Mercurial Hosting > traffic-intelligence
changeset 1050:9d4a06f49cb8
work in progress
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Fri, 06 Jul 2018 18:12:15 -0400 |
| parents | c9c03c97ed9f |
| children | c3c3a90c08f8 |
| files | scripts/process.py trafficintelligence/metadata.py trafficintelligence/storage.py |
| diffstat | 3 files changed, 191 insertions(+), 182 deletions(-) [+] |
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--- a/scripts/process.py Fri Jul 06 15:58:45 2018 -0400 +++ b/scripts/process.py Fri Jul 06 18:12:15 2018 -0400 @@ -61,10 +61,10 @@ parser.add_argument('--output', dest = 'output', help = 'kind of output to produce (interval means)', choices = ['figure', 'interval', 'event']) parser.add_argument('--min-user-duration', dest = 'minUserDuration', help = 'mininum duration we have to see the user to take into account in the analysis (s)', type = float, default = 0.1) parser.add_argument('--interval-duration', dest = 'intervalDuration', help = 'length of time interval to aggregate data (min)', type = float, default = 15.) -parser.add_argument('--aggregation', dest = 'aggMethod', help = 'aggregation method per user/event and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median']) +parser.add_argument('--aggregation', dest = 'aggMethod', help = 'aggregation method per user/interaction and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median']) parser.add_argument('--aggregation-centile', dest = 'aggCentiles', help = 'centile(s) to compute from the observations', nargs = '*', type = int) dpi = 150 -# unit of analysis: site or video sequence? +# unit of analysis: site - camera-view # need way of selecting sites as similar as possible to sql alchemy syntax # override tracking.cfg from db @@ -90,8 +90,7 @@ tmpsites = getSite(session, siteId) sites.extend(tmpsites) for site in tmpsites: - for cv in site.cameraViews: - videoSequences.extend(cv.videoSequences) + videoSequences.extend(getSiteVideoSequences(site)) else: print('No video/site to process') @@ -147,7 +146,7 @@ pool.join() elif args.process == 'prototype': # motion pattern learning - # learn by site by default -> group videos by site (or by camera view? TODO add cameraviews) + # learn by site by default -> group videos by camera view TODO # by default, load all objects, learn and then assign (BUT not save the assignments) for site in sites: print('Learning motion patterns for site {} ({})'.format(site.idx, site.name)) @@ -177,7 +176,6 @@ 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(str(parentPath/site.getPath()/outputPrototypeDatabaseFilename), [moving.Prototype(object2VideoSequences[trainingObjects[i]].getDatabaseFilename(False), trainingObjects[i].getNum(), prototypeType, clusterSizes[i]) for i in prototypeIndices]) @@ -232,7 +230,7 @@ objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object', args.nObjects) for o in objects: if o.length() > minUserDuration: - row = [vs.cameraView.siteIdx, d, utils.framesToTime(o.getFirstInstant(), vs.cameraView.cameraType.frameRate, t1), o.getUserType()] + row = [vs.cameraView.site.name, d, utils.framesToTime(o.getFirstInstant(), vs.cameraView.cameraType.frameRate, t1), o.getUserType()] tmp = o.getSpeeds() for method,func in aggFunctions.items(): aggSpeeds = vs.cameraView.cameraType.frameRate*3.6*func(tmp) @@ -252,7 +250,8 @@ plt.close() elif args.output == 'event': data.to_csv('speeds.csv', index = False) -if args.analyze == 'interaction': + +if args.analyze == 'interaction': # redo as for object, export in dataframe all interaction data indicatorIds = [2,5,7,10] conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30} maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.}
--- a/trafficintelligence/metadata.py Fri Jul 06 15:58:45 2018 -0400 +++ b/trafficintelligence/metadata.py Fri Jul 06 18:12:15 2018 -0400 @@ -366,6 +366,9 @@ 'Returns the site(s) matching the index' return session.query(CameraView).filter(CameraView.idx == int(viewId)).first() +def getSiteVideoSequences(site): + return [vs for cv in site.cameraViews for vs in cv.videoSequences] + def initializeSites(session, directoryName, nViewsPerSite = 1): '''Initializes default site objects and n camera views per site
--- a/trafficintelligence/storage.py Fri Jul 06 15:58:45 2018 -0400 +++ b/trafficintelligence/storage.py Fri Jul 06 18:12:15 2018 -0400 @@ -259,60 +259,61 @@ The number loaded is either the first objectNumbers objects, or the indices in objectNumbers from the database''' objects = [] - with sqlite3.connect(filename) as connection: - objects = loadTrajectoriesFromTable(connection, 'positions', trajectoryType, objectNumbers, timeStep) - objectVelocities = loadTrajectoriesFromTable(connection, 'velocities', trajectoryType, objectNumbers, timeStep) + if Path(filename).exists(): + with sqlite3.connect(filename) as connection: + objects = loadTrajectoriesFromTable(connection, 'positions', trajectoryType, objectNumbers, timeStep) + objectVelocities = loadTrajectoriesFromTable(connection, 'velocities', trajectoryType, objectNumbers, timeStep) - if len(objectVelocities) > 0: - for o,v in zip(objects, objectVelocities): - if o.getNum() == v.getNum(): - o.velocities = v.positions - o.velocities.duplicateLastPosition() # avoid having velocity shorter by one position than positions - else: - print('Could not match positions {0} with velocities {1}'.format(o.getNum(), v.getNum())) + if len(objectVelocities) > 0: + for o,v in zip(objects, objectVelocities): + if o.getNum() == v.getNum(): + o.velocities = v.positions + o.velocities.duplicateLastPosition() # avoid having velocity shorter by one position than positions + else: + print('Could not match positions {0} with velocities {1}'.format(o.getNum(), v.getNum())) - if trajectoryType == 'object': - cursor = connection.cursor() - try: - # attribute feature numbers to objects - queryStatement = 'SELECT trajectory_id, object_id FROM objects_features' - if objectNumbers is not None: - queryStatement += ' WHERE object_id '+getObjectCriteria(objectNumbers) - queryStatement += ' ORDER BY object_id' # order is important to group all features per object - logging.debug(queryStatement) - cursor.execute(queryStatement) + if trajectoryType == 'object': + cursor = connection.cursor() + try: + # attribute feature numbers to objects + queryStatement = 'SELECT trajectory_id, object_id FROM objects_features' + if objectNumbers is not None: + queryStatement += ' WHERE object_id '+getObjectCriteria(objectNumbers) + queryStatement += ' ORDER BY object_id' # order is important to group all features per object + logging.debug(queryStatement) + cursor.execute(queryStatement) - featureNumbers = {} - for row in cursor: - objId = row[1] - if objId not in featureNumbers: - featureNumbers[objId] = [row[0]] - else: - featureNumbers[objId].append(row[0]) + featureNumbers = {} + for row in cursor: + objId = row[1] + if objId not in featureNumbers: + featureNumbers[objId] = [row[0]] + else: + featureNumbers[objId].append(row[0]) - for obj in objects: - obj.featureNumbers = featureNumbers[obj.getNum()] + for obj in objects: + obj.featureNumbers = featureNumbers[obj.getNum()] - # load userType - attributes = loadObjectAttributesFromTable(cursor, objectNumbers, True) - for obj in objects: - userType, nObjects = attributes[obj.getNum()] - obj.setUserType(userType) - obj.setNObjects(nObjects) - - # add features - if withFeatures: + # load userType + attributes = loadObjectAttributesFromTable(cursor, objectNumbers, True) for obj in objects: - obj.features = loadTrajectoriesFromSqlite(filename, 'feature', obj.featureNumbers, timeStep = timeStep) - elif nLongestFeaturesPerObject is not None: - for obj in objects: - queryStatement = 'SELECT trajectory_id, max(frame_number)-min(frame_number) AS length FROM positions WHERE trajectory_id '+getObjectCriteria(obj.featureNumbers)+' GROUP BY trajectory_id ORDER BY length DESC' - logging.debug(queryStatement) - cursor.execute(queryStatement) - obj.features = loadTrajectoriesFromSqlite(filename, 'feature', [row[0] for i,row in enumerate(cursor) if i<nLongestFeaturesPerObject], timeStep = timeStep) + userType, nObjects = attributes[obj.getNum()] + obj.setUserType(userType) + obj.setNObjects(nObjects) - except sqlite3.OperationalError as error: - printDBError(error) + # add features + if withFeatures: + for obj in objects: + obj.features = loadTrajectoriesFromSqlite(filename, 'feature', obj.featureNumbers, timeStep = timeStep) + elif nLongestFeaturesPerObject is not None: + for obj in objects: + queryStatement = 'SELECT trajectory_id, max(frame_number)-min(frame_number) AS length FROM positions WHERE trajectory_id '+getObjectCriteria(obj.featureNumbers)+' GROUP BY trajectory_id ORDER BY length DESC' + logging.debug(queryStatement) + cursor.execute(queryStatement) + obj.features = loadTrajectoriesFromSqlite(filename, 'feature', [row[0] for i,row in enumerate(cursor) if i<nLongestFeaturesPerObject], timeStep = timeStep) + + except sqlite3.OperationalError as error: + printDBError(error) return objects def loadObjectFeatureFrameNumbers(filename, objectNumbers = None): @@ -451,19 +452,20 @@ Load descriptions?''' objects = [] - with sqlite3.connect(filename) as connection: - if objectType == 'bb': - topCorners = loadTrajectoriesFromTable(connection, 'bounding_boxes', 'bbtop', objectNumbers, timeStep) - bottomCorners = loadTrajectoriesFromTable(connection, 'bounding_boxes', 'bbbottom', objectNumbers, timeStep) - userTypes = loadObjectAttributesFromTable(connection.cursor(), objectNumbers) # string format is same as object + if Path(filename).exists(): + with sqlite3.connect(filename) as connection: + if objectType == 'bb': + topCorners = loadTrajectoriesFromTable(connection, 'bounding_boxes', 'bbtop', objectNumbers, timeStep) + bottomCorners = loadTrajectoriesFromTable(connection, 'bounding_boxes', 'bbbottom', objectNumbers, timeStep) + userTypes = loadObjectAttributesFromTable(connection.cursor(), objectNumbers) # string format is same as object - for t, b in zip(topCorners, bottomCorners): - num = t.getNum() - if t.getNum() == b.getNum(): - annotation = moving.BBMovingObject(num, t.getTimeInterval(), t, b, userTypes[num]) - objects.append(annotation) - else: - print ('Unknown type of bounding box {}'.format(objectType)) + for t, b in zip(topCorners, bottomCorners): + num = t.getNum() + if t.getNum() == b.getNum(): + annotation = moving.BBMovingObject(num, t.getTimeInterval(), t, b, userTypes[num]) + objects.append(annotation) + else: + print ('Unknown type of bounding box {}'.format(objectType)) return objects def saveInteraction(cursor, interaction): @@ -509,29 +511,30 @@ TODO choose the interactions to load''' interactions = [] - with sqlite3.connect(filename) as connection: - cursor = connection.cursor() - try: - cursor.execute('SELECT INT.id, INT.object_id1, INT.object_id2, INT.first_frame_number, INT.last_frame_number, IND.indicator_type, IND.frame_number, IND.value from interactions INT, indicators IND WHERE INT.id = IND.interaction_id ORDER BY INT.id, IND.indicator_type, IND.frame_number') - interactionNum = -1 - indicatorTypeNum = -1 - tmpIndicators = {} - for row in cursor: - if row[0] != interactionNum: - interactionNum = row[0] - interactions.append(events.Interaction(interactionNum, moving.TimeInterval(row[3],row[4]), row[1], row[2])) - interactions[-1].indicators = {} - if indicatorTypeNum != row[5] or row[0] != interactionNum: - indicatorTypeNum = row[5] - indicatorName = events.Interaction.indicatorNames[indicatorTypeNum] - indicatorValues = {row[6]:row[7]} - interactions[-1].indicators[indicatorName] = indicators.SeverityIndicator(indicatorName, indicatorValues, mostSevereIsMax = not indicatorName in events.Interaction.timeIndicators) - else: - indicatorValues[row[6]] = row[7] - interactions[-1].indicators[indicatorName].timeInterval.last = row[6] - except sqlite3.OperationalError as error: - printDBError(error) - return [] + if Path(filename).exists(): + with sqlite3.connect(filename) as connection: + cursor = connection.cursor() + try: + cursor.execute('SELECT INT.id, INT.object_id1, INT.object_id2, INT.first_frame_number, INT.last_frame_number, IND.indicator_type, IND.frame_number, IND.value from interactions INT, indicators IND WHERE INT.id = IND.interaction_id ORDER BY INT.id, IND.indicator_type, IND.frame_number') + interactionNum = -1 + indicatorTypeNum = -1 + tmpIndicators = {} + for row in cursor: + if row[0] != interactionNum: + interactionNum = row[0] + interactions.append(events.Interaction(interactionNum, moving.TimeInterval(row[3],row[4]), row[1], row[2])) + interactions[-1].indicators = {} + if indicatorTypeNum != row[5] or row[0] != interactionNum: + indicatorTypeNum = row[5] + indicatorName = events.Interaction.indicatorNames[indicatorTypeNum] + indicatorValues = {row[6]:row[7]} + interactions[-1].indicators[indicatorName] = indicators.SeverityIndicator(indicatorName, indicatorValues, mostSevereIsMax = not indicatorName in events.Interaction.timeIndicators) + else: + indicatorValues[row[6]] = row[7] + interactions[-1].indicators[indicatorName].timeInterval.last = row[6] + except sqlite3.OperationalError as error: + printDBError(error) + return [] return interactions # load first and last object instants # CREATE TEMP TABLE IF NOT EXISTS object_instants AS SELECT OF.object_id, min(frame_number) as first_instant, max(frame_number) as last_instant from positions P, objects_features OF WHERE P.trajectory_id = OF.trajectory_id group by OF.object_id order by OF.object_id @@ -554,18 +557,18 @@ def loadBoundingBoxTableForDisplay(filename): '''Loads bounding boxes from bounding_boxes table for display over trajectories''' boundingBoxes = {} # list of bounding boxes for each instant - with sqlite3.connect(filename) as connection: - cursor = connection.cursor() - try: - cursor.execute('SELECT name FROM sqlite_master WHERE type=\'table\' AND name=\'bounding_boxes\'') - result = cursor.fetchall() - if len(result) > 0: - cursor.execute('SELECT * FROM bounding_boxes') - for row in cursor: - boundingBoxes.setdefault(row[1], []).append([moving.Point(row[2], row[3]), moving.Point(row[4], row[5])]) - except sqlite3.OperationalError as error: - printDBError(error) - return boundingBoxes + if Path(filename).exists(): + with sqlite3.connect(filename) as connection: + cursor = connection.cursor() + try: + cursor.execute('SELECT name FROM sqlite_master WHERE type=\'table\' AND name=\'bounding_boxes\'') + result = cursor.fetchall() + if len(result) > 0: + cursor.execute('SELECT * FROM bounding_boxes') + for row in cursor: + boundingBoxes.setdefault(row[1], []).append([moving.Point(row[2], row[3]), moving.Point(row[4], row[5])]) + except sqlite3.OperationalError as error: + printDBError(error) return boundingBoxes ######################### @@ -624,49 +627,52 @@ connection.commit() def loadPrototypeAssignmentsFromSqlite(filename, objectType): - with sqlite3.connect(filename) as connection: - cursor = connection.cursor() - try: - tableName, objectIdColumnName = prototypeAssignmentNames(objectType) - cursor.execute('SELECT * FROM '+tableName) - prototypeAssignments = {} - for row in cursor: - p = moving.Prototype(row[1], row[2], row[3]) - if p in prototypeAssignments: - prototypeAssignments[p].append(row[0]) - else: - prototypeAssignments[p] = [row[0]] - return prototypeAssignments - except sqlite3.OperationalError as error: - printDBError(error) - + prototypeAssignments = {} + if Path(filename).exists(): + with sqlite3.connect(filename) as connection: + cursor = connection.cursor() + try: + tableName, objectIdColumnName = prototypeAssignmentNames(objectType) + cursor.execute('SELECT * FROM '+tableName) + for row in cursor: + p = moving.Prototype(row[1], row[2], row[3]) + if p in prototypeAssignments: + prototypeAssignments[p].append(row[0]) + else: + prototypeAssignments[p] = [row[0]] + return prototypeAssignments + except sqlite3.OperationalError as error: + printDBError(error) + return prototypeAssignments + def loadPrototypesFromSqlite(filename, withTrajectories = True): 'Loads prototype ids and matchings (if stored)' prototypes = [] - with sqlite3.connect(filename) as connection: - cursor = connection.cursor() - objects = [] - try: - cursor.execute('SELECT * FROM prototypes') - for row in cursor: - prototypes.append(moving.Prototype(row[0], row[1], row[2], row[3])) - if withTrajectories: - for p in prototypes: - p.setMovingObject(loadTrajectoriesFromSqlite(p.getFilename(), p.getTrajectoryType(), [p.getNum()])[0]) - # loadingInformation = {} # complicated slightly optimized - # for p in prototypes: - # dbfn = p.getFilename() - # trajType = p.getTrajectoryType() - # if (dbfn, trajType) in loadingInformation: - # loadingInformation[(dbfn, trajType)].append(p) - # else: - # loadingInformation[(dbfn, trajType)] = [p] - # for k, v in loadingInformation.iteritems(): - # objects += loadTrajectoriesFromSqlite(k[0], k[1], [p.getNum() for p in v]) - except sqlite3.OperationalError as error: - printDBError(error) - if len(set([p.getTrajectoryType() for p in prototypes])) > 1: - print('Different types of prototypes in database ({}).'.format(set([p.getTrajectoryType() for p in prototypes]))) + if Path(filename).exists(): + with sqlite3.connect(filename) as connection: + cursor = connection.cursor() + objects = [] + try: + cursor.execute('SELECT * FROM prototypes') + for row in cursor: + prototypes.append(moving.Prototype(row[0], row[1], row[2], row[3])) + if withTrajectories: + for p in prototypes: + p.setMovingObject(loadTrajectoriesFromSqlite(p.getFilename(), p.getTrajectoryType(), [p.getNum()])[0]) + # loadingInformation = {} # complicated slightly optimized + # for p in prototypes: + # dbfn = p.getFilename() + # trajType = p.getTrajectoryType() + # if (dbfn, trajType) in loadingInformation: + # loadingInformation[(dbfn, trajType)].append(p) + # else: + # loadingInformation[(dbfn, trajType)] = [p] + # for k, v in loadingInformation.iteritems(): + # objects += loadTrajectoriesFromSqlite(k[0], k[1], [p.getNum() for p in v]) + except sqlite3.OperationalError as error: + printDBError(error) + if len(set([p.getTrajectoryType() for p in prototypes])) > 1: + print('Different types of prototypes in database ({}).'.format(set([p.getTrajectoryType() for p in prototypes]))) return prototypes def savePOIsToSqlite(filename, gmm, gmmType, gmmId): @@ -703,46 +709,47 @@ from sklearn import mixture # todo if not avalaible, load data in duck-typed class with same fields from ast import literal_eval pois = [] - with sqlite3.connect(filename) as connection: - cursor = connection.cursor() - try: - cursor.execute('SELECT * from gaussians2d') - gmmId = None - gmm = [] - for row in cursor: - if gmmId is None or row[0] != gmmId: - if len(gmm) > 0: - tmp = mixture.GaussianMixture(len(gmm), covarianceType) - tmp.means_ = array([gaussian['mean'] for gaussian in gmm]) - tmp.covariances_ = array([gaussian['covar'] for gaussian in gmm]) - tmp.weights_ = array([gaussian['weight'] for gaussian in gmm]) - tmp.gmmTypes = [gaussian['type'] for gaussian in gmm] - tmp.precisions_cholesky_ = array([gaussian['precisions'] for gaussian in gmm]) - pois.append(tmp) - gaussian = {'type': row[2], - 'mean': row[3:5], - 'covar': array(literal_eval(row[5])), - 'weight': row[7], - 'precisions': array(literal_eval(row[8]))} - gmm = [gaussian] - covarianceType = row[6] - gmmId = row[0] - else: - gmm.append({'type': row[2], - 'mean': row[3:5], - 'covar': array(literal_eval(row[5])), - 'weight': row[7], - 'precisions': array(literal_eval(row[8]))}) - if len(gmm) > 0: - tmp = mixture.GaussianMixture(len(gmm), covarianceType) - tmp.means_ = array([gaussian['mean'] for gaussian in gmm]) - tmp.covariances_ = array([gaussian['covar'] for gaussian in gmm]) - tmp.weights_ = array([gaussian['weight'] for gaussian in gmm]) - tmp.gmmTypes = [gaussian['type'] for gaussian in gmm] - tmp.precisions_cholesky_ = array([gaussian['precisions'] for gaussian in gmm]) - pois.append(tmp) - except sqlite3.OperationalError as error: - printDBError(error) + if Path(filename).exists(): + with sqlite3.connect(filename) as connection: + cursor = connection.cursor() + try: + cursor.execute('SELECT * from gaussians2d') + gmmId = None + gmm = [] + for row in cursor: + if gmmId is None or row[0] != gmmId: + if len(gmm) > 0: + tmp = mixture.GaussianMixture(len(gmm), covarianceType) + tmp.means_ = array([gaussian['mean'] for gaussian in gmm]) + tmp.covariances_ = array([gaussian['covar'] for gaussian in gmm]) + tmp.weights_ = array([gaussian['weight'] for gaussian in gmm]) + tmp.gmmTypes = [gaussian['type'] for gaussian in gmm] + tmp.precisions_cholesky_ = array([gaussian['precisions'] for gaussian in gmm]) + pois.append(tmp) + gaussian = {'type': row[2], + 'mean': row[3:5], + 'covar': array(literal_eval(row[5])), + 'weight': row[7], + 'precisions': array(literal_eval(row[8]))} + gmm = [gaussian] + covarianceType = row[6] + gmmId = row[0] + else: + gmm.append({'type': row[2], + 'mean': row[3:5], + 'covar': array(literal_eval(row[5])), + 'weight': row[7], + 'precisions': array(literal_eval(row[8]))}) + if len(gmm) > 0: + tmp = mixture.GaussianMixture(len(gmm), covarianceType) + tmp.means_ = array([gaussian['mean'] for gaussian in gmm]) + tmp.covariances_ = array([gaussian['covar'] for gaussian in gmm]) + tmp.weights_ = array([gaussian['weight'] for gaussian in gmm]) + tmp.gmmTypes = [gaussian['type'] for gaussian in gmm] + tmp.precisions_cholesky_ = array([gaussian['precisions'] for gaussian in gmm]) + pois.append(tmp) + except sqlite3.OperationalError as error: + printDBError(error) return pois #########################