palette.py

from skimage import io, color
from skimage.transform import rescale
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import argparse

def palette(img, n=10, r=1):
    img = img[:,:,(0,1,2)]
    img = rescale(img, (r, r, 1), anti_aliasing=True)
    lab = color.rgb2lab(img).reshape(-1,3)
    km = KMeans(n_clusters=n, random_state=0).fit(lab)
    cc = km.cluster_centers_.reshape(-1,1,3).transpose((1,0,2))
    return color.lab2rgb(cc)

def p2h(p):
     r, g, b = np.round(p).astype(int)
     return f"#{r:02X}{g:02X}{b:02X}"


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('img')
    parser.add_argument('out')
    parser.add_argument('-n', default=10, type=int)
    parser.add_argument('-r', default=1, type=float)
    args = parser.parse_args()
    img = io.imread(args.img)
    p = palette(img, n=args.n, r=args.r)
    h = [p2h(rgb * 255) for rgb in p[0]]
    ax = plt.axes()
    ax.imshow(img)
    ax.set_axis_off()
    axin = ax.inset_axes([0.2,0.2, 0.6, 0.2])
    axin.set_axis_off()
    axin.imshow(p, aspect='auto')
    plt.savefig(args.out, dpi=800, pad_inches=0, bbox_inches='tight')
    print("\n".join(h))

if __name__ == "__main__":
    main()