#! /usr/bin/env python
'''Algorithms to process trajectories and moving objects'''
import numpy as np
from trafficintelligence import ml, storage, utils
def extractSpeeds(objects, zone):
speeds = {}
objectsNotInZone = []
import matplotlib.nxutils as nx
for o in objects:
inPolygon = nx.points_inside_poly(o.getPositions().asArray().T, zone.T)
if inPolygon.any():
objspeeds = [o.getVelocityAt(i).norm2() for i in range(int(o.length()-1)) if inPolygon[i]]
speeds[o.num] = np.mean(objspeeds) # km/h
else:
objectsNotInZone.append(o)
return speeds, objectsNotInZone
def extractVideoSequenceSpeeds(dbFilename, siteName, nObjects, startTime, frameRate, minDuration, aggMethods, aggCentiles):
data = []
d = startTime.date()
t1 = startTime.time()
print('Extracting speed from '+dbFilename)
aggFunctions, tmpheaders = utils.aggregationMethods(aggMethods, aggCentiles)
objects = storage.loadTrajectoriesFromSqlite(dbFilename, 'object', nObjects)
for o in objects:
if o.length() > minDuration:
row = [siteName, d, utils.framesToTime(o.getFirstInstant(), frameRate, t1), o.getUserType()]
tmp = o.getSpeeds()
for method,func in aggFunctions.items():
aggSpeeds = frameRate*3.6*func(tmp)
if method == 'centile':
row.extend(aggSpeeds.tolist())
else:
row.append(aggSpeeds)
data.append(row)
return data
def learnAssignMotionPatterns(learn, assign, objects, similarities, minSimilarity, similarityFunc, minClusterSize = 0, optimizeCentroid = False, randomInitialization = False, removePrototypesAfterAssignment = False, initialPrototypes = []):
'''Learns motion patterns
During assignments, if using minClusterSize > 0, prototypes can change (be removed)
The argument removePrototypesAfterAssignment indicates whether the prototypes are removed or not'''
if len(initialPrototypes) > 0:
initialPrototypeIndices = list(range(len(initialPrototypes)))
trajectories = [p.getMovingObject().getPositions().asArray().T for p in initialPrototypes]
else:
initialPrototypeIndices = None
trajectories = []
trajectories.extend([o.getPositions().asArray().T for o in objects])
if learn:
prototypeIndices = ml.prototypeCluster(trajectories, similarities, minSimilarity, similarityFunc, optimizeCentroid, randomInitialization, initialPrototypeIndices)
else:
prototypeIndices = initialPrototypeIndices
if assign:
assignedPrototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, minSimilarity, similarityFunc, minClusterSize)
if minClusterSize > 0 and removePrototypesAfterAssignment: # use prototypeIndices anyway
prototypeIndices = assignedPrototypeIndices
else:
labels = None
return prototypeIndices, labels
