Create cityscapes_labels.py

cityscapes_labels.py

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+#!/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 ))