Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/learn-motion-patterns.py @ 949:d6c1c05d11f5

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modified multithreading at the interaction level for safety computations
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 21 Jul 2017 17:52:56 -0400
parents 584b9405e494
children 2a4f174879dd
comparison
equal deleted inserted replaced
948:584b9405e494 949:d6c1c05d11f5
54 featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)] 54 featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)]
55 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate) 55 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate)
56 else: 56 else:
57 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate) 57 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate)
58 58
59 if args.inputPrototypeDatabaseFilename is not None:
60 prototypeIndices, dbFilenames, trajectoryTypes, nMatchings, prototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
61 59
62 trajectories = [o.getPositions().asArray().T for o in objects] 60 trajectories = [o.getPositions().asArray().T for o in objects]
61 if args.inputPrototypeDatabaseFilename is not None:
62 initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
63 trajectories = [p.getMovingObject().getPositions().asArray().T for p in initialPrototypes]+trajectories
64 initialPrototypeIndices = range(len(initialPrototypes))
65 else:
66 initialPrototypes = []
67 initialPrototypeIndices = None
63 68
64 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) 69 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
65 nTrajectories = len(trajectories) 70 nTrajectories = len(trajectories)
66 71
67 similarities = -np.ones((nTrajectories, nTrajectories)) 72 similarities = -np.ones((nTrajectories, nTrajectories))
68 73 # the next line can be called again without reinitializing similarities
69 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.optimizeCentroid, args.randomInitialization, True, None) # this line can be called again without reinitializing similarities 74 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.optimizeCentroid, args.randomInitialization, args.inputPrototypeDatabaseFilename is not None, initialPrototypeIndices) # assignment is done only if working on the same database, otherwise the matchings will not compare and one has to to matchings on a large scale at once
70 75
71 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) 76 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
72 print(clusterSizes) 77 print(clusterSizes)
73 78
74 prototypes = [moving.Prototype(args.databaseFilename, objects[i].getNum(), prototypeType, clusterSizes[i]) for i in prototypeIndices] 79 prototypes = []
80 for i in prototypeIndices:
81 if i<len(initialPrototypes):
82 initialPrototypes[i].nMatchings = 0
83 prototypes.append(initialPrototypes[i])
84 else:
85 if args.inputPrototypeDatabaseFilename is None:
86 nmatchings = clusterSizes[i]
87 else:
88 nmatchings = 0
89 prototypes.append(moving.Prototype(args.databaseFilename, objects[i].getNum(), prototypeType, nmatchings)
75 if args.outputPrototypeDatabaseFilename is None: 90 if args.outputPrototypeDatabaseFilename is None:
76 outputPrototypeDatabaseFilename = args.databaseFilename 91 outputPrototypeDatabaseFilename = args.databaseFilename
77 else: 92 else:
78 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename 93 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
79 storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes) 94 storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)