tfm.vision.augment.RandomErasing
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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
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
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.
|