def spectrum_to_XYZ(domain, values):
""" Converts a spectral distribution into a XYZ colour """
spd = colour.SpectralPowerDistribution(
values, domain=domain, interpolator=colour.NullInterpolator)
cmfs = colour.STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer']
illuminant = colour.ILLUMINANTS_RELATIVE_SPDS['D65']
return colour.spectral_to_XYZ(spd, cmfs, illuminant)
def spectr_LED(abc, spd_red0, spd_green0, spd_blue0):
sample_spd_data = {}
a, b, c = abc[0], abc[1], abc[2]
for lamb in np.arange(200, 800, 0.9):
chip_3d = a * spd_red0[lamb] + b * spd_green0[
lamb] + c * spd_blue0[lamb]
sample_spd_data[lamb] = float(chip_3d)
spd = colour.SpectralPowerDistribution('Sample', sample_spd_data)
spd.interpolate(colour.SpectralShape(200, 800, 1))
return spd
def calculateColorValues(splineT, splineR, settings):
'''Function calculates color values of the Transmission and Refelection side of the stack.
Input Arguments are tranmission and reflection spline functions.
Returns array of values/tuples of different standards.'''
import colour
import numpy as np
wvl = np.linspace(380, 780, 81)
dic_T = {}
dic_R = {}
dic_test = {}
for idx, value in enumerate(wvl):
dic_T[value] = splineT(value).item()
dic_R[value] = splineR(value).item()
#Removes warnings from conversions.
colour.filter_warnings()
cmfs = colour.STANDARD_OBSERVERS_CMFS[settings.color_cmfs] #1931 etc
illuminant = colour.ILLUMINANTS_RELATIVE_SPDS[
settings.color_illuminant] #D65, A, C
T_spd = colour.SpectralPowerDistribution('', dic_T)
T_XYZ = colour.spectral_to_XYZ(T_spd, cmfs, illuminant)
T_xy = colour.XYZ_to_xy(T_XYZ / 100)
T_ab = colour.XYZ_to_Lab(T_XYZ / 100,
illuminant=colour.ILLUMINANTS[settings.color_cmfs]
[settings.color_illuminant])
T_rgb = colour.XYZ_to_sRGB(
T_XYZ / 100,
illuminant=colour.ILLUMINANTS[settings.color_cmfs][
settings.color_illuminant])
R_spd = colour.SpectralPowerDistribution('', dic_R)
R_XYZ = colour.spectral_to_XYZ(R_spd, cmfs, illuminant)
R_xy = colour.XYZ_to_xy(R_XYZ / 100)
R_ab = colour.XYZ_to_Lab(R_XYZ / 100,
illuminant=colour.ILLUMINANTS[settings.color_cmfs]
[settings.color_illuminant])
R_rgb = colour.XYZ_to_sRGB(
R_XYZ / 100,
illuminant=colour.ILLUMINANTS[settings.color_cmfs][
settings.color_illuminant])
return (T_XYZ, T_xy, T_ab, T_rgb, R_XYZ, R_xy, R_ab, R_rgb)
def open_spd(sample, name):
sample_spd_data = {}
with open(sample) as f:
for iii in f:
(key, val) = iii.split()
key = float(key)
val = float(val)
key = round(key)
val = round(val, 5)
sample_spd_data[key] = float(val)
spd = colour.SpectralPowerDistribution(name, sample_spd_data)
clone_spd = spd.clone()
clone_spd.interpolate(colour.SpectralShape(200, 800, 1))
return clone_spd
def spectr_LED4(abca, spd_red0, spd_green0, spd_blue0, spd_yellow0):
sample_spd_data = {}
b1, b2, b3, a = abca[0], abca[1], abca[2], abca[3]
chip_values = []
for lamb in np.arange(200, 800, 0.9):
chip_4d = b1 * spd_red0[lamb] + b2 * spd_green0[lamb] + b3 * spd_blue0[
lamb] + a * spd_yellow0[lamb]
sample_spd_data[lamb] = float(chip_4d)
chip_values.append(chip_4d)
max_del = max(chip_values)
for sample in sample_spd_data:
sample_spd_data[sample] = sample_spd_data[sample] / max_del
spd = colour.SpectralPowerDistribution('Sample', sample_spd_data)
spd.interpolate(colour.SpectralShape(200, 800, 1))
return spd
660: 0.01347059,
665: 0.01424768,
670: 0.01215791,
675: 0.01209338,
680: 0.01155313,
685: 0.01061995,
690: 0.01014779,
695: 0.00864212,
700: 0.00951386,
705: 0.00786982,
710: 0.00841476,
715: 0.00741868,
720: 0.00637711,
725: 0.00556483,
730: 0.00590016,
735: 0.00416819,
740: 0.00422222,
745: 0.00345776,
750: 0.00336879,
755: 0.00298999,
760: 0.00367047,
765: 0.00340568,
770: 0.00261153,
775: 0.00258850,
780: 0.00293663
}
print(
colour.colour_quality_scale(
colour.SpectralPowerDistribution(SAMPLE_SPD_DATA, name='Sample')))
782: 84.19,
784: 84.21,
786: 84.22,
788: 84.25,
790: 84.29,
792: 84.29,
794: 84.31,
796: 84.31,
798: 84.34,
800: 84.34,
820: 84.47
}
message_box('Plotting various single spectral power distributions.')
single_spd_plot(
colour.SpectralPowerDistribution(sample_spd_data, name='Custom'))
single_spd_plot(
colour.SpectralPowerDistribution(galvanized_steel_metal_spd_data,
name='Galvanized Steel Metal'))
print('\n')
message_box('Plotting multiple spectral power distributions.')
multi_spd_plot(
(colour.SpectralPowerDistribution(galvanized_steel_metal_spd_data,
name='Galvanized Steel Metal'),
colour.SpectralPowerDistribution(white_marble_spd_data,
name='White Marble')))
print('\n')
660: 0.204,
665: 0.213,
670: 0.222,
675: 0.231,
680: 0.242,
685: 0.251,
690: 0.261,
695: 0.271,
700: 0.282,
705: 0.294,
710: 0.305,
715: 0.318,
720: 0.334,
725: 0.354,
730: 0.372,
735: 0.392,
740: 0.409,
745: 0.420,
750: 0.436,
755: 0.450,
760: 0.462,
765: 0.465,
770: 0.448,
775: 0.432,
780: 0.421
}
spd = colour.SpectralPowerDistribution(sample_spd_data, name='Sample')
uncorrected_values = spd.values
print(np.dstack((uncorrected_values, colour.bandpass_correction(spd).values)))
computations.
"""
import colour
from colour.utilities import message_box
message_box('"ACES" "Input Device Transform" Computations')
message_box(('Computing "ACES" relative exposure '
'values for some colour rendition chart spectral power '
'distributions:\n'
'\n\t("dark skin", \n\t"blue sky")'))
print(colour.spectral_to_aces_relative_exposure_values(
colour.COLOURCHECKERS_SPDS['ColorChecker N Ohta']['dark skin']))
print(colour.spectral_to_aces_relative_exposure_values(
colour.COLOURCHECKERS_SPDS['ColorChecker N Ohta']['blue sky']))
print('\n')
message_box(('Computing "ACES" relative exposure values for various ideal '
'reflectors:\n'
'\n\t("18%", \n\t"100%")'))
wavelengths = colour.models.ACES_RICD.wavelengths
gray_reflector = colour.SpectralPowerDistribution(
dict(zip(wavelengths, [0.18] * len(wavelengths))), name='18%')
print(repr(colour.spectral_to_aces_relative_exposure_values(gray_reflector)))
perfect_reflector = colour.SpectralPowerDistribution(
dict(zip(wavelengths, [1.] * len(wavelengths))), name='100%')
print(colour.spectral_to_aces_relative_exposure_values(perfect_reflector))
778: 84.18,
780: 84.19,
782: 84.19,
784: 84.21,
786: 84.22,
788: 84.25,
790: 84.29,
792: 84.29,
794: 84.31,
796: 84.31,
798: 84.34,
800: 84.34,
820: 84.47}
message_box('Plotting various single spectral power distributions.')
single_spd_plot(colour.SpectralPowerDistribution('Custom', sample_spd_data))
single_spd_plot(colour.SpectralPowerDistribution(
'Galvanized Steel Metal',
galvanized_steel_metal_spd_data))
print('\n')
message_box('Plotting multiple spectral power distributions.')
multi_spd_plot((colour.SpectralPowerDistribution(
'Galvanized Steel Metal',
galvanized_steel_metal_spd_data),
colour.SpectralPowerDistribution('White Marble', white_marble_spd_data)))
print('\n')
message_box('Plotting spectral bandpass dependence correction.')
580: 0.1128,
600: 0.1360,
620: 0.1511,
640: 0.1688,
660: 0.1996,
680: 0.2397,
700: 0.2852,
720: 0.0000,
740: 0.0000,
760: 0.0000,
780: 0.0000,
800: 0.0000,
820.9: 0.0000}
base_spd = colour.SpectralPowerDistribution(
'Reference',
uniform_spd_data)
uniform_interpolated_spd = colour.SpectralPowerDistribution(
'Uniform - Sprague Interpolation',
uniform_spd_data)
uniform_pchip_interpolated_spd = colour.SpectralPowerDistribution(
'Uniform - Pchip Interpolation',
uniform_spd_data)
non_uniform_interpolated_spd = colour.SpectralPowerDistribution(
'Non Uniform - Cubic Spline Interpolation',
non_uniform_spd_data)
uniform_interpolated_spd.interpolate(colour.SpectralShape(interval=1))
uniform_pchip_interpolated_spd.interpolate(colour.SpectralShape(interval=1),
method='Pchip')
non_uniform_interpolated_spd.interpolate(colour.SpectralShape(interval=1))
from random import random, seed
seed(20180501)
from iminuit import Minuit, describe, Struct
from iminuit.util import make_func_code # lo utiliza Chi2Functor
from math import pi, exp, sqrt
#import colour.colorimetry as colorimetry
"""
Conviete valores RGB cualquiera, dado como constante, a xy y lo grafica como un punto dentro chromaticity diagram standar
"""
from colour.plotting import (
colour_plotting_defaults, chromaticity_diagram_plot_CIE1931,render,single_spd_plot)
# Defining a sample spectral power distribution data.
sample_spd_data = {380+i*5: random() for i in range(80)}
spd = colour.SpectralPowerDistribution(sample_spd_data)
single_spd_plot(spd)
cmfs = colour.STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
XYZ = colour.spectral_to_XYZ(spd, cmfs)
#ch1=colour.XYZ_to_spectral(XYZ,Method='Meng 2015')
ch1=colour.XYZ_to_spectral(XYZ,Method='Smits 1999')
single_spd_plot(ch1)
#spd_rec=ch1.values
y=ch1.values
x=[360+i*5 for i in range(95)]
#for i, v in enumerate(spd_rec):
# x=360+i*5
# y=v
#this is very useful if you want to build a generic cost functor
#this is actually how probfit is implemented
#x=sorted(sample_spd_data.keys())
def colour_spectr(sample_spd_data, name):
spd = colour.SpectralPowerDistribution(name, sample_spd_data)
spd.interpolate(colour.SpectralShape(200, 800, 1))
return spd
def to_spd(self):
self.spd = colour.SpectralPowerDistribution(self.spec_dict,
name=self.name)
return self.spd
660: 0.01347059,
665: 0.01424768,
670: 0.01215791,
675: 0.01209338,
680: 0.01155313,
685: 0.01061995,
690: 0.01014779,
695: 0.00864212,
700: 0.00951386,
705: 0.00786982,
710: 0.00841476,
715: 0.00741868,
720: 0.00637711,
725: 0.00556483,
730: 0.00590016,
735: 0.00416819,
740: 0.00422222,
745: 0.00345776,
750: 0.00336879,
755: 0.00298999,
760: 0.00367047,
765: 0.00340568,
770: 0.00261153,
775: 0.00258850,
780: 0.00293663
}
print(
colour.colour_quality_scale(
colour.SpectralPowerDistribution('Sample', SAMPLE_SPD_DATA)))
720: 0.334,
725: 0.354,
730: 0.372,
735: 0.392,
740: 0.409,
745: 0.420,
750: 0.436,
755: 0.450,
760: 0.462,
765: 0.465,
770: 0.448,
775: 0.432,
780: 0.421
}
spd = colour.SpectralPowerDistribution('Sample', sample_spd_data)
message_box('Sample spectral power distribution shape.')
print(spd.shape)
print('\n')
message_box('Sample spectral power distribution uniformity.')
print(spd.is_uniform())
print('\n')
message_box(('Sample spectral power distribution cloning:\n'
'\n\t("Original Id", "Clone Id")\n'
'\nCloning is a convenient way to get a copy of the spectral '
'power distribution, this an important feature because some '
mLConvert = .05 / mL.shape[1]
#Need to do own michel-levy chart
def mlEQ(lam, ret):
'''lam: wavelength in nanometers
ret: retardence in microns'''
return np.sin(np.pi * ret / lam * 10**3)**2.
lamL = np.arange(360, 830, 5)
retL = np.arange(.05, 0.3, .0001) * 5
I = np.array([[mlEQ(lam, ret) for lam in lamL] for ret in retL])
XYZ = [
colour.spectral_to_XYZ(colour.SpectralPowerDistribution(
{lam: i
for (lam, i) in zip(lamL, iL)}),
illuminant=colour.ILLUMINANTS_RELATIVE_SPDS["D65"])
for iL in I
]
ilA = colour.ILLUMINANTS['CIE 1931 2 Degree Standard Observer'][
'D65'] #don't really know what this is, but I think I'm using a halogen lamp
M = np.array([[2.04414, -.5649, -0.3447], [-0.9693, 1.8760, 0.0416],
[0.0134, -0.1184, 1.0154]])
#RGB = [list(colour.XYZ_to_RGB(xyz/100,ilA,ilA,M)) for xyz in XYZ]
sRGB = [list(colour.XYZ_to_sRGB(xyz / 100)) for xyz in XYZ]
#psd = {lam:i for (lam,i) in zip(lamL,I)}
#psd = colour.SpectralPowerDistribution(psd)
#XYZ = colour.spectral_to_XYZ(psd)
#mlChart = np.array([[i for i in RGB] for j in np.arange(0,1000)])
mlChart2 = np.array([[i for i in sRGB] for j in np.arange(0, 200)])
