def calculateIndicatorPipe(pairs, predParam, timeHorizon=75,collisionDistanceThreshold=1.8):
collisionPoints, crossingZones = prediction.computeCrossingsCollisions(pairs.roadUser1, pairs.roadUser2, predParam, collisionDistanceThreshold, timeHorizon)
#print pairs.num
# Ignore empty collision points
empty = 1
for i in collisionPoints:
if(collisionPoints[i] != []):
empty = 0
if(empty == 1):
pairs.hasCP = 0
else:
pairs.hasCP = 1
pairs.CP = collisionPoints
# Ignore empty crossing zones
empty = 1
for i in crossingZones:
if(crossingZones[i] != []):
empty = 0
if(empty == 1):
pairs.hasCZ = 0
else:
pairs.hasCZ = 1
pairs.CZ = crossingZones
return pairs
def calculateIndicators(self,predParam,threads=1,timeHorizon=75,collisionDistanceThreshold=1.8):
if(threads > 1):
pool = multiprocessing.Pool(threads)
self.pairs = pool.map(calculateIndicatorPipe_star, itertools.izip(self.pairs, itertools.repeat(predParam)))
pool.close()
else:
#prog = Tools.ProgressBar(0, len(self.pairs), 77) #Removed in traffic-intelligenc port
for j in xrange(len(self.pairs)):
#prog.updateAmount(j) #Removed in traffic-intelligenc port
collisionPoints, crossingZones = prediction.computeCrossingsCollisions(self.pairs[j].roadUser1, self.pairs[j].roadUser2, predParam, collisionDistanceThreshold, timeHorizon)
# Ignore empty collision points
empty = 1
for i in collisionPoints:
if(collisionPoints[i] != []):
empty = 0
if(empty == 1):
self.pairs[j].hasCP = 0
else:
self.pairs[j].hasCP = 1
self.pairs[j].CP = collisionPoints
# Ignore empty crossing zones
empty = 1
for i in crossingZones:
if(crossingZones[i] != []):
empty = 0
if(empty == 1):
self.pairs[j].hasCZ = 0
else:
self.pairs[j].hasCZ = 1
self.pairs[j].CZ = crossingZones
for j in self.pairs:
self.interactionCount = self.interactionCount + len(j.CP)
self.CPcount = len(self.getCPlist())
self.Czcount = len(self.getCZlist())
print(videoFilename)
utils.mkdir(utils.cleanFilename(videoFilename))
# collision points, crossing zones, TTC and PET computations
objects = ubc_utils.loadTrajectories(dirname+ videoFilename+'-objects.txt')
features = ubc_utils.loadTrajectories(dirname+ videoFilename+'-features.txt') # needed only if using the feature positions
time = strftime('%Y-%m-%d-%H-%M-%S')
for roadUserNumbers in interactingRoadUsers[videoFilename]:
objects[roadUserNumbers[0]].setFeatures(features)
objects[roadUserNumbers[1]].setFeatures(features)
# choose the motion prediction methods to test: the list is [constantVelocityPredictionParameters, normalAdaptationPredictionParameters, featurePredictionParameters]
for params in [constantVelocityPredictionParameters]:
print(params.name)
outCP = openResultFile(videoFilename, params, time, '-collision-points.csv')
outCZ = openResultFile(videoFilename, params, time, '-crossing-zones.csv')
for roadUsers in interactingRoadUsers[videoFilename]:
collisionPoints, crossingZones = prediction.computeCrossingsCollisions(objects[roadUserNumbers[0]], objects[roadUserNumbers[1]], params, collisionDistanceThreshold, timeHorizon, outCP, outCZ)#, True, moving.TimeInterval(60,63)
outCP.close()
outCZ.close()
# compute probability of unsuccessful evasive action
# choose the motion prediction methods to test: the list is [evasiveActionPredictionParameters, featureEvasiveActionPredictionParameters]
for params in []:
outProba = openResultFile(videoFilename, params, time, '-probability-collision.csv')
for roadUsers in interactingRoadUsers[videoFilename]:
collisionProbabilities = prediction.computeCollisionProbability(objects[roadUsers[0]], objects[roadUsers[1]], params, collisionDistanceThreshold, timeHorizon, outProba) # True, moving.TimeInterval(90,93) # for last example
outProba.close()
