Datasets:
Tasks:
Image Segmentation
Modalities:
Image
| #!/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 " {:>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 "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 + "," , | |