Mercurial Hosting > traffic-intelligence
changeset 1046:f2ba9858e6c6
motion pattern learning seems to work
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
---|---|
date | Thu, 05 Jul 2018 23:12:11 -0400 |
parents | 25db2383e7ae |
children | 0b62e37991ab |
files | scripts/learn-motion-patterns.py scripts/process.py |
diffstat | 2 files changed, 13 insertions(+), 13 deletions(-) [+] |
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--- a/scripts/learn-motion-patterns.py Thu Jul 05 17:45:18 2018 -0400 +++ b/scripts/learn-motion-patterns.py Thu Jul 05 23:12:11 2018 -0400 @@ -14,7 +14,7 @@ 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 process', choices = ['feature', 'object'], default = 'feature') parser.add_argument('--nfeatures-per-object', dest = 'nLongestFeaturesPerObject', help = 'maximum number of features per object to load', type = int) -parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None) +parser.add_argument('-n', dest = 'nObjects', 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) @@ -44,7 +44,7 @@ print('Cannot learn and assign simultaneously') sys.exit(0) -objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nTrajectories, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject) +objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nObjects, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject) if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None: objectsWithFeatures = objects objects = [f for o in objectsWithFeatures for f in o.getFeatures()]
--- a/scripts/process.py Thu Jul 05 17:45:18 2018 -0400 +++ b/scripts/process.py Thu Jul 05 23:12:11 2018 -0400 @@ -7,17 +7,17 @@ #import matplotlib #atplotlib.use('Agg') import matplotlib.pyplot as plt -from numpy import percentile +from numpy import percentile, ones from pandas import DataFrame -from trafficintelligence import storage, events, prediction, cvutils, utils, moving +from trafficintelligence import storage, events, prediction, cvutils, utils, moving, processing, ml from trafficintelligence.metadata import * parser = argparse.ArgumentParser(description='This program manages the processing of several files based on a description of the sites and video data in an SQLite database following the metadata module.') # input parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True) parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int) -parser.add_argument('--sites', dest = 'siteIds', help = 'indices of the video sequences', nargs = '*', type = int) +parser.add_argument('--sites', dest = 'siteIds', help = 'indices of the video sequences', nargs = '*') # main function parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction']) @@ -32,17 +32,16 @@ parser.add_argument('--dry', dest = 'dryRun', help = 'dry run of processing', action = 'store_true') parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1) 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') ### process options # motion pattern learning and assignment -parser.add_argument('--prototype-filename', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes') +parser.add_argument('--prototype-filename', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes', default = 'prototypes.sqlite') #parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with') parser.add_argument('--nfeatures-per-object', dest = 'nLongestFeaturesPerObject', help = 'maximum number of features per object to load', type = int) -parser.add_argument('--epsilon', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True) +parser.add_argument('--epsilon', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float) parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance -parser.add_argument('--minsimil', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True) -parser.add_argument('-min-cluster-size', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0) +parser.add_argument('--minsimil', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float) +parser.add_argument('--min-cluster-size', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0) #parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true') 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') @@ -155,7 +154,7 @@ for cv in site.cameraViews: for vs in cv.videoSequences: print('Loading '+vs.getDatabaseFilename()) - objects[vs.idx] = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.trajectoryType, args.nTrajectories, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject) + objects[vs.idx] = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.trajectoryType, args.nObjects, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject) if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None: objectsWithFeatures = objects[vs.idx] objects[vs.idx] = [f for o in objectsWithFeatures for f in o.getFeatures()] @@ -167,14 +166,15 @@ lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) similarityFunc = lambda x,y : lcss.computeNormalized(x, y) allobjects = [o for tmpobjects in objects.values() for o in tmpobjects] - prototypeIndices, labels = processing.learnAssignMotionPatterns(True, True, allobjects, similarities, args.minsimil, similarityFunc, args.minClusterSize, args.optimizeCentroid, args.randomInitialization, True, []) + similarities = -ones((len(allobjects), len(allobjects))) + prototypeIndices, labels = processing.learnAssignMotionPatterns(True, True, allobjects, similarities, args.minSimilarity, similarityFunc, args.minClusterSize, args.optimizeCentroid, args.randomInitialization, True, []) if args.outputPrototypeDatabaseFilename is None: outputPrototypeDatabaseFilename = args.databaseFilename else: outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename # TODO maintain mapping from object prototype to db filename + compute nmatchings before clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) - storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, [moving.Prototype(object2VideoSequences[allobjects[i]].getDatabaseFilename(False), allobjects[i].getNum(), prototypeType) for i in prototypeIndices]) + storage.savePrototypesToSqlite(str(parentPath/site.getPath()/outputPrototypeDatabaseFilename), [moving.Prototype(object2VideoSequences[allobjects[i]].getDatabaseFilename(False), allobjects[i].getNum(), prototypeType, clusterSizes[i]) for i in prototypeIndices]) elif args.process == 'interaction':