import numpy as np
import cv2

img = cv2.imread('watermelon.jpg')
Z = np.float32(img.reshape((-1,3)))

# define criteria, number of clusters(K) and apply kmeans()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 4
ret,labels,centers = cv2.kmeans(Z, K, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
labels = labels.reshape((img.shape[:-1]))

for i, c in enumerate(centers):
    mask = cv2.inRange(labels, i, i)
    res = cv2.bitwise_and(img, np.dstack([mask]*3))
    cv2.imshow('', res)
    cv2.waitKey()