#!/usr/bin/python # SPDX-License-Identifier: Apache-2.0 # ''' This script contains label names, original ids, modified train ids and color maps for the categories in the cityscapes dataset. Used in the duc-inference.ipynb and duc-validation.ipynb ''' 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 you 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 , 'flat' , 1 , False , False , (128, 64,128) ), Label( 'sidewalk' , 8 , 1 , '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 , 'construction' , 2 , False , False , ( 70, 70, 70) ), Label( 'wall' , 12 , 3 , 'construction' , 2 , False , False , (102,102,156) ), Label( 'fence' , 13 , 4 , '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 , 'object' , 3 , False , False , (153,153,153) ), Label( 'polegroup' , 18 , 255 , 'object' , 3 , False , True , (153,153,153) ), Label( 'traffic light' , 19 , 6 , 'object' , 3 , False , False , (250,170, 30) ), Label( 'traffic sign' , 20 , 7 , 'object' , 3 , False , False , (220,220, 0) ), Label( 'vegetation' , 21 , 8 , 'nature' , 4 , False , False , (107,142, 35) ), Label( 'terrain' , 22 , 9 , 'nature' , 4 , False , False , (152,251,152) ), Label( 'sky' , 23 , 10 , 'sky' , 5 , False , False , ( 70,130,180) ), Label( 'person' , 24 , 11 , 'human' , 6 , True , False , (220, 20, 60) ), Label( 'rider' , 25 , 12 , 'human' , 6 , True , False , (255, 0, 0) ), Label( 'car' , 26 , 13 , 'vehicle' , 7 , True , False , ( 0, 0,142) ), Label( 'truck' , 27 , 14 , 'vehicle' , 7 , True , False , ( 0, 0, 70) ), Label( 'bus' , 28 , 15 , '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 , 'vehicle' , 7 , True , False , ( 0, 80,100) ), Label( 'motorcycle' , 32 , 17 , 'vehicle' , 7 , True , False , ( 0, 0,230) ), Label( 'bicycle' , 33 , 18 , '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 ))