Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/process.py @ 1043:b735895c8815

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work in progress on process (learn motion patterns)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 04 Jul 2018 17:39:39 -0400
parents 5621e4ad2428
children 75a6ad604cc5
comparison
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1042:b1ba6d44fcb9 1043:b735895c8815
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'])
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 = ['objectfeature', 'feature', 'object'], default = 'objectfeature')
31 parser.add_argument('--dry', dest = 'dryRun', help = 'dry run of processing', action = 'store_true') 32 parser.add_argument('--dry', dest = 'dryRun', help = 'dry run of processing', action = 'store_true')
32 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1) 33 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
34 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
35 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
36
37 ### process options
38 # motion pattern learning and assignment
39 parser.add_argument('--prototype-filename', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes')
40 #parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
41 parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 1)
42 parser.add_argument('--maxdist', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
43 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
44 parser.add_argument('-minsimil', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
45 parser.add_argument('-min-cluster-size', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0)
46 parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true')
47 parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true')
48 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
49 #parser.add_argument('--similarities-filename', dest = 'similaritiesFilename', help = 'filename of the similarities')
50 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
51 parser.add_argument('--save-assignments', dest = 'saveAssignments', help = 'saves the assignments of the objects to the prototypes', action = 'store_true')
52 parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true')
53
54 # safety analysis
55 parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (cvd: vector computation (approximate); cve: equation solving; cv: discrete time (approximate)), normal adaptation, point set prediction)', choices = ['cvd', 'cve', 'cv', 'na', 'ps', 'mp'])
56 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
57 # override other tracking config, erase sqlite?
58
33 59
34 # analysis options 60 # analysis options
35 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'])
36 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)
37 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.)
38 parser.add_argument('--aggregation', dest = 'aggMethod', help = 'aggregation method per user/event and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median']) 64 parser.add_argument('--aggregation', dest = 'aggMethod', help = 'aggregation method per user/event and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median'])
39 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)
40 dpi = 150 66 dpi = 150
41 # unit of analysis: site or video sequence? 67 # unit of analysis: site or video sequence?
42 68
43 # safety analysis
44 parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (cvd: vector computation (approximate); cve: equation solving; cv: discrete time (approximate)), normal adaptation, point set prediction)', choices = ['cvd', 'cve', 'cv', 'na', 'ps', 'mp'])
45 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
46 # override other tracking config, erase sqlite?
47
48 # need way of selecting sites as similar as possible to sql alchemy syntax 69 # need way of selecting sites as similar as possible to sql alchemy syntax
49 # override tracking.cfg from db 70 # override tracking.cfg from db
50 # manage cfg files, overwrite them (or a subset of parameters) 71 # manage cfg files, overwrite them (or a subset of parameters)
51 # delete sqlite files 72 # delete sqlite files
52 # info of metadata 73 # info of metadata
59 session = connectDatabase(args.metadataFilename) 80 session = connectDatabase(args.metadataFilename)
60 parentPath = Path(args.metadataFilename).parent # files are relative to metadata location 81 parentPath = Path(args.metadataFilename).parent # files are relative to metadata location
61 videoSequences = [] 82 videoSequences = []
62 if args.videoIds is not None: 83 if args.videoIds is not None:
63 videoSequences = [session.query(VideoSequence).get(videoId) for videoId in args.videoIds] 84 videoSequences = [session.query(VideoSequence).get(videoId) for videoId in args.videoIds]
85 siteIds = set([vs.cameraView.siteIdx for vs in videoSequences])
64 elif args.siteIds is not None: 86 elif args.siteIds is not None:
65 for siteId in args.siteIds: 87 siteIds = set(args.siteIds)
88 for siteId in siteIds:
66 for site in getSite(session, siteId): 89 for site in getSite(session, siteId):
67 for cv in site.cameraViews: 90 for cv in site.cameraViews:
68 videoSequences += cv.videoSequences 91 videoSequences += cv.videoSequences
69 else: 92 else:
70 print('No video/site to process') 93 print('No video/site to process')
119 print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename())) 142 print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename()))
120 pool.close() 143 pool.close()
121 pool.join() 144 pool.join()
122 145
123 elif args.process == 'prototype': # motion pattern learning 146 elif args.process == 'prototype': # motion pattern learning
124 pass 147 # learn by site by default -> group videos by site (or by camera view? TODO add cameraviews)
148 # by default, load all objects, learn and then assign
149 objects = {siteId: [] for siteId in siteIds}
150 for vs in videoSequences:
151 print('Loading '+vs.getDatabaseFilename())
152 objects[vs.cameraView.siteIdx] += storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.trajectoryType, args.nTrajectories, timeStep = args.positionSubsamplingRate)
153
125 154
126 elif args.process == 'interaction': 155 elif args.process == 'interaction':
127 # safety analysis TODO make function in safety analysis script 156 # safety analysis TODO make function in safety analysis script
128 if args.predictionMethod == 'cvd': 157 if args.predictionMethod == 'cvd':
129 predictionParameters = prediction.CVDirectPredictionParameters() 158 predictionParameters = prediction.CVDirectPredictionParameters()
181 row += aggSpeeds.tolist() 210 row += aggSpeeds.tolist()
182 else: 211 else:
183 row.append(aggSpeeds) 212 row.append(aggSpeeds)
184 data.append(row) 213 data.append(row)
185 data = DataFrame(data, columns = headers) 214 data = DataFrame(data, columns = headers)
186 if args.siteIds is None:
187 siteIds = set([vs.cameraView.siteIdx for vs in videoSequences])
188 else:
189 siteIds = set(args.siteIds)
190 if args.output == 'figure': 215 if args.output == 'figure':
191 for name in headers[4:]: 216 for name in headers[4:]:
192 plt.ioff() 217 plt.ioff()
193 plt.figure() 218 plt.figure()
194 plt.boxplot([data.loc[data['sites']==siteId, name] for siteId in siteIds], labels = [session.query(Site).get(siteId).name for siteId in siteIds]) 219 plt.boxplot([data.loc[data['sites']==siteId, name] for siteId in siteIds], labels = [session.query(Site).get(siteId).name for siteId in siteIds])