3D-printing/OrcaSlicer
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Better support for circular bed.

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This commit is contained in:
tamasmeszaros 2018-08-06 20:13:04 +02:00
parent 3c32a7c3db
commit e1edb05bbb
5 changed files with 172 additions and 530 deletions
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52
xs/src/libslic3r/ModelArrange.hpp
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@ -102,9 +102,9 @@ using SpatIndex = bgi::rtree< SpatElement, bgi::rstar<16, 4> >;
std::tuple<double /*score*/, Box /*farthest point from bin center*/>
objfunc(const PointImpl& bincenter,
double /*bin_area*/,
double bin_area,
ShapeLike::Shapes<PolygonImpl>& pile, // The currently arranged pile
double /*pile_area*/,
double pile_area,
const Item &item,
double norm, // A norming factor for physical dimensions
std::vector<double>& areacache, // pile item areas will be cached
@ -115,12 +115,16 @@ objfunc(const PointImpl& bincenter,
using pl = PointLike;
using sl = ShapeLike;
static const double BIG_ITEM_TRESHOLD = 0.2;
static const double BIG_ITEM_TRESHOLD = 0.04;
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
auto normarea = [norm](double area) { return std::sqrt(area)/norm; };
auto isBig = [&areacache, bin_area](double a) {
bool t = areacache.empty() ? true : a > 0.5*areacache.front();
return a/bin_area > BIG_ITEM_TRESHOLD || t;
};
// If a new bin has been created:
if(pile.size() < areacache.size()) {
@ -133,7 +137,7 @@ objfunc(const PointImpl& bincenter,
for(auto& p : pile) {
if(idx == areacache.size()) {
areacache.emplace_back(sl::area(p));
if(normarea(areacache[idx]) > BIG_ITEM_TRESHOLD)
if(isBig(areacache[idx]))
spatindex.insert({sl::boundingBox(p), idx});
}
@ -157,14 +161,10 @@ objfunc(const PointImpl& bincenter,
boost::geometry::convert(boostbb, bigbb);
}
// The size indicator of the candidate item. This is not the area,
// but almost...
double item_normarea = normarea(item.area());
// Will hold the resulting score
double score = 0;
if(item_normarea > BIG_ITEM_TRESHOLD) {
if(isBig(item.area())) {
// 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
@ -223,10 +223,9 @@ objfunc(const PointImpl& bincenter,
// The final mix of the score is the balance between the distance
// from the full pile center, the pack density and the
// alignment with the neigbours
auto C = 0.33;
score = C * dist + C * density + C * alignment_score;
score = 0.4 * dist + 0.4 * density + 0.2 * alignment_score;
} else if( item_normarea < BIG_ITEM_TRESHOLD && spatindex.empty()) {
} else if( !isBig(item.area()) && spatindex.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;
@ -349,17 +348,26 @@ public:
auto result = objfunc(bin.center(), bin_area_, pile,
pile_area, item, norm, areacache_, rtree_);
double score = std::get<0>(result);
// Circle fitting detection is very rough at the moment but
// we still need something that tells how badly the arrangement
// misses the print bed.
auto& fullbb = std::get<1>(result);
auto bbr = 0.5*PointLike::distance(fullbb.minCorner(),
fullbb.maxCorner());
auto diff = bbr - bin.radius();
if(diff > 0) score += std::pow(diff, 2) / norm;
auto d = PointLike::distance(fullbb.minCorner(),
fullbb.maxCorner());
auto diff = d - 2*bin.radius();
if(diff > 0) {
if( item.area() > 0.01*bin_area_ && item.vertexCount() < 20) {
pile.emplace_back(item.transformedShape());
auto chull = ShapeLike::convexHull(pile);
pile.pop_back();
auto C = strategies::boundingCircle(chull);
auto rdiff = C.radius() - bin.radius();
if(rdiff > 0) {
score += std::pow(rdiff, 3) / norm;
}
}
}
return score;
};
@ -695,6 +703,8 @@ bool arrange(Model &model, coordf_t min_obj_distance,
}
};
if(result.empty()) return false;
if(first_bin_only) {
applyResult(result.front(), 0, shapemap);
} else {