Mercurial > hg > nsaunier > traffic-intelligence

annotate scripts/process.py @ 984:a69695d14e59

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work on script for large datasets
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 06 Mar 2018 08:26:13 -0500
parents 7463c9bc846b
children 668a85c963c3
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983
7463c9bc846b work in progress on script to manage large dataset with multiple sites
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1 #! /usr/bin/env python
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2
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3 import sys, argparse
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4 from pathlib2 import Path
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5
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6 import storage, events, prediction
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7 from metadata import *
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8
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9 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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10 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True)
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11 parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int)
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12 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
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13
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14 # need way of selecting sites as similar as possible to sql alchemy syntax
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15 # override tracking.cfg from db
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16 # manage cfg files, overwrite them (or a subset of parameters)
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17 # delete sqlite files
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18
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19 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
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20
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21 args = parser.parse_args()
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22 # files are relative to metadata location
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23
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24 session = createDatabase(args.metadataFilename)
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25 parentDir = Path(args.metadataFilename).parent
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26
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27 # todo change prediction parameters
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28 predictionParameters = prediction.CVExactPredictionParameters()
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29
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30 for videoId in args.videoIds:
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31 vs = session.query(VideoSequence).get(videoId)
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32 print(vs.getDatabaseFilename())
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33 objects = storage.loadTrajectoriesFromSqlite(str(parentDir/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
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34 interactions = events.createInteractions(objects)
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35 #if args.nProcesses == 1:
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36 params = storage.ProcessParameters(str(parentDir/vs.cameraView.getTrackingConfigurationFilename()))
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37 #print(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones)
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38 processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
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39 storage.saveIndicatorsToSqlite(str(parentDir/vs.getDatabaseFilename()), processed)
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40
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41 # else:
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42 # pool = Pool(processes = args.nProcesses)
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43 # nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
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44 # 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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45 # processed = []
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46 # for job in jobs:
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47 # processed += job.get()
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48 # pool.close()