filter_images#
- Dataset.filter_images(index: Any, mode: Literal['loc', 'iloc'] = 'loc') Self[source]#
Method equivalent of
Dataset.locandDataset.iloc- Parameters:
index – Index object used in
self.images.loc[]orself.images.iloc[]mode – whether to be equivalent to
Dataset.locorDataset.iloc. Defaults to “loc”
- Returns:
Filtered dataset
See also
Example
>>> from lours.utils.doc_utils import dummy_dataset >>> example = dummy_dataset(2, 2, seed=1) >>> example Dataset object containing 2 images and 2 objects Name : shake_effort_many Images root : care/suggest Images : width height relative_path type split id 0 955 229 determine/story.jpg .jpg train 1 131 840 air/method.bmp .bmp train Annotations : image_id category_str category_id ... box_y_min box_width box_height id ... 0 1 listen 14 ... 276.974642 9.718823 184.684056 1 0 reach 22 ... 6.311037 123.141689 174.239136 [2 rows x 8 columns] Label map : {14: 'listen', 15: 'marriage', 22: 'reach'} >>> example.filter_images(example.images["type"] == ".jpg", mode="loc") Dataset object containing 1 image and 1 object Name : shake_effort_many Images root : care/suggest Images : width height relative_path type split id 0 955 229 determine/story.jpg .jpg train Annotations : image_id category_str category_id ... box_y_min box_width box_height id ... 1 0 reach 22 ... 6.311037 123.141689 174.239136 [1 rows x 8 columns] Label map : {14: 'listen', 15: 'marriage', 22: 'reach'}
>>> example.filter_images(0, mode="iloc") Dataset object containing 1 image and 1 object Name : shake_effort_many Images root : care/suggest Images : width height relative_path type split id 0 955 229 determine/story.jpg .jpg train Annotations : image_id category_str category_id ... box_y_min box_width box_height id ... 1 0 reach 22 ... 6.311037 123.141689 174.239136 [1 rows x 8 columns] Label map : {14: 'listen', 15: 'marriage', 22: 'reach'}