#!/usr/bin/env python3
#
# Copyright 2021 Graviti. Licensed under MIT License.
#
"""LabeledPolygon, PolygonSubcatalog.
:class:`PolygonSubcatalog` defines the subcatalog for polygon type of labels.
:class:`LabeledPolygon` is the polygon type of label,
which is often used for CV tasks such as semantic segmentation.
"""
from typing import Any, Dict, Iterable, Optional, Type, TypeVar
from ..geometry import RLE, MultiPolygon, Polygon
from ..utility import ReprType, attr_base, common_loads
from .basic import SubcatalogBase, _LabelBase
from .supports import AttributesMixin, CategoriesMixin, IsTrackingMixin
[docs]class PolygonSubcatalog(SubcatalogBase, IsTrackingMixin, CategoriesMixin, AttributesMixin):
"""This class defines the subcatalog for polygon type of labels.
Arguments:
is_tracking: A boolean value indicates whether the corresponding
subcatalog contains tracking information.
Attributes:
description: The description of the entire polygon subcatalog.
categories: All the possible categories in the corresponding dataset
stored in a :class:`~tensorbay.utility.name.NameList`
with the category names as keys
and the :class:`~tensorbay.label.supports.CategoryInfo` as values.
category_delimiter: The delimiter in category values indicating parent-child relationship.
attributes: All the possible attributes in the corresponding dataset
stored in a :class:`~tensorbay.utility.name.NameList`
with the attribute names as keys
and the :class:`~tensorbay.label.attribute.AttributeInfo` as values.
is_tracking: Whether the Subcatalog contains tracking information.
Examples:
*Initialization Method 1:* Init from ``PolygonSubcatalog.loads()`` method.
>>> catalog = {
... "POLYGON": {
... "isTracking": True,
... "categories": [{"name": "0"}, {"name": "1"}],
... "attributes": [{"name": "gender", "enum": ["male", "female"]}],
... }
... }
>>> PolygonSubcatalog.loads(catalog["POLYGON"])
PolygonSubcatalog(
(is_tracking): True,
(categories): NameList [...],
(attributes): NameList [...]
)
*Initialization Method 2:* Init an empty PolygonSubcatalog and then add the attributes.
>>> from tensorbay.utility import NameList
>>> from tensorbay.label import CategoryInfo, AttributeInfo
>>> categories = NameList()
>>> categories.append(CategoryInfo("a"))
>>> attributes = NameList()
>>> attributes.append(AttributeInfo("gender", enum=["female", "male"]))
>>> polygon_subcatalog = PolygonSubcatalog()
>>> polygon_subcatalog.is_tracking = True
>>> polygon_subcatalog.categories = categories
>>> polygon_subcatalog.attributes = attributes
>>> polygon_subcatalog
PolygonSubcatalog(
(is_tracking): True,
(categories): NameList [...],
(attributes): NameList [...]
)
"""
def __init__(self, is_tracking: bool = False) -> None:
SubcatalogBase.__init__(self)
IsTrackingMixin.__init__(self, is_tracking)
[docs]class MultiPolygonSubcatalog(SubcatalogBase, IsTrackingMixin, CategoriesMixin, AttributesMixin):
"""This class defines the subcatalog for multiple polygon type of labels.
Arguments:
is_tracking: A boolean value indicates whether the corresponding
subcatalog contains tracking information.
Attributes:
description: The description of the entire multiple polygon subcatalog.
categories: All the possible categories in the corresponding dataset
stored in a :class:`~tensorbay.utility.name.NameList`
with the category names as keys
and the :class:`~tensorbay.label.supports.CategoryInfo` as values.
category_delimiter: The delimiter in category values indicating parent-child relationship.
attributes: All the possible attributes in the corresponding dataset
stored in a :class:`~tensorbay.utility.name.NameList`
with the attribute names as keys
and the :class:`~tensorbay.label.attribute.AttributeInfo` as values.
is_tracking: Whether the Subcatalog contains tracking information.
Examples:
*Initialization Method 1:* Init from ``MultiPolygonSubcatalog.loads()`` method.
>>> catalog = {
... "MULTI_POLYGON": {
... "isTracking": True,
... "categories": [{"name": "0"}, {"name": "1"}],
... "attributes": [{"name": "gender", "enum": ["male", "female"]}],
... }
... }
>>> MultiPolygonSubcatalog.loads(catalog["MULTI_POLYGON"])
MultiPolygonSubcatalog(
(is_tracking): True,
(categories): NameList [...],
(attributes): NameList [...]
)
*Initialization Method 2:* Init an empty MultiPolygonSubcatalog
and then add the attributes.
>>> from tensorbay.label import CategoryInfo, AttributeInfo
>>> multi_polygon_subcatalog = MultiPolygonSubcatalog()
>>> multi_polygon_subcatalog.is_tracking = True
>>> multi_polygon_subcatalog.add_category("a")
>>> multi_polygon_subcatalog.add_attribute("gender", enum=["female", "male"])
>>> multi_polygon_subcatalog
MultiPolyline2DSubcatalog(
(is_tracking): True,
(categories): NameList [...],
(attributes): NameList [...]
)
"""
def __init__(self, is_tracking: bool = False) -> None:
SubcatalogBase.__init__(self)
IsTrackingMixin.__init__(self, is_tracking)
[docs]class RLESubcatalog(SubcatalogBase, IsTrackingMixin, CategoriesMixin, AttributesMixin):
"""This class defines the subcatalog for rle type of labels.
Arguments:
is_tracking: A boolean value indicating whether the corresponding
subcatalog contains tracking information.
Attributes:
description: The description of the rle subcatalog.
categories: All the possible categories in the corresponding dataset
stored in a :class:`~tensorbay.utility.name.NameList`
with the category names as keys
and the :class:`~tensorbay.label.supports.CategoryInfo` as values.
category_delimiter: The delimiter in category values indicating parent-child relationship.
attributes: All the possible attributes in the corresponding dataset
stored in a :class:`~tensorbay.utility.name.NameList`
with the attribute names as keys
and the :class:`~tensorbay.label.attribute.AttributeInfo` as values.
is_tracking: Whether the Subcatalog contains tracking information.
Examples:
*Initialization Method 1:* Init from ``RLESubcatalog.loads()`` method.
>>> catalog = {
... "RLE": {
... "isTracking": True,
... "categories": [{"name": "0"}, {"name": "1"}],
... "attributes": [{"name": "gender", "enum": ["male", "female"]}],
... }
... }
>>> RLESubcatalog.loads(catalog["RLESubcatalog"])
RLESubcatalog(
(is_tracking): True,
(categories): NameList [...],
(attributes): NameList [...]
)
*Initialization Method 2:* Init an empty RLESubcatalog
and then add the attributes.
>>> from tensorbay.label import CategoryInfo, AttributeInfo
>>> rle_subcatalog = RLESubcatalog()
>>> rle_subcatalog.is_tracking = True
>>> rle_subcatalog.add_category("a")
>>> rle_subcatalog.add_attribute("gender", enum=["female", "male"])
>>> rle_subcatalog
RLESubcatalog(
(is_tracking): True,
(categories): NameList [...],
(attributes): NameList [...]
)
"""
def __init__(self, is_tracking: bool = False) -> None:
SubcatalogBase.__init__(self)
IsTrackingMixin.__init__(self, is_tracking)
[docs]class LabeledPolygon(_LabelBase, Polygon): # pylint: disable=too-many-ancestors
"""This class defines the concept of polygon label.
:class:`LabeledPolygon` is the polygon type of label,
which is often used for CV tasks such as semantic segmentation.
Arguments:
points: A list of 2D points representing the vertexes of the polygon.
category: The category of the label.
attributes: The attributs of the label.
instance: The instance id of the label.
Attributes:
category: The category of the label.
attributes: The attributes of the label.
instance: The instance id of the label.
Examples:
>>> LabeledPolygon(
... [(1, 2), (2, 3), (1, 3)],
... category = "example",
... attributes = {"key": "value"},
... instance = "123",
... )
LabeledPolygon [
Vector2D(1, 2),
Vector2D(2, 3),
Vector2D(1, 3)
](
(category): 'example',
(attributes): {...},
(instance): '123'
)
"""
_T = TypeVar("_T", bound="LabeledPolygon")
_repr_type = ReprType.SEQUENCE
_repr_attrs = _LabelBase._repr_attrs
_attrs_base: Polygon = attr_base(key="polygon")
def __init__(
self,
points: Optional[Iterable[Iterable[float]]] = None,
*,
category: Optional[str] = None,
attributes: Optional[Dict[str, Any]] = None,
instance: Optional[str] = None,
):
Polygon.__init__(self, points) # type: ignore[arg-type]
_LabelBase.__init__(self, category, attributes, instance)
[docs] @classmethod
def loads(cls: Type[_T], contents: Dict[str, Any]) -> _T: # type: ignore[override]
"""Loads a LabeledPolygon from a dict containing the information of the label.
Arguments:
contents: A dict containing the information of the polygon label.
Returns:
The loaded :class:`LabeledPolygon` object.
Examples:
>>> contents = {
... "polygon": [
... {"x": 1, "y": 2},
... {"x": 2, "y": 3},
... {"x": 1, "y": 3},
... ],
... "category": "example",
... "attributes": {"key": "value"},
... "instance": "12345",
... }
>>> LabeledPolygon.loads(contents)
LabeledPolygon [
Vector2D(1, 2),
Vector2D(2, 3),
Vector2D(1, 3)
](
(category): 'example',
(attributes): {...},
(instance): '12345'
)
"""
return common_loads(cls, contents)
[docs] def dumps(self) -> Dict[str, Any]: # type: ignore[override]
"""Dumps the current polygon label into a dict.
Returns:
A dict containing all the information of the polygon label.
Examples:
>>> labeledpolygon = LabeledPolygon(
... [(1, 2), (2, 3), (1, 3)],
... category = "example",
... attributes = {"key": "value"},
... instance = "123",
... )
>>> labeledpolygon.dumps()
{
'category': 'example',
'attributes': {'key': 'value'},
'instance': '123',
'polygon': [{'x': 1, 'y': 2}, {'x': 2, 'y': 3}, {'x': 1, 'y': 3}],
}
"""
return self._dumps()
[docs]class LabeledMultiPolygon( # type: ignore[misc]
_LabelBase, MultiPolygon
): # pylint: disable=too-many-ancestors
"""This class defines the concept of multiple polygon label.
:class:`LabeledMultiPolygon` is the multipolygon type of label,
which is often used for CV tasks such as semantic segmentation.
Arguments:
points: A list of 2D points representing the vertices of the polygon.
category: The category of the label.
attributes: The attributs of the label.
instance: The instance id of the label.
Attributes:
category: The category of the label.
attributes: The attributes of the label.
instance: The instance id of the label.
Examples:
>>> LabeledMultiPolygon(
... [[(1.0, 2.0), (2.0, 3.0), (1.0, 3.0)], [(1.0, 4.0), (2.0, 3.0), (1.0, 8.0)]],
... category = "example",
... attributes = {"key": "value"},
... instance = "12345",
... )
LabeledMultiPolygon [
Polygon [...],
Polygon [...]
](
(category): 'example',
(attributes): {...},
(instance): '12345'
)
"""
_T = TypeVar("_T", bound="LabeledMultiPolygon")
_repr_type = ReprType.SEQUENCE
_repr_attrs = _LabelBase._repr_attrs
_attrs_base: MultiPolygon = attr_base(key="multiPolygon")
def __init__(
self,
polygons: Optional[Iterable[Iterable[Iterable[float]]]] = None,
*,
category: Optional[str] = None,
attributes: Optional[Dict[str, Any]] = None,
instance: Optional[str] = None,
):
MultiPolygon.__init__(self, polygons=polygons)
_LabelBase.__init__(self, category, attributes, instance)
[docs] @classmethod
def loads(cls: Type[_T], contents: Dict[str, Any]) -> _T: # type: ignore[override]
"""Loads a LabeledMultiPolygon from a list of dict containing the information of the label.
Arguments:
contents: A dict containing the information of the multipolygon label.
Returns:
The loaded :class:`LabeledMultiPolygon` object.
Examples:
>>> contents = {
... "multiPolygon": [
... [
... {"x": 1.0, "y": 2.0},
... {"x": 2.0, "y": 3.0},
... {"x": 1.0, "y": 3.0},
... ],
... [{"x": 1.0, "y": 4.0}, {"x": 2.0, "y": 3.0}, {"x": 1.0, "y": 8.0}],
... ],
... "category": "example",
... "attributes": {"key": "value"},
... "instance": "12345",
... }
>>> LabeledMultiPolygon.loads(contents)
LabeledMultiPolygon [
Polygon [...],
Polygon [...]
](
(category): 'example',
(attributes): {...},
(instance): '12345'
)
"""
return common_loads(cls, contents)
[docs] def dumps(self) -> Dict[str, Any]: # type: ignore[override]
"""Dumps the current multipolygon label into a dict.
Returns:
A dict containing all the information of the multipolygon label.
Examples:
>>> labeledmultipolygon = LabeledMultiPolygon(
... [[(1, 2), (2, 3), (1, 3)],[(1, 2), (2, 3), (1, 3)]],
... category = "example",
... attributes = {"key": "value"},
... instance = "123",
... )
>>> labeledmultipolygon.dumps()
{
'category': 'example',
'attributes': {'key': 'value'},
'instance': '123',
'multiPolygon': [
[{'x': 1, 'y': 2}, {'x': 2, 'y': 3}, {'x': 1, 'y': 3}],
[{"x": 1.0, "y": 4.0}, {"x": 2.0, "y": 3.0}, {"x": 1.0, "y": 8.0}]
]
}
"""
return self._dumps()
[docs]class LabeledRLE(_LabelBase, RLE): # type: ignore[misc] # pylint: disable=too-many-ancestors
"""This class defines the concept of rle label.
:class:`LabeledRLE` is the rle type of label,
which is often used for CV tasks such as semantic segmentation.
Arguments:
rle: A rle format mask.
category: The category of the label.
attributes: The attributs of the label.
instance: The instance id of the label.
Attributes:
category: The category of the label.
attributes: The attributes of the label.
instance: The instance id of the label.
Examples:
>>> LabeledRLE(
... [272, 2, 4, 4, 2, 9],
... category = "example",
... attributes = {"key": "value"},
... instance = "12345",
... )
LabeledRLE [
272,
2,
...
](
(category): 'example',
(attributes): {...},
(instance): '12345'
)
"""
_T = TypeVar("_T", bound="LabeledRLE")
_repr_type = ReprType.SEQUENCE
_repr_attrs = _LabelBase._repr_attrs
_attrs_base: RLE = attr_base(key="rle")
def __init__(
self,
rle: Optional[Iterable[int]] = None,
*,
category: Optional[str] = None,
attributes: Optional[Dict[str, Any]] = None,
instance: Optional[str] = None,
):
RLE.__init__(self, rle)
_LabelBase.__init__(self, category, attributes, instance)
[docs] @classmethod
def loads(cls: Type[_T], contents: Dict[str, Any]) -> _T: # type: ignore[override]
"""Loads a LabeledRLE from a dict containing the information of the label.
Arguments:
contents: A dict containing the information of the rle label.
Returns:
The loaded :class:`LabeledRLE` object.
Examples:
>>> contents = {
... "rle": [272, 2, 4, 4, 2, 9],
... "category": "example",
... "attributes": {"key": "value"},
... "instance": "12345",
... }
>>> LabeledRLE.loads(contents)
LabeledRLE [
272,
2,
...
](
(category): 'example',
(attributes): {...},
(instance): '12345'
)
"""
return common_loads(cls, contents)
[docs] def dumps(self) -> Dict[str, Any]: # type: ignore[override]
"""Dumps the current rle label into a dict.
Returns:
A dict containing all the information of the rle label.
Examples:
>>> labeled_rle = LabeledRLE(
... [272, 2, 4, 4, 2, 9],
... category = "example",
... attributes = {"key": "value"},
... instance = "123",
... )
>>> labeled_rle.dumps()
{
'category': 'example',
'attributes': {'key': 'value'},
'instance': '123',
'rle': [272, 2, 4, 4, 2, 9]
}
"""
return self._dumps()