mindspore.dataset.vision.c_transforms.RandomCropDecodeResize

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

A combination of Crop, Decode and Resize. It will get better performance for JPEG images. This operator will crop the input image at a random location, decode the cropped image in RGB mode, and resize the decoded image.

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.BILINEAR, means interpolation method is bilinear interpolation.

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

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

  • 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
>>> resize_crop_decode_op = c_vision.RandomCropDecodeResize(size=(50, 75),
...                                                         scale=(0.25, 0.5),
...                                                         interpolation=Inter.NEAREST,
...                                                         max_attempts=5)
>>> transforms_list = [resize_crop_decode_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])