Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/process.py @ 988:dc0be55e2bf5

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new process functionalities
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 08 Mar 2018 16:44:20 -0500
parents f026ce2af637
children 933670761a57
comparison
equal deleted inserted replaced
987:f026ce2af637 988:dc0be55e2bf5
4 from pathlib2 import Path 4 from pathlib2 import Path
5 5
6 import matplotlib 6 import matplotlib
7 matplotlib.use('Agg') 7 matplotlib.use('Agg')
8 import matplotlib.pyplot as plt 8 import matplotlib.pyplot as plt
9 from numpy import percentile
9 10
10 import storage, events, prediction 11 import storage, events, prediction
11 from metadata import * 12 from metadata import *
12 13
13 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.') 14 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.')
14 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True) 15 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True)
15 parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int) 16 parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int)
17 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects/interactions to process', type = int)
16 parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (cvd: vector computation (approximate); cve: equation solving; cv: discrete time (approximate)), normal adaptation, point set prediction)', choices = ['cvd', 'cve', 'cv', 'na', 'ps', 'mp']) 18 parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (cvd: vector computation (approximate); cve: equation solving; cv: discrete time (approximate)), normal adaptation, point set prediction)', choices = ['cvd', 'cve', 'cv', 'na', 'ps', 'mp'])
17 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true') 19 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
18 parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction']) 20 parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction'])
19 parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'interaction']) 21 parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'interaction'])
20 parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction']) 22 parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction'])
63 # processed = [] 65 # processed = []
64 # for job in jobs: 66 # for job in jobs:
65 # processed += job.get() 67 # processed += job.get()
66 # pool.close() 68 # pool.close()
67 69
70 if args.analyze == 'object': # user speed for now
71 medianSpeeds = {}
72 speeds85 = {}
73 minLength = 2*30
74 for videoId in args.videoIds:
75 vs = session.query(VideoSequence).get(videoId)
76 if not vs.cameraView.siteIdx in medianSpeeds:
77 medianSpeeds[vs.cameraView.siteIdx] = []
78 speeds85[vs.cameraView.siteIdx] = []
79 print('Extracting speed from '+vs.getDatabaseFilename())
80 objects = storage.loadTrajectoriesFromSqlite(str(parentDir/vs.getDatabaseFilename()), 'object')
81 for o in objects:
82 if o.length() > minLength:
83 speeds = 30*3.6*percentile(o.getSpeeds(), [50, 85])
84 medianSpeeds[vs.cameraView.siteIdx].append(speeds[0])
85 speeds85[vs.cameraView.siteIdx].append(speeds[1])
86 for speeds, name in zip([medianSpeeds, speeds85], ['Median', '85th Centile']):
87 plt.ioff()
88 plt.figure()
89 plt.boxplot(speeds.values(), labels = [session.query(Site).get(siteId).name for siteId in speeds])
90 plt.ylabel(name+' Speeds (km/h)')
91 plt.savefig(name.lower()+'-speeds.png', dpi=150)
92 plt.close()
93
68 if args.analyze == 'interaction': 94 if args.analyze == 'interaction':
69 indicatorIds = [2,5,7,10] 95 indicatorIds = [2,5,7,10]
70 conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30} 96 conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30}
97 maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.}
71 indicators = {} 98 indicators = {}
72 interactions = {} 99 interactions = {}
73 for videoId in args.videoIds: 100 for videoId in args.videoIds:
74 vs = session.query(VideoSequence).get(videoId) 101 vs = session.query(VideoSequence).get(videoId)
75 if not vs.cameraView.siteIdx in interactions: 102 if not vs.cameraView.siteIdx in interactions:
81 print(vs.getDatabaseFilename(), len(interactions[vs.cameraView.siteIdx])) 108 print(vs.getDatabaseFilename(), len(interactions[vs.cameraView.siteIdx]))
82 for inter in interactions[vs.cameraView.siteIdx]: 109 for inter in interactions[vs.cameraView.siteIdx]:
83 for i in indicatorIds: 110 for i in indicatorIds:
84 indic = inter.getIndicator(events.Interaction.indicatorNames[i]) 111 indic = inter.getIndicator(events.Interaction.indicatorNames[i])
85 if indic is not None: 112 if indic is not None:
86 indicators[vs.cameraView.siteIdx][i].append(indic.getMostSevereValue()*conversionFactors[i]) 113 v = indic.getMostSevereValue()*conversionFactors[i]
114 if v < maxIndicatorValue[i]:
115 indicators[vs.cameraView.siteIdx][i].append(v)
87 116
88 for i in indicatorIds: 117 for i in indicatorIds:
89 tmp = [indicators[siteId][i] for siteId in indicators] 118 tmp = [indicators[siteId][i] for siteId in indicators]
90 plt.ioff() 119 plt.ioff()
91 plt.figure() 120 plt.figure()
92 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators]) 121 plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators])
93 plt.title(events.Interaction.indicatorNames[i]) 122 plt.ylabel(events.Interaction.indicatorNames[i]+' ('+events.Interaction.indicatorUnits[i]+')')
94 plt.savefig(events.Interaction.indicatorNames[i]+'.png') 123 plt.savefig(events.Interaction.indicatorNames[i]+'.png', dpi=150)
95 plt.close() 124 plt.close()