Mercurial Hosting > traffic-intelligence

comparison scripts/process.py @ 1058:16575ca4537d

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work in progress
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 10 Jul 2018 17:16:38 -0400
parents 9d4a06f49cb8
children a87b3072bd26
comparison
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1057:a7ada64b8214 1058:16575ca4537d
21 21
22 # main function 22 # main function
23 parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction']) 23 parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction'])
24 parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'prototype', 'interaction']) 24 parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'prototype', 'interaction'])
25 parser.add_argument('--display', dest = 'display', help = 'data to display (replay over video)', choices = ['feature', 'object', 'classification', 'interaction']) 25 parser.add_argument('--display', dest = 'display', help = 'data to display (replay over video)', choices = ['feature', 'object', 'classification', 'interaction'])
26 parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction']) 26 parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction', 'event'])
27 27
28 # common options 28 # common options
29 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file') 29 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
30 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects/interactions to process', type = int) 30 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects/interactions to process', type = int)
31 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories', choices = ['feature', 'object'], default = 'feature') 31 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories', choices = ['feature', 'object'], default = 'feature')
59 59
60 # analysis options 60 # analysis options
61 parser.add_argument('--output', dest = 'output', help = 'kind of output to produce (interval means)', choices = ['figure', 'interval', 'event']) 61 parser.add_argument('--output', dest = 'output', help = 'kind of output to produce (interval means)', choices = ['figure', 'interval', 'event'])
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) 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)
63 parser.add_argument('--interval-duration', dest = 'intervalDuration', help = 'length of time interval to aggregate data (min)', type = float, default = 15.) 63 parser.add_argument('--interval-duration', dest = 'intervalDuration', help = 'length of time interval to aggregate data (min)', type = float, default = 15.)
64 parser.add_argument('--aggregation', dest = 'aggMethod', help = 'aggregation method per user/interaction and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median']) 64 parser.add_argument('--aggregation', dest = 'aggMethods', help = 'aggregation method per user/interaction and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median'])
65 parser.add_argument('--aggregation-centile', dest = 'aggCentiles', help = 'centile(s) to compute from the observations', nargs = '*', type = int) 65 parser.add_argument('--aggregation-centile', dest = 'aggCentiles', help = 'centile(s) to compute from the observations', nargs = '*', type = int)
66 parser.add_argument('--event-filename', dest = 'eventFilename', help = 'filename of the event data')
66 dpi = 150 67 dpi = 150
67 # unit of analysis: site - camera-view 68 # unit of analysis: site - camera-view
68 69
69 # need way of selecting sites as similar as possible to sql alchemy syntax 70 # need way of selecting sites as similar as possible to sql alchemy syntax
70 # override tracking.cfg from db 71 # override tracking.cfg from db
211 if args.analyze == 'object': 212 if args.analyze == 'object':
212 # user speeds, accelerations 213 # user speeds, accelerations
213 # aggregation per site 214 # aggregation per site
214 data = [] # list of observation per site-user with time 215 data = [] # list of observation per site-user with time
215 headers = ['sites', 'date', 'time', 'user_type'] 216 headers = ['sites', 'date', 'time', 'user_type']
216 aggFunctions = {} 217 aggFunctions, tmpheaders = utils.aggregationMethods(arg.aggMethods, args.aggCentiles)
217 for method in args.aggMethod: 218 headers.extend(tmpheaders)
218 if method == 'centile':
219 aggFunctions[method] = utils.aggregationFunction(method, args.aggCentiles)
220 for c in args.aggCentiles:
221 headers.append('{}{}'.format(method,c))
222 else:
223 aggFunctions[method] = utils.aggregationFunction(method)
224 headers.append(method)
225 for vs in videoSequences: 219 for vs in videoSequences:
226 d = vs.startTime.date() 220 d = vs.startTime.date()
227 t1 = vs.startTime.time() 221 t1 = vs.startTime.time()
228 minUserDuration = args.minUserDuration*vs.cameraView.cameraType.frameRate 222 minUserDuration = args.minUserDuration*vs.cameraView.cameraType.frameRate
229 print('Extracting speed from '+vs.getDatabaseFilename()) 223 print('Extracting speed from '+vs.getDatabaseFilename())
279 plt.figure() 273 plt.figure()
280 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators]) 274 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators])
281 plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')') 275 plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')')
282 plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150) 276 plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150)
283 plt.close() 277 plt.close()
278
279 if args.analyze == 'event': # aggregate event data by 15 min interval (arg.intervalDuration)
280 data = pd.read_csv(arg.eventFilename)
281 # create time for end of each 15 min, then group by, using the agg method for each data column
282 headers = ['sites', 'date', 'intervalend15']
283 # add n road users (by type?)
284 aggFunctions, tmpheaders = utils.aggregationMethods(arg.aggMethods, args.aggCentiles)
285 headers.extend(tmpheaders)