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Datasets:
kumuji
/
lost_and_found

Tasks:
Image Segmentation
Modalities:
Image
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  1. camera.zip +3 -0
  2. gtCoarse.zip +3 -0
  3. labels.py +181 -0
  4. laf_table.pdf +0 -0
  5. timestamp.tgz +3 -0
  6. vehicle.zip +3 -0
camera.zip ADDED
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gtCoarse.zip ADDED
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labels.py ADDED
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+ #!/usr/bin/python
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+ #
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+ # Cityscapes labels
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+ #
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+
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+ from collections import namedtuple
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+
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+
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+ #--------------------------------------------------------------------------------
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+ # Definitions
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+ #--------------------------------------------------------------------------------
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+
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+ # a label and all meta information
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+ Label = namedtuple( 'Label' , [
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+
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+ 'name' , # The identifier of this label, e.g. 'car', 'person', ... .
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+ # We use them to uniquely name a class
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+
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+ 'id' , # An integer ID that is associated with this label.
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+ # The IDs are used to represent the label in ground truth images
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+ # An ID of -1 means that this label does not have an ID and thus
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+ # is ignored when creating ground truth images (e.g. license plate).
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+
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+ 'trainId' , # An integer ID that overwrites the ID above, when creating ground truth
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+ # images for training.
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+ # For training, multiple labels might have the same ID. Then, these labels
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+ # are mapped to the same class in the ground truth images. For the inverse
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+ # mapping, we use the label that is defined first in the list below.
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+ # For example, mapping all void-type classes to the same ID in training,
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+ # might make sense for some approaches.
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+
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+ 'category' , # The name of the category that this label belongs to
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+
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+ 'categoryId' , # The ID of this category. Used to create ground truth images
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+ # on category level.
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+
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+ 'hasInstances', # Whether this label distinguishes between single instances or not
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+
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+ 'ignoreInEval', # Whether pixels having this class as ground truth label are ignored
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+ # during evaluations or not
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+
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+ 'color' , # The color of this label
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+ ] )
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+
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+
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+ #--------------------------------------------------------------------------------
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+ # A list of all labels
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+ #--------------------------------------------------------------------------------
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+
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+ # Please adapt the train IDs as appropriate for you approach.
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+ # Note that you might want to ignore labels with ID 255 during training.
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+ # Make sure to provide your results using the original IDs and not the training IDs.
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+ # Note that many IDs are ignored in evaluation and thus you never need to predict these!
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+
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+ labels = [
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+ # name id trainId hasInstances ignoreInEval color
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+ Label( 'unlabeled' , 0 , 0 , False , True , ( 0, 0, 0) ),
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+ Label( 'ego vehicle' , 0 , 0 , False , True , ( 0, 0, 0) ),
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+ Label( 'rectification border' , 0 , 0 , False , True , ( 0, 0, 0) ),
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+ Label( 'out of roi' , 0 , 0 , False , True , ( 0, 0, 0) ),
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+ Label( 'background' , 0 , 0 , False , False , ( 0, 0, 0) ),
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+ Label( 'free' , 1 , 1 , False , False , (128, 64,128) ),
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+ Label( '01' , 2 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '02' , 3 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '03' , 4 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '04' , 5 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '05' , 6 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '06' , 7 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '07' , 8 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '08' , 9 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '09' , 10 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '10' , 11 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '11' , 12 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '12' , 13 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '13' , 14 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '14' , 15 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '15' , 16 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '16' , 17 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '17' , 18 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '18' , 19 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '19' , 20 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '20' , 21 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '21' , 22 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '22' , 23 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '23' , 24 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '24' , 25 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '25' , 26 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '26' , 27 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '27' , 28 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '28' , 29 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '29' , 30 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '30' , 31 , 0 , True , False , ( 0, 0, 0) ),
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+ Label( '31' , 32 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '32' , 33 , 0 , True , False , ( 0, 0, 0) ),
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+ Label( '33' , 34 , 0 , True , False , ( 0, 0, 0) ),
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+ Label( '34' , 35 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '35' , 36 , 0 , True , False , ( 0, 0, 0) ),
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+ Label( '36' , 37 , 0 , True , False , ( 0, 0, 0) ),
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+ Label( '37' , 38 , 0 , True , False , ( 0, 0, 0) ),
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+ Label( '38' , 39 , 0 , True , False , ( 0, 0, 0) ),
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+ Label( '39' , 40 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '40' , 41 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '41' , 42 , 2 , True , False , ( 0, 0,142) ),
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+ Label( '42' , 43 , 2 , True , False , ( 0, 0,142) ),
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+
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+ ]
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+
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+
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+ #--------------------------------------------------------------------------------
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+ # Create dictionaries for a fast lookup
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+ #--------------------------------------------------------------------------------
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+
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+ name2label = { label.name : label for label in labels }
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+ id2label = { label.id : label for label in labels }
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+ trainId2label = { label.trainId : label for label in reversed(labels) }
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+ category2labels = {}
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+ for label in labels:
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+ category = label.category
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+ if category in category2labels:
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+ category2labels[category].append(label)
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+ else:
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+ category2labels[category] = [label]
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+
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+ #--------------------------------------------------------------------------------
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+ # Assure single instance name
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+ #--------------------------------------------------------------------------------
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+
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+ def assureSingleInstanceName( name ):
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+ # if the name is known, it is not a group
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+ if name in name2label:
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+ return name
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+ # test if the name actually denotes a group
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+ if not name.endswith("group"):
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+ return name
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+ # remove group
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+ name = name[:-len("group")]
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+ # test if the new name exists
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+ if not name in name2label:
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+ return None
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+ # test if the new name denotes a label that actually has instances
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+ if not name2label[name].hasInstances:
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+ return None
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+ # all good then
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+ return name
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+
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+ #--------------------------------------------------------------------------------
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+ # Main for testing
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+ #--------------------------------------------------------------------------------
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+
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+ if __name__ == "__main__":
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+ # Print all the labels
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+ print "List of cityscapes labels:"
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+ print
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+ print " {:>13} | {:>3} | {:>7} | {:>14} | {:>7} | {:>12} | {:>12}".format( 'name', 'id', 'trainId', 'category', 'categoryId', 'hasInstances', 'ignoreInEval' )
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+ print " " + ('-' * 88)
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+ for label in labels:
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+ print " {:>13} | {:>3} | {:>7} | {:>14} | {:>7} | {:>12} | {:>12}".format( label.name, label.id, label.trainId, label.category, label.categoryId, label.hasInstances, label.ignoreInEval )
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+ print
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+
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+ print "Example usages:"
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+
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+ # Map from name to label
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+ name = 'car'
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+ id = name2label[name].id
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+ print "ID of label '{name}': {id}".format( name=name, id=id )
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+
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+ # Map from ID to label
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+ category = id2label[id].category
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+ print "Category of label with ID '{id}': {category}".format( id=id, category=category )
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+
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+ # Map from trainID to label
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+ trainId = 0
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+ name = trainId2label[trainId].name
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+ print "Name of label with trainID '{id}': {name}".format( id=trainId, name=name )
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+
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+ # Print list of label names for each train ID
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+ print "Labels for train IDs: ", trainId2label.keys()
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+ print " ",
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+ for trainId in trainId2label:
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+ print trainId2label[trainId].name + "," ,
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+ print
laf_table.pdf ADDED
Binary file (25.4 kB). View file
 
timestamp.tgz ADDED
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vehicle.zip ADDED
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