HongChuang/ultralytics-ascend
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Ruff Docstring formatting (#15793)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-08-25 04:27:55 +08:00 • committed by GitHub
parent d27664216b
commit 776ca86369
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60 changed files with 241 additions and 309 deletions
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22
ultralytics/utils/instance.py
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@ -96,8 +96,11 @@ class Bboxes:
def mul(self, scale):
"""
Multiply bounding box coordinates by scale factor(s).
Args:
scale (tuple | list | int): the scale for four coords.
scale (int | tuple | list): Scale factor(s) for four coordinates.
If int, the same scale is applied to all coordinates.
"""
if isinstance(scale, Number):
scale = to_4tuple(scale)
@ -110,8 +113,11 @@ class Bboxes:
def add(self, offset):
"""
Add offset to bounding box coordinates.
Args:
offset (tuple | list | int): the offset for four coords.
offset (int | tuple | list): Offset(s) for four coordinates.
If int, the same offset is applied to all coordinates.
"""
if isinstance(offset, Number):
offset = to_4tuple(offset)
@ -210,10 +216,14 @@ class Instances:
def __init__(self, bboxes, segments=None, keypoints=None, bbox_format="xywh", normalized=True) -> None:
"""
Initialize the object with bounding boxes, segments, and keypoints.
Args:
bboxes (ndarray): bboxes with shape [N, 4].
segments (list | ndarray): segments.
keypoints (ndarray): keypoints(x, y, visible) with shape [N, 17, 3].
bboxes (np.ndarray): Bounding boxes, shape [N, 4].
segments (list | np.ndarray, optional): Segmentation masks. Defaults to None.
keypoints (np.ndarray, optional): Keypoints, shape [N, 17, 3] and format (x, y, visible). Defaults to None.
bbox_format (str, optional): Format of bboxes. Defaults to "xywh".
normalized (bool, optional): Whether the coordinates are normalized. Defaults to True.
"""
self._bboxes = Bboxes(bboxes=bboxes, format=bbox_format)
self.keypoints = keypoints
@ -230,7 +240,7 @@ class Instances:
return self._bboxes.areas()
def scale(self, scale_w, scale_h, bbox_only=False):
"""This might be similar with denormalize func but without normalized sign."""
"""Similar to denormalize func but without normalized sign."""
self._bboxes.mul(scale=(scale_w, scale_h, scale_w, scale_h))
if bbox_only:
return