Emboss

Targets:

image

Image Types:uint8, float32

Apply emboss effect (directional highlight and shadow). strength_range controls intensity. Pseudo-3D look; for texture or style augmentation.

This transform creates an emboss effect by highlighting edges and creating a 3D-like texture in the image. It works by applying a specific convolution kernel to the image that emphasizes differences in adjacent pixel values.

Arguments

alpha

tuple[float, float]

[0.2,0.5]

Range to choose the visibility of the embossed image. At 0, only the original image is visible, at 1.0 only its embossed version is visible. Values should be in the range [0, 1]. Alpha will be randomly selected from this range for each image. Default: (0.2, 0.5)

strength

tuple[float, float]

[0.2,0.7]

Range to choose the strength of the embossing effect. Higher values create a more pronounced 3D effect. Values should be non-negative. Strength will be randomly selected from this range for each image. Default: (0.2, 0.7)

p

float

0.5

Probability of applying the transform. Should be in the range [0, 1]. Default: 0.5

Examples

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> transform = A.Emboss(alpha=(0.2, 0.5), strength=(0.2, 0.7), p=0.5)
>>> result = transform(image=image)
>>> embossed_image = result['image']

Notes

The emboss effect is created using a 3x3 convolution kernel.
The 'alpha' parameter controls the blend between the original image and the embossed version. A higher alpha value will result in a more pronounced emboss effect.
The 'strength' parameter affects the intensity of the embossing. Higher strength values will create more contrast in the embossed areas, resulting in a stronger 3D-like effect.
This transform can be useful for creating artistic effects or for data augmentation in tasks where edge information is important.

References

Image Embossinghttps://en.wikipedia.org/wiki/Image_embossing
Application of Emboss Filtering in Image Processinghttps://www.researchgate.net/publication/303412455_Application_of_Emboss_Filtering_in_Image_Processing

>>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8) >>> transform = A.Emboss(alpha=(0.2, 0.5), strength=(0.2, 0.7), p=0.5) >>> result = transform(image=image) >>> embossed_image = result['image']