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311
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1 #! /usr/bin/env python
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2 '''Various utilities to load data saved by the POLY new output(s)'''
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3 import sys
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4 import utils
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5 from moving import TimeInterval
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6 import numpy as np
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7
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8 __metaclass__ = type
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598
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9 from indicators import SeverityIndicator
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311
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10
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11
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598
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12 def loadNewInteractions(videoFilename,interactionType,dirname, extension, indicatorsNames, roaduserNum1,roaduserNum2, selectedIndicators=[]):
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311
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13 '''Loads interactions from the POLY traffic event format'''
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14 from events import Interaction
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598
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15 filename= dirname + videoFilename + extension
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16 #filename= dirname + interactionType+ '-' + videoFilename + extension # case of min distance todo: change the saving format to be matched with all outputs
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311
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17 file = utils.openCheck(filename)
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18 if (not file):
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19 return []
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598
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20 #interactions = []
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311
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21 interactionNum = 0
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22 data= np.loadtxt(filename)
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23 indicatorFrameNums= data[:,0]
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24 inter = Interaction(interactionNum, TimeInterval(indicatorFrameNums[0],indicatorFrameNums[-1]), roaduserNum1, roaduserNum2)
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25 inter.addVideoFilename(videoFilename)
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26 inter.addInteractionType(interactionType)
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27 for key in indicatorsNames.keys():
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28 values= {}
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29 for i,t in enumerate(indicatorFrameNums):
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30 values[t] = data[i,key]
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31 inter.addIndicator(SeverityIndicator(indicatorsNames[key], values))
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32 if selectedIndicators !=[]:
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33 values= {}
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34 for i,t in enumerate(indicatorFrameNums):
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35 values[t] = [data[i,index] for index in selectedIndicators]
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36 inter.addIndicator(SeverityIndicator('selectedIndicators', values))
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37
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598
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38 #interactions.append(inter)
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311
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39 file.close()
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598
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40 #return interactions
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41 return inter
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42
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43 # Plotting results
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44
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45 frameRate = 15.
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46
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47 # To run in directory that contains the directories that contain the results (Miss-xx and Incident-xx)
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48 #dirname = '/home/nicolas/Research/Data/kentucky-db/'
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49
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50 interactingRoadUsers = {'Miss/0404052336': [(0,3)] # 0,2 and 1 vs 3
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51 #,
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52 #'Incident/0306022035': [(1,3)]
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53 #,
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54 #'Miss/0208030956': [(4,5),(5,7)]
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55 }
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56
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57
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58 def getIndicatorName(filename, withUnit = False):
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59 if withUnit:
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60 unit = ' (s)'
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61 else:
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62 unit = ''
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63 if 'collision-point' in filename:
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64 return 'TTC'+unit
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65 elif 'crossing' in filename:
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66 return 'pPET'+unit
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67 elif 'probability' in filename:
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68 return 'P(UEA)'
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69
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70 def getMethodName(fileprefix):
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71 if fileprefix == 'constant-velocity':
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72 return 'Con. Vel.'
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73 elif fileprefix == 'normal-adaptation':
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74 return 'Norm. Ad.'
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75 elif fileprefix == 'point-set':
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76 return 'Pos. Set'
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77 elif fileprefix == 'evasive-action':
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78 return 'Ev. Act.'
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79 elif fileprefix == 'point-set-evasive-action':
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80 return 'Pos. Set'
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81
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82 indicator2TimeIdx = {'TTC':2,'pPET':2, 'P(UEA)':3}
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311
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83
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598
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84 def getDataAtInstant(data, i):
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85 return data[data[:,2] == i]
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86
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87 def getPointsAtInstant(data, i):
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88 return getDataAtInstant(i)[3:5]
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89
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90 def getIndicator(data, roadUserNumbers, indicatorName):
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91 if data.ndim ==1:
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92 data.shape = (1,data.shape[0])
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93
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94 # find the order for the roadUserNumbers
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95 uniqueObj1 = np.unique(data[:,0])
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96 uniqueObj2 = np.unique(data[:,1])
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97 found = False
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98 if roadUserNumbers[0] in uniqueObj1 and roadUserNumbers[1] in uniqueObj2:
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99 objNum1 = roadUserNumbers[0]
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100 objNum2 = roadUserNumbers[1]
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101 found = True
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102 if roadUserNumbers[1] in uniqueObj1 and roadUserNumbers[0] in uniqueObj2:
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103 objNum1 = roadUserNumbers[1]
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104 objNum2 = roadUserNumbers[0]
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105 found = True
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106
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107 # get subset of data for road user numbers
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108 if found:
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109 roadUserData = data[np.logical_and(data[:,0] == objNum1, data[:,1] == objNum2),:]
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110 if roadUserData.size > 0:
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111 time = np.unique(roadUserData[:,indicator2TimeIdx[indicatorName]])
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112 values = {}
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113 if indicatorName == 'P(UEA)':
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114 tmp = roadUserData[:,4]
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115 for k,v in zip(time, tmp):
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116 values[k]=v
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117 return SeverityIndicator(indicatorName, values, mostSevereIsMax = False, maxValue = 1.), roadUserData
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118 else:
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119 for i in xrange(time[0],time[-1]+1):
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120 try:
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121 tmp = getDataAtInstant(roadUserData, i)
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122 values[i] = np.sum(tmp[:,5]*tmp[:,6])/np.sum(tmp[:,5])/frameRate
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123 except IOError:
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124 values[i] = np.inf
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125 return SeverityIndicator(indicatorName, values, mostSevereIsMax = False), roadUserData
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126 return None, None |
