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class paddle.vision.transforms. Resize ( size: Size2, interpolation: _InterpolationPil | _InterpolationCv2 = 'bilinear', keys: _TransformInputKeys | None = None ) [source]
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Resize the input Image to the given size.

Parameters

  • size (int|list|tuple) – Desired output size. If size is a sequence like (h, w), output size will be matched to this. If size is an int, smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size)

  • interpolation (int|str, optional) – Interpolation method. Default: ‘bilinear’. when use pil backend, support method are as following: - “nearest”: Image.NEAREST, - “bilinear”: Image.BILINEAR, - “bicubic”: Image.BICUBIC, - “box”: Image.BOX, - “lanczos”: Image.LANCZOS, - “hamming”: Image.HAMMING when use cv2 backend, support method are as following: - “nearest”: cv2.INTER_NEAREST, - “bilinear”: cv2.INTER_LINEAR, - “area”: cv2.INTER_AREA, - “bicubic”: cv2.INTER_CUBIC, - “lanczos”: cv2.INTER_LANCZOS4

  • 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 resized image.

Returns

A callable object of Resize.

Examples

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

>>> fake_img = Image.fromarray((np.random.rand(256, 300, 3) * 255.).astype(np.uint8))
>>> transform = Resize(size=224)
>>> converted_img = transform(fake_img)
>>> print(converted_img.size)
(262, 224)
>>> transform = Resize(size=(200,150))
>>> converted_img = transform(fake_img)
>>> print(converted_img.size)
(150, 200)