Mercurial Hosting > traffic-intelligence
comparison scripts/learn-poi.py @ 914:f228fd649644
corrected bugs in learn-pois.py
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 28 Jun 2017 23:43:52 -0400 |
parents | 1cd878812529 |
children | 13434f5017dd |
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913:1cd878812529 | 914:f228fd649644 |
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6 from sklearn import mixture | 6 from sklearn import mixture |
7 import matplotlib.pyplot as plt | 7 import matplotlib.pyplot as plt |
8 | 8 |
9 import storage, ml | 9 import storage, ml |
10 | 10 |
11 parser = argparse.ArgumentParser(description='The program learns and displays Gaussians fit to beginnings and ends of object trajectories (based on Mohamed Gomaa Mohamed 2015 PhD). TODO: save the data') | 11 parser = argparse.ArgumentParser(description='The program learns and displays Gaussians fit to beginnings and ends of object trajectories (based on Mohamed Gomaa Mohamed 2015 PhD).') |
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 = ['feature', 'object'], default = 'object') | 13 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['feature', 'object'], default = 'object') |
14 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to display', type = int) | |
14 parser.add_argument('-norigins', dest = 'nOriginClusters', help = 'number of clusters for trajectory origins', required = True, type = int) | 15 parser.add_argument('-norigins', dest = 'nOriginClusters', help = 'number of clusters for trajectory origins', required = True, type = int) |
15 parser.add_argument('-ndestinations', dest = 'nDestinationClusters', help = 'number of clusters for trajectory destinations (=norigins if not provided)', type = int) | 16 parser.add_argument('-ndestinations', dest = 'nDestinationClusters', help = 'number of clusters for trajectory destinations (=norigins if not provided)', type = int) |
16 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full") | 17 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full") |
17 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image') | 18 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image') |
18 parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.) | 19 parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.) |
19 parser.add_argument('--display', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance | 20 parser.add_argument('--display', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance |
20 parser.add_argument('--assign', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance | 21 parser.add_argument('--assign', dest = 'assign', help = 'display points of interests', action = 'store_true') |
21 | 22 |
22 args = parser.parse_args() | 23 args = parser.parse_args() |
23 | 24 |
24 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType) | 25 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nObjects) |
25 | 26 |
26 beginnings = [] | 27 beginnings = [] |
27 ends = [] | 28 ends = [] |
28 for o in objects: | 29 for o in objects: |
29 beginnings.append(o.getPositionAt(0).aslist()) | 30 beginnings.append(o.getPositionAt(0).aslist()) |
43 # estimation | 44 # estimation |
44 gmm = mixture.GaussianMixture(n_components=nClusters, covariance_type = args.covarianceType) | 45 gmm = mixture.GaussianMixture(n_components=nClusters, covariance_type = args.covarianceType) |
45 model=gmm.fit(points) | 46 model=gmm.fit(points) |
46 if not model.converged_: | 47 if not model.converged_: |
47 print('Warning: model for '+gmmType+' points did not converge') | 48 print('Warning: model for '+gmmType+' points did not converge') |
49 if args.display or args.assign: | |
50 labels = model.predict(points) | |
48 # plot | 51 # plot |
49 if args.display: | 52 if args.display: |
50 fig = plt.figure() | 53 fig = plt.figure() |
51 if args.worldImageFilename is not None and args.unitsPerPixel is not None: | 54 if args.worldImageFilename is not None and args.unitsPerPixel is not None: |
52 img = plt.imread(args.worldImageFilename) | 55 img = plt.imread(args.worldImageFilename) |
53 plt.imshow(img) | 56 plt.imshow(img) |
54 labels = model.predict(points) | 57 ml.plotGMMClusters(model, labels, points, fig, nUnitsPerPixel = args.unitsPerPixel) |
55 labels = ml.plotGMMClusters(model, labels, points, fig, nUnitsPerPixel = args.unitsPerPixel) | |
56 plt.axis('image') | 58 plt.axis('image') |
57 plt.title(gmmType) | 59 plt.title(gmmType) |
58 print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components)))) | 60 print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components)))) |
59 # save | 61 # save |
60 storage.savePOIs(args.databaseFilename, model, gmmType, gmmId) | 62 storage.savePOIs(args.databaseFilename, model, gmmType, gmmId) |
61 # save assignments | 63 # save assignments |
62 if args.assign: | 64 if args.assign: |
63 pass | 65 pass # savePOIAssignments( |
64 gmmId += 1 | 66 gmmId += 1 |
65 | 67 |
66 if args.display: | 68 if args.display: |
67 plt.axis('equal') | 69 plt.axis('equal') |
68 plt.show() | 70 plt.show() |