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annotate scripts/learn-motion-patterns.py @ 998:933670761a57

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updated code to python 3 (tests pass and scripts run, but non-executed parts of code are probably still not correct)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Sun, 27 May 2018 23:22:48 -0400
parents 23f98ebb113f
children cc5cb04b04b0
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
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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 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, moving
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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('-o', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes')
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14 parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
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15 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to learn from', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures')
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16 parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 1)
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17 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
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18 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
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19 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
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20 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
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21 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0)
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22 parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true')
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23 parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true')
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24 parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
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25 parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
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26 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
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27 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
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28 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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29 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
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31 args = parser.parse_args()
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32
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33 # use cases
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34 # 1. learn proto from one file, save in same or another
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35 # 2. load proto, load objects, update proto, save proto
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36 # 3. assign objects from one db to proto
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37 # 4. load objects from several files, save in another -> see metadata: site with view and times
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38 # 5. keep prototypes, with positions/velocities, in separate db (keep link to original data through filename, type and index)
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39
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40 # TODO add possibility to cluster with velocities
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41 # TODO add possibilite to load all trajectories and use minclustersize
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42 # save the objects that match the prototypes
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43 # write an assignment function for objects
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44
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45 # load trajectories to cluster or assign
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46 if args.trajectoryType == 'objectfeatures':
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47 trajectoryType = 'feature'
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48 objectFeatureNumbers = storage.loadObjectFeatureFrameNumbers(args.databaseFilename, objectNumbers = args.nTrajectories)
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49 featureNumbers = []
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50 for numbers in objectFeatureNumbers.values():
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51 featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)]
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52 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate)
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53 else:
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54 trajectoryType = args.trajectoryType
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55 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate)
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57 trajectories = [o.getPositions().asArray().T for o in objects]
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58
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59 # load initial prototypes, if any
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60 if args.inputPrototypeDatabaseFilename is not None:
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61 initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
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62 trajectories = [p.getMovingObject().getPositions().asArray().T for p in initialPrototypes]+trajectories
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63 if len(initialPrototypes) > 0:
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64 initialPrototypeIndices = list(range(len(initialPrototypes)))
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65 else:
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66 initialPrototypeIndices = None
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67 else:
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68 initialPrototypes = []
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69 initialPrototypeIndices = None
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70
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71 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
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72 nTrajectories = len(trajectories)
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73
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74 similarities = -np.ones((nTrajectories, nTrajectories))
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75 similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
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76 # the next line can be called again without reinitializing similarities
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77 if args.learn:
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78 prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices)
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79 # assignment is done if explicitly passed as argument or if working on the same database (starting prototypes from scratch and assigning them)
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80 else:
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81 prototypeIndices = initialPrototypeIndices
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82
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83 if args.assign:
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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 initialPrototypes[i].nMatchings += nMatchings
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97 prototypes.append(initialPrototypes[i])
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98 else:
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99 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType, nMatchings))
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100
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101 if args.outputPrototypeDatabaseFilename is None:
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102 outputPrototypeDatabaseFilename = args.databaseFilename
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103 else:
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104 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
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105 if args.inputPrototypeDatabaseFilename == args.outputPrototypeDatabaseFilename:
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106 storage.deleteFromSqlite(args.outputPrototypeDatabaseFilename, 'prototype')
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107 storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)
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108
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109 if args.saveSimilarities:
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110 # todo save trajectories and prototypes
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111 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')
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112
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113 labelsToProtoIndices = {protoId: i for i, protoId in enumerate(prototypeIndices)}
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114 if args.assign and args.saveMatches:
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115 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objects, trajectoryType, [labelsToProtoIndices[l] for l in labels], prototypes)
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117 if args.display and args.assign:
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parents: 951
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118 from matplotlib.pyplot import figure, show, axis
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119 figure()
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parents: 951
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120 for i,o in enumerate(objects):
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121 if i not in prototypeIndices:
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parents: 951
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122 if labels[i] < 0:
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123 o.plot('kx')
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parents: 951
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124 else:
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parents: 951
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125 o.plot(utils.colors[labels[i]])
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126 for i in prototypeIndices:
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127 objects[i].plot(utils.colors[i]+'o')
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128 axis('equal')
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129 show()
921
630934595871 work in progress with prototype class
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130 else:
952
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parents: 951
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131 print('Not learning nor assigning: doing nothing')