def apply_adjust_gamma(input: torch.Tensor,
params: Dict[str, torch.Tensor],
return_transform: bool = False):
r"""Perform gamma correction on an image.
The input image is expected to be in the range of [0, 1].
Args:
input (torch.Tensor): Image/Tensor to be adjusted in the shape of (\*, N).
gamma (float): Non negative real number, same as γ\gammaγ in the equation.
gamma larger than 1 make the shadows darker, while gamma smaller than 1 make
dark regions lighter.
gain (float, optional): The constant multiplier. Default 1.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(
input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transformed = adjust_gamma(input, params['gamma_factor'].to(input.dtype))
if return_transform:
identity: torch.Tensor = torch.eye(3,
device=input.device,
dtype=input.dtype).repeat(
input.shape[0], 1, 1)
return transformed, identity
return transformed
def apply_adjust_gamma(input: torch.Tensor, params: Dict[str, torch.Tensor]) -> torch.Tensor:
r"""Perform gamma correction on an image.
Args:
input (torch.Tensor): Image/Tensor to be adjusted in the shape of (\*, N).
params (Dict[str, torch.Tensor]):
- params['gamma_factor']: Non negative real number, same as γ\gammaγ in the equation.
gamma larger than 1 make the shadows darker, while gamma smaller than 1 make
dark regions lighter.
Returns:
torch.Tensor: Adjusted image.
"""
input = _transform_input(input)
_validate_input_dtype(input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])
transformed = adjust_gamma(input, params['gamma_factor'].to(input.dtype))
return transformed