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docs: convert fenced examples to doctests in dataset/formats/coco.py (#2474) 

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
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Swapnil Gautam 2026-08-05 08:51:58 -04:00 committed by GitHub
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48
src/supervision/dataset/formats/coco.py
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@ -65,15 +65,13 @@ def classes_to_coco_categories(classes: list[str]) -> list[CocoDict]:
Returns:
A list of COCO category dictionaries with 1-indexed ``id`` values.
Examples:
```python
from supervision.dataset.formats.coco import classes_to_coco_categories
Example:
```pycon
>>> from supervision.dataset.formats.coco import classes_to_coco_categories
>>> classes_to_coco_categories(classes=["cat", "dog"])
[{'id': 1, 'name': 'cat', 'supercategory': 'common-objects'},
{'id': 2, 'name': 'dog', 'supercategory': 'common-objects'}]
classes_to_coco_categories(classes=["cat", "dog"])
# [
# {"id": 1, "name": "cat", "supercategory": "common-objects"},
# {"id": 2, "name": "dog", "supercategory": "common-objects"},
# ]
```
"""
return [
@ -280,23 +278,25 @@ def detections_to_coco_annotations(
(``iscrowd=0``). Supply ``data={"iscrowd": np.array([0])}`` to
force polygon output regardless of mask topology.
Examples:
```python
import numpy as np
from supervision import Detections
from supervision.dataset.formats.coco import (
detections_to_coco_annotations,
)
Example:
```pycon
>>> import numpy as np
>>> from supervision import Detections
>>> from supervision.dataset.formats.coco import (
... detections_to_coco_annotations,
... )
>>> detections = Detections(
... xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
... class_id=np.array([0], dtype=int),
... )
>>> annotations, next_id = detections_to_coco_annotations(
... detections=detections, image_id=1, annotation_id=1
... )
>>> annotations[0]["category_id"]
1
>>> next_id
2
detections = Detections(
xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
class_id=np.array([0], dtype=int),
)
annotations, next_id = detections_to_coco_annotations(
detections=detections, image_id=1, annotation_id=1
)
annotations[0]["category_id"]
# 1
```
"""
coco_annotations: list[CocoDict] = []