mirror of
https://github.com/SoftFever/OrcaSlicer.git
synced 2025-07-12 01:07:57 -06:00
Bug fixes for the neighborhood detection
This commit is contained in:
tamasmeszaros
parent
08fb677583
commit
20b7aad6d1
12 changed files with 190 additions and 206 deletions
|
|
@ -99,6 +99,7 @@ namespace bgi = boost::geometry::index;
|
|||
|
||||
using SpatElement = std::pair<Box, unsigned>;
|
||||
using SpatIndex = bgi::rtree< SpatElement, bgi::rstar<16, 4> >;
|
||||
using ItemGroup = std::vector<std::reference_wrapper<Item>>;
|
||||
|
||||
std::tuple<double /*score*/, Box /*farthest point from bin center*/>
|
||||
objfunc(const PointImpl& bincenter,
|
||||
|
|
@ -109,24 +110,21 @@ objfunc(const PointImpl& bincenter,
|
|||
double norm, // A norming factor for physical dimensions
|
||||
std::vector<double>& areacache, // pile item areas will be cached
|
||||
// a spatial index to quickly get neighbors of the candidate item
|
||||
SpatIndex& spatindex
|
||||
SpatIndex& spatindex,
|
||||
const ItemGroup& remaining
|
||||
)
|
||||
{
|
||||
using pl = PointLike;
|
||||
using sl = ShapeLike;
|
||||
using Coord = TCoord<PointImpl>;
|
||||
|
||||
static const double BIG_ITEM_TRESHOLD = 0.02;
|
||||
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 isBig = [&areacache, bin_area](double a) {
|
||||
double farea = areacache.empty() ? 0 : areacache.front();
|
||||
bool fbig = farea / bin_area > BIG_ITEM_TRESHOLD;
|
||||
bool abig = a/bin_area > BIG_ITEM_TRESHOLD;
|
||||
bool rbig = fbig && a > 0.5*farea;
|
||||
return abig || rbig;
|
||||
return a/bin_area > BIG_ITEM_TRESHOLD ;
|
||||
};
|
||||
|
||||
// If a new bin has been created:
|
||||
|
|
@ -195,39 +193,74 @@ objfunc(const PointImpl& bincenter,
|
|||
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;
|
||||
double density = 0;
|
||||
|
||||
// Prepare a variable for the alignment score.
|
||||
// This will indicate: how well is the candidate item aligned with
|
||||
// its neighbors. We will check the aligment with all neighbors and
|
||||
// return the score for the best alignment. So it is enough for the
|
||||
// candidate to be aligned with only one item.
|
||||
auto alignment_score = std::numeric_limits<double>::max();
|
||||
if(remaining.empty()) {
|
||||
pile.emplace_back(item.transformedShape());
|
||||
auto chull = sl::convexHull(pile);
|
||||
pile.pop_back();
|
||||
strategies::EdgeCache<PolygonImpl> ec(chull);
|
||||
|
||||
auto& trsh = item.transformedShape();
|
||||
double circ = ec.circumference() / norm;
|
||||
double bcirc = 2.0*(fullbb.width() + fullbb.height()) / norm;
|
||||
score = 0.5*circ + 0.5*bcirc;
|
||||
|
||||
auto querybb = item.boundingBox();
|
||||
} else {
|
||||
// Prepare a variable for the alignment score.
|
||||
// This will indicate: how well is the candidate item aligned with
|
||||
// its neighbors. We will check the aligment with all neighbors and
|
||||
// return the score for the best alignment. So it is enough for the
|
||||
// candidate to be aligned with only one item.
|
||||
auto alignment_score = std::numeric_limits<double>::max();
|
||||
|
||||
// Query the spatial index for the neigbours
|
||||
std::vector<SpatElement> result;
|
||||
spatindex.query(bgi::intersects(querybb), std::back_inserter(result));
|
||||
density = (fullbb.width()*fullbb.height()) / (norm*norm);
|
||||
auto& trsh = item.transformedShape();
|
||||
auto querybb = item.boundingBox();
|
||||
auto wp = querybb.width()*0.2;
|
||||
auto hp = querybb.height()*0.2;
|
||||
auto pad = PointImpl( Coord(wp), Coord(hp));
|
||||
querybb = Box({ querybb.minCorner() - pad,
|
||||
querybb.maxCorner() + pad
|
||||
});
|
||||
|
||||
for(auto& e : result) { // now get the score for the best alignment
|
||||
auto idx = e.second;
|
||||
auto& p = pile[idx];
|
||||
auto parea = areacache[idx];
|
||||
auto bb = sl::boundingBox(sl::Shapes<PolygonImpl>{p, trsh});
|
||||
auto bbarea = bb.area();
|
||||
auto ascore = 1.0 - (item.area() + parea)/bbarea;
|
||||
// Query the spatial index for the neigbours
|
||||
std::vector<SpatElement> result;
|
||||
result.reserve(spatindex.size());
|
||||
spatindex.query(bgi::intersects(querybb),
|
||||
std::back_inserter(result));
|
||||
|
||||
// if(result.empty()) {
|
||||
// std::cout << "Error while arranging!" << std::endl;
|
||||
// std::cout << spatindex.size() << " " << pile.size() << std::endl;
|
||||
|
||||
// auto ib = spatindex.bounds();
|
||||
// Box ibb;
|
||||
// boost::geometry::convert(ib, ibb);
|
||||
// std::cout << "Inside: " << (sl::isInside<PolygonImpl>(querybb, ibb) ||
|
||||
// boost::geometry::intersects(querybb, ibb)) << std::endl;
|
||||
// }
|
||||
|
||||
for(auto& e : result) { // now get the score for the best alignment
|
||||
auto idx = e.second;
|
||||
auto& p = pile[idx];
|
||||
auto parea = areacache[idx];
|
||||
auto bb = sl::boundingBox(sl::Shapes<PolygonImpl>{p, trsh});
|
||||
auto bbarea = bb.area();
|
||||
auto ascore = 1.0 - (item.area() + parea)/bbarea;
|
||||
|
||||
if(ascore < alignment_score) alignment_score = ascore;
|
||||
}
|
||||
|
||||
// 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
|
||||
if(result.empty())
|
||||
score = 0.5 * dist + 0.5 * density;
|
||||
else
|
||||
score = 0.45 * dist + 0.45 * density + 0.1 * alignment_score;
|
||||
|
||||
if(ascore < alignment_score) alignment_score = ascore;
|
||||
}
|
||||
|
||||
// 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
|
||||
score = 0.45 * dist + 0.45 * density + 0.1 * alignment_score;
|
||||
|
||||
} 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
|
||||
|
|
@ -312,10 +345,12 @@ public:
|
|||
const Item &item,
|
||||
double pile_area,
|
||||
double norm,
|
||||
double /*penality*/) {
|
||||
const ItemGroup& rem) {
|
||||
|
||||
auto result = objfunc(bin.center(), bin_area_, pile,
|
||||
pile_area, item, norm, areacache_, rtree_);
|
||||
pile_area, item, norm, areacache_,
|
||||
rtree_,
|
||||
rem);
|
||||
double score = std::get<0>(result);
|
||||
auto& fullbb = std::get<1>(result);
|
||||
|
||||
|
|
@ -346,10 +381,11 @@ public:
|
|||
const Item &item,
|
||||
double pile_area,
|
||||
double norm,
|
||||
double /*penality*/) {
|
||||
const ItemGroup& rem) {
|
||||
|
||||
auto result = objfunc(bin.center(), bin_area_, pile,
|
||||
pile_area, item, norm, areacache_, rtree_);
|
||||
pile_area, item, norm, areacache_,
|
||||
rtree_, rem);
|
||||
double score = std::get<0>(result);
|
||||
auto& fullbb = std::get<1>(result);
|
||||
|
||||
|
|
@ -391,11 +427,12 @@ public:
|
|||
const Item &item,
|
||||
double pile_area,
|
||||
double norm,
|
||||
double /*penality*/) {
|
||||
const ItemGroup& rem) {
|
||||
|
||||
auto binbb = ShapeLike::boundingBox(bin);
|
||||
auto result = objfunc(binbb.center(), bin_area_, pile,
|
||||
pile_area, item, norm, areacache_, rtree_);
|
||||
pile_area, item, norm, areacache_,
|
||||
rtree_, rem);
|
||||
double score = std::get<0>(result);
|
||||
|
||||
return score;
|
||||
|
|
@ -417,10 +454,11 @@ public:
|
|||
const Item &item,
|
||||
double pile_area,
|
||||
double norm,
|
||||
double /*penality*/) {
|
||||
const ItemGroup& rem) {
|
||||
|
||||
auto result = objfunc({0, 0}, 0, pile, pile_area,
|
||||
item, norm, areacache_, rtree_);
|
||||
item, norm, areacache_,
|
||||
rtree_, rem);
|
||||
return std::get<0>(result);
|
||||
};
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue