Adjust the saturation of RGB images by a random factor deterministically.
tf.image.stateless_random_saturation(
    image, lower, upper, seed=None
)
Equivalent to adjust_saturation() but uses a saturation_factor randomly
picked in the interval [lower, upper).
Guarantees the same results given the same seed independent of how many
times the function is called, and independent of global seed settings (e.g.
tf.random.set_seed).
Usage Example:
x = [[[1.0, 2.0, 3.0],
      [4.0, 5.0, 6.0]],
     [[7.0, 8.0, 9.0],
      [10.0, 11.0, 12.0]]]
seed = (1, 2)
tf.image.stateless_random_saturation(x, 0.5, 1.0, seed)
<tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
array([[[ 1.1559395,  2.0779698,  3.       ],
        [ 4.1559396,  5.07797  ,  6.       ]],
       [[ 7.1559396,  8.07797  ,  9.       ],
        [10.155939 , 11.07797  , 12.       ]]], dtype=float32)>
Args | 
image
 | 
RGB image or images. The size of the last dimension must be 3.
 | 
lower
 | 
float.  Lower bound for the random saturation factor.
 | 
upper
 | 
float.  Upper bound for the random saturation factor.
 | 
seed
 | 
A shape [2] Tensor, the seed to the random number generator. Must have
dtype int32 or int64. (When using XLA, only int32 is allowed.)
 | 
Returns | 
Adjusted image(s), same shape and DType as image.
 | 
Raises | 
ValueError
 | 
if upper <= lower or if lower < 0.
 |