RandomSharpness layer

• 원본 링크 : https://keras.io/api/keras_cv/layers/augmentation/random_sharpness/
• 최종 확인 : 2024-11-25

source

RandomSharpness class

keras_cv.layers.RandomSharpness(factor, value_range, seed=None, **kwargs)

Randomly performs the sharpness operation on given images.

The sharpness operation first performs a blur operation, then blends between the original image and the blurred image. This operation makes the edges of an image less sharp than they were in the original image.

References

• PIL

Arguments

• factor: A tuple of two floats, a single float or keras_cv.FactorSampler. factor controls the extent to which the image sharpness is impacted. factor=0.0 makes this layer perform a no-op operation, while a value of 1.0 uses the sharpened result entirely. Values between 0 and 1 result in linear interpolation between the original image and the sharpened image. Values should be between 0.0 and 1.0. If a tuple is used, a factor is sampled between the two values for every image augmented. If a single float is used, a value between 0.0 and the passed float is sampled. In order to ensure the value is always the same, please pass a tuple with two identical floats: (0.5, 0.5).
• value_range: the range of values the incoming images will have. Represented as a two number tuple written [low, high]. This is typically either [0, 1] or [0, 255] depending on how your preprocessing pipeline is set up.