Mercurial Hosting > traffic-intelligence
diff scripts/learn-motion-patterns.py @ 1043:b735895c8815
work in progress on process (learn motion patterns)
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 04 Jul 2018 17:39:39 -0400 |
parents | 5621e4ad2428 |
children | 75a6ad604cc5 |
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--- a/scripts/learn-motion-patterns.py Wed Jul 04 16:21:09 2018 -0400 +++ b/scripts/learn-motion-patterns.py Wed Jul 04 17:39:39 2018 -0400 @@ -12,7 +12,7 @@ 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 = ['objectfeature', 'feature', 'object'], default = 'objectfeatures') +parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to learn from', choices = ['objectfeature', 'feature', 'object'], default = 'objectfeature') 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) @@ -24,6 +24,7 @@ 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('--similarities-filename', dest = 'similaritiesFilename', help = 'filename of the similarities') parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true') parser.add_argument('--save-assignments', dest = 'saveAssignments', help = 'saves the assignments of the objects to the prototypes', action = 'store_true') parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true') @@ -40,17 +41,7 @@ # TODO add possibility to load all trajectories and use minclustersize # load trajectories to cluster or assign -if args.trajectoryType == 'objectfeature': - trajectoryType = 'feature' - 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: - trajectoryType = args.trajectoryType - objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate) - +objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nTrajectories, timeStep = args.positionSubsamplingRate) trajectories = [o.getPositions().asArray().T for o in objects] # load initial prototypes, if any @@ -66,9 +57,10 @@ initialPrototypeIndices = None lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) -nTrajectories = len(trajectories) - -similarities = -np.ones((nTrajectories, nTrajectories)) +if args.similaritiesFilename is not None: + similarities = np.loadtxt(args.similaritiesFilename) +if args.similaritiesFilename is None or similarities.shape[0] != len(trajectories) or similarities.shape[1] != len(trajectories): + similarities = -np.ones((len(trajectories), len(trajectories))) similarityFunc = lambda x,y : lcss.computeNormalized(x, y) # the next line can be called again without reinitializing similarities if args.learn: @@ -90,6 +82,10 @@ if args.learn and not args.assign: prototypes = [] + if args.trajectoryType == 'objectfeature': + trajectoryType = 'feature' + else: + trajectoryType = args.trajectoryType for i in prototypeIndices: if i<len(initialPrototypes): prototypes.append(initialPrototypes[i]) @@ -136,7 +132,7 @@ objectNumbers.append(objNum) storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objectNumbers, 'object', objectLabels, initialPrototypes) else: - storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, [obj.getNum() for obj in objects], trajectoryType, labels[len(initialPrototypes):], initialPrototypes) + storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, [obj.getNum() for obj in objects], args.trajectoryType, labels[len(initialPrototypes):], initialPrototypes) if args.display: plt.figure() for i,o in enumerate(objects): @@ -150,4 +146,7 @@ plt.show() if (args.learn or args.assign) and args.saveSimilarities: - np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f') + if args.similaritiesFilename is not None: + np.savetxt(args.similaritiesFilename, similarities, '%.4f') + else: + np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')