Merge remote-tracking branch 'origin/master' into ys_cut
This commit is contained in:
YuSanka
commit
04145abdfc
26 changed files with 844 additions and 226 deletions
|
|
@ -1,2 +1,4 @@
|
|||
min_slic3r_version = 2.4.2
|
||||
1.0.1 Added 350mm Voron v1 variant. Updated max print heights. Removed redundant v1 volcano nozzle variants.
|
||||
min_slic3r_version = 2.4.0-beta0
|
||||
1.0.0 Initial version
|
||||
|
|
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|||
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@ -7,7 +7,7 @@
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|||
name = Voron
|
||||
# Configuration version of this file. Config file will only be installed, if the config_version differs.
|
||||
# This means, the server may force the PrusaSlicer configuration to be downgraded.
|
||||
config_version = 1.0.0
|
||||
config_version = 1.0.1
|
||||
# Where to get the updates from?
|
||||
config_update_url = https://files.prusa3d.com/wp-content/uploads/repository/PrusaSlicer-settings-master/live/Voron/
|
||||
|
||||
|
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@ -72,21 +72,30 @@ default_materials = Basic PLA @VORON; Basic PLA VOLCANO @VORON; Basic PET @VORON
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|||
|
||||
[printer_model:Voron_v1_250_afterburner]
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||||
name = Voron v1 250mm3
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||||
variants = 0.4; 0.25; 0.3; 0.5; 0.6; 0.8; volcano 0.6; volcano 0.8; volcano 1.0; volcano 1.2
|
||||
variants = 0.4; 0.25; 0.3; 0.5; 0.6; 0.8
|
||||
technology = FFF
|
||||
family = Voron v1 Afterburner
|
||||
bed_model = printbed-v1-250.stl
|
||||
bed_texture = bedtexture-v1-250.png
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||||
default_materials = Basic PLA @VORON; Basic PLA VOLCANO @VORON; Basic PET @VORON; Basic PET VOLCANO @VORON; Basic ABS @VORON; Basic ABS VOLCANO @VORON
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||||
default_materials = Basic PLA @VORON; Basic PET @VORON; Basic ABS @VORON
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||||
|
||||
[printer_model:Voron_v1_300_afterburner]
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||||
name = Voron v1 300mm3
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||||
variants = 0.4; 0.25; 0.3; 0.5; 0.6; 0.8; volcano 0.6; volcano 0.8; volcano 1.0; volcano 1.2
|
||||
variants = 0.4; 0.25; 0.3; 0.5; 0.6; 0.8
|
||||
technology = FFF
|
||||
family = Voron v1 Afterburner
|
||||
bed_model = printbed-v1-300.stl
|
||||
bed_texture = bedtexture-v1-300.png
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||||
default_materials = Basic PLA @VORON; Basic PLA VOLCANO @VORON; Basic PET @VORON; Basic PET VOLCANO @VORON; Basic ABS @VORON; Basic ABS VOLCANO @VORON
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||||
default_materials = Basic PLA @VORON; Basic PET @VORON; Basic ABS @VORON
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||||
|
||||
[printer_model:Voron_v1_350_afterburner]
|
||||
name = Voron v1 350mm3
|
||||
variants = 0.4; 0.25; 0.3; 0.5; 0.6; 0.8
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||||
technology = FFF
|
||||
family = Voron v1 Afterburner
|
||||
bed_model = printbed-v1-350.stl
|
||||
bed_texture = bedtexture-v2-350.png
|
||||
default_materials = Basic PLA @VORON; Basic PET @VORON; Basic ABS @VORON
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||||
|
||||
[printer_model:Voron_v0_120]
|
||||
name = Voron Zero 120mm3
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@ -239,21 +248,21 @@ retract_speed = 50
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[printer:*Voron_v2_250*]
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inherits = *common*
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bed_shape = 0x0,250x0,250x250,0x250
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max_print_height = 250
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||||
max_print_height = 230
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printer_model = Voron_v2_250
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||||
printer_notes = Unoffical profile.\nPRINTER_HAS_BOWDEN\nE3DV6
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||||
|
||||
[printer:*Voron_v2_300*]
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||||
inherits = *common*
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||||
bed_shape = 0x0,300x0,300x300,0x300
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||||
max_print_height = 300
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||||
max_print_height = 280
|
||||
printer_model = Voron_v2_300
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||||
printer_notes = Unoffical profile.\nPRINTER_HAS_BOWDEN\nE3DV6
|
||||
|
||||
[printer:*Voron_v2_350*]
|
||||
inherits = *common*
|
||||
bed_shape = 0x0,350x0,350x350,0x350
|
||||
max_print_height = 350
|
||||
max_print_height = 330
|
||||
printer_model = Voron_v2_350
|
||||
printer_notes = Unoffical profile.\nPRINTER_HAS_BOWDEN\nE3DV6
|
||||
|
||||
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@ -282,10 +291,17 @@ printer_notes = Unoffical profile.\nE3DV6
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|||
[printer:*Voron_v1_300_afterburner*]
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||||
inherits = *common*; *afterburner*
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||||
bed_shape = 0x0,300x0,300x300,0x300
|
||||
max_print_height = 230
|
||||
max_print_height = 280
|
||||
printer_model = Voron_v1_300_afterburner
|
||||
printer_notes = Unoffical profile.\nE3DV6
|
||||
|
||||
[printer:*Voron_v1_350_afterburner*]
|
||||
inherits = *common*; *afterburner*
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||||
bed_shape = 0x0,350x0,350x350,0x350
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||||
max_print_height = 330
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||||
printer_model = Voron_v1_350_afterburner
|
||||
printer_notes = Unoffical profile.\nE3DV6
|
||||
|
||||
[printer:*Voron_v0_120*]
|
||||
inherits = *common*
|
||||
bed_shape = 0x0,120x0,120x120,0x120
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||||
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@ -455,6 +471,24 @@ inherits = *Voron_v1_300_afterburner*; *0.6nozzle*
|
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[printer:Voron_v1_300_afterburner 0.8 nozzle]
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||||
inherits = *Voron_v1_300_afterburner*; *0.8nozzle*
|
||||
|
||||
[printer:Voron_v1_350_afterburner 0.25 nozzle]
|
||||
inherits = *Voron_v1_350_afterburner*; *0.25nozzle*
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||||
|
||||
[printer:Voron_v1_350_afterburner 0.3 nozzle]
|
||||
inherits = *Voron_v1_350_afterburner*; *0.3nozzle*
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||||
|
||||
[printer:Voron_v1_350_afterburner 0.4 nozzle]
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||||
inherits = *Voron_v1_350_afterburner*; *0.4nozzle*
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||||
|
||||
[printer:Voron_v1_350_afterburner 0.5 nozzle]
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||||
inherits = *Voron_v1_350_afterburner*; *0.5nozzle*
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||||
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||||
[printer:Voron_v1_350_afterburner 0.6 nozzle]
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||||
inherits = *Voron_v1_350_afterburner*; *0.6nozzle*
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||||
|
||||
[printer:Voron_v1_350_afterburner 0.8 nozzle]
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||||
inherits = *Voron_v1_350_afterburner*; *0.8nozzle*
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||||
|
||||
[printer:Voron_v2_250_afterburner 0.25 nozzle]
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||||
inherits = *Voron_v2_250_afterburner*; *0.25nozzle*
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||||
|
||||
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@ -652,7 +686,7 @@ fill_angle = 45
|
|||
fill_density = 15%
|
||||
fill_pattern = gyroid
|
||||
first_layer_acceleration = 1000
|
||||
first_layer_height = 75%
|
||||
first_layer_height = 0.2
|
||||
first_layer_speed = 30
|
||||
gap_fill_speed = 40
|
||||
gcode_comments = 0
|
||||
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|
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Binary file not shown.
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Before Width: | Height: | Size: 63 KiB After Width: | Height: | Size: 39 KiB |
Binary file not shown.
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Before Width: | Height: | Size: 63 KiB After Width: | Height: | Size: 39 KiB |
Binary file not shown.
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Before Width: | Height: | Size: 63 KiB After Width: | Height: | Size: 39 KiB |
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@ -771,8 +771,8 @@ inline bool is_any_triangle_in_radius(
|
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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:
|
||||
|
|
|
|||
182
src/libslic3r/AStar.hpp
Normal file
182
src/libslic3r/AStar.hpp
Normal file
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|
@ -0,0 +1,182 @@
|
|||
#ifndef ASTAR_HPP
|
||||
#define ASTAR_HPP
|
||||
|
||||
#include "libslic3r/Point.hpp"
|
||||
#include "libslic3r/MutablePriorityQueue.hpp"
|
||||
|
||||
#include <unordered_map>
|
||||
|
||||
namespace Slic3r { namespace astar {
|
||||
|
||||
// Input interface for the Astar algorithm. Specialize this struct for a
|
||||
// particular type and implement all the 4 methods and specify the Node type
|
||||
// to register the new type for the astar implementation.
|
||||
template<class T> struct TracerTraits_
|
||||
{
|
||||
// The type of a node used by this tracer. Usually a point in space.
|
||||
using Node = typename T::Node;
|
||||
|
||||
// Call fn for every new node reachable from node 'src'. fn should have the
|
||||
// candidate node as its only argument.
|
||||
template<class Fn>
|
||||
static void foreach_reachable(const T &tracer, const Node &src, Fn &&fn)
|
||||
{
|
||||
tracer.foreach_reachable(src, fn);
|
||||
}
|
||||
|
||||
// Get the distance from node 'a' to node 'b'. This is sometimes referred
|
||||
// to as the g value of a node in AStar context.
|
||||
static float distance(const T &tracer, const Node &a, const Node &b)
|
||||
{
|
||||
return tracer.distance(a, b);
|
||||
}
|
||||
|
||||
// Get the estimated distance heuristic from node 'n' to the destination.
|
||||
// This is referred to as the h value in AStar context.
|
||||
// If node 'n' is the goal, this function should return a negative value.
|
||||
static float goal_heuristic(const T &tracer, const Node &n)
|
||||
{
|
||||
return tracer.goal_heuristic(n);
|
||||
}
|
||||
|
||||
// Return a unique identifier (hash) for node 'n'.
|
||||
static size_t unique_id(const T &tracer, const Node &n)
|
||||
{
|
||||
return tracer.unique_id(n);
|
||||
}
|
||||
};
|
||||
|
||||
// Helper definition to get the node type of a tracer
|
||||
template<class T>
|
||||
using TracerNodeT = typename TracerTraits_<remove_cvref_t<T>>::Node;
|
||||
|
||||
namespace detail {
|
||||
// Helper functions dispatching calls through the TracerTraits_ interface
|
||||
|
||||
template<class T> using TracerTraits = TracerTraits_<remove_cvref_t<T>>;
|
||||
|
||||
template<class T, class Fn>
|
||||
void foreach_reachable(const T &tracer, const TracerNodeT<T> &from, Fn &&fn)
|
||||
{
|
||||
TracerTraits<T>::foreach_reachable(tracer, from, fn);
|
||||
}
|
||||
|
||||
template<class T>
|
||||
float trace_distance(const T &tracer, const TracerNodeT<T> &a, const TracerNodeT<T> &b)
|
||||
{
|
||||
return TracerTraits<T>::distance(tracer, a, b);
|
||||
}
|
||||
|
||||
template<class T>
|
||||
float goal_heuristic(const T &tracer, const TracerNodeT<T> &n)
|
||||
{
|
||||
return TracerTraits<T>::goal_heuristic(tracer, n);
|
||||
}
|
||||
|
||||
template<class T>
|
||||
size_t unique_id(const T &tracer, const TracerNodeT<T> &n)
|
||||
{
|
||||
return TracerTraits<T>::unique_id(tracer, n);
|
||||
}
|
||||
|
||||
} // namespace astar_detail
|
||||
|
||||
// Run the AStar algorithm on a tracer implementation.
|
||||
// The 'tracer' argument encapsulates the domain (grid, point cloud, etc...)
|
||||
// The 'source' argument is the starting node.
|
||||
// The 'out' argument is the output iterator into which the output nodes are
|
||||
// written.
|
||||
// Note that no destination node is given. The tracer's goal_heuristic() method
|
||||
// should return a negative value if a node is a destination node.
|
||||
template<class Tracer, class It>
|
||||
bool search_route(const Tracer &tracer, const TracerNodeT<Tracer> &source, It out)
|
||||
{
|
||||
using namespace detail;
|
||||
|
||||
using Node = TracerNodeT<Tracer>;
|
||||
enum class QueueType { Open, Closed, None };
|
||||
|
||||
struct QNode // Queue node. Keeps track of scores g, and h
|
||||
{
|
||||
Node node; // The actual node itself
|
||||
QueueType qtype = QueueType::None; // Which queue holds this node
|
||||
|
||||
float g = 0.f, h = 0.f;
|
||||
float f() const { return g + h; }
|
||||
};
|
||||
|
||||
// TODO: apply a linear memory allocator
|
||||
using QMap = std::unordered_map<size_t, QNode>;
|
||||
|
||||
// The traversed nodes are stored here encapsulated in QNodes
|
||||
QMap cached_nodes;
|
||||
|
||||
struct LessPred { // Comparison functor needed by MutablePriorityQueue
|
||||
QMap &m;
|
||||
bool operator ()(size_t node_a, size_t node_b) {
|
||||
auto ait = m.find(node_a);
|
||||
auto bit = m.find(node_b);
|
||||
assert (ait != m.end() && bit != m.end());
|
||||
|
||||
return ait->second.f() < bit->second.f();
|
||||
}
|
||||
};
|
||||
|
||||
auto qopen =
|
||||
make_mutable_priority_queue<size_t, false>([](size_t, size_t){},
|
||||
LessPred{cached_nodes});
|
||||
|
||||
auto qclosed =
|
||||
make_mutable_priority_queue<size_t, false>([](size_t, size_t){},
|
||||
LessPred{cached_nodes});
|
||||
|
||||
QNode initial{source, QueueType::Open};
|
||||
cached_nodes.insert({unique_id(tracer, source), initial});
|
||||
qopen.push(unique_id(tracer, source));
|
||||
|
||||
bool goal_reached = false;
|
||||
|
||||
while (!goal_reached && !qopen.empty()) {
|
||||
size_t q_id = qopen.top();
|
||||
qopen.pop();
|
||||
QNode q = cached_nodes.at(q_id);
|
||||
|
||||
foreach_reachable(tracer, q.node, [&](const Node &nd) {
|
||||
if (goal_reached) return goal_reached;
|
||||
|
||||
float h = goal_heuristic(tracer, nd);
|
||||
if (h < 0.f) {
|
||||
goal_reached = true;
|
||||
} else {
|
||||
float dst = trace_distance(tracer, q.node, nd);
|
||||
QNode qnd{nd, QueueType::None, q.g + dst, h};
|
||||
size_t qnd_id = unique_id(tracer, nd);
|
||||
|
||||
auto it = cached_nodes.find(qnd_id);
|
||||
|
||||
if (it == cached_nodes.end() ||
|
||||
(it->second.qtype != QueueType::None && qnd.f() < it->second.f())) {
|
||||
qnd.qtype = QueueType::Open;
|
||||
cached_nodes.insert_or_assign(qnd_id, qnd);
|
||||
qopen.push(qnd_id);
|
||||
}
|
||||
}
|
||||
|
||||
return goal_reached;
|
||||
});
|
||||
|
||||
q.qtype = QueueType::Closed;
|
||||
cached_nodes.insert_or_assign(q_id, q);
|
||||
qclosed.push(q_id);
|
||||
|
||||
// write the output
|
||||
*out = q.node;
|
||||
++out;
|
||||
}
|
||||
|
||||
return goal_reached;
|
||||
}
|
||||
|
||||
}} // namespace Slic3r::astar
|
||||
|
||||
#endif // ASTAR_HPP
|
||||
|
|
@ -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_; }
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ endif()
|
|||
set(SLIC3R_SOURCES
|
||||
pchheader.cpp
|
||||
pchheader.hpp
|
||||
AStar.hpp
|
||||
BoundingBox.cpp
|
||||
BoundingBox.hpp
|
||||
BridgeDetector.cpp
|
||||
|
|
@ -212,6 +213,7 @@ set(SLIC3R_SOURCES
|
|||
PrintObject.cpp
|
||||
PrintObjectSlice.cpp
|
||||
PrintRegion.cpp
|
||||
PointGrid.hpp
|
||||
PNGReadWrite.hpp
|
||||
PNGReadWrite.cpp
|
||||
QuadricEdgeCollapse.cpp
|
||||
|
|
|
|||
|
|
@ -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> >
|
||||
|
|
|
|||
|
|
@ -85,7 +85,7 @@ public:
|
|||
virtual ExtrusionEntity* clone() const = 0;
|
||||
// Create a new object, initialize it with this object using the move semantics.
|
||||
virtual ExtrusionEntity* clone_move() = 0;
|
||||
virtual ~ExtrusionEntity() {}
|
||||
virtual ~ExtrusionEntity() = default;
|
||||
virtual void reverse() = 0;
|
||||
virtual const Point& first_point() const = 0;
|
||||
virtual const Point& last_point() const = 0;
|
||||
|
|
|
|||
|
|
@ -36,9 +36,13 @@ public:
|
|||
ExtrusionEntityCollection(ExtrusionEntityCollection &&other) : entities(std::move(other.entities)), no_sort(other.no_sort) {}
|
||||
explicit ExtrusionEntityCollection(const ExtrusionPaths &paths);
|
||||
ExtrusionEntityCollection& operator=(const ExtrusionEntityCollection &other);
|
||||
ExtrusionEntityCollection& operator=(ExtrusionEntityCollection &&other)
|
||||
{ this->entities = std::move(other.entities); this->no_sort = other.no_sort; return *this; }
|
||||
~ExtrusionEntityCollection() { clear(); }
|
||||
ExtrusionEntityCollection& operator=(ExtrusionEntityCollection &&other) {
|
||||
this->clear();
|
||||
this->entities = std::move(other.entities);
|
||||
this->no_sort = other.no_sort;
|
||||
return *this;
|
||||
}
|
||||
~ExtrusionEntityCollection() override { clear(); }
|
||||
explicit operator ExtrusionPaths() const;
|
||||
|
||||
bool is_collection() const override { return true; }
|
||||
|
|
|
|||
|
|
@ -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 ¢er,
|
||||
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 ¢er,
|
||||
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
|
||||
|
||||
|
|
|
|||
|
|
@ -17,6 +17,14 @@ public:
|
|||
{}
|
||||
~MutablePriorityQueue() { clear(); }
|
||||
|
||||
MutablePriorityQueue(MutablePriorityQueue &&) = default;
|
||||
MutablePriorityQueue& operator=(MutablePriorityQueue &&) = default;
|
||||
|
||||
// This class modifies the outside data through the m_index_setter
|
||||
// and thus it should not be copied. The semantics are similar to std::unique_ptr
|
||||
MutablePriorityQueue(const MutablePriorityQueue &) = delete;
|
||||
MutablePriorityQueue& operator=(const MutablePriorityQueue &) = delete;
|
||||
|
||||
void clear();
|
||||
void reserve(size_t cnt) { m_heap.reserve(cnt); }
|
||||
void push(const T &item);
|
||||
|
|
|
|||
|
|
@ -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
74
src/libslic3r/PointGrid.hpp
Normal 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 {
|
||||
int iz = idx / XY;
|
||||
int iy = (idx / m_size.x()) % m_size.y();
|
||||
int 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) {
|
||||
int iz = i / XY;
|
||||
int iy = (i / numpts[X]) % numpts[Y];
|
||||
int 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
|
||||
|
|
@ -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();
|
||||
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ add_executable(${_TEST_NAME}_tests
|
|||
${_TEST_NAME}_tests.cpp
|
||||
test_3mf.cpp
|
||||
test_aabbindirect.cpp
|
||||
test_kdtreeindirect.cpp
|
||||
test_clipper_offset.cpp
|
||||
test_clipper_utils.cpp
|
||||
test_color.cpp
|
||||
|
|
@ -25,6 +26,7 @@ add_executable(${_TEST_NAME}_tests
|
|||
test_png_io.cpp
|
||||
test_timeutils.cpp
|
||||
test_indexed_triangle_set.cpp
|
||||
test_astar.cpp
|
||||
../libnest2d/printer_parts.cpp
|
||||
)
|
||||
|
||||
|
|
|
|||
71
tests/libslic3r/test_astar.cpp
Normal file
71
tests/libslic3r/test_astar.cpp
Normal file
|
|
@ -0,0 +1,71 @@
|
|||
#include <catch2/catch.hpp>
|
||||
|
||||
#include "libslic3r/BoundingBox.hpp"
|
||||
#include "libslic3r/AStar.hpp"
|
||||
#include "libslic3r/Execution/ExecutionSeq.hpp"
|
||||
#include "libslic3r/PointGrid.hpp"
|
||||
|
||||
using namespace Slic3r;
|
||||
|
||||
struct PointGridTracer {
|
||||
using Node = size_t;
|
||||
const PointGrid<float> &grid;
|
||||
size_t final;
|
||||
|
||||
PointGridTracer(const PointGrid<float> &g, size_t goal) :
|
||||
grid{g}, final{goal} {}
|
||||
|
||||
template<class Fn>
|
||||
void foreach_reachable(size_t from, Fn &&fn) const
|
||||
{
|
||||
Vec3i from_crd = grid.get_coord(from);
|
||||
REQUIRE(grid.get_idx(from_crd) == from);
|
||||
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{ 1, 0, 0}); i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{ 0, 1, 0}); i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{ 0, 0, 1}); i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{ 1, 1, 0}); i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{ 0, 1, 1}); i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{ 1, 1, 1}); i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{-1, 0, 0}); from_crd.x() > 0 && i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{ 0, -1, 0}); from_crd.y() > 0 && i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{ 0, 0, -1}); from_crd.z() > 0 && i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{-1, -1, 0}); from_crd.x() > 0 && from_crd.y() > 0 && i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{ 0, -1, -1}); from_crd.y() > 0 && from_crd.z() && i < grid.point_count()) fn(i);
|
||||
if (size_t i = grid.get_idx(from_crd + Vec3i{-1, -1, -1}); from_crd.x() > 0 && from_crd.y() > 0 && from_crd.z() && i < grid.point_count()) fn(i);
|
||||
|
||||
}
|
||||
|
||||
float distance(size_t a, size_t b) const
|
||||
{
|
||||
return (grid.get(a) - grid.get(b)).squaredNorm();
|
||||
}
|
||||
|
||||
float goal_heuristic(size_t n) const
|
||||
{
|
||||
return n == final ? -1.f : (grid.get(n) - grid.get(final)).squaredNorm();
|
||||
}
|
||||
|
||||
size_t unique_id(size_t n) const { return n; }
|
||||
};
|
||||
|
||||
TEST_CASE("astar algorithm test over 3D point grid", "[AStar]") {
|
||||
auto vol = BoundingBox3Base<Vec3f>{{0.f, 0.f, 0.f}, {1.f, 1.f, 1.f}};
|
||||
|
||||
auto pgrid = point_grid(ex_seq, vol, {0.1f, 0.1f, 0.1f});
|
||||
|
||||
size_t target = pgrid.point_count() - 1;
|
||||
|
||||
std::cout << "Tracing route to " << pgrid.get_coord(target).transpose() << std::endl;
|
||||
PointGridTracer pgt{pgrid, pgrid.point_count() - 1};
|
||||
std::vector<size_t> out;
|
||||
bool found = astar::search_route(pgt, size_t(0), std::back_inserter(out));
|
||||
|
||||
std::cout << "Route taken: ";
|
||||
for (size_t i : out) {
|
||||
std::cout << "(" << pgrid.get_coord(i).transpose() << ") ";
|
||||
}
|
||||
std::cout << std::endl;
|
||||
|
||||
REQUIRE(found);
|
||||
}
|
||||
142
tests/libslic3r/test_kdtreeindirect.cpp
Normal file
142
tests/libslic3r/test_kdtreeindirect.cpp
Normal 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());
|
||||
//}
|
||||
|
|
@ -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;
|
||||
|
|
|
|||
Loading…
Reference in a new issue