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import numpy as np
from colour import *
from colour.plotting import *
from matplotlib import pyplot as plt
from jakob_hanika import jakob_hanika, model_sd
illuminant = "D65"
ill_xy = ILLUMINANTS["CIE 1931 2 Degree Standard Observer"][illuminant]
ill_sd = SpectralDistribution(ILLUMINANT_SDS[illuminant])
# Makes a comparison plot with SDs and swatches
def plot_comparison(target, matched_sd, label, error, ill_sd, show=True):
if type(target) is SpectralDistribution:
target_XYZ = sd_to_XYZ(target, illuminant=ill_sd) / 100
else:
target_XYZ = target
target_RGB = np.clip(XYZ_to_sRGB(target_XYZ), 0, 1)
target_swatch = ColourSwatch(label, target_RGB)
matched_XYZ = sd_to_XYZ(matched_sd, illuminant=ill_sd) / 100
matched_RGB = np.clip(XYZ_to_sRGB(matched_XYZ), 0, 1)
matched_swatch = ColourSwatch("Model", matched_RGB)
axes = plt.subplot(2, 1, 1)
plt.title(label)
if type(target) is SpectralDistribution:
plot_multi_sds([target, matched_sd], axes=axes, standalone=False)
else:
plot_single_sd(matched_sd, axes=axes, standalone=False)
axes = plt.subplot(2, 1, 2)
plt.title("ΔE = %g" % error)
plot_multi_colour_swatches([target_swatch, matched_swatch],
standalone=show, axes=axes)
if __name__ == "__main__":
# This demo goes through SDs in a color checker
for name, sd in COLOURCHECKERS_SDS['ColorChecker N Ohta'].items():
XYZ = sd_to_XYZ(sd, illuminant=ill_sd) / 100
print("Color checker: The target is '%s' with X=%g, Y=%g, Z=%g" % (name, *XYZ))
ccp, error = jakob_hanika(XYZ, ill_sd, ill_xy)
matched_sd = model_sd(ccp)
plot_comparison(sd, matched_sd, name, error, ill_sd)
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