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annotate scripts/learn-motion-patterns.py @ 1222:69b531c7a061

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added methods to reset trajectories and change object coordinates (including features)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 20 Jun 2023 15:42:19 -0400
parents f2ba9858e6c6
children
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rev   line source
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933670761a57 updated code to python 3 (tests pass and scripts run, but non-executed parts of code are probably still not correct)
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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, processing
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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''') #, 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 process', choices = ['feature', 'object'], default = 'feature')
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16 parser.add_argument('--nfeatures-per-object', dest = 'nLongestFeaturesPerObject', help = 'maximum number of features per object to load', type = int)
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17 parser.add_argument('-n', dest = 'nObjects', 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('--similarities-filename', dest = 'similaritiesFilename', help = 'filename of the similarities')
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28 parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
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29 parser.add_argument('--save-assignments', dest = 'saveAssignments', help = 'saves the assignments of the objects to the prototypes', action = 'store_true')
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30 parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true')
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32 args = parser.parse_args()
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33
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34 # use cases
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35 # 1. learn proto from one file, save in same or another
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36 # 2. load proto, load objects (from same or other db), update proto matchings, save proto
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37 # TODO 3. on same dataset, learn and assign trajectories (could be done with min cluster size)
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38 # 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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39
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40 # TODO add possibility to cluster with velocities
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41 # TODO add possibility to load all trajectories and use minclustersize
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42
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43 if args.learn and args.assign:
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44 print('Cannot learn and assign simultaneously')
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45 sys.exit(0)
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46
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47 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nObjects, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject)
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48 if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None:
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49 objectsWithFeatures = objects
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50 objects = [f for o in objectsWithFeatures for f in o.getFeatures()]
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51 prototypeType = 'feature'
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52 else:
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53 prototypeType = args.trajectoryType
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55 # load initial prototypes, if any
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56 if args.inputPrototypeDatabaseFilename is not None:
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57 initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
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58 else:
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59 initialPrototypes = []
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61 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
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62 similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
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63 nTrajectories = len(initialPrototypes)+len(objects)
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64 if args.similaritiesFilename is not None:
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65 similarities = np.loadtxt(args.similaritiesFilename)
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66 if args.similaritiesFilename is None or similarities.shape[0] != nTrajectories or similarities.shape[1] != nTrajectories:
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67 similarities = -np.ones((nTrajectories, nTrajectories))
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68
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69 prototypeIndices, labels = processing.learnAssignMotionPatterns(args.learn, args.assign, objects, similarities, args.minSimilarity, similarityFunc, 0, args.optimizeCentroid, args.randomInitialization, False, initialPrototypes)
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70
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71 if args.learn:# and not args.assign:
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72 prototypes = []
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73 for i in prototypeIndices:
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74 if i<len(initialPrototypes):
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75 prototypes.append(initialPrototypes[i])
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76 else:
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77 prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), prototypeType))
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78
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79 if args.outputPrototypeDatabaseFilename is None:
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80 outputPrototypeDatabaseFilename = args.databaseFilename
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81 else:
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82 outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
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83 if args.inputPrototypeDatabaseFilename == args.outputPrototypeDatabaseFilename:
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84 storage.deleteFromSqlite(args.outputPrototypeDatabaseFilename, 'prototype')
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85 storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)
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86 if args.display:
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87 plt.figure()
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88 for p in prototypes:
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89 p.getMovingObject().plot()
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90 plt.axis('equal')
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91 plt.show()
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92
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93 if args.assign: # not args.learn and no modification to prototypes, can work with initialPrototypes
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94 clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
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95 for i in prototypeIndices:
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96 nMatchings = clusterSizes[i]-1 # external prototypes
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97 if initialPrototypes[i].nMatchings is None:
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98 initialPrototypes[i].nMatchings = nMatchings
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99 else:
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100 initialPrototypes[i].nMatchings += nMatchings
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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 storage.setPrototypeMatchingsInSqlite(outputPrototypeDatabaseFilename, initialPrototypes)
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106 if args.saveAssignments:
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107 if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None:
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108 # consider that the object is assigned through its longest features
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109 # issues are inconsistencies in the number of matchings per prototype and display (will display features, not objects)
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110 objectNumbers = []
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111 objectLabels = []
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112 i = 0
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113 for obj in objectsWithFeatures:
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114 objLabels = []
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115 for f in obj.getFeatures():
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116 if f == objects[i]:
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117 objLabels.append(labels[i+len(initialPrototypes)])
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118 i += 1
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119 else:
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120 print('Issue with obj {} and feature {} (trajectory {})'.format(obj.getNum(), f.getNum(), i))
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121 objectLabels.append(utils.mostCommon(objLabels))
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122 objectNumbers.append(obj.getNum())
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123 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objectNumbers, 'object', objectLabels, initialPrototypes)
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124 else:
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125 storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, [obj.getNum() for obj in objects], args.trajectoryType, labels[len(initialPrototypes):], initialPrototypes)
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126 if args.display:
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127 plt.figure()
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128 for i,o in enumerate(objects):
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129 if labels[i+len(initialPrototypes)] < 0:
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130 o.plot('kx-')
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131 else:
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132 o.plot(utils.colors[labels[i+len(initialPrototypes)]])
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133 for i,p in enumerate(initialPrototypes):
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134 p.getMovingObject().plot(utils.colors[i]+'o')
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135 plt.axis('equal')
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136 plt.show()
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137
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138 if (args.learn or args.assign) and args.saveSimilarities:
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139 if args.similaritiesFilename is not None:
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140 np.savetxt(args.similaritiesFilename, similarities, '%.4f')
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141 else:
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142 np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')