Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/learn-motion-patterns.py @ 920:499154254f37

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improved prototype loading
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 05 Jul 2017 16:30:04 -0400
parents 7b3f2e0a2652
children 630934595871
comparison
equal deleted inserted replaced
919:7b3f2e0a2652 920:499154254f37
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('-r', dest = 'initialPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
13 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to learn from', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures') 14 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) 15 parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 1)
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('-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) 17 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 18 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) 19 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
19 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None) 20 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None)
28 29
29 # use cases 30 # use cases
30 # 1. learn proto from one file, save in same or another (with traj) 31 # 1. learn proto from one file, save in same or another (with traj)
31 # 2. load proto, load objects, update proto, save proto 32 # 2. load proto, load objects, update proto, save proto
32 # 3. assign objects from one db to proto 33 # 3. assign objects from one db to proto
33 # 4. load objects from several files, save in another 34 # 4. load objects from several files, save in another -> see metadata: site with view and times
34 # 5. keep prototypes, with positions/velocities, in separate db (keep link to original data through filename, type and index) 35 # 5. keep prototypes, with positions/velocities, in separate db (keep link to original data through filename, type and index)
35 36
36 # TODO add possibility to cluter with velocities 37 # TODO add possibility to cluter with velocities
37 # TODO add possibility to start with saved prototypes so that one can incrementally learn from several databases 38 # TODO add possibility to start with saved prototypes so that one can incrementally learn from several databases
38 # save prototypes with database name, add option to keep trajectory along: if saved in same db, no need
39 # load proto must load the movingobject
40 # save the objects that match the prototypes 39 # save the objects that match the prototypes
41 # write an assignment function for objects 40 # write an assignment function for objects
42 41
43 trajectoryType = args.trajectoryType 42 trajectoryType = args.trajectoryType
44 prototypeType = args.trajectoryType 43 prototypeType = args.trajectoryType
66 65
67 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) 66 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
68 print(clusterSizes) 67 print(clusterSizes)
69 68
70 prototypes = [objects[i] for i in prototypeIndices] 69 prototypes = [objects[i] for i in prototypeIndices]
71 storage.savePrototypesToSqlite(args.databaseFilename, [p.getNum() for p in prototypes], prototypeType, prototypes, [clusterSizes[i] for i in prototypeIndices]) # if saving filenames, add for example [objects[i].dbFilename for i in prototypeIndices] 70 storage.savePrototypesToSqlite(args.databaseFilename, [p.getNum() for p in prototypes], prototypeType, [clusterSizes[i] for i in prototypeIndices]) # if saving filenames, add for example [objects[i].dbFilename for i in prototypeIndices]
72 71
73 if args.saveSimilarities: 72 if args.saveSimilarities:
74 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f') 73 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')
75 74
76 # if args.saveMatches: 75 # if args.saveMatches: