mindspore.dataset.vision.RandomResizedCrop

class mindspore.dataset.vision.RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation=Inter.BILINEAR, max_attempts=10)[source]

This operation will crop the input image randomly, and resize the cropped image using a selected interpolation mode Inter .

Note

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

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, a square of size (size, size) will be cropped with this value. If size is a sequence of length 2, an image of size (height, width) will be cropped.
scale (Union[list, tuple], optional) – Range [min, max) of respective size of the original size to be cropped, which must be non-negative. Default: (0.08, 1.0).
ratio (Union[list, tuple], optional) – Range [min, max) of aspect ratio to be cropped, which must be non-negative. Default: (3. / 4., 4. / 3.).
interpolation (Inter, optional) – Image interpolation method defined by Inter . Default: Inter.BILINEAR.
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.

Raises

TypeError – If size is not of type int or Sequence[int].
TypeError – If scale is not of type tuple or list.
TypeError – If ratio is not of type tuple or list.
TypeError – If interpolation is not of type Inter .
TypeError – If max_attempts is not of type int.
ValueError – If size is not positive.
ValueError – If scale is negative.
ValueError – If ratio is negative.
ValueError – If max_attempts is not positive.

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_crop_op = vision.RandomResizedCrop(size=(50, 75), scale=(0.25, 0.5),
...                                           interpolation=Inter.BILINEAR)
>>> transforms_list = [decode_op, resize_crop_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