lost_and_found

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lost_and_found / labels.py

Gak1o

Duplicate from kumuji/lost_and_found

0b060d8 16 days ago

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	#!/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
	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

	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 + "," ,
	print