tlc.core.objects.tables.from_url.table_from_yolo_url

A table populated from a single YOLO dataset folder

Module Contents

Classes

Class

Description

TableFromYoloUrl

A table populated from a YOLO URL

TableFromYoloUrlDetection

TableFromYoloUrlKeypoints

TableFromYoloUrlOBB

TableFromYoloUrlSegmentation

API

class TableFromYoloUrl(
*,
url: Url | None = None,
created: str | None = None,
description: str | None = None,
row_cache_url: Url | None = None,
row_cache_populated: bool | None = None,
override_table_rows_schema: Any = None,
init_parameters: Any = None,
input_tables: list[Url] | None = None,
input_urls: list[Url] | None = None,
categories: str | Sequence[str] | Sequence[dict[str, str]] | Sequence[MapElement] | dict[float, str] | dict[int, str] | dict[float, MapElement] | dict[int, MapElement] | None = None,
max_depth: int | None = None,
allow_fetch_remote_data: bool | None = None,
**kwargs: Any,
)

Bases: tlc.core.objects.tables.in_memory_columns_table._InMemoryColumnsTable, abc.ABC

A table populated from a YOLO URL

Create a table from a YOLO dataset folder.

Parameters:

  • url – The URL of the table.

  • created – The creation time of the table.

  • description – The description of the table.

  • row_cache_url – The URL of the row cache.

  • row_cache_populated – Whether the row cache is populated.

  • override_table_rows_schema – The schema to override the table rows schema.

  • init_parameters – The initial parameters of the table.

  • input_tables – A list of Table URLs that are considered direct predecessors in this table’s lineage.

  • input_urls – The URLs of the input folder/files.

  • categories – The categories of the table.

  • max_depth – The maximum depth to search for images. If None (default), the limit is set to 1 (i.e. only immediate children) for remote input URLs and unlimited for local files. Must be at least 1.

  • allow_fetch_remote_data – Whether to allow fetching remote images and label files if on remote storage. Defaults to False, meaning no remote data can be fetched, and an error is raised if required to. If True, the remote data is fetched as part of the table creating process. For large datasets, this will lead to two requests for each image, one for the full image and one for the corresponding label file. In such cases it is recommended to download a local copy and create the table from that.

  • **kwargs – Additional keyword arguments for task pose.

class TableFromYoloUrlDetection(
*,
url: Url | None = None,
created: str | None = None,
description: str | None = None,
row_cache_url: Url | None = None,
row_cache_populated: bool | None = None,
override_table_rows_schema: Any = None,
init_parameters: Any = None,
input_tables: list[Url] | None = None,
input_urls: list[Url] | None = None,
categories: str | Sequence[str] | Sequence[dict[str, str]] | Sequence[MapElement] | dict[float, str] | dict[int, str] | dict[float, MapElement] | dict[int, MapElement] | None = None,
max_depth: int | None = None,
allow_fetch_remote_data: bool | None = None,
**kwargs: Any,
)

Bases: tlc.core.objects.tables.from_url.table_from_yolo_url._YoloDetectionTaskHandlerNoDimensions, tlc.core.objects.tables.from_url.table_from_yolo_url.TableFromYoloUrl

class TableFromYoloUrlKeypoints(
*,
kpt_shape: tuple[int, int] | None = None,
points: list[float] | None = None,
point_attributes: str | Sequence[str] | Sequence[dict[str, str]] | Sequence[MapElement] | dict[float, str] | dict[int, str] | dict[float, MapElement] | dict[int, MapElement] | None = None,
lines: list[int] | None = None,
line_attributes: str | Sequence[str] | Sequence[dict[str, str]] | Sequence[MapElement] | dict[float, str] | dict[int, str] | dict[float, MapElement] | dict[int, MapElement] | None = None,
triangles: list[int] | None = None,
triangle_attributes: str | Sequence[str] | Sequence[dict[str, str]] | Sequence[MapElement] | dict[float, str] | dict[int, str] | dict[float, MapElement] | dict[int, MapElement] | None = None,
flip_indices: list[int] | None = None,
oks_sigmas: list[float] | None = None,
**kwargs: Any,
)

Bases: tlc.core.objects.tables.from_url.table_from_yolo._YoloKeypointsTaskHandler, tlc.core.objects.tables.from_url.table_from_yolo_url.TableFromYoloUrl

class TableFromYoloUrlOBB(
*,
url: Url | None = None,
created: str | None = None,
description: str | None = None,
row_cache_url: Url | None = None,
row_cache_populated: bool | None = None,
override_table_rows_schema: Any = None,
init_parameters: Any = None,
input_tables: list[Url] | None = None,
input_urls: list[Url] | None = None,
categories: str | Sequence[str] | Sequence[dict[str, str]] | Sequence[MapElement] | dict[float, str] | dict[int, str] | dict[float, MapElement] | dict[int, MapElement] | None = None,
max_depth: int | None = None,
allow_fetch_remote_data: bool | None = None,
**kwargs: Any,
)

Bases: tlc.core.objects.tables.from_url.table_from_yolo._YoloOBBTaskHandler, tlc.core.objects.tables.from_url.table_from_yolo_url.TableFromYoloUrl

class TableFromYoloUrlSegmentation(
*,
url: Url | None = None,
created: str | None = None,
description: str | None = None,
row_cache_url: Url | None = None,
row_cache_populated: bool | None = None,
override_table_rows_schema: Any = None,
init_parameters: Any = None,
input_tables: list[Url] | None = None,
input_urls: list[Url] | None = None,
categories: str | Sequence[str] | Sequence[dict[str, str]] | Sequence[MapElement] | dict[float, str] | dict[int, str] | dict[float, MapElement] | dict[int, MapElement] | None = None,
max_depth: int | None = None,
allow_fetch_remote_data: bool | None = None,
**kwargs: Any,
)

Bases: tlc.core.objects.tables.from_url.table_from_yolo._YoloSegmentationTaskHandler, tlc.core.objects.tables.from_url.table_from_yolo_url.TableFromYoloUrl