mindspore.dataset.vision.py_transforms.RandomErasing

class mindspore.dataset.vision.py_transforms.RandomErasing(prob=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False, max_attempts=10)[source]

Erase the pixels, within a selected rectangle region, to the given value.

Randomly applied on the input NumPy image array of shape (C, H, W) with a given probability.

Zhun Zhong et al. ‘Random Erasing Data Augmentation’ 2017 See https://arxiv.org/pdf/1708.04896.pdf

Parameters

  • prob (float, optional) – Probability of applying RandomErasing (default=0.5).

  • scale (sequence of floats, optional) – Range of the relative erase area to the original image (default=(0.02, 0.33)).

  • ratio (sequence of floats, optional) – Range of the aspect ratio of the erase area (default=(0.3, 3.3)).

  • value (Union[int, sequence, string]) – Erasing value (default=0). If value is a single intieger, it is applied to all pixels to be erased. If value is a sequence of length 3, it is applied to R, G, B channels respectively. If value is a string ‘random’, the erase value will be obtained from a standard normal distribution.

  • inplace (bool, optional) – Apply this transform in-place (default=False).

  • max_attempts (int, optional) – The maximum number of attempts to propose a valid erase_area (default=10). If exceeded, return the original image.

Examples

>>> from mindspore.dataset.transforms.py_transforms import Compose
>>> transforms_list = Compose([py_vision.Decode(),
...                            py_vision.ToTensor(),
...                            py_vision.RandomErasing(value='random')])
>>> # apply the transform to dataset through map function
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns="image")