Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/learn-motion-patterns.py @ 878:8e8ec4ece66e

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minor + bug corrected in motion pattern learning
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 10 Mar 2017 15:31:26 -0500
parents 1535251a1f40
children 9fd7b18f75b4
comparison
equal deleted inserted replaced
877:d1ff6917d082 878:8e8ec4ece66e
8 import ml, utils, storage 8 import ml, utils, storage
9 9
10 parser = argparse.ArgumentParser(description='The program learns prototypes for the motion patterns') #, epilog = '' 10 parser = argparse.ArgumentParser(description='The program learns prototypes for the motion patterns') #, 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('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures') 13 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to learn from', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures')
14 parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 3) 14 parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 3)
15 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None) 15 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
16 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True) 16 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
17 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance 17 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
18 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True) 18 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
26 26
27 # TODO parameters (random init?) and what to learn from: objects, features, longest features from objects 27 # TODO parameters (random init?) and what to learn from: objects, features, longest features from objects
28 # TODO add possibility to cluter with velocities 28 # TODO add possibility to cluter with velocities
29 29
30 trajectoryType = args.trajectoryType 30 trajectoryType = args.trajectoryType
31 prototypeType = args.trajectoryType
31 if args.trajectoryType == 'objectfeatures': 32 if args.trajectoryType == 'objectfeatures':
32 trajectoryType = 'object' 33 trajectoryType = 'object'
34 prototypeType = 'feature'
33 35
34 #features = storage.loadTrajectoriesFromSqlite(databaseFilename, args.trajectoryType) 36 #features = storage.loadTrajectoriesFromSqlite(databaseFilename, args.trajectoryType)
35 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate) 37 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate)
36 38
37 if args.trajectoryType == 'objectfeatures': 39 if args.trajectoryType == 'objectfeatures':
38 features = [] 40 features = []
39 for o in objects: 41 for o in objects:
40 o.getNLongestFeatures(args.maxNObjectFeatures) 42 features += o.getNLongestFeatures(args.maxNObjectFeatures)
41 objects = features 43 objects = features
42 44
43 trajectories = [o.getPositions().asArray().T for o in objects] 45 trajectories = [o.getPositions().asArray().T for o in objects]
44 46
45 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) 47 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
50 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.randomInitialization) # this line can be called again without reinitializing similarities 52 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.randomInitialization) # this line can be called again without reinitializing similarities
51 53
52 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) 54 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
53 print(clusterSizes) 55 print(clusterSizes)
54 56
55 storage.savePrototypesToSqlite(args.databaseFilename, [objects[i].getNum() for i in prototypeIndices], args.trajectoryType, [clusterSizes[i] for i in prototypeIndices]) # if saving filenames, add for example [objects[i].dbFilename for i in prototypeIndices] 57 storage.savePrototypesToSqlite(args.databaseFilename, [objects[i].getNum() for i in prototypeIndices], prototypeType, [clusterSizes[i] for i in prototypeIndices]) # if saving filenames, add for example [objects[i].dbFilename for i in prototypeIndices]
56 58
57 if args.saveSimilarities: 59 if args.saveSimilarities:
58 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f') 60 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')
59 61
60 if args.display: 62 if args.display: