Mercurial Hosting > traffic-intelligence
comparison scripts/learn-motion-patterns.py @ 907:9fd7b18f75b4
re arranged motion pattern learning
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 23 Jun 2017 23:50:02 -0400 |
parents | 8e8ec4ece66e |
children | b297525b2cbf |
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906:a57e6fbcd8e3 | 907:9fd7b18f75b4 |
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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) |
19 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None) | 19 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None) |
20 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true') | 20 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true') |
21 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int, default = None) | 21 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int) |
22 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true') | 22 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true') |
23 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true') | 23 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true') |
24 #parser.add_argument('--save-matches', dest = 'saveMatches', help = 'save the matched prototype information', action = 'store_true') | |
24 | 25 |
25 args = parser.parse_args() | 26 args = parser.parse_args() |
26 | 27 |
27 # TODO parameters (random init?) and what to learn from: objects, features, longest features from objects | 28 # use cases |
29 # 1. learn proto from one file, save in same or another (with traj) | |
30 # 2. load proto, load objects, update proto, save proto | |
31 # 3. assign objects from one db to proto | |
32 # 4. load objects from several files, save in another | |
33 | |
28 # TODO add possibility to cluter with velocities | 34 # TODO add possibility to cluter with velocities |
35 # TODO add possibility to start with saved prototypes so that one can incrementally learn from several databases | |
36 # save prototypes with database name, add option to keep trajectory along: if saved in same db, no need | |
37 # load proto must load the movingobject | |
38 # save the objects that match the prototypes | |
39 # write an assignment function for objects | |
29 | 40 |
30 trajectoryType = args.trajectoryType | 41 trajectoryType = args.trajectoryType |
31 prototypeType = args.trajectoryType | 42 prototypeType = args.trajectoryType |
32 if args.trajectoryType == 'objectfeatures': | 43 if args.trajectoryType == 'objectfeatures': |
33 trajectoryType = 'object' | 44 trajectoryType = 'object' |
47 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) | 58 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) |
48 nTrajectories = len(trajectories) | 59 nTrajectories = len(trajectories) |
49 | 60 |
50 similarities = -np.ones((nTrajectories, nTrajectories)) | 61 similarities = -np.ones((nTrajectories, nTrajectories)) |
51 | 62 |
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 | 63 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.randomInitialization, True, None) # this line can be called again without reinitializing similarities |
53 | 64 |
54 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) | 65 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) |
55 print(clusterSizes) | 66 print(clusterSizes) |
56 | 67 |
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] | 68 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] |
58 | 69 |
59 if args.saveSimilarities: | 70 if args.saveSimilarities: |
60 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f') | 71 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f') |
72 | |
73 # if args.saveMatches: | |
74 # out = storage.openCheck(utils.removeExtension(args.databaseFilename)+'prototypes-matches.csv', 'w') | |
75 # for o in ojbects: | |
76 # out.write('') | |
61 | 77 |
62 if args.display: | 78 if args.display: |
63 from matplotlib.pyplot import figure, show, axis | 79 from matplotlib.pyplot import figure, show, axis |
64 figure() | 80 figure() |
65 for i,o in enumerate(objects): | 81 for i,o in enumerate(objects): |
70 o.plot(utils.colors[labels[i]]) | 86 o.plot(utils.colors[labels[i]]) |
71 for i in prototypeIndices: | 87 for i in prototypeIndices: |
72 objects[i].plot(utils.colors[i]+'o') | 88 objects[i].plot(utils.colors[i]+'o') |
73 axis('equal') | 89 axis('equal') |
74 show() | 90 show() |
75 | |
76 # TODO store the prototypes trajectories, add option so store similarities (the most expensive stuff) with limited accuracy |