tfm.vision.augment.RandomErasing

Applies RandomErasing to a single image.

Inherits From: ImageAugment

tfm.vision.augment.RandomErasing(
    probability: float = 0.25,
    min_area: float = 0.02,
    max_area: float = (1 / 3),
    min_aspect: float = 0.3,
    max_aspect: Optional[float] = None,
    min_count=1,
    max_count=1,
    trials=10
)

Reference: https://arxiv.org/abs/1708.04896

Implementation is inspired by https://github.com/rwightman/pytorch-image-models

Args
`probability`	Probability of augmenting the image. Defaults to `0.25`.
`min_area`	Minimum area of the random erasing rectangle. Defaults to `0.02`.
`max_area`	Maximum area of the random erasing rectangle. Defaults to `1/3`.
`min_aspect`	Minimum aspect rate of the random erasing rectangle. Defaults to `0.3`.
`max_aspect`	Maximum aspect rate of the random erasing rectangle. Defaults to `None`.
`min_count`	Minimum number of erased rectangles. Defaults to `1`.
`max_count`	Maximum number of erased rectangles. Defaults to `1`.
`trials`	Maximum number of trials to randomly sample a rectangle that fulfills constraint. Defaults to `10`.

Methods

`distort`

View source

distort(
    image: tf.Tensor
) -> tf.Tensor

Applies RandomErasing to single image.

Args

Args
`image`	`tf.Tensor` Of shape [height, width, 3] representing an image.

image

tf.Tensor

Of shape [height, width, 3] representing an image.

Returns
`tf.Tensor`	The augmented version of `image`.

`distort_with_boxes`

View source

distort_with_boxes(
    image: tf.Tensor, bboxes: tf.Tensor
) -> Tuple[tf.Tensor, tf.Tensor]

Distorts the image and bounding boxes.

Expect the image tensor values are in the range [0, 255].

Args
`image`	`Tensor` of shape [height, width, 3] or [num_frames, height, width, 3] representing an image or image sequence.
`bboxes`	`Tensor` of shape [num_boxes, 4] or [num_frames, num_boxes, 4] representing bounding boxes for an image or image sequence.

Returns
The augmented version of `image` and `bboxes`.