datumaro.plugins.data_formats.datumaro.base#

Classes

`DatumaroBase`(path, *[, subset, stream, ctx])
`JsonReader`(path, subset, rootpath, ...)
`StreamJsonReader`(path, subset, rootpath, ...)

class datumaro.plugins.data_formats.datumaro.base.DatumaroBase(path: str, *, subset: str | None = None, stream: bool = False, ctx: ImportContext | None = None)[source]#

Bases: SubsetBase

LEGACY_VERSION = 'legacy'#

CURRENT_DATUMARO_FORMAT_VERSION = '1.0'#

ALLOWED_VERSIONS = {'1.0', 'legacy'}#

property is_stream: bool#

Boolean indicating whether the dataset is a stream

If the dataset is a stream, the dataset item is generated on demand from its iterator.

infos()[source]#: Returns meta-info of dataset.

categories()[source]#: Returns metainfo about dataset labels.

media_type()[source]#

Returns media type of the dataset items.

All the items are supposed to have the same media type. Supposed to be constant and known immediately after the object construction (i.e. doesn’t require dataset iteration).

ann_types()[source]#: Returns available task type from dataset annotation types.

class datumaro.plugins.data_formats.datumaro.base.AnnotationType(value)[source]#

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#

rotated_bbox = 15#

class datumaro.plugins.data_formats.datumaro.base.Bbox(x, y, w, h, *args, **kwargs)[source]#

Bases: _Shape

Method generated by attrs for class _Shape.

property x#

property y#

property w#

property h#

get_area()[source]#

get_bbox()[source]#: Returns [x, y, w, h]

as_polygon() → List[float][source]#

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

wrap(**kwargs)[source]#: Returns a modified copy of the object

class datumaro.plugins.data_formats.datumaro.base.Caption(caption, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)[source]#

Bases: Annotation

Represents arbitrary text annotations.

Method generated by attrs for class Caption.

caption: str#

class datumaro.plugins.data_formats.datumaro.base.Cuboid3d(position, rotation=None, scale=None, **kwargs)[source]#

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]

exception datumaro.plugins.data_formats.datumaro.base.DatasetImportError[source]#: Bases: DatumaroError

Bases: object

id: str#

subset: str#

media: MediaElement | None#

annotations: List[Annotation]#

attributes: Dict[str, Any]#

wrap(**kwargs)[source]#

media_as(t: Type[T]) → T[source]#

class datumaro.plugins.data_formats.datumaro.base.DatumPageMapper(path: str)[source]#

Bases: object

Construct page maps for items and annotations from the JSON file, which are used for the stream importer.

It also provides __iter__() to produce item and annotation dictionaries in stream manner after constructing the page map.

get_item_dict(item_key: str) → Dict | None[source]#

iter_item_ids() → Iterator[str][source]#

property dm_format_version: str | None#: Parse “dm_format_version” section from the given JSON file using the stream json parser

property media_type: MediaType | None#: Parse “media_type” section from the given JSON file using the stream json parser

property ann_types: Set[AnnotationType] | None#: Parse “media_type” section from the given JSON file using the stream json parser

property infos: Dict[str, Any]#: Parse “infos” section from the given JSON file using the stream json parser

property categories: Dict[str, Any]#: Parse “categories” section from the given JSON file using the stream json parser

class datumaro.plugins.data_formats.datumaro.base.DatumaroPath[source]#

Bases: object

IMAGES_DIR = 'images'#

ANNOTATIONS_DIR = 'annotations'#

PCD_DIR = 'point_clouds'#

VIDEO_DIR = 'videos'#

MASKS_DIR = 'masks'#

ANNOTATION_EXT = '.json'#

IMAGE_EXT = '.jpg'#

MASK_EXT = '.png'#

class datumaro.plugins.data_formats.datumaro.base.Ellipse(x1: float, y1: float, x2: float, y2: float, *args, **kwargs)[source]#

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()[source]#

get_bbox()[source]#: Returns [x, y, w, h]

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

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][source]#

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][source]#

wrap(**kwargs) → Ellipse[source]#: Returns a modified copy of the object

class datumaro.plugins.data_formats.datumaro.base.GroupType(value)[source]#

Bases: IntEnum

An enumeration.

EXCLUSIVE = 0#

INCLUSIVE = 1#

RESTRICTED = 2#

to_str() → str[source]#

classmethod from_str(text: str) → GroupType[source]#

class datumaro.plugins.data_formats.datumaro.base.Image(size: Tuple[int, int] | None = None, ext: str | None = None, *args, **kwargs)[source]#

Bases: MediaElement[ndarray]

classmethod from_file(path: str, *args, **kwargs)[source]#

classmethod from_numpy(data: ndarray | Callable[[], ndarray], *args, **kwargs)[source]#

classmethod from_bytes(data: bytes | Callable[[], bytes], *args, **kwargs)[source]#

property has_size: bool#: Indicates that size info is cached and won’t require image loading

property size: Tuple[int, int] | None#: Returns (H, W)

property ext: str | None#: Media file extension (with the leading dot)

set_crypter(crypter: Crypter)[source]#

class datumaro.plugins.data_formats.datumaro.base.ImportContext(progress_reporter=None, error_policy=None)[source]#

Bases: object

Method generated by attrs for class ImportContext.

progress_reporter: ProgressReporter#

error_policy: ImportErrorPolicy#

class datumaro.plugins.data_formats.datumaro.base.JsonReader(path: str, subset: str, rootpath: str, images_dir: str, pcd_dir: str, video_dir: str, ctx: ImportContext)[source]#: Bases: object

class datumaro.plugins.data_formats.datumaro.base.Label(label, *, id: int = 0, attributes: Dict[str, Any] = _Nothing.NOTHING, group: int = 0, object_id: int = -1)[source]#

Bases: Annotation

Method generated by attrs for class Label.

label: int#

class datumaro.plugins.data_formats.datumaro.base.LabelCategories(items: List[str] = _Nothing.NOTHING, label_groups: List[LabelGroup] = _Nothing.NOTHING, *, attributes: Set[str] = _Nothing.NOTHING)[source]#

Bases: Categories

Method generated by attrs for class LabelCategories.

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

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)[source]#

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[source]#

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[source]#

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

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

class datumaro.plugins.data_formats.datumaro.base.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)[source]#

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[source]#

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.plugins.data_formats.datumaro.base.MediaElement(crypter: ~datumaro.components.crypter.Crypter = <datumaro.components.crypter.NullCrypter object>)[source]#

Bases: Generic[AnyData]

as_dict() → Dict[str, Any][source]#

from_self(**kwargs)[source]#

property is_encrypted: bool#

set_crypter(crypter: Crypter)[source]#

property type: MediaType#

property data: AnyData | None#

property has_data: bool#

property bytes: bytes | None#

save(fp: str | ~io.IOBase, crypter: ~datumaro.components.crypter.Crypter = <datumaro.components.crypter.NullCrypter object>)[source]#

class datumaro.plugins.data_formats.datumaro.base.MediaType(value)[source]#

Bases: IntEnum

An enumeration.

NONE = 0#

MEDIA_ELEMENT = 1#

IMAGE = 2#

BYTE_IMAGE = 3#

VIDEO_FRAME = 4#

VIDEO = 5#

POINT_CLOUD = 6#

MULTIFRAME_IMAGE = 7#

ROI_IMAGE = 8#

MOSAIC_IMAGE = 9#

TABLE_ROW = 10#

property media: Type[MediaElement] | None#

exception datumaro.plugins.data_formats.datumaro.base.MediaTypeError[source]#: Bases: DatumaroError

class datumaro.plugins.data_formats.datumaro.base.PointCloud(extra_images: List[Image] | Callable[[], List[Image]] | None = None, *args, **kwargs)[source]#

Bases: MediaElement[bytes]

classmethod from_file(path: str, *args, **kwargs)[source]#

classmethod from_bytes(data: bytes | Callable[[], bytes], *args, **kwargs)[source]#

property extra_images: List[Image]#

class datumaro.plugins.data_formats.datumaro.base.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)[source]#

Bases: _Shape

Represents an ordered set of points.

Method generated by attrs for class Points.

class Visibility(value)[source]#

Bases: IntEnum

An enumeration.

absent = 0#

hidden = 1#

visible = 2#

visibility: List[IntEnum]#

get_area()[source]#

get_bbox()[source]#: Returns [x, y, w, h]

class datumaro.plugins.data_formats.datumaro.base.PointsCategories(items: Dict[int, Category] = _Nothing.NOTHING, *, attributes: Set[str] = _Nothing.NOTHING)[source]#

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)[source]#

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[source]#

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)[source]#

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

Bases: _Shape

Method generated by attrs for class PolyLine.

as_polygon()[source]#

get_area()[source]#

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

Bases: _Shape

Method generated by attrs for class Polygon.

get_area()[source]#

as_polygon() → List[float][source]#

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

Bases: Mask

An RLE-encoded instance segmentation mask.

Method generated by attrs for class RleMask.

property image: ndarray#

property rle#

get_area() → int[source]#

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

Computes the bounding box of the mask.

Returns: [x, y, w, h]

class datumaro.plugins.data_formats.datumaro.base.StreamJsonReader(path: str, subset: str, rootpath: str, images_dir: str, pcd_dir: str, video_dir: str, ctx: ImportContext)[source]#: Bases: JsonReader

class datumaro.plugins.data_formats.datumaro.base.SubsetBase(*, length: int | None = None, subset: str | None = None, media_type: ~typing.Type[~datumaro.components.media.MediaElement] = <class 'datumaro.components.media.Image'>, ann_types: ~typing.List[~datumaro.components.annotation.AnnotationType] | None = None, ctx: ~datumaro.components.contexts.importer.ImportContext | None = None)[source]#

Bases: DatasetBase

A base class for simple, single-subset extractors. Should be used by default for user-defined extractors.

infos()[source]#: Returns meta-info of dataset.

categories()[source]#: Returns metainfo about dataset labels.

get(id, subset=None)[source]#: Provides random access to dataset items.

property subset: str#: Subset name of this instance.

class datumaro.plugins.data_formats.datumaro.base.Video(path: str, *, step: int = 1, start_frame: int = 0, end_frame: int | None = None)[source]#

Bases: MediaElement, Iterable[VideoFrame]

close()[source]#

get_frame_data(idx: int) → VideoFrame[source]#

property length: int | None#

Returns frame count, if video provides such information.

Note that not all videos provide length / duration metainfo, so the result may be undefined.

Also note, that information may be inaccurate because of variable FPS in video or incorrect metainfo. The count is only guaranteed to be valid after video is completely read once.

The count is affected by the frame filtering options of the object, i.e. start frame, end frame and frame step.

property frame_size: Tuple[int, int]#: Returns (H, W)

save(fp: str | ~io.IOBase, crypter: ~datumaro.components.crypter.Crypter = <datumaro.components.crypter.NullCrypter object>)[source]#

property path: str#: Path to the media file

property ext: str#: Media file extension (with the leading dot)

class datumaro.plugins.data_formats.datumaro.base.VideoFrame(video: Video, index: int)[source]#

Bases: ImageFromNumpy

as_dict() → Dict[str, Any][source]#

property size: Tuple[int, int]#: Returns (H, W)

property index: int#

property video: Video#

property path: str#

datumaro.plugins.data_formats.datumaro.base.parse_json_file(path: str)[source]#