2 # -*- coding: utf-8 -*-
5 Implements support for *ARRI* colorspaces conversions and transfer functions.
11 import aces_ocio.generate_lut as genlut
12 from aces_ocio.utilities import ColorSpace, mat44_from_mat33
15 __author__ = 'ACES Developers'
16 __copyright__ = 'Copyright (C) 2014 - 2015 - ACES Developers'
18 __maintainer__ = 'ACES Developers'
19 __email__ = 'aces@oscars.org'
20 __status__ = 'Production'
22 __all__ = ['create_log_c',
26 def create_log_c(gamut,
40 Parameter description.
45 Return value description.
48 name = '%s (EI%s) - %s' % (transfer_function, exposure_index, gamut)
49 if transfer_function == '':
50 name = 'Linear - %s' % gamut
52 name = '%s (EI%s)' % (transfer_function, exposure_index)
56 cs.equality_group = ''
61 IDT_maker_version = '0.08'
64 black_signal = 0.003907
65 mid_gray_signal = 0.01
66 encoding_gain = 0.256598
67 encoding_offset = 0.391007
70 return (math.log(EI / nominal_EI) / math.log(2) * (
71 0.89 - 1) / 3 + 1) * encoding_gain
73 def log_c_inverse_parameters_for_EI(EI):
75 slope = 1.0 / (cut * math.log(10))
76 offset = math.log10(cut) - slope * cut
77 gain = EI / nominal_EI
78 gray = mid_gray_signal / gain
79 # The higher the EI, the lower the gamma
80 enc_gain = gain_for_EI(EI)
81 enc_offset = encoding_offset
83 nz = ((95.0 / 1023.0 - enc_offset) / enc_gain - offset) / slope
84 enc_offset = encoding_offset - math.log10(1 + nz) * enc_gain
85 # Calculate some intermediate values
87 b = nz - black_signal / gray
88 e = slope * a * enc_gain
89 f = enc_gain * (slope * b + offset) + enc_offset
90 # Manipulations so we can return relative exposure
100 'cut': (cut - b) / a,
106 def log_c_to_linear(code_value, exposure_index):
107 p = log_c_inverse_parameters_for_EI(exposure_index)
108 breakpoint = p['e'] * p['cut'] + p['f']
109 if (code_value > breakpoint):
110 linear = ((pow(10, (code_value / 1023.0 - p['d']) / p['c']) -
113 linear = (code_value / 1023.0 - p['f']) / p['e']
115 # print(codeValue, linear)
119 cs.to_reference_transforms = []
121 if transfer_function == 'V3 LogC':
122 data = array.array('f', '\0' * lut_resolution_1d * 4)
123 for c in range(lut_resolution_1d):
124 data[c] = log_c_to_linear(1023.0 * c / (lut_resolution_1d - 1),
127 lut = '%s_to_linear.spi1d' % (
128 '%s_%s' % (transfer_function, exposure_index))
130 # Remove spaces and parentheses
131 lut = lut.replace(' ', '_').replace(')', '_').replace('(', '_')
133 genlut.write_SPI_1d(lut_directory + '/' + lut,
140 # print('Writing %s' % lut)
141 cs.to_reference_transforms.append({
144 'interpolation': 'linear',
145 'direction': 'forward'
148 if gamut == 'Wide Gamut':
149 cs.to_reference_transforms.append({
151 'matrix': mat44_from_mat33([0.680206, 0.236137, 0.083658,
152 0.085415, 1.017471, -0.102886,
153 0.002057, -0.062563, 1.060506]),
154 'direction': 'forward'
157 cs.from_reference_transforms = []
161 def create_colorspaces(lut_directory, lut_resolution_1d):
163 Generates the colorspace conversions.
168 Parameter description.
173 Return value description.
178 transfer_function = 'V3 LogC'
181 # EIs = [160.0, 200.0, 250.0, 320.0, 400.0, 500.0, 640.0, 800.0,
182 # 1000.0, 1280.0, 1600.0, 2000.0, 2560.0, 3200.0]
183 EIs = [160, 200, 250, 320, 400, 500, 640, 800,
184 1000, 1280, 1600, 2000, 2560, 3200]
189 log_c_EI_full = create_log_c(
196 colorspaces.append(log_c_EI_full)
200 log_c_EI_linearization = create_log_c(
207 colorspaces.append(log_c_EI_linearization)
210 log_c_EI_primaries = create_log_c(
217 colorspaces.append(log_c_EI_primaries)