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annotate scripts/learn-motion-patterns.py @ 1032:d0e339359d8a

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work in progress
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 19 Jun 2018 17:07:50 -0400
parents cc5cb04b04b0
children 8ffb3ae9f3d2
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1 #! /usr/bin/env python3
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2
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3 import sys, argparse
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4
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5 import numpy as np
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6
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7 from trafficintelligence import ml, utils, storage, moving
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8
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9 parser = argparse.ArgumentParser(description='The program learns prototypes for the motion patterns') #, epilog = ''
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10 #parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
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11 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
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12 parser.add_argument('-o', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes')
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13 parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
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14 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to learn from', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures')
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15 parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 1)
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16 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
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17 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
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18 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
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19 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
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20 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0)
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21 parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true')
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22 parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true')
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23 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
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24 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
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25 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
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26 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
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27 parser.add_argument('--save-matches', dest = 'saveMatches', help = 'saves the assignments of the objects (not for features) to the prototypes', action = 'store_true')
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28 parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves them (do not use min cluster size as it will discard prototypes at the beginning if the initial cluster is too small)', action = 'store_true')
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30 args = parser.parse_args()
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31
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32 # use cases
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33 # 1. learn proto from one file, save in same or another
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34 # 2. load proto, load objects, update proto, save proto
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35 # 3. assign objects from one db to proto
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36 # 4. load objects from several files, save in another -> see metadata: site with view and times
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37 # 5. keep prototypes, with positions/velocities, in separate db (keep link to original data through filename, type and index)
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38
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39 # TODO add possibility to cluster with velocities
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40 # TODO add possibilite to load all trajectories and use minclustersize
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41 # save the objects that match the prototypes
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42 # write an assignment function for objects
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43
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44 # load trajectories to cluster or assign
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45 if args.trajectoryType == 'objectfeatures':
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46 trajectoryType = 'feature'
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47 objectFeatureNumbers = storage.loadObjectFeatureFrameNumbers(args.databaseFilename, objectNumbers = args.nTrajectories)
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48 featureNumbers = []
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49 for numbers in objectFeatureNumbers.values():
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50 featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)]
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51 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate)
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52 else:
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53 trajectoryType = args.trajectoryType
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54 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate)
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56 trajectories = [o.getPositions().asArray().T for o in objects]
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58 # load initial prototypes, if any
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59 if args.inputPrototypeDatabaseFilename is not None:
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60 initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
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61 trajectories = [p.getMovingObject().getPositions().asArray().T for p in initialPrototypes]+trajectories
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62 if len(initialPrototypes) > 0:
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63 initialPrototypeIndices = list(range(len(initialPrototypes)))
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64 else:
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65 initialPrototypeIndices = None
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66 else:
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67 initialPrototypes = []
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68 initialPrototypeIndices = None
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70 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
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71 nTrajectories = len(trajectories)
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72
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73 similarities = -np.ones((nTrajectories, nTrajectories))
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74 similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
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75 # the next line can be called again without reinitializing similarities
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76 if args.learn:
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77 prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices)
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78 else:
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79 prototypeIndices = initialPrototypeIndices
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81 if args.assign:
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82 if not args.learn and args.minClusterSize >= 1:
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83 print('Warning: you did not learn the prototypes and you are using minimum cluster size of {}, which may lead to removing prototypes and assigning them to others'.format(args.minClusterSize))
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84 prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc, args.minClusterSize)
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85 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
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86 print(clusterSizes)
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87
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88 if args.learn or args.assign:
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89 prototypes = []
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90 for i in prototypeIndices:
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91 if args.assign:
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92 nMatchings = clusterSizes[i]-1
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93 #else:
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94 # nMatchings = 0
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95 if i<len(initialPrototypes):
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96 if args.assign:
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97 initialPrototypes[i].nMatchings += nMatchings
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98 prototypes.append(initialPrototypes[i])
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99 else:
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100 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType, nMatchings))
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101
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102 if args.outputPrototypeDatabaseFilename is None:
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103 outputPrototypeDatabaseFilename = args.databaseFilename
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104 else:
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105 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
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106 if args.inputPrototypeDatabaseFilename == args.outputPrototypeDatabaseFilename:
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107 storage.deleteFromSqlite(args.outputPrototypeDatabaseFilename, 'prototype')
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108 storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)
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109
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110 if args.saveSimilarities:
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111 # todo save trajectories and prototypes
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112 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')
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113
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114 labelsToProtoIndices = {protoId: i for i, protoId in enumerate(prototypeIndices)}
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115 if args.assign and args.saveMatches:
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116 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objects, trajectoryType, [labelsToProtoIndices[l] for l in labels], prototypes)
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parents: 951
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117
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parents: 951
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118 if args.display and args.assign:
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parents: 951
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119 from matplotlib.pyplot import figure, show, axis
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120 figure()
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parents: 951
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121 for i,o in enumerate(objects):
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parents: 951
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122 if i not in prototypeIndices:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 951
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123 if labels[i] < 0:
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parents: 951
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124 o.plot('kx')
a9b2beef0db4 loading and assigning motion patterns works
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 951
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125 else:
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parents: 951
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126 o.plot(utils.colors[labels[i]])
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parents: 951
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127 for i in prototypeIndices:
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parents: 951
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128 objects[i].plot(utils.colors[i]+'o')
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parents: 951
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129 axis('equal')
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 951
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130 show()
921
630934595871 work in progress with prototype class
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parents: 920
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131 else:
952
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parents: 951
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132 print('Not learning nor assigning: doing nothing')