debooleanize

Dataset.debooleanize(dataframe: Literal['both', 'images', 'annotations'] = 'both') → Self

Convert booleanized columns back to list form, for exporting purpose.

Note

This will only debooleanize columns that have been explicitly booleanized, and not just boolean columns. It will look for values in self.booleanized_columns and retrieve all the column with the name in the form column_name.entry to reconstruct the column_name column.

Parameters:

dataframe – Which dataframe you want to booleanize. Can be either “images”, “annotations” or None. If set to None, will debooleanize both dataframes. Defaults to None.

Returns:

New dataset object with converted columns, booleanized columns are dropped.

See also

related tutorial

Example

>>> from lours.utils.doc_utils import dummy_dataset
>>> example = dummy_dataset(
...     n_imgs=3,
...     n_annot=3,
...     n_list_columns_images=[2, 3],
...     n_list_columns_annotations=1,
... )
>>> example
Dataset object containing 3 images and 3 objects
Name :
    inside_else_memory
Images root :
    such/serious
Images :
    width  height  ...                         beyond                father
id                 ...
0     342     167  ...                       [enough]  [challenge, someone]
1     377     114  ...          [present, successful]           [challenge]
2     136     257  ...  [present, successful, enough]  [challenge, someone]

[3 rows x 7 columns]
Annotations :
    image_id category_str  ...  box_height                                   where
id                         ...
0          2          why  ...  138.451739  [no, season, play, choice, force, bit]
1          1          why  ...   63.576932                     [no, choice, force]
2          2         step  ...   99.999123           [no, season, play, week, bit]

[3 rows x 9 columns]
Label map :
{15: 'step', 19: 'why', 25: 'interview'}
>>> modified = example.booleanize(column_names=["beyond", "where"])
>>> modified
Dataset object containing 3 images and 3 objects
Name :
    inside_else_memory
Images root :
    such/serious
Images :
    width  height  ... beyond.present beyond.successful
id                 ...
0     342     167  ...          False             False
1     377     114  ...           True              True
2     136     257  ...           True              True

[3 rows x 9 columns]
Annotations :
    image_id category_str  category_id  ... where.play  where.season  where.week
id                                      ...
0          2          why           19  ...       True          True       False
1          1          why           19  ...      False         False       False
2          2         step           15  ...       True          True        True

[3 rows x 15 columns]
Label map :
{15: 'step', 19: 'why', 25: 'interview'}
>>> modified.debooleanize()
Dataset object containing 3 images and 3 objects
Name :
    inside_else_memory
Images root :
    such/serious
Images :
    width  height  ...                         beyond                father
id                 ...
0     342     167  ...                       [enough]  [challenge, someone]
1     377     114  ...          [present, successful]           [challenge]
2     136     257  ...  [enough, present, successful]  [challenge, someone]

[3 rows x 7 columns]
Annotations :
    image_id category_str  ...  box_height                                   where
id                         ...
0          2          why  ...  138.451739  [bit, choice, force, no, play, season]
1          1          why  ...   63.576932                     [choice, force, no]
2          2         step  ...   99.999123           [bit, no, play, season, week]

[3 rows x 9 columns]
Label map :
{15: 'step', 19: 'why', 25: 'interview'}
>>> modified.debooleanize(dataframe="images")
Dataset object containing 3 images and 3 objects
Name :
    inside_else_memory
Images root :
    such/serious
Images :
    width  height  ...                         beyond                father
id                 ...
0     342     167  ...                       [enough]  [challenge, someone]
1     377     114  ...          [present, successful]           [challenge]
2     136     257  ...  [enough, present, successful]  [challenge, someone]

[3 rows x 7 columns]
Annotations :
    image_id category_str  category_id  ... where.play  where.season  where.week
id                                      ...
0          2          why           19  ...       True          True       False
1          1          why           19  ...      False         False       False
2          2         step           15  ...       True          True        True

[3 rows x 15 columns]
Label map :
{15: 'step', 19: 'why', 25: 'interview'}