Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/process.py @ 985:668a85c963c3

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work on processing and managing large video datasets
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 06 Mar 2018 22:44:33 -0500
parents a69695d14e59
children 3be8aaa47651
comparison
equal deleted inserted replaced
984:a69695d14e59 985:668a85c963c3
8 8
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.') 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.')
10 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True) 10 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True)
11 parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int) 11 parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int)
12 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true') 12 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
13 parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction'])
14 parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'interaction'])
15 parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction'])
13 16
14 # need way of selecting sites as similar as possible to sql alchemy syntax 17 # need way of selecting sites as similar as possible to sql alchemy syntax
15 # override tracking.cfg from db 18 # override tracking.cfg from db
16 # manage cfg files, overwrite them (or a subset of parameters) 19 # manage cfg files, overwrite them (or a subset of parameters)
17 # delete sqlite files 20 # delete sqlite files
19 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1) 22 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
20 23
21 args = parser.parse_args() 24 args = parser.parse_args()
22 # files are relative to metadata location 25 # files are relative to metadata location
23 26
24 session = createDatabase(args.metadataFilename) 27 session = connectDatabase(args.metadataFilename)
25 parentDir = Path(args.metadataFilename).parent 28 parentDir = Path(args.metadataFilename).parent
26 29
27 # todo change prediction parameters 30 if args.delete is not None:
28 predictionParameters = prediction.CVExactPredictionParameters() 31 if args.delete in ['object', 'interaction']:
32 #parser.add_argument('-t', dest = 'dataType', help = 'type of the data to remove', required = True, choices = ['object','interaction', 'bb', 'pois', 'prototype'])
33 for videoId in args.videoIds:
34 vs = session.query(VideoSequence).get(videoId)
35 storage.deleteFromSqlite(str(parentDir/vs.getDatabaseFilename()), args.delete)
29 36
30 for videoId in args.videoIds: 37 if args.process == 'interaction':
31 vs = session.query(VideoSequence).get(videoId) 38 # safety analysis TODO make function in safety analysis script
32 print(vs.getDatabaseFilename()) 39 predictionParameters = prediction.CVExactPredictionParameters()
33 objects = storage.loadTrajectoriesFromSqlite(str(parentDir/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp')) 40 for videoId in args.videoIds:
34 interactions = events.createInteractions(objects) 41 vs = session.query(VideoSequence).get(videoId)
35 #if args.nProcesses == 1: 42 print('Processing '+vs.getDatabaseFilename())
36 params = storage.ProcessParameters(str(parentDir/vs.cameraView.getTrackingConfigurationFilename())) 43 objects = storage.loadTrajectoriesFromSqlite(str(parentDir/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
37 #print(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones) 44 interactions = events.createInteractions(objects)
38 processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None) 45 #if args.nProcesses == 1:
39 storage.saveIndicatorsToSqlite(str(parentDir/vs.getDatabaseFilename()), processed) 46 #print(str(parentDir/vs.cameraView.getTrackingConfigurationFilename()))
40 47 params = storage.ProcessParameters(str(parentDir/vs.cameraView.getTrackingConfigurationFilename()))
41 # else: 48 #print(len(interactions), args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones)
42 # pool = Pool(processes = args.nProcesses) 49 processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
43 # nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses))) 50 storage.saveIndicatorsToSqlite(str(parentDir/vs.getDatabaseFilename()), processed)
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)] 51 # else:
45 # processed = [] 52 # pool = Pool(processes = args.nProcesses)
46 # for job in jobs: 53 # nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
47 # processed += job.get() 54 # 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)]
48 # pool.close() 55 # processed = []
56 # for job in jobs:
57 # processed += job.get()
58 # pool.close()
59
60 if args.analyze == 'interaction':
61 indicatorIds = [2,5,7,10]
62 indicators = {}
63 interactions = {}
64 for videoId in args.videoIds:
65 vs = session.query(VideoSequence).get(videoId)
66 if not vs.cameraView.siteIdx in interactions:
67 interactions[vs.cameraView.siteIdx] = []
68 for i in indicatorIds:
69 indicators[events.Interaction.indicatorNames[i]][vs.cameraView.siteIdx] = []
70 interactions[vs.cameraView.siteIdx].append(storage.loadInteractionsFromSqlite(str(parentDir/vs.getDatabaseFilename())))
71 print(vs.getDatabaseFilename(), len(interactions))
72 for inter in interactions[videoId]:
73 for i in indicatorIds:
74 indic = inter.getIndicator(events.Interaction.indicatorNames[i])
75 if indic is not None:
76 indicators[events.Interaction.indicatorNames[i]][vs.cameraView.siteIdx].append(indic.getMostSevereValue())