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annotate scripts/learn-poi.py @ 844:5a68779d7777

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added capability to save prototypes
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 14 Jul 2016 00:34:59 -0400
parents 181bcb6dad3a
children 6db83beb5350
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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). TODO: save the data')
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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('-norigins', dest = 'nOriginClusters', help = 'number of clusters for trajectory origins', required = True, type = int)
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15 parser.add_argument('-ndestinations', dest = 'nDestinationClusters', help = 'number of clusters for trajectory destinations (=norigins if not provided)', type = int)
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16 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full")
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17 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image')
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18 parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.)
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19 parser.add_argument('--display', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance
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20
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21 args = parser.parse_args()
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22
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23 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType)
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24
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25 beginnings = []
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26 ends = []
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27 for o in objects:
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28 beginnings.append(o.getPositionAt(0).aslist())
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29 ends.append(o.getPositionAt(int(o.length())-1).aslist())
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30
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31 beginnings = np.array(beginnings)
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32 ends = np.array(ends)
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33
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34 nDestinationClusters = args.nDestinationClusters
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35 if args.nDestinationClusters is None:
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36 nDestinationClusters = args.nOriginClusters
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37
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38 gmmId=0
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39 for nClusters, points, gmmType in zip([args.nOriginClusters, nDestinationClusters],
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40 [beginnings, ends],
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41 ['beginning', 'end']):
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42 # estimation
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43 gmm = mixture.GMM(n_components=nClusters, covariance_type = args.covarianceType)
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44 model=gmm.fit(beginnings)
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45 if not model.converged_:
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46 print('Warning: model for '+gmmType+' points did not converge')
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47 # plot
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48 if args.display:
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49 fig = plt.figure()
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50 if args.worldImageFilename is not None and args.unitsPerPixel is not None:
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51 img = plt.imread(args.worldImageFilename)
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52 plt.imshow(img)
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53 labels = ml.plotGMMClusters(model, points, fig, nUnitsPerPixel = args.unitsPerPixel)
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54 plt.axis('image')
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55 plt.title(gmmType)
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56 print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components))))
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57 # save
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58 storage.savePOIs(args.databaseFilename, model, gmmType, gmmId)
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59 gmmId += 1
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60
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61 if args.display:
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62 plt.axis('equal')
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63 plt.show()
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64
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65 # fig = plt.figure()
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66 # if args.worldImageFilename is not None and args.pixelsPerUnit is not None:
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67 # img = plt.imread(args.worldImageFilename)
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68 # plt.imshow(img)
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69 # ml.plotGMMClusters(, , fig, nPixelsPerUnit = args.pixelsPerUnit)
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70 # plt.axis('equal')
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71 # plt.title()
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72 # print('Destination Clusters:\n{}'.format(ml.computeClusterSizes(endModel.predict(ends), range(args.nClusters))))