像素矢量量化

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.metrics import pairwise_distances_argmin
from skimage.io import imread
from sklearn.utils import shuffle
from skimage import img_as_float
from time import time
img=imread("C:/Users/xpp/Desktop/Lena.png")
plt.figure(1),plt.clf()
ax=plt.axes([0,0,1,1])
plt.axis('off'),plt.title('Original image (%d colors)' %(len(np.unique(img)))),plt.imshow(img)
n_colors=64
img=np.array(img,dtype=np.float64)/255
w,h,d=original_shape=tuple(img.shape)
assert d==3
image_array=np.reshape(img,(w*h,d))
def recreate_image(codebook,labels,w,h):   
    d=codebook.shape[1]
    image=np.zeros((w,h,d))
    label_idx=0
    for i in range(w):
        for j in range(h):
            image[i][j]=codebook[labels[label_idx]]
            label_idx+=1
    return image
plt.figure(1)
plt.clf()
ax=plt.axes([0,0,1,1])
plt.axis('off')
plt.title('Original image (96,615 colors)')
plt.imshow(img)
plt.figure(2,figsize=(10,10))
plt.clf()
i=1
for k in [64,32,16,4]:
    t0=time()
    plt.subplot(2,2,i)
    plt.axis('off')
    image_array_sample=shuffle(image_array, random_state=0)[:1000]
    kmeans=KMeans(n_clusters=k, random_state=0).fit(image_array_sample)
    print("done in %0.3fs." % (time()-t0))
    print("Predicting color indices on the full image (k-means)")
    t0=time()
    labels=kmeans.predict(image_array)
    print("done in %0.3fs."%(time()-t0))
    plt.title('Quantized image ('+str(k)+' colors, K-Means)')
    plt.imshow(recreate_image(kmeans.cluster_centers_,labels,w,h))
    i+=1
plt.show()
plt.figure(3, figsize=(10,10))
plt.clf()
i=1
for k in [64,32,16,4]:
    t0=time()
    plt.subplot(2,2,i)
    plt.axis('off')
    codebook_random=shuffle(image_array, random_state=0)[:k+1]
    print("Predicting color indices on the full image (random)")
    t0=time()
    labels_random=pairwise_distances_argmin(codebook_random,image_array,axis=0)
    print("done in %0.3fs."%(time()-t0))
    plt.title('Quantized image ('+str(k)+'colors,nRandom)')
    plt.imshow(recreate_image(codebook_random,labels_random,w,h))
    i+=1
plt.show()