add_detection_annotation#

Add one or multiple detection annotations to the current dataset. In the case of a single annotation, every option can be a single value, but in the case of multiple annotations, every option needs to be an array of such values, and every array needs to be the same length.

Note

In additions to the following options, you can add other fields as well, with keyword arguments.

Parameters:

image_id – image identifier to link each detection to the corresponding image
bbox_coordinates – list of coordinates for the bounding box. Can follow any compatible format, as long as it is given in the next format
category_id – category of each detection. Label will be deduced from dataset’s label map
format_string – format of coordinates, whether coordinates are relatives, using corner points of the box, box dimensions, etc. See import_bbox() for more info
inplace – if set to True, will modify the dataset inplace and return self. Else, will return a modified Dataset. Defaults to False.
label_map – In the case the current dataset’s label map is incomplete, merge it with this new label map. current label map and new label map must be compatible, see merge_label_maps(). Defaults to None.
category_ids_mapping – Optional dictionary to map annotated category ids into the right ids. This is useful for example when a neural network can only use a contiguous label map. Defaults to None
confidence – Optional field for confidence, in the case annotations are actually predictions. Defaults to None.
**other_columns – kwargs of additional optional fields

Raises:

ValueError – raised when giving numpy arrays are not the same number of elements, or if the bounding box coordinates is not of the shape either [4], or [N, 4]

Returns:

A new Dataset with appended annotations to self.annotations if inplace is False, or itself otherwise.

Example

>>> from lours.utils.doc_utils import dummy_dataset
>>> example = dummy_dataset()
>>> example
Dataset object containing 2 images and 2 objects
Name :
    inside_else_memory
Images root :
    such/serious
Images :
    width  height      relative_path   type  split
id
0     342     136       help/me.jpeg  .jpeg  train
1     377     167  whatever/wait.png   .png  train
Annotations :
    image_id category_str  category_id  ...  box_y_min   box_width  box_height
id                                      ...
0          0         step           15  ...  73.932999   71.552480   42.673983
1          0          why           19  ...   4.567638  248.551257  122.602211

[2 rows x 8 columns]
Label map :
{15: 'step', 19: 'why', 25: 'interview'}
>>> example.add_detection_annotation(
...     image_id=0,
...     bbox_coordinates=[0, 0, 0.5, 0.5],
...     format_string="xyxy",
...     category_id=14,
...     confidence=0.5,
... )
Dataset object containing 2 images and 3 objects
Name :
    inside_else_memory
Images root :
    such/serious
Images :
    width  height      relative_path   type  split
id
0     342     136       help/me.jpeg  .jpeg  train
1     377     167  whatever/wait.png   .png  train
Annotations :
    image_id category_str  category_id  ...   box_width  box_height  confidence
id                                      ...
0          0         step           15  ...   71.552480   42.673983         NaN
1          0          why           19  ...  248.551257  122.602211         NaN
2          0           14           14  ...  171.000000   68.000000         0.5

[3 rows x 9 columns]
Label map :
{14: '14', 15: 'step', 19: 'why', 25: 'interview'}