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Add docformatter to pre-commit (#5279)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
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Glenn Jocher 2023-10-09 02:25:22 +02:00 • committed by GitHub
parent c7aa83da31
commit 7517667a33
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90 changed files with 1396 additions and 497 deletions
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19
ultralytics/utils/ops.py
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@ -50,6 +50,7 @@ class Profile(contextlib.ContextDecorator):
self.t += self.dt # accumulate dt
def __str__(self):
"""Returns a human-readable string representing the accumulated elapsed time in the profiler."""
return f'Elapsed time is {self.t} s'
def time(self):
@ -303,7 +304,7 @@ def clip_coords(coords, shape):
def scale_image(masks, im0_shape, ratio_pad=None):
"""
Takes a mask, and resizes it to the original image size
Takes a mask, and resizes it to the original image size.
Args:
masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3].
@ -403,8 +404,8 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
"""
Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height, normalized) format.
x, y, width and height are normalized to image dimensions
Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height, normalized) format. x, y,
width and height are normalized to image dimensions.
Args:
x (np.ndarray | torch.Tensor): The input bounding box coordinates in (x1, y1, x2, y2) format.
@ -445,7 +446,7 @@ def xywh2ltwh(x):
def xyxy2ltwh(x):
"""
Convert nx4 bounding boxes from [x1, y1, x2, y2] to [x1, y1, w, h], where xy1=top-left, xy2=bottom-right
Convert nx4 bounding boxes from [x1, y1, x2, y2] to [x1, y1, w, h], where xy1=top-left, xy2=bottom-right.
Args:
x (np.ndarray | torch.Tensor): The input tensor with the bounding boxes coordinates in the xyxy format
@ -461,7 +462,7 @@ def xyxy2ltwh(x):
def ltwh2xywh(x):
"""
Convert nx4 boxes from [x1, y1, w, h] to [x, y, w, h] where xy1=top-left, xy=center
Convert nx4 boxes from [x1, y1, w, h] to [x, y, w, h] where xy1=top-left, xy=center.
Args:
x (torch.Tensor): the input tensor
@ -544,7 +545,7 @@ def xywhr2xyxyxyxy(center):
def ltwh2xyxy(x):
"""
It converts the bounding box from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
It converts the bounding box from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right.
Args:
x (np.ndarray | torch.Tensor): the input image
@ -616,8 +617,8 @@ def crop_mask(masks, boxes):
def process_mask_upsample(protos, masks_in, bboxes, shape):
"""
Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher
quality but is slower.
Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher quality
but is slower.
Args:
protos (torch.Tensor): [mask_dim, mask_h, mask_w]
@ -713,7 +714,7 @@ def scale_masks(masks, shape, padding=True):
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None, normalize=False, padding=True):
"""
Rescale segment coordinates (xy) from img1_shape to img0_shape
Rescale segment coordinates (xy) from img1_shape to img0_shape.
Args:
img1_shape (tuple): The shape of the image that the coords are from.