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=None,
    min_count=1,
    max_count=1,
    trials=10
)

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

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

probability (float, optional): Probability of augmenting the image. Defaults to 0.25. min_area (float, optional): Minimum area of the random erasing rectangle. Defaults to 0.02. max_area (float, optional): Maximum area of the random erasing rectangle. Defaults to 1/3. min_aspect (float, optional): Minimum aspect rate of the random erasing rectangle. Defaults to 0.3. max_aspect ([type], optional): Maximum aspect rate of the random erasing rectangle. Defaults to None. min_count (int, optional): Minimum number of erased rectangles. Defaults to 1. max_count (int, optional): Maximum number of erased rectangles. Defaults to 1. trials (int, optional): 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
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.

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.