Mercurial > hg > nsaunier > traffic-intelligence

annotate scripts/learn-motion-patterns.py @ 1033:8ffb3ae9f3d2

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work in progress
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 20 Jun 2018 00:07:03 -0400
parents d0e339359d8a
children 933588568bec
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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 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 the assignments', 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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57
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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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80
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81 if args.assign: # TODO don't touch initial prototypes if not from same db as trajectories
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82 #if not args.learn and args.minClusterSize >= 1: # allow only
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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 # if args.minClusterSize >= 1:
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85 # if initialPrototypeIndices is None:
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86 # prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc, args.minClusterSize)
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87 # else:
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88 # print('Not assigning with non-zero minimum cluster size and initial prototypes (would remove initial prototypes based on other trajectories')
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89 # else:
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90 # prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
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91 prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
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92 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
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93 print(clusterSizes)
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94
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95 if args.learn and not args.assign:
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96 prototypes = []
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97 for i in prototypeIndices:
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98 if i<len(initialPrototypes):
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99 prototypes.append(initialPrototypes[i])
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100 else:
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101 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType))
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102
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103 if args.outputPrototypeDatabaseFilename is None:
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104 outputPrototypeDatabaseFilename = args.databaseFilename
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105 else:
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106 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
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107 if args.inputPrototypeDatabaseFilename == args.outputPrototypeDatabaseFilename:
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108 storage.deleteFromSqlite(args.outputPrototypeDatabaseFilename, 'prototype')
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109 storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)
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110
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111 if not args.learn and args.assign: # no new prototypes # not save assignments of past prototypes if removes with minClusterSize
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112 prototypes = []
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113 for i in prototypeIndices:
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114 nMatchings = clusterSizes[i]-1
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115 if initialPrototypes[i].nMatchings is None:
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116 initialPrototypes[i].nMatchings = nMatchings
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117 else:
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118 initialPrototypes[i].nMatchings += nMatchings
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119 prototypes.append(initialPrototypes[i])
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120 if args.outputPrototypeDatabaseFilename is None:
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121 outputPrototypeDatabaseFilename = args.databaseFilename
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122 else:
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123 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
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124 storage.setPrototypeMatchingsInSqlite(outputPrototypeDatabaseFilename, prototypes)
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125
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126 labelsToProtoIndices = {protoId: i for i, protoId in enumerate(prototypeIndices)}
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127 if args.saveMatches:
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128 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objects, trajectoryType, [labelsToProtoIndices[l] for l in labels], prototypes)
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129
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130 if (args.learn or args.assign) and args.saveSimilarities:
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131 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')
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132
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133 if args.display and args.assign:
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134 from matplotlib.pyplot import figure, show, axis
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135 figure()
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136 for i,o in enumerate(objects):
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137 if i not in prototypeIndices:
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138 if labels[i] < 0:
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139 o.plot('kx')
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140 else:
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141 o.plot(utils.colors[labels[i]])
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142 for i in prototypeIndices:
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143 objects[i].plot(utils.colors[i]+'o')
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144 axis('equal')
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145 show()