Mercurial > hg > nsaunier > traffic-intelligence

annotate scripts/learn-motion-patterns.py @ 804:17e54690af8a dev

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work in progress, not fully functional yet
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 01 Jun 2016 17:57:49 -0400
parents 0e875a7f5759
children 181bcb6dad3a
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1 #! /usr/bin/env python
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2
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3 import sys, argparse
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4
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5 #import matplotlib.pyplot as plt
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6 import numpy as np
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7
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8 import ml, utils, storage
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9
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10 parser = argparse.ArgumentParser(description='The program learns prototypes for the motion patterns') #, epilog = ''
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11 #parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
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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 = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures')
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14 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
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15 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
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16 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
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17 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
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18 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None)
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19 parser.add_argument('--display', dest = 'display', help = 'display trajectories', 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 # TODO parameters (random init?) and what to learn from: objects, features, longest features from objects
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24 # TODO add possibility to cluter with velocities
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25
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26 trajectoryType = args.trajectoryType
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27 if args.trajectoryType == 'objectfeatures':
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28 trajectoryType = 'object'
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29
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30 #features = storage.loadTrajectoriesFromSqlite(databaseFilename, args.trajectoryType)
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31 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories)
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32
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33 if args.trajectoryType == 'objectfeatures':
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34 features = []
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35 for o in objects:
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36 tmp = utils.sortByLength(o.getFeatures(), reverse = True)
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37 features += tmp[:min(len(tmp), 3)]
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38 objects = features
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39
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40 trajectories = [o.getPositions().asArray().T for o in objects]
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41
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42 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
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43 nTrajectories = len(trajectories)
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44
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45 similarities = -np.ones((nTrajectories, nTrajectories))
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46 # for i in xrange(nTrajectories):
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47 # for j in xrange(i):
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48 # similarities[i,j] = lcss.computeNormalized(trajectories[i], trajectories[j])
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49 # similarities[j,i] = similarities[i,j]
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51 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize) # this line can be called again without reinitializing similarities
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52
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53 if args.display:
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54 from matplotlib.pyplot import figure
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55 figure()
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56 for i,o in enumerate(objects):
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57 if i not in prototypeIndices:
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58 if labels[i] < 0:
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59 o.plot('kx')
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60 else:
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61 o.plot(utils.colors[labels[i]])
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62 for i in prototypeIndices:
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63 objects[i].plot(utils.colors[i]+'o')
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64
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65 # TODO store the prototypes (if features, easy, if objects, info must be stored about the type)