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comparison python/moving.py @ 619:dc2d0a0d7fe1

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merged code from Mohamed Gomaa Mohamed for the use of points of interests in mation pattern learning and motion prediction (TRB 2015)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 10 Dec 2014 15:27:08 -0500
parents 04a8304e13f0 0791b3b55b8f
children 582508610572
comparison
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596:04a8304e13f0 619:dc2d0a0d7fe1
793 if count > lengthThreshold: 793 if count > lengthThreshold:
794 return True 794 return True
795 return False 795 return False
796 796
797 def wiggliness(self): 797 def wiggliness(self):
798 return self.cumulatedDisplacement()/float(Point.distanceNorm2(self.__getitem__(0),self.__getitem__(self.length()-1))) 798 return self.getCumulativeDistance(self.length()-1)/float(Point.distanceNorm2(self.__getitem__(0),self.__getitem__(self.length()-1)))
799 799
800 def getIntersections(self, p1, p2): 800 def getIntersections(self, p1, p2):
801 '''Returns a list of the indices at which the trajectory 801 '''Returns a list of the indices at which the trajectory
802 intersects with the segment of extremities p1 and p2 802 intersects with the segment of extremities p1 and p2
803 the list is empty if there is no crossing''' 803 the list is empty if there is no crossing'''
1066 tMaxFeatures = t 1066 tMaxFeatures = t
1067 return MovingObject.maxDistance(self, self, tMaxFeatures) 1067 return MovingObject.maxDistance(self, self, tMaxFeatures)
1068 else: 1068 else:
1069 print('Load features to compute a maximum size') 1069 print('Load features to compute a maximum size')
1070 return None 1070 return None
1071 1071
1072 def setRoutes(self, startRouteID, endRouteID): 1072 def setRoutes(self, startRouteID, endRouteID):
1073 self.startRouteID = startRouteID 1073 self.startRouteID = startRouteID
1074 self.endRouteID = endRouteID 1074 self.endRouteID = endRouteID
1075 1075
1076 def getInstantsCrossingLane(self, p1, p2): 1076 def getInstantsCrossingLane(self, p1, p2):
1163 # User Type Classification 1163 # User Type Classification
1164 ### 1164 ###
1165 def classifyUserTypeSpeedMotorized(self, threshold, aggregationFunc = median, ignoreNInstantsAtEnds = 0): 1165 def classifyUserTypeSpeedMotorized(self, threshold, aggregationFunc = median, ignoreNInstantsAtEnds = 0):
1166 '''Classifies slow and fast road users 1166 '''Classifies slow and fast road users
1167 slow: non-motorized -> pedestrians 1167 slow: non-motorized -> pedestrians
1168 fast: motorized -> cars''' 1168 fast: motorized -> cars
1169
1170 aggregationFunc can be any function that can be applied to a vector of speeds, including percentile:
1171 aggregationFunc = lambda x: percentile(x, percentileFactor) # where percentileFactor is 85 for 85th percentile'''
1169 if ignoreNInstantsAtEnds > 0: 1172 if ignoreNInstantsAtEnds > 0:
1170 speeds = self.getSpeeds()[ignoreNInstantsAtEnds:-ignoreNInstantsAtEnds] 1173 speeds = self.getSpeeds()[ignoreNInstantsAtEnds:-ignoreNInstantsAtEnds]
1171 else: 1174 else:
1172 speeds = self.getSpeeds() 1175 speeds = self.getSpeeds()
1173 if aggregationFunc(speeds) >= threshold: 1176 if aggregationFunc(speeds) >= threshold: