K-means算法应用：图片压缩

from sklearn.datasets import load_sample_image
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
#加载flower的原图
flower=load_sample_image(‘flower.jpg‘)
plt.imshow(flower)
plt.show()
#降低分辨率
image=flower[::3,::3]
x=image.reshape(-1,3)
print(flower.shape,image.shape,x.shape)
#聚类
n_colors=64
model=KMeans(n_colors)
labels=model.fit_predict(x)
colors=model.cluster_centers_
#用聚类中心的颜色代替原来的颜色值
new_image=colors[labels]
new_image=new_image.reshape(image.shape)

plt.imshow(image)
plt.show()
plt.imshow(new_image.astype(np.uint8))
plt.show()

#原始图片与新图片所占用内存的大小
import sys
print(sys.getsizeof(flower))
print(sys.getsizeof(new_image))

#原始图片与新图片保存成文件
import matplotlib.image as img
img.imsave("F:\\flower.jpg",flower)
img.imsave("F:\\flower_zip.jpg",image)