Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/learn-motion-patterns.py @ 979:cc89267b5ff9

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work on learning and assigning
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 19 Feb 2018 10:47:19 -0500
parents 989917b1ed85
children 23f98ebb113f
comparison
equal deleted inserted replaced
978:184f1dd307f9 979:cc89267b5ff9
40 # TODO add possibility to cluster with velocities 40 # TODO add possibility to cluster with velocities
41 # TODO add possibilite to load all trajectories and use minclustersize 41 # TODO add possibilite to load all trajectories and use minclustersize
42 # save the objects that match the prototypes 42 # save the objects that match the prototypes
43 # write an assignment function for objects 43 # write an assignment function for objects
44 44
45 trajectoryType = args.trajectoryType 45 # load trajectories to cluster or assign
46 prototypeType = args.trajectoryType
47 if args.trajectoryType == 'objectfeatures': 46 if args.trajectoryType == 'objectfeatures':
48 trajectoryType = 'object' 47 trajectoryType = 'feature'
49 prototypeType = 'feature'
50
51 if args.trajectoryType == 'objectfeatures':
52 objectFeatureNumbers = storage.loadObjectFeatureFrameNumbers(args.databaseFilename, objectNumbers = args.nTrajectories) 48 objectFeatureNumbers = storage.loadObjectFeatureFrameNumbers(args.databaseFilename, objectNumbers = args.nTrajectories)
53 featureNumbers = [] 49 featureNumbers = []
54 for numbers in objectFeatureNumbers.values(): 50 for numbers in objectFeatureNumbers.values():
55 featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)] 51 featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)]
56 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate) 52 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate)
57 else: 53 else:
58 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate) 54 trajectoryType = args.trajectoryType
55 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate)
59 56
60
61 trajectories = [o.getPositions().asArray().T for o in objects] 57 trajectories = [o.getPositions().asArray().T for o in objects]
58
59 # load initial prototypes, if any
62 if args.inputPrototypeDatabaseFilename is not None: 60 if args.inputPrototypeDatabaseFilename is not None:
63 initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True) 61 initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
64 trajectories = [p.getMovingObject().getPositions().asArray().T for p in initialPrototypes]+trajectories 62 trajectories = [p.getMovingObject().getPositions().asArray().T for p in initialPrototypes]+trajectories
65 if len(initialPrototypes) > 0: 63 if len(initialPrototypes) > 0:
66 initialPrototypeIndices = range(len(initialPrototypes)) 64 initialPrototypeIndices = range(len(initialPrototypes))
97 nMatchings = 0 95 nMatchings = 0
98 if i<len(initialPrototypes): 96 if i<len(initialPrototypes):
99 initialPrototypes[i].nMatchings += nMatchings 97 initialPrototypes[i].nMatchings += nMatchings
100 prototypes.append(initialPrototypes[i]) 98 prototypes.append(initialPrototypes[i])
101 else: 99 else:
102 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), prototypeType, nMatchings)) 100 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType, nMatchings))
103 101
104 if args.outputPrototypeDatabaseFilename is None: 102 if args.outputPrototypeDatabaseFilename is None:
105 outputPrototypeDatabaseFilename = args.databaseFilename 103 outputPrototypeDatabaseFilename = args.databaseFilename
106 else: 104 else:
107 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename 105 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
112 if args.saveSimilarities: 110 if args.saveSimilarities:
113 # todo save trajectories and prototypes 111 # todo save trajectories and prototypes
114 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f') 112 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')
115 113
116 labelsToProtoIndices = {protoId: i for i, protoId in enumerate(prototypeIndices)} 114 labelsToProtoIndices = {protoId: i for i, protoId in enumerate(prototypeIndices)}
117 if args.assign and args.saveMatches: # or args.assign 115 if args.assign and args.saveMatches:
118 # save in the db that contained originally the data 116 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objects, trajectoryType, [labelsToProtoIndices[l] for l in labels], prototypes)
119 # retirer les assignations anterieures?
120 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objects, [labelsToProtoIndices[l] for l in labels], prototypes)
121 117
122 if args.display and args.assign: 118 if args.display and args.assign:
123 from matplotlib.pyplot import figure, show, axis 119 from matplotlib.pyplot import figure, show, axis
124 figure() 120 figure()
125 for i,o in enumerate(objects): 121 for i,o in enumerate(objects):