colour.models.log_encoding_FLog#
- colour.models.log_encoding_FLog(in_r: Annotated[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], 1], bit_depth: int = 10, out_normalised_code_value: bool = True, in_reflection: bool = True, constants: Structure | None = None) Annotated[ndarray[tuple[Any, ...], dtype[float16 | float32 | float64]], 1][source]#
Apply the Fujifilm F-Log log encoding opto-electronic transfer function (OETF).
- Parameters:
in_r (Annotated[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], 1]) – Linear reflection data \(in\).
bit_depth (int) – Bit-depth used for conversion.
out_normalised_code_value (bool) – Whether the Fujifilm F-Log non-linear data \(out\) is encoded as normalised code values.
in_reflection (bool) – Whether the light level \(in\) to a camera is reflection.
constants (Structure | None) – Fujifilm F-Log constants.
- Returns:
Fujifilm F-Log non-linear encoded data \(out\).
- Return type:
Notes
Domain
Scale - Reference
Scale - 1
in_r1
1
Range
Scale - Reference
Scale - 1
out_r1
1
References
Examples
>>> log_encoding_FLog(0.18) 0.4593184...
The values of 2-2. F-Log Code Value table in [Fujifilm22a] are obtained as follows:
>>> x = np.array([0, 18, 90]) / 100 >>> np.around(log_encoding_FLog(x, 10, False) * 100, 1) array([ 3.5, 46.3, 73.2]) >>> np.around(log_encoding_FLog(x) * (2**10 - 1)).astype(np.int_) array([ 95, 470, 705])