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754 lines
22 KiB
Plaintext
754 lines
22 KiB
Plaintext
/**
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*
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* Copyright (C) Jacobs University Bremen
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*
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* @author Vaibhav Kumar Mehta
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* @file normals.cc
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*/
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#include <iostream>
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#include <string>
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#include <fstream>
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#include <errno.h>
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#include <boost/program_options.hpp>
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#include <slam6d/io_types.h>
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#include <slam6d/globals.icc>
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#include <slam6d/scan.h>
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#include "slam6d/fbr/panorama.h"
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#include <scanserver/clientInterface.h>
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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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using namespace NEWMAT;
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#ifdef _MSC_VER
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#define strcasecmp _stricmp
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#define strncasecmp _strnicmp
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#else
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#include <strings.h>
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#endif
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namespace po = boost::program_options;
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using namespace std;
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enum normal_method {AKNN, ADAPTIVE_AKNN, PANORAMA, PANORAMA_FAST};
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/*
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* validates normal calculation method specification
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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(), "AKNN") == 0) v = AKNN;
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else if(strcasecmp(arg.c_str(), "ADAPTIVE_AKNN") == 0) v = ADAPTIVE_AKNN;
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else if(strcasecmp(arg.c_str(), "PANORAMA") == 0) v = PANORAMA;
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else if(strcasecmp(arg.c_str(), "PANORAMA_FAST") == 0) v = PANORAMA_FAST;
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else throw std::runtime_error(std::string("normal calculation method ") + arg + std::string(" is unknown"));
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}
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/// validate IO types
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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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/// Parse commandline options
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void parse_options(int argc, char **argv, int &start, int &end, bool &scanserver, int &max_dist, int &min_dist, string &dir,
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IOType &iotype, int &k1, int &k2, normal_method &ntype,int &width,int &height)
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{
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/// ----------------------------------
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/// set up program commandline options
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/// ----------------------------------
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po::options_description cmd_options("Usage: calculateNormals <options> where options are (default values in brackets)");
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cmd_options.add_options()
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("help,?", "Display this help message")
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("start,s", po::value<int>(&start)->default_value(0), "Start at scan number <arg>")
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("end,e", po::value<int>(&end)->default_value(-1), "Stop at scan number <arg>")
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("scanserver,S", po::value<bool>(&scanserver)->default_value(false), "Use the scanserver as an input method")
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("format,f", po::value<IOType>(&iotype)->default_value(UOS),
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"using shared library <arg> for input. (chose format from [uos|uosr|uos_map|"
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"uos_rgb|uos_frames|uos_map_frames|old|rts|rts_map|ifp|"
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"riegl_txt|riegl_rgb|riegl_bin|zahn|ply])")
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("max,M", po::value<int>(&max_dist)->default_value(-1),"neglegt all data points with a distance larger than <arg> 'units")
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("min,m", po::value<int>(&min_dist)->default_value(-1),"neglegt all data points with a distance smaller than <arg> 'units")
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("normal,g", po::value<normal_method>(&ntype)->default_value(AKNN), "normal calculation method "
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"(AKNN, ADAPTIVE_AKNN, PANORAMA, PANORAMA_FAST)")
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("K1,k", po::value<int>(&k1)->default_value(20), "<arg> value of K value used in the nearest neighbor search of ANN or" "kmin for k-adaptation")
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("K2,K", po::value<int>(&k2)->default_value(20), "<arg> value of Kmax for k-adaptation")
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("width,w", po::value<int>(&width)->default_value(1280),"width of panorama image")
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("height,h", po::value<int>(&height)->default_value(960),"height of panorama image")
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;
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po::options_description hidden("Hidden options");
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hidden.add_options()
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("input-dir", po::value<string>(&dir), "input dir");
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po::positional_options_description pd;
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pd.add("input-dir", 1);
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po::options_description all;
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all.add(cmd_options).add(hidden);
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po::variables_map vmap;
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po::store(po::command_line_parser(argc, argv).options(all).positional(pd).run(), vmap);
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po::notify(vmap);
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if (vmap.count("help")) {
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cout << cmd_options << endl << endl;
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cout << "SAMPLE COMMAND FOR CALCULATING NORMALS" << endl;
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cout << " bin/normals -s 0 -e 0 -f UOS -g AKNN -k 20 dat/" <<endl;
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cout << endl << endl;
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cout << "SAMPLE COMMAND FOR VIEWING CALCULATING NORMALS IN RGB SPACE" << endl;
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cout << " bin/show -c -f UOS_RGB dat/normals/" << endl;
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exit(-1);
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}
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// read scan path
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if (dir[dir.length()-1] != '/') dir = dir + "/";
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}
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///////////////////////////////////////////////////////
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/////////////NORMALS USING AKNN METHOD ////////////////
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///////////////////////////////////////////////////////
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void calculateNormalsAKNN(vector<Point> &normals,vector<Point> &points, int k, const double _rPos[3] )
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{
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cout<<"Total number of points: "<<points.size()<<endl;
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int nr_neighbors = k;
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ColumnVector rPos(3);
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for (int i = 0; i < 3; ++i)
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rPos(i+1) = _rPos[i];
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ANNpointArray pa = annAllocPts(points.size(), 3);
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for (size_t i=0; i<points.size(); ++i)
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{
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pa[i][0] = points[i].x;
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pa[i][1] = points[i].y;
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pa[i][2] = points[i].z;
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}
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ANNkd_tree t(pa, points.size(), 3);
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ANNidxArray nidx = new ANNidx[nr_neighbors];
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ANNdistArray d = new ANNdist[nr_neighbors];
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for (size_t i=0; i<points.size(); ++i)
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{
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ANNpoint p = pa[i];
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//ANN search for k nearest neighbors
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//indexes of the neighbors along with the query point
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//stored in the array n
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t.annkSearch(p, nr_neighbors, nidx, d, 0.0);
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Point mean(0.0,0.0,0.0);
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Matrix X(nr_neighbors,3);
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SymmetricMatrix A(3);
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Matrix U(3,3);
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DiagonalMatrix D(3);
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//calculate mean for all the points
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for (int j=0; j<nr_neighbors; ++j)
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{
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mean.x += points[nidx[j]].x;
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mean.y += points[nidx[j]].y;
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mean.z += points[nidx[j]].z;
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}
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mean.x /= nr_neighbors;
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mean.y /= nr_neighbors;
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mean.z /= nr_neighbors;
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//calculate covariance = A for all the points
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for (int i = 0; i < nr_neighbors; ++i) {
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X(i+1, 1) = points[nidx[i]].x - mean.x;
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X(i+1, 2) = points[nidx[i]].y - mean.y;
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X(i+1, 3) = points[nidx[i]].z - mean.z;
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}
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A << 1.0/nr_neighbors * X.t() * X;
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EigenValues(A, D, U);
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//normal = eigenvector corresponding to lowest
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//eigen value that is the 1st column of matrix U
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ColumnVector n(3);
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n(1) = U(1,1);
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n(2) = U(2,1);
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n(3) = U(3,1);
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ColumnVector point_vector(3);
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point_vector(1) = p[0] - rPos(1);
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point_vector(2) = p[1] - rPos(2);
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point_vector(3) = p[2] - rPos(3);
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point_vector = point_vector / point_vector.NormFrobenius();
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Real angle = (n.t() * point_vector).AsScalar();
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if (angle < 0) {
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n *= -1.0;
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}
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n = n / n.NormFrobenius();
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normals.push_back(Point(n(1), n(2), n(3)));
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}
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delete[] nidx;
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delete[] d;
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annDeallocPts(pa);
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}
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////////////////////////////////////////////////////////////////
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/////////////NORMALS USING ADAPTIVE AKNN METHOD ////////////////
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////////////////////////////////////////////////////////////////
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void calculateNormalsAdaptiveAKNN(vector<Point> &normals,vector<Point> &points,
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int kmin, int kmax, const double _rPos[3])
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{
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ColumnVector rPos(3);
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for (int i = 0; i < 3; ++i)
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rPos(i+1) = _rPos[i];
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cout<<"Total number of points: "<<points.size()<<endl;
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int nr_neighbors;
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ANNpointArray pa = annAllocPts(points.size(), 3);
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for (size_t i=0; i<points.size(); ++i)
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{
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pa[i][0] = points[i].x;
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pa[i][1] = points[i].y;
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pa[i][2] = points[i].z;
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}
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ANNkd_tree t(pa, points.size(), 3);
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Point mean(0.0,0.0,0.0);
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double temp_n[3],norm_n = 0.0;
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double e1,e2,e3;
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for (size_t i=0; i<points.size(); ++i)
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{
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Matrix U(3,3);
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ANNpoint p = pa[i];
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for(int kidx = kmin; kidx < kmax; kidx++)
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{
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nr_neighbors=kidx+1;
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ANNidxArray nidx = new ANNidx[nr_neighbors];
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ANNdistArray d = new ANNdist[nr_neighbors];
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//ANN search for k nearest neighbors
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//indexes of the neighbors along with the query point
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//stored in the array n
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t.annkSearch(p, nr_neighbors, nidx, d, 0.0);
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mean.x=0,mean.y=0,mean.z=0;
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//calculate mean for all the points
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for (int j=0; j<nr_neighbors; ++j)
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{
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mean.x += points[nidx[j]].x;
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mean.y += points[nidx[j]].y;
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mean.z += points[nidx[j]].z;
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}
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mean.x /= nr_neighbors;
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mean.y /= nr_neighbors;
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mean.z /= nr_neighbors;
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Matrix X(nr_neighbors,3);
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SymmetricMatrix A(3);
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DiagonalMatrix D(3);
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//calculate covariance = A for all the points
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for (int j = 0; j < nr_neighbors; ++j) {
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X(j+1, 1) = points[nidx[j]].x - mean.x;
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X(j+1, 2) = points[nidx[j]].y - mean.y;
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X(j+1, 3) = points[nidx[j]].z - mean.z;
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}
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A << 1.0/nr_neighbors * X.t() * X;
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EigenValues(A, D, U);
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e1 = D(1);
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e2 = D(2);
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e3 = D(3);
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delete[] nidx;
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delete[] d;
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//We take the particular k if the second maximum eigen value
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//is at least 25 percent of the maximum eigen value
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if ((e1 > 0.25 * e2) && (fabs(1.0 - (double)e2/(double)e3) < 0.25))
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break;
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}
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//normal = eigenvector corresponding to lowest
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//eigen value that is the 1rd column of matrix U
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ColumnVector n(3);
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n(1) = U(1,1);
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n(2) = U(2,1);
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n(3) = U(3,1);
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ColumnVector point_vector(3);
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point_vector(1) = p[0] - rPos(1);
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point_vector(2) = p[1] - rPos(2);
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point_vector(3) = p[2] - rPos(3);
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point_vector = point_vector / point_vector.NormFrobenius();
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Real angle = (n.t() * point_vector).AsScalar();
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if (angle < 0) {
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n *= -1.0;
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}
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n = n / n.NormFrobenius();
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normals.push_back(Point(n(1), n(2), n(3)));
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}
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annDeallocPts(pa);
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}
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///////////////////////////////////////////////////////
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/////////////NORMALS USING IMAGE NEIGHBORS ////////////
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///////////////////////////////////////////////////////
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void calculateNormalsPANORAMA(vector<Point> &normals,
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vector<Point> &points,
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vector< vector< vector< cv::Vec3f > > > extendedMap,
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const double _rPos[3])
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{
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ColumnVector rPos(3);
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for (int i = 0; i < 3; ++i)
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rPos(i+1) = _rPos[i];
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cout<<"Total number of points: "<<points.size()<<endl;
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points.clear();
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int nr_neighbors = 0;
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cout << "height of Image: "<<extendedMap.size()<<endl;
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cout << "width of Image: "<<extendedMap[0].size()<<endl;
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// as the nearest neighbors and then the same PCA method as done in AKNN
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//temporary dynamic array for all the neighbors of a given point
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vector<cv::Vec3f> neighbors;
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for (size_t i=0; i< extendedMap.size(); i++)
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{
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for (size_t j=0; j<extendedMap[i].size(); j++)
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{
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if (extendedMap[i][j].size() == 0) continue;
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neighbors.clear();
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Point mean(0.0,0.0,0.0);
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double temp_n[3],norm_n = 0.0;
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// Offset for neighbor computation
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int offset[2][5] = {{-1,0,1,0,0},{0,-1,0,1,0}};
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// Traversing all the cells in the extended map
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for (int n = 0; n < 5; ++n) {
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int x = i + offset[0][n];
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int y = j + offset[1][n];
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// Copy the neighboring buckets into the vector
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if (x >= 0 && x < (int)extendedMap.size() &&
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y >= 0 && y < (int)extendedMap[x].size()) {
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for (unsigned int k = 0; k < extendedMap[x][y].size(); k++) {
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neighbors.push_back(extendedMap[x][y][k]);
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}
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}
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}
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nr_neighbors = neighbors.size();
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cv::Vec3f p = extendedMap[i][j][0];
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//if no neighbor point is found in the 4-neighboring pixels then normal is set to zero
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if (nr_neighbors < 3)
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{
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points.push_back(Point(p[0], p[1], p[2]));
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normals.push_back(Point(0.0,0.0,0.0));
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continue;
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}
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//calculate mean for all the points
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Matrix X(nr_neighbors,3);
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SymmetricMatrix A(3);
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Matrix U(3,3);
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DiagonalMatrix D(3);
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//calculate mean for all the points
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for(int k = 0; k < nr_neighbors; k++)
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{
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cv::Vec3f pp = neighbors[k];
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mean.x += pp[0];
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mean.y += pp[1];
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mean.z += pp[2];
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}
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mean.x /= nr_neighbors;
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mean.y /= nr_neighbors;
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mean.z /= nr_neighbors;
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//calculate covariance = A for all the points
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for (int i = 0; i < nr_neighbors; ++i) {
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cv::Vec3f pp = neighbors[i];
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X(i+1, 1) = pp[0] - mean.x;
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X(i+1, 2) = pp[1] - mean.y;
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X(i+1, 3) = pp[2] - mean.z;
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}
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A << 1.0/nr_neighbors * X.t() * X;
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EigenValues(A, D, U);
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//normal = eigenvector corresponding to lowest
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//eigen value that is the 1st column of matrix U
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ColumnVector n(3);
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n(1) = U(1,1);
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n(2) = U(2,1);
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n(3) = U(3,1);
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ColumnVector point_vector(3);
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point_vector(1) = p[0] - rPos(1);
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point_vector(2) = p[1] - rPos(2);
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point_vector(3) = p[2] - rPos(3);
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point_vector = point_vector / point_vector.NormFrobenius();
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Real angle = (n.t() * point_vector).AsScalar();
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if (angle < 0) {
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n *= -1.0;
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}
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n = n / n.NormFrobenius();
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for (unsigned int k = 0; k < extendedMap[i][j].size(); k++) {
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cv::Vec3f p = extendedMap[i][j][k];
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points.push_back(Point(p[0], p[1], p[2]));
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normals.push_back(Point(n(1), n(2), n(3)));
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}
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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///////////FAST NORMALS USING PANORAMA EQUIRECTANGULAR RANGE IMAGE //////////////////////////
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/////////////////////////////////////////////////////////////////////////////////////////////
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/*
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void calculateNormalsFAST(vector<Point> &normals,vector<Point> &points,cv::Mat &img,vector<vector<vector<cv::Vec3f>>> extendedMap)
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{
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cout<<"Total number of points: "<<points.size()<<endl;
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points.clear();
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int nr_points = 0;
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//int nr_neighbors = 0,nr_neighbors_center = 0;
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cout << "height of Image: "<<extendedMap.size()<<endl;
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cout << "width of Image: "<<extendedMap[0].size()<<endl;
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for (size_t i=0; i< extendedMap.size(); ++i)
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{
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for (size_t j=0; j<extendedMap[0].size(); j++)
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{
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double theta,phi,rho;
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double x,y,z;
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double dRdTheta,dRdPhi;
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double n[3],m;
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nr_points = extendedMap[i][j].size();
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if (nr_points == 0 ) continue;
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for (int k = 0; k< nr_points; k++)
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{
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cv::Vec3f p = extendedMap[i][j][k];
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x = p[0];
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y = p[1];
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z = p[2];
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rho = sqrt(x*x + y*y + z*z);
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theta = atan(y/x);
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phi = atan(z/x);
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|
//Sobel Filter for the derivative
|
|
dRdTheta = dRdPhi = 0.0;
|
|
|
|
if (i == 0 || i == extendedMap.size()-1 || j == 0 || j == extendedMap[0].size()-1)
|
|
{
|
|
points.push_back(Point(x, y, z));
|
|
normals.push_back(Point(0.0,0.0,0.0));
|
|
continue;
|
|
}
|
|
dRdPhi += 10*img.at<uchar>(i-1,j);
|
|
dRdPhi += 3 *img.at<uchar>(i-1,j-1);
|
|
dRdPhi += 3 *img.at<uchar>(i-1,j+1);
|
|
dRdPhi -= 10*img.at<uchar>(i+1,j);
|
|
dRdPhi -= 3 *img.at<uchar>(i+1,j-1);
|
|
dRdPhi -= 3 *img.at<uchar>(i+1,j+1);
|
|
|
|
dRdTheta += 10*img.at<uchar>(i,j-1);
|
|
dRdTheta += 3 *img.at<uchar>(i-1,j-1);
|
|
dRdTheta += 3 *img.at<uchar>(i+1,j-1);
|
|
dRdTheta -= 10*img.at<uchar>(i,j+1);
|
|
dRdTheta -= 3 *img.at<uchar>(i-1,j+1);
|
|
dRdTheta -= 3 *img.at<uchar>(i+1,j+1);
|
|
|
|
n[0] = cos(theta) * sin(phi) - sin(theta) * dRdTheta / rho / sin(phi) +
|
|
cos(theta) * cos(phi) * dRdPhi / rho;
|
|
|
|
n[1] = sin(theta) * sin(phi) + cos(theta) * dRdTheta / rho / sin(phi) +
|
|
sin(theta) * cos(phi) * dRdPhi / rho;
|
|
|
|
n[2] = cos(phi) - sin(phi) * dRdPhi / rho;
|
|
|
|
//n[2] = -n[2];
|
|
|
|
m = sqrt(n[0]*n[0]+n[1]*n[1]+n[2]*n[2]);
|
|
n[0] /= m; n[1] /= m; n[2] /= m;
|
|
|
|
points.push_back(Point(x, y, z));
|
|
normals.push_back(Point(n[0],n[1],n[2]));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
*/
|
|
|
|
/*
|
|
* 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
|
|
*/
|
|
cv::Mat scan2mat(Scan *source)
|
|
{
|
|
DataXYZ xyz = source->get("xyz");
|
|
|
|
DataReflectance xyz_reflectance = source->get("reflectance");
|
|
|
|
unsigned int nPoints = xyz.size();
|
|
cv::Mat scan(nPoints,1,CV_32FC(4));
|
|
scan = cv::Scalar::all(0);
|
|
|
|
cv::MatIterator_<cv::Vec4f> it;
|
|
|
|
it = scan.begin<cv::Vec4f>();
|
|
for(unsigned int i = 0; i < nPoints; i++){
|
|
float x, y, z, reflectance;
|
|
x = xyz[i][0];
|
|
y = xyz[i][1];
|
|
z = xyz[i][2];
|
|
if(xyz_reflectance.size() != 0)
|
|
{
|
|
reflectance = xyz_reflectance[i];
|
|
|
|
//normalize the reflectance
|
|
reflectance += 32;
|
|
reflectance /= 64;
|
|
reflectance -= 0.2;
|
|
reflectance /= 0.3;
|
|
if (reflectance < 0) reflectance = 0;
|
|
if (reflectance > 1) reflectance = 1;
|
|
}
|
|
|
|
(*it)[0] = x;
|
|
(*it)[1] = y;
|
|
(*it)[2] = z;
|
|
if(xyz_reflectance.size() != 0)
|
|
(*it)[3] = reflectance;
|
|
else
|
|
(*it)[3] = 0;
|
|
|
|
++it;
|
|
}
|
|
return scan;
|
|
}
|
|
/*
|
|
* convert a matrix of float values (range image) to a matrix of unsigned
|
|
* eight bit characters using different techniques
|
|
*/
|
|
cv::Mat float2uchar(cv::Mat &source, bool logarithm, float cutoff)
|
|
{
|
|
cv::Mat result(source.size(), CV_8U, cv::Scalar::all(0));
|
|
float max = 0;
|
|
// find maximum value
|
|
if (cutoff == 0.0) {
|
|
// without cutoff, just iterate through all values to find the largest
|
|
for (cv::MatIterator_<float> it = source.begin<float>();
|
|
it != source.end<float>(); ++it) {
|
|
float val = *it;
|
|
if (val > max) {
|
|
max = val;
|
|
}
|
|
}
|
|
} else {
|
|
// when a cutoff is specified, sort all the points by value and then
|
|
// specify the max so that <cutoff> values are larger than it
|
|
vector<float> sorted(source.cols*source.rows);
|
|
int i = 0;
|
|
for (cv::MatIterator_<float> it = source.begin<float>();
|
|
it != source.end<float>(); ++it, ++i) {
|
|
sorted[i] = *it;
|
|
}
|
|
std::sort(sorted.begin(), sorted.end());
|
|
max = sorted[(int)(source.cols*source.rows*(1.0-cutoff))];
|
|
cout << "A cutoff of " << cutoff << " resulted in a max value of " << max << endl;
|
|
}
|
|
|
|
cv::MatIterator_<float> src = source.begin<float>();
|
|
cv::MatIterator_<uchar> dst = result.begin<uchar>();
|
|
cv::MatIterator_<float> end = source.end<float>();
|
|
if (logarithm) {
|
|
// stretch values from 0 to max logarithmically over 0 to 255
|
|
// using the logarithm allows to represent smaller values with more
|
|
// precision and larger values with less
|
|
max = log(max+1);
|
|
for (; src != end; ++src, ++dst) {
|
|
float val = (log(*src+1)*255.0)/max;
|
|
if (val > 255)
|
|
*dst = 255;
|
|
else
|
|
*dst = (uchar)val;
|
|
}
|
|
} else {
|
|
// stretch values from 0 to max linearly over 0 to 255
|
|
for (; src != end; ++src, ++dst) {
|
|
float val = (*src*255.0)/max;
|
|
if (val > 255)
|
|
*dst = 255;
|
|
else
|
|
*dst = (uchar)val;
|
|
}
|
|
}
|
|
return result;
|
|
}
|
|
/// Write a pose file with the specofied name
|
|
void writePoseFiles(string dir, const double* rPos, const double* rPosTheta,int scanNumber)
|
|
{
|
|
string poseFileName = dir + "/scan" + to_string(scanNumber, 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, vector<Point> &points, vector<Point> &normals, int scanNumber)
|
|
{
|
|
string ofilename = dir + "/scan" + to_string(scanNumber, 3) + ".3d";
|
|
ofstream normptsout(ofilename.c_str());
|
|
|
|
for (size_t i=0; i<points.size(); ++i)
|
|
{
|
|
int r,g,b;
|
|
r = (int)(normals[i].x * (127.5) + 127.5);
|
|
g = (int)(normals[i].y * (127.5) + 127.5);
|
|
b = (int)(fabs(normals[i].z) * (255.0));
|
|
normptsout <<points[i].x<<" "<<points[i].y<<" "<<points[i].z<<" "<<r<<" "<<g<<" "<<b<<" "<<endl;
|
|
}
|
|
normptsout.clear();
|
|
normptsout.close();
|
|
}
|
|
|
|
/// =============================================
|
|
/// Main
|
|
/// =============================================
|
|
int main(int argc, char** argv)
|
|
{
|
|
int start, end;
|
|
bool scanserver;
|
|
int max_dist, min_dist;
|
|
string dir;
|
|
IOType iotype;
|
|
int k1, k2;
|
|
normal_method ntype;
|
|
int width, height;
|
|
|
|
parse_options(argc, argv, start, end, scanserver, max_dist, min_dist,
|
|
dir, iotype, k1, k2, ntype, width, height);
|
|
|
|
/// ----------------------------------
|
|
/// 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 normdir = dir + "normals";
|
|
|
|
#ifdef _MSC_VER
|
|
int success = mkdir(normdir.c_str());
|
|
#else
|
|
int success = mkdir(normdir.c_str(), S_IRWXU|S_IRWXG|S_IRWXO);
|
|
#endif
|
|
if(success == 0) {
|
|
cout << "Writing segments to " << normdir << endl;
|
|
} else if(errno == EEXIST) {
|
|
cout << "WARN: Directory " << normdir << " exists already. Contents will be overwriten" << endl;
|
|
} else {
|
|
cerr << "Creating directory " << normdir << " 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);
|
|
}
|
|
|
|
cv::Mat img;
|
|
|
|
/// --------------------------------------------
|
|
/// Initialize and perform segmentation
|
|
/// --------------------------------------------
|
|
std::vector<Scan*>::iterator it = Scan::allScans.begin();
|
|
int scanNumber = 0;
|
|
|
|
for( ; it != Scan::allScans.end(); ++it) {
|
|
Scan* scan = *it;
|
|
|
|
// apply optional filtering
|
|
scan->setRangeFilter(max_dist, min_dist);
|
|
|
|
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());
|
|
vector<Point> normals;
|
|
normals.reserve(xyz.size());
|
|
|
|
for(unsigned int j = 0; j < xyz.size(); j++) {
|
|
points.push_back(Point(xyz[j][0], xyz[j][1], xyz[j][2]));
|
|
}
|
|
|
|
if(ntype == AKNN)
|
|
calculateNormalsAKNN(normals,points, k1, rPos);
|
|
else if(ntype == ADAPTIVE_AKNN)
|
|
calculateNormalsAdaptiveAKNN(normals,points, k1, k2, rPos);
|
|
else
|
|
{
|
|
// create panorama
|
|
fbr::panorama fPanorama(width, height, fbr::EQUIRECTANGULAR, 1, 0, fbr::EXTENDED);
|
|
fPanorama.createPanorama(scan2mat(scan));
|
|
|
|
// the range image has to be converted from float to uchar
|
|
img = fPanorama.getRangeImage();
|
|
img = float2uchar(img, 0, 0.0);
|
|
|
|
if(ntype == PANORAMA)
|
|
calculateNormalsPANORAMA(normals,points,fPanorama.getExtendedMap(), rPos);
|
|
else if(ntype == PANORAMA_FAST)
|
|
cout << "PANORAMA_FAST is not working yet" << endl;
|
|
// calculateNormalsFAST(normals,points,img,fPanorama.getExtendedMap());
|
|
}
|
|
|
|
// pose file (repeated for the number of segments
|
|
writePoseFiles(normdir, rPos, rPosTheta, scanNumber);
|
|
// scan files for all segments
|
|
writeScanFiles(normdir, points,normals,scanNumber);
|
|
|
|
scanNumber++;
|
|
}
|
|
|
|
// shutdown everything
|
|
if (scanserver)
|
|
ClientInterface::destroy();
|
|
else
|
|
Scan::closeDirectory();
|
|
|
|
cout << "Normal program end" << endl;
|
|
|
|
return 0;
|
|
}
|
|
|