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annotate scripts/learn-poi.py @ 787:0a428b449b80 dev

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improved script to display over world image
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 17 Mar 2016 16:01:19 -0400
parents 1f2b2d1f4fbf
children 180b6b0231c0
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786
1f2b2d1f4fbf added script and code to learn POIs
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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('-n', dest = 'nClusters', help = 'number of point clusters', required = True, type = int)
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15 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full")
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16 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image')
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17 parser.add_argument('-u', dest = 'pixelsPerUnit', help = 'number pixels per unit of distance', type = float, default = 1.)
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18
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19 args = parser.parse_args()
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20
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21 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType)
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22
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23 beginnings = []
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24 ends = []
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25 for o in objects:
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26 beginnings.append(o.getPositionAt(0).aslist())
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27 ends.append(o.getPositionAt(int(o.length())-1).aslist())
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28
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29 beginnings = np.array(beginnings)
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30 ends = np.array(ends)
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31
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32 gmm = mixture.GMM(n_components=args.nClusters, covariance_type = args.covarianceType)
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33 beginningModel=gmm.fit(beginnings)
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34 gmm = mixture.GMM(n_components=args.nClusters, covariance_type = args.covarianceType)
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35 endModel=gmm.fit(ends)
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36
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37 fig = plt.figure()
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38 if args.worldImageFilename is not None and args.pixelsPerUnit is not None:
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39 img = plt.imread(args.worldImageFilename)
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40 plt.imshow(img)
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41 ml.plotGMMClusters(beginningModel, beginnings, fig, nPixelsPerUnit = args.pixelsPerUnit)
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42 plt.axis('equal')
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43 plt.title('Origins')
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44 print('Origin Clusters:\n{}'.format(ml.computeClusterSizes(beginningModel.predict(beginnings), range(args.nClusters))))
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45
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46 fig = plt.figure()
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47 if args.worldImageFilename is not None and args.pixelsPerUnit is not None:
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48 img = plt.imread(args.worldImageFilename)
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49 plt.imshow(img)
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50 ml.plotGMMClusters(endModel, ends, fig, nPixelsPerUnit = args.pixelsPerUnit)
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51 plt.axis('equal')
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52 plt.title('Destinations')
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53 print('Destination Clusters:\n{}'.format(ml.computeClusterSizes(endModel.predict(ends), range(args.nClusters))))