datumaro.components.annotation

Module Attributes

Colormap

Represents { index -> color } mapping for segmentation masks

Classes

Annotation(*[, id, attributes, group, object_id])	A base annotation class.
AnnotationType(value)	An enumeration.
Bbox(x, y, w, h, *args, **kwargs)	Method generated by attrs for class _Shape.
Caption(caption, *[, id, attributes, group, ...])	Represents arbitrary text annotations.
Categories(*[, attributes])	A base class for annotation metainfo.
CompiledMask([class_mask, instance_mask])	Represents class- and instance- segmentation masks with all the instances (opposed to single-instance masks).
Cuboid3d(position[, rotation, scale])	Method generated by attrs for class Annotation.
DepthAnnotation(image, *[, id, attributes, ...])	Represents depth images.
Ellipse(x1, y1, x2, y2, *args, **kwargs)	Ellipse represents an ellipse that is encapsulated by a rectangle.
FeatureVector(vector, *[, id, attributes, ...])	Method generated by attrs for class FeatureVector.
GroupType(value)	An enumeration.
HashKey(hash_key, *[, id, attributes, ...])	Method generated by attrs for class HashKey.
Label(label, *[, id, attributes, group, ...])	Method generated by attrs for class Label.
LabelCategories([items, label_groups, ...])	Method generated by attrs for class LabelCategories.
Mask(image, *[, id, attributes, group, ...])	Represents a 2d single-instance binary segmentation mask.
MaskCategories([colormap, inverse_colormap, ...])	Describes a color map for segmentation masks.
Points(points[, visibility, id, attributes, ...])	Represents an ordered set of points.
PointsCategories([items, attributes])	Describes (key-)point metainfo such as point names and joints.
PolyLine(points, *[, id, attributes, group, ...])	Method generated by attrs for class PolyLine.
Polygon(points, *[, id, attributes, group, ...])	Method generated by attrs for class Polygon.
RleMask(rle, *[, id, attributes, group, ...])	An RLE-encoded instance segmentation mask.
SuperResolutionAnnotation(image, *[, id, ...])	Represents high resolution images.
Tabular(values, *[, id, attributes, group, ...])	Represents values of target columns in a tabular dataset.
TabularCategories([items, attributes])	Describes tabular data metainfo such as column names and types.

class datumaro.components.annotation.AnnotationType(value)

Bases: IntEnum

An enumeration.

unknown = 0

label = 1

mask = 2

points = 3

polygon = 4

polyline = 5

bbox = 6

caption = 7

cuboid_3d = 8

super_resolution_annotation = 9

depth_annotation = 10

ellipse = 11

hash_key = 12

feature_vector = 13

tabular = 14

class datumaro.components.annotation.Annotation(*, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)

Bases: object

A base annotation class.

Derived classes must define the ‘_type’ class variable with a value from the AnnotationType enum.

Method generated by attrs for class Annotation.

id: int

attributes: Dict[str, Any]

group: int

object_id: int

property type: AnnotationType

as_dict() → Dict[str, Any]

Returns a dictionary { field_name: value }

wrap(**kwargs)

Returns a modified copy of the object

class datumaro.components.annotation.Categories(*, attributes: Set[str] = _Nothing.NOTHING)

Bases: object

A base class for annotation metainfo. It is supposed to include dataset-wide metainfo like available labels, label colors, label attributes etc.

Method generated by attrs for class Categories.

attributes: Set[str]

class datumaro.components.annotation.GroupType(value)

Bases: IntEnum

An enumeration.

EXCLUSIVE = 0

INCLUSIVE = 1

RESTRICTED = 2

to_str() → str

classmethod from_str(text: str) → GroupType

class datumaro.components.annotation.LabelCategories(items: List[str] = _Nothing.NOTHING, label_groups: List[LabelGroup] = _Nothing.NOTHING, *, attributes: Set[str] = _Nothing.NOTHING)

Bases: Categories

Method generated by attrs for class LabelCategories.

class Category(name, parent: str = '', attributes: Set[str] = _Nothing.NOTHING)

Bases: object

Method generated by attrs for class LabelCategories.Category.

name: str

parent: str

attributes: Set[str]

class LabelGroup(name, labels: List[str] = [], group_type: GroupType = GroupType.EXCLUSIVE)

Bases: object

Method generated by attrs for class LabelCategories.LabelGroup.

name: str

labels: List[str]

group_type: GroupType

items: List[str]

label_groups: List[LabelGroup]

classmethod from_iterable(iterable: Iterable[str | Tuple[str] | Tuple[str, str] | Tuple[str, str, List[str]]]) → LabelCategories

Creates a LabelCategories from iterable.

Parameters:

iterable –

This iterable object can be:

• a list of str - will be interpreted as list of Category names

• a list of positional arguments - will generate Categories with these arguments

Returns: a LabelCategories object

add(name: str, parent: str | None = None, attributes: Set[str] | None = None) → int

add_label_group(name: str, labels: List[str], group_type: GroupType) → int

find(name: str) → Tuple[int | None, Category | None]

class datumaro.components.annotation.Label(label, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)

Bases: Annotation

Method generated by attrs for class Label.

label: int

class datumaro.components.annotation.HashKey(hash_key: ndarray, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)

Bases: Annotation

Method generated by attrs for class HashKey.

hash_key: ndarray

class datumaro.components.annotation.FeatureVector(vector: ndarray, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)

Bases: Annotation

Method generated by attrs for class FeatureVector.

vector: ndarray

id: int

attributes: Dict[str, Any]

group: int

object_id: int

datumaro.components.annotation.Colormap

Represents { index -> color } mapping for segmentation masks

alias of Dict[int, Tuple[int, int, int]]

class datumaro.components.annotation.MaskCategories(colormap: Dict[int, Tuple[int, int, int]] = _Nothing.NOTHING, inverse_colormap: Dict[Tuple[int, int, int], int] | None = None, *, attributes: Set[str] = _Nothing.NOTHING)

Bases: Categories

Describes a color map for segmentation masks.

Method generated by attrs for class MaskCategories.

classmethod generate(size: int = 255, include_background: bool = True) → MaskCategories

Generates MaskCategories with the specified size.

If include_background is True, the result will include the item

“0: (0, 0, 0)”, which is typically used as a background color.

colormap: Dict[int, Tuple[int, int, int]]

property inverse_colormap: Dict[Tuple[int, int, int], int]

class datumaro.components.annotation.Mask(image, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1, label=None, z_order: int = 0)

Bases: Annotation

Represents a 2d single-instance binary segmentation mask.

Method generated by attrs for class Mask.

label: int | None

z_order: int

property image: ndarray

as_class_mask(label_id: int | None = None, ignore_index: int = 0, dtype: dtype | None = None) → ndarray

Produces a class index mask based on the binary mask.

Parameters:

• label_id – Scalar value to represent the class index of the mask. If not specified, self.label will be used. Defaults to None.

• ignore_index – Scalar value to fill in the zeros in the binary mask. Defaults to 0.

• dtype – Data type for the resulting mask. If not specified, it will be inferred from the provided label_id to hold its value. For example, if label_id=255, the inferred dtype will be np.uint8. Defaults to None.

Returns:

Class index mask generated from the binary mask.

Return type:

IndexMaskImage

as_instance_mask(instance_id: int, ignore_index: int = 0, dtype: dtype | None = None) → ndarray

Produces an instance index mask based on the binary mask.

Parameters:

• instance_id – Scalar value to represent the instance id.

• ignore_index – Scalar value to fill in the zeros in the binary mask. Defaults to 0.

• dtype – Data type for the resulting mask. If not specified, it will be inferred from the provided label_id to hold its value. For example, if label_id=255, the inferred dtype will be np.uint8. Defaults to None.

Returns:

Instance index mask generated from the binary mask.

Return type:

IndexMaskImage

get_area() → int

get_bbox() → Tuple[int, int, int, int]

Computes the bounding box of the mask.

Returns: [x, y, w, h]

paint(colormap: Dict[int, Tuple[int, int, int]]) → ndarray

Applies a colormap to the mask and produces the resulting image.

class datumaro.components.annotation.RleMask(rle, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1, label=None, z_order: int = 0)

Bases: Mask

An RLE-encoded instance segmentation mask.

Method generated by attrs for class RleMask.

property image: ndarray

property rle

get_area() → int

get_bbox() → Tuple[int, int, int, int]

Computes the bounding box of the mask.

Returns: [x, y, w, h]

class datumaro.components.annotation.CompiledMask(class_mask: None | ndarray | Callable[[], ndarray] = None, instance_mask: None | ndarray | Callable[[], ndarray] = None)

Bases: object

Represents class- and instance- segmentation masks with all the instances (opposed to single-instance masks).

static from_instance_masks(instance_masks: Iterable[Mask], instance_ids: Iterable[int] | None = None, instance_labels: Iterable[int] | None = None) → CompiledMask

Joins instance masks into a single mask. Masks are sorted by z_order (ascending) prior to merging.

Parameters:

• instance_ids – Instance id values for the produced instance mask. By default, mask positions are used.

• instance_labels – Instance label id values for the produced class mask. By default, mask labels are used.

property class_mask: ndarray | None

property instance_mask: ndarray | None

property instance_count: int

get_instance_labels() → Dict[int, int]

Matches the class and instance masks.

Returns: { instance id: class id }

extract(instance_id: int) → ndarray

Extracts a single-instance mask from the compiled mask.

lazy_extract(instance_id: int) → Callable[[], ndarray]

class datumaro.components.annotation.PolyLine(points, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1, label=None, z_order: int = 0)

Bases: _Shape

Method generated by attrs for class PolyLine.

as_polygon()

get_area()

class datumaro.components.annotation.Cuboid3d(position, rotation=None, scale=None, **kwargs)

Bases: Annotation

Method generated by attrs for class Annotation.

label: int | None

property position

[x, y, z]

property rotation

[rx, ry, rz]

property scale

[sx, sy, sz]

class datumaro.components.annotation.Polygon(points, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1, label=None, z_order: int = 0)

Bases: _Shape

Method generated by attrs for class Polygon.

get_area()

as_polygon() → List[float]

class datumaro.components.annotation.Bbox(x, y, w, h, *args, **kwargs)

Bases: _Shape

Method generated by attrs for class _Shape.

property x

property y

property w

property h

get_area()

get_bbox()

Returns [x, y, w, h]

as_polygon() → List[float]

iou(other: _Shape) → float | ~typing.Literal[-1]

wrap(**kwargs)

Returns a modified copy of the object

class datumaro.components.annotation.PointsCategories(items: Dict[int, Category] = _Nothing.NOTHING, *, attributes: Set[str] = _Nothing.NOTHING)

Bases: Categories

Describes (key-)point metainfo such as point names and joints.

Method generated by attrs for class PointsCategories.

class Category(labels: List[str] = _Nothing.NOTHING, joints: Set[Tuple[int, int]] = _Nothing.NOTHING)

Bases: object

Method generated by attrs for class PointsCategories.Category.

labels: List[str]

joints: Set[Tuple[int, int]]

items: Dict[int, Category]

classmethod from_iterable(iterable: Tuple[int, List[str]] | Tuple[int, List[str], Set[Tuple[int, int]]]) → PointsCategories

Create PointsCategories from an iterable.

Parameters:

iterable –

An Iterable with the following elements:

• a label id

• a list of positional arguments for Categories

Returns:

PointsCategories object

Return type:

PointsCategories

add(label_id: int, labels: Iterable[str] | None = None, joints: Iterable[Tuple[int, int]] | None = None)

class datumaro.components.annotation.Points(points, visibility: List[IntEnum] | None = None, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1, label=None, z_order: int = 0)

Bases: _Shape

Represents an ordered set of points.

Method generated by attrs for class Points.

class Visibility(value)

Bases: IntEnum

An enumeration.

absent = 0

hidden = 1

visible = 2

visibility: List[IntEnum]

get_area()

get_bbox()

Returns [x, y, w, h]

class datumaro.components.annotation.Caption(caption, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)

Bases: Annotation

Represents arbitrary text annotations.

Method generated by attrs for class Caption.

caption: str

class datumaro.components.annotation.SuperResolutionAnnotation(image: Image, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)

Bases: _ImageAnnotation

Represents high resolution images.

Method generated by attrs for class SuperResolutionAnnotation.

class datumaro.components.annotation.DepthAnnotation(image: Image, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)

Bases: _ImageAnnotation

Represents depth images.

Method generated by attrs for class DepthAnnotation.

class datumaro.components.annotation.Ellipse(x1: float, y1: float, x2: float, y2: float, *args, **kwargs)

Bases: _Shape

Ellipse represents an ellipse that is encapsulated by a rectangle.

• x1 and y1 represent the top-left coordinate of the encapsulating rectangle

• x2 and y2 representing the bottom-right coordinate of the encapsulating rectangle

Parameters:

• x1 (float) – left x coordinate of encapsulating rectangle

• y1 (float) – top y coordinate of encapsulating rectangle

• x2 (float) – right x coordinate of encapsulating rectangle

• y2 (float) – bottom y coordinate of encapsulating rectangle

Method generated by attrs for class _Shape.

property x1

property y1

property x2

property y2

property w

property h

property c_x

property c_y

get_area()

get_bbox()

Returns [x, y, w, h]

get_points(num_points: int = 720) → List[Tuple[float, float]]

Return points as a list of tuples, e.g. [(x0, y0), (x1, y1), …].

Parameters:

num_points (int) – The number of boundary points of the ellipse. By default, one point is created for every 1 degree of interior angle (num_points=360).

as_polygon(num_points: int = 720) → List[float]

Return a polygon as a list of tuples, e.g. [x0, y0, x1, y1, …].

Parameters:

num_points (int) – The number of boundary points of the ellipse. By default, one point is created for every 1 degree of interior angle (num_points=360).

iou(other: _Shape) → float | ~typing.Literal[-1]

wrap(**kwargs) → Ellipse

Returns a modified copy of the object

class datumaro.components.annotation.TabularCategories(items: List[Category] = _Nothing.NOTHING, *, attributes: Set[str] = _Nothing.NOTHING)

Bases: Categories

Describes tabular data metainfo such as column names and types.

Method generated by attrs for class TabularCategories.

class Category(name, dtype: Type[TableDtype], labels: Set[str | int] = _Nothing.NOTHING)

Bases: object

Method generated by attrs for class TabularCategories.Category.

name: str

dtype: Type[TableDtype]

labels: Set[str | int]

items: List[Category]

classmethod from_iterable(iterable: Iterable[Tuple[str, Type[TableDtype]] | Tuple[str, Type[TableDtype], Set[str]]]) → TabularCategories

Creates a TabularCategories from iterable.

Parameters:

iterable – a list of (Category name, type) or (Category name, type, set of labels)

Returns: a TabularCategories object

add(name: str, dtype: Type[TableDtype], labels: Set[str] | None = None) → int

Add a Tabular Category.

Parameters:

• name (str) – Column name

• dtype (type) – Type of the corresponding column. (str, int, or float)

• labels (optional, set(str)) – Label values where the column can have.

Returns:

A index of added category.

Return type:

int

find(name: str) → Tuple[int | None, Category | None]

Find Category information for the given column name.

Parameters:

name (str) – Column name

Returns:

A index and Category information.

Return type:

tuple(int, Category)

class datumaro.components.annotation.Tabular(values, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)

Bases: Annotation

Represents values of target columns in a tabular dataset.

Method generated by attrs for class Tabular.

values: Dict[str, TableDtype]