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Although official docs and the init's comments state channel_wise as available args for transforms.RandScaleIntensityFixedMean,
it is currently not available.
Is it still in progress? or just missing by mistake?
class RandScaleIntensityFixedMean(RandomizableTransform):
"""
Randomly scale the intensity of input image by ``v = v * (1 + factor)`` where the `factor`
is randomly picked. Subtract the mean intensity before scaling with `factor`, then add the same value after scaling
to ensure that the output has the same mean as the input.
"""
backend = ScaleIntensityFixedMean.backend
def __init__(
self,
prob: float = 0.1,
factors: Sequence[float] | float = 0,
fixed_mean: bool = True,
preserve_range: bool = False,
dtype: DtypeLike = np.float32,
) -> None:
"""
Args:
factors: factor range to randomly scale by ``v = v * (1 + factor)``.
if single number, factor value is picked from (-factors, factors).
preserve_range: clips the output array/tensor to the range of the input array/tensor
fixed_mean: subtract the mean intensity before scaling with `factor`, then add the same value after scaling
to ensure that the output has the same mean as the input.
channel_wise: if True, scale on each channel separately. `preserve_range` and `fixed_mean` are also applied
on each channel separately if `channel_wise` is True. Please ensure that the first dimension represents the
channel of the image if True.
dtype: output data type, if None, same as input image. defaults to float32.
"""
The text was updated successfully, but these errors were encountered:
Although official docs and the init's comments state channel_wise as available args for transforms.RandScaleIntensityFixedMean,
it is currently not available.
Is it still in progress? or just missing by mistake?
The text was updated successfully, but these errors were encountered: