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import numpy as np
import multiprocessing
from colour import *
from colour.models import eotf_inverse_sRGB
from colour.recovery import coefficients_Jakob2019
from gsoc_common import model_sd, D65, D65_xy, plot_comparison
# Solve for a specific RGB color
def optimize_RGB(linear_RGB, coeffs_0):
RGB = eotf_inverse_sRGB(linear_RGB)
XYZ = sRGB_to_XYZ(RGB, D65_xy)
coeffs, error = coefficients_Jakob2019(
XYZ,
dimensionalise=False,
coefficients_0=coeffs_0
)
if error > 1e-6:
with open("out/bad.txt", "a") as fd:
fd.write("\n")
fd.write("linear_RGB=%s\n" % linear_RGB)
fd.write("XYZ=%s\n" % XYZ)
fd.write("coeffs_0=%s\n" % coeffs_0)
fd.write("coeffs=%s, error=%g\n" % (coeffs, error))
# A low budget graph to quickly see what's going on
log_error = np.log10(error)
bars = "|" * max(0, int(5 * (log_error + 9)))
print("%12.5g %12.5g %12.5g %5.3f %s" % (*XYZ, log_error, bars))
return coeffs
# Solve for all lightness values of a fully saturated RGB color
def optimize_chromaticity(linear_RGB):
# alpha's aren't spaced equally (see the article)
def smoothstep(x):
return x**2 * (3 - 2 * x)
steps = np.arange(0, LIGHTNESS_STEPS)
alphas = smoothstep(smoothstep(steps / LIGHTNESS_STEPS))
i_mid = LIGHTNESS_STEPS // 5
coeffs_mid = optimize_RGB(linear_RGB * alphas[i_mid], (0, 0, 0))
coeffs_0 = coeffs_mid
for i in range(i_mid + 1, LIGHTNESS_STEPS):
coeffs_0 = optimize_RGB(linear_RGB * alphas[i], coeffs_0)
coeffs_0 = coeffs_mid
for i in reversed(range(0, i_mid)):
coeffs_0 = optimize_RGB(linear_RGB * alphas[i], coeffs_0)
# This program runs XYZ_to_sd_Jakob2019 converges on a large number of inputs,
# covering an entire RGB gamut, to see if it'll diverge somewhere.
if __name__ == "__main__":
CHROMA_STEPS = 8
LIGHTNESS_STEPS = 64
with open("out/bad.txt", "w") as fd:
fd.write("Going through %dx%dx%d cubes\n"
% (LIGHTNESS_STEPS, CHROMA_STEPS, CHROMA_STEPS))
args = []
for A in np.linspace(0, 1, CHROMA_STEPS):
for B in np.linspace(0, 1, CHROMA_STEPS):
for RGB in [np.array([1, A, B]),
np.array([A, 1, B]),
np.array([A, B, 1])]:
args.append(RGB)
pool = multiprocessing.Pool()
pool.map(optimize_chromaticity, args)
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