update svn to r733 (4)
Razvan Mihalyi
12 years ago
parent
b03c2a3e0c
commit
5255e4f9f0
@ -1,7 +0,0 @@
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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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@ -1,643 +0,0 @@
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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;
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else if(strcasecmp(arg.c_str(), "AKNN_PCA") == 0) v = AKNN_PCA;
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else if(strcasecmp(arg.c_str(), "PANO_PCA") == 0) v = PANO_PCA;
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else if(strcasecmp(arg.c_str(), "PANO_SRI") == 0) v = PANO_SRI;
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else throw std::runtime_error(std::string("normal method ") + arg + std::string(" is unknown"));
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}
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/*
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* parse commandline options, fill arguments
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*/
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void parse_options(int argc, char **argv, int &start, int &end,
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bool &scanserver, string &dir, IOType &iotype,
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int &maxDist, int &minDist, normal_method &normalMethod, int &knn,
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int &kmin, int &kmax, double& alpha, int &width, int &height,
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bool &flipnormals, double &factor)
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{
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po::options_description generic("Generic options");
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generic.add_options()
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("help,h", "output this help message");
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po::options_description input("Input options");
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input.add_options()
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("start,s", po::value<int>(&start)->default_value(0),
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"start at scan <arg> (i.e., neglects the first <arg> scans) "
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"[ATTENTION: counting naturally starts with 0]")
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("end,e", po::value<int>(&end)->default_value(-1),
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"end after scan <arg>")
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("format,f", po::value<IOType>(&iotype)->default_value(UOS),
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"using shared library <arg> for input. (chose F from {uos, 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>(&maxDist)->default_value(-1),
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"neglegt all data points with a distance larger than <arg> 'units")
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("min,m", po::value<int>(&minDist)->default_value(-1),
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"neglegt all data points with a distance smaller than <arg> 'units")
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("scanserver,S", po::bool_switch(&scanserver),
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"Use the scanserver as an input method and handling of scan data")
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;
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po::options_description normal("Normal options");
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normal.add_options()
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("normalMethod,N", po::value<normal_method>(&normalMethod)->default_value(KNN_PCA),
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"choose the method for computing normals:\n"
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"KNN_PCA -- use kNN and PCA\n"
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"AKNN_PCA -- use adaptive kNN and PCA\n"
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"PANO_PCA -- use panorama image neighbors and PCA\n"
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"PANO_SRI -- use panorama image neighbors and spherical range image differentiation\n")
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("knn,K", po::value<int>(&knn),
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"select the k in kNN search")
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("kmin,1", po::value<int>(&kmin),
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"select k_min in adaptive kNN search")
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("kmax,2", po::value<int>(&kmax),
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"select k_max in adaptive kNN search")
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("alpha,a", po::value<double>(&alpha),
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"select the alpha parameter for detecting an ill-conditioned neighborhood")
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("width,w", po::value<int>(&width),
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"width of panorama")
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("height,h", po::value<int>(&height),
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"height of panorama")
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("flipnormals,F", po::bool_switch(&flipnormals),
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"flip orientation of normals towards scan pose")
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("factor,c", po::value<double>(&factor),
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"factor for SRI computation")
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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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// all options
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po::options_description all;
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all.add(generic).add(input).add(normal).add(hidden);
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// options visible with --help
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po::options_description cmdline_options;
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cmdline_options.add(generic).add(input).add(normal);
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// positional argument
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po::positional_options_description pd;
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pd.add("input-dir", 1);
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// process options
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po::variables_map vm;
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po::store(po::command_line_parser(argc, argv).
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options(all).positional(pd).run(), vm);
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po::notify(vm);
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// display help
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if (vm.count("help")) {
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cout << cmdline_options;
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exit(0);
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}
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normal_option_dependency(vm, KNN_PCA, "knn");
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normal_option_conflict(vm, KNN_PCA, "kmin");
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normal_option_conflict(vm, KNN_PCA, "kmax");
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normal_option_conflict(vm, KNN_PCA, "alpha");
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normal_option_conflict(vm, KNN_PCA, "width");
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normal_option_conflict(vm, KNN_PCA, "height");
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normal_option_conflict(vm, KNN_PCA, "factor");
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normal_option_conflict(vm, AKNN_PCA, "knn");
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normal_option_dependency(vm, AKNN_PCA, "kmin");
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normal_option_dependency(vm, AKNN_PCA, "kmax");
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normal_option_dependency(vm, AKNN_PCA, "alpha");
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normal_option_conflict(vm, AKNN_PCA, "width");
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normal_option_conflict(vm, AKNN_PCA, "height");
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normal_option_conflict(vm, AKNN_PCA, "factor");
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//normal_option_conflict(vm, PANO_PCA, "knn");
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normal_option_dependency(vm, KNN_PCA, "knn");
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normal_option_conflict(vm, PANO_PCA, "kmin");
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normal_option_conflict(vm, PANO_PCA, "kmax");
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normal_option_conflict(vm, PANO_PCA, "alpha");
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normal_option_dependency(vm, PANO_PCA, "width");
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normal_option_dependency(vm, PANO_PCA, "height");
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normal_option_conflict(vm, PANO_PCA, "factor");
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normal_option_conflict(vm, PANO_SRI, "knn");
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normal_option_conflict(vm, PANO_SRI, "kmin");
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normal_option_conflict(vm, PANO_SRI, "kmax");
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normal_option_conflict(vm, PANO_SRI, "alpha");
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normal_option_conflict(vm, PANO_SRI, "width");
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normal_option_conflict(vm, PANO_SRI, "height");
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normal_option_dependency(vm, PANO_SRI, "factor");
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// add trailing slash to directory if not present yet
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if (dir[dir.length()-1] != '/') dir = dir + "/";
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}
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/*
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* retrieve a cv::Mat with x,y,z,r from a scan object
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* functionality borrowed from scan_cv::convertScanToMat but this function
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* does not allow a scanserver to be used, prints to stdout and can only
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* handle a single scan
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*/
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void scan2mat(Scan* scan, cv::Mat& scan_cv) {
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DataXYZ xyz = scan->get("xyz");
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unsigned int nPoints = xyz.size();
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scan_cv.create(nPoints,1,CV_32FC(4));
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scan_cv = cv::Scalar::all(0);
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cv::MatIterator_<cv::Vec3f> it = scan_cv.begin<cv::Vec3f>();
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for(unsigned int i = 0; i < nPoints; i++){
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(*it)[0] = xyz[i][0];
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(*it)[1] = xyz[i][1];
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(*it)[2] = xyz[i][2];
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++it;
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}
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}
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/**
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* Helper function that maps x, y, z to R, G, B using a linear function
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*/
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void mapNormalToRGB(const Point& normal, Point& rgb)
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{
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rgb.x = 127.5 * normal.x + 127.5;
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rgb.y = 127.5 * normal.y + 127.5;
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rgb.z = 255.0 * fabs(normal.z);
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}
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/**
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* Write normals to .3d files using the uos_rgb format
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*/
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void writeNormals(const Scan* scan, const string& dir,
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const vector<Point>& points, const vector<Point>& normals)
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{
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stringstream ss;
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ss << dir << "scan" << string(scan->getIdentifier()) << ".3d";
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ofstream scan_file;
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scan_file.open(ss.str().c_str());
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for(size_t i = 0; i < points.size(); ++i) {
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Point rgb;
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mapNormalToRGB(normals[i], rgb);
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scan_file
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<< points[i].x << " " << points[i].y << " " << points[i].z << " "
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<< (unsigned int) rgb.x << " " << (unsigned int) rgb.y << " "
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<< (unsigned int) rgb.z << "\n";
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}
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scan_file.close();
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ss.clear(); ss.str(string());
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ss << dir << "scan" << string(scan->getIdentifier()) << ".pose";
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ofstream pose_file;
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pose_file.open(ss.str().c_str());
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pose_file << 0 << " " << 0 << " " << 0 << "\n" << 0 << " " << 0 << " " << 0 << "\n";
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pose_file.close();
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}
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/**
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* Compute eigen decomposition of a point and its neighbors using the NEWMAT library
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* @param point - input points with corresponding neighbors
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* @param e_values - out parameter returns the eigenvalues
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* @param e_vectors - out parameter returns the eigenvectors
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*/
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void computeEigenDecomposition(const PointNeighbor& point,
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DiagonalMatrix& e_values, Matrix& e_vectors)
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{
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Point centroid(0, 0, 0);
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vector<Point> neighbors = point.neighbors;
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for (size_t j = 0; j < neighbors.size(); ++j) {
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centroid.x += neighbors[j].x;
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centroid.y += neighbors[j].y;
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centroid.z += neighbors[j].z;
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}
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centroid.x /= (double) neighbors.size();
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centroid.y /= (double) neighbors.size();
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centroid.z /= (double) neighbors.size();
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Matrix S(3, 3);
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S = 0.0;
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for (size_t j = 0; j < neighbors.size(); ++j) {
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ColumnVector point_prime(3);
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point_prime(1) = neighbors[j].x - centroid.x;
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point_prime(2) = neighbors[j].y - centroid.y;
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point_prime(3) = neighbors[j].z - centroid.z;
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S = S + point_prime * point_prime.t();
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}
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// normalize S
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for (int j = 0; j < 3; ++j)
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for (int k = 0; k < 3; ++k)
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S(j+1, k+1) /= (double) neighbors.size();
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SymmetricMatrix C;
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C << S;
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// the decomposition
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Jacobi(C, e_values, e_vectors);
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#ifdef DEBUG
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// Print the result
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cout << "The eigenvalues matrix:" << endl;
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cout << e_values << endl;
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#endif
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}
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/**
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* Compute neighbors using kNN search
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* @param points - input set of points
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* @param points_neighbors - output set of points with corresponding neighbors
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* @param knn - k constant in kNN search
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* @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
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* @param alpha - to be used in adaptive knn search for detecting ill-conditioned neighborhoods
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* @param eps - parameter required by the ANN library in kNN search
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*/
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void computeKNearestNeighbors(const vector<Point>& points,
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vector<PointNeighbor>& points_neighbors, int knn, int kmax=-1,
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double alpha=1000.0, double eps=1.0)
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{
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ANNpointArray point_array = annAllocPts(points.size(), 3);
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for (size_t i = 0; i < points.size(); ++i) {
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point_array[i] = new ANNcoord[3];
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point_array[i][0] = points[i].x;
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point_array[i][1] = points[i].y;
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point_array[i][2] = points[i].z;
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}
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ANNkd_tree t(point_array, points.size(), 3);
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ANNidxArray n;
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ANNdistArray d;
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if (kmax < 0) {
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/// regular kNN search, allocate memory for knn
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n = new ANNidx[knn];
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d = new ANNdist[knn];
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} else {
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/// adaptive kNN search, allocate memory for kmax
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n = new ANNidx[kmax];
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d = new ANNdist[kmax];
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}
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for (size_t i = 0; i < points.size(); ++i) {
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vector<Point> neighbors;
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ANNpoint p = point_array[i];
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t.annkSearch(p, knn, n, d, eps);
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neighbors.push_back(points[i]);
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for (int j = 0; j < knn; ++j) {
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if ( n[j] != (int)i )
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neighbors.push_back(points[n[j]]);
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}
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PointNeighbor current_point(points[i], neighbors);
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points_neighbors.push_back( current_point );
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Matrix e_vectors(3,3); e_vectors = 0.0;
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DiagonalMatrix e_values(3); e_values = 0.0;
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computeEigenDecomposition( current_point, e_values, e_vectors );
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if (kmax > 0) {
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/// detecting an ill-conditioned neighborhood
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if (e_values(3) / e_values(2) > alpha && e_values(2) > 0.0) {
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if (knn < kmax)
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cout << "Increasing kmin to " << ++knn << endl;
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}
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}
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}
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delete[] n;
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delete[] d;
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}
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/**
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* Compute neighbors using kNN search
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* @param point - input point with neighbors
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* @param new_point - output point with new neighbors
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* @param knn - k constant in kNN search
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* @param eps - parameter required by the ANN library in kNN search
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*/
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void computeKNearestNeighbors(const PointNeighbor& point,
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PointNeighbor& new_point, int knn,
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double eps=1.0)
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{
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/// allocate memory for all neighbors of point plus the point itself
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ANNpointArray point_array = annAllocPts(point.neighbors.size()+1, 3);
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for (size_t i = 0; i < point.neighbors.size(); ++i) {
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point_array[i] = new ANNcoord[3];
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point_array[i][0] = point.neighbors[i].x;
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point_array[i][1] = point.neighbors[i].y;
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point_array[i][2] = point.neighbors[i].z;
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}
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int last = point.neighbors.size();
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point_array[last] = new ANNcoord[3];
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point_array[last][0] = point.point.x;
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point_array[last][1] = point.point.y;
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point_array[last][2] = point.point.z;
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ANNkd_tree t(point_array, point.neighbors.size()+1, 3);
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ANNidxArray n;
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ANNdistArray d;
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/// regular kNN search, allocate memory for knn
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n = new ANNidx[knn];
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d = new ANNdist[knn];
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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;
|
||||
}
|
||||
@ -1,95 +0,0 @@
|
||||
/*******************************************************
|
||||
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;
|
||||
}
|
||||
Loading…
Reference in New Issue