435 lines
17 KiB
C++
Executable File
435 lines
17 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 <KLib/math/filter/merging/InteractingMultipleModelParticleFilter.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/IPIN2017/code/data/";
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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 BERKWERK = {
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mapDir + "SHL/SHL25.xml",
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{
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dataDir + "bergwerk/path1/nexus/vor/1454775984079.csv",
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dataDir + "bergwerk/path1/galaxy/vor/1454776168794.csv",
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dataDir + "bergwerk/path2/nexus/vor/1454779863041.csv",
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dataDir + "bergwerk/path2/galaxy/vor/1454780113404.csv",
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dataDir + "bergwerk/path3/nexus/vor/1454782562231.csv",
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dataDir + "bergwerk/path3/galaxy/vor/1454782896548.csv",
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dataDir + "bergwerk/path4/nexus/vor/1454776525797.csv",
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dataDir + "bergwerk/path4/galaxy/vor/1454779020844.csv"
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},
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dataDir + "bergwerk/wifiParams.txt",
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40,
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VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO,
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mapDir + "SHL/grid25.dat"
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};
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DataSetup IPIN2015 = {
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mapDir + "SHL/SHL_IPIN2015_gt.xml",
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{
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dataDir + "ipin2015/galaxy/Path0/1433581471902.csv",
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dataDir + "ipin2015/nexus/Path0/1433606195078.csv",
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dataDir + "ipin2015/galaxy/Path1/1433587749492.csv",
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dataDir + "ipin2015/nexus/Path1/1433606670723.csv", // wlan für 71 sekunden weg. verlaufen uns aufm klo.
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dataDir + "ipin2015/galaxy/Path2/1433581471902.csv",
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dataDir + "ipin2015/nexus/Path2/1433607251262.csv",
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dataDir + "eiszeit/path2/1479986737368.csv"
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},
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dataDir + "bergwerk/wifiParams.txt",
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40,
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VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO,
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mapDir + "SHL/grid_IPIN2015_gt.dat"
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};
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DataSetup IPIN2017 = {
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mapDir + "SHL38.xml",
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{
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dataDir + "ipin2017/nogps/i-building/path1/1489769326868.csv",
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dataDir + "ipin2017/nogps/i-building/path1/1489769510080.csv",
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dataDir + "ipin2017/nogps/i-building/path2/1489774173022.csv",
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dataDir + "ipin2017/nogps/i-building/path2/1489774361865.csv",
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dataDir + "ipin2017/nogps/i-building/path3/1489776812891.csv",
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dataDir + "ipin2017/nogps/i-building/path3/1489776979143.csv",
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dataDir + "ipin2017/nogps/all/path1/1490031549543.csv",
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dataDir + "ipin2017/nogps/all/path1/1490031883742.csv",
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dataDir + "ipin2017/nogps/all/path2/1490032575999.csv",
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dataDir + "ipin2017/nogps/all/path2/1490032861864.csv",
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dataDir + "ipin2017/nogps/all/path3/EMPTY.csv",
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dataDir + "ipin2017/nogps/all/path3/EMPTY.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_SHL38.dat"
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};
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} data;
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Floorplan::IndoorMap* MyState::map;
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void 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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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(20);
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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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grid.write(onp);
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} else {
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grid.read(inp);
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}
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// add node-importance
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Importance::addImportance(grid);
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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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// 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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//init the mode filters
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std::vector<K::ParticleFilterMixing<MyState, MyControl, MyObs>> modes;
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//std::shared_ptr<K::ParticleFilterInitializer<MyState>> init(new PFInitFixed(grid, GridPoint(1120.0f, 750.0f, 740.0f), 90.0f));
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// mode 1
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std::shared_ptr<K::ParticleFilterInitializer<MyState>> initMode1(new PFInit(grid, 1));
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K::ParticleFilterMixing<MyState, MyControl, MyObs> mode1(Settings::numParticles, initMode1, Settings::Mode1::modeProbability);
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mode1.setTransition(std::shared_ptr<PFTrans>(new PFTrans(grid, &ctrl)));
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mode1.setEvaluation(std::shared_ptr<PFEval>(new PFEval(WiFiModel, beaconModel, grid)));
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mode1.setResampling(std::shared_ptr<K::ParticleFilterResamplingSimple<MyState>>(new K::ParticleFilterResamplingSimple<MyState>()));
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mode1.setNEffThreshold(0.95);
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mode1.setEstimator(std::shared_ptr<K::ParticleFilterEstimationWeightedAverage<MyState>>(new K::ParticleFilterEstimationWeightedAverage<MyState>()));
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modes.push_back(mode1);
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// mode 2
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std::shared_ptr<K::ParticleFilterInitializer<MyState>> initMode2(new PFInit(grid, 2));
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K::ParticleFilterMixing<MyState, MyControl, MyObs> mode2(Settings::numParticles, initMode2, Settings::Mode2::modeProbability);
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mode2.setTransition(std::shared_ptr<PFTransSimple>(new PFTransSimple(grid)));
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mode2.setEvaluation(std::shared_ptr<PFEval>(new PFEval(WiFiModel, beaconModel, grid)));
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mode2.setResampling(std::shared_ptr<K::ParticleFilterResamplingSimple<MyState>>(new K::ParticleFilterResamplingSimple<MyState>()));
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mode2.setNEffThreshold(0.95);
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mode2.setEstimator(std::shared_ptr<K::ParticleFilterEstimationWeightedAverage<MyState>>(new K::ParticleFilterEstimationWeightedAverage<MyState>()));
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modes.push_back(mode2);
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//init mixing
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Eigen::MatrixXd transitionProbabilityMatrix(2,2);
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transitionProbabilityMatrix << 1,0,0,1;
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K::InteractingMultipleModelParticleFilter<MyState, MyControl, MyObs> IMMAPF(modes, transitionProbabilityMatrix);
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IMMAPF.setMixingSampler(std::unique_ptr<K::MixingSamplerDivergency<MyState, MyControl, MyObs>>(new K::MixingSamplerDivergency<MyState, MyControl, MyObs>()));
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IMMAPF.setJointEstimation(std::unique_ptr<K::JointEstimationPosteriorOnly<MyState, MyControl, MyObs>>(new K::JointEstimationPosteriorOnly<MyState, MyControl, MyObs>()));
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//IMMAPF.setMarkovTransitionProbability(std::unique_ptr<ModeProbabilityTransition>(new ModeProbabilityTransition(grid, Settings::Mixing::lambda)));
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IMMAPF.setMarkovTransitionProbability(std::unique_ptr<ModeProbabilityTransitionNormal>(new ModeProbabilityTransitionNormal(Settings::Mixing::lambda)));
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Timestamp lastTimestamp = Timestamp::fromMS(0);
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StepDetection sd;
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TurnDetection td;
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MotionDetection md;
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ActivityButterPressure act;
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RelativePressure relBaro; relBaro.setCalibrationTimeframe( Timestamp::fromMS(5000) );
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K::Statistics<float> errorStats;
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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 + 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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// 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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td.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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obs.activity = act.add(ts, fr.getBarometer()[e.idx].data);
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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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ctrl.motionDeltaAngle_rad = md.getMotionChangeInRad();
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}
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if (ts.ms() - lastTimestamp.ms() > 500) {
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obs.currentTime = ts;
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MyState est;
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//update filter
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est = IMMAPF.update(&ctrl, obs);
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if(kld_data.empty()){
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kld_data.push_back(0.0);
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} else{
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kld_data.push_back(__KLD);
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}
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Point3 estPos = est.position.inMeter();
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//current ground truth position
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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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//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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plot.setEst(estPos);
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plot.setGT(gtPos);
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plot.addParticles1(IMMAPF.getModes()[0].getParticles());
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plot.addParticles2(IMMAPF.getModes()[1].getParticles());
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plot.addEstimationNode(estPos);
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plot.addEstimationNodeSmoothed(IMMAPF.getModes()[1].getEstimation().position.inMeter());
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//plot.gp << "set arrow 919 from " << tt.pos.x << "," << tt.pos.y << "," << tt.pos.z << " to "<< tt.pos.x << "," << tt.pos.y << "," << tt.pos.z+1 << "lw 3\n";
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//plot.gp << "set label 1001 at screen 0.02, 0.98 'base:" << relBaro.getBaseAvg() << " sigma:" << relBaro.getSigma() << " cur:" << relBaro.getPressureRealtiveToStart() << " hPa " << -relBaro.getPressureRealtiveToStart()/0.10/4.0f << " floor'\n";
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int minutes = static_cast<int>(ts.sec()) / 60;
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plot.gp << "set label 1002 at screen 0.02, 0.94 'Time: " << minutes << ":" << static_cast<int>(static_cast<int>(ts.sec())%60) << "'\n";
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plot.gp << "set label 1003 at screen 0.02, 0.92 'KLD: " << ":" << kld_data.back() << "'\n";
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plot.gp << "set label 1004 at screen 0.90, 0.98 'act:" << obs.activity << "'\n";
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plot.gp << "set label 1005 at screen 0.90, 0.08 'Prob. Mode1:" << IMMAPF.getModes()[0].getModePosteriorProbability() << "'\n";
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plot.gp << "set label 1006 at screen 0.90, 0.06 'Prob. Mode2:" << IMMAPF.getModes()[1].getModePosteriorProbability() << "'\n";
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int ones = 0;
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int twos = 0;
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for(int i = 0; i < IMMAPF.getModes()[0].getParticles().size(); ++i){
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int mode1 = IMMAPF.getModes()[0].getParticles()[i].state.curMode;
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int mode2 = IMMAPF.getModes()[1].getParticles()[i].state.curMode;
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if(mode1 == 1){
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++ones;
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} else {
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++twos;
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}
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if(mode2 == 1){
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++ones;
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} else {
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++twos;
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}
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}
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plot.gp << "set label 1007 at screen 0.90, 0.04 'Part. Mode1:" << ones << "'\n";
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plot.gp << "set label 1008 at screen 0.90, 0.02 'Part. Mode2:" << twos << "'\n";
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// error between GT and estimation
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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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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: " << std::endl;
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std::cout << "Median: " << errorStats.getMedian() << " Average: " << errorStats.getAvg() << 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(errorDir + 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 kld
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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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//save as screenshot for klb
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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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std::cout << "finished" << std::endl;
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sleep(1);
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}
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int main(int argc, char** argv) {
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//Testing files
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//run(data.BERKWERK, 6, "EVALBERGWERK"); // Nexus vor
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//for(int i = 0; i < 5; ++i){
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// Settings::useKLB = false;
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// //run(data.IPIN2017, 0, "ipin2017", Settings::Paths_IPIN2017::path1);
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// run(data.IPIN2017, 1, "ipin2017", Settings::Paths_IPIN2017::path1);
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// run(data.IPIN2017, 2, "ipin2017", Settings::Paths_IPIN2017::path2);
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// //run(data.IPIN2017, 3, "ipin2017", Settings::Paths_IPIN2017::path2);
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// run(data.IPIN2017, 5, "ipin2017", Settings::Paths_IPIN2017::path3);
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// //run(data.IPIN2017, 4, "ipin2017", Settings::Paths_IPIN2017::path3);
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//run(data.IPIN2017, 0, "ipin2017", Settings::Paths_IPIN2017::path1);
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run(data.IPIN2017, 1, "ipin2017", Settings::Paths_IPIN2017::path1);
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run(data.IPIN2017, 2, "ipin2017", Settings::Paths_IPIN2017::path2);
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//run(data.IPIN2017, 3, "ipin2017", Settings::Paths_IPIN2017::path2);
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run(data.IPIN2017, 5, "ipin2017", Settings::Paths_IPIN2017::path3);
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//run(data.IPIN2017, 4, "ipin2017", Settings::Paths_IPIN2017::path3);
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//}
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}
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