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annotate scripts/process.py @ 1050:9d4a06f49cb8

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work in progress
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 06 Jul 2018 18:12:15 -0400
parents c9c03c97ed9f
children 16575ca4537d
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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 from pandas import DataFrame
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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'])
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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 = float, default = 15.)
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64 parser.add_argument('--aggregation', dest = 'aggMethod', 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 dpi = 150
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67 # unit of analysis: site - camera-view
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68
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69 # need way of selecting sites as similar as possible to sql alchemy syntax
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70 # override tracking.cfg from db
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71 # manage cfg files, overwrite them (or a subset of parameters)
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72 # delete sqlite files
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73 # info of metadata
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74
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75 args = parser.parse_args()
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76
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77 #################################
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78 # Data preparation
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79 #################################
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80 session = connectDatabase(args.metadataFilename)
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81 parentPath = Path(args.metadataFilename).parent # files are relative to metadata location
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82 videoSequences = []
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83 sites = []
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84 if args.videoIds is not None:
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85 videoSequences = [session.query(VideoSequence).get(videoId) for videoId in args.videoIds]
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86 siteIds = set([vs.cameraView.siteIdx for vs in videoSequences])
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87 elif args.siteIds is not None:
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88 siteIds = set(args.siteIds)
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89 for siteId in siteIds:
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90 tmpsites = getSite(session, siteId)
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91 sites.extend(tmpsites)
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92 for site in tmpsites:
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93 videoSequences.extend(getSiteVideoSequences(site))
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94 else:
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95 print('No video/site to process')
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96
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97 if args.nProcesses > 1:
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98 pool = Pool(args.nProcesses)
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100 #################################
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101 # Delete
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102 #################################
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103 if args.delete is not None:
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104 if args.delete == 'feature':
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105 response = input('Are you sure you want to delete the tracking results (SQLite files) of all these sites (y/n)?')
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106 if response == 'y':
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107 for vs in videoSequences:
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108 p = parentPath.absolute()/vs.getDatabaseFilename()
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109 p.unlink()
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110 elif args.delete in ['object', 'interaction']:
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111 #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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112 for vs in videoSequences:
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113 storage.deleteFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.delete)
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parents: 1007
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115 #################################
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116 # Process
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117 #################################
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118 if args.process in ['feature', 'object']: # tracking
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119 if args.nProcesses == 1:
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120 for vs in videoSequences:
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121 if not (parentPath/vs.getDatabaseFilename()).exists() or args.process == 'object':
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122 if args.configFilename is None:
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123 configFilename = str(parentPath/vs.cameraView.getTrackingConfigurationFilename())
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124 else:
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125 configFilename = args.configFilename
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126 if vs.cameraView.cameraType is None:
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127 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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128 else:
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diff changeset
129 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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130 else:
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131 print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename()))
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132 else:
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133 for vs in videoSequences:
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134 if not (parentPath/vs.getDatabaseFilename()).exists() or args.process == 'object':
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135 if args.configFilename is None:
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diff changeset
136 configFilename = str(parentPath/vs.cameraView.getTrackingConfigurationFilename())
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137 else:
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138 configFilename = args.configFilename
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139 if vs.cameraView.cameraType is None:
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140 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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141 else:
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142 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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143 else:
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144 print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename()))
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145 pool.close()
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146 pool.join()
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147
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148 elif args.process == 'prototype': # motion pattern learning
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149 # learn by site by default -> group videos by camera view TODO
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diff changeset
150 # by default, load all objects, learn and then assign (BUT not save the assignments)
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parents: 1044
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151 for site in sites:
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152 print('Learning motion patterns for site {} ({})'.format(site.idx, site.name))
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153 objects = {}
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diff changeset
154 object2VideoSequences = {}
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diff changeset
155 for cv in site.cameraViews:
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parents: 1044
diff changeset
156 for vs in cv.videoSequences:
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diff changeset
157 print('Loading '+vs.getDatabaseFilename())
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diff changeset
158 objects[vs.idx] = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.trajectoryType, args.nObjects, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject)
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diff changeset
159 if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None:
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diff changeset
160 objectsWithFeatures = objects[vs.idx]
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1044
diff changeset
161 objects[vs.idx] = [f for o in objectsWithFeatures for f in o.getFeatures()]
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diff changeset
162 prototypeType = 'feature'
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diff changeset
163 else:
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parents: 1044
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164 prototypeType = args.trajectoryType
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parents: 1044
diff changeset
165 for obj in objects[vs.idx]:
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diff changeset
166 object2VideoSequences[obj] = vs
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diff changeset
167 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
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parents: 1044
diff changeset
168 similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
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parents: 1047
diff changeset
169 trainingObjects = [o for tmpobjects in objects.values() for o in tmpobjects]
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1048
diff changeset
170 if args.nMPObjects is not None and args.nMPObjects < len(trainingObjects):
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diff changeset
171 m = int(np.floor(float(len(trainingObjects))/args.nMPObjects))
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diff changeset
172 trainingObjects = trainingObjects[::m]
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diff changeset
173 similarities = -np.ones((len(trainingObjects), len(trainingObjects)))
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diff changeset
174 prototypeIndices, labels = processing.learnAssignMotionPatterns(True, True, trainingObjects, similarities, args.minSimilarity, similarityFunc, args.minClusterSize, args.optimizeCentroid, args.randomInitialization, True, [])
1045
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diff changeset
175 if args.outputPrototypeDatabaseFilename is None:
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diff changeset
176 outputPrototypeDatabaseFilename = args.databaseFilename
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parents: 1044
diff changeset
177 else:
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diff changeset
178 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
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diff changeset
179 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
1048
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diff changeset
180 storage.savePrototypesToSqlite(str(parentPath/site.getPath()/outputPrototypeDatabaseFilename), [moving.Prototype(object2VideoSequences[trainingObjects[i]].getDatabaseFilename(False), trainingObjects[i].getNum(), prototypeType, clusterSizes[i]) for i in prototypeIndices])
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parents: 1039
diff changeset
181
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diff changeset
182
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diff changeset
183 elif args.process == 'interaction':
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diff changeset
184 # safety analysis TODO make function in safety analysis script
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 986
diff changeset
185 if args.predictionMethod == 'cvd':
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 986
diff changeset
186 predictionParameters = prediction.CVDirectPredictionParameters()
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diff changeset
187 if args.predictionMethod == 'cve':
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diff changeset
188 predictionParameters = prediction.CVExactPredictionParameters()
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189 for vs in videoSequences:
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diff changeset
190 print('Processing '+vs.getDatabaseFilename())
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diff changeset
191 objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
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diff changeset
192 interactions = events.createInteractions(objects)
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diff changeset
193 #if args.nProcesses == 1:
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diff changeset
194 #print(str(parentPath/vs.cameraView.getTrackingConfigurationFilename()))
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diff changeset
195 params = storage.ProcessParameters(str(parentPath/vs.cameraView.getTrackingConfigurationFilename()))
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parents: 984
diff changeset
196 #print(len(interactions), args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones)
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parents: 984
diff changeset
197 processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
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diff changeset
198 storage.saveIndicatorsToSqlite(str(parentPath/vs.getDatabaseFilename()), processed)
985
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diff changeset
199 # else:
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diff changeset
200 # pool = Pool(processes = args.nProcesses)
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parents: 984
diff changeset
201 # nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
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parents: 984
diff changeset
202 # 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>
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diff changeset
203 # processed = []
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diff changeset
204 # for job in jobs:
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diff changeset
205 # processed += job.get()
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diff changeset
206 # pool.close()
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parents: 984
diff changeset
207
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diff changeset
208 #################################
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diff changeset
209 # Analyze
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parents: 1007
diff changeset
210 #################################
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diff changeset
211 if args.analyze == 'object':
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parents: 1021
diff changeset
212 # user speeds, accelerations
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
213 # aggregation per site
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
214 data = [] # list of observation per site-user with time
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
215 headers = ['sites', 'date', 'time', 'user_type']
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
216 aggFunctions = {}
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parents: 1021
diff changeset
217 for method in args.aggMethod:
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parents: 1021
diff changeset
218 if method == 'centile':
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parents: 1021
diff changeset
219 aggFunctions[method] = utils.aggregationFunction(method, args.aggCentiles)
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parents: 1021
diff changeset
220 for c in args.aggCentiles:
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parents: 1021
diff changeset
221 headers.append('{}{}'.format(method,c))
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parents: 1021
diff changeset
222 else:
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diff changeset
223 aggFunctions[method] = utils.aggregationFunction(method)
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
224 headers.append(method)
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diff changeset
225 for vs in videoSequences:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
226 d = vs.startTime.date()
a13f47c8931d work on processing large datasets (generate speed data)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
227 t1 = vs.startTime.time()
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
228 minUserDuration = args.minUserDuration*vs.cameraView.cameraType.frameRate
988
dc0be55e2bf5 new process functionalities
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 987
diff changeset
229 print('Extracting speed from '+vs.getDatabaseFilename())
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 1021
diff changeset
230 objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object', args.nObjects)
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parents: 987
diff changeset
231 for o in objects:
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parents: 1021
diff changeset
232 if o.length() > minUserDuration:
1050
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
233 row = [vs.cameraView.site.name, d, utils.framesToTime(o.getFirstInstant(), vs.cameraView.cameraType.frameRate, t1), o.getUserType()]
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234 tmp = o.getSpeeds()
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235 for method,func in aggFunctions.items():
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236 aggSpeeds = vs.cameraView.cameraType.frameRate*3.6*func(tmp)
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237 if method == 'centile':
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238 row += aggSpeeds.tolist()
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239 else:
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240 row.append(aggSpeeds)
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241 data.append(row)
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242 data = DataFrame(data, columns = headers)
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243 if args.output == 'figure':
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244 for name in headers[4:]:
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245 plt.ioff()
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246 plt.figure()
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247 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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248 plt.ylabel(name+' Speeds (km/h)')
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249 plt.savefig(name.lower()+'-speeds.png', dpi=dpi)
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250 plt.close()
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251 elif args.output == 'event':
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252 data.to_csv('speeds.csv', index = False)
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253
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254 if args.analyze == 'interaction': # redo as for object, export in dataframe all interaction data
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255 indicatorIds = [2,5,7,10]
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256 conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30}
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257 maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.}
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258 indicators = {}
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259 interactions = {}
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260 for vs in videoSequences:
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261 if not vs.cameraView.siteIdx in interactions:
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262 interactions[vs.cameraView.siteIdx] = []
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263 indicators[vs.cameraView.siteIdx] = {}
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264 for i in indicatorIds:
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265 indicators[vs.cameraView.siteIdx][i] = []
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266 interactions[vs.cameraView.siteIdx] += storage.loadInteractionsFromSqlite(str(parentPath/vs.getDatabaseFilename()))
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267 print(vs.getDatabaseFilename(), len(interactions[vs.cameraView.siteIdx]))
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268 for inter in interactions[vs.cameraView.siteIdx]:
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269 for i in indicatorIds:
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diff changeset
270 indic = inter.getIndicator(events.Interaction.indicatorNames[i])
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271 if indic is not None:
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diff changeset
272 v = indic.getMostSevereValue()*conversionFactors[i]
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diff changeset
273 if v < maxIndicatorValue[i]:
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diff changeset
274 indicators[vs.cameraView.siteIdx][i].append(v)
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diff changeset
275
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diff changeset
276 for i in indicatorIds:
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diff changeset
277 tmp = [indicators[siteId][i] for siteId in indicators]
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diff changeset
278 plt.ioff()
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279 plt.figure()
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diff changeset
280 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators])
988
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diff changeset
281 plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')')
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diff changeset
282 plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150)
986
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283 plt.close()