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| #!/usr/bin/python | |
| # | |
| # Cityscapes labels | |
| # | |
| from __future__ import print_function, absolute_import, division | |
| 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). | |
| # Do not modify these IDs, since exactly these IDs are expected by the | |
| # evaluation server. | |
| 'trainId' , # Feel free to modify these IDs as suitable for your method. Then create | |
| # ground truth images with train IDs, using the tools provided in the | |
| # 'preparation' folder. However, make sure to validate or submit results | |
| # to our evaluation server using the regular IDs above! | |
| # For trainIds, 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. | |
| # Max value is 255! | |
| '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 your approach. | |
| # Note that you might want to ignore labels with ID 255 during training. | |
| # Further note that the current train IDs are only a suggestion. You can use whatever you like. | |
| # 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 category catId hasInstances ignoreInEval color | |
| Label( 'unlabeled' , 0 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ), | |
| Label( 'ego vehicle' , 1 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ), | |
| Label( 'rectification border' , 2 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ), | |
| Label( 'out of roi' , 3 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ), | |
| Label( 'static' , 4 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ), | |
| Label( 'dynamic' , 5 , 255 , 'void' , 0 , False , True , (111, 74, 0) ), | |
| Label( 'ground' , 6 , 255 , 'void' , 0 , False , True , ( 81, 0, 81) ), | |
| Label( 'road' , 7 , 0 + 8, 'flat' , 1 , False , False , (128, 64,128) ), | |
| Label( 'sidewalk' , 8 , 1 + 8, 'flat' , 1 , False , False , (244, 35,232) ), | |
| Label( 'parking' , 9 , 255 , 'flat' , 1 , False , True , (250,170,160) ), | |
| Label( 'rail track' , 10 , 255 , 'flat' , 1 , False , True , (230,150,140) ), | |
| Label( 'building' , 11 , 2 + 8, 'construction' , 2 , False , False , ( 70, 70, 70) ), | |
| Label( 'wall' , 12 , 3 + 8, 'construction' , 2 , False , False , (102,102,156) ), | |
| Label( 'fence' , 13 , 4 + 8, 'construction' , 2 , False , False , (190,153,153) ), | |
| Label( 'guard rail' , 14 , 255 , 'construction' , 2 , False , True , (180,165,180) ), | |
| Label( 'bridge' , 15 , 255 , 'construction' , 2 , False , True , (150,100,100) ), | |
| Label( 'tunnel' , 16 , 255 , 'construction' , 2 , False , True , (150,120, 90) ), | |
| Label( 'pole' , 17 , 5 + 8, 'object' , 3 , False , False , (153,153,153) ), | |
| Label( 'polegroup' , 18 , 255 , 'object' , 3 , False , True , (153,153,153) ), | |
| Label( 'traffic light' , 19 , 6 + 8, 'object' , 3 , False , False , (250,170, 30) ), | |
| Label( 'traffic sign' , 20 , 7 + 8, 'object' , 3 , False , False , (220,220, 0) ), | |
| Label( 'vegetation' , 21 , 8 + 8, 'nature' , 4 , False , False , (107,142, 35) ), | |
| Label( 'terrain' , 22 , 9 + 8, 'nature' , 4 , False , False , (152,251,152) ), | |
| Label( 'sky' , 23 , 10 + 8, 'sky' , 5 , False , False , ( 70,130,180) ), | |
| Label( 'person' , 24 , 11 - 11 , 'human' , 6 , True , False , (220, 20, 60) ), | |
| Label( 'rider' , 25 , 12 - 11 , 'human' , 6 , True , False , (255, 0, 0) ), | |
| Label( 'car' , 26 , 13 - 11, 'vehicle' , 7 , True , False , ( 0, 0,142) ), | |
| Label( 'truck' , 27 , 14 - 11, 'vehicle' , 7 , True , False , ( 0, 0, 70) ), | |
| Label( 'bus' , 28 , 15 - 11, 'vehicle' , 7 , True , False , ( 0, 60,100) ), | |
| Label( 'caravan' , 29 , 255 , 'vehicle' , 7 , True , True , ( 0, 0, 90) ), | |
| Label( 'trailer' , 30 , 255 , 'vehicle' , 7 , True , True , ( 0, 0,110) ), | |
| Label( 'train' , 31 , 16 - 11, 'vehicle' , 7 , True , False , ( 0, 80,100) ), | |
| Label( 'motorcycle' , 32 , 17 - 11, 'vehicle' , 7 , True , False , ( 0, 0,230) ), | |
| Label( 'bicycle' , 33 , 18 - 11, 'vehicle' , 7 , True , False , (119, 11, 32) ), | |
| Label( 'license plate' , -1 , -1 , 'vehicle' , 7 , False , True , ( 0, 0,142) ), | |
| ] | |
| #-------------------------------------------------------------------------------- | |
| # Create dictionaries for a fast lookup | |
| #-------------------------------------------------------------------------------- | |
| # Please refer to the main method below for example usages! | |
| # name to label object | |
| name2label = { label.name : label for label in labels } | |
| # id to label object | |
| id2label = { label.id : label for label in labels } | |
| # trainId to label object | |
| trainId2label = { label.trainId : label for label in reversed(labels) } | |
| # category to list of label objects | |
| category2labels = {} | |
| for label in labels: | |
| category = label.category | |
| if category in category2labels: | |
| category2labels[category].append(label) | |
| else: | |
| category2labels[category] = [label] | |
| #-------------------------------------------------------------------------------- | |
| # Assure single instance name | |
| #-------------------------------------------------------------------------------- | |
| # returns the label name that describes a single instance (if possible) | |
| # e.g. input | output | |
| # ---------------------- | |
| # car | car | |
| # cargroup | car | |
| # foo | None | |
| # foogroup | None | |
| # skygroup | None | |
| 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 None | |
| # 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 | |
| #-------------------------------------------------------------------------------- | |
| # just a dummy main | |
| if __name__ == "__main__": | |
| # Print all the labels | |
| print("List of cityscapes labels:") | |
| print("") | |
| print(" {:>21} | {:>3} | {:>7} | {:>14} | {:>10} | {:>12} | {:>12}".format( 'name', 'id', 'trainId', 'category', 'categoryId', 'hasInstances', 'ignoreInEval' )) | |
| print(" " + ('-' * 98)) | |
| for label in labels: | |
| print(" {:>21} | {:>3} | {:>7} | {:>14} | {:>10} | {:>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 )) | |