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comparison scripts/learn-motion-patterns.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
10 parser = argparse.ArgumentParser(description='''The program clusters trajectories, each cluster being represented by a trajectory. It can either work on the same dataset (database) or different ones, but only does learning or assignment at a time to avoid issues (the minimum cluster size argument is not used for now as it may change prototypes when assigning other trajectories)''') #, epilog = '' 10 parser = argparse.ArgumentParser(description='''The program clusters trajectories, each cluster being represented by a trajectory. It can either work on the same dataset (database) or different ones, but only does learning or assignment at a time to avoid issues (the minimum cluster size argument is not used for now as it may change prototypes when assigning other trajectories)''') #, epilog = ''
11 #parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file') 11 #parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True) 12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
13 parser.add_argument('-o', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes') 13 parser.add_argument('-o', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes')
14 parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with') 14 parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
15 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to learn from', choices = ['objectfeature', 'feature', 'object'], default = 'objectfeatures') 15 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to learn from', choices = ['objectfeature', 'feature', 'object'], default = 'objectfeature')
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)
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')
27 parser.add_argument('--similarities-filename', dest = 'similaritiesFilename', help = 'filename of the similarities')
27 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true') 28 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
28 parser.add_argument('--save-assignments', dest = 'saveAssignments', help = 'saves the assignments of the objects to the prototypes', action = 'store_true') 29 parser.add_argument('--save-assignments', dest = 'saveAssignments', help = 'saves the assignments of the objects to the prototypes', action = 'store_true')
29 parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true') 30 parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true')
30 31
31 args = parser.parse_args() 32 args = parser.parse_args()
38 39
39 # TODO add possibility to cluster with velocities 40 # TODO add possibility to cluster with velocities
40 # TODO add possibility to load all trajectories and use minclustersize 41 # TODO add possibility to load all trajectories and use minclustersize
41 42
42 # load trajectories to cluster or assign 43 # load trajectories to cluster or assign
43 if args.trajectoryType == 'objectfeature': 44 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nTrajectories, timeStep = args.positionSubsamplingRate)
44 trajectoryType = 'feature'
45 objectFeatureNumbers = storage.loadObjectFeatureFrameNumbers(args.databaseFilename, objectNumbers = args.nTrajectories)
46 featureNumbers = []
47 for numbers in objectFeatureNumbers.values():
48 featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)]
49 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate)
50 else:
51 trajectoryType = args.trajectoryType
52 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate)
53
54 trajectories = [o.getPositions().asArray().T for o in objects] 45 trajectories = [o.getPositions().asArray().T for o in objects]
55 46
56 # load initial prototypes, if any 47 # load initial prototypes, if any
57 if args.inputPrototypeDatabaseFilename is not None: 48 if args.inputPrototypeDatabaseFilename is not None:
58 initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True) 49 initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
64 else: 55 else:
65 initialPrototypes = [] 56 initialPrototypes = []
66 initialPrototypeIndices = None 57 initialPrototypeIndices = None
67 58
68 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) 59 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
69 nTrajectories = len(trajectories) 60 if args.similaritiesFilename is not None:
70 61 similarities = np.loadtxt(args.similaritiesFilename)
71 similarities = -np.ones((nTrajectories, nTrajectories)) 62 if args.similaritiesFilename is None or similarities.shape[0] != len(trajectories) or similarities.shape[1] != len(trajectories):
63 similarities = -np.ones((len(trajectories), len(trajectories)))
72 similarityFunc = lambda x,y : lcss.computeNormalized(x, y) 64 similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
73 # the next line can be called again without reinitializing similarities 65 # the next line can be called again without reinitializing similarities
74 if args.learn: 66 if args.learn:
75 prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices) 67 prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices)
76 else: 68 else:
88 # prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc) 80 # prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
89 assignedPrototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc) 81 assignedPrototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
90 82
91 if args.learn and not args.assign: 83 if args.learn and not args.assign:
92 prototypes = [] 84 prototypes = []
85 if args.trajectoryType == 'objectfeature':
86 trajectoryType = 'feature'
87 else:
88 trajectoryType = args.trajectoryType
93 for i in prototypeIndices: 89 for i in prototypeIndices:
94 if i<len(initialPrototypes): 90 if i<len(initialPrototypes):
95 prototypes.append(initialPrototypes[i]) 91 prototypes.append(initialPrototypes[i])
96 else: 92 else:
97 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType)) 93 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType))
134 objLabels.append(labels[i+len(initialPrototypes)]) 130 objLabels.append(labels[i+len(initialPrototypes)])
135 objectLabels.append(utils.mostCommon(objLabels)) 131 objectLabels.append(utils.mostCommon(objLabels))
136 objectNumbers.append(objNum) 132 objectNumbers.append(objNum)
137 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objectNumbers, 'object', objectLabels, initialPrototypes) 133 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objectNumbers, 'object', objectLabels, initialPrototypes)
138 else: 134 else:
139 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, [obj.getNum() for obj in objects], trajectoryType, labels[len(initialPrototypes):], initialPrototypes) 135 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, [obj.getNum() for obj in objects], args.trajectoryType, labels[len(initialPrototypes):], initialPrototypes)
140 if args.display: 136 if args.display:
141 plt.figure() 137 plt.figure()
142 for i,o in enumerate(objects): 138 for i,o in enumerate(objects):
143 if labels[i+len(initialPrototypes)] < 0: 139 if labels[i+len(initialPrototypes)] < 0:
144 o.plot('kx-') 140 o.plot('kx-')
148 p.getMovingObject().plot(utils.colors[i]+'o') 144 p.getMovingObject().plot(utils.colors[i]+'o')
149 plt.axis('equal') 145 plt.axis('equal')
150 plt.show() 146 plt.show()
151 147
152 if (args.learn or args.assign) and args.saveSimilarities: 148 if (args.learn or args.assign) and args.saveSimilarities:
153 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f') 149 if args.similaritiesFilename is not None:
150 np.savetxt(args.similaritiesFilename, similarities, '%.4f')
151 else:
152 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')