datumaro.plugins.data_formats.yolo.base

Functions

localize_path(path)

Removes the "data/" prefix from the path

Classes

YoloLooseBase(config_path[, image_info, ...])
YoloStrictBase(config_path[, image_info, ...])
YoloUltralyticsBase(config_path[, ...])

class datumaro.plugins.data_formats.yolo.base.YoloStrictBase(config_path: str, image_info: None | str | Dict[str, Tuple[int, int]] = None, *, subset: str | None = None, ctx: ImportContext | None = None, **kwargs)

Bases: SubsetBase

static parse_image_info(rootpath: str, image_info: None | str | Dict[str, Tuple[int, int]] = None) → Dict[str, Tuple[int, int]]

classmethod name_from_path(path: str) → str

Obtains <image name> from the path like [data/]<subset>_obj/<image_name>.ext

<image name> can be <a/b/c/filename>, so it is more involved than just calling “basename()”.

get_subset(name)

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.

class datumaro.plugins.data_formats.yolo.base.YoloLooseBase(config_path: str, image_info: None | str | Dict[str, Tuple[int, int]] = None, urls: List[str] | None = None, *, subset: str | None = None, ctx: ImportContext | None = None)

Bases: SubsetBase

META_FILE = 'obj.names'

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.

class datumaro.plugins.data_formats.yolo.base.YoloUltralyticsBase(config_path: str, image_info: None | str | Dict[str, Tuple[int, int]] = None, urls: List[str] | None = None, **kwargs)

Bases: YoloLooseBase

META_FILE = 'data.yaml'

class datumaro.plugins.data_formats.yolo.base.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.plugins.data_formats.yolo.base.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

rotated_bbox = 15

class datumaro.plugins.data_formats.yolo.base.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.plugins.data_formats.yolo.base.DatasetBase(*, length: int | None = None, subsets: ~typing.Sequence[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)

Bases: _DatasetBase, CliPlugin

A base class for user-defined and built-in extractors. Should be used in cases, where SubsetBase is not enough, or its use makes problems with performance, implementation etc.

media_type()

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()

Returns available task type from dataset annotation types.

exception datumaro.plugins.data_formats.yolo.base.DatasetImportError

Bases: DatumaroError

class datumaro.plugins.data_formats.yolo.base.DatasetItem(id: str, *, subset: str | None = None, media: str | MediaElement | None = None, annotations: List[Annotation] | None = None, attributes: Dict[str, Any] | None = None)

Bases: object

id: str

subset: str

media: MediaElement | None

annotations: List[Annotation]

attributes: Dict[str, Any]

wrap(**kwargs)

media_as(t: Type[T]) → T

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

Bases: MediaElement[ndarray]

classmethod from_file(path: str, *args, **kwargs)

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

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

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)

class datumaro.plugins.data_formats.yolo.base.ImageFromFile(path: str, *args, **kwargs)

Bases: FromFileMixin, Image

property data: ndarray | None

Image data in BGRA HWC [0; 255] (uint8) format

property size: Tuple[int, int] | None

Returns (H, W)

save(fp: str | ~io.IOBase, ext: str | None = None, crypter: ~datumaro.components.crypter.Crypter = <datumaro.components.crypter.NullCrypter object>)

set_crypter(crypter: Crypter)

class datumaro.plugins.data_formats.yolo.base.ImportContext(progress_reporter=None, error_policy=None)

Bases: object

Method generated by attrs for class ImportContext.

progress_reporter: ProgressReporter

error_policy: ImportErrorPolicy

exception datumaro.plugins.data_formats.yolo.base.InvalidAnnotationError

Bases: DatasetImportError

A basic dataset parsing error. Should include the problem description in the message.

class datumaro.plugins.data_formats.yolo.base.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.plugins.data_formats.yolo.base.OrderedDict

Bases: dict

Dictionary that remembers insertion order

clear() → None.  Remove all items from od.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

keys() → a set-like object providing a view on D's keys

items() → a set-like object providing a view on D's items

values() → an object providing a view on D's values

pop(key[, default]) → v, remove specified key and return the corresponding value.

If the key is not found, return the default if given; otherwise, raise a KeyError.

setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

copy() → a shallow copy of od

fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

class datumaro.plugins.data_formats.yolo.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)

Bases: DatasetBase

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

infos()

Returns meta-info of dataset.

categories()

Returns metainfo about dataset labels.

get(id, subset=None)

Provides random access to dataset items.

property subset: str

Subset name of this instance.

class datumaro.plugins.data_formats.yolo.base.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False)

Bases: _Final, _Immutable, _TypeVarLike

Type variable.

Usage:

T = TypeVar('T')  # Can be anything
A = TypeVar('A', str, bytes)  # Must be str or bytes

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function definitions. See class Generic for more information on generic types. Generic functions work as follows:

def repeat(x: T, n: int) -> List[T]:

‘’’Return a list containing n references to x.’’’ return [x]*n

def longest(x: A, y: A) -> A:

‘’’Return the longest of two strings.’’’ return x if len(x) >= len(y) else y

The latter example’s signature is essentially the overloading of (str, str) -> str and (bytes, bytes) -> bytes. Also note that if the arguments are instances of some subclass of str, the return type is still plain str.

At runtime, isinstance(x, T) and issubclass(C, T) will raise TypeError.

Type variables defined with covariant=True or contravariant=True can be used to declare covariant or contravariant generic types. See PEP 484 for more details. By default generic types are invariant in all type variables.

Type variables can be introspected. e.g.:

T.__name__ == ‘T’ T.__constraints__ == () T.__covariant__ == False T.__contravariant__ = False A.__constraints__ == (str, bytes)

Note that only type variables defined in global scope can be pickled.

exception datumaro.plugins.data_formats.yolo.base.UndeclaredLabelError(id: str)

Bases: InvalidAnnotationError

Method generated by attrs for class UndeclaredLabelError.

id: str

Index or name

class datumaro.plugins.data_formats.yolo.base.YoloLoosePath

Bases: object

NAMES_FILE = 'obj.names'

class datumaro.plugins.data_formats.yolo.base.YoloPath

Bases: object

DEFAULT_SUBSET_NAME = 'train'

SUBSET_NAMES = ['train', 'valid']

RESERVED_CONFIG_KEYS = {'backup', 'classes', 'names'}

class datumaro.plugins.data_formats.yolo.base.YoloUltralyticsPath

Bases: object

META_FILE = 'data.yaml'

datumaro.plugins.data_formats.yolo.base.extract_subset_name_from_parent(url: str, start: str) → str

Extract subset name from the given url.

For example, if url = “/a/b/images/train/img.jpg” and start = “/a/b”, it will return “train”. On the other hand, if url = “/a/b/images/img.jpg” and start = “/a/b”, it will return DEFAULT_SUBSET_NAME.

Parameters:

• url (str) – Given url to extract subset

• start – The head path of url to obtain the relative path from the url

Returns:

Subset name

Return type:

str

datumaro.plugins.data_formats.yolo.base.find_files(dirpath: str, exts: str | Iterable[str], recursive: bool = False, max_depth: int | None = None, min_depth: int | None = None) → Iterator[str]

datumaro.plugins.data_formats.yolo.base.has_meta_file(path)

datumaro.plugins.data_formats.yolo.base.load_image_meta_file(image_meta_path: str) → Dict[str, Tuple[int, int]]

Loads image metadata from a file with the following format:

<image name 1> <height 1> <width 1>

<image name 2> <height 2> <width 2>

…

Shell-like comments and quoted fields are allowed.

This can be useful to support datasets in which image dimensions are required to interpret annotations.

datumaro.plugins.data_formats.yolo.base.localize_path(path: str) → str

Removes the “data/” prefix from the path

datumaro.plugins.data_formats.yolo.base.parse_meta_file(path)

datumaro.plugins.data_formats.yolo.base.split_path(path)