"""
Canon Log Encodings
===================
Defines the *Canon Log* encodings:
- :func:`colour.models.log_encoding_CanonLog`
- :func:`colour.models.log_decoding_CanonLog`
- :func:`colour.models.log_encoding_CanonLog2`
- :func:`colour.models.log_decoding_CanonLog2`
- :func:`colour.models.log_encoding_CanonLog3`
- :func:`colour.models.log_decoding_CanonLog3`
Notes
-----
- :cite:`Canona` is available as a *Drivers & Downloads* *Software* for
Windows 10 (x64) *Operating System*, a copy of the archive is hosted at
this url: https://drive.google.com/open?id=0B_IQZQdc4Vy8ZGYyY29pMEVwZU0
References
----------
- :cite:`Canona` : Canon. (2016). EOS C300 Mark II - EOS C300 Mark II Input
Transform Version 2.0 (for Cinema Gamut / BT.2020). Retrieved August 23,
2016, from
https://www.usa.canon.com/internet/portal/us/home/support/details/cameras/cinema-eos/eos-c300-mark-ii
- :cite:`Thorpe2012a` : Thorpe, L. (2012). CANON-LOG TRANSFER CHARACTERISTIC.
Retrieved September 25, 2014, from
http://downloads.canon.com/CDLC/Canon-Log_Transfer_Characteristic_6-20-2012.pdf
"""
from __future__ import annotations
import numpy as np
from colour.hints import (
Boolean,
FloatingOrArrayLike,
FloatingOrNDArray,
Integer,
)
from colour.models.rgb.transfer_functions import full_to_legal, legal_to_full
from colour.utilities import (
as_float,
domain_range_scale,
from_range_1,
to_domain_1,
)
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "New BSD License - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"
__all__ = [
"log_encoding_CanonLog",
"log_decoding_CanonLog",
"log_encoding_CanonLog2",
"log_decoding_CanonLog2",
"log_encoding_CanonLog3",
"log_decoding_CanonLog3",
]
[docs]def log_encoding_CanonLog(
x: FloatingOrArrayLike,
bit_depth: Integer = 10,
out_normalised_code_value: Boolean = True,
in_reflection: Boolean = True,
) -> FloatingOrNDArray:
"""
Define the *Canon Log* log encoding curve / opto-electronic transfer
function.
Parameters
----------
x
Linear data :math:`x`.
bit_depth
Bit depth used for conversion.
out_normalised_code_value
Whether the *Canon Log* non-linear data is encoded as normalised code
values.
in_reflection
Whether the light level :math:`x` to a camera is reflection.
Returns
-------
:class:`numpy.floating` or :class:`numpy.ndarray`
*Canon Log* non-linear data.
References
----------
:cite:`Thorpe2012a`
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 # doctest: +ELLIPSIS
34.3389651...
The values of *Table 2 Canon-Log Code Values* table in :cite:`Thorpe2012a`
are obtained as follows:
>>> x = np.array([0, 2, 18, 90, 720]) / 100
>>> np.around(log_encoding_CanonLog(x) * (2 ** 10 - 1)).astype(np.int)
array([ 128, 169, 351, 614, 1016])
>>> np.around(log_encoding_CanonLog(x, 10, False) * 100, 1)
array([ 7.3, 12. , 32.8, 62.7, 108.7])
"""
x = to_domain_1(x)
if in_reflection:
x = x / 0.9
with domain_range_scale("ignore"):
clog = np.where(
x < log_decoding_CanonLog(0.0730597, bit_depth, False),
-(0.529136 * (np.log10(-x * 10.1596 + 1)) - 0.0730597),
0.529136 * np.log10(10.1596 * x + 1) + 0.0730597,
)
clog_cv = (
full_to_legal(clog, bit_depth) if out_normalised_code_value else clog
)
return as_float(from_range_1(clog_cv))
[docs]def log_decoding_CanonLog(
clog: FloatingOrArrayLike,
bit_depth: Integer = 10,
in_normalised_code_value: Boolean = True,
out_reflection: Boolean = True,
) -> FloatingOrNDArray:
"""
Define the *Canon Log* log decoding curve / electro-optical transfer
function.
Parameters
----------
clog
*Canon Log* non-linear data.
bit_depth
Bit depth used for conversion.
in_normalised_code_value
Whether the *Canon Log* non-linear data is encoded with normalised
code values.
out_reflection
Whether the light level :math:`x` to a camera is reflection.
Returns
-------
:class:`numpy.floating` or :class:`numpy.ndarray`
Linear data :math:`x`.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``clog`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``x`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Thorpe2012a`
Examples
--------
>>> log_decoding_CanonLog(34.338965172606912 / 100) # doctest: +ELLIPSIS
0.17999999...
"""
clog = to_domain_1(clog)
clog = legal_to_full(clog, bit_depth) if in_normalised_code_value else clog
x = np.where(
clog < 0.0730597,
-(10 ** ((0.0730597 - clog) / 0.529136) - 1) / 10.1596,
(10 ** ((clog - 0.0730597) / 0.529136) - 1) / 10.1596,
)
if out_reflection:
x = x * 0.9
return as_float(from_range_1(x))
[docs]def log_encoding_CanonLog2(
x: FloatingOrArrayLike,
bit_depth: Integer = 10,
out_normalised_code_value: Boolean = True,
in_reflection: Boolean = True,
) -> FloatingOrNDArray:
"""
Define the *Canon Log 2* log encoding curve / opto-electronic transfer
function.
Parameters
----------
x
Linear data :math:`x`.
bit_depth
Bit depth used for conversion.
out_normalised_code_value
Whether the *Canon Log 2* non-linear data is encoded as normalised
code values.
in_reflection
Whether the light level :math:`x` to a camera is reflection.
Returns
-------
:class:`numpy.floating` or :class:`numpy.ndarray`
*Canon Log 2* non-linear data.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``x`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``clog2`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Canona`
Examples
--------
>>> log_encoding_CanonLog2(0.18) * 100 # doctest: +ELLIPSIS
39.8254694...
"""
x = to_domain_1(x)
if in_reflection:
x = x / 0.9
with domain_range_scale("ignore"):
clog2 = np.where(
x < log_decoding_CanonLog2(0.035388128, bit_depth, False),
-(0.281863093 * (np.log10(-x * 87.09937546 + 1)) - 0.035388128),
0.281863093 * np.log10(x * 87.09937546 + 1) + 0.035388128,
)
clog2_cv = (
full_to_legal(clog2, bit_depth) if out_normalised_code_value else clog2
)
return as_float(from_range_1(clog2_cv))
[docs]def log_decoding_CanonLog2(
clog2: FloatingOrArrayLike,
bit_depth: Integer = 10,
in_normalised_code_value: Boolean = True,
out_reflection: Boolean = True,
) -> FloatingOrNDArray:
"""
Define the *Canon Log 2* log decoding curve / electro-optical transfer
function.
Parameters
----------
clog2
*Canon Log 2* non-linear data.
bit_depth
Bit depth used for conversion.
in_normalised_code_value
Whether the *Canon Log 2* non-linear data is encoded with normalised
code values.
out_reflection
Whether the light level :math:`x` to a camera is reflection.
Returns
-------
:class:`numpy.floating` or :class:`numpy.ndarray`
Linear data :math:`x`.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``clog2`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``x`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Canona`
Examples
--------
>>> log_decoding_CanonLog2(39.825469498316735 / 100) # doctest: +ELLIPSIS
0.1799999...
"""
clog2 = to_domain_1(clog2)
clog2 = (
legal_to_full(clog2, bit_depth) if in_normalised_code_value else clog2
)
x = np.where(
clog2 < 0.035388128,
-(10 ** ((0.035388128 - clog2) / 0.281863093) - 1) / 87.09937546,
(10 ** ((clog2 - 0.035388128) / 0.281863093) - 1) / 87.09937546,
)
if out_reflection:
x = x * 0.9
return as_float(from_range_1(x))
[docs]def log_encoding_CanonLog3(
x: FloatingOrArrayLike,
bit_depth: Integer = 10,
out_normalised_code_value: Boolean = True,
in_reflection: Boolean = True,
) -> FloatingOrNDArray:
"""
Define the *Canon Log 3* log encoding curve / opto-electronic transfer
function.
Parameters
----------
x
Linear data :math:`x`.
bit_depth
Bit depth used for conversion.
out_normalised_code_value
Whether the *Canon Log 3* non-linear data is encoded as normalised code
values.
in_reflection
Whether the light level :math:`x` to a camera is reflection.
Returns
-------
:class:`numpy.floating` or :class:`numpy.ndarray`
*Canon Log 3* non-linear data.
Notes
-----
- Introspection of the grafting points by Shaw, N. (2018) shows that the
*Canon Log 3* IDT was likely derived from its encoding curve as the
later is grafted at *+/-0.014*::
>>> clog3 = 0.04076162
>>> (clog3 - 0.073059361) / 2.3069815
-0.014000000000000002
>>> clog3 = 0.105357102
>>> (clog3 - 0.073059361) / 2.3069815
0.013999999999999997
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``x`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``clog3`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Canona`
Examples
--------
>>> log_encoding_CanonLog3(0.18) * 100 # doctest: +ELLIPSIS
34.3389369...
"""
x = to_domain_1(x)
if in_reflection:
x = x / 0.9
with domain_range_scale("ignore"):
clog3 = np.select(
(
x
< log_decoding_CanonLog3(0.04076162, bit_depth, False, False),
x
<= log_decoding_CanonLog3(
0.105357102, bit_depth, False, False
),
x
> log_decoding_CanonLog3(0.105357102, bit_depth, False, False),
),
(
-0.42889912 * np.log10(-x * 14.98325 + 1) + 0.07623209,
2.3069815 * x + 0.073059361,
0.42889912 * np.log10(x * 14.98325 + 1) + 0.069886632,
),
)
clog3_cv = (
full_to_legal(clog3, bit_depth) if out_normalised_code_value else clog3
)
return as_float(from_range_1(clog3_cv))
[docs]def log_decoding_CanonLog3(
clog3: FloatingOrArrayLike,
bit_depth: Integer = 10,
in_normalised_code_value: Boolean = True,
out_reflection: Boolean = True,
) -> FloatingOrNDArray:
"""
Define the *Canon Log 3* log decoding curve / electro-optical transfer
function.
Parameters
----------
clog3
*Canon Log 3* non-linear data.
bit_depth
Bit depth used for conversion.
in_normalised_code_value
Whether the *Canon Log 3* non-linear data is encoded with normalised
code values.
out_reflection
Whether the light level :math:`x` to a camera is reflection.
Returns
-------
:class:`numpy.floating` or :class:`numpy.ndarray`
Linear data :math:`x`.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``clog3`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``x`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Canona`
Examples
--------
>>> log_decoding_CanonLog3(34.338936938868677 / 100) # doctest: +ELLIPSIS
0.1800000...
"""
clog3 = to_domain_1(clog3)
clog3 = (
legal_to_full(clog3, bit_depth) if in_normalised_code_value else clog3
)
x = np.select(
(clog3 < 0.04076162, clog3 <= 0.105357102, clog3 > 0.105357102),
(
-(10 ** ((0.07623209 - clog3) / 0.42889912) - 1) / 14.98325,
(clog3 - 0.073059361) / 2.3069815,
(10 ** ((clog3 - 0.069886632) / 0.42889912) - 1) / 14.98325,
),
)
if out_reflection:
x = x * 0.9
return as_float(from_range_1(x))