update svn to r765
This commit is contained in:
Razvan Mihalyi
parent
c559c1eec9
commit
1268ff8dc2
7 changed files with 838 additions and 0 deletions
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#ifndef __PAIRINGMODE_H__
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#define __PAIRINGMODE_H__
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enum PairingMode {
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CLOSEST_POINT,
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CLOSEST_POINT_ALONG_NORMAL,
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CLOSEST_PLANE
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};
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#endif // PAIRINGMODE_H
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#ifndef NORMALS_H
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#define NORMALS_H
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#include <vector>
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#include <slam6d/scan.h>
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#if (CV_MAJOR_VERSION == 2) && (CV_MINOR_VERSION < 2)
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#include <opencv/cv.h>
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#include <opencv/highgui.h>
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#else
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#include <opencv2/opencv.hpp>
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#endif
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void calculateNormalsAKNN(std::vector<Point> &normals,vector<Point> &points, int k,
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const double _rPos[3] );
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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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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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// TODO should be exported to separate library
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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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static inline cv::Mat scan2mat(Scan *source)
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{
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DataXYZ xyz = source->get("xyz");
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DataReflectance xyz_reflectance = source->get("reflectance");
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unsigned int nPoints = xyz.size();
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cv::Mat scan(nPoints,1,CV_32FC(4));
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scan = cv::Scalar::all(0);
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cv::MatIterator_<cv::Vec4f> it;
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it = scan.begin<cv::Vec4f>();
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for(unsigned int i = 0; i < nPoints; i++){
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float x, y, z, reflectance;
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x = xyz[i][0];
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y = xyz[i][1];
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z = xyz[i][2];
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if(xyz_reflectance.size() != 0)
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{
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reflectance = xyz_reflectance[i];
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//normalize the reflectance
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reflectance += 32;
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reflectance /= 64;
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reflectance -= 0.2;
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reflectance /= 0.3;
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if (reflectance < 0) reflectance = 0;
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if (reflectance > 1) reflectance = 1;
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}
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(*it)[0] = x;
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(*it)[1] = y;
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(*it)[2] = z;
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if(xyz_reflectance.size() != 0)
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(*it)[3] = reflectance;
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else
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(*it)[3] = 0;
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++it;
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}
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return scan;
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}
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// TODO should be exported to separate library
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/*
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* convert a matrix of float values (range image) to a matrix of unsigned
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* eight bit characters using different techniques
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*/
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static inline cv::Mat float2uchar(cv::Mat &source, bool logarithm, float cutoff)
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{
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cv::Mat result(source.size(), CV_8U, cv::Scalar::all(0));
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float max = 0;
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// find maximum value
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if (cutoff == 0.0) {
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// without cutoff, just iterate through all values to find the largest
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for (cv::MatIterator_<float> it = source.begin<float>();
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it != source.end<float>(); ++it) {
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float val = *it;
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if (val > max) {
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max = val;
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}
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}
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} else {
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// when a cutoff is specified, sort all the points by value and then
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// specify the max so that <cutoff> values are larger than it
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vector<float> sorted(source.cols*source.rows);
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int i = 0;
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for (cv::MatIterator_<float> it = source.begin<float>();
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it != source.end<float>(); ++it, ++i) {
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sorted[i] = *it;
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}
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std::sort(sorted.begin(), sorted.end());
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max = sorted[(int)(source.cols*source.rows*(1.0-cutoff))];
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cout << "A cutoff of " << cutoff << " resulted in a max value of " << max << endl;
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}
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cv::MatIterator_<float> src = source.begin<float>();
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cv::MatIterator_<uchar> dst = result.begin<uchar>();
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cv::MatIterator_<float> end = source.end<float>();
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if (logarithm) {
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// stretch values from 0 to max logarithmically over 0 to 255
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// using the logarithm allows to represent smaller values with more
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// precision and larger values with less
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max = log(max+1);
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for (; src != end; ++src, ++dst) {
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float val = (log(*src+1)*255.0)/max;
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if (val > 255)
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*dst = 255;
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else
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*dst = (uchar)val;
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}
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} else {
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// stretch values from 0 to max linearly over 0 to 255
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for (; src != end; ++src, ++dst) {
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float val = (*src*255.0)/max;
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if (val > 255)
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*dst = 255;
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else
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*dst = (uchar)val;
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}
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}
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return result;
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}
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#endif // NORMALS_H
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/**
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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 calc_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 <normals/normals.h>
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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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/// Write a pose file with the specofied name
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void writePoseFiles(string dir, const double* rPos, const double* rPosTheta,int scanNumber)
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{
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string poseFileName = dir + "/scan" + to_string(scanNumber, 3) + ".pose";
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ofstream posout(poseFileName.c_str());
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posout << rPos[0] << " "
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<< rPos[1] << " "
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<< rPos[2] << endl
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<< deg(rPosTheta[0]) << " "
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<< deg(rPosTheta[1]) << " "
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<< deg(rPosTheta[2]) << endl;
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posout.clear();
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posout.close();
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}
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/// write scan files for all segments
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void writeScanFiles(string dir, vector<Point> &points, vector<Point> &normals, int scanNumber)
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{
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string ofilename = dir + "/scan" + to_string(scanNumber, 3) + ".3d";
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ofstream normptsout(ofilename.c_str());
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for (size_t i=0; i<points.size(); ++i)
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{
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int r,g,b;
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r = (int)(normals[i].x * (127.5) + 127.5);
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g = (int)(normals[i].y * (127.5) + 127.5);
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b = (int)(fabs(normals[i].z) * (255.0));
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normptsout <<points[i].x<<" "<<points[i].y<<" "<<points[i].z<<" "<<r<<" "<<g<<" "<<b<<" "<<endl;
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}
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normptsout.clear();
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normptsout.close();
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}
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/// =============================================
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/// Main
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/// =============================================
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int main(int argc, char** argv)
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{
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int start, end;
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bool scanserver;
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int max_dist, min_dist;
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string dir;
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IOType iotype;
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int k1, k2;
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normal_method ntype;
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int width, height;
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parse_options(argc, argv, start, end, scanserver, max_dist, min_dist,
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dir, iotype, k1, k2, ntype, width, height);
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/// ----------------------------------
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/// Prepare and read scans
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/// ----------------------------------
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if (scanserver) {
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try {
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ClientInterface::create();
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} catch(std::runtime_error& e) {
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cerr << "ClientInterface could not be created: " << e.what() << endl;
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cerr << "Start the scanserver first." << endl;
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exit(-1);
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}
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}
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/// Make directory for saving the scan segments
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string normdir = dir + "normals";
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#ifdef _MSC_VER
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int success = mkdir(normdir.c_str());
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#else
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int success = mkdir(normdir.c_str(), S_IRWXU|S_IRWXG|S_IRWXO);
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#endif
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if(success == 0) {
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cout << "Writing segments to " << normdir << endl;
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} else if(errno == EEXIST) {
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cout << "WARN: Directory " << normdir << " exists already. Contents will be overwriten" << endl;
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} else {
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cerr << "Creating directory " << normdir << " failed" << endl;
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exit(1);
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}
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/// Read the scans
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Scan::openDirectory(scanserver, dir, iotype, start, end);
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if(Scan::allScans.size() == 0) {
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cerr << "No scans found. Did you use the correct format?" << endl;
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exit(-1);
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}
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cv::Mat img;
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/// --------------------------------------------
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/// Initialize and perform segmentation
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/// --------------------------------------------
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std::vector<Scan*>::iterator it = Scan::allScans.begin();
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int scanNumber = 0;
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for( ; it != Scan::allScans.end(); ++it) {
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Scan* scan = *it;
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// apply optional filtering
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scan->setRangeFilter(max_dist, min_dist);
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const double* rPos = scan->get_rPos();
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const double* rPosTheta = scan->get_rPosTheta();
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/// read scan into points
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DataXYZ xyz(scan->get("xyz"));
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vector<Point> points;
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points.reserve(xyz.size());
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vector<Point> normals;
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normals.reserve(xyz.size());
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for(unsigned int j = 0; j < xyz.size(); j++) {
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points.push_back(Point(xyz[j][0], xyz[j][1], xyz[j][2]));
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}
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if(ntype == AKNN)
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calculateNormalsAKNN(normals,points, k1, rPos);
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else if(ntype == ADAPTIVE_AKNN)
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calculateNormalsAdaptiveAKNN(normals,points, k1, k2, rPos);
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else
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{
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// create panorama
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fbr::panorama fPanorama(width, height, fbr::EQUIRECTANGULAR, 1, 0, fbr::EXTENDED);
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fPanorama.createPanorama(scan2mat(scan));
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// the range image has to be converted from float to uchar
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img = fPanorama.getRangeImage();
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img = float2uchar(img, 0, 0.0);
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if(ntype == PANORAMA)
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calculateNormalsPANORAMA(normals,points,fPanorama.getExtendedMap(), rPos);
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else if(ntype == PANORAMA_FAST)
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cout << "PANORAMA_FAST is not working yet" << endl;
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// calculateNormalsFAST(normals,points,img,fPanorama.getExtendedMap());
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}
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// pose file (repeated for the number of segments
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writePoseFiles(normdir, rPos, rPosTheta, scanNumber);
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// scan files for all segments
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writeScanFiles(normdir, points,normals,scanNumber);
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scanNumber++;
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}
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// shutdown everything
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if (scanserver)
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ClientInterface::destroy();
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Scan::closeDirectory();
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cout << "Normal program end" << endl;
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return 0;
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}
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BIN
.svn/wc.db
BIN
.svn/wc.db
Binary file not shown.
134
include/normals/normals.h
Normal file
134
include/normals/normals.h
Normal file
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#ifndef NORMALS_H
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#define NORMALS_H
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#include <vector>
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#include <slam6d/scan.h>
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#if (CV_MAJOR_VERSION == 2) && (CV_MINOR_VERSION < 2)
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#include <opencv/cv.h>
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#include <opencv/highgui.h>
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#else
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#include <opencv2/opencv.hpp>
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#endif
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void calculateNormalsAKNN(std::vector<Point> &normals,vector<Point> &points, int k,
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const double _rPos[3] );
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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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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]);
|
||||
// TODO should be exported to separate library
|
||||
/*
|
||||
* 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
|
||||
*/
|
||||
static inline 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;
|
||||
}
|
||||
// TODO should be exported to separate library
|
||||
/*
|
||||
* convert a matrix of float values (range image) to a matrix of unsigned
|
||||
* eight bit characters using different techniques
|
||||
*/
|
||||
static inline 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;
|
||||
}
|
||||
|
||||
|
||||
|
||||
#endif // NORMALS_H
|
||||
10
include/slam6d/pairingMode.h
Normal file
10
include/slam6d/pairingMode.h
Normal file
|
|
@ -0,0 +1,10 @@
|
|||
#ifndef __PAIRINGMODE_H__
|
||||
#define __PAIRINGMODE_H__
|
||||
|
||||
enum PairingMode {
|
||||
CLOSEST_POINT,
|
||||
CLOSEST_POINT_ALONG_NORMAL,
|
||||
CLOSEST_PLANE
|
||||
};
|
||||
|
||||
#endif // PAIRINGMODE_H
|
||||
275
src/normals/calc_normals.cc
Normal file
275
src/normals/calc_normals.cc
Normal file
|
|
@ -0,0 +1,275 @@
|
|||
/**
|
||||
*
|
||||
* Copyright (C) Jacobs University Bremen
|
||||
*
|
||||
* @author Vaibhav Kumar Mehta
|
||||
* @file calc_normals.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 "slam6d/fbr/panorama.h"
|
||||
#include <scanserver/clientInterface.h>
|
||||
|
||||
#include <normals/normals.h>
|
||||
|
||||
|
||||
#ifdef _MSC_VER
|
||||
#define strcasecmp _stricmp
|
||||
#define strncasecmp _strnicmp
|
||||
#else
|
||||
#include <strings.h>
|
||||
#endif
|
||||
|
||||
namespace po = boost::program_options;
|
||||
using namespace std;
|
||||
|
||||
enum normal_method {AKNN, ADAPTIVE_AKNN, PANORAMA, PANORAMA_FAST};
|
||||
|
||||
/*
|
||||
* validates normal calculation method specification
|
||||
*/
|
||||
void validate(boost::any& v, const std::vector<std::string>& values,
|
||||
normal_method*, int) {
|
||||
if (values.size() == 0)
|
||||
throw std::runtime_error("Invalid model specification");
|
||||
string arg = values.at(0);
|
||||
if(strcasecmp(arg.c_str(), "AKNN") == 0) v = AKNN;
|
||||
else if(strcasecmp(arg.c_str(), "ADAPTIVE_AKNN") == 0) v = ADAPTIVE_AKNN;
|
||||
else if(strcasecmp(arg.c_str(), "PANORAMA") == 0) v = PANORAMA;
|
||||
else if(strcasecmp(arg.c_str(), "PANORAMA_FAST") == 0) v = PANORAMA_FAST;
|
||||
else throw std::runtime_error(std::string("normal calculation method ") + arg + std::string(" is unknown"));
|
||||
}
|
||||
|
||||
/// 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, int &k1, int &k2, normal_method &ntype,int &width,int &height)
|
||||
{
|
||||
/// ----------------------------------
|
||||
/// set up program commandline options
|
||||
/// ----------------------------------
|
||||
po::options_description cmd_options("Usage: calculateNormals <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")
|
||||
("normal,g", po::value<normal_method>(&ntype)->default_value(AKNN), "normal calculation method "
|
||||
"(AKNN, ADAPTIVE_AKNN, PANORAMA, PANORAMA_FAST)")
|
||||
("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")
|
||||
("K2,K", po::value<int>(&k2)->default_value(20), "<arg> value of Kmax for k-adaptation")
|
||||
("width,w", po::value<int>(&width)->default_value(1280),"width of panorama image")
|
||||
("height,h", po::value<int>(&height)->default_value(960),"height of panorama image")
|
||||
;
|
||||
|
||||
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 << endl;
|
||||
cout << "SAMPLE COMMAND FOR CALCULATING NORMALS" << endl;
|
||||
cout << " bin/normals -s 0 -e 0 -f UOS -g AKNN -k 20 dat/" <<endl;
|
||||
cout << endl << endl;
|
||||
cout << "SAMPLE COMMAND FOR VIEWING CALCULATING NORMALS IN RGB SPACE" << endl;
|
||||
cout << " bin/show -c -f UOS_RGB dat/normals/" << endl;
|
||||
exit(-1);
|
||||
}
|
||||
|
||||
// read scan path
|
||||
if (dir[dir.length()-1] != '/') dir = dir + "/";
|
||||
|
||||
}
|
||||
|
||||
/// 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();
|
||||
|
||||
Scan::closeDirectory();
|
||||
|
||||
cout << "Normal program end" << endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
Loading…
Reference in a new issue