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annotate scripts/process.py @ 987:f026ce2af637

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found bug with direct ttc computation
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 07 Mar 2018 23:37:00 -0500
parents 3be8aaa47651
children dc0be55e2bf5
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1 #! /usr/bin/env python
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2
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3 import sys, argparse
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4 from pathlib2 import Path
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5
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6 import matplotlib
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7 matplotlib.use('Agg')
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8 import matplotlib.pyplot as plt
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9
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10 import storage, events, prediction
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11 from metadata import *
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12
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13 parser = argparse.ArgumentParser(description='This program manages the processing of several files based on a description of the sites and video data in an SQLite database following the metadata module.')
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14 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True)
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15 parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int)
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16 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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17 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
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18 parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction'])
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19 parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'interaction'])
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20 parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction'])
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21
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22 # need way of selecting sites as similar as possible to sql alchemy syntax
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23 # override tracking.cfg from db
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24 # manage cfg files, overwrite them (or a subset of parameters)
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25 # delete sqlite files
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26
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27 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
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28
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29 args = parser.parse_args()
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30 # files are relative to metadata location
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31
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32 session = connectDatabase(args.metadataFilename)
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33 parentDir = Path(args.metadataFilename).parent
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34
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35 if args.delete is not None:
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36 if args.delete in ['object', 'interaction']:
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37 #parser.add_argument('-t', dest = 'dataType', help = 'type of the data to remove', required = True, choices = ['object','interaction', 'bb', 'pois', 'prototype'])
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38 for videoId in args.videoIds:
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39 vs = session.query(VideoSequence).get(videoId)
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40 storage.deleteFromSqlite(str(parentDir/vs.getDatabaseFilename()), args.delete)
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41
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42 if args.process == 'interaction':
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43 # safety analysis TODO make function in safety analysis script
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44 if args.predictionMethod == 'cvd':
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45 predictionParameters = prediction.CVDirectPredictionParameters()
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46 if args.predictionMethod == 'cve':
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47 predictionParameters = prediction.CVExactPredictionParameters()
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48 for videoId in args.videoIds:
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49 vs = session.query(VideoSequence).get(videoId)
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50 print('Processing '+vs.getDatabaseFilename())
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51 objects = storage.loadTrajectoriesFromSqlite(str(parentDir/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
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52 interactions = events.createInteractions(objects)
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53 #if args.nProcesses == 1:
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54 #print(str(parentDir/vs.cameraView.getTrackingConfigurationFilename()))
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55 params = storage.ProcessParameters(str(parentDir/vs.cameraView.getTrackingConfigurationFilename()))
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56 #print(len(interactions), args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones)
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57 processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
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58 storage.saveIndicatorsToSqlite(str(parentDir/vs.getDatabaseFilename()), processed)
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59 # else:
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60 # pool = Pool(processes = args.nProcesses)
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61 # nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
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62 # 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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63 # processed = []
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64 # for job in jobs:
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65 # processed += job.get()
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66 # pool.close()
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67
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68 if args.analyze == 'interaction':
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69 indicatorIds = [2,5,7,10]
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70 conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30}
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71 indicators = {}
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72 interactions = {}
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73 for videoId in args.videoIds:
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74 vs = session.query(VideoSequence).get(videoId)
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75 if not vs.cameraView.siteIdx in interactions:
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76 interactions[vs.cameraView.siteIdx] = []
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77 indicators[vs.cameraView.siteIdx] = {}
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78 for i in indicatorIds:
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79 indicators[vs.cameraView.siteIdx][i] = []
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80 interactions[vs.cameraView.siteIdx] += storage.loadInteractionsFromSqlite(str(parentDir/vs.getDatabaseFilename()))
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81 print(vs.getDatabaseFilename(), len(interactions[vs.cameraView.siteIdx]))
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82 for inter in interactions[vs.cameraView.siteIdx]:
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83 for i in indicatorIds:
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84 indic = inter.getIndicator(events.Interaction.indicatorNames[i])
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85 if indic is not None:
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86 indicators[vs.cameraView.siteIdx][i].append(indic.getMostSevereValue()*conversionFactors[i])
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87
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88 for i in indicatorIds:
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89 tmp = [indicators[siteId][i] for siteId in indicators]
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90 plt.ioff()
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91 plt.figure()
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92 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators])
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93 plt.title(events.Interaction.indicatorNames[i])
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94 plt.savefig(events.Interaction.indicatorNames[i]+'.png')
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95 plt.close()