colour.models.log_encoding_CanonLog#

colour.models.log_encoding_CanonLog(x: ArrayLike, bit_depth: int = 10, out_normalised_code_value: bool = True, in_reflection: bool = True, method: Literal['v1', 'v1.2'] | str = 'v1.2') → NDArrayFloat[source]#

Define the Canon Log log encoding curve / opto-electronic transfer function.

Parameters:

x (ArrayLike) – Linear data \(x\).
bit_depth (int) – Bit-depth used for conversion.
out_normalised_code_value (bool) – Whether the Canon Log non-linear data is encoded as normalised code values.
in_reflection (bool) – Whether the light level \(x\) to a camera is reflection.
method (Literal['v1', 'v1.2'] | str) – Computation method.

Returns:

Canon Log non-linear data.

Return type:

numpy.ndarray

References

[Canon16], [Canon20], [Tho12]

Notes

Domain	Scale - Reference	Scale - 1
`x`	[0, 1]	[0, 1]

Range	Scale - Reference	Scale - 1
`clog`	[0, 1]	[0, 1]

Examples

>>> log_encoding_CanonLog(0.18) * 100  
34.3389649...
>>> log_encoding_CanonLog(0.18, method="v1") * 100  
34.3389651...

The values of Table 2 Canon-Log Code Values table in [Tho12] are obtained as follows:

>>> x = np.array([0, 2, 18, 90, 720]) / 100
>>> np.around(log_encoding_CanonLog(x, method="v1") * (2**10 - 1)).astype(np.int_)
array([ 128,  169,  351,  614, 1016])
>>> np.around(log_encoding_CanonLog(x, 10, False, method="v1") * 100, 1)
array([   7.3,   12. ,   32.8,   62.7,  108.7])

colour.models.log_encoding_CanonLog#

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