Mercurial Hosting > traffic-intelligence
view trafficintelligence/events.py @ 1275:9f1711a85c56
added code to make sure TTC is replaced of recomputed and not having a value
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 19 Jun 2024 16:11:35 -0400 |
parents | 655a1646f0d5 |
children |
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#! /usr/bin/env python '''Libraries for events Interactions, pedestrian crossing...''' from trafficintelligence import moving, prediction, indicators, utils, cvutils, ml from trafficintelligence.base import VideoFilenameAddable import numpy as np from matplotlib.pyplot import subplot, figure, ylabel import multiprocessing import itertools, logging def findRoute(prototypes,objects,i,j,noiseEntryNums,noiseExitNums,minSimilarity= 0.3, spatialThreshold=1.0, delta=180): if i[0] not in noiseEntryNums: prototypesRoutes= [ x for x in sorted(prototypes.keys()) if i[0]==x[0]] elif i[1] not in noiseExitNums: prototypesRoutes=[ x for x in sorted(prototypes.keys()) if i[1]==x[1]] else: prototypesRoutes=[x for x in sorted(prototypes.keys())] routeSim={} lcss = utils.LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta) for y in prototypesRoutes: if y in prototypes: prototypesIDs=prototypes[y] similarity=[] for x in prototypesIDs: s=lcss.computeNormalized(objects[j].positions, objects[x].positions) similarity.append(s) routeSim[y]=max(similarity) route=max(routeSim, key=routeSim.get) if routeSim[route]>=minSimilarity: return route else: return i def getRoute(obj,prototypes,objects,noiseEntryNums,noiseExitNums,useDestination=True): route=(obj.startRouteID,obj.endRouteID) if useDestination: if route not in prototypes: route= findRoute(prototypes,objects,route,obj.getNum(),noiseEntryNums,noiseExitNums) return route class Interaction(moving.STObject, VideoFilenameAddable): '''Class for an interaction between two road users or a road user and an obstacle link to the moving objects contains the indicators in a dictionary with the names as keys ''' categories = {'headon': 0, 'rearend': 1, 'side': 2, 'parallel': 3, 'stationary': 4} indicatorNames = ['Collision Course Dot Product', 'Collision Course Angle', 'Distance', 'Minimum Distance', 'Velocity Angle', 'Speed Differential', 'Collision Probability', 'Time to Collision', # 7 'Probability of Successful Evasive Action', 'predicted Post Encroachment Time', 'Post Encroachment Time'] indicatorNameToIndices = utils.inverseEnumeration(indicatorNames) indicatorShortNames = ['CCDP', 'CCA', 'Dist', 'MinDist', 'VA', 'SD', 'PoC', 'TTC', 'P(SEA)', 'pPET', 'PET'] indicatorUnits = ['', 'rad', 'm', 'm', 'rad', 'km/h', '', 's', '', 's', 's'] mostSevereIsMinIndicators = ['Distance', 'Time to Collision', 'predicted Post Encroachment Time'] def __init__(self, num = None, timeInterval = None, roaduserNum1 = None, roaduserNum2 = None, roadUser1 = None, roadUser2 = None): moving.STObject.__init__(self, num, timeInterval) if timeInterval is None and roadUser1 is not None and roadUser2 is not None: self.timeInterval = roadUser1.commonTimeInterval(roadUser2) self.roadUser1 = roadUser1 self.roadUser2 = roadUser2 if roaduserNum1 is not None and roaduserNum2 is not None: self.roadUserNumbers = set([roaduserNum1, roaduserNum2]) elif roadUser1 is not None and roadUser2 is not None: self.roadUserNumbers = set([roadUser1.getNum(), roadUser2.getNum()]) else: self.roadUserNumbers = None self.indicators = {} # list for collison points and crossing zones self.collisionPoints = None self.crossingZones = None def getRoadUserNumbers(self): return self.roadUserNumbers def setRoadUsers(self, objects): tmpobjects = {o.getNum():o for o in objects} i, j = self.roadUserNumbers if i in tmpobjects: self.roadUser1 = tmpobjects[i] if j in tmpobjects: self.roadUser2 = tmpobjects[j] def getIndicator(self, indicatorName): return self.indicators.get(indicatorName, None) def resetIndicator(self, indicatorName): if indicatorName in self.indicators: del self.indicators[indicatorName] def addIndicator(self, indicator): if indicator is not None: self.indicators[indicator.name] = indicator def getIndicatorValueAtInstant(self, indicatorName, instant): indicator = self.getIndicator(indicatorName) if indicator is not None: return indicator[instant] else: return None def getIndicatorValuesAtInstant(self, instant): '''Returns list of indicator values at instant as dict (with keys from indicators dict)''' values = {} for k, indicator in self.indicators.items(): values[k] = indicator[instant] return values def plot(self, options = '', withOrigin = False, timeStep = 1, withFeatures = False, restricted = True, **kwargs): if restricted: self.roadUser1.getObjectInTimeInterval(self.timeInterval).plot(options, withOrigin, timeStep, withFeatures, **kwargs) self.roadUser2.getObjectInTimeInterval(self.timeInterval).plot(options, withOrigin, timeStep, withFeatures, **kwargs) else: self.roadUser1.plot(options, withOrigin, timeStep, withFeatures, **kwargs) self.roadUser2.plot(options, withOrigin, timeStep, withFeatures, **kwargs) def plotOnWorldImage(self, nPixelsPerUnitDistance, options = '', withOrigin = False, timeStep = 1, **kwargs): self.roadUser1.plotOnWorldImage(nPixelsPerUnitDistance, options, withOrigin, timeStep, **kwargs) self.roadUser2.plotOnWorldImage(nPixelsPerUnitDistance, options, withOrigin, timeStep, **kwargs) def plotIndicators(self, _indicatorNames = indicatorNames): nrows = int(np.ceil(len(_indicatorNames)/2)) ncols = 2 #subplot(nrows, 2) for i, indicatorName in enumerate(_indicatorNames): if i==0: ax = subplot(nrows, ncols, i+1) else: subplot(nrows, ncols, i+1, sharex = ax) ind = self.getIndicator(indicatorName) if ind is not None: ind.plot() ylabel(indicatorName) def play(self, videoFilename, homography = None, undistort = False, intrinsicCameraMatrix = None, distortionCoefficients = None, undistortedImageMultiplication = 1., allUserInstants = False): if self.roadUser1 is not None and self.roadUser2 is not None: if allUserInstants: firstFrameNum = min(self.roadUser1.getFirstInstant(), self.roadUser2.getFirstInstant()) lastFrameNum = max(self.roadUser1.getLastInstant(), self.roadUser2.getLastInstant()) else: firstFrameNum = self.getFirstInstant() lastFrameNum = self.getLastInstant() cvutils.displayTrajectories(videoFilename, [self.roadUser1, self.roadUser2], homography = homography, firstFrameNum = firstFrameNum, lastFrameNumArg = lastFrameNum, undistort = undistort, intrinsicCameraMatrix = intrinsicCameraMatrix, distortionCoefficients = distortionCoefficients, undistortedImageMultiplication = undistortedImageMultiplication) else: print('Please set the interaction road user attributes roadUser1 and roadUser1 through the method setRoadUsers') def computeIndicators(self): '''Computes all cinematic indicators but the expensive safety indicators (TTC, PET)''' collisionCourseDotProducts = {} collisionCourseAngles = {} velocityAngles = {} distances = {} speedDifferentials = {} for instant in self.timeInterval: deltap = self.roadUser1.getPositionAtInstant(instant)-self.roadUser2.getPositionAtInstant(instant) v1 = self.roadUser1.getVelocityAtInstant(instant) v2 = self.roadUser2.getVelocityAtInstant(instant) deltav = v2-v1 v1Norm = v1.norm2() v2Norm = v2.norm2() if v1Norm != 0. and v2Norm != 0.: velocityAngles[instant] = np.arccos(max(-1, min(1, moving.Point.dot(v1, v2)/(v1Norm*v2Norm)))) collisionCourseDotProducts[instant] = moving.Point.dot(deltap, deltav) distances[instant] = deltap.norm2() speedDifferentials[instant] = deltav.norm2() if distances[instant] != 0 and speedDifferentials[instant] != 0: collisionCourseAngles[instant] = np.arccos(max(-1, min(1, collisionCourseDotProducts[instant]/(distances[instant]*speedDifferentials[instant])))) # avoid values slightly higher than 1.0 self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[0], collisionCourseDotProducts)) self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[1], collisionCourseAngles)) self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[2], distances, mostSevereIsMax = False)) self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[4], velocityAngles)) self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[5], speedDifferentials)) # if we have features, compute other indicators if self.roadUser1.hasFeatures() and self.roadUser2.hasFeatures(): minDistances={} for instant in self.timeInterval: minDistances[instant] = moving.MovingObject.minDistance(self.roadUser1, self.roadUser2, instant) self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[3], minDistances, mostSevereIsMax = False)) def categorize(self, velocityAngleTolerance, parallelAngleTolerance, headonCollisionCourseAngleTolerance = None, speedThreshold = 0.): '''Computes the interaction category by instant all 3 angle arguments in radian velocityAngleTolerance: indicates the angle threshold for rear and head on (180-velocityAngleTolerance), as well as the maximum collision course angle for head on (if headonCollisionCourseAngleTolerance is None) parallelAngleTolerance: indicates the tolerance on the expected 90 deg angle between velocity vector (average for parallel) and position vector for a parallel interaction speedThreshold defines stationary users: a stationary interaction is between one moving and one stationary user, and their distance decreases an instant may not be categorized if it matches the side definition (angle) but the distance is growing (at least one user is probably past the point of trajectory crossing)''' minParallelAngleCosine = np.cos(np.pi/2+parallelAngleTolerance) maxParallelAngleCosine = np.cos(np.pi/2-parallelAngleTolerance) if headonCollisionCourseAngleTolerance is None: headonCollisionCourseAngleTolerance = velocityAngleTolerance speedThreshold2 = speedThreshold**2 self.categories = {} collisionCourseDotProducts = self.getIndicator(Interaction.indicatorNames[0]) collisionCourseAngles = self.getIndicator(Interaction.indicatorNames[1]) distances = self.getIndicator(Interaction.indicatorNames[2]) velocityAngles = self.getIndicator(Interaction.indicatorNames[4]) for instant in self.timeInterval: stationaryUser1 = self.roadUser1.getVelocityAtInstant(instant).norm2Squared() <= speedThreshold2 stationaryUser2 = self.roadUser2.getVelocityAtInstant(instant).norm2Squared() <= speedThreshold2 if stationaryUser1 != stationaryUser2 and collisionCourseDotProducts[instant] > 0: # only one is not moving and is getting closer self.categories[instant] = Interaction.categories["stationary"] # true stationary would be all the times (parked, difficult without semantic knowledge of the scene # alternatively, one could get the previous or next non zero velocity to identify user orientation elif velocityAngles.existsAtInstant(instant): if velocityAngles[instant] < velocityAngleTolerance: # parallel or rear end midVelocity = self.roadUser1.getVelocityAtInstant(instant) + self.roadUser2.getVelocityAtInstant(instant) deltap = self.roadUser1.getPositionAtInstant(instant)-self.roadUser2.getPositionAtInstant(instant) if minParallelAngleCosine < abs(moving.Point.dot(midVelocity, deltap)/(midVelocity.norm2()*distances[instant])) < maxParallelAngleCosine: self.categories[instant] = Interaction.categories["parallel"] else: self.categories[instant] = Interaction.categories["rearend"] elif velocityAngles[instant] > np.pi - velocityAngleTolerance and collisionCourseAngles[instant] < headonCollisionCourseAngleTolerance: # head on self.categories[instant] = Interaction.categories["headon"] elif collisionCourseDotProducts[instant] > 0: self.categories[instant] = Interaction.categories["side"] # true stationary is when object does not move for the whole period of the interaction, otherwise get last (or next) velocity vector for user orientation # leaving is not a good interaction category (issue in Etienne's 2022 paper): # means we are past the situation in which users are approaching # could try to predict what happened before, but it's not observed def computeCrossingsCollisions(self, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, speedThreshold = 0.): '''Computes all crossing and collision points at each common instant for two road users. speedThreshold defines when users are stationary: TTC is not computed when both users are stationary''' TTCs = {} collisionProbabilities = {} if timeInterval is not None: commonTimeInterval = timeInterval else: commonTimeInterval = self.timeInterval self.collisionPoints, crossingZones = predictionParameters.computeCrossingsCollisions(self.roadUser1, self.roadUser2, collisionDistanceThreshold, timeHorizon, computeCZ, debug, commonTimeInterval, speedThreshold) for i, cps in self.collisionPoints.items(): TTCs[i] = prediction.SafetyPoint.computeExpectedIndicator(cps) collisionProbabilities[i] = sum([p.probability for p in cps]) if len(TTCs) > 0: self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[7], TTCs, mostSevereIsMax=False)) self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[6], collisionProbabilities)) else: for i in [6,7]: self.resetIndicator(Interaction.indicatorNames[i]) # crossing zones and pPET if computeCZ: self.crossingZones = crossingZones pPETs = {} for i, cz in self.crossingZones.items(): pPETs[i] = prediction.SafetyPoint.computeExpectedIndicator(cz) self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[9], pPETs, mostSevereIsMax=False)) # TODO add probability of collision, and probability of successful evasive action def computePET(self, collisionDistanceThreshold, computePetWithBoundingPoly): 'Warning: when computing PET from interactions, there could be PETs between objects that do not coexist and therefore are not considered interactions' pet, t1, t2= moving.MovingObject.computePET(self.roadUser1, self.roadUser2, collisionDistanceThreshold, computePetWithBoundingPoly) if pet is not None: self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[10], {min(t1, t2): pet, max(t1, t2): pet}, mostSevereIsMax = False)) else: self.resetIndicator(Interaction.indicatorNames[10]) def setCollision(self, collision): '''indicates if it is a collision: argument should be boolean''' self.collision = collision def isCollision(self): if hasattr(self, 'collision'): return self.collision else: return None def getCollisionPoints(self): return self.collisionPoints def getCrossingZones(self): return self.crossingZones def createInteractions(objects, _others = None, maxDurationApart = 0): '''Create all interactions of two co-existing road users''' if _others is not None: others = _others interactions = [] num = 0 for i in range(len(objects)): if _others is None: others = objects[:i] for j in range(len(others)): commonTimeInterval = objects[i].commonTimeInterval(others[j]) if not commonTimeInterval.empty() or (maxDurationApart > 0 and objects[i].getTimeInterval().distance(objects[j].getTimeInterval()) < maxDurationApart): interactions.append(Interaction(num, commonTimeInterval, objects[i].num, others[j].num, objects[i], others[j])) num += 1 return interactions def findInteraction(interactions, roadUserNum1, roadUserNum2): 'Returns the right interaction in the set' i=0 while i<len(interactions) and set([roadUserNum1, roadUserNum2]) != interactions[i].getRoadUserNumbers(): i+=1 if i<len(interactions): return interactions[i] else: return None def computeIndicators(interactions, computeMotionPrediction, computePET, predictionParameters, collisionDistanceThreshold, computePetWithBoundingPoly, timeHorizon, computeCZ = False, debug = False, timeInterval = None): for inter in interactions: print('processing interaction {}'.format(inter.getNum())) # logging.debug('processing interaction {}'.format(inter.getNum())) inter.computeIndicators() if computeMotionPrediction: inter.computeCrossingsCollisions(predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ, debug, timeInterval) if computePET: inter.computePET(collisionDistanceThreshold, computePetWithBoundingPoly) return interactions def aggregateSafetyPoints(interactions, pointType = 'collision'): '''Put all collision points or crossing zones in a list for display''' allPoints = [] if pointType == 'collision': for i in interactions: for points in i.collisionPoints.values(): allPoints += points elif pointType == 'crossing': for i in interactions: for points in i.crossingZones.values(): allPoints += points else: print('unknown type of point: '+pointType) return allPoints def prototypeCluster(interactions, similarities, indicatorName, minSimilarity, similarityFunc = None, minClusterSize = None, randomInitialization = False): return ml.prototypeCluster([inter.getIndicator(indicatorName) for inter in interactions], similarities, minSimilarity, similarityFunc, minClusterSize, randomInitialization) class Crossing(moving.STObject): '''Class for the event of a street crossing TODO: detecter passage sur la chaussee identifier origines et destination (ou uniquement chaussee dans FOV) carac traversee detecter proximite veh (retirer si trop similaire simultanement carac interaction''' def __init__(self, roaduserNum = None, num = None, timeInterval = None): moving.STObject.__init__(self, num, timeInterval) self.roaduserNum = roaduserNum if __name__ == "__main__": import doctest import unittest suite = doctest.DocFileSuite('tests/events.txt') #suite = doctest.DocTestSuite() unittest.TextTestRunner().run(suite)