Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/learn-motion-patterns.py @ 953:989917b1ed85

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author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 25 Jul 2017 17:36:07 -0400
parents a9b2beef0db4
children cc89267b5ff9
comparison
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952:a9b2beef0db4 953:989917b1ed85
16 parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 1) 16 parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 1)
17 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None) 17 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
18 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True) 18 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
19 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance 19 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
20 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True) 20 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
21 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None) 21 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0)
22 parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true') 22 parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true')
23 parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true') 23 parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true')
24 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true') 24 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
25 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int) 25 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
26 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true') 26 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
75 75
76 similarities = -np.ones((nTrajectories, nTrajectories)) 76 similarities = -np.ones((nTrajectories, nTrajectories))
77 similarityFunc = lambda x,y : lcss.computeNormalized(x, y) 77 similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
78 # the next line can be called again without reinitializing similarities 78 # the next line can be called again without reinitializing similarities
79 if args.learn: 79 if args.learn:
80 prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.minClusterSize, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices) 80 prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices)
81 # assignment is done if explicitly passed as argument or if working on the same database (starting prototypes from scratch and assigning the ) 81 # assignment is done if explicitly passed as argument or if working on the same database (starting prototypes from scratch and assigning the )
82 # (otherwise the matchings will not compare and one has to to matchings on a large scale at once) 82 # (otherwise the matchings will not compare and one has to to matchings on a large scale at once)
83 else:
84 prototypeIndices = initialPrototypeIndices
83 85
84 if args.assign: 86 if args.assign:
85 prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc, args.minClusterSize) 87 prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc, args.minClusterSize)
86 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) 88 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
87 print(clusterSizes) 89 print(clusterSizes)
88 90
89 if args.learn or args.assign: 91 if args.learn or args.assign:
90 prototypes = [] 92 prototypes = []
91 for i in prototypeIndices: 93 for i in prototypeIndices:
92 if args.assign: 94 if args.assign:
93 nMatchings = clusterSizes[i] 95 nMatchings = clusterSizes[i]-1
94 else: 96 else:
95 nMatchings = 0 97 nMatchings = 0
96 if i<len(initialPrototypes): 98 if i<len(initialPrototypes):
97 initialPrototypes[i].nMatchings += nMatchings 99 initialPrototypes[i].nMatchings += nMatchings
98 prototypes.append(initialPrototypes[i]) 100 prototypes.append(initialPrototypes[i])