mindspore.dataset.vision.Resize

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

Resize the input image to the given size with a given interpolation mode mindspore.dataset.vision.Inter .

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 Inter.BILINEAR, Inter.LINEAR, Inter.NEAREST, Inter.BICUBIC, Inter.AREA, Inter.PILCUBIC, Inter.ANTIALIAS.
- Inter.BILINEAR, bilinear interpolation.
- Inter.LINEAR, bilinear interpolation, here is the same as Inter.BILINEAR.
- Inter.NEAREST, nearest-neighbor interpolation.
- Inter.BICUBIC, bicubic interpolation.
- Inter.AREA, pixel area interpolation.
- Inter.PILCUBIC, bicubic interpolation like implemented in Pillow, only valid when the input is a 3-channel image in the numpy.ndarray format.
- Inter.ANTIALIAS, antialias interpolation.

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

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>> from mindspore.dataset.vision import Inter
>>>
>>> decode_op = vision.Decode()
>>> resize_op = vision.Resize([100, 75], Inter.BICUBIC)
>>> transforms_list = [decode_op, resize_op]
>>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory")
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])

Tutorial Examples:

Illustration of vision transforms