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]
Randomly erase the pixels within a random selected rectangle region with a given probability.
See Zhun Zhong et al. ‘Random Erasing Data Augmentation’ 2017 on https://arxiv.org/pdf/1708.04896.pdf
- Parameters
prob (float, optional) – Probability of the image being randomly erased (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, optional) – Range of aspect ratio of the erased area (default=(0.3, 3.3)).
value (Union[int, sequence, str]) – Erasing value (default=0). If value is a single integer, it is used to erase all pixels. If value is a sequence of length 3, it is used to erase R, G, B channels respectively. If value is a string of ‘random’, each pixel will be erased with a random value obtained from a standard normal distribution.
inplace (bool, optional) – Whether to apply this transformation inplace (default=False).
max_attempts (int, optional) – The maximum number of attempts to propose a valid area to be erased (default=10). If exceeded, return the original image.
- Raises
TypeError – If prob is not of type float.
TypeError – If scale is not of type sequence.
TypeError – If ratio is not of type sequence.
TypeError – If value is not of type integer, sequence or string.
TypeError – If inplace is not of type boolean.
TypeError – If max_attempts is not of type integer.
ValueError – If prob is not in range [0, 1].
ValueError – If scale is negative.
ValueError – If ratio is negative.
ValueError – If value is not in range [0, 255].
ValueError – If max_attempts is not positive.
- Supported Platforms:
CPU
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")