# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import sys sys.path.append("../input/n3l5-a/"); import Nelsa semData = """ import Nelsa import numpy as np # linear algebra import pymap3d class mapII(Nelsa.mapI): def tce(self, elem): return elem.traffic_control_element def pdelta(self, elem): return {'px': self.cm_Norm(elem.points_x_deltas_cm), 'py': self.cm_Norm(elem.points_y_deltas_cm), 'pz': self.cm_Norm(elem.points_z_deltas_cm)}; def ldelta(self, elem): return {'px': self.cm_Norm(elem.vertex_deltas_x_cm), 'py': self.cm_Norm(elem.vertex_deltas_y_cm), 'pz': self.cm_Norm(elem.vertex_deltas_z_cm)}; pass; class gpsData: def __init__(self): self.HC_Lat = 37.429333 self.HC_Lng = -122.154361 self.HC_Ex, self.HC_Ey, self.HC_Ez = pymap3d.geodetic2ecef(self.HC_Lat, 0, 0); ## try lngZero? self.degree2kmNS = self.toDist(self.ecefNorm(1, 0))[1] / 1000; self.degree2kmEW = self.toDist(self.ecefNorm(0, 1))[0] / 1000; def ecefNorm(self, dLat, dLng): rx1, ry1, rz1 = pymap3d.geodetic2ecef(dLat+self.HC_Lat, 0, 0); ## NS Dist // EW Centre rx2, ry2, rz2 = pymap3d.geodetic2ecef(dLat+self.HC_Lat, dLng, 0); ## EW Dist @ lat return rx2 - rx1, ry2 - ry1, rx1 - self.HC_Ex, rz1 - self.HC_Ez; ##EW-Fall, East-west, NS-Fall, Northsouth ## East-West Horizontal? // NS Direction skew def toDist(self, tupl): return [np.hypot(tupl[0], tupl[1]) *np.sign(tupl[1]), np.hypot(tupl[2], tupl[3]) *np.sign(tupl[3])] def world2gps(self, wx, wy): gX = (wx/1000) / self.degree2kmEW; gY = (wy/1000) / self.degree2kmNS; return gY, gX def gps2world(self, lat, lng): return self.toDist(self.ecefNorm(lat - self.HC_Lat, lng - self.HC_Lng)); class mapData: def scan_Labels(self, xstr): return [self.map.label_Mask(i, xstr) for i in range(0, self.maplen)]; def collect_Labels(self, bvec): return [self.map[int(x)] for x in np.nonzero(bvec)[0]]; def delta_Offsets(self): for x in self.traffics: #x.delta_Offset = self.map.gps_to_World(x.Centre); #x.delta_Offset = self.gps.gps2world(x.Centre[0], x.Centre[1]); x.delta_Offset = self.gps.gps2world(x.Origin[0], x.Origin[1]); for x in self.lanes: #x.delta_Offset = self.map.gps_to_World(x.Centre); #x.delta_Offset = self.gps.gps2world(x.Centre[0], x.Centre[1]); x.delta_Offset = self.gps.gps2world(x.Origin[0], x.Origin[1]); def __init__(self, xmap): self.lanes = []; self.traffics = []; self.junctions = []; self.map = xmap; ##self.map.Nelsa_? self.maplen = len(xmap); self.setlanes(self.collect_Labels(self.scan_Labels("lane"))); self.settraffic(self.collect_Labels(self.scan_Labels("traffic_control_element"))); self.setjunction(self.collect_Labels(self.scan_Labels("junction"))); for x in range(0, len(self.lanes)): self.lanes[x] = self.lane(self.lanes[x]); for x in range(0, len(self.traffics)): self.traffics[x] = self.traffic(self.traffics[x]); self.gps = gpsData(); self.delta_Offsets(); class common: #def __init__(self, xob): # self.obj = xob; def __init__(self, xob): self.obj = xob; self.Id = self.get_Id(); self.Centre = self.get_Centroid(); def get_Id(self): return self.obj.id.id; def get_Centroid(self): #print(type(self)); #if(type(self) == mapData.traffic): print("trafficlight"); #if(type(self) == mapData.lane): print("lane"); bound = self.obj.bounding_box; e27 = 20000000; lat = (bound.south_west.lat_e7 + bound.north_east.lat_e7) / e27; #2e7 long = (bound.south_west.lng_e7 + bound.north_east.lng_e7) / e27; #2e7 return [lat, long];##? def get_Origin(self): #print(type(self)); #origin = self.obj.element; if(type(self) == mapData.traffic): origin = self.obj.element.traffic_control_element.geo_frame.origin; if(type(self) == mapData.lane): origin = self.obj.element.lane.geo_frame.origin; e17 = 10000000; lat = origin.lat_e7 / e17; #2e7 long = origin.lng_e7 / e17; #2e7 return [lat, long];##? #pass; def get_Delta(self): #print(type(self), type(self.obj)) if(type(self) == mapData.traffic): return [mapII.pdelta(self, self.obj.element.traffic_control_element)]; if(type(self) == mapData.lane): lane1 = mapII.ldelta(self, self.obj.element.lane.left_boundary); lane2 = mapII.ldelta(self, self.obj.element.lane.right_boundary); #print(self.obj == mII[self.obj.get_Id()]) #return mII.pdelta(mII[self.obj.get_Id()]); ## return [lane1, lane2]; #pass; def cm_Norm(self, seq): ## to metres absolute return np.cumsum(np.asarray(seq) / 100) #pass; class node(common): pass; # def __init__(self, xob): # self.obj = xob; ## road segments ## junction class segment(common): pass; # def __init__(self, xob): # self.obj = xob; ## start node / end node ## lanes class junction(common): pass; # def __init__(self, xob): # self.obj = xob; ## road net nodes ## traffic ## lanes class lane(common): def __init__(self, xob): self.obj = xob; self.Id = self.get_Id(); self.Centre = self.get_Centroid(); self.Origin = self.get_Origin(); self.Delta = self.get_Delta(); self.delta_Offset = np.array([0., 0.]); class traffic(common): def __init__(self, xob): self.obj = xob; self.Id = self.get_Id(); self.Centre = self.get_Centroid(); self.Origin = self.get_Origin(); self.Delta = self.get_Delta(); self.delta_Offset = np.array([0., 0.]); def setlanes(self, xl): self.lanes.extend(xl); def settraffic(self, xt): self.traffics.extend(xt); def setjunction(self, xt): self.junctions.extend(xt); #""" if(semData != ""): f = open("./semData.py", "w") f.write(semData); f.close(); # Input data files are available in the read-only "../input/" directory # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory #import os #for dirname, _, filenames in os.walk('/kaggle/input'): # for filename in filenames: # print(os.path.join(dirname, filename)) # You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session #mapi = mapII("/kaggle/input/lyft-motion-prediction-autonomous-vehicles/semantic_map/semantic_map.pb", [[1,0],[0,1]]) #mapii = mapData(mapi);