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annotate scripts/process.py @ 988:dc0be55e2bf5

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new process functionalities
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 08 Mar 2018 16:44:20 -0500
parents f026ce2af637
children 933670761a57
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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 from numpy import percentile
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10
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11 import storage, events, prediction
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12 from metadata import *
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13
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14 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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15 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True)
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16 parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int)
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17 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects/interactions to process', type = int)
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18 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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19 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
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20 parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction'])
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21 parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'interaction'])
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22 parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction'])
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23
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24 # need way of selecting sites as similar as possible to sql alchemy syntax
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25 # override tracking.cfg from db
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26 # manage cfg files, overwrite them (or a subset of parameters)
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27 # delete sqlite files
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28
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29 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
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30
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31 args = parser.parse_args()
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32 # files are relative to metadata location
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33
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34 session = connectDatabase(args.metadataFilename)
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35 parentDir = Path(args.metadataFilename).parent
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36
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37 if args.delete is not None:
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38 if args.delete in ['object', 'interaction']:
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39 #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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40 for videoId in args.videoIds:
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41 vs = session.query(VideoSequence).get(videoId)
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42 storage.deleteFromSqlite(str(parentDir/vs.getDatabaseFilename()), args.delete)
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43
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44 if args.process == 'interaction':
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45 # safety analysis TODO make function in safety analysis script
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46 if args.predictionMethod == 'cvd':
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47 predictionParameters = prediction.CVDirectPredictionParameters()
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48 if args.predictionMethod == 'cve':
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49 predictionParameters = prediction.CVExactPredictionParameters()
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50 for videoId in args.videoIds:
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51 vs = session.query(VideoSequence).get(videoId)
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52 print('Processing '+vs.getDatabaseFilename())
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53 objects = storage.loadTrajectoriesFromSqlite(str(parentDir/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
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54 interactions = events.createInteractions(objects)
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55 #if args.nProcesses == 1:
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56 #print(str(parentDir/vs.cameraView.getTrackingConfigurationFilename()))
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57 params = storage.ProcessParameters(str(parentDir/vs.cameraView.getTrackingConfigurationFilename()))
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58 #print(len(interactions), args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones)
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59 processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
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60 storage.saveIndicatorsToSqlite(str(parentDir/vs.getDatabaseFilename()), processed)
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61 # else:
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62 # pool = Pool(processes = args.nProcesses)
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63 # nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
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64 # 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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65 # processed = []
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66 # for job in jobs:
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67 # processed += job.get()
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68 # pool.close()
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69
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70 if args.analyze == 'object': # user speed for now
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71 medianSpeeds = {}
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72 speeds85 = {}
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73 minLength = 2*30
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74 for videoId in args.videoIds:
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75 vs = session.query(VideoSequence).get(videoId)
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76 if not vs.cameraView.siteIdx in medianSpeeds:
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77 medianSpeeds[vs.cameraView.siteIdx] = []
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78 speeds85[vs.cameraView.siteIdx] = []
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79 print('Extracting speed from '+vs.getDatabaseFilename())
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80 objects = storage.loadTrajectoriesFromSqlite(str(parentDir/vs.getDatabaseFilename()), 'object')
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81 for o in objects:
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82 if o.length() > minLength:
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83 speeds = 30*3.6*percentile(o.getSpeeds(), [50, 85])
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84 medianSpeeds[vs.cameraView.siteIdx].append(speeds[0])
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85 speeds85[vs.cameraView.siteIdx].append(speeds[1])
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86 for speeds, name in zip([medianSpeeds, speeds85], ['Median', '85th Centile']):
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87 plt.ioff()
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88 plt.figure()
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89 plt.boxplot(speeds.values(), labels = [session.query(Site).get(siteId).name for siteId in speeds])
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90 plt.ylabel(name+' Speeds (km/h)')
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91 plt.savefig(name.lower()+'-speeds.png', dpi=150)
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92 plt.close()
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93
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94 if args.analyze == 'interaction':
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95 indicatorIds = [2,5,7,10]
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96 conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30}
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97 maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.}
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98 indicators = {}
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99 interactions = {}
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100 for videoId in args.videoIds:
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101 vs = session.query(VideoSequence).get(videoId)
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102 if not vs.cameraView.siteIdx in interactions:
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103 interactions[vs.cameraView.siteIdx] = []
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104 indicators[vs.cameraView.siteIdx] = {}
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105 for i in indicatorIds:
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106 indicators[vs.cameraView.siteIdx][i] = []
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107 interactions[vs.cameraView.siteIdx] += storage.loadInteractionsFromSqlite(str(parentDir/vs.getDatabaseFilename()))
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108 print(vs.getDatabaseFilename(), len(interactions[vs.cameraView.siteIdx]))
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109 for inter in interactions[vs.cameraView.siteIdx]:
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110 for i in indicatorIds:
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111 indic = inter.getIndicator(events.Interaction.indicatorNames[i])
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112 if indic is not None:
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113 v = indic.getMostSevereValue()*conversionFactors[i]
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114 if v < maxIndicatorValue[i]:
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115 indicators[vs.cameraView.siteIdx][i].append(v)
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116
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117 for i in indicatorIds:
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118 tmp = [indicators[siteId][i] for siteId in indicators]
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119 plt.ioff()
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120 plt.figure()
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121 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators])
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dc0be55e2bf5 new process functionalities
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 987
diff changeset
122 plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')')
dc0be55e2bf5 new process functionalities
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 987
diff changeset
123 plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150)
986
3be8aaa47651 work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 985
diff changeset
124 plt.close()