d.py

from sklearn.cluster import MiniBatchKMeans
import numpy as np
import argparse
import cv2


ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
ap.add_argument("-c", "--clusters", required = True, type = int, help = "# of clusters")
args = vars(ap.parse_args())

image = cv2.imread(args["rgb.jpg"])
(height, width) = image.shape[:2]

image =  cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
print(image.shape)

image = image.reshape((image.shape[0] * image.shape[1], 3))

clt = MiniBatchKMeans(n_clusters = args["clusters"])
labels = clt.fit_predict(image)
quant = clt.cluster_centers_.astype("uint8")[labels]

quant = quant.reshape((height, width, 3))
image = image.reshape((height, width, 3))

quant = cv2.cvtColor(quant, cv2.COLOR_LAB2BGR)
image = cv2.cvtColor(image, cv2.COLOR_LAB2BGR)

cv2.imshow("image", np.hstack([image, quant]))
cv2.waitKey(0)