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annotate scripts/process.py @ 1021:16932cefabc1

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work on paths in line with new configurations from tracker
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 05 Jun 2018 17:02:28 -0400
parents 0d29b75f74ea
children a13f47c8931d
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1 #! /usr/bin/env python3
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2
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3 import sys, argparse
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4 from pathlib import Path
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5 from multiprocessing.pool import Pool
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6
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7 import matplotlib
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8 matplotlib.use('Agg')
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9 import matplotlib.pyplot as plt
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10 from numpy import percentile
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11
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12 import storage, events, prediction, cvutils
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13 from metadata import *
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14
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15 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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16 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True)
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17 parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int)
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18 parser.add_argument('--sites', dest = 'siteIds', help = 'indices of the video sequences', nargs = '*', type = int)
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19 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
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20 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects/interactions to process', type = int)
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21 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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22 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
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23 # override other tracking config, erase sqlite?
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24 parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction'])
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25 parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'interaction'])
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26 parser.add_argument('--display', dest = 'display', help = 'data to display (replay over video)', choices = ['feature', 'object', 'classification', 'interaction'])
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27 parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction'])
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28 parser.add_argument('--dry', dest = 'dryRun', help = 'dry run of processing', action = 'store_true')
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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 # need way of selecting sites as similar as possible to sql alchemy syntax
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32 # override tracking.cfg from db
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33 # manage cfg files, overwrite them (or a subset of parameters)
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34 # delete sqlite files
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35 # info of metadata
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36
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37 args = parser.parse_args()
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38
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39 #################################
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40 # Data preparation
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41 #################################
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42 session = connectDatabase(args.metadataFilename)
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43 parentPath = Path(args.metadataFilename).parent # files are relative to metadata location
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44 videoSequences = []
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45 if args.videoIds is not None:
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46 videoSequences = [session.query(VideoSequence).get(videoId) for videoId in args.videoIds]
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47 elif args.siteIds is not None:
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48 for siteId in args.siteIds:
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49 for site in getSite(session, siteId):
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50 for cv in site.cameraViews:
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51 videoSequences += cv.videoSequences
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52 else:
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53 print('No video/site to process')
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54
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55 #################################
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56 # Delete
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57 #################################
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58 if args.delete is not None:
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59 if args.delete == 'feature':
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60 pass
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61 elif args.delete in ['object', 'interaction']:
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62 #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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63 for vs in videoSequences:
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64 storage.deleteFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.delete)
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65
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66 #################################
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67 # Process
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68 #################################
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69 if args.process in ['feature', 'object']: # tracking
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70 if args.nProcesses == 1:
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71 for vs in videoSequences:
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72 if not (parentPath/vs.getDatabaseFilename()).exists() or args.process == 'object':
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73 if args.configFilename is None:
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74 configFilename = str(parentPath/vs.cameraView.getTrackingConfigurationFilename())
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75 else:
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76 configFilename = args.configFilename
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77 if vs.cameraView.cameraType is None:
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78 cvutils.tracking(configFilename, args.process == 'object', str(parentPath.absolute()/vs.getVideoSequenceFilename()), str(parentPath.absolute()/vs.getDatabaseFilename()), str(parentPath.absolute()/vs.cameraView.getHomographyFilename()), str(parentPath.absolute()/vs.cameraView.getMaskFilename()), False, None, None, args.dryRun)
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79 else:
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80 cvutils.tracking(configFilename, args.process == 'object', str(parentPath.absolute()/vs.getVideoSequenceFilename()), str(parentPath.absolute()/vs.getDatabaseFilename()), str(parentPath.absolute()/vs.cameraView.getHomographyFilename()), str(parentPath.absolute()/vs.cameraView.getMaskFilename()), True, vs.cameraView.cameraType.intrinsicCameraMatrix, vs.cameraView.cameraType.distortionCoefficients, args.dryRun)
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81 else:
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82 print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename()))
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83 else:
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84 pool = Pool(args.nProcesses)
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85 for vs in videoSequences:
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86 if not (parentPath/vs.getDatabaseFilename()).exists() or args.process == 'object':
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87 if args.configFilename is None:
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88 configFilename = str(parentPath/vs.cameraView.getTrackingConfigurationFilename())
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89 else:
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90 configFilename = args.configFilename
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91 if vs.cameraView.cameraType is None:
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92 pool.apply_async(cvutils.tracking, args = (configFilename, args.process == 'object', str(parentPath.absolute()/vs.getVideoSequenceFilename()), str(parentPath.absolute()/vs.getDatabaseFilename()), str(parentPath.absolute()/vs.cameraView.getHomographyFilename()), str(parentPath.absolute()/vs.cameraView.getMaskFilename()), False, None, None, args.dryRun))
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93 else:
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94 pool.apply_async(cvutils.tracking, args = (configFilename, args.process == 'object', str(parentPath.absolute()/vs.getVideoSequenceFilename()), str(parentPath.absolute()/vs.getDatabaseFilename()), str(parentPath.absolute()/vs.cameraView.getHomographyFilename()), str(parentPath.absolute()/vs.cameraView.getMaskFilename()), True, vs.cameraView.cameraType.intrinsicCameraMatrix, vs.cameraView.cameraType.distortionCoefficients, args.dryRun))
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95 else:
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96 print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename()))
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97 pool.close()
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98 pool.join()
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99
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100 elif args.process == 'interaction':
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101 # safety analysis TODO make function in safety analysis script
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102 if args.predictionMethod == 'cvd':
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103 predictionParameters = prediction.CVDirectPredictionParameters()
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104 if args.predictionMethod == 'cve':
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105 predictionParameters = prediction.CVExactPredictionParameters()
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106 for vs in videoSequences:
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107 print('Processing '+vs.getDatabaseFilename())
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108 objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
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109 interactions = events.createInteractions(objects)
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110 #if args.nProcesses == 1:
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111 #print(str(parentPath/vs.cameraView.getTrackingConfigurationFilename()))
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112 params = storage.ProcessParameters(str(parentPath/vs.cameraView.getTrackingConfigurationFilename()))
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113 #print(len(interactions), args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones)
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114 processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
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115 storage.saveIndicatorsToSqlite(str(parentPath/vs.getDatabaseFilename()), processed)
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116 # else:
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117 # pool = Pool(processes = args.nProcesses)
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118 # nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
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119 # 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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120 # processed = []
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121 # for job in jobs:
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122 # processed += job.get()
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123 # pool.close()
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124
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125 #################################
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126 # Analyze
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127 #################################
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128 if args.analyze == 'object': # user speed for now
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129 medianSpeeds = {}
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130 speeds85 = {}
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131 minLength = 2*30
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132 for vs in videoSequences:
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133 if not vs.cameraView.siteIdx in medianSpeeds:
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134 medianSpeeds[vs.cameraView.siteIdx] = []
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135 speeds85[vs.cameraView.siteIdx] = []
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136 print('Extracting speed from '+vs.getDatabaseFilename())
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137 objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object')
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138 for o in objects:
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139 if o.length() > minLength:
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140 speeds = 30*3.6*percentile(o.getSpeeds(), [50, 85])
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141 medianSpeeds[vs.cameraView.siteIdx].append(speeds[0])
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142 speeds85[vs.cameraView.siteIdx].append(speeds[1])
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143 for speeds, name in zip([medianSpeeds, speeds85], ['Median', '85th Centile']):
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144 plt.ioff()
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145 plt.figure()
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146 plt.boxplot(list(speeds.values()), labels = [session.query(Site).get(siteId).name for siteId in speeds])
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147 plt.ylabel(name+' Speeds (km/h)')
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148 plt.savefig(name.lower()+'-speeds.png', dpi=150)
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149 plt.close()
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150
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151 if args.analyze == 'interaction':
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152 indicatorIds = [2,5,7,10]
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153 conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30}
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154 maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.}
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155 indicators = {}
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156 interactions = {}
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157 for vs in videoSequences:
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158 if not vs.cameraView.siteIdx in interactions:
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159 interactions[vs.cameraView.siteIdx] = []
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160 indicators[vs.cameraView.siteIdx] = {}
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161 for i in indicatorIds:
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162 indicators[vs.cameraView.siteIdx][i] = []
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163 interactions[vs.cameraView.siteIdx] += storage.loadInteractionsFromSqlite(str(parentPath/vs.getDatabaseFilename()))
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164 print(vs.getDatabaseFilename(), len(interactions[vs.cameraView.siteIdx]))
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165 for inter in interactions[vs.cameraView.siteIdx]:
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166 for i in indicatorIds:
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167 indic = inter.getIndicator(events.Interaction.indicatorNames[i])
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168 if indic is not None:
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169 v = indic.getMostSevereValue()*conversionFactors[i]
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170 if v < maxIndicatorValue[i]:
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171 indicators[vs.cameraView.siteIdx][i].append(v)
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172
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173 for i in indicatorIds:
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174 tmp = [indicators[siteId][i] for siteId in indicators]
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175 plt.ioff()
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176 plt.figure()
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177 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators])
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178 plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')')
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179 plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150)
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180 plt.close()