In [1]:

    

%matplotlib inline
from matplotlib import pyplot as plt, cm
from sklearn.cluster import MiniBatchKMeans
import numpy as np
import cv2


    

In [2]:

    

image = cv2.imread("./data/IMG_3654_1024.jpg")
(h, w) = image.shape[:2]

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

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

clt = MiniBatchKMeans(n_clusters = 5)
labels = clt.fit_predict(image)
quant = clt.cluster_centers_.astype("uint8")[labels]

quant = quant.reshape((h, w, 3))
image = image.reshape((h, w, 3))

# convert from L*a*b* to RGB
quant = cv2.cvtColor(quant, cv2.COLOR_LAB2BGR)
image = cv2.cvtColor(image, cv2.COLOR_LAB2BGR)

# display the images and wait for a keypress
plt.figure(figsize=(12,8))
plt.subplot(111), plt.imshow(np.hstack([image, quant]))


    

    
Out[2]:





(<matplotlib.axes._subplots.AxesSubplot at 0x10983f190>,
 <matplotlib.image.AxesImage at 0x10c52dc10>)






    

In [3]:

    

image = cv2.imread("./data/IMG_3654_1024.jpg")
(h, w) = image.shape[:2]

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

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

clt = MiniBatchKMeans(n_clusters = 5)
labels = clt.fit_predict(image)
quant = clt.cluster_centers_.astype("uint8")[labels]

quant = quant.reshape((h, w, 3))
image = image.reshape((h, w, 3))

# convert from L*a*b* to RGB
quant = cv2.cvtColor(quant, cv2.COLOR_LAB2RGB)
image = cv2.cvtColor(image, cv2.COLOR_LAB2RGB)

# display the images and wait for a keypress
plt.figure(figsize=(12,8))
plt.subplot(111), plt.imshow(np.hstack([image, quant]))


    

    
Out[3]:





(<matplotlib.axes._subplots.AxesSubplot at 0x10da124d0>,
 <matplotlib.image.AxesImage at 0x10e654410>)