update svn to r733 (3)
This commit is contained in:
Razvan Mihalyi
parent
5aed26340d
commit
b03c2a3e0c
11 changed files with 1793 additions and 0 deletions
50
include/segmentation/FHGraph.h
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50
include/segmentation/FHGraph.h
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/**
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* Point Cloud Segmentation using Felzenszwalb-Huttenlocher Algorithm
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*
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* Copyright (C) Jacobs University Bremen
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*
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* Released under the GPL version 3.
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*
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* @author Mihai-Cotizo Sima
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*/
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#ifndef __FHGRAPH_H_
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#define __FHGRAPH_H_
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#include <vector>
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#include <list>
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#include <slam6d/point.h>
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#include <slam6d/scan.h>
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#include <segmentation/segment-graph.h>
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#include <ANN/ANN.h>
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class FHGraph {
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public:
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FHGraph(std::vector< Point >& ps, double weight(Point, Point), double sigma, double eps, int neighbors, float radius);
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edge* getGraph();
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Point operator[](int index);
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int getNumPoints();
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int getNumEdges();
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void dispose();
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private:
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void compute_neighbors(double weight(Point, Point), double eps);
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void do_gauss(double sigma);
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void without_gauss();
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std::vector<edge> edges;
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std::vector<Point>& points;
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int V;
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int E;
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int nr_neighbors;
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float radius;
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struct he{ int x; float w; };
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std::vector< std::list<he> > adjency_list;
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};
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#endif
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48
include/segmentation/disjoint-set.h
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48
include/segmentation/disjoint-set.h
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/*
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Copyright (C) 2006 Pedro Felzenszwalb
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This program is free software; you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation; either version 2 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program; if not, write to the Free Software
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Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*/
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#ifndef DISJOINT_SET
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#define DISJOINT_SET
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// disjoint-set forests using union-by-rank and path compression (sort of).
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typedef struct {
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int rank;
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int p;
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int size;
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} uni_elt;
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class universe {
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public:
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universe(int elements);
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~universe();
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int find(int x);
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void join(int x, int y);
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int size(int x) const {
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return elts[x].size;
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}
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int num_sets() const {
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return num;
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}
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private:
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uni_elt *elts;
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int num;
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};
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#endif
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49
include/segmentation/segment-graph.h
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49
include/segmentation/segment-graph.h
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/*
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Copyright (C) 2006 Pedro Felzenszwalb
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This program is free software; you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation; either version 2 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program; if not, write to the Free Software
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Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*/
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#ifndef SEGMENT_GRAPH
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#define SEGMENT_GRAPH
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#include <algorithm>
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#include <cmath>
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#include "disjoint-set.h"
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// threshold function
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#define THRESHOLD(size, c) (c/size)
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typedef struct {
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float w;
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int a, b;
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} edge;
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bool operator<(const edge &a, const edge &b);
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/*
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* Segment a graph
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*
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* Returns a disjoint-set forest representing the segmentation.
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*
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* num_vertices: number of vertices in graph.
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* num_edges: number of edges in graph
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* edges: array of edges.
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* c: constant for treshold function.
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*/
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universe *segment_graph(int num_vertices, int num_edges, edge *edges,
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float c);
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#endif
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238
include/slam6d/kdTreeImpl.h
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238
include/slam6d/kdTreeImpl.h
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/** @file
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* @brief Representation of the optimized k-d tree.
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* @author Remus Dumitru. Jacobs University Bremen, Germany
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* @author Corneliu-Claudiu Prodescu. Jacobs University Bremen, Germany
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* @author Andreas Nuechter. Institute of Computer Science, University of Osnabrueck, Germany.
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* @author Kai Lingemann. Institute of Computer Science, University of Osnabrueck, Germany.
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* @author Thomas Escher. Institute of Computer Science, University of Osnabrueck, Germany.
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*/
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#ifndef __KD_TREE_IMPL_H__
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#define __KD_TREE_IMPL_H__
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#include "slam6d/kdparams.h"
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#include "globals.icc"
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#ifdef _MSC_VER
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#if !defined _OPENMP && defined OPENMP
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#define _OPENMP
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#endif
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#endif
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#ifdef _OPENMP
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#include <omp.h>
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#endif
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/**
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* @brief The optimized k-d tree.
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*
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* A kD tree for points, with limited
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* capabilities (find nearest point to
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* a given point, or to a ray).
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**/
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template<class PointData, class AccessorData, class AccessorFunc>
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class KDTreeImpl {
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public:
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inline KDTreeImpl() { }
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virtual inline ~KDTreeImpl() {
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if (!npts) {
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#ifdef WITH_OPENMP_KD
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omp_set_num_threads(OPENMP_NUM_THREADS);
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#pragma omp parallel for schedule(dynamic)
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#endif
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for (int i = 0; i < 2; i++) {
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if (i == 0 && node.child1) delete node.child1;
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if (i == 1 && node.child2) delete node.child2;
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}
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} else {
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if (leaf.p) delete [] leaf.p;
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}
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}
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virtual void create(PointData pts, AccessorData *indices, size_t n) {
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AccessorFunc point;
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// Find bbox
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double xmin = point(pts, indices[0])[0], xmax = point(pts, indices[0])[0];
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double ymin = point(pts, indices[0])[1], ymax = point(pts, indices[0])[1];
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double zmin = point(pts, indices[0])[2], zmax = point(pts, indices[0])[2];
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for(unsigned int i = 1; i < n; i++) {
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xmin = min(xmin, point(pts, indices[i])[0]);
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xmax = max(xmax, point(pts, indices[i])[0]);
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ymin = min(ymin, point(pts, indices[i])[1]);
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ymax = max(ymax, point(pts, indices[i])[1]);
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zmin = min(zmin, point(pts, indices[i])[2]);
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zmax = max(zmax, point(pts, indices[i])[2]);
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}
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// Leaf nodes
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if ((n > 0) && (n <= 10)) {
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npts = n;
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leaf.p = new AccessorData[n];
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// fill leaf index array with indices
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for(unsigned int i = 0; i < n; ++i) {
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leaf.p[i] = indices[i];
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}
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return;
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}
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// Else, interior nodes
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npts = 0;
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node.center[0] = 0.5 * (xmin+xmax);
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node.center[1] = 0.5 * (ymin+ymax);
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node.center[2] = 0.5 * (zmin+zmax);
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node.dx = 0.5 * (xmax-xmin);
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node.dy = 0.5 * (ymax-ymin);
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node.dz = 0.5 * (zmax-zmin);
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node.r2 = sqr(node.dx) + sqr(node.dy) + sqr(node.dz);
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// Find longest axis
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if (node.dx > node.dy) {
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if (node.dx > node.dz) {
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node.splitaxis = 0;
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} else {
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node.splitaxis = 2;
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}
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} else {
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if (node.dy > node.dz) {
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node.splitaxis = 1;
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} else {
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node.splitaxis = 2;
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}
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}
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// Partition
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double splitval = node.center[node.splitaxis];
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if ( fabs(max(max(node.dx,node.dy),node.dz)) < 0.01 ) {
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npts = n;
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leaf.p = new AccessorData[n];
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// fill leaf index array with indices
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for(unsigned int i = 0; i < n; ++i) {
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leaf.p[i] = indices[i];
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}
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return;
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}
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AccessorData* left = indices, * right = indices + n - 1;
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while(true) {
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while(point(pts, *left)[node.splitaxis] < splitval)
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left++;
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while(point(pts, *right)[node.splitaxis] >= splitval)
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right--;
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if(right < left)
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break;
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std::swap(*left, *right);
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}
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// Build subtrees
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int i;
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#ifdef WITH_OPENMP_KD // does anybody know the reason why this is slower ?? --Andreas
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omp_set_num_threads(OPENMP_NUM_THREADS);
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#pragma omp parallel for schedule(dynamic)
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#endif
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for (i = 0; i < 2; i++) {
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if (i == 0) {
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node.child1 = new KDTreeImpl();
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node.child1->create(pts, indices, left - indices);
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}
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if (i == 1) {
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node.child2 = new KDTreeImpl();
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node.child2->create(pts, left, n - (left - indices));
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}
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}
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}
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protected:
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/**
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* storing the parameters of the k-d tree, i.e., the current closest point,
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* the distance to the current closest point and the point itself.
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* These global variable are needed in this search.
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*
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* Padded in the parallel case.
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*/
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#ifdef _OPENMP
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#ifdef __INTEL_COMPILER
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__declspec (align(16)) static KDParams params[MAX_OPENMP_NUM_THREADS];
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#else
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static KDParams params[MAX_OPENMP_NUM_THREADS];
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#endif //__INTEL_COMPILER
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#else
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static KDParams params[MAX_OPENMP_NUM_THREADS];
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#endif
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/**
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* number of points. If this is 0: intermediate node. If nonzero: leaf.
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*/
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int npts;
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/**
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* Cue the standard rant about anon unions but not structs in C++
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*/
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union {
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/**
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* in case of internal node...
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*/
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struct {
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double center[3]; ///< storing the center of the voxel (R^3)
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double dx, ///< defining the voxel itself
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dy, ///< defining the voxel itself
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dz, ///< defining the voxel itself
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r2; ///< defining the voxel itself
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int splitaxis; ///< defining the kind of splitaxis
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KDTreeImpl *child1; ///< pointers to the childs
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KDTreeImpl *child2; ///< pointers to the childs
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} node;
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/**
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* in case of leaf node ...
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*/
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struct {
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/**
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* store the value itself
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* Here we store just a pointer to the data
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*/
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AccessorData* p;
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} leaf;
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};
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void _FindClosest(const PointData& pts, int threadNum) const {
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AccessorFunc point;
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// Leaf nodes
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if (npts) {
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for (int i = 0; i < npts; i++) {
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double myd2 = Dist2(params[threadNum].p, point(pts, leaf.p[i]));
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if (myd2 < params[threadNum].closest_d2) {
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params[threadNum].closest_d2 = myd2;
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params[threadNum].closest = point(pts, leaf.p[i]);
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}
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}
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return;
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}
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// Quick check of whether to abort
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double approx_dist_bbox = max(max(fabs(params[threadNum].p[0]-node.center[0])-node.dx,
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fabs(params[threadNum].p[1]-node.center[1])-node.dy),
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fabs(params[threadNum].p[2]-node.center[2])-node.dz);
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if (approx_dist_bbox >= 0 && sqr(approx_dist_bbox) >= params[threadNum].closest_d2)
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return;
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// Recursive case
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double myd = node.center[node.splitaxis] - params[threadNum].p[node.splitaxis];
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if (myd >= 0.0) {
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node.child1->_FindClosest(pts, threadNum);
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if (sqr(myd) < params[threadNum].closest_d2) {
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node.child2->_FindClosest(pts, threadNum);
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}
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} else {
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node.child2->_FindClosest(pts, threadNum);
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if (sqr(myd) < params[threadNum].closest_d2) {
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node.child1->_FindClosest(pts, threadNum);
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}
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}
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}
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};
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#endif
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7
src/normals/CMakeLists.txt~
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7
src/normals/CMakeLists.txt~
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IF(WITH_NORMALS)
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FIND_PACKAGE(OpenCV REQUIRED)
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add_executable(calculateNormals calculate_normals.cc ../slam6d/fbr/fbr_global.cc ../slam6d/fbr/panorama.cc)
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target_link_libraries(calculateNormals scan ANN newmat fbr_panorama fbr_cv_io ${Boost_LIBRARIES} ${OpenCV_LIBS})
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ENDIF(WITH_NORMALS)
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643
src/normals/calculate_normals.cc~
Normal file
643
src/normals/calculate_normals.cc~
Normal file
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@ -0,0 +1,643 @@
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/*
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* calculateNormals implementation
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*
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* Copyright (C) Johannes Schauer, Razvan Mihaly
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*
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* Released under the GPL version 3
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*
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*/
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#include "ANN/ANN.h"
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#include "newmat/newmat.h"
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#include "newmat/newmatap.h"
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#include "newmat/newmatio.h"
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using namespace NEWMAT;
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#include "slam6d/point.h"
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#include "normals/pointNeighbor.h"
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#include "slam6d/scan.h"
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#include "slam6d/globals.icc"
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#include "slam6d/fbr/panorama.h"
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#include "normals/point.h"
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#include "normals/SRI.h"
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#include <string>
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using std::string;
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#include <iostream>
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using std::cout;
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using std::endl;
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using std::vector;
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#include <algorithm>
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#include <boost/program_options.hpp>
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#include <boost/filesystem/operations.hpp>
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#include <boost/filesystem/fstream.hpp>
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namespace po = boost::program_options;
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enum normal_method {KNN_PCA, AKNN_PCA, PANO_PCA, PANO_SRI};
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void normal_option_dependency(const po::variables_map & vm, normal_method ntype, const char *option)
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{
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if (vm.count("normalMethod") && vm["normalMethod"].as<normal_method>() == ntype) {
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if (!vm.count(option)) {
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throw std::logic_error (string("this normal method needs ")+option+" to be set");
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}
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}
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}
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void normal_option_conflict(const po::variables_map & vm, normal_method ntype, const char *option)
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{
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if (vm.count("normalMethod") && vm["normalMethod"].as<normal_method>() == ntype) {
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if (vm.count(option)) {
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throw std::logic_error (string("this normal method is incompatible with ")+option);
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}
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}
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}
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/*
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* validates input type specification
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*/
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void validate(boost::any& v, const std::vector<std::string>& values,
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IOType*, int) {
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if (values.size() == 0)
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throw std::runtime_error("Invalid model specification");
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string arg = values.at(0);
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try {
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v = formatname_to_io_type(arg.c_str());
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} catch (...) { // runtime_error
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throw std::runtime_error("Format " + arg + " unknown.");
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}
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}
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void validate(boost::any& v, const std::vector<std::string>& values,
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normal_method*, int) {
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if (values.size() == 0)
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throw std::runtime_error("Invalid model specification");
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string arg = values.at(0);
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if(strcasecmp(arg.c_str(), "KNN_PCA") == 0) v = KNN_PCA;
|
||||
else if(strcasecmp(arg.c_str(), "AKNN_PCA") == 0) v = AKNN_PCA;
|
||||
else if(strcasecmp(arg.c_str(), "PANO_PCA") == 0) v = PANO_PCA;
|
||||
else if(strcasecmp(arg.c_str(), "PANO_SRI") == 0) v = PANO_SRI;
|
||||
else throw std::runtime_error(std::string("normal method ") + arg + std::string(" is unknown"));
|
||||
}
|
||||
|
||||
/*
|
||||
* parse commandline options, fill arguments
|
||||
*/
|
||||
void parse_options(int argc, char **argv, int &start, int &end,
|
||||
bool &scanserver, string &dir, IOType &iotype,
|
||||
int &maxDist, int &minDist, normal_method &normalMethod, int &knn,
|
||||
int &kmin, int &kmax, double& alpha, int &width, int &height,
|
||||
bool &flipnormals, double &factor)
|
||||
{
|
||||
po::options_description generic("Generic options");
|
||||
generic.add_options()
|
||||
("help,h", "output this help message");
|
||||
|
||||
po::options_description input("Input options");
|
||||
input.add_options()
|
||||
("start,s", po::value<int>(&start)->default_value(0),
|
||||
"start at scan <arg> (i.e., neglects the first <arg> scans) "
|
||||
"[ATTENTION: counting naturally starts with 0]")
|
||||
("end,e", po::value<int>(&end)->default_value(-1),
|
||||
"end after scan <arg>")
|
||||
("format,f", po::value<IOType>(&iotype)->default_value(UOS),
|
||||
"using shared library <arg> for input. (chose F from {uos, uos_map, "
|
||||
"uos_rgb, uos_frames, uos_map_frames, old, rts, rts_map, ifp, "
|
||||
"riegl_txt, riegl_rgb, riegl_bin, zahn, ply})")
|
||||
("max,M", po::value<int>(&maxDist)->default_value(-1),
|
||||
"neglegt all data points with a distance larger than <arg> 'units")
|
||||
("min,m", po::value<int>(&minDist)->default_value(-1),
|
||||
"neglegt all data points with a distance smaller than <arg> 'units")
|
||||
("scanserver,S", po::bool_switch(&scanserver),
|
||||
"Use the scanserver as an input method and handling of scan data")
|
||||
;
|
||||
|
||||
po::options_description normal("Normal options");
|
||||
normal.add_options()
|
||||
("normalMethod,N", po::value<normal_method>(&normalMethod)->default_value(KNN_PCA),
|
||||
"choose the method for computing normals:\n"
|
||||
"KNN_PCA -- use kNN and PCA\n"
|
||||
"AKNN_PCA -- use adaptive kNN and PCA\n"
|
||||
"PANO_PCA -- use panorama image neighbors and PCA\n"
|
||||
"PANO_SRI -- use panorama image neighbors and spherical range image differentiation\n")
|
||||
("knn,K", po::value<int>(&knn),
|
||||
"select the k in kNN search")
|
||||
("kmin,1", po::value<int>(&kmin),
|
||||
"select k_min in adaptive kNN search")
|
||||
("kmax,2", po::value<int>(&kmax),
|
||||
"select k_max in adaptive kNN search")
|
||||
("alpha,a", po::value<double>(&alpha),
|
||||
"select the alpha parameter for detecting an ill-conditioned neighborhood")
|
||||
("width,w", po::value<int>(&width),
|
||||
"width of panorama")
|
||||
("height,h", po::value<int>(&height),
|
||||
"height of panorama")
|
||||
("flipnormals,F", po::bool_switch(&flipnormals),
|
||||
"flip orientation of normals towards scan pose")
|
||||
("factor,c", po::value<double>(&factor),
|
||||
"factor for SRI computation")
|
||||
;
|
||||
|
||||
po::options_description hidden("Hidden options");
|
||||
hidden.add_options()
|
||||
("input-dir", po::value<string>(&dir), "input dir");
|
||||
|
||||
// all options
|
||||
po::options_description all;
|
||||
all.add(generic).add(input).add(normal).add(hidden);
|
||||
|
||||
// options visible with --help
|
||||
po::options_description cmdline_options;
|
||||
cmdline_options.add(generic).add(input).add(normal);
|
||||
|
||||
// positional argument
|
||||
po::positional_options_description pd;
|
||||
pd.add("input-dir", 1);
|
||||
|
||||
// process options
|
||||
po::variables_map vm;
|
||||
po::store(po::command_line_parser(argc, argv).
|
||||
options(all).positional(pd).run(), vm);
|
||||
po::notify(vm);
|
||||
|
||||
// display help
|
||||
if (vm.count("help")) {
|
||||
cout << cmdline_options;
|
||||
exit(0);
|
||||
}
|
||||
|
||||
normal_option_dependency(vm, KNN_PCA, "knn");
|
||||
normal_option_conflict(vm, KNN_PCA, "kmin");
|
||||
normal_option_conflict(vm, KNN_PCA, "kmax");
|
||||
normal_option_conflict(vm, KNN_PCA, "alpha");
|
||||
normal_option_conflict(vm, KNN_PCA, "width");
|
||||
normal_option_conflict(vm, KNN_PCA, "height");
|
||||
normal_option_conflict(vm, KNN_PCA, "factor");
|
||||
|
||||
normal_option_conflict(vm, AKNN_PCA, "knn");
|
||||
normal_option_dependency(vm, AKNN_PCA, "kmin");
|
||||
normal_option_dependency(vm, AKNN_PCA, "kmax");
|
||||
normal_option_dependency(vm, AKNN_PCA, "alpha");
|
||||
normal_option_conflict(vm, AKNN_PCA, "width");
|
||||
normal_option_conflict(vm, AKNN_PCA, "height");
|
||||
normal_option_conflict(vm, AKNN_PCA, "factor");
|
||||
|
||||
//normal_option_conflict(vm, PANO_PCA, "knn");
|
||||
normal_option_dependency(vm, KNN_PCA, "knn");
|
||||
normal_option_conflict(vm, PANO_PCA, "kmin");
|
||||
normal_option_conflict(vm, PANO_PCA, "kmax");
|
||||
normal_option_conflict(vm, PANO_PCA, "alpha");
|
||||
normal_option_dependency(vm, PANO_PCA, "width");
|
||||
normal_option_dependency(vm, PANO_PCA, "height");
|
||||
normal_option_conflict(vm, PANO_PCA, "factor");
|
||||
|
||||
normal_option_conflict(vm, PANO_SRI, "knn");
|
||||
normal_option_conflict(vm, PANO_SRI, "kmin");
|
||||
normal_option_conflict(vm, PANO_SRI, "kmax");
|
||||
normal_option_conflict(vm, PANO_SRI, "alpha");
|
||||
normal_option_conflict(vm, PANO_SRI, "width");
|
||||
normal_option_conflict(vm, PANO_SRI, "height");
|
||||
normal_option_dependency(vm, PANO_SRI, "factor");
|
||||
|
||||
// add trailing slash to directory if not present yet
|
||||
if (dir[dir.length()-1] != '/') dir = dir + "/";
|
||||
}
|
||||
|
||||
/*
|
||||
* retrieve a cv::Mat with x,y,z,r from a scan object
|
||||
* functionality borrowed from scan_cv::convertScanToMat but this function
|
||||
* does not allow a scanserver to be used, prints to stdout and can only
|
||||
* handle a single scan
|
||||
*/
|
||||
void scan2mat(Scan* scan, cv::Mat& scan_cv) {
|
||||
DataXYZ xyz = scan->get("xyz");
|
||||
unsigned int nPoints = xyz.size();
|
||||
scan_cv.create(nPoints,1,CV_32FC(4));
|
||||
scan_cv = cv::Scalar::all(0);
|
||||
cv::MatIterator_<cv::Vec3f> it = scan_cv.begin<cv::Vec3f>();
|
||||
for(unsigned int i = 0; i < nPoints; i++){
|
||||
(*it)[0] = xyz[i][0];
|
||||
(*it)[1] = xyz[i][1];
|
||||
(*it)[2] = xyz[i][2];
|
||||
++it;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper function that maps x, y, z to R, G, B using a linear function
|
||||
*/
|
||||
void mapNormalToRGB(const Point& normal, Point& rgb)
|
||||
{
|
||||
rgb.x = 127.5 * normal.x + 127.5;
|
||||
rgb.y = 127.5 * normal.y + 127.5;
|
||||
rgb.z = 255.0 * fabs(normal.z);
|
||||
}
|
||||
|
||||
/**
|
||||
* Write normals to .3d files using the uos_rgb format
|
||||
*/
|
||||
void writeNormals(const Scan* scan, const string& dir,
|
||||
const vector<Point>& points, const vector<Point>& normals)
|
||||
{
|
||||
|
||||
stringstream ss;
|
||||
ss << dir << "scan" << string(scan->getIdentifier()) << ".3d";
|
||||
ofstream scan_file;
|
||||
scan_file.open(ss.str().c_str());
|
||||
for(size_t i = 0; i < points.size(); ++i) {
|
||||
Point rgb;
|
||||
mapNormalToRGB(normals[i], rgb);
|
||||
scan_file
|
||||
<< points[i].x << " " << points[i].y << " " << points[i].z << " "
|
||||
<< (unsigned int) rgb.x << " " << (unsigned int) rgb.y << " "
|
||||
<< (unsigned int) rgb.z << "\n";
|
||||
}
|
||||
scan_file.close();
|
||||
|
||||
ss.clear(); ss.str(string());
|
||||
ss << dir << "scan" << string(scan->getIdentifier()) << ".pose";
|
||||
ofstream pose_file;
|
||||
pose_file.open(ss.str().c_str());
|
||||
pose_file << 0 << " " << 0 << " " << 0 << "\n" << 0 << " " << 0 << " " << 0 << "\n";
|
||||
pose_file.close();
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute eigen decomposition of a point and its neighbors using the NEWMAT library
|
||||
* @param point - input points with corresponding neighbors
|
||||
* @param e_values - out parameter returns the eigenvalues
|
||||
* @param e_vectors - out parameter returns the eigenvectors
|
||||
*/
|
||||
void computeEigenDecomposition(const PointNeighbor& point,
|
||||
DiagonalMatrix& e_values, Matrix& e_vectors)
|
||||
{
|
||||
Point centroid(0, 0, 0);
|
||||
vector<Point> neighbors = point.neighbors;
|
||||
|
||||
for (size_t j = 0; j < neighbors.size(); ++j) {
|
||||
centroid.x += neighbors[j].x;
|
||||
centroid.y += neighbors[j].y;
|
||||
centroid.z += neighbors[j].z;
|
||||
}
|
||||
centroid.x /= (double) neighbors.size();
|
||||
centroid.y /= (double) neighbors.size();
|
||||
centroid.z /= (double) neighbors.size();
|
||||
|
||||
Matrix S(3, 3);
|
||||
S = 0.0;
|
||||
for (size_t j = 0; j < neighbors.size(); ++j) {
|
||||
ColumnVector point_prime(3);
|
||||
point_prime(1) = neighbors[j].x - centroid.x;
|
||||
point_prime(2) = neighbors[j].y - centroid.y;
|
||||
point_prime(3) = neighbors[j].z - centroid.z;
|
||||
S = S + point_prime * point_prime.t();
|
||||
}
|
||||
// normalize S
|
||||
for (int j = 0; j < 3; ++j)
|
||||
for (int k = 0; k < 3; ++k)
|
||||
S(j+1, k+1) /= (double) neighbors.size();
|
||||
|
||||
SymmetricMatrix C;
|
||||
C << S;
|
||||
// the decomposition
|
||||
Jacobi(C, e_values, e_vectors);
|
||||
|
||||
#ifdef DEBUG
|
||||
// Print the result
|
||||
cout << "The eigenvalues matrix:" << endl;
|
||||
cout << e_values << endl;
|
||||
#endif
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute neighbors using kNN search
|
||||
* @param points - input set of points
|
||||
* @param points_neighbors - output set of points with corresponding neighbors
|
||||
* @param knn - k constant in kNN search
|
||||
* @param kmax - to be used in adaptive knn search as the upper bound on adapting the k constant, defaults to -1 for regular kNN search
|
||||
* @param alpha - to be used in adaptive knn search for detecting ill-conditioned neighborhoods
|
||||
* @param eps - parameter required by the ANN library in kNN search
|
||||
*/
|
||||
void computeKNearestNeighbors(const vector<Point>& points,
|
||||
vector<PointNeighbor>& points_neighbors, int knn, int kmax=-1,
|
||||
double alpha=1000.0, double eps=1.0)
|
||||
{
|
||||
ANNpointArray point_array = annAllocPts(points.size(), 3);
|
||||
for (size_t i = 0; i < points.size(); ++i) {
|
||||
point_array[i] = new ANNcoord[3];
|
||||
point_array[i][0] = points[i].x;
|
||||
point_array[i][1] = points[i].y;
|
||||
point_array[i][2] = points[i].z;
|
||||
}
|
||||
|
||||
ANNkd_tree t(point_array, points.size(), 3);
|
||||
ANNidxArray n;
|
||||
ANNdistArray d;
|
||||
|
||||
if (kmax < 0) {
|
||||
/// regular kNN search, allocate memory for knn
|
||||
n = new ANNidx[knn];
|
||||
d = new ANNdist[knn];
|
||||
} else {
|
||||
/// adaptive kNN search, allocate memory for kmax
|
||||
n = new ANNidx[kmax];
|
||||
d = new ANNdist[kmax];
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < points.size(); ++i) {
|
||||
vector<Point> neighbors;
|
||||
ANNpoint p = point_array[i];
|
||||
|
||||
t.annkSearch(p, knn, n, d, eps);
|
||||
|
||||
neighbors.push_back(points[i]);
|
||||
for (int j = 0; j < knn; ++j) {
|
||||
if ( n[j] != (int)i )
|
||||
neighbors.push_back(points[n[j]]);
|
||||
}
|
||||
|
||||
PointNeighbor current_point(points[i], neighbors);
|
||||
points_neighbors.push_back( current_point );
|
||||
Matrix e_vectors(3,3); e_vectors = 0.0;
|
||||
DiagonalMatrix e_values(3); e_values = 0.0;
|
||||
computeEigenDecomposition( current_point, e_values, e_vectors );
|
||||
|
||||
if (kmax > 0) {
|
||||
/// detecting an ill-conditioned neighborhood
|
||||
if (e_values(3) / e_values(2) > alpha && e_values(2) > 0.0) {
|
||||
if (knn < kmax)
|
||||
cout << "Increasing kmin to " << ++knn << endl;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
delete[] n;
|
||||
delete[] d;
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute neighbors using kNN search
|
||||
* @param point - input point with neighbors
|
||||
* @param new_point - output point with new neighbors
|
||||
* @param knn - k constant in kNN search
|
||||
* @param eps - parameter required by the ANN library in kNN search
|
||||
*/
|
||||
void computeKNearestNeighbors(const PointNeighbor& point,
|
||||
PointNeighbor& new_point, int knn,
|
||||
double eps=1.0)
|
||||
{
|
||||
/// allocate memory for all neighbors of point plus the point itself
|
||||
ANNpointArray point_array = annAllocPts(point.neighbors.size()+1, 3);
|
||||
for (size_t i = 0; i < point.neighbors.size(); ++i) {
|
||||
point_array[i] = new ANNcoord[3];
|
||||
point_array[i][0] = point.neighbors[i].x;
|
||||
point_array[i][1] = point.neighbors[i].y;
|
||||
point_array[i][2] = point.neighbors[i].z;
|
||||
}
|
||||
int last = point.neighbors.size();
|
||||
point_array[last] = new ANNcoord[3];
|
||||
point_array[last][0] = point.point.x;
|
||||
point_array[last][1] = point.point.y;
|
||||
point_array[last][2] = point.point.z;
|
||||
|
||||
ANNkd_tree t(point_array, point.neighbors.size()+1, 3);
|
||||
ANNidxArray n;
|
||||
ANNdistArray d;
|
||||
|
||||
/// regular kNN search, allocate memory for knn
|
||||
n = new ANNidx[knn];
|
||||
d = new ANNdist[knn];
|
||||
|
||||
vector<Point> new_neighbors;
|
||||
/// last point in the array is the current point
|
||||
ANNpoint p = point_array[point.neighbors.size()];
|
||||
|
||||
t.annkSearch(p, knn, n, d, eps);
|
||||
new_neighbors.push_back(point.point);
|
||||
|
||||
for (int j = 0; j < knn; ++j) {
|
||||
if ( n[j] != (int) point.neighbors.size() )
|
||||
new_neighbors.push_back(point.neighbors[n[j]]);
|
||||
}
|
||||
|
||||
new_point.point = point.point;
|
||||
new_point.neighbors = new_neighbors;
|
||||
|
||||
delete[] n;
|
||||
delete[] d;
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute neighbors using panorama images
|
||||
* @param fPanorama - input panorama image created from the current scan
|
||||
* @param points_neighbors - output set of points with corresponding neighbors
|
||||
*/
|
||||
void computePanoramaNeighbors(Scan* scan,
|
||||
vector<PointNeighbor>& points_neighbors, int width, int height, int knn)
|
||||
{
|
||||
cv::Mat scan_cv;
|
||||
scan2mat(scan, scan_cv);
|
||||
fbr::panorama fPanorama(width, height, fbr::EQUIRECTANGULAR, 1, 0, fbr::EXTENDED);
|
||||
fPanorama.createPanorama(scan_cv);
|
||||
cv::Mat img = fPanorama.getRangeImage();
|
||||
vector<vector<vector<cv::Vec3f> > > extended_map = fPanorama.getExtendedMap();
|
||||
for (int row = 0; row < height; ++row) {
|
||||
for (int col = 0; col < width; ++col) {
|
||||
vector<cv::Vec3f> points_panorama = extended_map[row][col];
|
||||
/// if no points found, skip pixel
|
||||
if (points_panorama.size() < 1) continue;
|
||||
/// foreach point from panorama consider all points in the bucket as its neighbors
|
||||
for (size_t point_idx = 0; point_idx < points_panorama.size(); ++point_idx) {
|
||||
Point point;
|
||||
point.x = points_panorama[point_idx][0];
|
||||
point.y = points_panorama[point_idx][1];
|
||||
point.z = points_panorama[point_idx][2];
|
||||
vector<Point> neighbors;
|
||||
for (size_t i = 0; i < points_panorama.size(); ++i) {
|
||||
if (i != point_idx)
|
||||
neighbors.push_back(Point (points_panorama[i][0], points_panorama[i][1], points_panorama[i][2]) );
|
||||
}
|
||||
/// add neighbors from adjacent pixels and buckets
|
||||
for (int i = -1; i <= 1; ++i) {
|
||||
for (int j = -1; j <= 1; ++j) {
|
||||
if (!(i==0 && j==0) && !(row+i < 0 || col+j < 0)
|
||||
&& !(row+i >= height || col+j >= width) ) {
|
||||
vector<cv::Vec3f> neighbors_panorama = extended_map[row+i][col+j];
|
||||
for (size_t k = 0; k < neighbors_panorama.size(); ++k)
|
||||
neighbors.push_back(Point (neighbors_panorama[k][0],
|
||||
neighbors_panorama[k][1],
|
||||
neighbors_panorama[k][2]) );
|
||||
}
|
||||
}
|
||||
}
|
||||
/// filter the point by kNN search
|
||||
PointNeighbor current_point(point, neighbors);
|
||||
if (knn > 0) {
|
||||
PointNeighbor filtered_point;
|
||||
computeKNearestNeighbors(current_point, filtered_point, knn);
|
||||
points_neighbors.push_back(filtered_point);
|
||||
} else {
|
||||
points_neighbors.push_back(current_point);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute normals using PCA given a set of points and their neighbors
|
||||
* @param scan - pointer to current scan, used to compute the position vectors
|
||||
* @param points - input set of points with corresponding neighbors
|
||||
* @param normals - output set of normals
|
||||
*/
|
||||
void computePCA(const Scan* scan, const vector<PointNeighbor>& points,
|
||||
vector<Point>& normals, bool flipnormals)
|
||||
{
|
||||
ColumnVector origin(3);
|
||||
const double *scan_pose = scan->get_rPos();
|
||||
for (int i = 0; i < 3; ++i)
|
||||
origin(i+1) = scan_pose[i];
|
||||
|
||||
for(size_t i = 0; i < points.size(); ++i) {
|
||||
vector<Point> neighbors = points[i].neighbors;
|
||||
|
||||
if (points[i].neighbors.size() < 2) {
|
||||
normals.push_back( Point(0,0,0) );
|
||||
continue;
|
||||
}
|
||||
|
||||
ColumnVector point_vector(3);
|
||||
point_vector(1) = points[i].point.x - origin(1);
|
||||
point_vector(2) = points[i].point.y - origin(2);
|
||||
point_vector(3) = points[i].point.z - origin(3);
|
||||
point_vector = point_vector / point_vector.NormFrobenius();
|
||||
|
||||
Matrix e_vectors(3,3); e_vectors = 0.0;
|
||||
DiagonalMatrix e_values(3); e_values = 0.0;
|
||||
computeEigenDecomposition(points[i], e_values, e_vectors);
|
||||
|
||||
ColumnVector v1(3);
|
||||
v1(1) = e_vectors(1,1);
|
||||
v1(2) = e_vectors(2,1);
|
||||
v1(3) = e_vectors(3,1);
|
||||
// consider first (smallest) eigenvector as the normal
|
||||
Real angle = (v1.t() * point_vector).AsScalar();
|
||||
|
||||
// orient towards scan pose
|
||||
// works better when orientation is not flipped
|
||||
if (flipnormals && angle < 0) {
|
||||
v1 *= -1.0;
|
||||
}
|
||||
normals.push_back( Point(v1(1), v1(2), v1(3)) );
|
||||
}
|
||||
}
|
||||
|
||||
void computeSRI(int factor, vector<Point>& points, vector<Point>& normals)
|
||||
{
|
||||
SRI *sri2 = new SRI(0, factor);
|
||||
|
||||
for (size_t i = 0; i < points.size(); i++) {
|
||||
sri2->addPoint(points[i].x, points[i].y, points[i].z);
|
||||
}
|
||||
|
||||
points.clear();
|
||||
|
||||
for (unsigned int i = 0; i < sri2->points.size(); i++) {
|
||||
double rgbN[3], x, y, z;
|
||||
PointN* p = sri2->points[i];
|
||||
p->getCartesian(x, y, z);
|
||||
sri2->getNormalSRI(p, rgbN);
|
||||
normals.push_back(Point(rgbN[0], rgbN[1], rgbN[2]));
|
||||
points.push_back(Point(x, z, y));
|
||||
}
|
||||
}
|
||||
|
||||
void scan2points(Scan* scan, vector<Point> &points)
|
||||
{
|
||||
DataXYZ xyz = scan->get("xyz");
|
||||
unsigned int nPoints = xyz.size();
|
||||
for(unsigned int i = 0; i < nPoints; ++i) {
|
||||
points.push_back(Point(xyz[i][0], xyz[i][1], xyz[i][2]));
|
||||
}
|
||||
}
|
||||
|
||||
int main(int argc, char **argv)
|
||||
{
|
||||
// commandline arguments
|
||||
int start, end;
|
||||
bool scanserver;
|
||||
int maxDist, minDist;
|
||||
string dir;
|
||||
IOType iotype;
|
||||
normal_method normalMethod;
|
||||
int knn, kmin, kmax;
|
||||
double alpha;
|
||||
int width, height;
|
||||
bool flipnormals;
|
||||
double factor;
|
||||
|
||||
parse_options(argc, argv, start, end, scanserver, dir, iotype, maxDist,
|
||||
minDist, normalMethod, knn, kmin, kmax, alpha, width, height,
|
||||
flipnormals, factor);
|
||||
|
||||
Scan::openDirectory(scanserver, dir, iotype, start, end);
|
||||
|
||||
if(Scan::allScans.size() == 0) {
|
||||
cerr << "No scans found. Did you use the correct format?" << endl;
|
||||
exit(-1);
|
||||
}
|
||||
|
||||
boost::filesystem::path boost_dir(dir + "normals/");
|
||||
boost::filesystem::create_directory(boost_dir);
|
||||
|
||||
for(ScanVector::iterator it = Scan::allScans.begin(); it != Scan::allScans.end(); ++it) {
|
||||
Scan* scan = *it;
|
||||
|
||||
// apply optional filtering
|
||||
scan->setRangeFilter(maxDist, minDist);
|
||||
|
||||
vector<PointNeighbor> points_neighbors;
|
||||
vector<Point> normals;
|
||||
vector<Point> points;
|
||||
|
||||
scan2points(scan, points);
|
||||
|
||||
switch (normalMethod) {
|
||||
case KNN_PCA:
|
||||
computeKNearestNeighbors(points, points_neighbors, knn);
|
||||
computePCA(scan, points_neighbors, normals, flipnormals);
|
||||
break;
|
||||
case AKNN_PCA:
|
||||
computeKNearestNeighbors(points, points_neighbors, kmin, kmax, alpha);
|
||||
computePCA(scan, points_neighbors, normals, flipnormals);
|
||||
break;
|
||||
case PANO_PCA:
|
||||
computePanoramaNeighbors(scan, points_neighbors, width, height, knn);
|
||||
computePCA(scan, points_neighbors, normals, flipnormals);
|
||||
break;
|
||||
case PANO_SRI:
|
||||
computeSRI(factor, points, normals);
|
||||
break;
|
||||
default:
|
||||
cerr << "unknown normal method" << endl;
|
||||
return 1;
|
||||
break;
|
||||
}
|
||||
|
||||
if (points.size() != normals.size()) {
|
||||
cerr << "got " << points.size() << " points but " << normals.size() << " normals" << endl;
|
||||
return 1;
|
||||
}
|
||||
|
||||
writeNormals(scan, dir + "normals/", points, normals);
|
||||
}
|
||||
|
||||
Scan::closeDirectory();
|
||||
|
||||
return 0;
|
||||
}
|
||||
95
src/normals/test.cc
Normal file
95
src/normals/test.cc
Normal file
|
|
@ -0,0 +1,95 @@
|
|||
/*******************************************************
|
||||
A simple program that demonstrates NewMat10 library.
|
||||
The program defines a random symmetric matrix
|
||||
and computes its eigendecomposition.
|
||||
For further details read the NewMat10 Reference Manual
|
||||
********************************************************/
|
||||
|
||||
#include <stdlib.h>
|
||||
#include <time.h>
|
||||
#include <string.h>
|
||||
|
||||
// the following two are needed for printing
|
||||
#include <iostream.h>
|
||||
#include <iomanip.h>
|
||||
/**************************************
|
||||
/* The NewMat10 include files */
|
||||
#include <include.h>
|
||||
#include <newmat.h>
|
||||
#include <newmatap.h>
|
||||
#include <newmatio.h>
|
||||
/***************************************/
|
||||
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
int M = 3, N = 5;
|
||||
Matrix X(M,N); // Define an M x N general matrix
|
||||
|
||||
// Fill X by random numbers between 0 and 9
|
||||
// Note that indexing into matrices in NewMat is 1-based!
|
||||
srand(time(NULL));
|
||||
for (int i = 1; i <= M; ++i) {
|
||||
for (int j = 1; j <= N; ++j) {
|
||||
X(i,j) = rand() % 10;
|
||||
}
|
||||
}
|
||||
|
||||
SymmetricMatrix C;
|
||||
C << X * X.t(); // fill in C by X * X^t.
|
||||
// Works because we *know* that the result is symmetric
|
||||
|
||||
cout << "The symmetrix matrix C" << endl;
|
||||
cout << setw(5) << setprecision(0) << C << endl;
|
||||
|
||||
|
||||
// compute eigendecomposition of C
|
||||
Matrix V(3,3); // for eigenvectors
|
||||
DiagonalMatrix D(3); // for eigenvalues
|
||||
|
||||
// the decomposition
|
||||
Jacobi(C, D, V);
|
||||
|
||||
// Print the result
|
||||
cout << "The eigenvalues matrix:" << endl;
|
||||
cout << setw(10) << setprecision(5) << D << endl;
|
||||
cout << "The eigenvectors matrix:" << endl;
|
||||
cout << setw(10) << setprecision(5) << V << endl;
|
||||
|
||||
// Check that the first eigenvector indeed has the eigenvector property
|
||||
ColumnVector v1(3);
|
||||
v1(1) = V(1,1);
|
||||
v1(2) = V(2,1);
|
||||
v1(3) = V(3,1);
|
||||
|
||||
ColumnVector Cv1 = C * v1;
|
||||
ColumnVector lambda1_v1 = D(1) * v1;
|
||||
|
||||
cout << "The max-norm of the difference between C*v1 and lambda1*v1 is " <<
|
||||
NormInfinity(Cv1 - lambda1_v1) << endl << endl;
|
||||
|
||||
// Build the inverse and check the result
|
||||
Matrix Ci = C.i();
|
||||
Matrix I = Ci * C;
|
||||
|
||||
cout << "The inverse of C is" << endl;
|
||||
cout << setw(10) << setprecision(5) << Ci << endl;
|
||||
cout << "And the inverse times C is identity" << endl;
|
||||
cout << setw(10) << setprecision(5) << I << endl;
|
||||
|
||||
// Example for multiple solves (see NewMat documentation)
|
||||
ColumnVector r1(3), r2(3);
|
||||
for (i = 1; i <= 3; ++i) {
|
||||
r1(i) = rand() % 10;
|
||||
r2(i) = rand() % 10;
|
||||
}
|
||||
LinearEquationSolver CLU = C; // decomposes C
|
||||
ColumnVector s1 = CLU.i() * r1;
|
||||
ColumnVector s2 = CLU.i() * r2;
|
||||
|
||||
cout << "solution for right hand side r1" << endl;
|
||||
cout << setw(10) << setprecision(5) << s1 << endl;
|
||||
cout << "solution for right hand side r2" << endl;
|
||||
cout << setw(10) << setprecision(5) << s2 << endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
269
src/segmentation/FHGraph.cc
Normal file
269
src/segmentation/FHGraph.cc
Normal file
|
|
@ -0,0 +1,269 @@
|
|||
/**
|
||||
* Point Cloud Segmentation using Felzenszwalb-Huttenlocher Algorithm
|
||||
*
|
||||
* Copyright (C) Jacobs University Bremen
|
||||
*
|
||||
* Released under the GPL version 3.
|
||||
*
|
||||
* @author Mihai-Cotizo Sima
|
||||
*/
|
||||
|
||||
#include <segmentation/FHGraph.h>
|
||||
#include <map>
|
||||
#include <omp.h>
|
||||
#include <algorithm>
|
||||
|
||||
using namespace std;
|
||||
|
||||
|
||||
|
||||
FHGraph::FHGraph(std::vector< Point >& ps, double weight(Point, Point), double sigma, double eps, int neighbors, float radius) :
|
||||
points( ps ), V( ps.size() )
|
||||
{
|
||||
/*
|
||||
* 1. create adjency list using a map<int, vector<half_edge> >
|
||||
* 2. use get_neighbors(e, max_dist) to get all the edges e' that are at a distance smaller than max_dist than e
|
||||
* 3. using all these edges, compute the gaussian smoothed weight
|
||||
* 4. insert the edges in a new list
|
||||
*/
|
||||
nr_neighbors = neighbors;
|
||||
this->radius = radius;
|
||||
|
||||
compute_neighbors(weight, eps);
|
||||
|
||||
|
||||
if ( sigma > 0.01 )
|
||||
{
|
||||
do_gauss(sigma);
|
||||
}
|
||||
else
|
||||
{
|
||||
without_gauss();
|
||||
}
|
||||
|
||||
adjency_list.clear();
|
||||
}
|
||||
|
||||
void FHGraph::compute_neighbors(double weight(Point, Point), double eps)
|
||||
{
|
||||
|
||||
adjency_list.reserve(points.size());
|
||||
adjency_list.resize(points.size());
|
||||
|
||||
ANNpointArray pa = annAllocPts(points.size(), 3);
|
||||
for (size_t i=0; i<points.size(); ++i)
|
||||
{
|
||||
pa[i] = new ANNcoord[3];
|
||||
pa[i][0] = points[i].x;
|
||||
pa[i][1] = points[i].y;
|
||||
pa[i][2] = points[i].z;
|
||||
}
|
||||
|
||||
ANNkd_tree t(pa, points.size(), 3);
|
||||
|
||||
if ( radius < 0 ) // Using knn search
|
||||
{
|
||||
nr_neighbors++;
|
||||
ANNidxArray n = new ANNidx[nr_neighbors];
|
||||
ANNdistArray d = new ANNdist[nr_neighbors];
|
||||
|
||||
for (size_t i=0; i<points.size(); ++i)
|
||||
{
|
||||
ANNpoint p = pa[i];
|
||||
|
||||
t.annkSearch(p, nr_neighbors, n, d, eps);
|
||||
|
||||
for (int j=0; j<nr_neighbors; ++j)
|
||||
{
|
||||
if ( n[j] == (int)i ) continue;
|
||||
|
||||
he e;
|
||||
e.x = n[j];
|
||||
e.w = weight(points[i], points[n[j]]);
|
||||
|
||||
adjency_list[i].push_back(e);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
delete[] n;
|
||||
delete[] d;
|
||||
}
|
||||
else // Using radius search
|
||||
{
|
||||
float sqradius = radius*radius;
|
||||
|
||||
|
||||
ANNidxArray n;
|
||||
ANNdistArray d;
|
||||
int nret;
|
||||
int total = 0;
|
||||
|
||||
const int MOD = 1000;
|
||||
int TMP = MOD;
|
||||
|
||||
for (size_t i=0; i<points.size(); ++i)
|
||||
{
|
||||
ANNpoint p = pa[i];
|
||||
|
||||
nret = t.annkFRSearch(p, sqradius, 0, NULL, NULL, eps);
|
||||
total += nret;
|
||||
|
||||
n = new ANNidx[nret];
|
||||
d = new ANNdist[nret];
|
||||
t.annkFRSearch(p, sqradius, nret, n, d, eps);
|
||||
|
||||
if ( nr_neighbors > 0 && nr_neighbors < nret )
|
||||
{
|
||||
random_shuffle(n, n+nret);
|
||||
nret = nr_neighbors;
|
||||
}
|
||||
|
||||
for (int j=0; j<nret; ++j)
|
||||
{
|
||||
if ( n[j] == (int)i ) continue;
|
||||
|
||||
he e;
|
||||
e.x = n[j];
|
||||
e.w = weight(points[i], points[n[j]]);
|
||||
|
||||
adjency_list[i].push_back(e);
|
||||
}
|
||||
|
||||
delete[] n;
|
||||
delete[] d;
|
||||
if ( TMP==0 )
|
||||
{
|
||||
TMP = MOD;
|
||||
cout << "Point " << i << "/" << V << ", or "<< (i*100.0 / V) << "%\r"; cout.flush();
|
||||
}
|
||||
TMP --;
|
||||
}
|
||||
cout << "Average nr of neighbors: " << (float) total / points.size() << endl;
|
||||
|
||||
}
|
||||
|
||||
annDeallocPts(pa);
|
||||
}
|
||||
|
||||
static double gauss(double x, double miu, double sigma)
|
||||
{
|
||||
double tmp = ((x-miu)/sigma);
|
||||
return exp(- .5 * tmp * tmp);
|
||||
}
|
||||
|
||||
static void normalize(std::vector<double>& v)
|
||||
{
|
||||
double s = 0;
|
||||
for (size_t i=0; i<v.size(); ++i)
|
||||
s += v[i];
|
||||
for (size_t i=0; i<v.size(); ++i)
|
||||
v[i] /= s;
|
||||
}
|
||||
|
||||
string tostr(int x)
|
||||
{
|
||||
stringstream ss;
|
||||
ss << x;
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
void FHGraph::do_gauss(double sigma)
|
||||
{
|
||||
edges.reserve( V * nr_neighbors);
|
||||
list<he>::iterator k, j;
|
||||
vector<double> gauss_weight, edge_weight;
|
||||
edge e;
|
||||
#pragma omp parallel for private(j, k, e, gauss_weight, edge_weight) schedule(dynamic)
|
||||
for (int i=0; i<V; ++i)
|
||||
{
|
||||
for (j=adjency_list[i].begin();
|
||||
j!=adjency_list[i].end();
|
||||
j++)
|
||||
{
|
||||
|
||||
gauss_weight.clear();
|
||||
edge_weight.clear();
|
||||
|
||||
for (k=adjency_list[i].begin();
|
||||
k!=adjency_list[i].end();
|
||||
++k)
|
||||
{
|
||||
gauss_weight.push_back(gauss(k->w, j->w, sigma));
|
||||
edge_weight.push_back(k->w);
|
||||
}
|
||||
for (k=adjency_list[j->x].begin();
|
||||
k!=adjency_list[j->x].end();
|
||||
++k)
|
||||
{
|
||||
gauss_weight.push_back(gauss(k->w, j->w, sigma));
|
||||
edge_weight.push_back(k->w);
|
||||
}
|
||||
normalize(gauss_weight);
|
||||
|
||||
e.a = i; e.b = j->x;
|
||||
e.w = 0;
|
||||
for (size_t k=0; k<edge_weight.size(); ++k)
|
||||
e.w += gauss_weight[k] * edge_weight[k];
|
||||
|
||||
#pragma omp critical
|
||||
{
|
||||
edges.push_back(e);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void FHGraph::without_gauss()
|
||||
{
|
||||
edges.reserve( V * nr_neighbors);
|
||||
list<he>::iterator j;
|
||||
edge e;
|
||||
|
||||
for (int i=0; i<V; ++i)
|
||||
{
|
||||
for (j=adjency_list[i].begin();
|
||||
j!=adjency_list[i].end();
|
||||
j++)
|
||||
{
|
||||
e.a = i; e.b = j->x; e.w = j->w;
|
||||
edges.push_back(e);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
edge* FHGraph::getGraph()
|
||||
{
|
||||
edge* ret = new edge[edges.size()];
|
||||
for (size_t i=0; i<edges.size(); ++i)
|
||||
ret[i] = edges[i];
|
||||
return ret;
|
||||
}
|
||||
|
||||
Point FHGraph::operator[](int index)
|
||||
{
|
||||
return points[index];
|
||||
}
|
||||
|
||||
int FHGraph::getNumPoints()
|
||||
{
|
||||
return V;
|
||||
}
|
||||
|
||||
int FHGraph::getNumEdges()
|
||||
{
|
||||
return edges.size();
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
void vectorFree(T& t) {
|
||||
T tmp;
|
||||
t.swap(tmp);
|
||||
}
|
||||
|
||||
void FHGraph::dispose() {
|
||||
vectorFree(edges);
|
||||
vectorFree(points);
|
||||
vectorFree(adjency_list);
|
||||
}
|
||||
|
||||
47
src/segmentation/disjoint-set.cc
Normal file
47
src/segmentation/disjoint-set.cc
Normal file
|
|
@ -0,0 +1,47 @@
|
|||
/**
|
||||
* Point Cloud Segmentation using Felzenszwalb-Huttenlocher Algorithm
|
||||
*
|
||||
* Copyright (C) Jacobs University Bremen
|
||||
*
|
||||
* Released under the GPL version 3.
|
||||
*
|
||||
* @author Mihai-Cotizo Sima
|
||||
*/
|
||||
|
||||
|
||||
#include <segmentation/disjoint-set.h>
|
||||
|
||||
universe::universe(int elements) {
|
||||
elts = new uni_elt[elements];
|
||||
num = elements;
|
||||
for (int i = 0; i < elements; i++) {
|
||||
elts[i].rank = 0;
|
||||
elts[i].size = 1;
|
||||
elts[i].p = i;
|
||||
}
|
||||
}
|
||||
|
||||
universe::~universe() {
|
||||
delete [] elts;
|
||||
}
|
||||
|
||||
int universe::find(int x) {
|
||||
int y = x;
|
||||
while (y != elts[y].p)
|
||||
y = elts[y].p;
|
||||
elts[x].p = y;
|
||||
return y;
|
||||
}
|
||||
|
||||
void universe::join(int x, int y) {
|
||||
if (elts[x].rank > elts[y].rank) {
|
||||
elts[y].p = x;
|
||||
elts[x].size += elts[y].size;
|
||||
} else {
|
||||
elts[x].p = y;
|
||||
elts[y].size += elts[x].size;
|
||||
if (elts[x].rank == elts[y].rank)
|
||||
elts[y].rank++;
|
||||
}
|
||||
num--;
|
||||
}
|
||||
297
src/segmentation/fhsegmentation.cc
Normal file
297
src/segmentation/fhsegmentation.cc
Normal file
|
|
@ -0,0 +1,297 @@
|
|||
/**
|
||||
* Point Cloud Segmentation using Felzenszwalb-Huttenlocher Algorithm
|
||||
*
|
||||
* Copyright (C) Jacobs University Bremen
|
||||
*
|
||||
* Released under the GPL version 3.
|
||||
*
|
||||
* @author Billy Okal <b.okal@jacobs-university.de>
|
||||
* @author Mihai-Cotizo Sima
|
||||
* @file fhsegmentation.cc
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
#include <fstream>
|
||||
#include <errno.h>
|
||||
|
||||
#include <boost/program_options.hpp>
|
||||
|
||||
#include <slam6d/io_types.h>
|
||||
#include <slam6d/globals.icc>
|
||||
#include <slam6d/scan.h>
|
||||
#include <scanserver/clientInterface.h>
|
||||
|
||||
#include <segmentation/FHGraph.h>
|
||||
|
||||
#ifdef _MSC_VER
|
||||
#define strcasecmp _stricmp
|
||||
#define strncasecmp _strnicmp
|
||||
#else
|
||||
#include <strings.h>
|
||||
#endif
|
||||
|
||||
namespace po = boost::program_options;
|
||||
using namespace std;
|
||||
|
||||
/// validate IO types
|
||||
void validate(boost::any& v, const std::vector<std::string>& values,
|
||||
IOType*, int) {
|
||||
if (values.size() == 0)
|
||||
throw std::runtime_error("Invalid model specification");
|
||||
string arg = values.at(0);
|
||||
try {
|
||||
v = formatname_to_io_type(arg.c_str());
|
||||
} catch (...) { // runtime_error
|
||||
throw std::runtime_error("Format " + arg + " unknown.");
|
||||
}
|
||||
}
|
||||
|
||||
/// Parse commandline options
|
||||
void parse_options(int argc, char **argv, int &start, int &end, bool &scanserver, int &max_dist, int &min_dist, string &dir,
|
||||
IOType &iotype, float &sigma, int &k, int &neighbors, float &eps, float &radius, int &min_size)
|
||||
{
|
||||
/// ----------------------------------
|
||||
/// set up program commandline options
|
||||
/// ----------------------------------
|
||||
po::options_description cmd_options("Usage: fhsegmentation <options> where options are (default values in brackets)");
|
||||
cmd_options.add_options()
|
||||
("help,?", "Display this help message")
|
||||
("start,s", po::value<int>(&start)->default_value(0), "Start at scan number <arg>")
|
||||
("end,e", po::value<int>(&end)->default_value(-1), "Stop at scan number <arg>")
|
||||
("scanserver,S", po::value<bool>(&scanserver)->default_value(false), "Use the scanserver as an input method")
|
||||
("format,f", po::value<IOType>(&iotype)->default_value(UOS),
|
||||
"using shared library <arg> for input. (chose format from [uos|uosr|uos_map|"
|
||||
"uos_rgb|uos_frames|uos_map_frames|old|rts|rts_map|ifp|"
|
||||
"riegl_txt|riegl_rgb|riegl_bin|zahn|ply])")
|
||||
("max,M", po::value<int>(&max_dist)->default_value(-1),"neglegt all data points with a distance larger than <arg> 'units")
|
||||
("min,m", po::value<int>(&min_dist)->default_value(-1), "neglegt all data points with a distance smaller than <arg> 'units")
|
||||
("K,k", po::value<int>(&k)->default_value(1), "<arg> value of K value used in the FH segmentation")
|
||||
("neighbors,N", po::value<int>(&neighbors)->default_value(1), "use approximate <arg>-nearest neighbors search or limit the number of points")
|
||||
("sigma,v", po::value<float>(&sigma)->default_value(1.0), "Set the Gaussian variance for smoothing to <arg>")
|
||||
("radius,r", po::value<float>(&radius)->default_value(-1.0), "Set the range of radius search to <arg>")
|
||||
("eps,E", po::value<float>(&eps)->default_value(1.0), "Set error threshold used by the AKNN algorithm to <arg>")
|
||||
("minsize,z", po::value<int>(&min_size)->default_value(0), "Keep segments of size at least <arg>")
|
||||
;
|
||||
|
||||
po::options_description hidden("Hidden options");
|
||||
hidden.add_options()
|
||||
("input-dir", po::value<string>(&dir), "input dir");
|
||||
|
||||
po::positional_options_description pd;
|
||||
pd.add("input-dir", 1);
|
||||
|
||||
po::options_description all;
|
||||
all.add(cmd_options).add(hidden);
|
||||
|
||||
po::variables_map vmap;
|
||||
po::store(po::command_line_parser(argc, argv).options(all).positional(pd).run(), vmap);
|
||||
po::notify(vmap);
|
||||
|
||||
if (vmap.count("help")) {
|
||||
cout << cmd_options << endl;
|
||||
exit(-1);
|
||||
}
|
||||
|
||||
// read scan path
|
||||
if (dir[dir.length()-1] != '/') dir = dir + "/";
|
||||
|
||||
}
|
||||
|
||||
/// distance measures
|
||||
double weight1(Point a, Point b)
|
||||
{
|
||||
return a.distance(b);
|
||||
}
|
||||
|
||||
double weight2(Point a, Point b)
|
||||
{
|
||||
return a.distance(b) * .5 + fabs(a.reflectance-b.reflectance) * .5;
|
||||
}
|
||||
|
||||
|
||||
/// Write a pose file with the specofied name
|
||||
void writePoseFiles(string dir, const double* rPos, const double* rPosTheta, int num, int outnum)
|
||||
{
|
||||
for (int i = outnum; i < num; i++) {
|
||||
string poseFileName = dir + "segments/scan" + to_string(i, 3) + ".pose";
|
||||
ofstream posout(poseFileName.c_str());
|
||||
|
||||
posout << rPos[0] << " "
|
||||
<< rPos[1] << " "
|
||||
<< rPos[2] << endl
|
||||
<< deg(rPosTheta[0]) << " "
|
||||
<< deg(rPosTheta[1]) << " "
|
||||
<< deg(rPosTheta[2]) << endl;
|
||||
posout.clear();
|
||||
posout.close();
|
||||
}
|
||||
}
|
||||
|
||||
/// write scan files for all segments
|
||||
void writeScanFiles(string dir, int outnum, const vector<vector<Point>* > cloud)
|
||||
{
|
||||
for (int i = outnum, j = 0; i < (int)cloud.size() && j < (int)cloud.size(); i++, j++) {
|
||||
vector<Point>* segment = cloud[j];
|
||||
string scanFileName = dir + "segments/scan" + to_string(i,3) + ".3d";
|
||||
ofstream scanout(scanFileName.c_str());
|
||||
|
||||
for (int k = 0; k < (int)segment->size(); k++) {
|
||||
Point p = segment->at(k);
|
||||
scanout << p.x << " " << p.y << " " << p.z << endl;
|
||||
}
|
||||
scanout.close();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
/// =============================================
|
||||
/// Main
|
||||
/// =============================================
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
int start, end;
|
||||
bool scanserver;
|
||||
int max_dist, min_dist;
|
||||
string dir;
|
||||
IOType iotype;
|
||||
float sigma;
|
||||
int k, neighbors;
|
||||
float eps;
|
||||
float radius;
|
||||
int min_size;
|
||||
|
||||
parse_options(argc, argv, start, end, scanserver, max_dist, min_dist,
|
||||
dir, iotype, sigma, k, neighbors, eps, radius, min_size);
|
||||
|
||||
/// ----------------------------------
|
||||
/// Prepare and read scans
|
||||
/// ----------------------------------
|
||||
if (scanserver) {
|
||||
try {
|
||||
ClientInterface::create();
|
||||
} catch(std::runtime_error& e) {
|
||||
cerr << "ClientInterface could not be created: " << e.what() << endl;
|
||||
cerr << "Start the scanserver first." << endl;
|
||||
exit(-1);
|
||||
}
|
||||
}
|
||||
|
||||
/// Make directory for saving the scan segments
|
||||
string segdir = dir + "segments";
|
||||
|
||||
#ifdef _MSC_VER
|
||||
int success = mkdir(segdir.c_str());
|
||||
#else
|
||||
int success = mkdir(segdir.c_str(), S_IRWXU|S_IRWXG|S_IRWXO);
|
||||
#endif
|
||||
if(success == 0) {
|
||||
cout << "Writing segments to " << segdir << endl;
|
||||
} else if(errno == EEXIST) {
|
||||
cout << "WARN: Directory " << segdir << " exists already. Contents will be overwriten" << endl;
|
||||
} else {
|
||||
cerr << "Creating directory " << segdir << " failed" << endl;
|
||||
exit(1);
|
||||
}
|
||||
|
||||
/// Read the scans
|
||||
Scan::openDirectory(scanserver, dir, iotype, start, end);
|
||||
if(Scan::allScans.size() == 0) {
|
||||
cerr << "No scans found. Did you use the correct format?" << endl;
|
||||
exit(-1);
|
||||
}
|
||||
|
||||
/// --------------------------------------------
|
||||
/// Initialize and perform segmentation
|
||||
/// --------------------------------------------
|
||||
std::vector<Scan*>::iterator it = Scan::allScans.begin();
|
||||
int outscan = start;
|
||||
|
||||
for( ; it != Scan::allScans.end(); ++it) {
|
||||
Scan* scan = *it;
|
||||
const double* rPos = scan->get_rPos();
|
||||
const double* rPosTheta = scan->get_rPosTheta();
|
||||
|
||||
/// read scan into points
|
||||
DataXYZ xyz(scan->get("xyz"));
|
||||
vector<Point> points;
|
||||
points.reserve(xyz.size());
|
||||
|
||||
for(unsigned int j = 0; j < xyz.size(); j++) {
|
||||
Point p(xyz[j][0], xyz[j][1], xyz[j][2]);
|
||||
points.push_back(p);
|
||||
}
|
||||
|
||||
/// create the graph and get the segments
|
||||
cout << "creating graph" << endl;
|
||||
FHGraph sgraph(points, weight2, sigma, eps, neighbors, radius);
|
||||
|
||||
cout << "segmenting graph" << endl;
|
||||
edge* sedges = sgraph.getGraph();
|
||||
universe* segmented = segment_graph(sgraph.getNumPoints(),
|
||||
sgraph.getNumEdges(),
|
||||
sedges, k);
|
||||
|
||||
cout << "post processing" << endl;
|
||||
for (int i = 0; i < sgraph.getNumEdges(); ++i)
|
||||
{
|
||||
int a = sedges[i].a;
|
||||
int b = sedges[i].b;
|
||||
|
||||
int aa = segmented->find(a);
|
||||
int bb = segmented->find(b);
|
||||
|
||||
if ( (aa!=bb) &&
|
||||
(segmented->size(aa) < min_size ||
|
||||
segmented->size(bb) < min_size) )
|
||||
segmented->join(aa, bb);
|
||||
}
|
||||
|
||||
delete[] sedges;
|
||||
|
||||
int nr = segmented->num_sets();
|
||||
cout << "Obtained " << nr << " segment(s)" << endl;
|
||||
|
||||
/// write point clouds with segments
|
||||
vector< vector<Point>* > clouds;
|
||||
clouds.reserve(nr);
|
||||
for (int i=0; i<nr; ++i)
|
||||
clouds.push_back( new vector<Point> );
|
||||
|
||||
map<int, int> components2cloud;
|
||||
int kk = 0;
|
||||
|
||||
for (int i = 0; i < sgraph.getNumPoints(); ++i)
|
||||
{
|
||||
int component = segmented->find(i);
|
||||
if ( components2cloud.find(component)==components2cloud.end() )
|
||||
{
|
||||
components2cloud[component] = kk++;
|
||||
clouds[components2cloud[component]]->reserve(segmented->size(component));
|
||||
}
|
||||
clouds[components2cloud[component]]->push_back(sgraph[i]);
|
||||
}
|
||||
|
||||
// pose file (repeated for the number of segments
|
||||
writePoseFiles(dir, rPos, rPosTheta, clouds.size(), outscan);
|
||||
// scan files for all segments
|
||||
writeScanFiles(dir, outscan, clouds);
|
||||
|
||||
outscan += clouds.size();
|
||||
|
||||
/// clean up
|
||||
sgraph.dispose();
|
||||
}
|
||||
|
||||
// shutdown everything
|
||||
if (scanserver)
|
||||
ClientInterface::destroy();
|
||||
else
|
||||
Scan::closeDirectory();
|
||||
|
||||
cout << "Normal program end" << endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
50
src/segmentation/segment-graph.cc
Normal file
50
src/segmentation/segment-graph.cc
Normal file
|
|
@ -0,0 +1,50 @@
|
|||
/**
|
||||
* Point Cloud Segmentation using Felzenszwalb-Huttenlocher Algorithm
|
||||
*
|
||||
* Copyright (C) Jacobs University Bremen
|
||||
*
|
||||
* Released under the GPL version 3.
|
||||
*
|
||||
* @author Mihai-Cotizo Sima
|
||||
*/
|
||||
|
||||
#include <segmentation/segment-graph.h>
|
||||
|
||||
bool operator<(const edge &a, const edge &b) {
|
||||
return a.w < b.w;
|
||||
}
|
||||
|
||||
universe *segment_graph(int num_vertices, int num_edges, edge *edges,
|
||||
float c) {
|
||||
// sort edges by weight
|
||||
std::sort(edges, edges + num_edges);
|
||||
|
||||
// make a disjoint-set forest
|
||||
universe *u = new universe(num_vertices);
|
||||
|
||||
// init thresholds
|
||||
float *threshold = new float[num_vertices];
|
||||
for (int i = 0; i < num_vertices; i++)
|
||||
threshold[i] = THRESHOLD(1,c);
|
||||
|
||||
// for each edge, in non-decreasing weight order...
|
||||
for (int i = 0; i < num_edges; i++) {
|
||||
edge *pedge = &edges[i];
|
||||
|
||||
// components conected by this edge
|
||||
int a = u->find(pedge->a);
|
||||
int b = u->find(pedge->b);
|
||||
if (a != b) {
|
||||
if ((pedge->w <= threshold[a]) &&
|
||||
(pedge->w <= threshold[b])) {
|
||||
u->join(a, b);
|
||||
a = u->find(a);
|
||||
threshold[a] = pedge->w + THRESHOLD(u->size(a), c);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// free up
|
||||
delete threshold;
|
||||
return u;
|
||||
}
|
||||
Loading…
Reference in a new issue