769 lines
24 KiB
C++
769 lines
24 KiB
C++
#include "ModelArrange.hpp"
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#include "Model.hpp"
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#include "SVG.hpp"
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#include <libnest2d.h>
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#include <numeric>
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#include <ClipperUtils.hpp>
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#include <boost/geometry/index/rtree.hpp>
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namespace Slic3r {
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namespace arr {
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using namespace libnest2d;
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std::string toString(const Model& model, bool holes = true) {
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std::stringstream ss;
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ss << "{\n";
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for(auto objptr : model.objects) {
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if(!objptr) continue;
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auto rmesh = objptr->raw_mesh();
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for(auto objinst : objptr->instances) {
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if(!objinst) continue;
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Slic3r::TriangleMesh tmpmesh = rmesh;
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// CHECK_ME -> Is the following correct ?
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tmpmesh.scale(objinst->get_scaling_factor());
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objinst->transform_mesh(&tmpmesh);
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ExPolygons expolys = tmpmesh.horizontal_projection();
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for(auto& expoly_complex : expolys) {
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auto tmp = expoly_complex.simplify(1.0/SCALING_FACTOR);
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if(tmp.empty()) continue;
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auto expoly = tmp.front();
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expoly.contour.make_clockwise();
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for(auto& h : expoly.holes) h.make_counter_clockwise();
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ss << "\t{\n";
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ss << "\t\t{\n";
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for(auto v : expoly.contour.points) ss << "\t\t\t{"
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<< v(0) << ", "
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<< v(1) << "},\n";
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{
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auto v = expoly.contour.points.front();
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ss << "\t\t\t{" << v(0) << ", " << v(1) << "},\n";
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}
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ss << "\t\t},\n";
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// Holes:
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ss << "\t\t{\n";
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if(holes) for(auto h : expoly.holes) {
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ss << "\t\t\t{\n";
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for(auto v : h.points) ss << "\t\t\t\t{"
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<< v(0) << ", "
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<< v(1) << "},\n";
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{
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auto v = h.points.front();
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ss << "\t\t\t\t{" << v(0) << ", " << v(1) << "},\n";
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}
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ss << "\t\t\t},\n";
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}
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ss << "\t\t},\n";
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ss << "\t},\n";
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}
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}
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}
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ss << "}\n";
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return ss.str();
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}
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void toSVG(SVG& svg, const Model& model) {
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for(auto objptr : model.objects) {
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if(!objptr) continue;
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auto rmesh = objptr->raw_mesh();
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for(auto objinst : objptr->instances) {
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if(!objinst) continue;
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Slic3r::TriangleMesh tmpmesh = rmesh;
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tmpmesh.scale(objinst->get_scaling_factor());
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objinst->transform_mesh(&tmpmesh);
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ExPolygons expolys = tmpmesh.horizontal_projection();
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svg.draw(expolys);
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}
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}
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}
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namespace bgi = boost::geometry::index;
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using SpatElement = std::pair<Box, unsigned>;
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using SpatIndex = bgi::rtree< SpatElement, bgi::rstar<16, 4> >;
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using ItemGroup = std::vector<std::reference_wrapper<Item>>;
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template<class TBin>
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using TPacker = typename placers::_NofitPolyPlacer<PolygonImpl, TBin>;
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const double BIG_ITEM_TRESHOLD = 0.02;
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Box boundingBox(const Box& pilebb, const Box& ibb ) {
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auto& pminc = pilebb.minCorner();
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auto& pmaxc = pilebb.maxCorner();
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auto& iminc = ibb.minCorner();
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auto& imaxc = ibb.maxCorner();
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PointImpl minc, maxc;
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setX(minc, std::min(getX(pminc), getX(iminc)));
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setY(minc, std::min(getY(pminc), getY(iminc)));
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setX(maxc, std::max(getX(pmaxc), getX(imaxc)));
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setY(maxc, std::max(getY(pmaxc), getY(imaxc)));
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return Box(minc, maxc);
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}
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std::tuple<double /*score*/, Box /*farthest point from bin center*/>
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objfunc(const PointImpl& bincenter,
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const shapelike::Shapes<PolygonImpl>& merged_pile,
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const Box& pilebb,
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const ItemGroup& items,
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const Item &item,
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double bin_area,
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double norm, // A norming factor for physical dimensions
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// a spatial index to quickly get neighbors of the candidate item
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const SpatIndex& spatindex,
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const SpatIndex& smalls_spatindex,
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const ItemGroup& remaining
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)
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{
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using Coord = TCoord<PointImpl>;
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static const double ROUNDNESS_RATIO = 0.5;
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static const double DENSITY_RATIO = 1.0 - ROUNDNESS_RATIO;
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// We will treat big items (compared to the print bed) differently
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auto isBig = [bin_area](double a) {
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return a/bin_area > BIG_ITEM_TRESHOLD ;
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};
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// Candidate item bounding box
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auto ibb = sl::boundingBox(item.transformedShape());
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// Calculate the full bounding box of the pile with the candidate item
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auto fullbb = boundingBox(pilebb, ibb);
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// The bounding box of the big items (they will accumulate in the center
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// of the pile
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Box bigbb;
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if(spatindex.empty()) bigbb = fullbb;
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else {
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auto boostbb = spatindex.bounds();
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boost::geometry::convert(boostbb, bigbb);
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}
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// Will hold the resulting score
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double score = 0;
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if(isBig(item.area()) || spatindex.empty()) {
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// This branch is for the bigger items..
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auto minc = ibb.minCorner(); // bottom left corner
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auto maxc = ibb.maxCorner(); // top right corner
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// top left and bottom right corners
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auto top_left = PointImpl{getX(minc), getY(maxc)};
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auto bottom_right = PointImpl{getX(maxc), getY(minc)};
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// Now the distance of the gravity center will be calculated to the
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// five anchor points and the smallest will be chosen.
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std::array<double, 5> dists;
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auto cc = fullbb.center(); // The gravity center
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dists[0] = pl::distance(minc, cc);
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dists[1] = pl::distance(maxc, cc);
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dists[2] = pl::distance(ibb.center(), cc);
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dists[3] = pl::distance(top_left, cc);
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dists[4] = pl::distance(bottom_right, cc);
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// The smalles distance from the arranged pile center:
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auto dist = *(std::min_element(dists.begin(), dists.end())) / norm;
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auto bindist = pl::distance(ibb.center(), bincenter) / norm;
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dist = 0.8*dist + 0.2*bindist;
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// Density is the pack density: how big is the arranged pile
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double density = 0;
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if(remaining.empty()) {
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auto mp = merged_pile;
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mp.emplace_back(item.transformedShape());
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auto chull = sl::convexHull(mp);
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placers::EdgeCache<PolygonImpl> ec(chull);
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double circ = ec.circumference() / norm;
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double bcirc = 2.0*(fullbb.width() + fullbb.height()) / norm;
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score = 0.5*circ + 0.5*bcirc;
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} else {
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// Prepare a variable for the alignment score.
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// This will indicate: how well is the candidate item aligned with
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// its neighbors. We will check the alignment with all neighbors and
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// return the score for the best alignment. So it is enough for the
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// candidate to be aligned with only one item.
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auto alignment_score = 1.0;
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density = std::sqrt((fullbb.width() / norm )*
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(fullbb.height() / norm));
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auto querybb = item.boundingBox();
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// Query the spatial index for the neighbors
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std::vector<SpatElement> result;
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result.reserve(spatindex.size());
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if(isBig(item.area())) {
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spatindex.query(bgi::intersects(querybb),
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std::back_inserter(result));
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} else {
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smalls_spatindex.query(bgi::intersects(querybb),
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std::back_inserter(result));
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}
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for(auto& e : result) { // now get the score for the best alignment
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auto idx = e.second;
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Item& p = items[idx];
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auto parea = p.area();
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if(std::abs(1.0 - parea/item.area()) < 1e-6) {
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auto bb = boundingBox(p.boundingBox(), ibb);
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auto bbarea = bb.area();
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auto ascore = 1.0 - (item.area() + parea)/bbarea;
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if(ascore < alignment_score) alignment_score = ascore;
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}
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}
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// The final mix of the score is the balance between the distance
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// from the full pile center, the pack density and the
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// alignment with the neighbors
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if(result.empty())
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score = 0.5 * dist + 0.5 * density;
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else
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score = 0.40 * dist + 0.40 * density + 0.2 * alignment_score;
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}
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} else {
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// Here there are the small items that should be placed around the
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// already processed bigger items.
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// No need to play around with the anchor points, the center will be
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// just fine for small items
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score = pl::distance(ibb.center(), bigbb.center()) / norm;
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}
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return std::make_tuple(score, fullbb);
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}
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template<class PConf>
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void fillConfig(PConf& pcfg) {
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// Align the arranged pile into the center of the bin
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pcfg.alignment = PConf::Alignment::CENTER;
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// Start placing the items from the center of the print bed
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pcfg.starting_point = PConf::Alignment::CENTER;
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// TODO cannot use rotations until multiple objects of same geometry can
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// handle different rotations
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// arranger.useMinimumBoundigBoxRotation();
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pcfg.rotations = { 0.0 };
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// The accuracy of optimization.
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// Goes from 0.0 to 1.0 and scales performance as well
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pcfg.accuracy = 0.65f;
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pcfg.parallel = true;
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}
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template<class TBin>
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class AutoArranger {};
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template<class TBin>
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class _ArrBase {
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protected:
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using Placer = TPacker<TBin>;
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using Selector = FirstFitSelection;
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using Packer = Nester<Placer, Selector>;
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using PConfig = typename Packer::PlacementConfig;
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using Distance = TCoord<PointImpl>;
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using Pile = sl::Shapes<PolygonImpl>;
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Packer m_pck;
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PConfig m_pconf; // Placement configuration
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double m_bin_area;
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SpatIndex m_rtree;
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SpatIndex m_smallsrtree;
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double m_norm;
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Pile m_merged_pile;
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Box m_pilebb;
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ItemGroup m_remaining;
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ItemGroup m_items;
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public:
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_ArrBase(const TBin& bin, Distance dist,
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std::function<void(unsigned)> progressind,
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std::function<bool(void)> stopcond):
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m_pck(bin, dist), m_bin_area(sl::area(bin)),
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m_norm(std::sqrt(sl::area(bin)))
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{
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fillConfig(m_pconf);
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m_pconf.before_packing =
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[this](const Pile& merged_pile, // merged pile
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const ItemGroup& items, // packed items
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const ItemGroup& remaining) // future items to be packed
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{
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m_items = items;
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m_merged_pile = merged_pile;
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m_remaining = remaining;
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m_pilebb = sl::boundingBox(merged_pile);
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m_rtree.clear();
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m_smallsrtree.clear();
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// We will treat big items (compared to the print bed) differently
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auto isBig = [this](double a) {
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return a/m_bin_area > BIG_ITEM_TRESHOLD ;
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};
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for(unsigned idx = 0; idx < items.size(); ++idx) {
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Item& itm = items[idx];
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if(isBig(itm.area())) m_rtree.insert({itm.boundingBox(), idx});
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m_smallsrtree.insert({itm.boundingBox(), idx});
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}
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};
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m_pck.progressIndicator(progressind);
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m_pck.stopCondition(stopcond);
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}
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template<class...Args> inline IndexedPackGroup operator()(Args&&...args) {
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m_rtree.clear();
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return m_pck.executeIndexed(std::forward<Args>(args)...);
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}
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};
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template<>
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class AutoArranger<Box>: public _ArrBase<Box> {
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public:
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AutoArranger(const Box& bin, Distance dist,
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std::function<void(unsigned)> progressind,
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std::function<bool(void)> stopcond):
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_ArrBase<Box>(bin, dist, progressind, stopcond)
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{
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m_pconf.object_function = [this, bin] (const Item &item) {
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auto result = objfunc(bin.center(),
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m_merged_pile,
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m_pilebb,
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m_items,
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item,
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m_bin_area,
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m_norm,
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m_rtree,
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m_smallsrtree,
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m_remaining);
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double score = std::get<0>(result);
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auto& fullbb = std::get<1>(result);
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double miss = Placer::overfit(fullbb, bin);
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miss = miss > 0? miss : 0;
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score += miss*miss;
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return score;
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};
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m_pck.configure(m_pconf);
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}
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};
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using lnCircle = libnest2d::_Circle<libnest2d::PointImpl>;
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inline lnCircle to_lnCircle(const Circle& circ) {
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return lnCircle({circ.center()(0), circ.center()(1)}, circ.radius());
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}
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template<>
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class AutoArranger<lnCircle>: public _ArrBase<lnCircle> {
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public:
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AutoArranger(const lnCircle& bin, Distance dist,
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std::function<void(unsigned)> progressind,
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std::function<bool(void)> stopcond):
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_ArrBase<lnCircle>(bin, dist, progressind, stopcond) {
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m_pconf.object_function = [this, &bin] (const Item &item) {
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auto result = objfunc(bin.center(),
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m_merged_pile,
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m_pilebb,
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m_items,
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item,
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m_bin_area,
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m_norm,
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m_rtree,
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m_smallsrtree,
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m_remaining);
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double score = std::get<0>(result);
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auto isBig = [this](const Item& itm) {
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return itm.area()/m_bin_area > BIG_ITEM_TRESHOLD ;
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};
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if(isBig(item)) {
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auto mp = m_merged_pile;
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mp.push_back(item.transformedShape());
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auto chull = sl::convexHull(mp);
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double miss = Placer::overfit(chull, bin);
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if(miss < 0) miss = 0;
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score += miss*miss;
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}
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return score;
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};
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m_pck.configure(m_pconf);
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}
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};
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template<>
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class AutoArranger<PolygonImpl>: public _ArrBase<PolygonImpl> {
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public:
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AutoArranger(const PolygonImpl& bin, Distance dist,
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std::function<void(unsigned)> progressind,
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std::function<bool(void)> stopcond):
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_ArrBase<PolygonImpl>(bin, dist, progressind, stopcond)
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{
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m_pconf.object_function = [this, &bin] (const Item &item) {
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auto binbb = sl::boundingBox(bin);
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auto result = objfunc(binbb.center(),
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m_merged_pile,
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m_pilebb,
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m_items,
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item,
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m_bin_area,
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m_norm,
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m_rtree,
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m_smallsrtree,
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m_remaining);
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double score = std::get<0>(result);
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return score;
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};
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m_pck.configure(m_pconf);
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}
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};
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template<> // Specialization with no bin
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class AutoArranger<bool>: public _ArrBase<Box> {
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public:
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AutoArranger(Distance dist, std::function<void(unsigned)> progressind,
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std::function<bool(void)> stopcond):
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_ArrBase<Box>(Box(0, 0), dist, progressind, stopcond)
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{
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this->m_pconf.object_function = [this] (const Item &item) {
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auto result = objfunc({0, 0},
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m_merged_pile,
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m_pilebb,
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m_items,
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item,
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0,
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m_norm,
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m_rtree,
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m_smallsrtree,
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m_remaining);
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return std::get<0>(result);
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};
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this->m_pck.configure(m_pconf);
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}
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};
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// A container which stores a pointer to the 3D object and its projected
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// 2D shape from top view.
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using ShapeData2D =
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std::vector<std::pair<Slic3r::ModelInstance*, Item>>;
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ShapeData2D projectModelFromTop(const Slic3r::Model &model) {
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ShapeData2D ret;
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auto s = std::accumulate(model.objects.begin(), model.objects.end(), size_t(0),
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[](size_t s, ModelObject* o){
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return s + o->instances.size();
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});
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ret.reserve(s);
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for(ModelObject* objptr : model.objects) {
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if(objptr) {
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TriangleMesh rmesh = objptr->raw_mesh();
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ModelInstance * finst = objptr->instances.front();
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// Object instances should carry the same scaling and
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// x, y rotation that is why we use the first instance
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rmesh.scale(finst->get_scaling_factor());
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rmesh.rotate_x(float(finst->get_rotation()(X)));
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rmesh.rotate_y(float(finst->get_rotation()(Y)));
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// TODO export the exact 2D projection
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auto p = rmesh.convex_hull();
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p.make_clockwise();
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p.append(p.first_point());
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auto clpath = Slic3rMultiPoint_to_ClipperPath(p);
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for(ModelInstance* objinst : objptr->instances) {
|
|
if(objinst) {
|
|
ClipperLib::PolygonImpl pn;
|
|
pn.Contour = clpath;
|
|
|
|
// Efficient conversion to item.
|
|
Item item(std::move(pn));
|
|
|
|
// Invalid geometries would throw exceptions when arranging
|
|
if(item.vertexCount() > 3) {
|
|
item.rotation(objinst->get_rotation(Z));
|
|
item.translation({
|
|
ClipperLib::cInt(objinst->get_offset(X)/SCALING_FACTOR),
|
|
ClipperLib::cInt(objinst->get_offset(Y)/SCALING_FACTOR)
|
|
});
|
|
ret.emplace_back(objinst, item);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
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 transformation data from the item object and scale it
|
|
// appropriately
|
|
auto off = item.translation();
|
|
Radians rot = item.rotation();
|
|
|
|
Vec3d foff(off.X*SCALING_FACTOR + batch_offset,
|
|
off.Y*SCALING_FACTOR,
|
|
inst_ptr->get_offset()(Z));
|
|
|
|
// write the transformation data into the model instance
|
|
inst_ptr->set_rotation(Z, rot);
|
|
inst_ptr->set_offset(foff);
|
|
}
|
|
}
|
|
|
|
BedShapeHint bedShape(const Polyline &bed) {
|
|
BedShapeHint ret;
|
|
|
|
auto x = [](const Point& p) { return p(0); };
|
|
auto y = [](const Point& p) { return p(1); };
|
|
|
|
auto width = [x](const BoundingBox& box) {
|
|
return x(box.max) - x(box.min);
|
|
};
|
|
|
|
auto height = [y](const BoundingBox& box) {
|
|
return y(box.max) - y(box.min);
|
|
};
|
|
|
|
auto area = [&width, &height](const BoundingBox& box) {
|
|
double w = width(box);
|
|
double h = height(box);
|
|
return w*h;
|
|
};
|
|
|
|
auto poly_area = [](Polyline p) {
|
|
Polygon pp; pp.points.reserve(p.points.size() + 1);
|
|
pp.points = std::move(p.points);
|
|
pp.points.emplace_back(pp.points.front());
|
|
return std::abs(pp.area());
|
|
};
|
|
|
|
auto distance_to = [x, y](const Point& p1, const Point& p2) {
|
|
double dx = x(p2) - x(p1);
|
|
double dy = y(p2) - y(p1);
|
|
return std::sqrt(dx*dx + dy*dy);
|
|
};
|
|
|
|
auto bb = bed.bounding_box();
|
|
|
|
auto isCircle = [bb, distance_to](const Polyline& polygon) {
|
|
auto center = bb.center();
|
|
std::vector<double> vertex_distances;
|
|
double avg_dist = 0;
|
|
for (auto pt: polygon.points)
|
|
{
|
|
double distance = distance_to(center, pt);
|
|
vertex_distances.push_back(distance);
|
|
avg_dist += distance;
|
|
}
|
|
|
|
avg_dist /= vertex_distances.size();
|
|
|
|
Circle ret(center, avg_dist);
|
|
for (auto el: vertex_distances)
|
|
{
|
|
if (abs(el - avg_dist) > 10 * SCALED_EPSILON)
|
|
ret = Circle();
|
|
break;
|
|
}
|
|
|
|
return ret;
|
|
};
|
|
|
|
auto parea = poly_area(bed);
|
|
|
|
if( (1.0 - parea/area(bb)) < 1e-3 ) {
|
|
ret.type = BedShapeType::BOX;
|
|
ret.shape.box = bb;
|
|
}
|
|
else if(auto c = isCircle(bed)) {
|
|
ret.type = BedShapeType::CIRCLE;
|
|
ret.shape.circ = c;
|
|
} else {
|
|
ret.type = BedShapeType::IRREGULAR;
|
|
ret.shape.polygon = bed;
|
|
}
|
|
|
|
// Determine the bed shape by hand
|
|
return ret;
|
|
}
|
|
|
|
bool arrange(Model &model,
|
|
coord_t min_obj_distance,
|
|
const Polyline &bed,
|
|
BedShapeHint bedhint,
|
|
bool first_bin_only,
|
|
std::function<void (unsigned)> progressind,
|
|
std::function<bool ()> stopcondition)
|
|
{
|
|
using ArrangeResult = _IndexedPackGroup<PolygonImpl>;
|
|
|
|
bool ret = true;
|
|
|
|
// Get the 2D projected shapes with their 3D model instance pointers
|
|
auto shapemap = arr::projectModelFromTop(model);
|
|
|
|
// 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] (ShapeData2D::value_type& it)
|
|
{
|
|
shapes.push_back(std::ref(it.second));
|
|
});
|
|
|
|
IndexedPackGroup result;
|
|
|
|
// If there is no hint about the shape, we will try to guess
|
|
if(bedhint.type == BedShapeType::WHO_KNOWS) bedhint = bedShape(bed);
|
|
|
|
BoundingBox bbb(bed);
|
|
|
|
auto& cfn = stopcondition;
|
|
|
|
auto binbb = Box({
|
|
static_cast<libnest2d::Coord>(bbb.min(0)),
|
|
static_cast<libnest2d::Coord>(bbb.min(1))
|
|
},
|
|
{
|
|
static_cast<libnest2d::Coord>(bbb.max(0)),
|
|
static_cast<libnest2d::Coord>(bbb.max(1))
|
|
});
|
|
|
|
switch(bedhint.type) {
|
|
case BedShapeType::BOX: {
|
|
|
|
// Create the arranger for the box shaped bed
|
|
AutoArranger<Box> arrange(binbb, min_obj_distance, progressind, cfn);
|
|
|
|
// Arrange and return the items with their respective indices within the
|
|
// input sequence.
|
|
result = arrange(shapes.begin(), shapes.end());
|
|
break;
|
|
}
|
|
case BedShapeType::CIRCLE: {
|
|
|
|
auto c = bedhint.shape.circ;
|
|
auto cc = to_lnCircle(c);
|
|
|
|
AutoArranger<lnCircle> arrange(cc, min_obj_distance, progressind, cfn);
|
|
result = arrange(shapes.begin(), shapes.end());
|
|
break;
|
|
}
|
|
case BedShapeType::IRREGULAR:
|
|
case BedShapeType::WHO_KNOWS: {
|
|
|
|
using P = libnest2d::PolygonImpl;
|
|
|
|
auto ctour = Slic3rMultiPoint_to_ClipperPath(bed);
|
|
P irrbed = sl::create<PolygonImpl>(std::move(ctour));
|
|
|
|
AutoArranger<P> arrange(irrbed, min_obj_distance, progressind, cfn);
|
|
|
|
// Arrange and return the items with their respective indices within the
|
|
// input sequence.
|
|
result = arrange(shapes.begin(), shapes.end());
|
|
break;
|
|
}
|
|
};
|
|
|
|
if(result.empty() || stopcondition()) return false;
|
|
|
|
if(first_bin_only) {
|
|
applyResult(result.front(), 0, shapemap);
|
|
} else {
|
|
|
|
const auto STRIDE_PADDING = 1.2;
|
|
|
|
Coord stride = static_cast<Coord>(STRIDE_PADDING*
|
|
binbb.width()*SCALING_FACTOR);
|
|
Coord batch_offset = 0;
|
|
|
|
for(auto& group : result) {
|
|
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
|
|
// print bed
|
|
batch_offset += stride;
|
|
}
|
|
}
|
|
|
|
for(auto objptr : model.objects) objptr->invalidate_bounding_box();
|
|
|
|
return ret && result.size() == 1;
|
|
}
|
|
|
|
}
|
|
}
|