def create_transfer_colorspace(name='transfer',
transfer_function_name='transfer_function',
transfer_function=lambda x: x,
lut_directory='/tmp',
lut_resolution_1d=1024,
aliases=[]):
"""
Object description.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
# A linear space needs allocation variables
cs.allocation_type = ocio.Constants.ALLOCATION_UNIFORM
cs.allocation_vars = [0, 1]
# Sample the transfer function
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = transfer_function(c / (lut_resolution_1d - 1))
# Write the sampled data to a LUT
lut = '%s_to_linear.spi1d' % transfer_function_name
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
# Create the 'to_reference' transforms
cs.to_reference_transforms = []
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
# Create the 'from_reference' transforms
cs.from_reference_transforms = []
return cs
def create_matrix_colorspace(name='matrix',
from_reference_values=None,
to_reference_values=None,
aliases=None):
"""
Object description.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
if from_reference_values is None:
from_reference_values = []
if to_reference_values is None:
to_reference_values = []
if aliases is None:
aliases = []
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
# A linear space needs allocation variables.
cs.allocation_type = ocio.Constants.ALLOCATION_UNIFORM
cs.allocation_vars = [0, 1]
cs.to_reference_transforms = []
if to_reference_values:
for matrix in to_reference_values:
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(matrix),
'direction': 'forward'})
cs.from_reference_transforms = []
if from_reference_values:
for matrix in from_reference_values:
cs.from_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(matrix),
'direction': 'forward'})
return cs
def create_ACES():
"""
Object description.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
# Defining the reference colorspace.
aces2065_1 = ColorSpace('ACES2065-1')
aces2065_1.description = (
'The Academy Color Encoding System reference color space')
aces2065_1.equality_group = ''
aces2065_1.aliases = ['lin_ap0', 'aces']
aces2065_1.family = 'ACES'
aces2065_1.is_data = False
aces2065_1.allocation_type = ocio.Constants.ALLOCATION_LG2
aces2065_1.allocation_vars = [-8, 5, 0.00390625]
return aces2065_1
def create_Dolby_PQ_scaled(aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name='pq',
aliases=None,
min_value=0.0,
max_value=1.0,
input_scale=1.0,
middle_grey=0.18,
min_exposure=-6.0,
max_exposure=6.5):
if aliases is None:
aliases = []
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
ctls = [os.path.join(
aces_ctl_directory,
'utilities',
'ACESlib.OCIOShaper_to_lin_param.a1.0.0.ctl')]
lut = '%s_to_linear.spi1d' % name
lut = sanitize(lut)
generate_1d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_1d,
'float',
input_scale,
1.0,
{'middleGrey': middle_grey,
'minExposure': min_exposure,
'maxExposure': max_exposure},
cleanup,
aces_ctl_directory,
min_value,
max_value)
cs.to_reference_transforms = []
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_ACEScg(aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name='ACEScg'):
"""
Creates the *ACEScg* colorspace.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
Colorspace
*ACEScg* colorspace.
"""
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = ["lin_ap1"]
cs.equality_group = ''
cs.family = 'ACES'
cs.is_data = False
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
cs.to_reference_transforms = []
# *AP1* primaries to *AP0* primaries.
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(ACES_AP1_TO_AP0),
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_dolbypq_scaled(
aces_CTL_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name="pq",
aliases=[],
min_value=0.0,
max_value=1.0,
input_scale=1.0,
middle_grey=0.18,
min_exposure=-6.0,
max_exposure=6.5,
):
cs = ColorSpace(name)
cs.description = "The %s color space" % name
cs.aliases = aliases
cs.equality_group = name
cs.family = "Utility"
cs.is_data = False
ctls = [os.path.join(aces_CTL_directory, "utilities", "ACESlib.DolbyPQ_to_lin_param.a1.0.0.ctl")]
lut = "%s_to_linear.spi1d" % name
lut = sanitize(lut)
generate_1d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_1d,
"float",
input_scale,
1.0,
{"middleGrey": middle_grey, "minExposure": min_exposure, "maxExposure": max_exposure},
cleanup,
aces_CTL_directory,
min_value,
max_value,
)
cs.to_reference_transforms = []
cs.to_reference_transforms.append(
{"type": "lutFile", "path": lut, "interpolation": "linear", "direction": "forward"}
)
cs.from_reference_transforms = []
return cs
def create_dolbypq(aces_CTL_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name='pq',
aliases=[],
min_value=0.0,
max_value=1.0,
input_scale=1.0):
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
ctls = [os.path.join(
aces_CTL_directory,
'utilities',
'ACESlib.OCIO_shaper_dolbypq_to_lin.a1.0.0.ctl')]
lut = '%s_to_linear.spi1d' % name
lut = sanitize(lut)
generate_1d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_1d,
'float',
input_scale,
1.0,
{},
cleanup,
aces_CTL_directory,
min_value,
max_value)
cs.to_reference_transforms = []
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_raw():
# *Raw* utility space
name = "Raw"
raw = ColorSpace(name)
raw.description = 'The %s color space' % name
raw.aliases = []
raw.equality_group = name
raw.family = 'Utility'
raw.is_data = True
return raw
def create_raw():
"""
Creates the *raw* colorspace.
Returns
-------
ColorSpace
*raw* and all its identifying information.
"""
# *Raw* utility space
name = 'Raw'
raw = ColorSpace(name)
raw.description = 'The {0} color space'.format(name)
raw.aliases = ['raw']
raw.equality_group = name
raw.family = 'Utility'
raw.is_data = True
return raw
def create_raw():
"""
Creates the *raw* color space
Parameters
----------
None
Returns
-------
ColorSpace
*raw* and all its identifying information
"""
# *Raw* utility space
name = 'Raw'
raw = ColorSpace(name)
raw.description = 'The %s color space' % name
raw.aliases = ['raw']
raw.equality_group = name
raw.family = 'Utility'
raw.is_data = True
return raw
def create_c_log(gamut,
transfer_function,
lut_directory,
lut_resolution_1d,
aliases):
"""
Object description.
Canon-Log to ACES.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
name = '%s - %s' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - Canon %s' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/Canon'
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]
def legal_to_full(code_value):
return (code_value - 64) / (940 - 64)
def c_log_to_linear(code_value):
# log = fullToLegal(c1 * log10(c2*linear + 1) + c3)
# linear = (pow(10, (legalToFul(log) - c3)/c1) - 1)/c2
c1 = 0.529136
c2 = 10.1596
c3 = 0.0730597
linear = (pow(10, (legal_to_full(code_value) - c3) / c1) - 1) / c2
linear *= 0.9
return linear
cs.to_reference_transforms = []
if transfer_function == 'Canon-Log':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = c_log_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
if gamut == 'Rec. 709 Daylight':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.561538969, 0.402060105, 0.036400926, 0,
0.092739623, 0.924121198, -0.016860821, 0,
0.084812961, 0.006373835, 0.908813204, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'Rec. 709 Tungsten':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.566996399, 0.365079418, 0.067924183, 0,
0.070901044, 0.880331008, 0.048767948, 0,
0.073013542, -0.066540862, 0.99352732, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'DCI-P3 Daylight':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.607160575, 0.299507286, 0.093332140, 0,
0.004968120, 1.050982224, -0.055950343, 0,
-0.007839939, 0.000809127, 1.007030813, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'DCI-P3 Tungsten':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.650279125, 0.253880169, 0.095840706, 0,
-0.026137986, 1.017900530, 0.008237456, 0,
0.007757558, -0.063081669, 1.055324110, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'Cinema Gamut Daylight':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.763064455, 0.149021161, 0.087914384, 0,
0.003657457, 1.10696038, -0.110617837, 0,
-0.009407794, -0.218383305, 1.227791099, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'Cinema Gamut Tungsten':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.817416293, 0.090755698, 0.091828009, 0,
-0.035361374, 1.065690585, -0.030329211, 0,
0.010390366, -0.299271107, 1.288880741, 0,
0, 0, 0, 1],
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_matrix_colorspace(name='matrix',
from_reference_values=None,
to_reference_values=None,
aliases=None):
"""
Creates a ColorSpace that only uses *Matrix Transforms*
Parameters
----------
name : str, optional
Aliases for this colorspace
from_reference_values : list of matrices
List of matrices to convert from the reference colorspace to this space
to_reference_values : list of matrices
List of matrices to convert to the reference colorspace from this space
aliases : list of str, optional
Aliases for this colorspace
Returns
-------
ColorSpace
A *Matrix Transform*-based ColorSpace
"""
if from_reference_values is None:
from_reference_values = []
if to_reference_values is None:
to_reference_values = []
if aliases is None:
aliases = []
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
# A linear space needs allocation variables.
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
cs.to_reference_transforms = []
if to_reference_values:
for matrix in to_reference_values:
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(matrix),
'direction': 'forward'})
cs.from_reference_transforms = []
if from_reference_values:
for matrix in from_reference_values:
cs.from_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(matrix),
'direction': 'forward'})
return cs
def create_red_log_film(gamut,
transfer_function,
lut_directory,
lut_resolution_1d,
aliases=None):
"""
Creates colorspace covering the conversion from RED spaces to ACES, with various
transfer functions and encoding gamuts covered
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
aliases : list of str
Aliases for this colorspace
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and identifying
information for the requested colorspace.
"""
if aliases is None:
aliases = []
name = '%s - %s' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - %s' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/RED'
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]
def cineon_to_linear(code_value):
n_gamma = 0.6
black_point = 95
white_point = 685
code_value_to_density = 0.002
black_linear = pow(10, (black_point - white_point) * (
code_value_to_density / n_gamma))
code_linear = pow(10, (code_value - white_point) * (
code_value_to_density / n_gamma))
return (code_linear - black_linear) / (1 - black_linear)
cs.to_reference_transforms = []
if transfer_function == 'REDlogFilm':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = cineon_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = 'CineonLog_to_linear.spi1d'
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
if gamut == 'DRAGONcolor':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33([0.532279, 0.376648, 0.091073,
0.046344, 0.974513, -0.020860,
-0.053976, -0.000320, 1.054267]),
'direction': 'forward'})
elif gamut == 'DRAGONcolor2':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33([0.468452, 0.331484, 0.200064,
0.040787, 0.857658, 0.101553,
-0.047504, -0.000282, 1.047756]),
'direction': 'forward'})
elif gamut == 'REDcolor':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33([0.451464, 0.388498, 0.160038,
0.062716, 0.866790, 0.070491,
-0.017541, 0.086921, 0.930590]),
'direction': 'forward'})
elif gamut == 'REDcolor2':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33([0.480997, 0.402289, 0.116714,
-0.004938, 1.000154, 0.004781,
-0.105257, 0.025320, 1.079907]),
'direction': 'forward'})
elif gamut == 'REDcolor3':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33([0.512136, 0.360370, 0.127494,
0.070377, 0.903884, 0.025737,
-0.020824, 0.017671, 1.003123]),
'direction': 'forward'})
elif gamut == 'REDcolor4':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33([0.474202, 0.333677, 0.192121,
0.065164, 0.836932, 0.097901,
-0.019281, 0.016362, 1.002889]),
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_log_c(gamut,
transfer_function,
exposure_index,
name,
lut_directory,
lut_resolution_1d,
aliases):
"""
Object description.
LogC to ACES.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
name = '%s (EI%s) - %s' % (transfer_function, exposure_index, gamut)
if transfer_function == '':
name = 'Linear - ARRI %s' % gamut
if gamut == '':
name = '%s (EI%s)' % (transfer_function, exposure_index)
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/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
black_signal = 0.003907
mid_gray_signal = 0.01
encoding_gain = 0.256598
encoding_offset = 0.391007
def gain_for_EI(EI):
return (math.log(EI / nominal_EI) / math.log(2) * (
0.89 - 1) / 3 + 1) * encoding_gain
def log_c_inverse_parameters_for_EI(EI):
cut = 1 / 9
slope = 1 / (cut * math.log(10))
offset = math.log10(cut) - slope * cut
gain = EI / nominal_EI
gray = mid_gray_signal / gain
# The higher the EI, the lower the gamma.
enc_gain = gain_for_EI(EI)
enc_offset = encoding_offset
for i in range(0, 3):
nz = ((95 / 1023 - enc_offset) / enc_gain - offset) / slope
enc_offset = encoding_offset - math.log10(1 + nz) * enc_gain
a = 1 / gray
b = nz - black_signal / gray
e = slope * a * enc_gain
f = enc_gain * (slope * b + offset) + enc_offset
# Ensuring we can return relative exposure.
s = 4 / (0.18 * EI)
t = black_signal
b += a * t
a *= s
f += e * t
e *= s
return {'a': a,
'b': b,
'cut': (cut - b) / a,
'c': enc_gain,
'd': enc_offset,
'e': e,
'f': f}
def log_c_to_linear(code_value, exposure_index):
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 - p['d']) / p['c']) -
p['b']) / p['a'])
else:
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 * c / (lut_resolution_1d - 1),
int(exposure_index))
lut = '%s_to_linear.spi1d' % (
'%s_%s' % (transfer_function, exposure_index))
lut = sanitize(lut)
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
if gamut == 'Wide Gamut':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33([0.680206, 0.236137, 0.083658,
0.085415, 1.017471, -0.102886,
0.002057, -0.062563, 1.060506]),
'direction': 'forward'
})
cs.from_reference_transforms = []
return cs
def create_ACES_RRT_plus_ODT(odt_name,
odt_values,
shaper_info,
aces_ctl_directory,
lut_directory,
lut_resolution_3d=64,
cleanup=True,
aliases=None):
"""
Object description.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
if aliases is None:
aliases = []
cs = ColorSpace('%s' % odt_name)
cs.description = '%s - %s Output Transform' % (
odt_values['transformUserNamePrefix'], odt_name)
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Output'
cs.is_data = False
cs.aces_transform_id = odt_values['transformID']
pprint.pprint(odt_values)
# Generating the *shaper* transform.
(shaper_name,
shaper_to_aces_ctl,
shaper_from_aces_ctl,
shaper_input_scale,
shaper_params) = shaper_info
if 'legalRange' in odt_values:
shaper_params['legalRange'] = odt_values['legalRange']
else:
shaper_params['legalRange'] = 0
shaper_lut = '%s_to_linear.spi1d' % shaper_name
shaper_lut = sanitize(shaper_lut)
shaper_ocio_transform = {
'type': 'lutFile',
'path': shaper_lut,
'interpolation': 'linear',
'direction': 'inverse'}
# Generating the *forward* transform.
cs.from_reference_transforms = []
if 'transformLUT' in odt_values:
transform_lut_file_name = os.path.basename(
odt_values['transformLUT'])
lut = os.path.join(lut_directory, transform_lut_file_name)
shutil.copy(odt_values['transformLUT'], lut)
cs.from_reference_transforms.append(shaper_ocio_transform)
cs.from_reference_transforms.append({
'type': 'lutFile',
'path': transform_lut_file_name,
'interpolation': 'tetrahedral',
'direction': 'forward'})
elif 'transformCTL' in odt_values:
ctls = [
shaper_to_aces_ctl % aces_ctl_directory,
os.path.join(aces_ctl_directory,
'rrt',
'RRT.a1.0.0.ctl'),
os.path.join(aces_ctl_directory,
'odt',
odt_values['transformCTL'])]
lut = '%s.RRT.a1.0.0.%s.spi3d' % (shaper_name, odt_name)
lut = sanitize(lut)
generate_3d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_3d,
'float',
1 / shaper_input_scale,
1,
shaper_params,
cleanup,
aces_ctl_directory)
cs.from_reference_transforms.append(shaper_ocio_transform)
cs.from_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'tetrahedral',
'direction': 'forward'})
# Generating the *inverse* transform.
cs.to_reference_transforms = []
if 'transformLUTInverse' in odt_values:
transform_lut_inverse_file_name = os.path.basename(
odt_values['transformLUTInverse'])
lut = os.path.join(lut_directory, transform_lut_inverse_file_name)
shutil.copy(odt_values['transformLUTInverse'], lut)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': transform_lut_inverse_file_name,
'interpolation': 'tetrahedral',
'direction': 'forward'})
shaper_inverse = shaper_ocio_transform.copy()
shaper_inverse['direction'] = 'forward'
cs.to_reference_transforms.append(shaper_inverse)
elif 'transformCTLInverse' in odt_values:
ctls = [os.path.join(aces_ctl_directory,
'odt',
odt_values['transformCTLInverse']),
os.path.join(aces_ctl_directory,
'rrt',
'InvRRT.a1.0.0.ctl'),
shaper_from_aces_ctl % aces_ctl_directory]
lut = 'InvRRT.a1.0.0.%s.%s.spi3d' % (odt_name, shaper_name)
lut = sanitize(lut)
generate_3d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_3d,
'half',
1,
shaper_input_scale,
shaper_params,
cleanup,
aces_ctl_directory)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'tetrahedral',
'direction': 'forward'})
shaper_inverse = shaper_ocio_transform.copy()
shaper_inverse['direction'] = 'forward'
cs.to_reference_transforms.append(shaper_inverse)
return cs
def create_v_log(gamut,
transfer_function,
lut_directory,
lut_resolution_1d,
aliases):
"""
Object description.
Panasonic V-Log to ACES.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
name = '%s - %s' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - %s' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/Panasonic'
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]
def v_log_to_linear(x):
cut_inv = 0.181
b = 0.00873
c = 0.241514
d = 0.598206
if x <= cut_inv:
return (x - 0.125) / 5.6
else:
return pow(10, (x - d) / c) - b
cs.to_reference_transforms = []
if transfer_function == 'V-Log':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = v_log_to_linear(float(c) / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0.0,
1.0,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
if gamut == 'V-Gamut':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.724382758, 0.166748484, 0.108497411, 0.0,
0.021354009, 0.985138372, -0.006319092, 0.0,
-0.009234278, -0.00104295, 1.010272625, 0.0,
0, 0, 0, 1.0],
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_VLog(gamut, transfer_function, lut_directory, lut_resolution_1D,
aliases):
"""
Creates colorspace covering the conversion from *VLog* to *ACES*, with various
transfer functions and encoding gamuts covered.
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use.
lut_directory : str or unicode
The directory to use when generating LUTs.
lut_resolution_1D : int
The resolution of generated 1D LUTs.
aliases : list of str
Aliases for this colorspace.
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and
identifying information for the requested colorspace.
"""
name = '{0} - {1}'.format(transfer_function, gamut)
if transfer_function == '':
name = 'Linear - {0}'.format(gamut)
if gamut == '':
name = 'Curve - {0}'.format(transfer_function)
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/Panasonic'
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]
def VLog_to_linear(x):
cut_inv = 0.181
b = 0.00873
c = 0.241514
d = 0.598206
if x <= cut_inv:
return (x - 0.125) / 5.6
else:
return pow(10, (x - d) / c) - b
cs.to_reference_transforms = []
if transfer_function == 'V-Log':
data = array.array('f', b'\0' * lut_resolution_1D * 4)
for c in range(lut_resolution_1D):
data[c] = VLog_to_linear(float(c) / (lut_resolution_1D - 1))
lut = '{0}_to_linear.spi1d'.format(transfer_function)
genlut.write_SPI_1D(os.path.join(lut_directory, lut), 0.0, 1.0, data,
lut_resolution_1D, 1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
if gamut == 'V-Gamut':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.724382758, 0.166748484, 0.108497411, 0.0, 0.021354009,
0.985138372, -0.006319092, 0.0, -0.009234278, -0.00104295,
1.010272625, 0.0, 0, 0, 0, 1.0
],
'direction':
'forward'
})
cs.from_reference_transforms = []
return cs
def create_ACES_LMT(lmt_name,
lmt_values,
shaper_info,
aces_ctl_directory,
lut_directory,
lut_resolution_3d=64,
cleanup=True,
aliases=None):
"""
Creates the *ACES LMT* colorspace.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
Colorspace
*ACES LMT* colorspace.
"""
if aliases is None:
aliases = []
cs = ColorSpace('%s' % lmt_name)
cs.description = 'The ACES Look Transform: %s' % lmt_name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Look'
cs.is_data = False
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
cs.aces_transform_id = lmt_values['transformID']
pprint.pprint(lmt_values)
# Generating the *shaper* transform.
(shaper_name,
shaper_to_aces_ctl,
shaper_from_aces_ctl,
shaper_input_scale,
shaper_params) = shaper_info
shaper_lut = '%s_to_linear.spi1d' % shaper_name
shaper_lut = sanitize(shaper_lut)
shaper_ocio_transform = {
'type': 'lutFile',
'path': shaper_lut,
'interpolation': 'linear',
'direction': 'inverse'}
# Generating the forward transform.
cs.from_reference_transforms = []
if 'transformCTL' in lmt_values:
ctls = [shaper_to_aces_ctl % aces_ctl_directory,
os.path.join(aces_ctl_directory,
lmt_values['transformCTL'])]
lut = '%s.%s.spi3d' % (shaper_name, lmt_name)
lut = sanitize(lut)
generate_3d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_3d,
'float',
1 / shaper_input_scale,
1,
shaper_params,
cleanup,
aces_ctl_directory)
cs.from_reference_transforms.append(shaper_ocio_transform)
cs.from_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'tetrahedral',
'direction': 'forward'})
# Generating the inverse transform.
cs.to_reference_transforms = []
if 'transformCTLInverse' in lmt_values:
ctls = [os.path.join(aces_ctl_directory,
lmt_values['transformCTLInverse']),
shaper_from_aces_ctl % aces_ctl_directory]
lut = 'Inverse.%s.%s.spi3d' % (lmt_name, shaper_name)
lut = sanitize(lut)
generate_3d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_3d,
'half',
1,
shaper_input_scale,
shaper_params,
cleanup,
aces_ctl_directory,
0)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'tetrahedral',
'direction': 'forward'})
shaper_inverse = shaper_ocio_transform.copy()
shaper_inverse['direction'] = 'forward'
cs.to_reference_transforms.append(shaper_inverse)
return cs
def create_c_log(gamut, transfer_function, lut_directory, lut_resolution_1d,
aliases):
"""
Creates a colorspace covering the conversion from CLog to ACES, with
various transfer functions and encoding gamuts covered.
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
aliases : list of str
Aliases for this colorspace.
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and
identifying information for the requested colorspace.
"""
name = '%s - %s' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - Canon %s' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/Canon'
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]
def legal_to_full(code_value):
return (code_value - 64) / (940 - 64)
def c_log_to_linear(code_value):
# log = fullToLegal(c1 * log10(c2*linear + 1) + c3)
# linear = (pow(10, (legalToFul(log) - c3)/c1) - 1)/c2
c1 = 0.529136
c2 = 10.1596
c3 = 0.0730597
linear = (pow(10, (legal_to_full(code_value) - c3) / c1) - 1) / c2
linear *= 0.9
return linear
def c_log2_to_linear(code_value):
# log = fullToLegal(c1 * log10(c2*linear + 1) + c3)
# linear = (pow(10, (legalToFul(log) - c3)/c1) - 1)/c2
c1 = 0.281863093
c2 = 87.09937546
c3 = 0.035388128
linear = (pow(10, (legal_to_full(code_value) - c3) / c1) - 1) / c2
linear *= 0.9
return linear
def c_log3_to_linear(code_value):
# if(clog3_ire < 0.04076162)
# out = -( pow( 10, ( 0.07623209 - clog3_ire ) / 0.42889912 )
# - 1 ) / 14.98325;
# else if(clog3_ire <= 0.105357102)
# out = ( clog3_ire - 0.073059361 ) / 2.3069815;
# else
# out = ( pow( 10, ( clog3_ire - 0.069886632 ) / 0.42889912 )
# - 1 ) / 14.98325;
c1 = 0.42889912
c2 = 14.98325
c3 = 0.069886632
c4 = 0.04076162
c5 = 0.07623209
c6 = 0.105357102
c7 = 0.073059361
c8 = 2.3069815
clog3_ire = legal_to_full(code_value)
if clog3_ire < c4:
linear = -(pow(10, (c5 - clog3_ire) / c1) - 1) / c2
elif clog3_ire <= c6:
linear = (clog3_ire - c7) / c8
else:
linear = (pow(10, (clog3_ire - c3) / c1) - 1) / c2
linear *= 0.9
return linear
cs.to_reference_transforms = []
if transfer_function:
if transfer_function == 'Canon-Log':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = c_log_to_linear(1023 * c / (lut_resolution_1d - 1))
elif transfer_function == 'Canon-Log2':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = c_log2_to_linear(1023 * c / (lut_resolution_1d - 1))
elif transfer_function == 'Canon-Log3':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = c_log3_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1d, 1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
if gamut == 'Rec. 709 Daylight':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.561538969, 0.402060105, 0.036400926, 0, 0.092739623,
0.924121198, -0.016860821, 0, 0.084812961, 0.006373835,
0.908813204, 0, 0, 0, 0, 1
],
'direction':
'forward'
})
elif gamut == 'Rec. 709 Tungsten':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.566996399, 0.365079418, 0.067924183, 0, 0.070901044,
0.880331008, 0.048767948, 0, 0.073013542, -0.066540862,
0.99352732, 0, 0, 0, 0, 1
],
'direction':
'forward'
})
elif gamut == 'DCI-P3 Daylight':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.607160575, 0.299507286, 0.093332140, 0, 0.004968120,
1.050982224, -0.055950343, 0, -0.007839939, 0.000809127,
1.007030813, 0, 0, 0, 0, 1
],
'direction':
'forward'
})
elif gamut == 'DCI-P3 Tungsten':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.650279125, 0.253880169, 0.095840706, 0, -0.026137986,
1.017900530, 0.008237456, 0, 0.007757558, -0.063081669,
1.055324110, 0, 0, 0, 0, 1
],
'direction':
'forward'
})
elif gamut == 'Cinema Gamut Daylight':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.763064455, 0.149021161, 0.087914384, 0, 0.003657457,
1.10696038, -0.110617837, 0, -0.009407794, -0.218383305,
1.227791099, 0, 0, 0, 0, 1
],
'direction':
'forward'
})
elif gamut == 'Cinema Gamut Tungsten':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.817416293, 0.090755698, 0.091828009, 0, -0.035361374,
1.065690585, -0.030329211, 0, 0.010390366, -0.299271107,
1.288880741, 0, 0, 0, 0, 1
],
'direction':
'forward'
})
elif gamut == 'Rec. 2020 Daylight':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.678891151, 0.158868422, 0.162240427, 0, 0.045570831,
0.860712772, 0.093716397, 0, -0.000485710, 0.025060196,
0.975425515, 0, 0, 0, 0, 1
],
'direction':
'forward'
})
elif gamut == 'Rec. 2020 Tungsten':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.724488568, 0.115140904, 0.160370529, 0, 0.010659276,
0.839605344, 0.149735380, 0, 0.014560161, 0.028562057,
1.014001897, 0, 0, 0, 0, 1
],
'direction':
'forward'
})
cs.from_reference_transforms = []
return cs
def create_log_c(gamut, transfer_function, exposure_index, lut_directory,
lut_resolution_1d, aliases):
"""
Creates a colorspace covering the conversion from LogC to ACES, with
various transfer functions and encoding gamuts covered.
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use.
exposure_index : str
The exposure index to use.
lut_directory : str or unicode
The directory to use when generating LUTs.
lut_resolution_1d : int
The resolution of generated 1D LUTs.
aliases : list of str
Aliases for this colorspace.
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and
identifying information for the requested colorspace.
"""
name = '%s (EI%s) - %s' % (transfer_function, exposure_index, gamut)
if transfer_function == '':
name = 'Linear - ALEXA %s' % gamut
if gamut == '':
name = 'Curve - %s (EI%s)' % (transfer_function, exposure_index)
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/ARRI'
cs.is_data = False
if gamut and transfer_function:
cs.aces_transform_id = ('IDT.ARRI.Alexa-v3-logC-EI%s.a1.v1' %
exposure_index)
# A linear space needs allocation variables.
if transfer_function == '':
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
IDT_maker_version = '0.09'
nominal_exposure_index = 400
black_signal = 16 / 4095 # 0.003907
mid_gray_signal = 0.01
encoding_gain = 500 / 1023 * 0.525 # 0.256598
encoding_offset = 400 / 1023 # 0.391007
def gain_for_EI(ei):
return (math.log(ei / nominal_exposure_index) / math.log(2) *
(0.89 - 1) / 3 + 1) * encoding_gain
def hermite_weights(x, x1, x2):
d = x2 - x1
s = (x - x1) / d
s2 = 1 - s
return [(1 + 2 * s) * s2 * s2, (3 - 2 * s) * s * s, d * s * s2 * s2,
-d * s * s * s2]
def normalized_sensor_to_relative_exposure(ns, ei):
return (ns - black_signal) * (
0.18 / (mid_gray_signal * nominal_exposure_index / ei))
def normalized_log_c_to_linear(code_value, exposure_index):
cut = 1 / 9
slope = 1 / (cut * math.log(10))
offset = math.log10(cut) - slope * cut
gain = exposure_index / nominal_exposure_index
gray = mid_gray_signal / gain
# The higher the EI, the lower the gamma.
enc_gain = (math.log(gain) / math.log(2) *
(0.89 - 1) / 3 + 1) * encoding_gain
enc_offset = encoding_offset
for i in range(0, 3):
nz = ((95 / 1023 - enc_offset) / enc_gain - offset) / slope
enc_offset = encoding_offset - math.log10(1 + nz) * enc_gain
# see if we need to bring the hermite spline into play
xm = math.log10((1 - black_signal) / gray + nz) * enc_gain + enc_offset
if xm > 1.0:
if code_value > 0.8:
hw = hermite_weights(code_value, 0.8, 1)
d = 0.2 / (xm - 0.8)
v = [0.8, xm, 1.0, 1 / (d * d)]
# reconstruct code value from spline
code_value = 0
for i in range(0, 4):
code_value += (hw[i] * v[i])
code_value = (code_value - enc_offset) / enc_gain
# compute normalized sensor value
ns = pow(10, code_value) if (code_value - offset) / slope > cut else (
code_value - offset) / slope
ns = (ns - nz) * gray + black_signal
return normalized_sensor_to_relative_exposure(ns, exposure_index)
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] = normalized_log_c_to_linear(c / (lut_resolution_1d - 1),
int(exposure_index))
lut = '%s_to_linear.spi1d' % ('%s_%s' %
(transfer_function, exposure_index))
lut = sanitize(lut)
genlut.write_SPI_1d(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1d, 1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
if gamut == 'Wide Gamut':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.680206, 0.236137, 0.083658, 0.085415, 1.017471, -0.102886,
0.002057, -0.062563, 1.060506
]),
'direction':
'forward'
})
cs.from_reference_transforms = []
return cs
def create_s_log(gamut, transfer_function, lut_directory, lut_resolution_1d,
aliases):
"""
Creates colorspace covering the conversion from Sony spaces to ACES, with various
transfer functions and encoding gamuts covered
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
aliases : list of str
Aliases for this colorspace
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and identifying
information for the requested colorspace.
"""
name = '%s - %s' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - %s' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/Sony'
cs.is_data = False
if gamut and transfer_function:
cs.aces_transform_id = 'IDT.Sony.%s_%s_10i.a1.v1' % (
transfer_function.replace('-', ''), gamut.replace('-', '').replace(
' ', '_'))
# A linear space needs allocation variables.
if transfer_function == '':
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
def s_log1_to_linear(s_log):
b = 64.
ab = 90.
w = 940.
if s_log >= ab:
linear = ((pow(10., (
((s_log - b) /
(w - b) - 0.616596 - 0.03) / 0.432699)) - 0.037584) * 0.9)
else:
linear = (((s_log - b) /
(w - b) - 0.030001222851889303) / 5.) * 0.9
return linear
def s_log2_to_linear(s_log):
b = 64.
ab = 90.
w = 940.
if s_log >= ab:
linear = ((219. * (pow(10., (
((s_log - b) /
(w - b) - 0.616596 - 0.03) / 0.432699)) - 0.037584) / 155.) *
0.9)
else:
linear = (
((s_log - b) /
(w - b) - 0.030001222851889303) / 3.53881278538813) * 0.9
return linear
def s_log3_to_linear(code_value):
if code_value >= 171.2102946929:
linear = (pow(10,
((code_value - 420) / 261.5)) * (0.18 + 0.01) - 0.01)
else:
linear = (code_value - 95) * 0.01125000 / (171.2102946929 - 95)
return linear
cs.to_reference_transforms = []
if transfer_function == 'S-Log1':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = s_log1_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1d, 1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
elif transfer_function == 'S-Log2':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = s_log2_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1d, 1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
elif transfer_function == 'S-Log3':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = s_log3_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1d, 1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
if gamut == 'S-Gamut':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.754338638, 0.133697046, 0.111968437, 0.021198141,
1.005410934, -0.026610548, -0.009756991, 0.004508563,
1.005253201
]),
'direction':
'forward'
})
elif gamut == 'S-Gamut Daylight':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.8764457030, 0.0145411681, 0.1090131290, 0.0774075345,
0.9529571767, -0.0303647111, 0.0573564351, -0.1151066335,
1.0577501984
]),
'direction':
'forward'
})
elif gamut == 'S-Gamut Tungsten':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
1.0110238740, -0.1362526051, 0.1252287310, 0.1011994504,
0.9562196265, -0.0574190769, 0.0600766530, -0.1010185315,
1.0409418785
]),
'direction':
'forward'
})
elif gamut == 'S-Gamut3.Cine':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.6387886672, 0.2723514337, 0.0888598992, -0.0039159061,
1.0880732308, -0.0841573249, -0.0299072021, -0.0264325799,
1.0563397820
]),
'direction':
'forward'
})
elif gamut == 'S-Gamut3':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.7529825954, 0.1433702162, 0.1036471884, 0.0217076974,
1.0153188355, -0.0370265329, -0.0094160528, 0.0033704179,
1.0060456349
]),
'direction':
'forward'
})
cs.from_reference_transforms = []
return cs
def create_matrix_colorspace(name='matrix',
from_reference_values=None,
to_reference_values=None,
aliases=None):
"""
Creates a ColorSpace that only uses *Matrix Transforms*
Parameters
----------
name : str, optional
Aliases for this colorspace
from_reference_values : list of matrices
List of matrices to convert from the reference colorspace to this space
to_reference_values : list of matrices
List of matrices to convert to the reference colorspace from this space
aliases : list of str, optional
Aliases for this colorspace
Returns
-------
ColorSpace
A *Matrix Transform*-based ColorSpace
"""
if from_reference_values is None:
from_reference_values = []
if to_reference_values is None:
to_reference_values = []
if aliases is None:
aliases = []
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
# A linear space needs allocation variables.
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
cs.to_reference_transforms = []
if to_reference_values:
for matrix in to_reference_values:
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33(matrix),
'direction':
'forward'
})
cs.from_reference_transforms = []
if from_reference_values:
for matrix in from_reference_values:
cs.from_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33(matrix),
'direction':
'forward'
})
return cs
def create_matrix_plus_transfer_colorspace(
name='matrix_plus_transfer',
transfer_function_name='transfer_function',
transfer_function=lambda x: x,
lut_directory='/tmp',
lut_resolution_1d=1024,
from_reference_values=None,
to_reference_values=None,
aliases=None):
"""
Creates a ColorSpace that uses transfer functions encoded as 1D LUTs and
matrice
Parameters
----------
name : str, optional
Aliases for this colorspace
transfer_function_name : str, optional
The name of the transfer function
transfer_function : function, optional
The transfer function to be evaluated
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
from_reference_values : list of matrices
List of matrices to convert from the reference colorspace to this space
to_reference_values : list of matrices
List of matrices to convert to the reference colorspace from this space
aliases : list of str
Aliases for this colorspace
Returns
-------
ColorSpace
A *Matrx and LUT1D Transform*-based ColorSpace representing a transfer
function and matrix
"""
if from_reference_values is None:
from_reference_values = []
if to_reference_values is None:
to_reference_values = []
if aliases is None:
aliases = []
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
# A linear space needs allocation variables.
cs.allocation_type = ocio.Constants.ALLOCATION_UNIFORM
cs.allocation_vars = [0, 1]
# Sampling the transfer function.
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = transfer_function(c / (lut_resolution_1d - 1))
# Writing the sampled data to a *LUT*.
lut = '%s_to_linear.spi1d' % transfer_function_name
genlut.write_SPI_1d(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1d, 1)
# Creating the *to_reference* transforms.
cs.to_reference_transforms = []
if to_reference_values:
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
for matrix in to_reference_values:
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33(matrix),
'direction':
'forward'
})
# Creating the *from_reference* transforms.
cs.from_reference_transforms = []
if from_reference_values:
for matrix in from_reference_values:
cs.from_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33(matrix),
'direction':
'forward'
})
cs.from_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'inverse'
})
return cs
def create_ODTs(
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
lut_resolution_3d,
odt_info,
shaper_name,
cleanup,
linear_display_space,
log_display_space,
):
"""
Object description.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
colorspaces = []
displays = {}
# -------------------------------------------------------------------------
# *RRT / ODT* Shaper Options
# -------------------------------------------------------------------------
shaper_data = {}
# Defining the *Log 2* shaper.
log2_shaper_name = shaper_name
log2_shaper_name_aliases = ["crv_%s" % compact(log2_shaper_name)]
log2_params = {"middleGrey": 0.18, "minExposure": -6, "maxExposure": 6.5}
log2_shaper_colorspace = create_generic_log(
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name=log2_shaper_name,
middle_grey=log2_params["middleGrey"],
min_exposure=log2_params["minExposure"],
max_exposure=log2_params["maxExposure"],
aliases=log2_shaper_name_aliases,
)
colorspaces.append(log2_shaper_colorspace)
shaper_input_scale_generic_log2 = 1
# *Log 2* shaper name and *CTL* transforms bundled up.
log2_shaper_data = [
log2_shaper_name,
os.path.join("%s", "utilities", "ACESlib.Log2_to_Lin_param.a1.0.0.ctl"),
os.path.join("%s", "utilities", "ACESlib.Lin_to_Log2_param.a1.0.0.ctl"),
shaper_input_scale_generic_log2,
log2_params,
]
shaper_data[log2_shaper_name] = log2_shaper_data
# Space with a more user-friendly name. Direct copy otherwise.
log2_shaper_copy_name = "Log2 Shaper"
log2_shaper_copy_colorspace = ColorSpace(log2_shaper_copy_name)
log2_shaper_copy_colorspace.description = "The %s color space" % log2_shaper_copy_name
log2_shaper_copy_colorspace.aliases = ["crv_%s" % compact(log2_shaper_copy_name)]
log2_shaper_copy_colorspace.equality_group = log2_shaper_copy_name
log2_shaper_copy_colorspace.family = log2_shaper_colorspace.family
log2_shaper_copy_colorspace.is_data = log2_shaper_colorspace.is_data
log2_shaper_copy_colorspace.to_reference_transforms = list(log2_shaper_colorspace.to_reference_transforms)
log2_shaper_copy_colorspace.from_reference_transforms = list(log2_shaper_colorspace.from_reference_transforms)
colorspaces.append(log2_shaper_copy_colorspace)
# Defining the *Log2 shaper that includes the AP1* primaries.
log2_shaper_api1_name = "%s - AP1" % "Log2 Shaper"
log2_shaper_api1_colorspace = ColorSpace(log2_shaper_api1_name)
log2_shaper_api1_colorspace.description = "The %s color space" % log2_shaper_api1_name
log2_shaper_api1_colorspace.aliases = ["%s_ap1" % compact(log2_shaper_copy_name)]
log2_shaper_api1_colorspace.equality_group = log2_shaper_api1_name
log2_shaper_api1_colorspace.family = log2_shaper_colorspace.family
log2_shaper_api1_colorspace.is_data = log2_shaper_colorspace.is_data
log2_shaper_api1_colorspace.to_reference_transforms = list(log2_shaper_colorspace.to_reference_transforms)
log2_shaper_api1_colorspace.from_reference_transforms = list(log2_shaper_colorspace.from_reference_transforms)
# *AP1* primaries to *AP0* primaries.
log2_shaper_api1_colorspace.to_reference_transforms.append(
{"type": "matrix", "matrix": mat44_from_mat33(ACES_AP1_TO_AP0), "direction": "forward"}
)
colorspaces.append(log2_shaper_api1_colorspace)
# Defining the *Log2 shaper that includes the AP1* primaries.
# Named with 'shaper_name' variable. Needed for some LUT baking steps.
shaper_api1_name = "%s - AP1" % shaper_name
shaper_api1_colorspace = ColorSpace(shaper_api1_name)
shaper_api1_colorspace.description = "The %s color space" % shaper_api1_name
shaper_api1_colorspace.aliases = ["%s_ap1" % compact(shaper_name)]
shaper_api1_colorspace.equality_group = shaper_api1_name
shaper_api1_colorspace.family = log2_shaper_colorspace.family
shaper_api1_colorspace.is_data = log2_shaper_colorspace.is_data
shaper_api1_colorspace.to_reference_transforms = list(log2_shaper_api1_colorspace.to_reference_transforms)
shaper_api1_colorspace.from_reference_transforms = list(log2_shaper_api1_colorspace.from_reference_transforms)
colorspaces.append(shaper_api1_colorspace)
# Define the base *Dolby PQ Shaper*
#
dolbypq_shaper_name = "Dolby PQ 10000"
dolbypq_shaper_name_aliases = ["crv_%s" % "dolbypq_10000"]
dolbypq_shaper_colorspace = create_dolbypq(
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name=dolbypq_shaper_name,
aliases=dolbypq_shaper_name_aliases,
)
colorspaces.append(dolbypq_shaper_colorspace)
# *Dolby PQ* shaper name and *CTL* transforms bundled up.
dolbypq_shaper_data = [
dolbypq_shaper_name,
os.path.join("%s", "utilities", "ACESlib.DolbyPQ_to_Lin.a1.0.0.ctl"),
os.path.join("%s", "utilities", "ACESlib.Lin_to_DolbyPQ.a1.0.0.ctl"),
1.0,
{},
]
shaper_data[dolbypq_shaper_name] = dolbypq_shaper_data
# Define the *Dolby PQ Shaper that considers a fixed linear range*
#
dolbypq_scaled_shaper_name = "Dolby PQ Scaled"
dolbypq_scaled_shaper_name_aliases = ["crv_%s" % "dolbypq_scaled"]
dolbypq_scaled_shaper_colorspace = create_dolbypq_scaled(
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name=dolbypq_scaled_shaper_name,
aliases=dolbypq_scaled_shaper_name_aliases,
)
colorspaces.append(dolbypq_scaled_shaper_colorspace)
# *Dolby PQ* shaper name and *CTL* transforms bundled up.
dolbypq_scaled_shaper_data = [
dolbypq_scaled_shaper_name,
os.path.join("%s", "utilities", "ACESlib.DolbyPQ_to_Lin_param.a1.0.0.ctl"),
os.path.join("%s", "utilities", "ACESlib.Lin_to_DolbyPQ_param.a1.0.0.ctl"),
1.0,
log2_params,
]
shaper_data[dolbypq_scaled_shaper_name] = dolbypq_scaled_shaper_data
#
# Pick a specific shaper
#
rrt_shaper = log2_shaper_data
# rrt_shaper = dolbypq_scaled_shaper_data
# *RRT + ODT* combinations.
sorted_odts = sorted(odt_info.iteritems(), key=lambda x: x[1])
print(sorted_odts)
for odt in sorted_odts:
(odt_name, odt_values) = odt
# Generating only full range transform for *ODTs* that can generate
# either *legal* or *full* output.
# Uncomment these lines and the lower section and flip the 'legalRange' value to 1
# to recover the old behavior, where both legal and full range LUTs were generated
if odt_values["transformHasFullLegalSwitch"]:
# odt_name_legal = '%s - Legal' % odt_values['transformUserName']
odt_legal["legalRange"] = 0
# else:
# odt_name_legal = odt_values['transformUserName']
odt_name_legal = odt_values["transformUserName"]
odt_legal = odt_values.copy()
odt_aliases = ["out_%s" % compact(odt_name_legal)]
cs = create_ACES_RRT_plus_ODT(
odt_name_legal,
odt_legal,
rrt_shaper,
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
lut_resolution_3d,
cleanup,
odt_aliases,
)
colorspaces.append(cs)
displays[odt_name_legal] = {"Raw": linear_display_space, "Log": log_display_space, "Output Transform": cs}
"""
# Generating full range transform for *ODTs* that can generate
# either *legal* or *full* output.
if odt_values['transformHasFullLegalSwitch']:
print('Generating full range ODT for %s' % odt_name)
odt_name_full = '%s - Full' % odt_values['transformUserName']
odt_full = odt_values.copy()
odt_full['legalRange'] = 0
odt_full_aliases = ["out_%s" % compact(odt_name_full)]
cs_full = create_ACES_RRT_plus_ODT(
odt_name_full,
odt_full,
rrt_shaper,
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
lut_resolution_3d,
cleanup,
odt_full_aliases)
colorspaces.append(cs_full)
displays[odt_name_full] = {
'Raw': linear_display_space,
'Log': log_display_space,
'Output Transform': cs_full}
"""
return (colorspaces, displays)
def create_ACEScc(aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name='ACEScc',
min_value=0,
max_value=1,
input_scale=1):
"""
Creates the *ACEScc* colorspace.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
Colorspace
*ACEScc* colorspace.
"""
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = ["acescc_ap1"]
cs.equality_group = ''
cs.family = 'ACES'
cs.is_data = False
cs.allocation_type = ocio.Constants.ALLOCATION_UNIFORM
cs.allocation_vars = [min_value, max_value]
ctls = [os.path.join(aces_ctl_directory,
'ACEScc',
'ACEScsc.ACEScc_to_ACES.a1.0.0.ctl')]
lut = '%s_to_linear.spi1d' % name
lut = sanitize(lut)
generate_1d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_1d,
'float',
input_scale,
1,
{'transferFunctionOnly':1},
cleanup,
aces_ctl_directory,
min_value,
max_value,
1)
cs.to_reference_transforms = []
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
# *AP1* primaries to *AP0* primaries.
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(ACES_AP1_TO_AP0),
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_matrix_plus_gamma_colorspace(name='matrix_plus_gamma',
gamma_value=1.0,
from_reference_values=None,
to_reference_values=None,
aliases=None):
"""
Creates a colorspace expressed as a single or multiple *MatrixTransform*
and an *ExponentTransform* transformations.
Parameters
----------
name : str, optional
Aliases for this colorspace.
gamma_value : function, optional
The gamma value.
from_reference_values : list of matrices
List of matrices to convert from the reference colorspace to this
colorspace.
to_reference_values : list of matrices
List of matrices to convert to the reference colorspace from this
colorspace.
aliases : list of str
Aliases for this colorspace.
Returns
-------
ColorSpace
A colorspace expressed as a single or multiple *MatrixTransform* and an
*ExponentTransform* transformations.
"""
if from_reference_values is None:
from_reference_values = []
if to_reference_values is None:
to_reference_values = []
if aliases is None:
aliases = []
cs = ColorSpace(name)
cs.description = 'The {0} color space'.format(name)
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
cs.allocation_type = ocio.Constants.ALLOCATION_UNIFORM
cs.allocation_vars = [0, 1]
# Creating the *to_reference* transforms.
cs.to_reference_transforms = []
if to_reference_values:
cs.to_reference_transforms.append({
'type':
'exponent',
'value': [gamma_value, gamma_value, gamma_value, 1]
})
for matrix in to_reference_values:
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33(matrix),
'direction':
'forward'
})
# Creating the *from_reference* transforms.
cs.from_reference_transforms = []
if from_reference_values:
for matrix in from_reference_values:
cs.from_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33(matrix),
'direction':
'forward'
})
cs.from_reference_transforms.append({
'type':
'exponent',
'value':
[1.0 / gamma_value, 1.0 / gamma_value, 1.0 / gamma_value, 1]
})
return cs
def create_ACES_LMT(
lmt_name,
lmt_values,
shaper_info,
aces_ctl_directory,
lut_directory,
lut_resolution_1d=1024,
lut_resolution_3d=64,
cleanup=True,
aliases=None,
):
"""
Creates the *ACES LMT* colorspace.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
Colorspace
*ACES LMT* colorspace.
"""
if aliases is None:
aliases = []
cs = ColorSpace("%s" % lmt_name)
cs.description = "The ACES Look Transform: %s" % lmt_name
cs.aliases = aliases
cs.equality_group = ""
cs.family = "Look"
cs.is_data = False
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
cs.aces_transform_id = lmt_values["transformID"]
pprint.pprint(lmt_values)
# Generating the *shaper* transform.
(shaper_name, shaper_to_ACES_CTL, shaper_from_ACES_CTL, shaper_input_scale, shaper_params) = shaper_info
# Add the shaper transform
shaper_lut = "%s_to_linear.spi1d" % shaper_name
shaper_lut = sanitize(shaper_lut)
shaper_OCIO_transform = {"type": "lutFile", "path": shaper_lut, "interpolation": "linear", "direction": "inverse"}
# Generating the forward transform.
cs.from_reference_transforms = []
if "transformCTL" in lmt_values:
ctls = [shaper_to_ACES_CTL % aces_ctl_directory, os.path.join(aces_ctl_directory, lmt_values["transformCTL"])]
lut = "%s.%s.spi3d" % (shaper_name, lmt_name)
lut = sanitize(lut)
generate_3d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_3d,
"float",
1 / shaper_input_scale,
1,
shaper_params,
cleanup,
aces_ctl_directory,
)
cs.from_reference_transforms.append(shaper_OCIO_transform)
cs.from_reference_transforms.append(
{"type": "lutFile", "path": lut, "interpolation": "tetrahedral", "direction": "forward"}
)
# Generating the inverse transform.
cs.to_reference_transforms = []
if "transformCTLInverse" in lmt_values:
ctls = [
os.path.join(aces_ctl_directory, lmt_values["transformCTLInverse"]),
shaper_from_ACES_CTL % aces_ctl_directory,
]
lut = "Inverse.%s.%s.spi3d" % (odt_name, shaper_name)
lut = sanitize(lut)
generate_3d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_3d,
"half",
1,
shaper_input_scale,
shaper_params,
cleanup,
aces_ctl_directory,
0,
1,
1,
)
cs.to_reference_transforms.append(
{"type": "lutFile", "path": lut, "interpolation": "tetrahedral", "direction": "forward"}
)
shaper_inverse = shaper_OCIO_transform.copy()
shaper_inverse["direction"] = "forward"
cs.to_reference_transforms.append(shaper_inverse)
return cs
def create_gamma_colorspace(name='gamma', gamma_value=1.0, aliases=None):
"""
Creates a colorspace expressed as an *ExponentTransform* transformation.
Parameters
----------
name : str, optional
Aliases for this colorspace.
gamma_value : function, optional
The gamma value.
aliases : list of str
Aliases for this colorspace.
Returns
-------
ColorSpace
A colorspace expressed as an *ExponentTransform* transformation.
"""
if aliases is None:
aliases = []
cs = ColorSpace(name)
cs.description = 'The {0} color space'.format(name)
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
# A linear space needs allocation variables.
cs.allocation_type = ocio.Constants.ALLOCATION_UNIFORM
cs.allocation_vars = [0, 1]
# Creating the *to_reference* transforms.
cs.to_reference_transforms = []
cs.to_reference_transforms.append({
'type':
'exponent',
'value': [gamma_value, gamma_value, gamma_value, 1]
})
# Creating the *from_reference* transforms.
cs.from_reference_transforms = []
return cs
def create_s_log(gamut,
transfer_function,
lut_directory,
lut_resolution_1d,
aliases):
"""
Creates colorspace covering the conversion from Sony spaces to ACES, with various
transfer functions and encoding gamuts covered
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
aliases : list of str
Aliases for this colorspace
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and identifying
information for the requested colorspace.
"""
name = '%s - %s' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - %s' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/Sony'
cs.is_data = False
if gamut and transfer_function:
cs.aces_transform_id = 'IDT.Sony.%s_%s_10i.a1.v1' % (
transfer_function.replace('-', ''),
gamut.replace('-', '').replace(' ', '_'))
# A linear space needs allocation variables.
if transfer_function == '':
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
def s_log1_to_linear(s_log):
b = 64.
ab = 90.
w = 940.
if s_log >= ab:
linear = ((pow(10.,
(((s_log - b) /
(w - b) - 0.616596 - 0.03) / 0.432699)) -
0.037584) * 0.9)
else:
linear = (((s_log - b) / (
w - b) - 0.030001222851889303) / 5.) * 0.9
return linear
def s_log2_to_linear(s_log):
b = 64.
ab = 90.
w = 940.
if s_log >= ab:
linear = ((219. * (pow(10.,
(((s_log - b) /
(w - b) - 0.616596 - 0.03) / 0.432699)) -
0.037584) / 155.) * 0.9)
else:
linear = (((s_log - b) / (
w - b) - 0.030001222851889303) / 3.53881278538813) * 0.9
return linear
def s_log3_to_linear(code_value):
if code_value >= 171.2102946929:
linear = (pow(10, ((code_value - 420) / 261.5)) *
(0.18 + 0.01) - 0.01)
else:
linear = (code_value - 95) * 0.01125000 / (171.2102946929 - 95)
return linear
cs.to_reference_transforms = []
if transfer_function == 'S-Log1':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = s_log1_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
elif transfer_function == 'S-Log2':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = s_log2_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
elif transfer_function == 'S-Log3':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = s_log3_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
if gamut == 'S-Gamut':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(
[0.754338638, 0.133697046, 0.111968437,
0.021198141, 1.005410934, -0.026610548,
-0.009756991, 0.004508563, 1.005253201]),
'direction': 'forward'})
elif gamut == 'S-Gamut Daylight':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(
[0.8764457030, 0.0145411681, 0.1090131290,
0.0774075345, 0.9529571767, -0.0303647111,
0.0573564351, -0.1151066335, 1.0577501984]),
'direction': 'forward'})
elif gamut == 'S-Gamut Tungsten':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(
[1.0110238740, -0.1362526051, 0.1252287310,
0.1011994504, 0.9562196265, -0.0574190769,
0.0600766530, -0.1010185315, 1.0409418785]),
'direction': 'forward'})
elif gamut == 'S-Gamut3.Cine':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(
[0.6387886672, 0.2723514337, 0.0888598992,
-0.0039159061, 1.0880732308, -0.0841573249,
-0.0299072021, -0.0264325799, 1.0563397820]),
'direction': 'forward'})
elif gamut == 'S-Gamut3':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(
[0.7529825954, 0.1433702162, 0.1036471884,
0.0217076974, 1.0153188355, -0.0370265329,
-0.0094160528, 0.0033704179, 1.0060456349]),
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_c_log(gamut,
transfer_function,
lut_directory,
lut_resolution_1d,
aliases):
"""
Creates colorspace covering the conversion from CLog to ACES, with various transfer
functions and encoding gamuts covered
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
aliases : list of str
Aliases for this colorspace
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and identifying
information for the requested colorspace.
"""
name = '%s - %s' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - Canon %s' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/Canon'
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]
def legal_to_full(code_value):
return (code_value - 64) / (940 - 64)
def c_log_to_linear(code_value):
# log = fullToLegal(c1 * log10(c2*linear + 1) + c3)
# linear = (pow(10, (legalToFul(log) - c3)/c1) - 1)/c2
c1 = 0.529136
c2 = 10.1596
c3 = 0.0730597
linear = (pow(10, (legal_to_full(code_value) - c3) / c1) - 1) / c2
linear *= 0.9
return linear
def c_log2_to_linear(code_value):
# log = fullToLegal(c1 * log10(c2*linear + 1) + c3)
# linear = (pow(10, (legalToFul(log) - c3)/c1) - 1)/c2
c1 = 0.281863093
c2 = 87.09937546
c3 = 0.035388128
linear = (pow(10, (legal_to_full(code_value) - c3) / c1) - 1) / c2
linear *= 0.9
return linear
cs.to_reference_transforms = []
if transfer_function:
if transfer_function == 'Canon-Log':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = c_log_to_linear(1023 * c / (lut_resolution_1d - 1))
elif transfer_function == 'Canon-Log2':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = c_log2_to_linear(1023 * c / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
if gamut == 'Rec. 709 Daylight':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.561538969, 0.402060105, 0.036400926, 0,
0.092739623, 0.924121198, -0.016860821, 0,
0.084812961, 0.006373835, 0.908813204, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'Rec. 709 Tungsten':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.566996399, 0.365079418, 0.067924183, 0,
0.070901044, 0.880331008, 0.048767948, 0,
0.073013542, -0.066540862, 0.99352732, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'DCI-P3 Daylight':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.607160575, 0.299507286, 0.093332140, 0,
0.004968120, 1.050982224, -0.055950343, 0,
-0.007839939, 0.000809127, 1.007030813, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'DCI-P3 Tungsten':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.650279125, 0.253880169, 0.095840706, 0,
-0.026137986, 1.017900530, 0.008237456, 0,
0.007757558, -0.063081669, 1.055324110, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'Cinema Gamut Daylight':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.763064455, 0.149021161, 0.087914384, 0,
0.003657457, 1.10696038, -0.110617837, 0,
-0.009407794, -0.218383305, 1.227791099, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'Cinema Gamut Tungsten':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.817416293, 0.090755698, 0.091828009, 0,
-0.035361374, 1.065690585, -0.030329211, 0,
0.010390366, -0.299271107, 1.288880741, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'Rec. 2020 Daylight':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.678891151, 0.158868422, 0.162240427, 0,
0.045570831, 0.860712772, 0.093716397, 0,
-0.000485710, 0.025060196, 0.975425515, 0,
0, 0, 0, 1],
'direction': 'forward'})
elif gamut == 'Rec. 2020 Tungsten':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.724488568, 0.115140904, 0.160370529, 0,
0.010659276, 0.839605344, 0.149735380, 0,
0.014560161, 0.028562057, 1.014001897, 0,
0, 0, 0, 1],
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_REDLog_film(gamut,
transfer_function,
lut_directory,
lut_resolution_1D,
aliases=None):
"""
Creates colorspace covering the conversion from *RED* spaces to *ACES*,
with various transfer functions and encoding gamuts covered.
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use.
lut_directory : str or unicode
The directory to use when generating LUTs.
lut_resolution_1D : int
The resolution of generated 1D LUTs.
aliases : list of str
Aliases for this colorspace.
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and
identifying information for the requested colorspace.
"""
if aliases is None:
aliases = []
name = '{0} - {1}'.format(transfer_function, gamut)
if transfer_function == '':
name = 'Linear - {0}'.format(gamut)
if gamut == '':
name = 'Curve - {0}'.format(transfer_function)
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/RED'
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]
def Cineon_to_linear(code_value):
n_gamma = 0.6
black_point = 95
white_point = 685
code_value_to_density = 0.002
black_linear = pow(10, (black_point - white_point) *
(code_value_to_density / n_gamma))
code_linear = pow(10, (code_value - white_point) *
(code_value_to_density / n_gamma))
return (code_linear - black_linear) / (1 - black_linear)
def Log3G10_to_linear(code_value):
a = 0.224282
b = 155.975327
c = 0.01
normalized_log = code_value / 1023.0
mirror = 1.0
if normalized_log < 0.0:
mirror = -1.0
normalized_log = -normalized_log
linear = (pow(10.0, normalized_log / a) - 1) / b
linear = linear * mirror - c
return linear
cs.to_reference_transforms = []
if transfer_function:
if transfer_function == 'REDlogFilm':
lut_name = "CineonLog"
data = array.array('f', b'\0' * lut_resolution_1D * 4)
for c in range(lut_resolution_1D):
data[c] = Cineon_to_linear(1023 * c / (lut_resolution_1D - 1))
elif transfer_function == 'REDLog3G10':
lut_name = "REDLog3G10"
data = array.array('f', b'\0' * lut_resolution_1D * 4)
for c in range(lut_resolution_1D):
data[c] = Log3G10_to_linear(1023 * c / (lut_resolution_1D - 1))
lut = '{0}_to_linear.spi1d'.format(lut_name)
genlut.write_SPI_1D(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1D, 1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
if gamut == 'DRAGONcolor':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.532279, 0.376648, 0.091073, 0.046344, 0.974513, -0.020860,
-0.053976, -0.000320, 1.054267
]),
'direction':
'forward'
})
elif gamut == 'DRAGONcolor2':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.468452, 0.331484, 0.200064, 0.040787, 0.857658, 0.101553,
-0.047504, -0.000282, 1.047756
]),
'direction':
'forward'
})
elif gamut == 'REDcolor':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.451464, 0.388498, 0.160038, 0.062716, 0.866790, 0.070491,
-0.017541, 0.086921, 0.930590
]),
'direction':
'forward'
})
elif gamut == 'REDcolor2':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.480997, 0.402289, 0.116714, -0.004938, 1.000154, 0.004781,
-0.105257, 0.025320, 1.079907
]),
'direction':
'forward'
})
elif gamut == 'REDcolor3':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.512136, 0.360370, 0.127494, 0.070377, 0.903884, 0.025737,
-0.020824, 0.017671, 1.003123
]),
'direction':
'forward'
})
elif gamut == 'REDcolor4':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.474202, 0.333677, 0.192121, 0.065164, 0.836932, 0.097901,
-0.019281, 0.016362, 1.002889
]),
'direction':
'forward'
})
elif gamut == 'REDWideGamutRGB':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.785043, 0.083844, 0.131118, 0.023172, 1.087892, -0.111055,
-0.073769, -0.314639, 1.388537
]),
'direction':
'forward'
})
cs.from_reference_transforms = []
return cs
def create_protune(gamut,
transfer_function,
lut_directory,
lut_resolution_1d,
aliases):
"""
Creates colorspace covering the conversion from ProTune to ACES, with various transfer
functions and encoding gamuts covered
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
aliases : list of str
Aliases for this colorspace
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and identifying
information for the requested colorspace.
"""
# The gamut should be marked as experimental until matrices are fully
# verified.
name = '%s - %s - Experimental' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - %s - Experimental' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/GoPro'
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]
def protune_to_linear(normalized_code_value):
c1 = 113.0
c2 = 1.0
c3 = 112.0
linear = ((pow(c1, normalized_code_value) - c2) / c3)
return linear
cs.to_reference_transforms = []
if transfer_function == 'Protune Flat':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = protune_to_linear(float(c) / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
lut = sanitize(lut)
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
if gamut == 'Protune Native':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.533448429, 0.32413911, 0.142412421, 0,
-0.050729924, 1.07572006, -0.024990416, 0,
0.071419661, -0.290521962, 1.219102381, 0,
0, 0, 0, 1],
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_ODTs(aces_ctl_directory,
lut_directory,
lut_resolution_1d,
lut_resolution_3d,
odt_info,
shaper_name,
cleanup,
linear_display_space,
log_display_space):
"""
Object description.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
colorspaces = []
displays = {}
# -------------------------------------------------------------------------
# *RRT / ODT* Shaper Options
# -------------------------------------------------------------------------
shaper_data = {}
# Defining the *Log 2* shaper.
log2_shaper_name = shaper_name
log2_shaper_name_aliases = ['crv_%s' % compact(log2_shaper_name)]
log2_params = {
'middleGrey': 0.18,
'minExposure': -6,
'maxExposure': 6.5}
log2_shaper_colorspace = create_generic_log(
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name=log2_shaper_name,
middle_grey=log2_params['middleGrey'],
min_exposure=log2_params['minExposure'],
max_exposure=log2_params['maxExposure'],
aliases=log2_shaper_name_aliases)
colorspaces.append(log2_shaper_colorspace)
shaper_input_scale_generic_log2 = 1
# *Log 2* shaper name and *CTL* transforms bundled up.
log2_shaper_data = [
log2_shaper_name,
os.path.join('%s',
'utilities',
'ACESlib.Log2_to_Lin_param.a1.0.0.ctl'),
os.path.join('%s',
'utilities',
'ACESlib.Lin_to_Log2_param.a1.0.0.ctl'),
shaper_input_scale_generic_log2,
log2_params]
shaper_data[log2_shaper_name] = log2_shaper_data
# Space with a more user-friendly name. Direct copy otherwise.
log2_shaper_copy_name = 'Log2 Shaper'
log2_shaper_copy_colorspace = ColorSpace(log2_shaper_copy_name)
log2_shaper_copy_colorspace.description = (
'The %s color space' % log2_shaper_copy_name)
log2_shaper_copy_colorspace.aliases = [
'crv_%s' % compact(log2_shaper_copy_name)]
log2_shaper_copy_colorspace.equality_group = log2_shaper_copy_name
log2_shaper_copy_colorspace.family = log2_shaper_colorspace.family
log2_shaper_copy_colorspace.is_data = log2_shaper_colorspace.is_data
log2_shaper_copy_colorspace.to_reference_transforms = list(
log2_shaper_colorspace.to_reference_transforms)
log2_shaper_copy_colorspace.from_reference_transforms = list(
log2_shaper_colorspace.from_reference_transforms)
colorspaces.append(log2_shaper_copy_colorspace)
# Defining the *Log2 shaper that includes the AP1* primaries.
log2_shaper_api1_name = '%s - AP1' % 'Log2 Shaper'
log2_shaper_api1_colorspace = ColorSpace(log2_shaper_api1_name)
log2_shaper_api1_colorspace.description = (
'The %s color space' % log2_shaper_api1_name)
log2_shaper_api1_colorspace.aliases = [
'%s_ap1' % compact(log2_shaper_copy_name)]
log2_shaper_api1_colorspace.equality_group = log2_shaper_api1_name
log2_shaper_api1_colorspace.family = log2_shaper_colorspace.family
log2_shaper_api1_colorspace.is_data = log2_shaper_colorspace.is_data
log2_shaper_api1_colorspace.to_reference_transforms = list(
log2_shaper_colorspace.to_reference_transforms)
log2_shaper_api1_colorspace.from_reference_transforms = list(
log2_shaper_colorspace.from_reference_transforms)
# *AP1* primaries to *AP0* primaries
log2_shaper_api1_colorspace.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(ACES_AP1_TO_AP0),
'direction': 'forward'
})
colorspaces.append(log2_shaper_api1_colorspace)
# Defining the *Log2* shaper that includes the *AP1* primaries.
# Named with `shaper_name` variable and needed for some *LUT* baking steps.
shaper_api1_name = '%s - AP1' % shaper_name
shaper_api1_colorspace = ColorSpace(shaper_api1_name)
shaper_api1_colorspace.description = (
'The %s color space' % shaper_api1_name)
shaper_api1_colorspace.aliases = ['%s_ap1' % compact(shaper_name)]
shaper_api1_colorspace.equality_group = shaper_api1_name
shaper_api1_colorspace.family = log2_shaper_colorspace.family
shaper_api1_colorspace.is_data = log2_shaper_colorspace.is_data
shaper_api1_colorspace.to_reference_transforms = list(
log2_shaper_api1_colorspace.to_reference_transforms)
shaper_api1_colorspace.from_reference_transforms = list(
log2_shaper_api1_colorspace.from_reference_transforms)
colorspaces.append(shaper_api1_colorspace)
# Define the base *Dolby PQ Shaper*
#
dolby_pq_shaper_name = 'Dolby PQ 10000'
dolby_pq_shaper_name_aliases = ['crv_%s' % 'dolbypq_10000']
dolby_pq_shaper_colorspace = create_Dolby_PQ(
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name=dolby_pq_shaper_name,
aliases=dolby_pq_shaper_name_aliases)
colorspaces.append(dolby_pq_shaper_colorspace)
# *Dolby PQ* shaper name and *CTL* transforms bundled up.
dolby_pq_shaper_data = [
dolby_pq_shaper_name,
os.path.join('%s',
'utilities',
'ACESlib.DolbyPQ_to_Lin.a1.0.0.ctl'),
os.path.join('%s',
'utilities',
'ACESlib.Lin_to_DolbyPQ.a1.0.0.ctl'),
1.0,
{}]
shaper_data[dolby_pq_shaper_name] = dolby_pq_shaper_data
# Define the *Dolby PQ Shaper that considers a fixed linear range*
dolby_pq_scaled_shaper_name = 'Dolby PQ Scaled'
dolby_pq_scaled_shaper_name_aliases = ['crv_%s' % 'dolbypq_scaled']
dolby_pq_scaled_shaper_colorspace = create_Dolby_PQ_scaled(
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name=dolby_pq_scaled_shaper_name,
aliases=dolby_pq_scaled_shaper_name_aliases)
colorspaces.append(dolby_pq_scaled_shaper_colorspace)
# *Dolby PQ* shaper name and *CTL* transforms bundled up.
dolby_pq_scaled_shaper_data = [
dolby_pq_scaled_shaper_name,
os.path.join('%s',
'utilities',
'ACESlib.OCIOShaper_to_Lin_param.a1.0.0.ctl'),
os.path.join('%s',
'utilities',
'ACESlib.Lin_to_OCIOShaper_param.a1.0.0.ctl'),
1.0,
log2_params]
shaper_data[dolby_pq_scaled_shaper_name] = dolby_pq_scaled_shaper_data
rrt_shaper = log2_shaper_data
# rrt_shaper = dolby_pq_scaled_shaper_data
# *RRT + ODT* combinations.
sorted_odts = sorted(odt_info.iteritems(), key=lambda x: x[1])
print(sorted_odts)
for odt in sorted_odts:
(odt_name, odt_values) = odt
# Defining full range transform for *ODTs* that can generate either
# *legal* or *full* output.
# Uncomment these lines and the lower section and
# flip the `legalRange` value to 1 to restore the old behavior,
# where both *legal* or *full* range *LUTs* were generated.
if odt_values['transformHasFullLegalSwitch']:
# odt_name_legal = '%s - Legal' % odt_values['transformUserName']
odt_legal['legalRange'] = 0
# else:
# odt_name_legal = odt_values['transformUserName']
odt_name_legal = odt_values['transformUserName']
odt_legal = odt_values.copy()
odt_aliases = ['out_%s' % compact(odt_name_legal)]
cs = create_ACES_RRT_plus_ODT(
odt_name_legal,
odt_legal,
rrt_shaper,
aces_ctl_directory,
lut_directory,
lut_resolution_3d,
cleanup,
odt_aliases)
colorspaces.append(cs)
displays[odt_name_legal] = {
'Raw': linear_display_space,
'Log': log_display_space,
'Output Transform': cs}
"""
# Generating full range transform for *ODTs* that can generate
# either *legal* or *full* output.
if odt_values['transformHasFullLegalSwitch']:
print('Generating full range ODT for %s' % odt_name)
odt_name_full = '%s - Full' % odt_values['transformUserName']
odt_full = odt_values.copy()
odt_full['legalRange'] = 0
odt_full_aliases = ['out_%s' % compact(odt_name_full)]
cs_full = create_ACES_RRT_plus_ODT(
odt_name_full,
odt_full,
rrt_shaper,
aces_ctl_directory,
lut_directory,
lut_resolution_1d,
lut_resolution_3d,
cleanup,
odt_full_aliases)
colorspaces.append(cs_full)
displays[odt_name_full] = {
'Raw': linear_display_space,
'Log': log_display_space,
'Output Transform': cs_full}
"""
return colorspaces, displays
def create_matrix_plus_transfer_colorspace(
name='matrix_plus_transfer',
transfer_function_name='transfer_function',
transfer_function=lambda x: x,
lut_directory='/tmp',
lut_resolution_1D=1024,
from_reference_values=None,
to_reference_values=None,
aliases=None):
"""
Creates a colorspace expressed as a single or multiple *MatrixTransform*
and 1D LUT *FileTransform* transformations.
Parameters
----------
name : str, optional
Aliases for this colorspace.
transfer_function_name : str, optional
The name of the transfer function.
transfer_function : function, optional
The transfer function to be evaluated.
lut_directory : str or unicode
The directory to use when generating LUTs.
lut_resolution_1D : int
The resolution of generated 1D LUTs.
from_reference_values : list of matrices
List of matrices to convert from the reference colorspace to this
colorspace.
to_reference_values : list of matrices
List of matrices to convert to the reference colorspace from this
colorspace.
aliases : list of str
Aliases for this colorspace.
Returns
-------
ColorSpace
A colorspace expressed as a single or multiple *MatrixTransform* and
1D LUT *FileTransform* transformations.
"""
if from_reference_values is None:
from_reference_values = []
if to_reference_values is None:
to_reference_values = []
if aliases is None:
aliases = []
cs = ColorSpace(name)
cs.description = 'The {0} color space'.format(name)
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
# A linear space needs allocation variables.
cs.allocation_type = ocio.Constants.ALLOCATION_UNIFORM
cs.allocation_vars = [0, 1]
# Sampling the transfer function.
data = array.array('f', b'\0' * lut_resolution_1D * 4)
for c in range(lut_resolution_1D):
data[c] = transfer_function(c / (lut_resolution_1D - 1))
# Writing the sampled data to a *LUT*.
lut = 'linear_to_{0}.spi1d'.format(transfer_function_name)
genlut.write_SPI_1D(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1D, 1)
# Creating the *to_reference* transforms.
cs.to_reference_transforms = []
if to_reference_values:
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'inverse'
})
for matrix in to_reference_values:
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33(matrix),
'direction':
'forward'
})
# Creating the *from_reference* transforms.
cs.from_reference_transforms = []
if from_reference_values:
for matrix in from_reference_values:
cs.from_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33(matrix),
'direction':
'forward'
})
cs.from_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
return cs
def create_ACESproxy(aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name='ACESproxy'):
"""
Creates the *ACESproxy* colorspace.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
Colorspace
*ACESproxy* colorspace.
"""
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = ['acesproxy', 'acesproxy_ap1']
cs.equality_group = ''
cs.family = 'ACES'
cs.is_data = False
cs.aces_transform_id = 'ACEScsc.ACESproxy10i_to_ACES.a1.0.0'
ctls = [os.path.join(aces_ctl_directory,
'ACESproxy',
'ACEScsc.ACESproxy10i_to_ACES.a1.0.0.ctl'),
# This transform gets back to the *AP1* primaries.
# Useful as the 1d LUT is only covering the transfer function.
# The primaries switch is covered by the matrix below:
os.path.join(aces_ctl_directory,
'ACEScg',
'ACEScsc.ACES_to_ACEScg.a1.0.0.ctl')]
lut = '%s_to_linear.spi1d' % name
lut = sanitize(lut)
generate_1d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_1d,
'float',
1,
1,
{},
cleanup,
aces_ctl_directory,
0,
1,
1)
cs.to_reference_transforms = []
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
# *AP1* primaries to *AP0* primaries
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(ACES_AP1_TO_AP0),
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_protune(gamut, transfer_function, lut_directory, lut_resolution_1d,
aliases):
"""
Creates colorspace covering the conversion from ProTune to ACES, with various transfer
functions and encoding gamuts covered
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
aliases : list of str
Aliases for this colorspace
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and identifying
information for the requested colorspace.
"""
# The gamut should be marked as experimental until matrices are fully
# verified.
name = '%s - %s - Experimental' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - %s - Experimental' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/GoPro'
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]
def protune_to_linear(normalized_code_value):
c1 = 113.0
c2 = 1.0
c3 = 112.0
linear = ((pow(c1, normalized_code_value) - c2) / c3)
return linear
cs.to_reference_transforms = []
if transfer_function == 'Protune Flat':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = protune_to_linear(float(c) / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
lut = sanitize(lut)
genlut.write_SPI_1d(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1d, 1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
if gamut == 'Protune Native':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix': [
0.533448429, 0.32413911, 0.142412421, 0, -0.050729924,
1.07572006, -0.024990416, 0, 0.071419661, -0.290521962,
1.219102381, 0, 0, 0, 0, 1
],
'direction':
'forward'
})
cs.from_reference_transforms = []
return cs
def create_ACEScg():
"""
Creates the *ACEScg* colorspace.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
Colorspace
*ACEScg* colorspace.
"""
name = 'ACEScg'
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = ['acescg', 'lin_ap1']
cs.equality_group = ''
cs.family = 'ACES'
cs.is_data = False
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
cs.aces_transform_id = 'ACEScsc.ACEScg_to_ACES.a1.0.0'
cs.to_reference_transforms = []
# *AP1* primaries to *AP0* primaries
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(ACES_AP1_TO_AP0),
'direction': 'forward'})
cs.from_reference_transforms = []
# *AP1* primaries to *AP0* primaries
cs.from_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(ACES_AP0_TO_AP1),
'direction': 'forward'})
return cs
def create_matrix_plus_transfer_colorspace(
name='matrix_plus_transfer',
transfer_function_name='transfer_function',
transfer_function=lambda x: x,
lut_directory='/tmp',
lut_resolution_1d=1024,
from_reference_values=None,
to_reference_values=None,
aliases=None):
"""
Creates a ColorSpace that uses transfer functions encoded as 1D LUTs and
matrice
Parameters
----------
name : str, optional
Aliases for this colorspace
transfer_function_name : str, optional
The name of the transfer function
transfer_function : function, optional
The transfer function to be evaluated
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
from_reference_values : list of matrices
List of matrices to convert from the reference colorspace to this space
to_reference_values : list of matrices
List of matrices to convert to the reference colorspace from this space
aliases : list of str
Aliases for this colorspace
Returns
-------
ColorSpace
A *Matrx and LUT1D Transform*-based ColorSpace representing a transfer
function and matrix
"""
if from_reference_values is None:
from_reference_values = []
if to_reference_values is None:
to_reference_values = []
if aliases is None:
aliases = []
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
# A linear space needs allocation variables.
cs.allocation_type = ocio.Constants.ALLOCATION_UNIFORM
cs.allocation_vars = [0, 1]
# Sampling the transfer function.
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = transfer_function(c / (lut_resolution_1d - 1))
# Writing the sampled data to a *LUT*.
lut = '%s_to_linear.spi1d' % transfer_function_name
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
# Creating the *to_reference* transforms.
cs.to_reference_transforms = []
if to_reference_values:
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
for matrix in to_reference_values:
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(matrix),
'direction': 'forward'})
# Creating the *from_reference* transforms.
cs.from_reference_transforms = []
if from_reference_values:
for matrix in from_reference_values:
cs.from_reference_transforms.append({
'type': 'matrix',
'matrix': mat44_from_mat33(matrix),
'direction': 'forward'})
cs.from_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'inverse'})
return cs
def create_ADX(lut_directory,
bit_depth=10,
name='ADX'):
"""
Creates the *ADX* colorspace.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
Colorspace
*ADX* colorspace.
"""
name = '%s%s' % (name, bit_depth)
cs = ColorSpace(name)
cs.description = '%s color space - used for film scans' % name
cs.aliases = ['adx%s' % str(bit_depth)]
cs.equality_group = ''
cs.family = 'ADX'
cs.is_data = False
if bit_depth == 10:
cs.aces_transform_id = 'ACEScsc.ADX10_to_ACES.a1.0.0'
cs.bit_depth = ocio.Constants.BIT_DEPTH_UINT10
ADX_to_CDD = [1023 / 500, 0, 0, 0,
0, 1023 / 500, 0, 0,
0, 0, 1023 / 500, 0,
0, 0, 0, 1]
offset = [-95 / 500, -95 / 500, -95 / 500, 0]
elif bit_depth == 16:
cs.aces_transform_id = 'ACEScsc.ADX16_to_ACES.a1.0.0'
cs.bit_depth = ocio.Constants.BIT_DEPTH_UINT16
ADX_to_CDD = [65535 / 8000, 0, 0, 0,
0, 65535 / 8000, 0, 0,
0, 0, 65535 / 8000, 0,
0, 0, 0, 1]
offset = [-1520 / 8000, -1520 / 8000, -1520 / 8000, 0]
cs.to_reference_transforms = []
# Converting from *ADX* to *Channel-Dependent Density*.
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': ADX_to_CDD,
'offset': offset,
'direction': 'forward'})
# Converting from *Channel-Dependent Density* to
# *Channel-Independent Density*.
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.75573, 0.22197, 0.02230, 0,
0.05901, 0.96928, -0.02829, 0,
0.16134, 0.07406, 0.76460, 0,
0, 0, 0, 1],
'direction': 'forward'})
# Copied from *Alex Fry*'s *adx_cid_to_rle.py*
def create_CID_to_RLE_LUT():
def interpolate_1d(x, xp, fp):
return numpy.interp(x, xp, fp)
LUT_1D_XP = [-0.190000000000000,
0.010000000000000,
0.028000000000000,
0.054000000000000,
0.095000000000000,
0.145000000000000,
0.220000000000000,
0.300000000000000,
0.400000000000000,
0.500000000000000,
0.600000000000000]
LUT_1D_FP = [-6.000000000000000,
-2.721718645000000,
-2.521718645000000,
-2.321718645000000,
-2.121718645000000,
-1.921718645000000,
-1.721718645000000,
-1.521718645000000,
-1.321718645000000,
-1.121718645000000,
-0.926545676714876]
REF_PT = ((7120 - 1520) / 8000 * (100 / 55) -
math.log(0.18, 10))
def cid_to_rle(x):
if x <= 0.6:
return interpolate_1d(x, LUT_1D_XP, LUT_1D_FP)
return (100 / 55) * x - REF_PT
def fit(value, from_min, from_max, to_min, to_max):
if from_min == from_max:
raise ValueError('from_min == from_max')
return (value - from_min) / (from_max - from_min) * (
to_max - to_min) + to_min
num_samples = 2 ** 12
domain = (-0.19, 3)
data = []
for i in xrange(num_samples):
x = i / (num_samples - 1)
x = fit(x, 0, 1, domain[0], domain[1])
data.append(cid_to_rle(x))
lut = 'ADX_CID_to_RLE.spi1d'
write_SPI_1d(os.path.join(lut_directory, lut),
domain[0],
domain[1],
data,
num_samples, 1)
return lut
# Converting *Channel Independent Density* values to
# *Relative Log Exposure* values.
lut = create_CID_to_RLE_LUT()
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
# Converting *Relative Log Exposure* values to
# *Relative Exposure* values.
cs.to_reference_transforms.append({
'type': 'log',
'base': 10,
'direction': 'inverse'})
# Convert *Relative Exposure* values to *ACES* values.
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.72286, 0.12630, 0.15084, 0,
0.11923, 0.76418, 0.11659, 0,
0.01427, 0.08213, 0.90359, 0,
0, 0, 0, 1],
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_generic_log(aces_ctl_directory,
lut_directory,
lut_resolution_1d,
cleanup,
name='log',
aliases=[],
min_value=0,
max_value=1,
input_scale=1,
middle_grey=0.18,
min_exposure=-6,
max_exposure=6.5):
"""
Creates the *Generic Log* colorspace.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
Colorspace
*Generic Log* colorspace.
"""
cs = ColorSpace(name)
cs.description = 'The %s color space' % name
cs.aliases = aliases
cs.equality_group = name
cs.family = 'Utility'
cs.is_data = False
ctls = [os.path.join(
aces_ctl_directory,
'utilities',
'ACESlib.OCIO_shaper_log2_to_lin_param.a1.0.0.ctl')]
lut = '%s_to_linear.spi1d' % name
lut = sanitize(lut)
generate_1d_LUT_from_CTL(
os.path.join(lut_directory, lut),
ctls,
lut_resolution_1d,
'float',
input_scale,
1,
{'middleGrey': middle_grey,
'minExposure': min_exposure,
'maxExposure': max_exposure},
cleanup,
aces_ctl_directory,
min_value,
max_value,
1)
cs.to_reference_transforms = []
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_ACES_RRT_plus_ODT(
odt_name,
odt_values,
shaper_info,
aces_ctl_directory,
lut_directory,
lut_resolution_1d=1024,
lut_resolution_3d=64,
cleanup=True,
aliases=None,
):
"""
Object description.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
if aliases is None:
aliases = []
cs = ColorSpace("%s" % odt_name)
cs.description = "%s - %s Output Transform" % (odt_values["transformUserNamePrefix"], odt_name)
cs.aliases = aliases
cs.equality_group = ""
cs.family = "Output"
cs.is_data = False
cs.aces_transform_id = odt_values["transformID"]
pprint.pprint(odt_values)
# Generating the *shaper* transform.
(shaper_name, shaper_to_ACES_CTL, shaper_from_ACES_CTL, shaper_input_scale, shaper_params) = shaper_info
if "legalRange" in odt_values:
shaper_params["legalRange"] = odt_values["legalRange"]
else:
shaper_params["legalRange"] = 0
# Add the shaper transform
shaper_lut = "%s_to_linear.spi1d" % shaper_name
shaper_lut = sanitize(shaper_lut)
shaper_OCIO_transform = {"type": "lutFile", "path": shaper_lut, "interpolation": "linear", "direction": "inverse"}
# Generating the *forward* transform.
cs.from_reference_transforms = []
if "transformLUT" in odt_values:
transform_LUT_file_name = os.path.basename(odt_values["transformLUT"])
lut = os.path.join(lut_directory, transform_LUT_file_name)
shutil.copy(odt_values["transformLUT"], lut)
cs.from_reference_transforms.append(shaper_OCIO_transform)
cs.from_reference_transforms.append(
{"type": "lutFile", "path": transform_LUT_file_name, "interpolation": "tetrahedral", "direction": "forward"}
)
elif "transformCTL" in odt_values:
ctls = [
shaper_to_ACES_CTL % aces_ctl_directory,
os.path.join(aces_ctl_directory, "rrt", "RRT.a1.0.0.ctl"),
os.path.join(aces_ctl_directory, "odt", odt_values["transformCTL"]),
]
lut = "%s.RRT.a1.0.0.%s.spi3d" % (shaper_name, odt_name)
lut = sanitize(lut)
generate_3d_LUT_from_CTL(
os.path.join(lut_directory, lut),
# shaperLUT,
ctls,
lut_resolution_3d,
"float",
1 / shaper_input_scale,
1,
shaper_params,
cleanup,
aces_ctl_directory,
)
cs.from_reference_transforms.append(shaper_OCIO_transform)
cs.from_reference_transforms.append(
{"type": "lutFile", "path": lut, "interpolation": "tetrahedral", "direction": "forward"}
)
# Generating the *inverse* transform.
cs.to_reference_transforms = []
if "transformLUTInverse" in odt_values:
transform_LUT_inverse_file_name = os.path.basename(odt_values["transformLUTInverse"])
lut = os.path.join(lut_directory, transform_LUT_inverse_file_name)
shutil.copy(odt_values["transformLUTInverse"], lut)
cs.to_reference_transforms.append(
{
"type": "lutFile",
"path": transform_LUT_inverse_file_name,
"interpolation": "tetrahedral",
"direction": "forward",
}
)
shaper_inverse = shaper_OCIO_transform.copy()
shaper_inverse["direction"] = "forward"
cs.to_reference_transforms.append(shaper_inverse)
elif "transformCTLInverse" in odt_values:
ctls = [
os.path.join(aces_ctl_directory, "odt", odt_values["transformCTLInverse"]),
os.path.join(aces_ctl_directory, "rrt", "InvRRT.a1.0.0.ctl"),
shaper_from_ACES_CTL % aces_ctl_directory,
]
lut = "InvRRT.a1.0.0.%s.%s.spi3d" % (odt_name, shaper_name)
lut = sanitize(lut)
generate_3d_LUT_from_CTL(
os.path.join(lut_directory, lut),
# None,
ctls,
lut_resolution_3d,
"half",
1,
shaper_input_scale,
shaper_params,
cleanup,
aces_ctl_directory,
)
cs.to_reference_transforms.append(
{"type": "lutFile", "path": lut, "interpolation": "tetrahedral", "direction": "forward"}
)
shaper_inverse = shaper_OCIO_transform.copy()
shaper_inverse["direction"] = "forward"
cs.to_reference_transforms.append(shaper_inverse)
return cs
def create_protune(gamut,
transfer_function,
lut_directory,
lut_resolution_1d,
aliases):
"""
Object description.
Protune to ACES.
Parameters
----------
parameter : type
Parameter description.
Returns
-------
type
Return value description.
"""
# The gamut should be marked as experimental until matrices are fully
# verified.
name = '%s - %s - Experimental' % (transfer_function, gamut)
if transfer_function == '':
name = 'Linear - %s - Experimental' % gamut
if gamut == '':
name = 'Curve - %s' % transfer_function
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/GoPro'
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]
def protune_to_linear(normalized_code_value):
c1 = 113.0
c2 = 1.0
c3 = 112.0
linear = ((pow(c1, normalized_code_value) - c2) / c3)
return linear
cs.to_reference_transforms = []
if transfer_function == 'Protune Flat':
data = array.array('f', '\0' * lut_resolution_1d * 4)
for c in range(lut_resolution_1d):
data[c] = protune_to_linear(float(c) / (lut_resolution_1d - 1))
lut = '%s_to_linear.spi1d' % transfer_function
lut = sanitize(lut)
genlut.write_SPI_1d(
os.path.join(lut_directory, lut),
0,
1,
data,
lut_resolution_1d,
1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'})
if gamut == 'Protune Native':
cs.to_reference_transforms.append({
'type': 'matrix',
'matrix': [0.533448429, 0.32413911, 0.142412421, 0,
-0.050729924, 1.07572006, -0.024990416, 0,
0.071419661, -0.290521962, 1.219102381, 0,
0, 0, 0, 1],
'direction': 'forward'})
cs.from_reference_transforms = []
return cs
def create_log_c(gamut, transfer_function, exposure_index, lut_directory,
lut_resolution_1d, aliases):
"""
Creates colorspace covering the conversion from LogC to ACES, with various transfer
functions and encoding gamuts covered
Parameters
----------
gamut : str
The name of the encoding gamut to use.
transfer_function : str
The name of the transfer function to use
exposure_index : str
The exposure index to use
lut_directory : str or unicode
The directory to use when generating LUTs
lut_resolution_1d : int
The resolution of generated 1D LUTs
aliases : list of str
Aliases for this colorspace
Returns
-------
ColorSpace
A ColorSpace container class referencing the LUTs, matrices and identifying
information for the requested colorspace.
"""
name = '%s (EI%s) - %s' % (transfer_function, exposure_index, gamut)
if transfer_function == '':
name = 'Linear - ARRI %s' % gamut
if gamut == '':
name = 'Curve - %s (EI%s)' % (transfer_function, exposure_index)
cs = ColorSpace(name)
cs.description = name
cs.aliases = aliases
cs.equality_group = ''
cs.family = 'Input/ARRI'
cs.is_data = False
if gamut and transfer_function:
cs.aces_transform_id = ('IDT.ARRI.Alexa-v3-logC-EI%s.a1.v1' %
exposure_index)
# A linear space needs allocation variables.
if transfer_function == '':
cs.allocation_type = ocio.Constants.ALLOCATION_LG2
cs.allocation_vars = [-8, 5, 0.00390625]
IDT_maker_version = '0.08'
nominal_EI = 400
black_signal = 0.003907
mid_gray_signal = 0.01
encoding_gain = 0.256598
encoding_offset = 0.391007
def gain_for_EI(EI):
return (math.log(EI / nominal_EI) / math.log(2) *
(0.89 - 1) / 3 + 1) * encoding_gain
def log_c_inverse_parameters_for_EI(EI):
cut = 1 / 9
slope = 1 / (cut * math.log(10))
offset = math.log10(cut) - slope * cut
gain = EI / nominal_EI
gray = mid_gray_signal / gain
# The higher the EI, the lower the gamma.
enc_gain = gain_for_EI(EI)
enc_offset = encoding_offset
for i in range(0, 3):
nz = ((95 / 1023 - enc_offset) / enc_gain - offset) / slope
enc_offset = encoding_offset - math.log10(1 + nz) * enc_gain
a = 1 / gray
b = nz - black_signal / gray
e = slope * a * enc_gain
f = enc_gain * (slope * b + offset) + enc_offset
# Ensuring we can return relative exposure.
s = 4 / (0.18 * EI)
t = black_signal
b += a * t
a *= s
f += e * t
e *= s
return {
'a': a,
'b': b,
'cut': (cut - b) / a,
'c': enc_gain,
'd': enc_offset,
'e': e,
'f': f
}
def normalized_log_c_to_linear(code_value, exposure_index):
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 - p['d']) / p['c']) - p['b']) / p['a'])
else:
linear = (code_value - 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] = normalized_log_c_to_linear(c / (lut_resolution_1d - 1),
int(exposure_index))
lut = '%s_to_linear.spi1d' % ('%s_%s' %
(transfer_function, exposure_index))
lut = sanitize(lut)
genlut.write_SPI_1d(os.path.join(lut_directory, lut), 0, 1, data,
lut_resolution_1d, 1)
cs.to_reference_transforms.append({
'type': 'lutFile',
'path': lut,
'interpolation': 'linear',
'direction': 'forward'
})
if gamut == 'Wide Gamut':
cs.to_reference_transforms.append({
'type':
'matrix',
'matrix':
mat44_from_mat33([
0.680206, 0.236137, 0.083658, 0.085415, 1.017471, -0.102886,
0.002057, -0.062563, 1.060506
]),
'direction':
'forward'
})
cs.from_reference_transforms = []
return cs