Mercurial > hg > nsaunier > traffic-intelligence

annotate scripts/learn-motion-patterns.py @ 1052:1748c02f9ac3

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Modifying storage.py [configloaders]
author Wendlasida
date Thu, 05 Jul 2018 13:48:12 -0400
parents 5621e4ad2428
children b735895c8815
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 numpy as np
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6 import matplotlib.pyplot as plt
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7
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8 from trafficintelligence import ml, utils, storage, moving
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9
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10 parser = argparse.ArgumentParser(description='''The program clusters trajectories, each cluster being represented by a trajectory. It can either work on the same dataset (database) or different ones, but only does learning or assignment at a time to avoid issues (the minimum cluster size argument is not used for now as it may change prototypes when assigning other trajectories)''') #, 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 = ['objectfeature', '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-assignments', dest = 'saveAssignments', help = 'saves the assignments of the objects 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 the assignments', action = 'store_true')
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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 (from same or other db), update proto matchings, save proto
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36 # TODO 3. on same dataset, learn and assign trajectories (could be done with min cluster size)
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37 # TODO? 4. when assigning, allow min cluster size only to avoid assigning to small clusters (but prototypes are not removed even if in small clusters, can be done after assignment with nmatchings)
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38
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39 # TODO add possibility to cluster with velocities
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40 # TODO add possibility to load all trajectories and use minclustersize
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41
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42 # load trajectories to cluster or assign
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43 if args.trajectoryType == 'objectfeature':
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44 trajectoryType = 'feature'
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45 objectFeatureNumbers = storage.loadObjectFeatureFrameNumbers(args.databaseFilename, objectNumbers = args.nTrajectories)
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46 featureNumbers = []
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47 for numbers in objectFeatureNumbers.values():
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48 featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)]
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49 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate)
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50 else:
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51 trajectoryType = args.trajectoryType
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52 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate)
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54 trajectories = [o.getPositions().asArray().T for o in objects]
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55
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56 # load initial prototypes, if any
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57 if args.inputPrototypeDatabaseFilename is not None:
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58 initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
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59 trajectories = [p.getMovingObject().getPositions().asArray().T for p in initialPrototypes]+trajectories
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60 if len(initialPrototypes) > 0:
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61 initialPrototypeIndices = list(range(len(initialPrototypes)))
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62 else:
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63 initialPrototypeIndices = None
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64 else:
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65 initialPrototypes = []
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66 initialPrototypeIndices = None
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68 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
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69 nTrajectories = len(trajectories)
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70
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71 similarities = -np.ones((nTrajectories, nTrajectories))
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72 similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
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73 # the next line can be called again without reinitializing similarities
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74 if args.learn:
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75 prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices)
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76 else:
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77 prototypeIndices = initialPrototypeIndices
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78
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79 if args.assign: # TODO don't touch initial prototypes if not from same db as trajectories
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80 #if not args.learn and args.minClusterSize >= 1: # allow only
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81 # 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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82 # if args.minClusterSize >= 1:
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83 # if initialPrototypeIndices is None:
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84 # prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc, args.minClusterSize)
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85 # else:
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86 # print('Not assigning with non-zero minimum cluster size and initial prototypes (would remove initial prototypes based on other trajectories')
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87 # else:
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88 # prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
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89 assignedPrototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
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90
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91 if args.learn and not args.assign:
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92 prototypes = []
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93 for i in prototypeIndices:
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94 if i<len(initialPrototypes):
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95 prototypes.append(initialPrototypes[i])
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96 else:
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97 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType))
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98
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99 if args.outputPrototypeDatabaseFilename is None:
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100 outputPrototypeDatabaseFilename = args.databaseFilename
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101 else:
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102 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
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103 if args.inputPrototypeDatabaseFilename == args.outputPrototypeDatabaseFilename:
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104 storage.deleteFromSqlite(args.outputPrototypeDatabaseFilename, 'prototype')
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105 storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)
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106 if args.display:
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107 plt.figure()
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108 for p in prototypes:
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109 p.getMovingObject().plot()
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110 plt.axis('equal')
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111 plt.show()
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112
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113 if not args.learn and args.assign: # no modification to prototypes, can work with initialPrototypes
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114 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
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115 for i in prototypeIndices:
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116 nMatchings = clusterSizes[i]-1
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117 if initialPrototypes[i].nMatchings is None:
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118 initialPrototypes[i].nMatchings = nMatchings
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119 else:
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120 initialPrototypes[i].nMatchings += nMatchings
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121 if args.outputPrototypeDatabaseFilename is None:
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122 outputPrototypeDatabaseFilename = args.databaseFilename
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123 else:
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124 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
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125 storage.setPrototypeMatchingsInSqlite(outputPrototypeDatabaseFilename, initialPrototypes)
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126 if args.saveAssignments:
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127 if args.trajectoryType == 'objectfeature': # consider that the object is assigned through its longest features
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128 objectNumbers = []
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129 objectLabels = []
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130 for objNum, objFeatureNumbers in objectFeatureNumbers.items():
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131 objLabels = []
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132 for i, o in enumerate(objects):
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133 if o.getNum() in objFeatureNumbers:
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134 objLabels.append(labels[i+len(initialPrototypes)])
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135 objectLabels.append(utils.mostCommon(objLabels))
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136 objectNumbers.append(objNum)
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137 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objectNumbers, 'object', objectLabels, initialPrototypes)
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138 else:
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139 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, [obj.getNum() for obj in objects], trajectoryType, labels[len(initialPrototypes):], initialPrototypes)
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140 if args.display:
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141 plt.figure()
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142 for i,o in enumerate(objects):
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143 if labels[i+len(initialPrototypes)] < 0:
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144 o.plot('kx-')
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145 else:
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146 o.plot(utils.colors[labels[i+len(initialPrototypes)]])
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147 for i,p in enumerate(initialPrototypes):
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148 p.getMovingObject().plot(utils.colors[i]+'o')
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149 plt.axis('equal')
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150 plt.show()
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151
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152 if (args.learn or args.assign) and args.saveSimilarities:
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153 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')