colour.models.log_decoding_FLog2

colour.models.log_decoding_FLog2(out_r: Annotated[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], 1], bit_depth: int = 10, in_normalised_code_value: bool = True, out_reflection: bool = True, constants: Structure | None = None) → Annotated[ndarray[tuple[Any, ...], dtype[float16 | float32 | float64]], 1]

Apply the Fujifilm F-Log2 log decoding inverse opto-electronic transfer function (OETF).

Parameters:

• out_r (Annotated[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], 1]) – Fujifilm F-Log2 non-linear encoded data \(out\).

• bit_depth (int) – Bit-depth used for conversion.

• in_normalised_code_value (bool) – Whether the Fujifilm F-Log2 non-linear data \(out\) is encoded as normalised code values.

• out_reflection (bool) – Whether the light level \(in\) to a camera is reflection.

• constants (Structure | None) – Fujifilm F-Log2 constants.

Returns:

Linear reflection data \(in\).

Return type:

numpy.floating or numpy.ndarray

Notes

Domain	Scale - Reference	Scale - 1
out_r	1	1

Range	Scale - Reference	Scale - 1
in_r	1	1

References

[Fujifilm22b]

Examples

>>> log_decoding_FLog2(0.39100724189123004)
np.float64(0.18...)