mindspore.dataset.vision.c_transforms.Resize

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.

Parameters

size (Union[int, Sequence[int]]) – 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.

Raises

TypeError – If size is not of type int or Sequence[int].
TypeError – If interpolation is not of type mindspore.dataset.vision.Inter.
ValueError – If size is not positive.
RuntimeError – If given tensor shape is not <H, W> or <H, W, C>.

Supported Platforms:: CPU

Examples

>>> 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"])