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annotate scripts/safety-analysis.py @ 1240:bb14f919d1cb

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cleaned use of centile (np only) and added info in classify-objects
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 05 Feb 2024 14:14:14 -0500
parents 14140b55e580
children fe35473acee3
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rev   line source
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933670761a57 updated code to python 3 (tests pass and scripts run, but non-executed parts of code are probably still not correct)
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1 #! /usr/bin/env python3
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2
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3 import sys, argparse, random
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4 from multiprocessing import Pool
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5
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6 import matplotlib.pyplot as plt
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7 import numpy as np
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8
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9 from trafficintelligence import storage, prediction, events, moving
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10
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11 # todo: very slow if too many predicted trajectories
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12 # add computation of probality of unsucessful evasive action
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13
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14 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
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15 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
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16 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
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17 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to analyse', type = int)
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18 # TODO analyze only
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19 parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (cvd: vector computation (approximate); cve: equation solving; cv: discrete time (approximate)), normal adaptation, point set prediction)', choices = ['cvd', 'cve', 'cv', 'na', 'ps', 'mp'])
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20 parser.add_argument('-p', dest = 'prototypeDatabaseFilename', help = 'name of the database containing the prototypes')
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21 parser.add_argument('-c', dest = 'minPrototypeNMatchings', help = 'minimum number of matchings per prototype', type = int, default = 1)
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22 # parser.add_argument('--categorize', dest = 'categorize', help = 'computes interaction categories', action = 'store_true') TODO, add angle parameters in tracking.cfg - the safety analysis parameters should probably be spun off tracking.cfg
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23 parser.add_argument('--no-motion-prediction', dest = 'noMotionPrediction', help = 'does not compute indicators like TTC depending on motion prediction', action = 'store_true')
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24 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
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25 parser.add_argument('--display-cp', dest = 'displayCollisionPoints', help = 'display collision points', action = 'store_true')
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26 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
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27 args = parser.parse_args()
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28
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29 params = storage.ProcessParameters(args.configFilename)
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31 # selected database to overide the configuration file
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32 if args.databaseFilename is not None:
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33 params.databaseFilename = args.databaseFilename
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34
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35 # parameters for prediction methods
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36 if args.predictionMethod is not None:
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37 predictionMethod = args.predictionMethod
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38 else:
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39 predictionMethod = params.predictionMethod
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41 def accelerationDistribution():
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42 return random.triangular(-params.maxNormalAcceleration, params.maxNormalAcceleration, 0.)
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43 def steeringDistribution():
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44 return random.triangular(-params.maxNormalSteering, params.maxNormalSteering, 0.)
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45
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46 if predictionMethod == 'cvd':
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47 predictionParameters = prediction.CVDirectPredictionParameters()
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48 if predictionMethod == 'cve':
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49 predictionParameters = prediction.CVExactPredictionParameters()
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50 elif predictionMethod == 'cv':
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51 predictionParameters = prediction.ConstantPredictionParameters(params.maxPredictedSpeed)
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52 elif predictionMethod == 'na':
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53 predictionParameters = prediction.NormalAdaptationPredictionParameters(params.maxPredictedSpeed,
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54 params.nPredictedTrajectories,
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55 accelerationDistribution,
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56 steeringDistribution,
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57 params.useFeaturesForPrediction)
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58 elif predictionMethod == 'ps':
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59 predictionParameters = prediction.PointSetPredictionParameters(params.maxPredictedSpeed)
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60 elif predictionMethod == 'mp':
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61 if args.prototypeDatabaseFilename is None:
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62 prototypes = storage.loadPrototypesFromSqlite(params.databaseFilename)
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63 else:
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64 prototypes = storage.loadPrototypesFromSqlite(args.prototypeDatabaseFilename)
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65 if args.minPrototypeNMatchings > 0:
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66 prototypes = [p for p in prototypes if p.getNMatchings() >= args.minPrototypeNMatchings]
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67 else:
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68 nProto0Matching = 0
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69 for p in prototypes:
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70 if p.getNMatchings() == 0:
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71 nProto0Matching += 1
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72 print("Prototype {} has 0 matchings".format(p))
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73 if len(prototypes) == 0 or nProto0Matching > 0:
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74 print('Database has {} prototypes without any matching. Exiting'.format(nProto0Matching))
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75 sys.exit()
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76 for p in prototypes:
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77 p.getMovingObject().computeCumulativeDistances()
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78 predictionParameters = prediction.PrototypePredictionParameters(prototypes, params.nPredictedTrajectories, params.maxLcssDistance, params.minLcssSimilarity, params.lcssMetric, params.minFeatureTime, params.constantSpeedPrototypePrediction, params.useFeaturesForPrediction)
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79 # else:
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80 # no else required, since parameters is required as argument
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81
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82 # evasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(params.maxPredictedSpeed,
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83 # params.nPredictedTrajectories,
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84 # params.minExtremeAcceleration,
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85 # params.maxExtremeAcceleration,
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86 # params.maxExtremeSteering,
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87 # params.useFeaturesForPrediction)
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88
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89 objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'object', args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
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90
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91 interactions = events.createInteractions(objects)
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92 if args.nProcesses == 1:
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93 processed = events.computeIndicators(interactions, not args.noMotionPrediction, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
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94 else:
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95 pool = Pool(processes = args.nProcesses)
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96 nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
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97 jobs = [pool.apply_async(events.computeIndicators, args = (interactions[i*nInteractionPerProcess:(i+1)*nInteractionPerProcess], not args.noMotionPrediction, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)) for i in range(args.nProcesses)]
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98 processed = []
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99 for job in jobs:
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100 processed += job.get()
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101 pool.close()
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102 storage.saveIndicatorsToSqlite(params.databaseFilename, processed)
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103
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104 if args.displayCollisionPoints:
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105 plt.figure()
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106 allCollisionPoints = []
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107 for inter in processed:
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108 for collisionPoints in inter.collisionPoints.values():
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109 allCollisionPoints += collisionPoints
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110 moving.Point.plotAll(allCollisionPoints)
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111 plt.axis('equal')
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112