Implements support for *ARRI* colorspaces conversions and transfer functions.
"""
+from __future__ import division
+
import array
import math
import os
+import PyOpenColorIO as ocio
+
import aces_ocio.generate_lut as genlut
-from aces_ocio.utilities import ColorSpace, mat44_from_mat33, sanitize_path, compact
+from aces_ocio.utilities import ColorSpace, mat44_from_mat33, sanitize
__author__ = 'ACES Developers'
cs.family = 'ARRI'
cs.is_data = False
+ # A linear space needs allocation variables
+ if transfer_function == '':
+ cs.allocation_type = ocio.Constants.ALLOCATION_LG2
+ cs.allocation_vars = [-8, 5, 0.00390625]
+
# Globals.
IDT_maker_version = '0.08'
- nominal_EI = 400.0
+ nominal_EI = 400
black_signal = 0.003907
mid_gray_signal = 0.01
encoding_gain = 0.256598
0.89 - 1) / 3 + 1) * encoding_gain
def log_c_inverse_parameters_for_EI(EI):
- cut = 1.0 / 9.0
- slope = 1.0 / (cut * math.log(10))
+ cut = 1 / 9
+ slope = 1 / (cut * math.log(10))
offset = math.log10(cut) - slope * cut
gain = EI / nominal_EI
gray = mid_gray_signal / gain
enc_gain = gain_for_EI(EI)
enc_offset = encoding_offset
for i in range(0, 3):
- nz = ((95.0 / 1023.0 - enc_offset) / enc_gain - offset) / slope
+ nz = ((95 / 1023 - enc_offset) / enc_gain - offset) / slope
enc_offset = encoding_offset - math.log10(1 + nz) * enc_gain
- a = 1.0 / gray
+ a = 1 / gray
b = nz - black_signal / gray
e = slope * a * enc_gain
f = enc_gain * (slope * b + offset) + enc_offset
p = log_c_inverse_parameters_for_EI(exposure_index)
breakpoint = p['e'] * p['cut'] + p['f']
if code_value > breakpoint:
- linear = ((pow(10, (code_value / 1023.0 - p['d']) / p['c']) -
+ linear = ((pow(10, (code_value / 1023 - p['d']) / p['c']) -
p['b']) / p['a'])
else:
- linear = (code_value / 1023.0 - p['f']) / p['e']
+ linear = (code_value / 1023 - p['f']) / p['e']
return linear
cs.to_reference_transforms = []
if transfer_function == 'V3 LogC':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
- data[c] = log_c_to_linear(1023.0 * c / (lut_resolution_1d - 1),
+ data[c] = log_c_to_linear(1023 * c / (lut_resolution_1d - 1),
int(exposure_index))
lut = '%s_to_linear.spi1d' % (
'%s_%s' % (transfer_function, exposure_index))
- lut = sanitize_path(lut)
+ lut = sanitize(lut)
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
- 0.0,
- 1.0,
+ 0,
+ 1,
data,
lut_resolution_1d,
1)
transfer_function = 'V3 LogC'
gamut = 'Wide Gamut'
- # EIs = [160.0, 200.0, 250.0, 320.0, 400.0, 500.0, 640.0, 800.0,
- # 1000.0, 1280.0, 1600.0, 2000.0, 2560.0, 3200.0]
+ # EIs = [160, 200, 250, 320, 400, 500, 640, 800,
+ # 1000, 1280, 1600, 2000, 2560, 3200]
EIs = [160, 200, 250, 320, 400, 500, 640, 800,
1000, 1280, 1600, 2000, 2560, 3200]
default_EI = 800