Mercurial Hosting > traffic-intelligence
changeset 984:a69695d14e59
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 |
files | scripts/process.py |
diffstat | 1 files changed, 33 insertions(+), 2 deletions(-) [+] |
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--- a/scripts/process.py Tue Feb 27 16:46:06 2018 -0500 +++ b/scripts/process.py Tue Mar 06 08:26:13 2018 -0500 @@ -1,17 +1,48 @@ #! /usr/bin/env python import sys, argparse +from pathlib2 import Path -import storage, cvutils, utils +import storage, events, prediction from metadata import * 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.') parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True) +parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int) +parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true') # need way of selecting sites as similar as possible to sql alchemy syntax # override tracking.cfg from db # manage cfg files, overwrite them (or a subset of parameters) # delete sqlite files -# nprocesses + +parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1) args = parser.parse_args() +# files are relative to metadata location + +session = createDatabase(args.metadataFilename) +parentDir = Path(args.metadataFilename).parent + +# todo change prediction parameters +predictionParameters = prediction.CVExactPredictionParameters() + +for videoId in args.videoIds: + vs = session.query(VideoSequence).get(videoId) + print(vs.getDatabaseFilename()) + objects = storage.loadTrajectoriesFromSqlite(str(parentDir/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp')) + interactions = events.createInteractions(objects) + #if args.nProcesses == 1: + params = storage.ProcessParameters(str(parentDir/vs.cameraView.getTrackingConfigurationFilename())) + #print(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones) + processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None) + storage.saveIndicatorsToSqlite(str(parentDir/vs.getDatabaseFilename()), processed) + +# else: +# pool = Pool(processes = args.nProcesses) +# nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses))) +# 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)] +# processed = [] +# for job in jobs: +# processed += job.get() +# pool.close()