Resize¶

class paddle.vision.transforms. Resize ( size, interpolation='bilinear', keys=None ) [source]

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)

          