Mercurial Hosting > traffic-intelligence
changeset 1072:c67f8c36ebc7
interaction extraction
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Thu, 19 Jul 2018 01:44:11 -0400 |
| parents | 58994b08be42 |
| children | b123fa0e5440 |
| files | scripts/process.py trafficintelligence/processing.py |
| diffstat | 2 files changed, 51 insertions(+), 30 deletions(-) [+] |
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--- a/scripts/process.py Wed Jul 18 02:12:47 2018 -0400 +++ b/scripts/process.py Thu Jul 19 01:44:11 2018 -0400 @@ -60,7 +60,7 @@ # analysis options parser.add_argument('--output', dest = 'output', help = 'kind of output to produce (interval means)', choices = ['figure', 'interval', 'event']) -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) +parser.add_argument('--min-duration', dest = 'minDuration', help = 'mininum duration we have to see the user or interaction to take into account in the analysis (s)', type = float) parser.add_argument('--interval-duration', dest = 'intervalDuration', help = 'length of time interval to aggregate data (min)', type = int, default = 15) parser.add_argument('--aggregation', dest = 'aggMethods', help = 'aggregation method per user/interaction and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median']) parser.add_argument('--aggregation-centiles', dest = 'aggCentiles', help = 'centile(s) to compute from the observations', nargs = '*', type = int) @@ -259,9 +259,9 @@ headers.extend(tmpheaders) if args.nProcesses == 1: for vs in videoSequences: - data.extend(processing.extractVideoSequenceSpeeds(str(parentPath/vs.getDatabaseFilename()), vs.cameraView.site.name, args.nObjects, vs.startTime, vs.cameraView.cameraType.frameRate, args.minUserDuration, args.aggMethods, args.aggCentiles)) + data.extend(processing.extractVideoSequenceSpeeds(str(parentPath/vs.getDatabaseFilename()), vs.cameraView.site.name, args.nObjects, vs.startTime, vs.cameraView.cameraType.frameRate, vs.cameraView.cameraType.frameRate*args.minDuration, args.aggMethods, args.aggCentiles)) else: - jobs = [pool.apply_async(processing.extractVideoSequenceSpeeds, args = (str(parentPath/vs.getDatabaseFilename()), vs.cameraView.site.name, args.nObjects, vs.startTime, vs.cameraView.cameraType.frameRate, args.minUserDuration, args.aggMethods, args.aggCentiles)) for vs in videoSequences] + jobs = [pool.apply_async(processing.extractVideoSequenceSpeeds, args = (str(parentPath/vs.getDatabaseFilename()), vs.cameraView.site.name, args.nObjects, vs.startTime, vs.cameraView.cameraType.frameRate, vs.cameraView.cameraType.frameRate*args.minDuration, args.aggMethods, args.aggCentiles)) for vs in videoSequences] for job in jobs: data.extend(job.get()) pool.close() @@ -280,33 +280,55 @@ if args.analyze == 'interaction': # redo as for object, export in dataframe all interaction data indicatorIds = [2,5,7,10] conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30} - maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.} + #maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.} + data = [] # list of observation per site-user with time + headers = ['site', 'date', 'time', events.Interaction.indicatorNames[10].replace(' ','-')] # user types? + aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles) + for i in indicatorIds[:3]: + for h in tmpheaders: + headers.append(events.Interaction.indicatorNames[i].replace(' ','-')+'-'+h) indicators = {} interactions = {} for vs in videoSequences: - if not vs.cameraView.siteIdx in interactions: - interactions[vs.cameraView.siteIdx] = [] - indicators[vs.cameraView.siteIdx] = {} - for i in indicatorIds: - indicators[vs.cameraView.siteIdx][i] = [] - interactions[vs.cameraView.siteIdx] += storage.loadInteractionsFromSqlite(str(parentPath/vs.getDatabaseFilename())) - print(vs.getDatabaseFilename(), len(interactions[vs.cameraView.siteIdx])) - for inter in interactions[vs.cameraView.siteIdx]: - for i in indicatorIds: - indic = inter.getIndicator(events.Interaction.indicatorNames[i]) - if indic is not None: - v = indic.getMostSevereValue()*conversionFactors[i] - if v < maxIndicatorValue[i]: - indicators[vs.cameraView.siteIdx][i].append(v) - - for i in indicatorIds: - tmp = [indicators[siteId][i] for siteId in indicators] - plt.ioff() - plt.figure() - plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators]) - plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')') - plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150) - plt.close() + print('Extracting SMoS from '+vs.getDatabaseFilename()) + interactions = storage.loadInteractionsFromSqlite(str(parentPath/vs.getDatabaseFilename())) + minDuration = vs.cameraView.cameraType.frameRate*args.minDuration + for inter in interactions: + if inter.length() > minDuration: + d = vs.startTime.date() + t = vs.startTime.time() + row = [vs.cameraView.site.name, d, utils.framesToTime(inter.getFirstInstant(), vs.cameraView.cameraType.frameRate, t)] + pet = inter.getIndicator('Post Encroachment Time') + if pet is None: + row.append(None) + else: + row.append(conversionFactors[10]*pet.getValues()[0]) + for i in indicatorIds[:3]: + indic = inter.getIndicator(events.Interaction.indicatorNames[i]) + if indic is not None: + #v = indic.getMostSevereValue()* + tmp = list(indic.values.values()) + for method,func in aggFunctions.items(): + agg = conversionFactors[i]*func(tmp) + if method == 'centile': + row.extend(agg.tolist()) + else: + row.append(agg) + else: + row.extend([None]*len(aggFunctions)) + data.append(row) + data = pd.DataFrame(data, columns = headers) + if args.output == 'figure': + for i in indicatorIds: + pass # tmp = [indicators[siteId][i] for siteId in indicators] + # plt.ioff() + # plt.figure() + # plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators]) + # plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')') + # plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150) + # plt.close() + elif args.output == 'event': + data.to_csv(args.eventFilename, index = False) if args.analyze == 'event': # aggregate event data by 15 min interval (args.intervalDuration), count events with thresholds data = pd.read_csv(args.eventFilename, parse_dates = [2])
--- a/trafficintelligence/processing.py Wed Jul 18 02:12:47 2018 -0400 +++ b/trafficintelligence/processing.py Thu Jul 19 01:44:11 2018 -0400 @@ -18,16 +18,15 @@ objectsNotInZone.append(o) return speeds, objectsNotInZone -def extractVideoSequenceSpeeds(dbFilename, siteName, nObjects, startTime, frameRate, minUserDurationSeconds, aggMethods, aggCentiles): +def extractVideoSequenceSpeeds(dbFilename, siteName, nObjects, startTime, frameRate, minDuration, aggMethods, aggCentiles): data = [] d = startTime.date() t1 = startTime.time() - minUserDuration = minUserDurationSeconds*frameRate print('Extracting speed from '+dbFilename) aggFunctions, tmpheaders = utils.aggregationMethods(aggMethods, aggCentiles) objects = storage.loadTrajectoriesFromSqlite(dbFilename, 'object', nObjects) for o in objects: - if o.length() > minUserDuration: + if o.length() > minDuration: row = [siteName, d, utils.framesToTime(o.getFirstInstant(), frameRate, t1), o.getUserType()] tmp = o.getSpeeds() for method,func in aggFunctions.items():