第九次作业

from sklearn.datasets import load_sample_image
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np

china = load_sample_image("china.jpg")#读取图片
plt.imshow(china)
plt.show()
print(china.shape)#观察图片存放数据特点

image = china[::3, ::3] #降低分辨率
X = image .reshape(-1,3)
plt.imshow(image)
plt.show()
print(image.shape,X.shape)

n_colors =64 #(256,256,256)
model = KMeans(n_colors) #k均值聚类算法，将图片中所有的颜色值做聚类
labels = model.fit_predict(X) #每个点的颜色分类，0-63
colors = model.cluster_centers_ #64个聚类中心，颜色值

new_image=colors[labels]  #用聚类中心的颜色代替原来的颜色值
new_image=new_image.reshape(image.shape) #形成新的照片
plt.imshow(new_image.astype(np.uint8))
plt.show()
import matplotlib.image as img
img.imsave('F:\\china.jpg',china)
img.imsave('F:\\china_zip.jpg',image)