mindspore.dataset.vision.c_transforms.RandomResizedCrop

class mindspore.dataset.vision.c_transforms.RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Inter.BILINEAR, max_attempts=10)[source]

Crop the input image to a random size and aspect ratio. This operator will crop the input image randomly, and resize the cropped image using a selected interpolation mode.

Note

If the input image is more than one, then make sure that the image size is the same.

Parameters

  • size (Union[int, sequence]) – The output size of the resized image. If size is an integer, a square crop of size (size, size) is returned. If size is a sequence of length 2, it should be (height, width).

  • scale (list, tuple, optional) – Range [min, max) of respective size of the original size to be cropped (default=(0.08, 1.0)).

  • ratio (list, tuple, optional) – Range [min, max) of aspect ratio to be cropped (default=(3. / 4., 4. / 3.)).

  • interpolation (Inter mode, optional) –

    Image interpolation mode for resize operator (default=Inter.BILINEAR). It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC, Inter.PILCUBIC].

    • Inter.BILINEAR, 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.

  • max_attempts (int, optional) – The maximum number of attempts to propose a valid crop_area (default=10). If exceeded, fall back to use center_crop instead.

Examples

>>> from mindspore.dataset.vision import Inter
>>> decode_op = c_vision.Decode()
>>> resize_crop_op = c_vision.RandomResizedCrop(size=(50, 75), scale=(0.25, 0.5),
...                                             interpolation=Inter.BILINEAR)
>>> transforms_list = [decode_op, resize_crop_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])