def generate_label_schema(not_empty_labels, multilabel=False):
assert len(not_empty_labels) > 1
label_schema = LabelSchemaEntity()
if multilabel:
emptylabel = LabelEntity(name="Empty label",
is_empty=True,
domain=Domain.CLASSIFICATION)
empty_group = LabelGroup(name="empty",
labels=[emptylabel],
group_type=LabelGroupType.EMPTY_LABEL)
single_groups = []
for label in not_empty_labels:
single_groups.append(
LabelGroup(name=label.name,
labels=[label],
group_type=LabelGroupType.EXCLUSIVE))
label_schema.add_group(single_groups[-1])
label_schema.add_group(empty_group, exclusive_with=single_groups)
else:
main_group = LabelGroup(name="labels",
labels=not_empty_labels,
group_type=LabelGroupType.EXCLUSIVE)
label_schema.add_group(main_group)
return label_schema
def add_hierarchy(
self, label_schema: LabelSchemaEntity
) -> Tuple[LabelEntity, LabelEntity, LabelEntity]:
"""Adds children to flowering, no_plant and vegetative"""
label_schema.add_group(
LabelGroup(
"plant_state",
[self.flowering, self.no_plant, self.vegetative],
LabelGroupType.EXCLUSIVE,
))
flower_partial_visible = self.new_label_by_name(
"flower_partial_visible")
flower_fully_visible = self.new_label_by_name("flower_fully_visible")
label_schema.add_group(
LabelGroup(
"flowering_state",
[flower_fully_visible, flower_partial_visible],
LabelGroupType.EXCLUSIVE,
))
label_schema.add_child(self.flowering, flower_partial_visible)
label_schema.add_child(self.flowering, flower_fully_visible)
assert self.flowering == label_schema.get_parent(
flower_partial_visible)
assert label_schema.get_parent(self.no_plant) is None
few_leaves = self.new_label_by_name("few_leaves")
label_schema.add_group(
LabelGroup("leaf_state", [few_leaves], LabelGroupType.EXCLUSIVE))
label_schema.add_child(self.vegetative, few_leaves)
return few_leaves, flower_fully_visible, flower_partial_visible
def generate_label_schema(dataset, task_type):
"""
Generates label schema depending on task type.
"""
if task_type == TaskType.CLASSIFICATION and dataset.is_multilabel():
not_empty_labels = dataset.get_labels()
assert len(not_empty_labels) > 1
label_schema = LabelSchemaEntity()
empty_label = LabelEntity(
name="Empty label", is_empty=True, domain=Domain.CLASSIFICATION
)
empty_group = LabelGroup(
name="empty", labels=[empty_label], group_type=LabelGroupType.EMPTY_LABEL
)
single_groups = []
for label in not_empty_labels:
single_groups.append(
LabelGroup(
name=label.name, labels=[label], group_type=LabelGroupType.EXCLUSIVE
)
)
label_schema.add_group(single_groups[-1])
label_schema.add_group(empty_group, exclusive_with=single_groups)
return label_schema
if task_type == TaskType.ANOMALY_CLASSIFICATION:
return LabelSchemaEntity.from_labels(
[
LabelEntity(
name="Normal", domain=Domain.ANOMALY_CLASSIFICATION, id=ID(0)
),
LabelEntity(
name="Anomalous", domain=Domain.ANOMALY_CLASSIFICATION, id=ID(1)
),
]
)
return LabelSchemaEntity.from_labels(dataset.get_labels())
