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Fixing the "last item doesn't fit" problem.

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This commit is contained in:
tamasmeszaros 2018-08-02 13:15:30 +02:00
parent 6cdec7ac9a
commit a7ba51bd11
5 changed files with 399 additions and 219 deletions
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31
xs/src/libnest2d/libnest2d/geometry_traits.hpp
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@ -684,6 +684,20 @@ struct ShapeLike {
return PointLike::distance(point, circ.center()) < circ.radius();
}
template<class RawShape>
static bool isInside(const TPoint<RawShape>& point,
const _Box<TPoint<RawShape>>& box)
{
auto px = getX(point);
auto py = getY(point);
auto minx = getX(box.minCorner());
auto miny = getY(box.minCorner());
auto maxx = getX(box.maxCorner());
auto maxy = getY(box.maxCorner());
return px > minx && px < maxx && py > miny && py < maxy;
}
template<class RawShape>
static bool isInside(const RawShape& sh,
const _Circle<TPoint<RawShape>>& circ)
@ -702,6 +716,23 @@ struct ShapeLike {
isInside<RawShape>(box.maxCorner(), circ);
}
template<class RawShape>
static bool isInside(const _Box<TPoint<RawShape>>& ibb,
const _Box<TPoint<RawShape>>& box)
{
auto iminX = getX(ibb.minCorner());
auto imaxX = getX(ibb.maxCorner());
auto iminY = getY(ibb.minCorner());
auto imaxY = getY(ibb.maxCorner());
auto minX = getX(box.minCorner());
auto maxX = getX(box.maxCorner());
auto minY = getY(box.minCorner());
auto maxY = getY(box.maxCorner());
return iminX > minX && imaxX < maxX && iminY > minY && imaxY < maxY;
}
template<class RawShape> // Potential O(1) implementation may exist
static inline TPoint<RawShape>& vertex(RawShape& sh, unsigned long idx)
{

3
xs/src/libnest2d/libnest2d/libnest2d.hpp
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@ -473,8 +473,7 @@ public:
template<class RawShape>
inline bool _Item<RawShape>::isInside(const _Box<TPoint<RawShape>>& box) const {
_Rectangle<RawShape> rect(box.width(), box.height());
return _Item<RawShape>::isInside(rect);
return ShapeLike::isInside<RawShape>(boundingBox(), box);
}
template<class RawShape> inline bool

75
xs/src/libnest2d/libnest2d/placers/nfpplacer.hpp
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@ -166,7 +166,7 @@ template<class RawShape> class EdgeCache {
using std::pow;
return static_cast<Coord>(
round( N/(ceil(pow(accuracy_, 2)*(N-1)) + 1) )
std::round(N/std::pow(N, std::pow(accuracy_, 1.0/3.0)))
);
}
@ -178,6 +178,7 @@ template<class RawShape> class EdgeCache {
contour_.corners.reserve(N / S + 1);
auto N_1 = N-1;
contour_.corners.emplace_back(0.0);
for(size_t i = 0; i < N_1; i += S) {
contour_.corners.emplace_back(
contour_.distances.at(i) / contour_.full_distance);
@ -192,6 +193,7 @@ template<class RawShape> class EdgeCache {
const auto S = stride(N);
auto N_1 = N-1;
hc.corners.reserve(N / S + 1);
hc.corners.emplace_back(0.0);
for(size_t i = 0; i < N_1; i += S) {
hc.corners.emplace_back(
hc.distances.at(i) / hc.full_distance);
@ -484,7 +486,7 @@ public:
bool static inline wouldFit(const RawShape& chull, const RawShape& bin) {
auto bbch = sl::boundingBox<RawShape>(chull);
auto bbin = sl::boundingBox<RawShape>(bin);
auto d = bbin.center() - bbch.center();
auto d = bbch.center() - bbin.center();
auto chullcpy = chull;
sl::translate(chullcpy, d);
return sl::isInside<RawShape>(chullcpy, bin);
@ -579,17 +581,21 @@ public:
pile_area += mitem.area();
}
auto merged_pile = Nfp::merge(pile);
// This is the kernel part of the object function that is
// customizable by the library client
auto _objfunc = config_.object_function?
config_.object_function :
[this](Nfp::Shapes<RawShape>& pile, const Item& item,
double occupied_area, double /*norm*/,
double penality)
[this, &merged_pile](
Nfp::Shapes<RawShape>& /*pile*/,
const Item& item,
double occupied_area, double norm,
double /*penality*/)
{
pile.emplace_back(item.transformedShape());
auto ch = sl::convexHull(pile);
pile.pop_back();
merged_pile.emplace_back(item.transformedShape());
auto ch = sl::convexHull(merged_pile);
merged_pile.pop_back();
// The pack ratio -- how much is the convex hull occupied
double pack_rate = occupied_area/sl::area(ch);
@ -602,7 +608,7 @@ public:
// (larger) values.
auto score = std::sqrt(waste);
if(!wouldFit(ch, bin_)) score = 2*penality - score;
if(!wouldFit(ch, bin_)) score += norm;
return score;
};
@ -622,9 +628,22 @@ public:
return score;
};
auto boundaryCheck = [&](const Optimum& o) {
auto v = getNfpPoint(o);
auto d = v - iv;
d += startpos;
item.translation(d);
merged_pile.emplace_back(item.transformedShape());
auto chull = sl::convexHull(merged_pile);
merged_pile.pop_back();
return wouldFit(chull, bin_);
};
opt::StopCriteria stopcr;
stopcr.max_iterations = 1000;
stopcr.absolute_score_difference = 1e-20*norm_;
stopcr.max_iterations = 100;
stopcr.relative_score_difference = 1e-6;
opt::TOptimizer<opt::Method::L_SUBPLEX> solver(stopcr);
Optimum optimum(0, 0);
@ -644,7 +663,7 @@ public:
std::for_each(cache.corners().begin(),
cache.corners().end(),
[ch, &contour_ofn, &solver, &best_score,
&optimum] (double pos)
&optimum, &boundaryCheck] (double pos)
{
try {
auto result = solver.optimize_min(contour_ofn,
@ -653,22 +672,15 @@ public:
);
if(result.score < best_score) {
best_score = result.score;
optimum.relpos = std::get<0>(result.optimum);
optimum.nfpidx = ch;
optimum.hidx = -1;
Optimum o(std::get<0>(result.optimum), ch, -1);
if(boundaryCheck(o)) {
best_score = result.score;
optimum = o;
}
}
} catch(std::exception& e) {
derr() << "ERROR: " << e.what() << "\n";
}
// auto sc = contour_ofn(pos);
// if(sc < best_score) {
// best_score = sc;
// optimum.relpos = pos;
// optimum.nfpidx = ch;
// optimum.hidx = -1;
// }
});
for(unsigned hidx = 0; hidx < cache.holeCount(); ++hidx) {
@ -683,7 +695,7 @@ public:
std::for_each(cache.corners(hidx).begin(),
cache.corners(hidx).end(),
[&hole_ofn, &solver, &best_score,
&optimum, ch, hidx]
&optimum, ch, hidx, &boundaryCheck]
(double pos)
{
try {
@ -693,21 +705,16 @@ public:
);
if(result.score < best_score) {
best_score = result.score;
Optimum o(std::get<0>(result.optimum),
ch, hidx);
optimum = o;
if(boundaryCheck(o)) {
best_score = result.score;
optimum = o;
}
}
} catch(std::exception& e) {
derr() << "ERROR: " << e.what() << "\n";
}
// auto sc = hole_ofn(pos);
// if(sc < best_score) {
// best_score = sc;
// optimum.relpos = pos;
// optimum.nfpidx = ch;
// optimum.hidx = hidx;
// }
});
}
}

490
xs/src/libslic3r/ModelArrange.hpp
View File

@ -93,6 +93,237 @@ void toSVG(SVG& svg, const Model& model) {
}
}
std::tuple<double /*score*/, Box /*farthest point from bin center*/>
objfunc(const PointImpl& bincenter,
ShapeLike::Shapes<PolygonImpl>& pile, // The currently arranged pile
const Item &item,
double norm // A norming factor for physical dimensions
)
{
using pl = PointLike;
static const double BIG_ITEM_TRESHOLD = 0.2;
static const double ROUNDNESS_RATIO = 0.5;
static const double DENSITY_RATIO = 1.0 - ROUNDNESS_RATIO;
// We will treat big items (compared to the print bed) differently
NfpPlacer::Pile bigs;
bigs.reserve(pile.size());
for(auto& p : pile) {
auto pbb = ShapeLike::boundingBox(p);
auto na = std::sqrt(pbb.width()*pbb.height())/norm;
if(na > BIG_ITEM_TRESHOLD) bigs.emplace_back(p);
}
// Candidate item bounding box
auto ibb = item.boundingBox();
// Calculate the full bounding box of the pile with the candidate item
pile.emplace_back(item.transformedShape());
auto fullbb = ShapeLike::boundingBox(pile);
pile.pop_back();
// The bounding box of the big items (they will accumulate in the center
// of the pile
auto bigbb = bigs.empty()? fullbb : ShapeLike::boundingBox(bigs);
// The size indicator of the candidate item. This is not the area,
// but almost...
auto itemnormarea = std::sqrt(ibb.width()*ibb.height())/norm;
// Will hold the resulting score
double score = 0;
if(itemnormarea > BIG_ITEM_TRESHOLD) {
// This branch is for the bigger items..
// Here we will use the closest point of the item bounding box to
// the already arranged pile. So not the bb center nor the a choosen
// corner but whichever is the closest to the center. This will
// prevent unwanted strange arrangements.
// Now the distance of the gravity center will be calculated to the
// five anchor points and the smallest will be chosen.
auto minc = ibb.minCorner(); // bottom left corner
auto maxc = ibb.maxCorner(); // top right corner
// top left and bottom right corners
auto top_left = PointImpl{getX(minc), getY(maxc)};
auto bottom_right = PointImpl{getX(maxc), getY(minc)};
// Now the distnce of the gravity center will be calculated to the
// five anchor points and the smallest will be chosen.
std::array<double, 5> dists;
auto cc = fullbb.center(); // The gravity center
dists[0] = pl::distance(minc, cc);
dists[1] = pl::distance(maxc, cc);
dists[2] = pl::distance(ibb.center(), cc);
dists[3] = pl::distance(top_left, cc);
dists[4] = pl::distance(bottom_right, cc);
auto dist = *(std::min_element(dists.begin(), dists.end())) / norm;
// Density is the pack density: how big is the arranged pile
auto density = std::sqrt(fullbb.width()*fullbb.height()) / norm;
// The score is a weighted sum of the distance from pile center
// and the pile size
score = ROUNDNESS_RATIO * dist + DENSITY_RATIO * density;
} else if(itemnormarea < BIG_ITEM_TRESHOLD && bigs.empty()) {
// If there are no big items, only small, we should consider the
// density here as well to not get silly results
auto bindist = pl::distance(ibb.center(), bincenter) / norm;
auto density = std::sqrt(fullbb.width()*fullbb.height()) / norm;
score = ROUNDNESS_RATIO * bindist + DENSITY_RATIO * density;
} else {
// Here there are the small items that should be placed around the
// already processed bigger items.
// No need to play around with the anchor points, the center will be
// just fine for small items
score = pl::distance(ibb.center(), bigbb.center()) / norm;
}
return std::make_tuple(score, fullbb);
}
template<class PConf>
void fillConfig(PConf& pcfg) {
// Align the arranged pile into the center of the bin
pcfg.alignment = PConf::Alignment::CENTER;
// Start placing the items from the center of the print bed
pcfg.starting_point = PConf::Alignment::CENTER;
// TODO cannot use rotations until multiple objects of same geometry can
// handle different rotations
// arranger.useMinimumBoundigBoxRotation();
pcfg.rotations = { 0.0 };
// The accuracy of optimization.
// Goes from 0.0 to 1.0 and scales performance as well
pcfg.accuracy = 0.35f;
}
template<class TBin>
class AutoArranger {};
template<class TBin>
class _ArrBase {
protected:
using Placer = strategies::_NofitPolyPlacer<PolygonImpl, TBin>;
using Selector = FirstFitSelection;
using Packer = Arranger<Placer, Selector>;
using PConfig = typename Packer::PlacementConfig;
using Distance = TCoord<PointImpl>;
using Pile = ShapeLike::Shapes<PolygonImpl>;
Packer pck_;
PConfig pconf_; // Placement configuration
public:
_ArrBase(const TBin& bin, Distance dist,
std::function<void(unsigned)> progressind):
pck_(bin, dist)
{
fillConfig(pconf_);
pck_.progressIndicator(progressind);
}
template<class...Args> inline IndexedPackGroup operator()(Args&&...args) {
return pck_.arrangeIndexed(std::forward<Args>(args)...);
}
};
template<>
class AutoArranger<Box>: public _ArrBase<Box> {
public:
AutoArranger(const Box& bin, Distance dist,
std::function<void(unsigned)> progressind):
_ArrBase<Box>(bin, dist, progressind)
{
pconf_.object_function = [bin] (
Pile& pile,
const Item &item,
double /*occupied_area*/,
double norm,
double penality) {
auto result = objfunc(bin.center(), pile, item, norm);
double score = std::get<0>(result);
auto& fullbb = std::get<1>(result);
auto wdiff = fullbb.width() - bin.width();
auto hdiff = fullbb.height() - bin.height();
if(wdiff > 0) score += std::pow(wdiff, 2) / norm;
if(hdiff > 0) score += std::pow(hdiff, 2) / norm;
return score;
};
pck_.configure(pconf_);
}
};
template<>
class AutoArranger<PolygonImpl>: public _ArrBase<PolygonImpl> {
public:
AutoArranger(const PolygonImpl& bin, Distance dist,
std::function<void(unsigned)> progressind):
_ArrBase<PolygonImpl>(bin, dist, progressind)
{
pconf_.object_function = [&bin] (
Pile& pile,
const Item &item,
double /*area*/,
double norm,
double /*penality*/) {
auto binbb = ShapeLike::boundingBox(bin);
auto result = objfunc(binbb.center(), pile, item, norm);
double score = std::get<0>(result);
pile.emplace_back(item.transformedShape());
auto chull = ShapeLike::convexHull(pile);
pile.pop_back();
// If it does not fit into the print bed we will beat it with a
// large penality. If we would not do this, there would be only one
// big pile that doesn't care whether it fits onto the print bed.
if(!Placer::wouldFit(chull, bin)) score += norm;
return score;
};
pck_.configure(pconf_);
}
};
template<> // Specialization with no bin
class AutoArranger<bool>: public _ArrBase<Box> {
public:
AutoArranger(Distance dist, std::function<void(unsigned)> progressind):
_ArrBase<Box>(Box(0, 0), dist, progressind)
{
this->pconf_.object_function = [] (
Pile& pile,
const Item &item,
double /*area*/,
double norm,
double /*penality*/) {
auto result = objfunc({0, 0}, pile, item, norm);
return std::get<0>(result);
};
this->pck_.configure(pconf_);
}
};
// A container which stores a pointer to the 3D object and its projected
// 2D shape from top view.
using ShapeData2D =
@ -147,6 +378,44 @@ ShapeData2D projectModelFromTop(const Slic3r::Model &model) {
return ret;
}
enum BedShapeHint {
BOX,
CIRCLE,
IRREGULAR,
WHO_KNOWS
};
BedShapeHint bedShape(const Slic3r::Polyline& /*bed*/) {
// Determine the bed shape by hand
return BOX;
}
void applyResult(
IndexedPackGroup::value_type& group,
Coord batch_offset,
ShapeData2D& shapemap)
{
for(auto& r : group) {
auto idx = r.first; // get the original item index
Item& item = r.second; // get the item itself
// Get the model instance from the shapemap using the index
ModelInstance *inst_ptr = shapemap[idx].first;
// Get the tranformation data from the item object and scale it
// appropriately
auto off = item.translation();
Radians rot = item.rotation();
Pointf foff(off.X*SCALING_FACTOR + batch_offset,
off.Y*SCALING_FACTOR);
// write the tranformation data into the model instance
inst_ptr->rotation = rot;
inst_ptr->offset = foff;
}
}
/**
* \brief Arranges the model objects on the screen.
*
@ -170,7 +439,9 @@ ShapeData2D projectModelFromTop(const Slic3r::Model &model) {
* bed or leave them untouched (let the user arrange them by hand or remove
* them).
*/
bool arrange(Model &model, coordf_t dist, const Slic3r::BoundingBoxf* bb,
bool arrange(Model &model, coordf_t min_obj_distance,
const Slic3r::Polyline& bed,
BedShapeHint bedhint,
bool first_bin_only,
std::function<void(unsigned)> progressind)
{
@ -178,215 +449,74 @@ bool arrange(Model &model, coordf_t dist, const Slic3r::BoundingBoxf* bb,
bool ret = true;
// Create the arranger config
auto min_obj_distance = static_cast<Coord>(dist/SCALING_FACTOR);
// Get the 2D projected shapes with their 3D model instance pointers
auto shapemap = arr::projectModelFromTop(model);
bool hasbin = bb != nullptr && bb->defined;
double area_max = 0;
// Copy the references for the shapes only as the arranger expects a
// sequence of objects convertible to Item or ClipperPolygon
std::vector<std::reference_wrapper<Item>> shapes;
shapes.reserve(shapemap.size());
std::for_each(shapemap.begin(), shapemap.end(),
[&shapes, min_obj_distance, &area_max, hasbin]
(ShapeData2D::value_type& it)
[&shapes] (ShapeData2D::value_type& it)
{
shapes.push_back(std::ref(it.second));
});
Box bin;
IndexedPackGroup result;
BoundingBox bbb(bed.points);
if(hasbin) {
// Scale up the bounding box to clipper scale.
BoundingBoxf bbb = *bb;
bbb.scale(1.0/SCALING_FACTOR);
auto binbb = Box({
static_cast<libnest2d::Coord>(bbb.min.x),
static_cast<libnest2d::Coord>(bbb.min.y)
},
{
static_cast<libnest2d::Coord>(bbb.max.x),
static_cast<libnest2d::Coord>(bbb.max.y)
});
bin = Box({
static_cast<libnest2d::Coord>(bbb.min.x),
static_cast<libnest2d::Coord>(bbb.min.y)
},
{
static_cast<libnest2d::Coord>(bbb.max.x),
static_cast<libnest2d::Coord>(bbb.max.y)
});
switch(bedhint) {
case BOX: {
// Create the arranger for the box shaped bed
AutoArranger<Box> arrange(binbb, min_obj_distance, progressind);
// Arrange and return the items with their respective indices within the
// input sequence.
result = arrange(shapes.begin(), shapes.end());
break;
}
case CIRCLE:
break;
case IRREGULAR:
case WHO_KNOWS: {
using P = libnest2d::PolygonImpl;
// Will use the DJD selection heuristic with the BottomLeft placement
// strategy
using Arranger = Arranger<NfpPlacer, FirstFitSelection>;
using PConf = Arranger::PlacementConfig;
using SConf = Arranger::SelectionConfig;
auto ctour = Slic3rMultiPoint_to_ClipperPath(bed);
P irrbed = ShapeLike::create<PolygonImpl>(std::move(ctour));
PConf pcfg; // Placement configuration
SConf scfg; // Selection configuration
std::cout << ShapeLike::toString(irrbed) << std::endl;
// Align the arranged pile into the center of the bin
pcfg.alignment = PConf::Alignment::CENTER;
AutoArranger<P> arrange(irrbed, min_obj_distance, progressind);
// Start placing the items from the center of the print bed
pcfg.starting_point = PConf::Alignment::CENTER;
// TODO cannot use rotations until multiple objects of same geometry can
// handle different rotations
// arranger.useMinimumBoundigBoxRotation();
pcfg.rotations = { 0.0 };
// The accuracy of optimization. Goes from 0.0 to 1.0 and scales performance
pcfg.accuracy = 0.4f;
// Magic: we will specify what is the goal of arrangement... In this case
// we override the default object function to make the larger items go into
// the center of the pile and smaller items orbit it so the resulting pile
// has a circle-like shape. This is good for the print bed's heat profile.
// We alse sacrafice a bit of pack efficiency for this to work. As a side
// effect, the arrange procedure is a lot faster (we do not need to
// calculate the convex hulls)
pcfg.object_function = [bin, hasbin](
NfpPlacer::Pile& pile, // The currently arranged pile
const Item &item,
double /*area*/, // Sum area of items (not needed)
double norm, // A norming factor for physical dimensions
double penality) // Min penality in case of bad arrangement
{
using pl = PointLike;
static const double BIG_ITEM_TRESHOLD = 0.2;
static const double GRAVITY_RATIO = 0.5;
static const double DENSITY_RATIO = 1.0 - GRAVITY_RATIO;
// We will treat big items (compared to the print bed) differently
NfpPlacer::Pile bigs;
bigs.reserve(pile.size());
for(auto& p : pile) {
auto pbb = ShapeLike::boundingBox(p);
auto na = std::sqrt(pbb.width()*pbb.height())/norm;
if(na > BIG_ITEM_TRESHOLD) bigs.emplace_back(p);
}
// Candidate item bounding box
auto ibb = item.boundingBox();
// Calculate the full bounding box of the pile with the candidate item
pile.emplace_back(item.transformedShape());
auto fullbb = ShapeLike::boundingBox(pile);
pile.pop_back();
// The bounding box of the big items (they will accumulate in the center
// of the pile
auto bigbb = bigs.empty()? fullbb : ShapeLike::boundingBox(bigs);
// The size indicator of the candidate item. This is not the area,
// but almost...
auto itemnormarea = std::sqrt(ibb.width()*ibb.height())/norm;
// Will hold the resulting score
double score = 0;
if(itemnormarea > BIG_ITEM_TRESHOLD) {
// This branch is for the bigger items..
// Here we will use the closest point of the item bounding box to
// the already arranged pile. So not the bb center nor the a choosen
// corner but whichever is the closest to the center. This will
// prevent unwanted strange arrangements.
auto minc = ibb.minCorner(); // bottom left corner
auto maxc = ibb.maxCorner(); // top right corner
// top left and bottom right corners
auto top_left = PointImpl{getX(minc), getY(maxc)};
auto bottom_right = PointImpl{getX(maxc), getY(minc)};
auto cc = fullbb.center(); // The gravity center
// Now the distnce of the gravity center will be calculated to the
// five anchor points and the smallest will be chosen.
std::array<double, 5> dists;
dists[0] = pl::distance(minc, cc);
dists[1] = pl::distance(maxc, cc);
dists[2] = pl::distance(ibb.center(), cc);
dists[3] = pl::distance(top_left, cc);
dists[4] = pl::distance(bottom_right, cc);
auto dist = *(std::min_element(dists.begin(), dists.end())) / norm;
// Density is the pack density: how big is the arranged pile
auto density = std::sqrt(fullbb.width()*fullbb.height()) / norm;
// The score is a weighted sum of the distance from pile center
// and the pile size
score = GRAVITY_RATIO * dist + DENSITY_RATIO * density;
} else if(itemnormarea < BIG_ITEM_TRESHOLD && bigs.empty()) {
// If there are no big items, only small, we should consider the
// density here as well to not get silly results
auto bindist = pl::distance(ibb.center(), bin.center()) / norm;
auto density = std::sqrt(fullbb.width()*fullbb.height()) / norm;
score = GRAVITY_RATIO * bindist + DENSITY_RATIO * density;
} else {
// Here there are the small items that should be placed around the
// already processed bigger items.
// No need to play around with the anchor points, the center will be
// just fine for small items
score = pl::distance(ibb.center(), bigbb.center()) / norm;
}
// If it does not fit into the print bed we will beat it
// with a large penality. If we would not do this, there would be only
// one big pile that doesn't care whether it fits onto the print bed.
if(!NfpPlacer::wouldFit(fullbb, bin)) score = 2*penality - score;
return score;
};
// Create the arranger object
Arranger arranger(bin, min_obj_distance, pcfg, scfg);
// Set the progress indicator for the arranger.
arranger.progressIndicator(progressind);
// Arrange and return the items with their respective indices within the
// input sequence.
auto result = arranger.arrangeIndexed(shapes.begin(), shapes.end());
auto applyResult = [&shapemap](ArrangeResult::value_type& group,
Coord batch_offset)
{
for(auto& r : group) {
auto idx = r.first; // get the original item index
Item& item = r.second; // get the item itself
// Get the model instance from the shapemap using the index
ModelInstance *inst_ptr = shapemap[idx].first;
// Get the tranformation data from the item object and scale it
// appropriately
auto off = item.translation();
Radians rot = item.rotation();
Pointf foff(off.X*SCALING_FACTOR + batch_offset,
off.Y*SCALING_FACTOR);
// write the tranformation data into the model instance
inst_ptr->rotation = rot;
inst_ptr->offset = foff;
}
// Arrange and return the items with their respective indices within the
// input sequence.
result = arrange(shapes.begin(), shapes.end());
break;
}
};
if(first_bin_only) {
applyResult(result.front(), 0);
applyResult(result.front(), 0, shapemap);
} else {
const auto STRIDE_PADDING = 1.2;
Coord stride = static_cast<Coord>(STRIDE_PADDING*
bin.width()*SCALING_FACTOR);
binbb.width()*SCALING_FACTOR);
Coord batch_offset = 0;
for(auto& group : result) {
applyResult(group, batch_offset);
applyResult(group, batch_offset, shapemap);
// Only the first pack group can be placed onto the print bed. The
// other objects which could not fit will be placed next to the

19
xs/src/slic3r/AppController.cpp
View File

@ -294,6 +294,8 @@ void AppController::arrange_model()
supports_asynch()? std::launch::async : std::launch::deferred,
[this]()
{
using Coord = libnest2d::TCoord<libnest2d::PointImpl>;
unsigned count = 0;
for(auto obj : model_->objects) count += obj->instances.size();
@ -311,14 +313,25 @@ void AppController::arrange_model()
auto dist = print_ctl()->config().min_object_distance();
// Create the arranger config
auto min_obj_distance = static_cast<Coord>(dist/SCALING_FACTOR);
BoundingBoxf bb(print_ctl()->config().bed_shape.values);
auto& bedpoints = print_ctl()->config().bed_shape.values;
Polyline bed; bed.points.reserve(bedpoints.size());
for(auto& v : bedpoints)
bed.append(Point::new_scale(v.x, v.y));
if(pind) pind->update(0, _(L("Arranging objects...")));
try {
arr::arrange(*model_, dist, &bb, false, [pind, count](unsigned rem){
if(pind) pind->update(count - rem, _(L("Arranging objects...")));
arr::arrange(*model_,
min_obj_distance,
bed,
arr::BOX,
false, // create many piles not just one pile
[pind, count](unsigned rem) {
if(pind)
pind->update(count - rem, _(L("Arranging objects...")));
});
} catch(std::exception& e) {
std::cerr << e.what() << std::endl;