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
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_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_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_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): """ 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] # 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_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): """ 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_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_v_log(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 = '%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_s_log(gamut, transfer_function, name, lut_directory, lut_resolution_1d, aliases): """ Object description. SLog 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/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 = 64.0 ab = 90.0 w = 940.0 if s_log >= ab: linear = (pow(10.0, (((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) * 0.9 return linear def s_log2_to_linear(s_log): b = 64.0 ab = 90.0 w = 940.0 if s_log >= ab: linear = ( 219.0 * (pow(10.0, (((s_log - b) / (w - b) - 0.616596 - 0.03) / 0.432699)) - 0.037584) / 155.0 ) * 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_RED_log_film(gamut, transfer_function, name, lut_directory, lut_resolution_1d, aliases=[]): """ Object description. RED colorspaces 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/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, 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 = "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
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
def create_s_log(gamut, transfer_function, name, lut_directory, lut_resolution_1d, aliases): """ Object description. SLog 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 = '%s' % transfer_function cs = ColorSpace(name) cs.description = name cs.aliases = aliases cs.equality_group = '' cs.family = 'Input/Sony' 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 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_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_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_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_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_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_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_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_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_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