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 image on all sides with the given padding parameters.
- Parameters
padding (Union[int, sequence]) – The number of pixels padded on the image borders. If a single number is provided, pad all borders with this value. If a sequence of length 2 is provided, pad the left and top with the first value and the right and bottom with the second value. If a sequence of length 4 is provided, pad the left, top, right and bottom respectively.
fill_value (Union[int, tuple], optional) – Pixel fill value to pad the borders, only valid when padding_mode is Border.CONSTANT (default=0). If fill_value is an integer, it is used for all RGB channels. If fill_value is a tuple of length 3, it is used to fill R, G, B channels respectively.
padding_mode (Border, optional) –
The method of padding (default=Border.CONSTANT). It can be any of [Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].
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 integer or sequence of integer.
TypeError – If fill_value is not of type integer or tuple of integer.
TypeError – If padding_mode is not of type 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
>>> 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")