2 # -*- coding: utf-8 -*-
5 Implements support for *ARRI* colorspaces conversions and transfer functions.
8 from __future__ import division
14 import PyOpenColorIO as ocio
16 import aces_ocio.generate_lut as genlut
17 from aces_ocio.utilities import ColorSpace, mat44_from_mat33, sanitize
19 __author__ = 'ACES Developers'
20 __copyright__ = 'Copyright (C) 2014 - 2015 - ACES Developers'
22 __maintainer__ = 'ACES Developers'
23 __email__ = 'aces@oscars.org'
24 __status__ = 'Production'
26 __all__ = ['create_log_c',
30 def create_log_c(gamut,
44 Parameter description.
49 Return value description.
52 name = '%s (EI%s) - %s' % (transfer_function, exposure_index, gamut)
53 if transfer_function == '':
54 name = 'Linear - ARRI %s' % gamut
56 name = 'Curve - %s (EI%s)' % (transfer_function, exposure_index)
61 cs.equality_group = ''
62 cs.family = 'Input/ARRI'
65 if gamut and transfer_function:
66 cs.aces_transform_id = (
67 'IDT.ARRI.Alexa-v3-logC-EI%s.a1.v1' % exposure_index)
69 # A linear space needs allocation variables.
70 if transfer_function == '':
71 cs.allocation_type = ocio.Constants.ALLOCATION_LG2
72 cs.allocation_vars = [-8, 5, 0.00390625]
74 IDT_maker_version = '0.08'
77 black_signal = 0.003907
78 mid_gray_signal = 0.01
79 encoding_gain = 0.256598
80 encoding_offset = 0.391007
83 return (math.log(EI / nominal_EI) / math.log(2) * (
84 0.89 - 1) / 3 + 1) * encoding_gain
86 def log_c_inverse_parameters_for_EI(EI):
88 slope = 1 / (cut * math.log(10))
89 offset = math.log10(cut) - slope * cut
90 gain = EI / nominal_EI
91 gray = mid_gray_signal / gain
92 # The higher the EI, the lower the gamma.
93 enc_gain = gain_for_EI(EI)
94 enc_offset = encoding_offset
96 nz = ((95 / 1023 - enc_offset) / enc_gain - offset) / slope
97 enc_offset = encoding_offset - math.log10(1 + nz) * enc_gain
100 b = nz - black_signal / gray
101 e = slope * a * enc_gain
102 f = enc_gain * (slope * b + offset) + enc_offset
104 # Ensuring we can return relative exposure.
114 'cut': (cut - b) / a,
120 def normalized_log_c_to_linear(code_value, exposure_index):
121 p = log_c_inverse_parameters_for_EI(exposure_index)
122 breakpoint = p['e'] * p['cut'] + p['f']
123 if code_value > breakpoint:
124 linear = ((pow(10, (code_value - p['d']) / p['c']) -
127 linear = (code_value - p['f']) / p['e']
130 cs.to_reference_transforms = []
132 if transfer_function == 'V3 LogC':
133 data = array.array('f', '\0' * lut_resolution_1d * 4)
134 for c in range(lut_resolution_1d):
135 data[c] = normalized_log_c_to_linear(c / (lut_resolution_1d - 1),
138 lut = '%s_to_linear.spi1d' % (
139 '%s_%s' % (transfer_function, exposure_index))
144 os.path.join(lut_directory, lut),
151 cs.to_reference_transforms.append({
154 'interpolation': 'linear',
155 'direction': 'forward'})
157 if gamut == 'Wide Gamut':
158 cs.to_reference_transforms.append({
160 'matrix': mat44_from_mat33([0.680206, 0.236137, 0.083658,
161 0.085415, 1.017471, -0.102886,
162 0.002057, -0.062563, 1.060506]),
163 'direction': 'forward'})
165 cs.from_reference_transforms = []
169 def create_colorspaces(lut_directory, lut_resolution_1d):
171 Generates the colorspace conversions.
176 Parameter description.
181 Return value description.
186 transfer_function = 'V3 LogC'
189 # EIs = [160, 200, 250, 320, 400, 500, 640, 800,
190 # 1000, 1280, 1600, 2000, 2560, 3200]
191 EIs = [160, 200, 250, 320, 400, 500, 640, 800,
192 1000, 1280, 1600, 2000, 2560, 3200]
197 log_c_EI_full = create_log_c(
203 ['%sei%s_%s' % ('logc3', str(EI), 'arriwide')])
204 colorspaces.append(log_c_EI_full)
208 log_c_EI_linearization = create_log_c(
214 ['crv_%sei%s' % ('logc3', str(EI))])
215 colorspaces.append(log_c_EI_linearization)
218 log_c_EI_primaries = create_log_c(
224 ['%s_%s' % ('lin', 'arriwide')])
225 colorspaces.append(log_c_EI_primaries)