mindspore.dataset.vision.Pad
- class mindspore.dataset.vision.Pad(padding, fill_value=0, padding_mode=Border.CONSTANT)[source]
Pad the image according to padding parameters.
- Parameters
padding (Union[int, Sequence[int, int], Sequence[int, int, int, int]]) – The number of pixels to pad each border of the image. If a single number is provided, it pads all borders with this value. If a tuple or lists of 2 values are provided, it pads the (left and right) with the first value and (top and bottom) with the second value. If 4 values are provided as a list or tuple, it pads the left, top, right and bottom respectively. The pad values must be non-negative.
fill_value (Union[int, tuple[int]], optional) – The pixel intensity of the borders, only valid for padding_mode
Border.CONSTANT
. If it is a 3-tuple, it is used to fill R, G, B channels respectively. If it is an integer, it is used for all RGB channels. The fill_value values must be in range [0, 255]. Default:0
.padding_mode (Border, optional) –
The method of padding. Default:
Border.CONSTANT
. Can beBorder.CONSTANT
,Border.EDGE
,Border.REFLECT
,Border.SYMMETRIC
.Border.CONSTANT
, means it fills the border with constant values.Border.EDGE
, means it pads with the last value on the edge.Border.REFLECT
, means it reflects the values on the edge omitting the last value of edge.Border.SYMMETRIC
, means it reflects the values on the edge repeating the last value of edge.
- Raises
TypeError – If padding is not of type int or Sequence[int, int], Sequence[int, int, int, int].
TypeError – If fill_value is not of type int or tuple[int].
TypeError – If padding_mode is not of type
mindspore.dataset.vision.Border
.ValueError – If padding is negative.
ValueError – If fill_value is not in range [0, 255].
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 >>> >>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory") >>> transforms_list = [vision.Decode(), vision.Pad([100, 100, 100, 100])] >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, ... input_columns=["image"])
- Tutorial Examples: