remap_from_preset

Dataset.remap_from_preset(input_dataset_map: str, output_dataset_map: str, remove_not_mapped: bool = True, remove_emptied_images: bool = False) → Self

Same as class remap, but instead of taking a dictionary, you give the name of a preset. Registered presets are stored in remap_presets folders, with csv files in the form {inputr_dataset_map}_to_{output_dataset_map}

Parameters:

• input_dataset_map – Name of label map to convert from.

• output_dataset_map – Name of label to convert to.

• remove_not_mapped – If set to True, will remove classes that are not in class mapping. Otherwise, keep them as is (with potential class fusion). Defaults to True.

• remove_emptied_images – If set to True, will remove from self.images the images that are now empty of annotation. Note that it will keep the images that were empty before the remapping. Defaults to False.

Returns:

New dataset object with remapped classes according to the preset

Raises:

KeyError – raised when the input/output pair does not exists in presets.

See also

• related tutorial

• remap_classes()

• remap_from_dataframe()

• remap_from_csv()

• remap_from_other()

• remove_classes()

• keep_classes()