Mercurial Hosting > traffic-intelligence
view scripts/learn-motion-patterns.py @ 948:584b9405e494
added safety analysis parameters for motion patterns
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 21 Jul 2017 12:11:55 -0400 |
parents | c030f735c594 |
children | d6c1c05d11f5 |
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#! /usr/bin/env python import sys, argparse #import matplotlib.pyplot as plt import numpy as np import ml, utils, storage, moving parser = argparse.ArgumentParser(description='The program learns prototypes for the motion patterns') #, epilog = '' #parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file') parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True) parser.add_argument('-o', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes') parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with') parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to learn from', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures') parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 1) parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None) parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True) parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True) parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None) parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true') parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true') parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int) parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true') parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true') parser.add_argument('--save-matches', dest = 'saveMatches', help = 'saves the assignments of the objects (not for features) to the prototypes', action = 'store_true') #parser.add_argument('--assign', dest = 'assign', help = 'saves the assignments of the objects (not for features) to the prototypes', action = 'store_true') # default is train, but one could want only to assign the objects to the loaded prototypes without learning args = parser.parse_args() # use cases # 1. learn proto from one file, save in same or another (with traj) # 2. load proto, load objects, update proto, save proto # 3. assign objects from one db to proto # 4. load objects from several files, save in another -> see metadata: site with view and times # 5. keep prototypes, with positions/velocities, in separate db (keep link to original data through filename, type and index) # TODO add possibility to cluster with velocities # TODO add possibility to start with saved prototypes so that one can incrementally learn from several databases # save the objects that match the prototypes # write an assignment function for objects trajectoryType = args.trajectoryType prototypeType = args.trajectoryType if args.trajectoryType == 'objectfeatures': trajectoryType = 'object' prototypeType = 'feature' if args.trajectoryType == 'objectfeatures': objectFeatureNumbers = storage.loadObjectFeatureFrameNumbers(args.databaseFilename, objectNumbers = args.nTrajectories) featureNumbers = [] for numbers in objectFeatureNumbers.values(): featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)] objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate) else: objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate) if args.inputPrototypeDatabaseFilename is not None: prototypeIndices, dbFilenames, trajectoryTypes, nMatchings, prototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True) trajectories = [o.getPositions().asArray().T for o in objects] lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) nTrajectories = len(trajectories) similarities = -np.ones((nTrajectories, nTrajectories)) prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize, args.optimizeCentroid, args.randomInitialization, True, None) # this line can be called again without reinitializing similarities clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) print(clusterSizes) prototypes = [moving.Prototype(args.databaseFilename, objects[i].getNum(), prototypeType, clusterSizes[i]) for i in prototypeIndices] if args.outputPrototypeDatabaseFilename is None: outputPrototypeDatabaseFilename = args.databaseFilename else: outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes) if args.saveSimilarities: np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f') labelsToProtoIndices = {protoId: i for i, protoId in enumerate(prototypeIndices)} if args.saveMatches: # or args.assign # save in the db that contained originally the data # retirer les assignations anterieures? storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objects, [labelsToProtoIndices[l] for l in labels], prototypes) if args.display: from matplotlib.pyplot import figure, show, axis figure() for i,o in enumerate(objects): if i not in prototypeIndices: if labels[i] < 0: o.plot('kx') else: o.plot(utils.colors[labels[i]]) for i in prototypeIndices: objects[i].plot(utils.colors[i]+'o') axis('equal') show()