Equalization layer
Equalization class
keras_cv.layers.Equalization(value_range, bins=256, **kwargs)
Equalization performs histogram equalization on a channel-wise basis.
Arguments
- value_range: a tuple or a list of two elements. The first value
represents the lower bound for values in passed images, the second
represents the upper bound. Images passed to the layer should have
values within
value_range. - bins: Integer indicating the number of bins to use in histogram equalization. Should be in the range [0, 256].
Example
equalize = Equalization()
(images, labels), _ = keras.datasets.cifar10.load_data()
# Note that images are an int8 Tensor with values in the range [0, 255]
images = equalize(images)
Call arguments
- images: Tensor of pixels in range [0, 255], in RGB format. Can be of type float or int. Should be in NHWC format.