Mercurial > hg > nsaunier > traffic-intelligence

annotate scripts/learn-poi.py @ 915:13434f5017dd

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work to save trajectory assignment to origin and destinations
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 04 Jul 2017 17:03:29 -0400
parents f228fd649644
children 7345f0d51faa
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1 #! /usr/bin/env python
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2
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3 import argparse
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4
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5 import numpy as np
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6 from sklearn import mixture
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7 import matplotlib.pyplot as plt
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8
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9 import storage, ml
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10
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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).')
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12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
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13 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['feature', 'object'], default = 'object')
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14 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to display', type = int)
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15 parser.add_argument('-norigins', dest = 'nOriginClusters', help = 'number of clusters for trajectory origins', required = True, type = int)
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16 parser.add_argument('-ndestinations', dest = 'nDestinationClusters', help = 'number of clusters for trajectory destinations (=norigins if not provided)', type = int)
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17 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full")
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18 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image')
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19 parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.)
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20 parser.add_argument('--display', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance
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21 parser.add_argument('--assign', dest = 'assign', help = 'display points of interests', action = 'store_true')
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22
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23 # TODO test Variational Bayesian Gaussian Mixture BayesianGaussianMixture
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24
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25 args = parser.parse_args()
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26
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27 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nObjects)
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28
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29 beginnings = []
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30 ends = []
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31 for o in objects:
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32 beginnings.append(o.getPositionAt(0).aslist())
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33 ends.append(o.getPositionAt(int(o.length())-1).aslist())
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34 if args.assign:
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35 o.od = [-1, -1]
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36
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37 beginnings = np.array(beginnings)
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38 ends = np.array(ends)
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39
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40 nDestinationClusters = args.nDestinationClusters
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41 if args.nDestinationClusters is None:
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42 nDestinationClusters = args.nOriginClusters
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43
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44 gmmId=0
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45 models = {}
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46 for nClusters, points, gmmType in zip([args.nOriginClusters, nDestinationClusters],
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47 [beginnings, ends],
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48 ['beginning', 'end']):
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49 # estimation
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50 gmm = mixture.GaussianMixture(n_components=nClusters, covariance_type = args.covarianceType)
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51 models[gmmType]=gmm.fit(points)
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52 if not models[gmmType].converged_:
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53 print('Warning: model for '+gmmType+' points did not converge')
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54 if args.display or args.assign:
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55 labels = models[gmmType].predict(points)
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56 # plot
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57 if args.display:
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58 fig = plt.figure()
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59 if args.worldImageFilename is not None and args.unitsPerPixel is not None:
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60 img = plt.imread(args.worldImageFilename)
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61 plt.imshow(img)
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62 ml.plotGMMClusters(models[gmmType], labels, points, fig, nUnitsPerPixel = args.unitsPerPixel)
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63 plt.axis('image')
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64 plt.title(gmmType)
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65 print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(models[gmmType].n_components))))
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66 # save
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67 storage.savePOIs(args.databaseFilename, models[gmmType], gmmType, gmmId)
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68 # save assignments
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69 if args.assign:
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70 for o, l in zip(objects, labels):
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71 if gmmType == 'beginning':
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72 o.od[0] = l
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73 elif gmmType == 'end':
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74 o.od[1] = l
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75 gmmId += 1
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76
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77 if args.assign:
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78 storage.savePOIAssignments(args.databaseFilename, objects)
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79
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80 if args.display:
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81 plt.axis('equal')
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82 plt.show()