mindspore.dataset.vision.py_transforms.Pad

class mindspore.dataset.vision.py_transforms.Pad(padding, fill_value=0, padding_mode=Border.CONSTANT)[source]

Pad the input PIL Image on all sides.

Parameters

padding (Union[int, Sequence[int, int], Sequence[int, int, int, int]]) – The number of pixels to pad on each border. If int is provided, pad all borders with this value. If Sequence[int, int] is provided, pad the left and top borders with the first value and the right and bottom borders with the second value. If Sequence[int, int, int, int] is provided, pad the left, top, right and bottom borders respectively.
fill_value (Union[int, tuple[int, int, int]], optional) – Pixel value used to pad the borders, only valid when padding_mode is Border.CONSTANT. If int is provided, it will be used for all RGB channels. If tuple[int, int, int] is provided, it will be used for R, G, B channels respectively. Default: 0.
padding_mode (Border, optional) –
Method of padding. It can be Border.CONSTANT, Border.EDGE, Border.REFLECT or Border.SYMMETRIC. Default: Border.CONSTANT. Default: Border.CONSTANT.
- Border.CONSTANT, pads with a constant value.
- Border.EDGE, pads with the last value at the edge of the image.
- Border.REFLECT, pads with reflection of the image omitting the last value on the edge.
- Border.SYMMETRIC, pads with reflection of the image repeating the last value on the edge.

Raises

TypeError – If padding is not of type int or Sequence[int, int].
TypeError – If fill_value is not of type int or tuple[int, int, 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 of [0, 255].
RuntimeError – If shape of the input image is not <H, W> or <H, W, C>.

Supported Platforms:: CPU

Examples

>>> from mindspore.dataset.transforms.py_transforms import Compose
>>>
>>> transforms_list = Compose([py_vision.Decode(),
...                            # adds 10 pixels (default black) to each border of the image
...                            py_vision.Pad(padding=10),
...                            py_vision.ToTensor()])
>>> # apply the transform to dataset through map function
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns="image")