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 be Border.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:

Illustration of vision transforms