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annotate scripts/process.py @ 1059:a87b3072bd26

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working version
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 11 Jul 2018 01:48:42 -0400
parents 16575ca4537d
children 671426ce0f3e
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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 #atplotlib.use('Agg')
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9 import matplotlib.pyplot as plt
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10 import numpy as np
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11 import pandas as pd
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12
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13 from trafficintelligence import storage, events, prediction, cvutils, utils, moving, processing, ml
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14 from trafficintelligence.metadata import *
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16 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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17 # input
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18 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True)
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19 parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int)
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20 parser.add_argument('--sites', dest = 'siteIds', help = 'indices of the video sequences', nargs = '*')
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21
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22 # main function
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23 parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction'])
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24 parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'prototype', 'interaction'])
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25 parser.add_argument('--display', dest = 'display', help = 'data to display (replay over video)', choices = ['feature', 'object', 'classification', 'interaction'])
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26 parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction', 'event'])
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27
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28 # common options
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29 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
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30 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects/interactions to process', type = int)
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31 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories', choices = ['feature', 'object'], default = 'feature')
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32 parser.add_argument('--dry', dest = 'dryRun', help = 'dry run of processing', action = 'store_true')
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33 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
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34 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
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35
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36 ### process options
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37 # motion pattern learning and assignment
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38 parser.add_argument('--prototype-filename', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes', default = 'prototypes.sqlite')
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39 #parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
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40 parser.add_argument('--nobjects-mp', dest = 'nMPObjects', help = 'number of objects/interactions to process', type = int)
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41 parser.add_argument('--nfeatures-per-object', dest = 'nLongestFeaturesPerObject', help = 'maximum number of features per object to load', type = int)
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42 parser.add_argument('--epsilon', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float)
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43 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
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44 parser.add_argument('--minsimil', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float)
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45 parser.add_argument('--min-cluster-size', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0)
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46 #parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true')
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47 parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true')
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48 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
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49 #parser.add_argument('--similarities-filename', dest = 'similaritiesFilename', help = 'filename of the similarities')
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50 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
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51 parser.add_argument('--save-assignments', dest = 'saveAssignments', help = 'saves the assignments of the objects to the prototypes', action = 'store_true')
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52 parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true')
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53
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54 # safety analysis
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55 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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56 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
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57 # override other tracking config, erase sqlite?
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58
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59
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60 # analysis options
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61 parser.add_argument('--output', dest = 'output', help = 'kind of output to produce (interval means)', choices = ['figure', 'interval', 'event'])
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62 parser.add_argument('--min-user-duration', dest = 'minUserDuration', help = 'mininum duration we have to see the user to take into account in the analysis (s)', type = float, default = 0.1)
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63 parser.add_argument('--interval-duration', dest = 'intervalDuration', help = 'length of time interval to aggregate data (min)', type = int, default = 15)
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64 parser.add_argument('--aggregation', dest = 'aggMethods', help = 'aggregation method per user/interaction and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median'])
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65 parser.add_argument('--aggregation-centile', dest = 'aggCentiles', help = 'centile(s) to compute from the observations', nargs = '*', type = int)
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66 parser.add_argument('--event-filename', dest = 'eventFilename', help = 'filename of the event data')
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67 dpi = 150
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68 # unit of analysis: site - camera-view
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69
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70 # need way of selecting sites as similar as possible to sql alchemy syntax
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71 # override tracking.cfg from db
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72 # manage cfg files, overwrite them (or a subset of parameters)
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73 # delete sqlite files
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74 # info of metadata
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75
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76 args = parser.parse_args()
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77
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78 #################################
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79 # Data preparation
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80 #################################
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81 session = connectDatabase(args.metadataFilename)
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82 parentPath = Path(args.metadataFilename).parent # files are relative to metadata location
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83 videoSequences = []
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84 sites = []
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85 if args.videoIds is not None:
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86 videoSequences = [session.query(VideoSequence).get(videoId) for videoId in args.videoIds]
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87 siteIds = set([vs.cameraView.siteIdx for vs in videoSequences])
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88 elif args.siteIds is not None:
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89 siteIds = set(args.siteIds)
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90 for siteId in siteIds:
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91 tmpsites = getSite(session, siteId)
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92 sites.extend(tmpsites)
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93 for site in tmpsites:
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94 videoSequences.extend(getSiteVideoSequences(site))
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95 else:
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96 print('No video/site to process')
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97
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98 if args.nProcesses > 1:
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99 pool = Pool(args.nProcesses)
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100
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101 #################################
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102 # Delete
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103 #################################
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104 if args.delete is not None:
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105 if args.delete == 'feature':
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106 response = input('Are you sure you want to delete the tracking results (SQLite files) of all these sites (y/n)?')
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107 if response == 'y':
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108 for vs in videoSequences:
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109 p = parentPath.absolute()/vs.getDatabaseFilename()
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110 p.unlink()
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111 elif args.delete in ['object', 'interaction']:
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112 #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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113 for vs in videoSequences:
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114 storage.deleteFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.delete)
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parents: 983
diff changeset
115
1009
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116 #################################
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parents: 1007
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117 # Process
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118 #################################
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119 if args.process in ['feature', 'object']: # tracking
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parents: 1003
diff changeset
120 if args.nProcesses == 1:
1009
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parents: 1007
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121 for vs in videoSequences:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1009
diff changeset
122 if not (parentPath/vs.getDatabaseFilename()).exists() or args.process == 'object':
1009
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parents: 1007
diff changeset
123 if args.configFilename is None:
1021
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parents: 1009
diff changeset
124 configFilename = str(parentPath/vs.cameraView.getTrackingConfigurationFilename())
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diff changeset
125 else:
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126 configFilename = args.configFilename
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127 if vs.cameraView.cameraType is None:
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parents: 1009
diff changeset
128 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)
1009
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diff changeset
129 else:
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diff changeset
130 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)
1009
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parents: 1007
diff changeset
131 else:
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diff changeset
132 print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename()))
1004
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parents: 1003
diff changeset
133 else:
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parents: 1003
diff changeset
134 for vs in videoSequences:
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parents: 1009
diff changeset
135 if not (parentPath/vs.getDatabaseFilename()).exists() or args.process == 'object':
1004
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diff changeset
136 if args.configFilename is None:
1021
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parents: 1009
diff changeset
137 configFilename = str(parentPath/vs.cameraView.getTrackingConfigurationFilename())
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diff changeset
138 else:
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parents: 1003
diff changeset
139 configFilename = args.configFilename
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diff changeset
140 if vs.cameraView.cameraType is None:
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parents: 1009
diff changeset
141 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))
1004
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parents: 1003
diff changeset
142 else:
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diff changeset
143 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))
1004
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parents: 1003
diff changeset
144 else:
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diff changeset
145 print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename()))
1004
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146 pool.close()
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147 pool.join()
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parents: 1003
diff changeset
148
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5621e4ad2428 removing prefix option to loadtrajectories from SQLite
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1036
diff changeset
149 elif args.process == 'prototype': # motion pattern learning
1050
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parents: 1049
diff changeset
150 # learn by site by default -> group videos by camera view TODO
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parents: 1044
diff changeset
151 # by default, load all objects, learn and then assign (BUT not save the assignments)
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
152 for site in sites:
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parents: 1047
diff changeset
153 print('Learning motion patterns for site {} ({})'.format(site.idx, site.name))
1045
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
154 objects = {}
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
155 object2VideoSequences = {}
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
156 for cv in site.cameraViews:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
157 for vs in cv.videoSequences:
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parents: 1044
diff changeset
158 print('Loading '+vs.getDatabaseFilename())
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parents: 1045
diff changeset
159 objects[vs.idx] = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.trajectoryType, args.nObjects, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject)
1045
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
160 if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None:
25db2383e7ae work in progress on process.py
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
161 objectsWithFeatures = objects[vs.idx]
25db2383e7ae work in progress on process.py
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
162 objects[vs.idx] = [f for o in objectsWithFeatures for f in o.getFeatures()]
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parents: 1044
diff changeset
163 prototypeType = 'feature'
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
164 else:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
165 prototypeType = args.trajectoryType
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
166 for obj in objects[vs.idx]:
25db2383e7ae work in progress on process.py
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
167 object2VideoSequences[obj] = vs
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
168 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
25db2383e7ae work in progress on process.py
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parents: 1044
diff changeset
169 similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
1048
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parents: 1047
diff changeset
170 trainingObjects = [o for tmpobjects in objects.values() for o in tmpobjects]
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c9c03c97ed9f bug fix to store numpy integers in SQLite
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1048
diff changeset
171 if args.nMPObjects is not None and args.nMPObjects < len(trainingObjects):
1048
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parents: 1047
diff changeset
172 m = int(np.floor(float(len(trainingObjects))/args.nMPObjects))
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parents: 1047
diff changeset
173 trainingObjects = trainingObjects[::m]
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parents: 1047
diff changeset
174 similarities = -np.ones((len(trainingObjects), len(trainingObjects)))
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parents: 1047
diff changeset
175 prototypeIndices, labels = processing.learnAssignMotionPatterns(True, True, trainingObjects, similarities, args.minSimilarity, similarityFunc, args.minClusterSize, args.optimizeCentroid, args.randomInitialization, True, [])
1045
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
176 if args.outputPrototypeDatabaseFilename is None:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
177 outputPrototypeDatabaseFilename = args.databaseFilename
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
178 else:
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parents: 1044
diff changeset
179 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
180 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
1048
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parents: 1047
diff changeset
181 storage.savePrototypesToSqlite(str(parentPath/site.getPath()/outputPrototypeDatabaseFilename), [moving.Prototype(object2VideoSequences[trainingObjects[i]].getDatabaseFilename(False), trainingObjects[i].getNum(), prototypeType, clusterSizes[i]) for i in prototypeIndices])
1043
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1039
diff changeset
182
1039
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parents: 1036
diff changeset
183
1003
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parents: 998
diff changeset
184 elif args.process == 'interaction':
985
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parents: 984
diff changeset
185 # safety analysis TODO make function in safety analysis script
987
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 986
diff changeset
186 if args.predictionMethod == 'cvd':
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 986
diff changeset
187 predictionParameters = prediction.CVDirectPredictionParameters()
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 986
diff changeset
188 if args.predictionMethod == 'cve':
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 986
diff changeset
189 predictionParameters = prediction.CVExactPredictionParameters()
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diff changeset
190 for vs in videoSequences:
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parents: 984
diff changeset
191 print('Processing '+vs.getDatabaseFilename())
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parents: 1009
diff changeset
192 objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
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parents: 984
diff changeset
193 interactions = events.createInteractions(objects)
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parents: 984
diff changeset
194 #if args.nProcesses == 1:
1021
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parents: 1009
diff changeset
195 #print(str(parentPath/vs.cameraView.getTrackingConfigurationFilename()))
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1009
diff changeset
196 params = storage.ProcessParameters(str(parentPath/vs.cameraView.getTrackingConfigurationFilename()))
985
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parents: 984
diff changeset
197 #print(len(interactions), args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones)
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 984
diff changeset
198 processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
1021
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parents: 1009
diff changeset
199 storage.saveIndicatorsToSqlite(str(parentPath/vs.getDatabaseFilename()), processed)
985
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parents: 984
diff changeset
200 # else:
668a85c963c3 work on processing and managing large video datasets
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diff changeset
201 # pool = Pool(processes = args.nProcesses)
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 984
diff changeset
202 # nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
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parents: 984
diff changeset
203 # 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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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 984
diff changeset
204 # processed = []
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diff changeset
205 # for job in jobs:
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diff changeset
206 # processed += job.get()
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parents: 984
diff changeset
207 # pool.close()
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parents: 984
diff changeset
208
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diff changeset
209 #################################
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1007
diff changeset
210 # Analyze
0d29b75f74ea cleaning
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1007
diff changeset
211 #################################
1023
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
212 if args.analyze == 'object':
a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
213 # user speeds, accelerations
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
214 # aggregation per site
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
215 data = [] # list of observation per site-user with time
1059
a87b3072bd26 working version
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1058
diff changeset
216 headers = ['site', 'date', 'time', 'user_type']
1058
16575ca4537d work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1050
diff changeset
217 aggFunctions, tmpheaders = utils.aggregationMethods(arg.aggMethods, args.aggCentiles)
16575ca4537d work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1050
diff changeset
218 headers.extend(tmpheaders)
1009
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diff changeset
219 for vs in videoSequences:
1023
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
220 d = vs.startTime.date()
a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
221 t1 = vs.startTime.time()
a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
222 minUserDuration = args.minUserDuration*vs.cameraView.cameraType.frameRate
988
dc0be55e2bf5 new process functionalities
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 987
diff changeset
223 print('Extracting speed from '+vs.getDatabaseFilename())
1023
a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
224 objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object', args.nObjects)
988
dc0be55e2bf5 new process functionalities
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 987
diff changeset
225 for o in objects:
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a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
226 if o.length() > minUserDuration:
1050
9d4a06f49cb8 work in progress
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1049
diff changeset
227 row = [vs.cameraView.site.name, d, utils.framesToTime(o.getFirstInstant(), vs.cameraView.cameraType.frameRate, t1), o.getUserType()]
1023
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
228 tmp = o.getSpeeds()
a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
229 for method,func in aggFunctions.items():
a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
230 aggSpeeds = vs.cameraView.cameraType.frameRate*3.6*func(tmp)
a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
231 if method == 'centile':
a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
232 row += aggSpeeds.tolist()
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233 else:
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234 row.append(aggSpeeds)
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235 data.append(row)
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236 data = pd.DataFrame(data, columns = headers)
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237 if args.output == 'figure':
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238 for name in headers[4:]:
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239 plt.ioff()
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240 plt.figure()
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241 plt.boxplot([data.loc[data['sites']==siteId, name] for siteId in siteIds], labels = [session.query(Site).get(siteId).name for siteId in siteIds])
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242 plt.ylabel(name+' Speeds (km/h)')
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243 plt.savefig(name.lower()+'-speeds.png', dpi=dpi)
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244 plt.close()
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245 elif args.output == 'event':
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246 data.to_csv('speeds.csv', index = False)
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247
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248 if args.analyze == 'interaction': # redo as for object, export in dataframe all interaction data
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249 indicatorIds = [2,5,7,10]
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250 conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30}
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251 maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.}
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252 indicators = {}
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253 interactions = {}
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254 for vs in videoSequences:
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255 if not vs.cameraView.siteIdx in interactions:
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256 interactions[vs.cameraView.siteIdx] = []
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257 indicators[vs.cameraView.siteIdx] = {}
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258 for i in indicatorIds:
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259 indicators[vs.cameraView.siteIdx][i] = []
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260 interactions[vs.cameraView.siteIdx] += storage.loadInteractionsFromSqlite(str(parentPath/vs.getDatabaseFilename()))
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261 print(vs.getDatabaseFilename(), len(interactions[vs.cameraView.siteIdx]))
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262 for inter in interactions[vs.cameraView.siteIdx]:
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263 for i in indicatorIds:
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264 indic = inter.getIndicator(events.Interaction.indicatorNames[i])
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265 if indic is not None:
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266 v = indic.getMostSevereValue()*conversionFactors[i]
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267 if v < maxIndicatorValue[i]:
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268 indicators[vs.cameraView.siteIdx][i].append(v)
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269
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270 for i in indicatorIds:
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271 tmp = [indicators[siteId][i] for siteId in indicators]
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272 plt.ioff()
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273 plt.figure()
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274 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators])
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275 plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')')
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276 plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150)
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277 plt.close()
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278
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279 if args.analyze == 'event': # aggregate event data by 15 min interval (arg.intervalDuration), count events with thresholds
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280 data = pd.read_csv(args.eventFilename, parse_dates = [2])
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diff changeset
281 #data = pd.read_csv('./speeds.csv', converters = {'time': lambda s: datetime.datetime.strptime(s, "%H:%M:%S").time()}, nrows = 5000)
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282 # create time for end of each 15 min, then group by, using the agg method for each data column
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diff changeset
283 headers = ['sites', 'date', 'intervalend15', 'duration', 'count']
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284 aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles)
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285 dataColumns = list(data.columns[4:])
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286 for h in dataColumns:
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287 for h2 in tmpheaders:
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288 headers.append(h+'-'+h2)
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289 data['intervalend15'] = data.time.apply(lambda t: (pd.Timestamp(year = t.year, month = t.month, day = t.day,hour = t.hour, minute = (t.minute // args.intervalDuration)*args.intervalDuration)+pd.Timedelta(minutes = 15)).time())
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290 outputData = []
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diff changeset
291 for name, group in data.groupby(['site', 'date', 'intervalend15']):
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292 # get duration as intervalend15-min(time), apply agg methods to each centile
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293 row = []
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294 row.extend(name)
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295 row.append((name[2].minute-group.time.min().minute) % 60)
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296 row.append(len(group))
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297 for h in dataColumns:
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298 for method,func in aggFunctions.items():
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299 aggregated = func(group[h])
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300 if method == 'centile':
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301 row.extend(aggregated)
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302 else:
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diff changeset
303 row.append(aggregated)
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diff changeset
304 outputData.append(row)
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diff changeset
305 pd.DataFrame(outputData, columns = headers).to_csv('aggregated-speeds.csv', index = False)