mindspore.dataset.vision.ResizeWithBBox

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

Resize the input image to the given size and adjust bounding boxes accordingly.

Parameters

size (Union[int, Sequence[int]]) – The output size of the resized image. If size is an integer, 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.LINEAR, Inter.NEAREST, Inter.BICUBIC.
- Inter.LINEAR , means interpolation method is bilinear interpolation.
- Inter.NEAREST , means interpolation method is nearest-neighbor interpolation.
- Inter.BICUBIC , means interpolation method is bicubic 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()
>>> bbox_op = vision.ResizeWithBBox(50, Inter.NEAREST)
>>> transforms_list = [decode_op, bbox_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