Mercurial Hosting > traffic-intelligence
annotate python/events.py @ 607:84690dfe5560
add some functions for behaviour analysis
| author | MohamedGomaa |
|---|---|
| date | Tue, 25 Nov 2014 22:49:47 -0500 |
| parents | 07b1bd0785cd |
| children | 0dc36203973d |
| rev | line source |
|---|---|
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1 #! /usr/bin/env python |
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2 '''Libraries for events |
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3 Interactions, pedestrian crossing...''' |
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4 |
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5 import numpy as np |
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6 from numpy import arccos |
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7 |
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8 import multiprocessing |
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9 import itertools |
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10 |
| 607 | 11 import sys |
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12 import moving, prediction, indicators, utils |
| 607 | 13 sys.path.append("D:/behaviourAnalysis/libs") |
| 14 import trajLearning | |
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15 __metaclass__ = type |
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16 |
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17 class Interaction(moving.STObject): |
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18 '''Class for an interaction between two road users |
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19 or a road user and an obstacle |
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20 |
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21 link to the moving objects |
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22 contains the indicators in a dictionary with the names as keys |
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23 ''' |
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24 |
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25 categories = {'Head On': 0, |
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26 'rearend': 1, |
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27 'side': 2, |
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28 'parallel': 3} |
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29 |
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30 indicatorNames = ['Collision Course Dot Product', |
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31 'Collision Course Angle', |
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32 'Distance', |
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33 'Minimum Distance', |
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34 'Velocity Angle', |
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35 'Speed Differential', |
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36 'Collision Probability', |
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37 'Time to Collision', |
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38 'Probability of Successful Evasive Action', |
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39 'predicted Post Encroachment Time'] |
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40 |
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41 indicatorNameToIndices = utils.inverseEnumeration(indicatorNames) |
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42 |
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43 indicatorShortNames = ['CCDP', |
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44 'CCA', |
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45 'Dist', |
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46 'MinDist', |
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47 'VA', |
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48 'SD', |
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49 'PoC', |
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50 'TTC', |
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51 'P(SEA)', |
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52 'pPET'] |
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53 |
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54 indicatorUnits = ['', |
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55 'rad', |
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56 'm', |
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57 'm', |
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58 'rad', |
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59 'm/s', |
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60 '', |
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61 's', |
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62 '', |
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63 ''] |
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64 |
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65 def __init__(self, num = None, timeInterval = None, roaduserNum1 = None, roaduserNum2 = None, roadUser1 = None, roadUser2 = None, categoryNum = None): |
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66 moving.STObject.__init__(self, num, timeInterval) |
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67 if timeInterval == None and roadUser1 != None and roadUser2 != None: |
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68 self.timeInterval = roadUser1.commonTimeInterval(roadUser2) |
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69 self.roadUser1 = roadUser1 |
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70 self.roadUser2 = roadUser2 |
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71 if roaduserNum1 != None and roaduserNum2 != None: |
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72 self.roadUserNumbers = set([roaduserNum1, roaduserNum2]) |
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73 elif roadUser1 != None and roadUser2 != None: |
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74 self.roadUserNumbers = set(roadUser1.getNum(), roadUser2.getNum()) |
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75 else: |
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76 self.roadUserNumbers = None |
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77 self.categoryNum = categoryNum |
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78 self.indicators = {} |
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79 self.interactionInterval = None |
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80 |
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81 def getRoadUserNumbers(self): |
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82 return self.roadUserNumbers |
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83 |
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84 def getIndicator(self, indicatorName): |
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85 return self.indicators.get(indicatorName, None) |
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86 |
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87 def addIndicator(self, indicator): |
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88 if indicator: |
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89 self.indicators[indicator.name] = indicator |
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90 |
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91 def computeIndicators(self): |
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92 '''Computes the collision course cosine only if the cosine is positive''' |
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93 collisionCourseDotProducts = {}#[0]*int(self.timeInterval.length()) |
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94 collisionCourseAngles = {} |
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95 velocityAngles = {} |
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96 distances = {}#[0]*int(self.timeInterval.length()) |
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97 speedDifferentials = {} |
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98 interactionInstants = [] |
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99 for instant in self.timeInterval: |
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100 deltap = self.roadUser1.getPositionAtInstant(instant)-self.roadUser2.getPositionAtInstant(instant) |
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101 v1 = self.roadUser1.getVelocityAtInstant(instant) |
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102 v2 = self.roadUser2.getVelocityAtInstant(instant) |
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103 deltav = v2-v1 |
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104 velocityAngles[instant] = arccos(moving.Point.dot(v1, v2)/(v1.norm2()*v2.norm2())) |
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105 collisionCourseDotProducts[instant] = moving.Point.dot(deltap, deltav) |
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106 distances[instant] = deltap.norm2() |
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107 speedDifferentials[instant] = deltav.norm2() |
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108 if collisionCourseDotProducts[instant] > 0: |
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109 interactionInstants.append(instant) |
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110 collisionCourseAngles[instant] = arccos(collisionCourseDotProducts[instant]/(distances[instant]*speedDifferentials[instant])) |
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111 |
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112 if len(interactionInstants) >= 2: |
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113 self.interactionInterval = moving.TimeInterval(interactionInstants[0], interactionInstants[-1]) |
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114 else: |
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115 self.interactionInterval = moving.TimeInterval() |
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116 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[0], collisionCourseDotProducts)) |
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117 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[1], collisionCourseAngles)) |
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118 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[2], distances)) |
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119 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[4], velocityAngles)) |
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120 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[5], speedDifferentials)) |
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121 |
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122 # if we have features, compute other indicators |
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123 if len(self.roadUser1.features) != 0 and len(self.roadUser2.features) != 0: |
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124 minDistance={} |
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125 for instant in self.timeInterval: |
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126 minDistance[instant] = moving.MovingObject.minDistance(self.roadUser1, self.roadUser2, instant) |
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127 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[3], minDistance)) |
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128 |
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129 def computeCrossingsCollisions(self, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1): |
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130 '''Computes all crossing and collision points at each common instant for two road users. ''' |
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131 self.collisionPoints={} |
| 339 | 132 self.crossingZones={} |
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133 TTCs = {} |
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134 |
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135 if timeInterval: |
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136 commonTimeInterval = timeInterval |
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137 else: |
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138 commonTimeInterval = self.timeInterval |
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139 self.collisionPoints, self.crossingZones = prediction.computeCrossingsCollisions(predictionParameters, self.roadUser1, self.roadUser2, collisionDistanceThreshold, timeHorizon, computeCZ, debug, commonTimeInterval, nProcesses) |
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140 for i, cp in self.collisionPoints.iteritems(): |
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141 TTCs[i] = prediction.SafetyPoint.computeExpectedIndicator(cp) |
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142 # add probability of collision, and probability of successful evasive action |
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143 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[7], TTCs)) |
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144 |
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145 if computeCZ: |
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146 pPETs = {} |
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147 for i in list(commonTimeInterval)[:-1]: |
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148 if len(self.crossingZones[i]) > 0: |
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149 pPETs[i] = prediction.SafetyPoint.computeExpectedIndicator(self.crossingZones[i]) |
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150 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[9], pPETs)) |
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151 |
| 607 | 152 def computeCrosssingCollisionsPrototypeAtInstant(self, instant,route1,route2,predictionParameters, collisionDistanceThreshold, timeHorizon, prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None, computeCZ = False, debug = False,useDestination=True,useSpeedPrototype=True): |
| 153 inter1=moving.Interval(self.roadUser1.timeInterval.first,instant) | |
| 154 inter2=moving.Interval(self.roadUser2.timeInterval.first,instant) | |
| 155 partialObjPositions1= self.roadUser1.getObjectInTimeInterval(inter1).positions | |
| 156 partialObjPositions2= self.roadUser2.getObjectInTimeInterval(inter2).positions | |
| 157 if useSpeedPrototype: | |
| 158 prototypeTrajectories1=trajLearning.findPrototypesSpeed(prototypes,secondStepPrototypes,nMatching,objects,route1,partialObjPositions1,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination) | |
| 159 prototypeTrajectories2=trajLearning.findPrototypesSpeed(prototypes,secondStepPrototypes,nMatching,objects,route2,partialObjPositions2,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination) | |
| 160 else: | |
| 161 prototypeTrajectories1=trajLearning.findPrototypes(prototypes,nMatching,objects,route1,partialObjPositions1,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched) | |
| 162 prototypeTrajectories2=trajLearning.findPrototypes(prototypes,nMatching,objects,route2,partialObjPositions2,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched) | |
| 163 if prototypeTrajectories1=={}: | |
| 164 print self.roadUser1.num, 'is abnormal at instant', str(instant) | |
| 165 return [],[] | |
| 166 elif prototypeTrajectories2=={}: | |
| 167 print self.roadUser2.num, 'is abnormal at instant', str(instant) | |
| 168 return [],[] | |
| 169 else: | |
| 170 currentInstant,collisionPoints, crossingZones = predictionParameters.computeCrossingsCollisionsAtInstant(instant, self.roadUser1, self.roadUser2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,prototypeTrajectories1,prototypeTrajectories2) | |
| 171 return collisionPoints,crossingZones | |
| 172 | |
| 173 def computeCrossingsCollisionsPrototype2stages(self, predictionParameters, collisionDistanceThreshold, timeHorizon, prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,step=10,minSimilarity=0.1,mostMatched=None, computeCZ = False, debug = False, timeInterval = None,acceptPartialLength=30,useDestination=True,useSpeedPrototype=True): | |
| 174 '''Computes all crossing and collision points at each common instant for two road users. ''' | |
| 175 self.collisionPoints={} | |
| 176 self.crossingZones={} | |
| 177 TTCs = {} | |
| 178 route1=(self.roadUser1.startRouteID,self.roadUser1.endRouteID) | |
| 179 route2=(self.roadUser2.startRouteID,self.roadUser2.endRouteID) | |
| 180 if useDestination: | |
| 181 if route1 not in prototypes.keys(): | |
| 182 route1= trajLearning.findRoute(prototypes,objects,route1,self.roadUser1.num,noiseEntryNums,noiseExitNums) | |
| 183 if route2 not in prototypes.keys(): | |
| 184 route2= trajLearning.findRoute(prototypes,objects,route2,self.roadUser2.num,noiseEntryNums,noiseExitNums) | |
| 185 | |
| 186 if timeInterval: | |
| 187 commonTimeInterval = timeInterval | |
| 188 else: | |
| 189 commonTimeInterval = self.timeInterval | |
| 190 reCompute=False | |
| 191 for i in xrange(commonTimeInterval.first,commonTimeInterval.last,step): # incremental calculation of CP,CZ to save time | |
| 192 if i-self.roadUser1.timeInterval.first >= acceptPartialLength and i-self.roadUser2.timeInterval.first >= acceptPartialLength: | |
| 193 self.collisionPoints[i], self.crossingZones[i] = self.computeCrosssingCollisionsPrototypeAtInstant(i,route1,route2,predictionParameters, collisionDistanceThreshold, timeHorizon, prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched, computeCZ, debug,useDestination,useSpeedPrototype) | |
| 194 if len(self.collisionPoints[i]) >0 or len(self.crossingZones[i])>0: | |
| 195 reCompute=True | |
| 196 break | |
| 197 if reCompute: | |
| 198 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors | |
| 199 if i-self.roadUser1.timeInterval.first >= acceptPartialLength and i-self.roadUser2.timeInterval.first >= acceptPartialLength: | |
| 200 self.collisionPoints[i], self.crossingZones[i] = self.computeCrosssingCollisionsPrototypeAtInstant(i,route1,route2,predictionParameters, collisionDistanceThreshold, timeHorizon, prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched, computeCZ, debug,useDestination,useSpeedPrototype) | |
| 201 if len(self.collisionPoints[i]) > 0: | |
| 202 TTCs[i] = prediction.SafetyPoint.computeExpectedIndicator(self.collisionPoints[i]) | |
| 203 # add probability of collision, and probability of successful evasive action | |
| 204 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[7], TTCs)) | |
| 205 | |
| 206 if computeCZ: | |
| 207 pPETs = {} | |
| 208 for i in list(commonTimeInterval)[:-1]: | |
| 209 if i in self.crossingZones.keys() and len(self.crossingZones[i]) > 0: | |
| 210 pPETs[i] = prediction.SafetyPoint.computeExpectedIndicator(self.crossingZones[i]) | |
| 211 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[9], pPETs)) | |
| 212 | |
|
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213 def addVideoFilename(self,videoFilename): |
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214 self.videoFilename= videoFilename |
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215 |
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216 def addInteractionType(self,interactionType): |
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217 ''' interaction types: conflict or collision if they are known''' |
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218 self.interactionType= interactionType |
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219 |
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220 def createInteractions(objects, _others = None): |
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221 '''Create all interactions of two co-existing road users''' |
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222 if _others != None: |
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223 others = _others |
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224 |
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225 interactions = [] |
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226 num = 0 |
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227 for i in xrange(len(objects)): |
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228 if _others == None: |
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229 others = objects[:i] |
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230 for j in xrange(len(others)): |
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231 commonTimeInterval = objects[i].commonTimeInterval(others[j]) |
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232 if not commonTimeInterval.empty(): |
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233 interactions.append(Interaction(num, commonTimeInterval, objects[i].num, others[j].num, objects[i], others[j])) |
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234 num += 1 |
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235 return interactions |
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236 |
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237 def prototypeCluster(interactions, similarityMatrix, alignmentMatrix, indicatorName, minSimilarity): |
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238 '''Finds exemplar indicator time series for all interactions |
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239 Returns the prototype indices (in the interaction list) and the label of each indicator (interaction) |
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240 |
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241 if an indicator profile (time series) is different enough (<minSimilarity), |
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242 it will become a new prototype. |
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243 Non-prototype interactions will be assigned to an existing prototype''' |
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244 |
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245 # sort indicators based on length |
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246 indices = range(similarityMatrix.shape[0]) |
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247 def compare(i, j): |
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248 if len(interactions[i].getIndicator(indicatorName)) > len(interactions[j].getIndicator(indicatorName)): |
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249 return -1 |
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250 elif len(interactions[i].getIndicator(indicatorName)) == len(interactions[j].getIndicator(indicatorName)): |
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251 return 0 |
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252 else: |
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253 return 1 |
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254 indices.sort(compare) |
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255 # go through all indicators |
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256 prototypeIndices = [indices[0]] |
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257 for i in indices[1:]: |
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258 if similarityMatrix[i][prototypeIndices].max() < minSimilarity: |
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259 prototypeIndices.append(i) |
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260 |
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261 # assignment |
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262 labels = [-1]*similarityMatrix.shape[0] |
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263 indices = [i for i in range(similarityMatrix.shape[0]) if i not in prototypeIndices] |
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264 for i in prototypeIndices: |
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265 labels[i] = i |
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266 for i in indices: |
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267 prototypeIndex = similarityMatrix[i][prototypeIndices].argmax() |
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268 labels[i] = prototypeIndices[prototypeIndex] |
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269 |
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270 return prototypeIndices, labels |
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293
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271 |
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272 def prototypeMultivariateCluster(interactions, similarityMatrics, indicatorNames, minSimilarities, minClusterSize): |
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273 '''Finds exmaple indicator time series (several indicators) for all interactions |
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274 |
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275 if any interaction indicator time series is different enough (<minSimilarity), |
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276 it will become a new prototype. |
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277 Non-prototype interactions will be assigned to an existing prototype if all indicators are similar enough''' |
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278 pass |
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279 |
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280 # TODO: |
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281 #http://stackoverflow.com/questions/3288595/multiprocessing-using-pool-map-on-a-function-defined-in-a-class |
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282 #http://www.rueckstiess.net/research/snippets/show/ca1d7d90 |
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283 def calculateIndicatorPipe(pairs, predParam, timeHorizon=75,collisionDistanceThreshold=1.8): |
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320
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284 collisionPoints, crossingZones = prediction.computeCrossingsCollisions(pairs.roadUser1, pairs.roadUser2, predParam, collisionDistanceThreshold, timeHorizon) |
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285 #print pairs.num |
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286 # Ignore empty collision points |
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287 empty = 1 |
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288 for i in collisionPoints: |
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289 if(collisionPoints[i] != []): |
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290 empty = 0 |
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291 if(empty == 1): |
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292 pairs.hasCP = 0 |
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293 else: |
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294 pairs.hasCP = 1 |
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295 pairs.CP = collisionPoints |
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296 |
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297 # Ignore empty crossing zones |
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298 empty = 1 |
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299 for i in crossingZones: |
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300 if(crossingZones[i] != []): |
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301 empty = 0 |
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302 if(empty == 1): |
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303 pairs.hasCZ = 0 |
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304 else: |
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305 pairs.hasCZ = 1 |
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306 pairs.CZ = crossingZones |
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307 return pairs |
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308 |
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309 def calculateIndicatorPipe_star(a_b): |
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310 """Convert `f([1,2])` to `f(1,2)` call.""" |
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311 return calculateIndicatorPipe(*a_b) |
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312 |
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313 class VehPairs(): |
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314 '''Create a veh-pairs object from objects list''' |
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315 def __init__(self,objects): |
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316 self.pairs = createInteractions(objects) |
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317 self.interactionCount = 0 |
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318 self.CPcount = 0 |
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319 self.CZcount = 0 |
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320 |
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321 # Process indicator calculation with support for multi-threading |
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322 def calculateIndicators(self,predParam,threads=1,timeHorizon=75,collisionDistanceThreshold=1.8): |
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323 if(threads > 1): |
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324 pool = multiprocessing.Pool(threads) |
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325 self.pairs = pool.map(calculateIndicatorPipe_star, itertools.izip(self.pairs, itertools.repeat(predParam))) |
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326 pool.close() |
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327 else: |
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328 #prog = Tools.ProgressBar(0, len(self.pairs), 77) #Removed in traffic-intelligenc port |
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329 for j in xrange(len(self.pairs)): |
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330 #prog.updateAmount(j) #Removed in traffic-intelligenc port |
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320
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331 collisionPoints, crossingZones = prediction.computeCrossingsCollisions(self.pairs[j].roadUser1, self.pairs[j].roadUser2, predParam, collisionDistanceThreshold, timeHorizon) |
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332 |
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333 # Ignore empty collision points |
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334 empty = 1 |
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335 for i in collisionPoints: |
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336 if(collisionPoints[i] != []): |
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337 empty = 0 |
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338 if(empty == 1): |
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339 self.pairs[j].hasCP = 0 |
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340 else: |
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341 self.pairs[j].hasCP = 1 |
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342 self.pairs[j].CP = collisionPoints |
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343 |
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344 # Ignore empty crossing zones |
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345 empty = 1 |
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346 for i in crossingZones: |
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347 if(crossingZones[i] != []): |
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348 empty = 0 |
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349 if(empty == 1): |
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350 self.pairs[j].hasCZ = 0 |
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351 else: |
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352 self.pairs[j].hasCZ = 1 |
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353 self.pairs[j].CZ = crossingZones |
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354 |
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355 for j in self.pairs: |
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356 self.interactionCount = self.interactionCount + len(j.CP) |
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357 self.CPcount = len(self.getCPlist()) |
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358 self.Czcount = len(self.getCZlist()) |
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359 |
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360 |
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361 def getPairsWCP(self): |
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362 lists = [] |
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363 for j in self.pairs: |
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364 if(j.hasCP): |
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365 lists.append(j.num) |
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366 return lists |
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367 |
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368 def getPairsWCZ(self): |
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369 lists = [] |
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370 for j in self.pairs: |
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371 if(j.hasCZ): |
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372 lists.append(j.num) |
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373 return lists |
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374 |
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375 def getCPlist(self,indicatorThreshold=float('Inf')): |
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376 lists = [] |
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377 for j in self.pairs: |
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378 if(j.hasCP): |
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379 for k in j.CP: |
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380 if(j.CP[k] != [] and j.CP[k][0].indicator < indicatorThreshold): |
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381 lists.append([k,j.CP[k][0]]) |
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382 return lists |
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383 |
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384 def getCZlist(self,indicatorThreshold=float('Inf')): |
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385 lists = [] |
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386 for j in self.pairs: |
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387 if(j.hasCZ): |
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388 for k in j.CZ: |
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389 if(j.CZ[k] != [] and j.CZ[k][0].indicator < indicatorThreshold): |
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390 lists.append([k,j.CZ[k][0]]) |
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391 return lists |
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392 |
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393 def genIndicatorHistogram(self, CPlist=False, bins=range(0,100,1)): |
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394 if(not CPlist): |
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395 CPlist = self.getCPlist() |
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396 if(not CPlist): |
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397 return False |
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398 TTC_list = [] |
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399 for i in CPlist: |
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400 TTC_list.append(i[1].indicator) |
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401 histo = np.histogram(TTC_list,bins=bins) |
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402 histo += (histo[0].astype(float)/np.sum(histo[0]),) |
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403 return histo |
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404 |
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405 class Crossing(moving.STObject): |
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406 '''Class for the event of a street crossing |
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407 |
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408 TODO: detecter passage sur la chaussee |
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409 identifier origines et destination (ou uniquement chaussee dans FOV) |
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410 carac traversee |
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411 detecter proximite veh (retirer si trop similaire simultanement |
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412 carac interaction''' |
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413 |
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414 def __init__(self, roaduserNum = None, num = None, timeInterval = None): |
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415 moving.STObject.__init__(self, num, timeInterval) |
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416 self.roaduserNum = roaduserNum |
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417 |
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418 |
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419 |
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420 if __name__ == "__main__": |
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421 import doctest |
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422 import unittest |
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423 suite = doctest.DocFileSuite('tests/events.txt') |
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424 #suite = doctest.DocTestSuite() |
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425 unittest.TextTestRunner().run(suite) |
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426 |