图像量化层
Posterization class
keras_cv.layers.Posterization(value_range, bits, **kwargs)
Reduces the number of bits for each color channel.
References
- AutoAugment: Learning Augmentation Policies from Data
- RandAugment: Practical automated data augmentation with a reduced search space
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. Defaults to(0, 255). - bits: integer, the number of bits to keep for each channel. Must be a value between 1-8.
Example
(images, labels), _ = keras.datasets.cifar10.load_data()
print(images[0, 0, 0])
# [59 62 63]
# Note that images are Tensors with values in the range [0, 255] and uint8
dtype
posterization = Posterization(bits=4, value_range=[0, 255])
images = posterization(images)
print(images[0, 0, 0])
# [48., 48., 48.]
# NOTE: the layer will output values in tf.float32, regardless of input
dtype.
Call arguments
- inputs: input tensor in two possible formats:
- single 3D (HWC) image or 4D (NHWC) batch of images.
- A dict of tensors where the images are under
"images"key.