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0b060d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | #!/usr/bin/python
#
# Cityscapes labels
#
from collections import namedtuple
#--------------------------------------------------------------------------------
# Definitions
#--------------------------------------------------------------------------------
# a label and all meta information
Label = namedtuple( 'Label' , [
'name' , # The identifier of this label, e.g. 'car', 'person', ... .
# We use them to uniquely name a class
'id' , # An integer ID that is associated with this label.
# The IDs are used to represent the label in ground truth images
# An ID of -1 means that this label does not have an ID and thus
# is ignored when creating ground truth images (e.g. license plate).
'trainId' , # An integer ID that overwrites the ID above, when creating ground truth
# images for training.
# For training, multiple labels might have the same ID. Then, these labels
# are mapped to the same class in the ground truth images. For the inverse
# mapping, we use the label that is defined first in the list below.
# For example, mapping all void-type classes to the same ID in training,
# might make sense for some approaches.
'category' , # The name of the category that this label belongs to
'categoryId' , # The ID of this category. Used to create ground truth images
# on category level.
'hasInstances', # Whether this label distinguishes between single instances or not
'ignoreInEval', # Whether pixels having this class as ground truth label are ignored
# during evaluations or not
'color' , # The color of this label
] )
#--------------------------------------------------------------------------------
# A list of all labels
#--------------------------------------------------------------------------------
# Please adapt the train IDs as appropriate for you approach.
# Note that you might want to ignore labels with ID 255 during training.
# Make sure to provide your results using the original IDs and not the training IDs.
# Note that many IDs are ignored in evaluation and thus you never need to predict these!
labels = [
# name id trainId hasInstances ignoreInEval color
Label( 'unlabeled' , 0 , 0 , False , True , ( 0, 0, 0) ),
Label( 'ego vehicle' , 0 , 0 , False , True , ( 0, 0, 0) ),
Label( 'rectification border' , 0 , 0 , False , True , ( 0, 0, 0) ),
Label( 'out of roi' , 0 , 0 , False , True , ( 0, 0, 0) ),
Label( 'background' , 0 , 0 , False , False , ( 0, 0, 0) ),
Label( 'free' , 1 , 1 , False , False , (128, 64,128) ),
Label( '01' , 2 , 2 , True , False , ( 0, 0,142) ),
Label( '02' , 3 , 2 , True , False , ( 0, 0,142) ),
Label( '03' , 4 , 2 , True , False , ( 0, 0,142) ),
Label( '04' , 5 , 2 , True , False , ( 0, 0,142) ),
Label( '05' , 6 , 2 , True , False , ( 0, 0,142) ),
Label( '06' , 7 , 2 , True , False , ( 0, 0,142) ),
Label( '07' , 8 , 2 , True , False , ( 0, 0,142) ),
Label( '08' , 9 , 2 , True , False , ( 0, 0,142) ),
Label( '09' , 10 , 2 , True , False , ( 0, 0,142) ),
Label( '10' , 11 , 2 , True , False , ( 0, 0,142) ),
Label( '11' , 12 , 2 , True , False , ( 0, 0,142) ),
Label( '12' , 13 , 2 , True , False , ( 0, 0,142) ),
Label( '13' , 14 , 2 , True , False , ( 0, 0,142) ),
Label( '14' , 15 , 2 , True , False , ( 0, 0,142) ),
Label( '15' , 16 , 2 , True , False , ( 0, 0,142) ),
Label( '16' , 17 , 2 , True , False , ( 0, 0,142) ),
Label( '17' , 18 , 2 , True , False , ( 0, 0,142) ),
Label( '18' , 19 , 2 , True , False , ( 0, 0,142) ),
Label( '19' , 20 , 2 , True , False , ( 0, 0,142) ),
Label( '20' , 21 , 2 , True , False , ( 0, 0,142) ),
Label( '21' , 22 , 2 , True , False , ( 0, 0,142) ),
Label( '22' , 23 , 2 , True , False , ( 0, 0,142) ),
Label( '23' , 24 , 2 , True , False , ( 0, 0,142) ),
Label( '24' , 25 , 2 , True , False , ( 0, 0,142) ),
Label( '25' , 26 , 2 , True , False , ( 0, 0,142) ),
Label( '26' , 27 , 2 , True , False , ( 0, 0,142) ),
Label( '27' , 28 , 2 , True , False , ( 0, 0,142) ),
Label( '28' , 29 , 2 , True , False , ( 0, 0,142) ),
Label( '29' , 30 , 2 , True , False , ( 0, 0,142) ),
Label( '30' , 31 , 0 , True , False , ( 0, 0, 0) ),
Label( '31' , 32 , 2 , True , False , ( 0, 0,142) ),
Label( '32' , 33 , 0 , True , False , ( 0, 0, 0) ),
Label( '33' , 34 , 0 , True , False , ( 0, 0, 0) ),
Label( '34' , 35 , 2 , True , False , ( 0, 0,142) ),
Label( '35' , 36 , 0 , True , False , ( 0, 0, 0) ),
Label( '36' , 37 , 0 , True , False , ( 0, 0, 0) ),
Label( '37' , 38 , 0 , True , False , ( 0, 0, 0) ),
Label( '38' , 39 , 0 , True , False , ( 0, 0, 0) ),
Label( '39' , 40 , 2 , True , False , ( 0, 0,142) ),
Label( '40' , 41 , 2 , True , False , ( 0, 0,142) ),
Label( '41' , 42 , 2 , True , False , ( 0, 0,142) ),
Label( '42' , 43 , 2 , True , False , ( 0, 0,142) ),
]
#--------------------------------------------------------------------------------
# Create dictionaries for a fast lookup
#--------------------------------------------------------------------------------
name2label = { label.name : label for label in labels }
id2label = { label.id : label for label in labels }
trainId2label = { label.trainId : label for label in reversed(labels) }
category2labels = {}
for label in labels:
category = label.category
if category in category2labels:
category2labels[category].append(label)
else:
category2labels[category] = [label]
#--------------------------------------------------------------------------------
# Assure single instance name
#--------------------------------------------------------------------------------
def assureSingleInstanceName( name ):
# if the name is known, it is not a group
if name in name2label:
return name
# test if the name actually denotes a group
if not name.endswith("group"):
return name
# remove group
name = name[:-len("group")]
# test if the new name exists
if not name in name2label:
return None
# test if the new name denotes a label that actually has instances
if not name2label[name].hasInstances:
return None
# all good then
return name
#--------------------------------------------------------------------------------
# Main for testing
#--------------------------------------------------------------------------------
if __name__ == "__main__":
# Print all the labels
print "List of cityscapes labels:"
print
print " {:>13} | {:>3} | {:>7} | {:>14} | {:>7} | {:>12} | {:>12}".format( 'name', 'id', 'trainId', 'category', 'categoryId', 'hasInstances', 'ignoreInEval' )
print " " + ('-' * 88)
for label in labels:
print " {:>13} | {:>3} | {:>7} | {:>14} | {:>7} | {:>12} | {:>12}".format( label.name, label.id, label.trainId, label.category, label.categoryId, label.hasInstances, label.ignoreInEval )
print
print "Example usages:"
# Map from name to label
name = 'car'
id = name2label[name].id
print "ID of label '{name}': {id}".format( name=name, id=id )
# Map from ID to label
category = id2label[id].category
print "Category of label with ID '{id}': {category}".format( id=id, category=category )
# Map from trainID to label
trainId = 0
name = trainId2label[trainId].name
print "Name of label with trainID '{id}': {name}".format( id=trainId, name=name )
# Print list of label names for each train ID
print "Labels for train IDs: ", trainId2label.keys()
print " ",
for trainId in trainId2label:
print trainId2label[trainId].name + "," ,
print
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