1, next_dimension, left, center - 1);
build_recursive(input, node * 2 + 2, next_dimension, center + 1, right);
}
// Partition the input to left / right pieces of the same length to produce a balanced tree.
size_t center = (left + right) / 2;
partition_input(input, dimension, left, right, center);
// Insert a node into the tree.
m_nodes[node] = input[center];
// Build up the left / right subtrees.
size_t next_dimension = dimension;
if (++ next_dimension == NumDimensions)
next_dimension = 0;
if (center > left)
build_recursive(input, node * 2 + 1, next_dimension, left, center - 1);
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:
// https://en.wikipedia.org/wiki/Quickselect
// 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",
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) {
size_t center = (left + right) / 2;
CoordType pivot;
{
// Bubble sort the input[left], input[center], input[right], so that a median of the three values
// will end up in input[center].
CoordType left_value = this->coordinate(input[left], dimension);
CoordType center_value = this->coordinate(input[center], dimension);
CoordType right_value = this->coordinate(input[right], dimension);
if (left_value > center_value) {
std::swap(input[left], input[center]);
std::swap(left_value, center_value);
}
if (left_value > right_value) {
std::swap(input[left], input[right]);
right_value = left_value;
}
if (center_value > right_value) {
std::swap(input[center], input[right]);
center_value = right_value;
}
pivot = center_value;
}
if (right <= left + 2)
// The <left, right> interval is already sorted.
break;
size_t i = left;
size_t j = right - 1;
std::swap(input[center], input[j]);
// Partition the set based on the pivot.
for (;;) {
// Skip left points that are already at correct positions.
// Search will certainly stop at position (right - 1), which stores the pivot.
while (this->coordinate(input[++ i], dimension) < pivot) ;
// Skip right points that are already at correct positions.
while (this->coordinate(input[-- j], dimension) > pivot && i < j) ;
if (i >= j)
break;
std::swap(input[i], input[j]);
}
// Restore pivot to the center of the sequence.
std::swap(input[i], input[right - 1]);
// Which side the kth element is in?
if (k < i)
right = i - 1;
else if (k == i)
// Sequence is partitioned, kth element is at its place.
break;
else
left = i + 1;
}
}
// Partition the input m_nodes <left, right> at "k" and "dimension" using the QuickSelect method:
// https://en.wikipedia.org/wiki/Quickselect
// 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",
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) {
size_t center = (left + right) / 2;
CoordType pivot;
{
// Bubble sort the input[left], input[center], input[right], so that a median of the three values
// will end up in input[center].
CoordType left_value = this->coordinate(input[left], dimension);
CoordType center_value = this->coordinate(input[center], dimension);
CoordType right_value = this->coordinate(input[right], dimension);
if (left_value > center_value) {
std::swap(input[left], input[center]);
std::swap(left_value, center_value);
}
if (left_value > right_value) {
std::swap(input[left], input[right]);
right_value = left_value;
}
if (center_value > right_value) {
std::swap(input[center], input[right]);
center_value = right_value;
}
pivot = center_value;
}
if (right <= left + 2)
// The <left, right> interval is already sorted.
break;
size_t i = left;
size_t j = right - 1;
std::swap(input[center], input[j]);
// Partition the set based on the pivot.
for (;;) {
// Skip left points that are already at correct positions.
// Search will certainly stop at position (right - 1), which stores the pivot.
while (this->coordinate(input[++ i], dimension) < pivot) ;
// Skip right points that are already at correct positions.
while (this->coordinate(input[-- j], dimension) > pivot && i < j) ;
if (i >= j)
break;
std::swap(input[i], input[j]);
}
// Restore pivot to the center of the sequence.
std::swap(input[i], input[right - 1]);
// Which side the kth element is in?
if (k < i)
right = i - 1;
else if (k == i)
// Sequence is partitioned, kth element is at its place.
break;
else
left = i + 1;
}
}
template<typename Visitor>
void visit_recursive(size_t node, size_t dimension, Visitor &visitor) const
{
assert(! m_nodes.empty());
if (node >= m_nodes.size() || m_nodes[node] == npos)
return;
template<typename Visitor>
void visit_recursive(size_t node, size_t dimension, Visitor &visitor) const
{
assert(! m_nodes.empty());
if (node >= m_nodes.size() || m_nodes[node] == npos)
return;
// Left / right child node index.
size_t left = node * 2 + 1;
size_t right = left + 1;
unsigned int mask = visitor(m_nodes[node], dimension);
if ((mask & (unsigned int)VisitorReturnMask::STOP) == 0) {
size_t next_dimension = (++ dimension == NumDimensions) ? 0 : dimension;
if (mask & (unsigned int)VisitorReturnMask::CONTINUE_LEFT)
visit_recursive(left, next_dimension, visitor);
if (mask & (unsigned int)VisitorReturnMask::CONTINUE_RIGHT)
visit_recursive(right, next_dimension, visitor);
}
}
// Left / right child node index.
size_t left = node * 2 + 1;
size_t right = left + 1;
unsigned int mask = visitor(m_nodes[node], dimension);
if ((mask & (unsigned int)VisitorReturnMask::STOP) == 0) {
size_t next_dimension = (++ dimension == NumDimensions) ? 0 : dimension;
if (mask & (unsigned int)VisitorReturnMask::CONTINUE_LEFT)
visit_recursive(left, next_dimension, visitor);
if (mask & (unsigned int)VisitorReturnMask::CONTINUE_RIGHT)
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.
// Returns npos if not found.
template<typename KDTreeIndirectType, typename PointType, typename FilterFn>
size_t find_closest_point(const KDTreeIndirectType &kdtree, const PointType &point, FilterFn filter)
template<size_t K,
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 {
const KDTreeIndirectType &kdtree;
const PointType &point;
const FilterFn filter;
size_t min_idx = KDTreeIndirectType::npos;
CoordType min_dist = std::numeric_limits<CoordType>::max();
struct Visitor
{
const Tree &kdtree;
const PointType &point;
const FilterFn filter;
Visitor(const KDTreeIndirectType &kdtree, const PointType &point, FilterFn filter) : kdtree(kdtree), point(point), filter(filter) {}
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);
std::array<std::pair<size_t, CoordT>, K> results;
kdtree.visit(visitor);
return visitor.min_idx;
Visitor(const Tree &kdtree, const PointType &point, FilterFn filter)
: 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>
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.
template<typename KDTreeIndirectType, typename PointType, typename FilterFn>
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;
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,
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) {
if (this->filter(idx)) {
@ -260,7 +305,7 @@ std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree, const P
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);
@ -270,13 +315,59 @@ std::vector<size_t> find_nearby_points(const KDTreeIndirectType &kdtree, const P
template<typename KDTreeIndirectType, typename PointType>
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 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

6
src/libslic3r/Optimize/Optimizer.hpp
View File

@ -41,13 +41,13 @@ template<size_t N> using Bounds = std::array<Bound, N>;
class StopCriteria {
// If the absolute value difference between two scores.
double m_abs_score_diff = std::nan("");
double m_abs_score_diff = NaNd;
// If the relative value difference between two scores.
double m_rel_score_diff = std::nan("");
double m_rel_score_diff = NaNd;
// Stop if this value or better is found.
double m_stop_score = std::nan("");
double m_stop_score = NaNd;
// A predicate that if evaluates to true, the optimization should terminate
// and the best result found prior to termination should be returned.

74
src/libslic3r/PointGrid.hpp Normal file
View File

@ -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

10
src/libslic3r/SLA/Rotfinder.cpp
View File

@ -76,7 +76,7 @@ struct Facestats {
// Try to guess the number of support points needed to support a mesh
double get_misalginment_score(const TriangleMesh &mesh, const Transform3f &tr)
{
if (mesh.its.vertices.empty()) return std::nan("");
if (mesh.its.vertices.empty()) return NaNd;
auto accessfn = [&mesh, &tr](size_t fi) {
Facestats fc{get_transformed_triangle(mesh, tr, fi)};
@ -117,7 +117,7 @@ inline double get_supportedness_score(const Facestats &fc)
// Try to guess the number of support points needed to support a mesh
double get_supportedness_score(const TriangleMesh &mesh, const Transform3f &tr)
{
if (mesh.its.vertices.empty()) return std::nan("");
if (mesh.its.vertices.empty()) return NaNd;
auto accessfn = [&mesh, &tr](size_t fi) {
Facestats fc{get_transformed_triangle(mesh, tr, fi)};
@ -149,10 +149,10 @@ float find_ground_level(const TriangleMesh &mesh,
return execution::reduce(ex_tbb, size_t(0), vsize, zmin, minfn, accessfn, granularity);
}
float get_supportedness_onfloor_score(const TriangleMesh &mesh,
const Transform3f & tr)
double get_supportedness_onfloor_score(const TriangleMesh &mesh,
const Transform3f &tr)
{
if (mesh.its.vertices.empty()) return std::nan("");
if (mesh.its.vertices.empty()) return NaNd;
size_t Nthreads = std::thread::hardware_concurrency();

2
src/libslic3r/SLA/SupportTreeBuildsteps.cpp
View File

@ -654,7 +654,7 @@ void SupportTreeBuildsteps::filter()
for (const SupportPoint &sp : m_support_pts) {
m_thr();
heads.emplace_back(
std::nan(""),
NaNd,
sp.head_front_radius,
0.,
m_cfg.head_penetration_mm,

6
src/libslic3r/SLAPrintSteps.cpp
View File

@ -408,9 +408,9 @@ void SLAPrint::Steps::drill_holes(SLAPrintObject &po)
AABBTreeIndirect::traverse(
tree,
AABBTreeIndirect::intersecting(ebb),
[&part_to_drill, &hollowed_mesh](size_t faceid)
[&part_to_drill, &hollowed_mesh](const auto& node)
{
part_to_drill.indices.emplace_back(hollowed_mesh.its.indices[faceid]);
part_to_drill.indices.emplace_back(hollowed_mesh.its.indices[node.idx]);
});
auto cgal_meshpart = MeshBoolean::cgal::triangle_mesh_to_cgal(
@ -1036,7 +1036,7 @@ void SLAPrint::Steps::merge_slices_and_eval_stats() {
// Estimated printing time
// A layers count o the highest object
if (printer_input.size() == 0)
print_statistics.estimated_print_time = std::nan("");
print_statistics.estimated_print_time = NaNd;
else {
print_statistics.estimated_print_time = estim_time;
print_statistics.layers_times = layers_times;

6
src/libslic3r/libslic3r.h
View File

@ -331,6 +331,12 @@ public:
inline bool empty() const { return size() == 0; }
};
template<class T, class = FloatingOnly<T>>
constexpr T NaN = std::numeric_limits<T>::quiet_NaN();
constexpr float NaNf = NaN<float>;
constexpr double NaNd = NaN<double>;
} // namespace Slic3r
#endif

2
src/slic3r/GUI/DoubleSlider.cpp
View File

@ -1163,7 +1163,7 @@ void Control::draw_ruler(wxDC& dc)
}
};
double short_tick = std::nan("");
double short_tick = NaNd;
int tick = 0;
double value = 0.0;
size_t sequence = 0;

2
src/slic3r/GUI/GLCanvas3D.hpp
View File

@ -822,7 +822,7 @@ public:
class WipeTowerInfo {
protected:
Vec2d m_pos = {std::nan(""), std::nan("")};
Vec2d m_pos = {NaNd, NaNd};
double m_rotation = 0.;
BoundingBoxf m_bb;
friend class GLCanvas3D;

1
tests/libslic3r/CMakeLists.txt
View File

@ -4,6 +4,7 @@ add_executable(${_TEST_NAME}_tests
${_TEST_NAME}_tests.cpp
test_3mf.cpp
test_aabbindirect.cpp
test_kdtreeindirect.cpp
test_clipper_offset.cpp
test_clipper_utils.cpp
test_color.cpp

142
tests/libslic3r/test_kdtreeindirect.cpp Normal file
View File

@ -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());
//}

2
tests/sla_print/sla_test_utils.cpp
View File

@ -386,7 +386,7 @@ long raster_pxsum(const sla::RasterGrayscaleAA &raster)
double raster_white_area(const sla::RasterGrayscaleAA &raster)
{
if (raster.resolution().pixels() == 0) return std::nan("");
if (raster.resolution().pixels() == 0) return NaNd;
auto res = raster.resolution();
double a = 0;