Mercurial > hg > nsaunier > traffic-intelligence

comparison trafficintelligence/processing.py @ 1077:3939ae415be0

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Merging
author Wendlasida
date Fri, 20 Jul 2018 14:03:34 -0400
parents 092bd9c7deaf
children c67f8c36ebc7
comparison
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1076:108c5dc4e34a 1077:3939ae415be0
1 #! /usr/bin/env python 1 #! /usr/bin/env python
2 '''Algorithms to process trajectories and moving objects''' 2 '''Algorithms to process trajectories and moving objects'''
3 3
4 import numpy as np 4 import numpy as np
5 5
6 from trafficintelligence import ml 6 from trafficintelligence import ml, storage, utils
7 7
8 def extractSpeeds(objects, zone): 8 def extractSpeeds(objects, zone):
9 speeds = {} 9 speeds = {}
10 objectsNotInZone = [] 10 objectsNotInZone = []
11 import matplotlib.nxutils as nx 11 import matplotlib.nxutils as nx
15 objspeeds = [o.getVelocityAt(i).norm2() for i in range(int(o.length()-1)) if inPolygon[i]] 15 objspeeds = [o.getVelocityAt(i).norm2() for i in range(int(o.length()-1)) if inPolygon[i]]
16 speeds[o.num] = np.mean(objspeeds) # km/h 16 speeds[o.num] = np.mean(objspeeds) # km/h
17 else: 17 else:
18 objectsNotInZone.append(o) 18 objectsNotInZone.append(o)
19 return speeds, objectsNotInZone 19 return speeds, objectsNotInZone
20
21 def extractVideoSequenceSpeeds(dbFilename, siteName, nObjects, startTime, frameRate, minUserDurationSeconds, aggMethods, aggCentiles):
22 data = []
23 d = startTime.date()
24 t1 = startTime.time()
25 minUserDuration = minUserDurationSeconds*frameRate
26 print('Extracting speed from '+dbFilename)
27 aggFunctions, tmpheaders = utils.aggregationMethods(aggMethods, aggCentiles)
28 objects = storage.loadTrajectoriesFromSqlite(dbFilename, 'object', nObjects)
29 for o in objects:
30 if o.length() > minUserDuration:
31 row = [siteName, d, utils.framesToTime(o.getFirstInstant(), frameRate, t1), o.getUserType()]
32 tmp = o.getSpeeds()
33 for method,func in aggFunctions.items():
34 aggSpeeds = frameRate*3.6*func(tmp)
35 if method == 'centile':
36 row.extend(aggSpeeds.tolist())
37 else:
38 row.append(aggSpeeds)
39 data.append(row)
40 return data
20 41
21 def learnAssignMotionPatterns(learn, assign, objects, similarities, minSimilarity, similarityFunc, minClusterSize = 0, optimizeCentroid = False, randomInitialization = False, removePrototypesAfterAssignment = False, initialPrototypes = []): 42 def learnAssignMotionPatterns(learn, assign, objects, similarities, minSimilarity, similarityFunc, minClusterSize = 0, optimizeCentroid = False, randomInitialization = False, removePrototypesAfterAssignment = False, initialPrototypes = []):
22 '''Learns motion patterns 43 '''Learns motion patterns
23 44
24 During assignments, if using minClusterSize > 0, prototypes can change (be removed) 45 During assignments, if using minClusterSize > 0, prototypes can change (be removed)