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[tuple]]) – 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 top) with the first value and (right 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 any of [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.
Note
The behavior when padding is a sequence of length 2 will change from padding left/top with the first value and right/bottom with the second, to padding left/right with the first one and top/bottom with the second in the future. Or you can pass in a 4-element sequence to specify left, top, right and bottom respectively.
- Raises
TypeError – If padding is not of type int or Sequence[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
>>> transforms_list = [vision.Decode(), vision.Pad([100, 100, 100, 100])] >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, ... input_columns=["image"])