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import os
import matplotlib.pyplot as plt
from colour import (
SpectralShape, COLOURCHECKER_SDS, ILLUMINANT_SDS, sd_to_XYZ)
from otsu2018 import load_Otsu2018_spectra, Clustering
if __name__ == '__main__':
print('Loading spectral data...')
sds = load_Otsu2018_spectra('CommonData/spectrum_m.csv', every_nth=1)
shape = SpectralShape(380, 730, 10)
print('Initializing the clustering...')
clustering = Clustering(sds, shape)
print('Clustering...')
before = clustering.root.total_reconstruction_error()
clustering.optimise()
after = clustering.root.total_reconstruction_error()
print('Error before: %g' % before)
print('Error after: %g' % after)
print('Saving the dataset...')
os.makedirs('datasets', exist_ok=True)
clustering.write_python_dataset('datasets/otsu2018.py')
print('Plotting...')
clustering.root.visualise()
plt.figure()
examples = COLOURCHECKER_SDS['ColorChecker N Ohta'].items()
for i, (name, sd) in enumerate(examples):
plt.subplot(2, 3, 1 + i)
plt.title(name)
plt.plot(sd.wavelengths, sd.values, label='Original')
XYZ = sd_to_XYZ(sd) / 100
recovered_sd = clustering.reconstruct(XYZ)
plt.plot(recovered_sd.wavelengths, recovered_sd.values,
label='Recovered')
plt.legend()
if i + 1 == 6:
break
plt.show()
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