480 lines
18 KiB
C++
Executable File
480 lines
18 KiB
C++
Executable File
#include <iostream>
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#include "filter/Structs.h"
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#include "filter/KLB.h"
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#include "Plotti.h"
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#include "filter/Logic.h"
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#include "Settings.h"
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#include <sys/types.h>
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#include <sys/stat.h>
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#include <Indoor/sensors/radio/model/WiFiModelFactory.h>
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#include <Indoor/sensors/radio/model/WiFiModelFactoryImpl.h>
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#include <Indoor/math/stats/Statistics.h>
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#include <Indoor/smc/smoothing/ForwardFilterHistory.h>
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#include <Indoor/smc/smoothing/FastKDESmoothing.h>
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//frank
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//const std::string mapDir = "/mnt/data/workspaces/IPIN2016/IPIN2016/competition/maps/";
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//const std::string dataDir = "/mnt/data/workspaces/IPIN2016/IPIN2016/competition/src/data/";
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//toni
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const std::string mapDir = "/home/toni/Documents/programme/localization/IndoorMap/maps/";
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//const std::string dataDir = "/home/toni/Documents/programme/localization/DynLag/code/data/";
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const std::string dataDir = "/home/toni/Documents/programme/localization/museum/measurements/shl/";
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//const std::string dataDir = "/home/toni/Documents/programme/localization/museum/measurements/motionAxisTest/";
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const std::string errorDir = dataDir + "results/";
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/** describes one dataset (map, training, parameter-estimation, ...) */
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struct DataSetup {
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std::string map;
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std::vector<std::string> training;
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std::string wifiParams;
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int minWifiOccurences;
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VAPGrouper::Mode vapMode;
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std::string grid;
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};
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/** all configured datasets */
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struct Data {
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DataSetup SecondFloorOnly = {
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mapDir + "SHL_Stock_2_01.xml",
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{
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dataDir + "Path1_1.csv",
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dataDir + "Path2_1.csv",
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dataDir + "Path3_1.csv",
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},
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mapDir + "wifi_fp_all.dat",
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40,
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VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO,
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mapDir + "grid_Stock_2_01.dat"
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};
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DataSetup FloorOneToThree = {
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mapDir + "SHL_Stock_1-3_03.xml",
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{
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dataDir + "Path4_0.csv",
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dataDir + "Path5_0.csv",
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dataDir + "Path6_0.csv",
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},
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mapDir + "wifi_fp_all.dat",
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40,
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VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO,
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mapDir + "grid_Stock_1-3_03.dat"
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};
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DataSetup MotionAxisTest = {
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mapDir + "SHL40_noElevator.xml",
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{
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dataDir + "Path0_0.csv"
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},
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mapDir + "wifi_fp_all.dat",
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40,
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VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO,
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mapDir + "grid_SHL40_noElevator.dat"
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};
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} data;
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Floorplan::IndoorMap* MyState::map;
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Stats::Statistics<float> run(DataSetup setup, int numFile, std::string folder, std::vector<int> gtPath) {
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std::vector<double> kld_data;
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// load the floorplan
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Floorplan::IndoorMap* map = Floorplan::Reader::readFromFile(setup.map);
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MyState::map = map;
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WiFiModelLogDistCeiling WiFiModel(map);
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WiFiModel.loadAPs(map, Settings::WiFiModel::TXP, Settings::WiFiModel::EXP, Settings::WiFiModel::WAF);
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Assert::isFalse(WiFiModel.getAllAPs().empty(), "no AccessPoints stored within the map.xml");
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//Wi-Fi model new
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// WiFiModelFactory factory(map);
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// WiFiModel* wifimodel= factory.loadXML("/home/toni/Documents/programme/localization/data/wifi/model/eachOptParPos_multimodel.xml");
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// Assert::isFalse(wifimodel->getAllAPs().empty(), "no AccessPoints stored within the map.xml");
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BeaconModelLogDistCeiling beaconModel(map);
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beaconModel.loadBeaconsFromMap(map, Settings::BeaconModel::TXP, Settings::BeaconModel::EXP, Settings::BeaconModel::WAF);
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//Assert::isFalse(beaconModel.getAllBeacons().empty(), "no Beacons stored within the map.xml");
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// build the grid
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std::ifstream inp(setup.grid, std::ifstream::binary);
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Grid<MyNode> grid(Settings::Grid::gridSize_cm);
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// grid.dat empty? -> build one and save it
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if (!inp.good() || (inp.peek()&&0) || inp.eof()) {
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std::ofstream onp;
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onp.open(setup.grid);
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GridFactory<MyNode> factory(grid);
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factory.build(map);
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// add node-importance
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Importance::addImportance(grid);
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grid.write(onp);
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} else {
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grid.read(inp);
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}
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// stamp WiFi signal-strengths onto the grid
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WiFiGridEstimator::estimate(grid, WiFiModel, Settings::smartphoneAboveGround);
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// reading file
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Offline::FileReader fr(setup.training[numFile]);
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//interpolator for ground truth
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Interpolator<uint64_t, Point3> gtInterpolator = fr.getGroundTruthPath(map, gtPath);
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//gnuplot plot
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Plotti plot;
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plot.addFloors(map);
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plot.addOutline(map);
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plot.addStairs(map);
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plot.gp << "set autoscale xy\n";
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//plot.addGrid(grid);
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plot.splot.getView().setEnabled(false);
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// init ctrl and observation
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MyControl ctrl;
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ctrl.resetAfterTransition();
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MyObs obs;
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//History of all estimated particles. Used for smoothing
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SMC::ForwardFilterHistory<MyState, MyControl, MyObs> pfHistory;
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//filter init
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SMC::ParticleFilterHistory<MyState, MyControl, MyObs> pf(Settings::numParticles, std::unique_ptr<PFInit>(new PFInit(grid)));
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//SMC::ParticleFilterHistory<MyState, MyControl, MyObs> pf(Settings::numParticles, std::unique_ptr<PFInitFixed>(new PFInitFixed(grid, GridPoint(55.5f * 100.0, 43.7f * 100.0, 740.0f), 180.0f)));
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pf.setTransition(std::unique_ptr<PFTrans>(new PFTrans(grid, &ctrl)));
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//pf.setTransition(std::unique_ptr<PFTransKLDSampling>(new PFTransKLDSampling(grid, &ctrl)));
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//pf.setTransition(std::unique_ptr<PFTransSimple>(new PFTransSimple(grid)));
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pf.setEvaluation(std::unique_ptr<PFEval>(new PFEval(WiFiModel, beaconModel, grid)));
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//resampling
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if(Settings::useKLB){
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pf.setResampling(std::unique_ptr<SMC::ParticleFilterResamplingDivergence<MyState>>(new SMC::ParticleFilterResamplingDivergence<MyState>()));
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} else {
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pf.setResampling(std::unique_ptr<SMC::ParticleFilterResamplingSimple<MyState>>(new SMC::ParticleFilterResamplingSimple<MyState>()));
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//pf.setResampling(std::unique_ptr<SMC::ParticleFilterResamplingPercent<MyState>>(new SMC::ParticleFilterResamplingPercent<MyState>(0.4)));
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//pf.setResampling(std::unique_ptr<NodeResampling<MyState, MyNode>>(new NodeResampling<MyState, MyNode>(*grid)););
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//pf.setResampling(std::unique_ptr<SMC::ParticleFilterResamplingKLD<MyState>>(new SMC::ParticleFilterResamplingKLD<MyState>()));
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}
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pf.setNEffThreshold(0.95);
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//estimation
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pf.setEstimation(std::unique_ptr<SMC::ParticleFilterEstimationWeightedAverage<MyState>>(new SMC::ParticleFilterEstimationWeightedAverage<MyState>()));
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//pf.setEstimation(std::unique_ptr<SMC::ParticleFilterEstimationRegionalWeightedAverage<MyState>>(new SMC::ParticleFilterEstimationRegionalWeightedAverage<MyState>()));
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//pf.setEstimation(std::unique_ptr<SMC::ParticleFilterEstimationOrderedWeightedAverage<MyState>>(new SMC::ParticleFilterEstimationOrderedWeightedAverage<MyState>(0.5)));
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//pf.setEstimation(std::unique_ptr<SMC::ParticleFilterEstimationKernelDensity<MyState, 3>>(new SMC::ParticleFilterEstimationKernelDensity<MyState, 3>()));
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/** Smoothing Init */
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SMC::FastKDESmoothing<MyState, MyControl, MyObs> bf(Settings::numParticles, map, Settings::Grid::gridSize_cm, Settings::KDE::bandwidth);
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if(Settings::Smoothing::activated){
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//create the backward smoothing filter
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bf.setSampler( std::unique_ptr<SMC::CumulativeSampler<MyState>>(new SMC::CumulativeSampler<MyState>()));
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bool smoothing_resample = false;
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//bf->setNEffThreshold(1.0);
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if(smoothing_resample)
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bf.setResampling( std::unique_ptr<SMC::ParticleFilterResamplingSimple<MyState>>(new SMC::ParticleFilterResamplingSimple<MyState>()) );
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//bf.setTransition(std::unique_ptr<BFTrans>( new BFTrans) );
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bf.setTransition(std::unique_ptr<PFTrans>(new PFTrans(grid, &ctrl)));
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//Smoothing estimation
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bf.setEstimation(std::unique_ptr<SMC::ParticleFilterEstimationWeightedAverage<MyState>>(new SMC::ParticleFilterEstimationWeightedAverage<MyState>()));
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//bf->setEstimation( std::unique_ptr<SMC::ParticleFilterEstimationRegionalWeightedAverage<MyState>>(new SMC::ParticleFilterEstimationRegionalWeightedAverage<MyState>()));
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//bf->setEstimation( std::unique_ptr<SMC::ParticleFilterEstimationOrderedWeightedAverage<MyState>>(new SMC::ParticleFilterEstimationOrderedWeightedAverage<MyState>(0.50f)));
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}
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Timestamp lastTimestamp = Timestamp::fromMS(0);
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StepDetection sd;
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PoseDetection pd;
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TurnDetection td(&pd);
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MotionDetection md;
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ActivityButterPressure act;
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//ActivityDetector act;
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RelativePressure relBaro;
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relBaro.setCalibrationTimeframe( Timestamp::fromMS(5000) );
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Stats::Statistics<float> errorStats;
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Stats::Statistics<float> errorStatsSmoothing;
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//file writing for error data
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const long int t = static_cast<long int>(time(NULL));
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const std::string evalDir = errorDir + folder + std::to_string(t);
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if(mkdir(evalDir.c_str(), S_IRWXU | S_IRWXG | S_IROTH | S_IXOTH) == -1){
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Assert::doThrow("Eval folder couldn't be created!");
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}
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std::ofstream errorFile;
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errorFile.open (evalDir + "/" + std::to_string(numFile) + "_" + std::to_string(t) + ".csv");
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std::ofstream errorFileSmoothing;
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errorFileSmoothing.open (evalDir + "/" + std::to_string(numFile) + "_" + std::to_string(t) + "_Smoothing.csv");
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// parse each sensor-value within the offline data
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for (const Offline::Entry& e : fr.getEntries()) {
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const Timestamp ts = Timestamp::fromMS(e.ts);
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if (e.type == Offline::Sensor::WIFI) {
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obs.wifi = fr.getWiFiGroupedByTime()[e.idx].data;
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} else if (e.type == Offline::Sensor::BEACON){
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obs.beacons.entries.push_back(fr.getBeacons()[e.idx].data);
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// remove to old beacon measurements
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obs.beacons.removeOld(ts);
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} else if (e.type == Offline::Sensor::ACC) {
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if (sd.add(ts, fr.getAccelerometer()[e.idx].data)) {
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++ctrl.numStepsSinceLastTransition;
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}
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const Offline::TS<AccelerometerData>& _acc = fr.getAccelerometer()[e.idx];
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pd.addAccelerometer(ts, _acc.data);
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} else if (e.type == Offline::Sensor::GYRO) {
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const Offline::TS<GyroscopeData>& _gyr = fr.getGyroscope()[e.idx];
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const float delta_gyro = td.addGyroscope(ts, _gyr.data);
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ctrl.turnSinceLastTransition_rad += delta_gyro;
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} else if (e.type == Offline::Sensor::BARO) {
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relBaro.add(ts, fr.getBarometer()[e.idx].data);
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obs.relativePressure = relBaro.getPressureRealtiveToStart();
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obs.sigmaPressure = relBaro.getSigma();
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//activity recognition
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act.add(ts, fr.getBarometer()[e.idx].data);
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obs.activity = act.get();
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//activity for transition
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} else if (e.type == Offline::Sensor::LIN_ACC) {
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md.addLinearAcceleration(ts, fr.getLinearAcceleration()[e.idx].data);
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} else if (e.type == Offline::Sensor::GRAVITY) {
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md.addGravity(ts, fr.getGravity()[e.idx].data);
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Eigen::Vector2f curVec = md.getCurrentMotionAxis();
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ctrl.motionDeltaAngle_rad = md.getMotionChangeInRad();
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}
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if (ts.ms() - lastTimestamp.ms() > 500) {
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/** filtering stuff */
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obs.currentTime = ts;
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MyState est = pf.update(&ctrl, obs);
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Point3 estPos = est.position.inMeter();
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Point3 gtPos = gtInterpolator.get(static_cast<uint64_t>(ts.ms()));
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/** plotting stuff */
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plot.pInterest.clear();
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plot.setEst(estPos);
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plot.setGT(gtPos);
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//plot.addEstimationNode(estPos);
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//plot.addParticles(pf.getParticles());
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/** error calculation stuff */
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float err_m = gtPos.getDistance(estPos);
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errorStats.add(err_m);
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errorFile << err_m << "\n";
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/** smoothing stuff */
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if(Settings::Smoothing::activated){
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// add everything from the forward step to the history
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pfHistory.add(ts, pf.getNonResamplingParticles(), ctrl, obs);
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//backward filtering
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//((BFTrans*)bf.getTransition())->setCurrentTime(tsHistory[(tsHistory.size() - 1) - i]);
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MyState estBF = bf.update(pfHistory, Settings::Smoothing::lag);
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// get ground truth position at lag time
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Point3 estPosSmoothing = estBF.position.inMeter();
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Point3 gtPosSmoothed;
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if(pfHistory.size() <= Settings::Smoothing::lag){
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gtPosSmoothed = gtInterpolator.get(static_cast<uint64_t>(pfHistory.getFirstTimestamp().ms()));
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} else {
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gtPosSmoothed = gtInterpolator.get(static_cast<uint64_t>(pfHistory.getTimestamp(Settings::Smoothing::lag).ms()));
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}
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//plot
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plot.addEstimationNodeSmoothed(estPosSmoothing);
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plot.addParticles(bf.getbackwardParticles().back());
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if(Settings::Smoothing::lag >= pfHistory.size()){
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// error between GT and smoothing
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float errSmoothing_m = gtPosSmoothed.getDistance(estPosSmoothing);
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errorStatsSmoothing.add(errSmoothing_m);
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errorFileSmoothing << errSmoothing_m << "\n";
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}
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}
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//plot misc
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plot.setTimeInMinute(static_cast<int>(ts.sec()) / 60, static_cast<int>(static_cast<int>(ts.sec())%60));
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if(Settings::useKLB){
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plot.gp << "set label 1002 at screen 0.04, 0.94 'KLD: " << ":" << kld_data.back() << "'\n";
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}
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plot.gp << "set label 1002 at screen 0.95, 0.98 'act:" << static_cast<int>(obs.activity) << "'\n";
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//draw gyro angle and motion angle
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//turn angle plot
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static float angleSumTurn = 0; angleSumTurn += ctrl.turnSinceLastTransition_rad;
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plot.showAngle(1, angleSumTurn + M_PI, Point2(0.9, 0.9), "Turn: ");
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//motion angle plot
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static float angleSumMotion = 0; angleSumMotion += ctrl.motionDeltaAngle_rad;
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plot.showAngle(2, angleSumMotion + M_PI, Point2(0.9, 0.8), "Motion: ");
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/** Draw everything */
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plot.show();
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usleep(10*10);
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lastTimestamp = ts;
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// reset control
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ctrl.resetAfterTransition();
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}
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}
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errorFile.close();
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std::cout << "Statistical Analysis Filtering: " << std::endl;
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std::cout << "Median: " << errorStats.getMedian() << " Average: " << errorStats.getAvg() << " Std: " << errorStats.getStdDev() << std::endl;
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std::cout << "Statistical Analysis Smoothing: " << std::endl;
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std::cout << "Median: " << errorStatsSmoothing.getMedian() << " Average: " << errorStatsSmoothing.getAvg() << " Std: " << errorStatsSmoothing.getStdDev() << std::endl;
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//Write the current plotti buffer into file
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std::ofstream plotFile;
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plotFile.open(evalDir + "/plot_" + std::to_string(numFile) + "_" + std::to_string(t) + ".gp");
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plot.saveToFile(plotFile);
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plotFile.close();
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for(int i = 0; i < map->floors.size(); ++i){
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plot.printSingleFloor(evalDir + "/image" + std::to_string(numFile) + "_" + std::to_string(t), i);
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plot.show();
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usleep(10*10);
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}
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plot.printSideView(evalDir + "/image" + std::to_string(numFile) + "_" + std::to_string(t), 90);
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plot.show();
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plot.printSideView(evalDir + "/image" + std::to_string(numFile) + "_" + std::to_string(t), 0);
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plot.show();
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plot.printOverview(evalDir + "/image" + std::to_string(numFile) + "_" + std::to_string(t));
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plot.show();
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/** Draw KLB */
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K::Gnuplot gp;
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K::GnuplotPlot plotkld;
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K::GnuplotPlotElementLines lines;
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if(Settings::useKLB){
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std::string path = evalDir + "/image" + std::to_string(numFile) + "_" + std::to_string(t);
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gp << "set terminal png size 1280,720\n";
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gp << "set output '" << path << "_shennendistance.png'\n";
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for(int i=0; i < kld_data.size()-1; ++i){
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K::GnuplotPoint2 p1(i, kld_data[i]);
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K::GnuplotPoint2 p2(i+1, kld_data[i+1]);
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lines.addSegment(p1, p2);
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}
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plotkld.add(&lines);
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gp.draw(plotkld);
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gp.flush();
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plot.splot.getView().setEnabled(false);
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}
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std::cout << "finished" << std::endl;
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sleep(1);
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return errorStats;
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}
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int main(int argc, char** argv) {
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Stats::Statistics<float> statsAVG;
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Stats::Statistics<float> statsMedian;
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Stats::Statistics<float> statsSTD;
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Stats::Statistics<float> statsQuantil;
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Stats::Statistics<float> tmp;
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for(int i = 0; i < 10; ++i){
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tmp = run(data.SecondFloorOnly, 0, "KDE-Smoothing-Test", Settings::Path_DongleTest::path1);
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statsMedian.add(tmp.getMedian());
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statsAVG.add(tmp.getAvg());
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statsSTD.add(tmp.getStdDev());
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statsQuantil.add(tmp.getQuantile(0.75));
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// tmp = run(data.MotionAxisTest, 0, "Motion-Axis-Test", Settings::Path_DongleTest::path1);
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// statsMedian.add(tmp.getMedian());
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// statsAVG.add(tmp.getAvg());
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// statsSTD.add(tmp.getStdDev());
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// statsQuantil.add(tmp.getQuantile(0.75));
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|
|
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std::cout << "Iteration " << i << " completed" << std::endl;;
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|
}
|
|
|
|
std::cout << "==========================================================" << std::endl;
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|
std::cout << "Average of all statistical data: " << std::endl;
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|
std::cout << "Median: " << statsMedian.getAvg() << std::endl;
|
|
std::cout << "Average: " << statsAVG.getAvg() << std::endl;
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|
std::cout << "Standard Deviation: " << statsSTD.getAvg() << std::endl;
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|
std::cout << "75 Quantil: " << statsQuantil.getAvg() << std::endl;
|
|
std::cout << "==========================================================" << std::endl;
|
|
|
|
//EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS
|
|
std::ofstream finalStatisticFile;
|
|
finalStatisticFile.open (errorDir + "/tmp.csv");
|
|
|
|
finalStatisticFile << "Average of all statistical data: \n";
|
|
finalStatisticFile << "Median: " << statsMedian.getAvg() << "\n";
|
|
finalStatisticFile << "Average: " << statsAVG.getAvg() << "\n";
|
|
finalStatisticFile << "Standard Deviation: " << statsSTD.getAvg() << "\n";
|
|
finalStatisticFile << "75 Quantil: " << statsQuantil.getAvg() << "\n";
|
|
|
|
finalStatisticFile.close();
|
|
//EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS
|
|
|
|
}
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