from_coco

from_coco(coco_json: Path | str, images_root: Path | str | None = None, dataset_name: str | None = None, split: str | None = None, label_map: dict[int, str] | None = None, box_format: str = 'XYWH', drop_columns: Iterable[str] = ('iscrowd', 'segmentation')) → Dataset

Load a coco json file into a dictionary. Note that there is only one split per file, which needs to be given by caller. See specifications (only Object detection)

Notes

• from_coco is compatible with bounding box annotations without category_id field, but then you will need to have a label map of only one entry, which will be assigned to every bounding box.

• If split value is not given, it will try to deduce it from the file name. More specifically, it will search a <name>_<split>.json pattern and assign name to the dataset name and split to the split value.

Parameters:

• coco_json – path of json file

• images_root – folder which file_name of images are relative to

• dataset_name – If specified, will be the dataset name, used when showing the dataset or exporting in other formats such as fiftyone. If not specified, the dataset name will be deduced from the name of the json file.

• split – split of given json file. If not set, will try to deduce from filename. Defaults to None.

• label_map – Optional dictionary to specify the name of each category id. If not set, will try to deduce it from the json itself, in the field categories at its root.

• box_format – what type of annotation the json file will have. It will be converted back to XYWH. Defaults to XYWH

• drop_columns – list of names of columns that need to be dropped from the parsed json dictionary.

Returns:

Loaded dataset object