mindspore.dataset.vision.c_transforms.RandomResizedCropWithBBox
- class mindspore.dataset.vision.c_transforms.RandomResizedCropWithBBox(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation=Inter.BILINEAR, max_attempts=10)[source]
Crop the input image to a random size and aspect ratio and adjust bounding boxes accordingly.
- Parameters
size (Union[int, sequence]) – The size of the output image. The size value(s) must be positive. 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, which must be non-negative (default=(0.08, 1.0)).
ratio (list, tuple, optional) – Range (min, max) of aspect ratio to be cropped, which must be non-negative (default=(3. / 4., 4. / 3.)).
interpolation (Inter mode, optional) –
Image interpolation mode (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.
- Raises
TypeError – If size is not of type integer or sequence of integer.
TypeError – If scale is not of type tuple.
TypeError – If ratio is not of type tuple.
TypeError – If interpolation is not of type Inter.
TypeError – If max_attempts is not of type integer.
ValueError – If size is not positive.
ValueError – If scale is negative.
ValueError – If ratio is negative.
ValueError – If max_attempts 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() >>> bbox_op = c_vision.RandomResizedCropWithBBox(size=50, interpolation=Inter.NEAREST) >>> transforms_list = [decode_op, bbox_op] >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, ... input_columns=["image"])