#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Panasonic V-Log Log Encoding
============================
Defines the *Panasonic V-Log* log encoding:
- :func:`log_encoding_VLog`
- :func:`log_decoding_VLog`
See Also
--------
`RGB Colourspaces Jupyter Notebook
<http://nbviewer.jupyter.org/github/colour-science/colour-notebooks/\
blob/master/notebooks/models/rgb.ipynb>`_
References
----------
.. [1] Panasonic. (2014). VARICAM V-Log/V-Gamut. Retrieved from
http://pro-av.panasonic.net/en/varicam/common/pdf/\
VARICAM_V-Log_V-Gamut.pdf
"""
from __future__ import division, unicode_literals
import numpy as np
from colour.utilities import Structure, as_numeric
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2017 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['VLOG_CONSTANTS',
'log_encoding_VLog',
'log_decoding_VLog']
VLOG_CONSTANTS = Structure(cut1=0.01,
cut2=0.181,
b=0.00873,
c=0.241514,
d=0.598206)
"""
*Panasonic V-Log* colourspace constants.
VLOG_CONSTANTS : Structure
"""
[docs]def log_encoding_VLog(L_in):
"""
Defines the *Panasonic V-Log* log encoding curve / opto-electronic transfer
function.
Parameters
----------
L_in : numeric or array_like
Linear reflection data :math`L_{in}`.
Returns
-------
numeric or ndarray
Non-linear data :math:`V_{out}`.
Examples
--------
>>> log_encoding_VLog(0.18) # doctest: +ELLIPSIS
0.4233114...
"""
L_in = np.asarray(L_in)
cut1 = VLOG_CONSTANTS.cut1
b = VLOG_CONSTANTS.b
c = VLOG_CONSTANTS.c
d = VLOG_CONSTANTS.d
L_in = np.where(L_in < cut1,
5.6 * L_in + 0.125,
c * np.log10(L_in + b) + d)
return as_numeric(L_in)
[docs]def log_decoding_VLog(V_out):
"""
Defines the *Panasonic V-Log* log decoding curve / electro-optical transfer
function.
Parameters
----------
V_out : numeric or array_like
Non-linear data :math:`V_{out}`.
Returns
-------
numeric or ndarray
Linear reflection data :math`L_{in}`.
Examples
--------
>>> log_decoding_VLog(0.423311448760136) # doctest: +ELLIPSIS
0.1799999...
"""
V_out = np.asarray(V_out)
cut2 = VLOG_CONSTANTS.cut2
b = VLOG_CONSTANTS.b
c = VLOG_CONSTANTS.c
d = VLOG_CONSTANTS.d
V_out = np.where(V_out < cut2,
(V_out - 0.125) / 5.6,
np.power(10, ((V_out - d) / c)) - b)
return as_numeric(V_out)