"""
Canon Log Encodings
===================
Defines the *Canon Log* encodings:
- :attr:`colour.models.CANON_LOG_ENCODING_METHODS`
- :func:`colour.models.log_encoding_CanonLog`
- :attr:`colour.models.CANON_LOG_DECODING_METHODS`
- :func:`colour.models.log_decoding_CanonLog`
- :attr:`colour.models.CANON_LOG_2_ENCODING_METHODS`
- :func:`colour.models.log_encoding_CanonLog2`
- :attr:`colour.models.CANON_LOG_2_DECODING_METHODS`
- :func:`colour.models.log_decoding_CanonLog2`
- :attr:`colour.models.CANON_LOG_3_ENCODING_METHODS`
- :func:`colour.models.log_encoding_CanonLog3`
- :attr:`colour.models.CANON_LOG_3_DECODING_METHODS`
- :func:`colour.models.log_decoding_CanonLog3`
Notes
-----
- :cite:`Canon2016` is available as a *Drivers & Downloads* *Software* for
Windows 7 *Operating System*, a copy of the archive is hosted at
this url: https://drive.google.com/open?id=0B_IQZQdc4Vy8ZGYyY29pMEVwZU0
- :cite:`Canon2020` is available as a *Drivers & Downloads* *Software* for
Windows 10 *Operating System*, a copy of the archive is hosted at
this url: https://drive.google.com/open?id=1Vcz8RVIXgXL54lhZsOwGUjjVZRObZSc5
References
----------
- :cite:`Canon2016` : Canon. (2016). Input Transform Version 201612 for EOS
C300 Mark II. Retrieved August 23, 2016, from https://www.usa.canon.com/\
internet/portal/us/home/support/details/cameras/cinema-eos/eos-c300-mark-ii
- :cite:`Canon2020` : Canon. (2020). Input Transform Version 202007 for EOS
C300 Mark II. Retrieved July 16, 2023, 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 ArrayLike, Literal, NDArrayFloat
from colour.models.rgb.transfer_functions import full_to_legal, legal_to_full
from colour.utilities import (
CanonicalMapping,
as_float,
domain_range_scale,
from_range_1,
to_domain_1,
validate_method,
)
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"
__all__ = [
"log_encoding_CanonLog_v1",
"log_decoding_CanonLog_v1",
"log_encoding_CanonLog_v1_2",
"log_decoding_CanonLog_v1_2",
"CANON_LOG_ENCODING_METHODS",
"log_encoding_CanonLog",
"CANON_LOG_DECODING_METHODS",
"log_decoding_CanonLog",
"log_encoding_CanonLog2_v1",
"log_decoding_CanonLog2_v1",
"log_encoding_CanonLog2_v1_2",
"log_decoding_CanonLog2_v1_2",
"CANON_LOG_2_ENCODING_METHODS",
"log_encoding_CanonLog2",
"CANON_LOG_2_DECODING_METHODS",
"log_decoding_CanonLog2",
"log_encoding_CanonLog3_v1",
"log_decoding_CanonLog3_v1",
"log_encoding_CanonLog3_v1_2",
"log_decoding_CanonLog3_v1_2",
"CANON_LOG_3_ENCODING_METHODS",
"log_encoding_CanonLog3",
"CANON_LOG_3_DECODING_METHODS",
"log_decoding_CanonLog3",
]
def log_encoding_CanonLog_v1(
x: ArrayLike,
bit_depth: int = 10,
out_normalised_code_value: bool = True,
in_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log* v1 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.ndarray`
*Canon Log* non-linear data.
References
----------
:cite:`Canon2016`, :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_v1(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_v1(x) * (2**10 - 1)).astype(np.int_)
array([ 128, 169, 351, 614, 1016])
>>> np.around(log_encoding_CanonLog_v1(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_v1(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))
def log_decoding_CanonLog_v1(
clog: ArrayLike,
bit_depth: int = 10,
in_normalised_code_value: bool = True,
out_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log* v1 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.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:`Canon2016`, :cite:`Thorpe2012a`
Examples
--------
>>> log_decoding_CanonLog_v1(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))
def log_encoding_CanonLog_v1_2(
x: ArrayLike,
bit_depth: int = 10,
out_normalised_code_value: bool = True,
in_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log* v1.2 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* 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.ndarray`
*Canon Log* non-linear data.
References
----------
:cite:`Canon2020`
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_v1_2(0.18) * 100 # doctest: +ELLIPSIS
34.3389649...
"""
x = to_domain_1(x)
if in_reflection:
x = x / 0.9
with domain_range_scale("ignore"):
clog = np.where(
x < (log_decoding_CanonLog_v1_2(0.12512248, bit_depth, True)),
-(0.45310179 * (np.log10(-x * 10.1596 + 1)) - 0.12512248),
0.45310179 * np.log10(10.1596 * x + 1) + 0.12512248,
)
# NOTE: *Canon Log* v1.2 constants are expressed in legal range
# (studio swing).
clog_cv = clog if out_normalised_code_value else legal_to_full(clog, bit_depth)
return as_float(from_range_1(clog_cv))
def log_decoding_CanonLog_v1_2(
clog: ArrayLike,
bit_depth: int = 10,
in_normalised_code_value: bool = True,
out_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log* v1.2 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.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:`Canon2020`
Examples
--------
>>> log_decoding_CanonLog_v1_2(34.338964929528061 / 100)
... # doctest: +ELLIPSIS
0.17999999...
"""
clog = to_domain_1(clog)
# NOTE: *Canon Log* v1.2 constants are expressed in legal range
# (studio swing).
clog = clog if in_normalised_code_value else full_to_legal(clog, bit_depth)
x = np.where(
clog < 0.12512248,
-(10 ** ((0.12512248 - clog) / 0.45310179) - 1) / 10.1596,
(10 ** ((clog - 0.12512248) / 0.45310179) - 1) / 10.1596,
)
if out_reflection:
x = x * 0.9
return as_float(from_range_1(x))
CANON_LOG_ENCODING_METHODS: CanonicalMapping = CanonicalMapping(
{
"v1": log_encoding_CanonLog_v1,
"v1.2": log_encoding_CanonLog_v1_2,
}
)
CANON_LOG_ENCODING_METHODS.__doc__ = """
Supported *CanonLog* log encoding curve / opto-electronic transfer function
methods.
References
----------
:cite:`Canon2016`, :cite:`Canon2020`
"""
[docs]
def 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:
"""
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.
method
Computation method.
Returns
-------
:class:`numpy.ndarray`
*Canon Log* non-linear data.
References
----------
:cite:`Canon2016`, :cite:`Canon2020`, :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
34.3389649...
>>> log_encoding_CanonLog(0.18, method="v1") * 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, 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])
"""
method = validate_method(method, tuple(CANON_LOG_ENCODING_METHODS))
return CANON_LOG_ENCODING_METHODS[method](
x, bit_depth, out_normalised_code_value, in_reflection
)
CANON_LOG_DECODING_METHODS: CanonicalMapping = CanonicalMapping(
{
"v1": log_decoding_CanonLog_v1,
"v1.2": log_decoding_CanonLog_v1_2,
}
)
CANON_LOG_DECODING_METHODS.__doc__ = """
Supported *CanonLog* log decoding curve / electro-optical transfer function
methods.
References
----------
:cite:`Canon2016`, :cite:`Canon2020`
"""
[docs]
def log_decoding_CanonLog(
clog: ArrayLike,
bit_depth: int = 10,
in_normalised_code_value: bool = True,
out_reflection: bool = True,
method: Literal["v1", "v1.2"] | str = "v1.2",
) -> NDArrayFloat:
"""
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.
method
Computation method.
Returns
-------
: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:`Canon2016`, :cite:`Canon2020`, :cite:`Thorpe2012a`
Examples
--------
>>> log_decoding_CanonLog(34.338964929528061 / 100) # doctest: +ELLIPSIS
0.17999999...
>>> log_decoding_CanonLog(34.338965172606912 / 100, method="v1")
... # doctest: +ELLIPSIS
0.17999999...
"""
method = validate_method(method, tuple(CANON_LOG_DECODING_METHODS))
return CANON_LOG_DECODING_METHODS[method](
clog, bit_depth, in_normalised_code_value, out_reflection
)
def log_encoding_CanonLog2_v1(
x: ArrayLike,
bit_depth: int = 10,
out_normalised_code_value: bool = True,
in_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log 2* v1 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.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:`Canon2016`
Examples
--------
>>> log_encoding_CanonLog2_v1(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_v1(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))
def log_decoding_CanonLog2_v1(
clog2: ArrayLike,
bit_depth: int = 10,
in_normalised_code_value: bool = True,
out_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log 2* v1 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.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:`Canon2016`
Examples
--------
>>> log_decoding_CanonLog2_v1(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))
def log_encoding_CanonLog2_v1_2(
x: ArrayLike,
bit_depth: int = 10,
out_normalised_code_value: bool = True,
in_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log 2* v1.2 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.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:`Canon2020`
Examples
--------
>>> log_encoding_CanonLog2_v1_2(0.18) * 100 # doctest: +ELLIPSIS
39.8254692...
"""
x = to_domain_1(x)
if in_reflection:
x = x / 0.9
with domain_range_scale("ignore"):
clog2 = np.where(
x < (log_decoding_CanonLog2_v1_2(0.092864125, bit_depth, True)),
-(0.24136077 * (np.log10(-x * 87.09937546 + 1)) - 0.092864125),
0.24136077 * np.log10(x * 87.09937546 + 1) + 0.092864125,
)
# NOTE: *Canon Log 2* v1.2 constants are expressed in legal range
# (studio swing).
clog2_cv = clog2 if out_normalised_code_value else legal_to_full(clog2, bit_depth)
return as_float(from_range_1(clog2_cv))
def log_decoding_CanonLog2_v1_2(
clog2: ArrayLike,
bit_depth: int = 10,
in_normalised_code_value: bool = True,
out_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log 2* v1.2 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.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:`Canon2020`
Examples
--------
>>> log_decoding_CanonLog2_v1_2(39.825469256149191 / 100)
... # doctest: +ELLIPSIS
0.1799999...
"""
clog2 = to_domain_1(clog2)
# NOTE: *Canon Log 2* v1.2 constants are expressed in legal range
# (studio swing).
clog2 = clog2 if in_normalised_code_value else full_to_legal(clog2, bit_depth)
x = np.where(
clog2 < 0.092864125,
-(10 ** ((0.092864125 - clog2) / 0.24136077) - 1) / 87.09937546,
(10 ** ((clog2 - 0.092864125) / 0.24136077) - 1) / 87.09937546,
)
if out_reflection:
x = x * 0.9
return as_float(from_range_1(x))
CANON_LOG_2_ENCODING_METHODS: CanonicalMapping = CanonicalMapping(
{
"v1": log_encoding_CanonLog2_v1,
"v1.2": log_encoding_CanonLog2_v1_2,
}
)
CANON_LOG_2_ENCODING_METHODS.__doc__ = """
Supported *Canon Log 2* log encoding curve / opto-electronic transfer function
methods.
References
----------
:cite:`Canon2016`, :cite:`Canon2020`
"""
[docs]
def log_encoding_CanonLog2(
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:
"""
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.
method
Computation method.
Returns
-------
: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:`Canon2016`, :cite:`Canon2020`
Examples
--------
>>> log_encoding_CanonLog2(0.18) * 100 # doctest: +ELLIPSIS
39.8254692...
"""
method = validate_method(method, tuple(CANON_LOG_2_ENCODING_METHODS))
return CANON_LOG_2_ENCODING_METHODS[method](
x, bit_depth, out_normalised_code_value, in_reflection
)
CANON_LOG_2_DECODING_METHODS: CanonicalMapping = CanonicalMapping(
{
"v1": log_decoding_CanonLog2_v1,
"v1.2": log_decoding_CanonLog2_v1_2,
}
)
CANON_LOG_2_DECODING_METHODS.__doc__ = """
Supported *Canon Log 2* log decoding curve / electro-optical transfer function
methods.
References
----------
:cite:`Canon2016`, :cite:`Canon2020`
"""
[docs]
def log_decoding_CanonLog2(
clog2: ArrayLike,
bit_depth: int = 10,
in_normalised_code_value: bool = True,
out_reflection: bool = True,
method: Literal["v1", "v1.2"] | str = "v1.2",
) -> NDArrayFloat:
"""
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.
method
Computation method.
Returns
-------
: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:`Canon2016`, :cite:`Canon2020`
Examples
--------
>>> log_decoding_CanonLog2(39.825469256149191 / 100) # doctest: +ELLIPSIS
0.1799999...
"""
method = validate_method(method, tuple(CANON_LOG_2_DECODING_METHODS))
return CANON_LOG_2_DECODING_METHODS[method](
clog2, bit_depth, in_normalised_code_value, out_reflection
)
def log_encoding_CanonLog3_v1(
x: ArrayLike,
bit_depth: int = 10,
out_normalised_code_value: bool = True,
in_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log 3* v1 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.ndarray`
*Canon Log 3* non-linear data.
Notes
-----
- Introspection of the grafting points by Shaw, N. (2018) shows that the
*Canon Log 3* v1 IDT was likely derived from its encoding curve as the
latter 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:`Canon2016`
Examples
--------
>>> log_encoding_CanonLog3_v1(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_v1(0.04076162, bit_depth, False, False),
x <= log_decoding_CanonLog3_v1(0.105357102, bit_depth, False, False),
x > log_decoding_CanonLog3_v1(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))
def log_decoding_CanonLog3_v1(
clog3: ArrayLike,
bit_depth: int = 10,
in_normalised_code_value: bool = True,
out_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log 3* v1 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.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:`Canon2016`
Examples
--------
>>> log_decoding_CanonLog3_v1(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))
def log_encoding_CanonLog3_v1_2(
x: ArrayLike,
bit_depth: int = 10,
out_normalised_code_value: bool = True,
in_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log 3* v1.2 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 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.ndarray`
*Canon Log 3* non-linear data.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``x`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``clog3`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Canon2020`
Examples
--------
>>> log_encoding_CanonLog3_v1_2(0.18) * 100 # doctest: +ELLIPSIS
34.3389370...
"""
x = to_domain_1(x)
if in_reflection:
x = x / 0.9
with domain_range_scale("ignore"):
clog3 = np.select(
(
x < log_decoding_CanonLog3_v1_2(0.097465473, bit_depth, True, False),
x <= log_decoding_CanonLog3_v1_2(0.15277891, bit_depth, True, False),
x > log_decoding_CanonLog3_v1_2(0.15277891, bit_depth, True, False),
),
(
-0.36726845 * np.log10(-x * 14.98325 + 1) + 0.12783901,
1.9754798 * x + 0.12512219,
0.36726845 * np.log10(x * 14.98325 + 1) + 0.12240537,
),
)
# NOTE: *Canon Log 3* v1.2 constants are expressed in legal range
# (studio swing).
clog3_cv = clog3 if out_normalised_code_value else legal_to_full(clog3, bit_depth)
return as_float(from_range_1(clog3_cv))
def log_decoding_CanonLog3_v1_2(
clog3: ArrayLike,
bit_depth: int = 10,
in_normalised_code_value: bool = True,
out_reflection: bool = True,
) -> NDArrayFloat:
"""
Define the *Canon Log 3* v1.2 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.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:`Canon2020`
Examples
--------
>>> log_decoding_CanonLog3_v1_2(34.338937037393549 / 100)
... # doctest: +ELLIPSIS
0.1799999...
"""
clog3 = to_domain_1(clog3)
# NOTE: *Canon Log 3* v1.2 constants are expressed in legal range
# (studio swing).
clog3 = clog3 if in_normalised_code_value else full_to_legal(clog3, bit_depth)
x = np.select(
(clog3 < 0.097465473, clog3 <= 0.15277891, clog3 > 0.15277891),
(
-(10 ** ((0.12783901 - clog3) / 0.36726845) - 1) / 14.98325,
(clog3 - 0.12512219) / 1.9754798,
(10 ** ((clog3 - 0.12240537) / 0.36726845) - 1) / 14.98325,
),
)
if out_reflection:
x = x * 0.9
return as_float(from_range_1(x))
CANON_LOG_3_ENCODING_METHODS: CanonicalMapping = CanonicalMapping(
{
"v1": log_encoding_CanonLog3_v1,
"v1.2": log_encoding_CanonLog3_v1_2,
}
)
CANON_LOG_3_ENCODING_METHODS.__doc__ = """
Supported *Canon Log 3* log encoding curve / opto-electronic transfer function
methods.
References
----------
:cite:`Canon2016`, :cite:`Canon2020`
"""
[docs]
def log_encoding_CanonLog3(
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:
"""
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.
method
Computation method.
Returns
-------
: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* v1 IDT was likely derived from its encoding curve as the
latter 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** |
+============+=======================+===============+
| ``clog2`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`Canon2016`, :cite:`Canon2020`
Examples
--------
>>> log_encoding_CanonLog3(0.18) * 100 # doctest: +ELLIPSIS
34.3389370...
"""
method = validate_method(method, tuple(CANON_LOG_3_ENCODING_METHODS))
return CANON_LOG_3_ENCODING_METHODS[method](
x, bit_depth, out_normalised_code_value, in_reflection
)
CANON_LOG_3_DECODING_METHODS: CanonicalMapping = CanonicalMapping(
{
"v1": log_decoding_CanonLog3_v1,
"v1.2": log_decoding_CanonLog3_v1_2,
}
)
CANON_LOG_3_DECODING_METHODS.__doc__ = """
Supported *Canon Log 3* log decoding curve / electro-optical transfer function
methods.
References
----------
:cite:`Canon2016`, :cite:`Canon2020`
"""
[docs]
def log_decoding_CanonLog3(
clog3: ArrayLike,
bit_depth: int = 10,
in_normalised_code_value: bool = True,
out_reflection: bool = True,
method: Literal["v1", "v1.2"] | str = "v1.2",
) -> NDArrayFloat:
"""
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.
method
Computation method.
Returns
-------
: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:`Canon2016`, :cite:`Canon2020`
Examples
--------
>>> log_decoding_CanonLog3(34.338937037393549 / 100) # doctest: +ELLIPSIS
0.1799999...
"""
method = validate_method(method, tuple(CANON_LOG_3_DECODING_METHODS))
return CANON_LOG_3_DECODING_METHODS[method](
clog3, bit_depth, in_normalised_code_value, out_reflection
)