class mindspore.dataset.vision.c_transforms.Resize(size, interpolation=Inter.LINEAR)[source]

Resize the input image to the given size with a given interpolation mode.

  • size (Union[int, sequence]) – The output size of the resized image. The size value(s) must be positive. If size is an integer, the smaller edge of the image will be resized to this value with the same image aspect ratio. If size is a sequence of length 2, it should be (height, width).

  • interpolation (Inter, optional) –

    Image interpolation mode (default=Inter.LINEAR). It can be any of [Inter.LINEAR, Inter.NEAREST, Inter.BICUBIC, Inter.AREA, Inter.PILCUBIC].

    • Inter.LINEAR, means interpolation method is bilinear interpolation.

    • Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.

    • Inter.BICUBIC, means interpolation method is bicubic interpolation.

    • Inter.AREA, means interpolation method is pixel area interpolation.

    • Inter.PILCUBIC, means interpolation method is bicubic interpolation like implemented in pillow, input should be in 3 channels format.

  • TypeError – If size is not of type integer or sequence of integer.

  • TypeError – If interpolation is not of type Inter.

  • ValueError – If size is not positive.

  • RuntimeError – If given tensor shape is not <H, W> or <H, W, C>.

Supported Platforms:



>>> from mindspore.dataset.vision import Inter
>>> decode_op = c_vision.Decode()
>>> resize_op = c_vision.Resize([100, 75], Inter.BICUBIC)
>>> transforms_list = [decode_op, resize_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])