Pad

class paddle.vision.transforms. Pad ( padding, fill=0, padding_mode='constant', keys=None ) [source]

Pads the given CV Image on all sides with the given “pad” value.

Parameters

  • padding (int|list|tuple) – Padding on each border. If a single int is provided this is used to pad all borders. If list/tuple of length 2 is provided this is the padding on left/right and top/bottom respectively. If a list/tuple of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.

  • fill (int|list|tuple) – Pixel fill value for constant fill. Default is 0. If a list/tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant

  • padding_mode (str) – Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant. constant means pads with a constant value, this value is specified with fill. edge means pads with the last value at the edge of the image. reflect means pads with reflection of image (without repeating the last value on the edge) padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2]. symmetric menas pads with reflection of image (repeating the last value on the edge) padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3].

  • keys (list[str]|tuple[str], optional) – Same as BaseTransform. Default: None.

Shape:

  • img(PIL.Image|np.ndarray|Paddle.Tensor): The input image with shape (H x W x C).

  • output(PIL.Image|np.ndarray|Paddle.Tensor): A padded image.

Returns

A callable object of Pad.

Examples

>>> import numpy as np
>>> from PIL import Image
>>> from paddle.vision.transforms import Pad

>>> transform = Pad(2)
>>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
>>> fake_img = transform(fake_img)
>>> print(fake_img.size)
(228, 228)