#!/usr/bin/env python3
#
# Copyright 2021 Graviti. Licensed under MIT License.
#
"""The implementation of the TensorBay label."""
from functools import partial
from typing import Any, Dict, Iterator, List, Type, TypeVar
from tensorbay.label.label_box import LabeledBox2D, LabeledBox3D
from tensorbay.label.label_classification import Classification
from tensorbay.label.label_keypoints import LabeledKeypoints2D
from tensorbay.label.label_mask import (
InstanceMaskBase,
PanopticMaskBase,
RemoteInstanceMask,
RemotePanopticMask,
RemoteSemanticMask,
SemanticMaskBase,
)
from tensorbay.label.label_polygon import LabeledMultiPolygon, LabeledPolygon, LabeledRLE
from tensorbay.label.label_polyline import LabeledMultiPolyline2D, LabeledPolyline2D
from tensorbay.label.label_sentence import LabeledSentence
from tensorbay.utility import AttrsMixin, ReprMixin, ReprType, attr, common_loads, upper
_ERROR_MESSAGE = "The '{attr_name}' label is not provided in this data"
_attr = partial(attr, is_dynamic=True, key=upper, error_message=_ERROR_MESSAGE)
_mask_attr = partial(_attr, dumper=lambda self: self.get_callback_body())
[docs]class Label(ReprMixin, AttrsMixin): # pylint: disable=too-many-instance-attributes
"""This class defines :attr:`~tensorbay.dataset.data.Data.label`.
It contains growing types of labels referring to different tasks.
Examples:
>>> from tensorbay.label import Classification
>>> label = Label()
>>> label.classification = Classification("example_category", {"example_attribute1": "a"})
>>> label
Label(
(classification): Classification(
(category): 'example_category',
(attributes): {...}
)
)
"""
_T = TypeVar("_T", bound="Label")
_repr_type = ReprType.INSTANCE
_repr_maxlevel = 2
classification: Classification = _attr()
box2d: List[LabeledBox2D] = _attr()
box3d: List[LabeledBox3D] = _attr()
polygon: List[LabeledPolygon] = _attr()
polyline2d: List[LabeledPolyline2D] = _attr()
multi_polyline2d: List[LabeledMultiPolyline2D] = _attr()
rle: List[LabeledRLE] = _attr()
keypoints2d: List[LabeledKeypoints2D] = _attr()
multi_polygon: List[LabeledMultiPolygon] = _attr()
sentence: List[LabeledSentence] = _attr()
semantic_mask: SemanticMaskBase = _mask_attr(loader=RemoteSemanticMask.from_response_body)
instance_mask: InstanceMaskBase = _mask_attr(loader=RemoteInstanceMask.from_response_body)
panoptic_mask: PanopticMaskBase = _mask_attr(loader=RemotePanopticMask.from_response_body)
def __bool__(self) -> bool:
return any(hasattr(self, key) for key in self._attrs_fields)
@property
def _repr_attrs(self) -> Iterator[str]: # type: ignore[override]
yield from self._attrs_fields
[docs] @classmethod
def loads(cls: Type[_T], contents: Dict[str, Any]) -> _T:
"""Loads data from a dict containing the labels information.
Arguments:
contents: A dict containing the labels information.
Returns:
A :class:`Label` instance containing labels information from the given dict.
Examples:
>>> contents = {
... "CLASSIFICATION": {
... "category": "example_category",
... "attributes": {"example_attribute1": "a"}
... }
... }
>>> Label.loads(contents)
Label(
(classification): Classification(
(category): 'example_category',
(attributes): {...}
)
)
"""
return common_loads(cls, contents)
[docs] def dumps(self) -> Dict[str, Any]:
"""Dumps all labels into a dict.
Returns:
Dumped labels dict.
Examples:
>>> from tensorbay.label import Classification
>>> label = Label()
>>> label.classification = Classification("category1", {"attribute1": "a"})
>>> label.dumps()
{'CLASSIFICATION': {'category': 'category1', 'attributes': {'attribute1': 'a'}}}
"""
return self._dumps()