4.K均值算法

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import matplotlib.image as img
import numpy as np
import sys

saber = img.imread(‘C://Users/无语/saber.jpg‘)  #导入图片
plt.imshow(saber)
plt.show()

print(‘原图片大小：‘,saber.size)
print(‘原图片内存大小：‘,sys.getsizeof(saber))
print(‘原图片维度：‘,saber.shape)
print(‘原图片数据结构：\n‘,saber)

# 颜色聚类
X = saber.reshape(-1,3)          # 图片线性化，保留颜色顺序
n_color = 64
model = KMeans(n_color)          # 构建模型，将所有颜色聚类成64种
labels = model.fit_predict(X)    # 训练模型并进行预测
colors = model.cluster_centers_  # 聚类中心
new_saber = colors[labels].reshape(saber.shape)   # 还原具体颜色
plt.imshow(new_saber.astype(np.uint8))
plt.show()

# 按比例压缩图片
print(‘颜色聚类后图片大小：‘,new_saber.size)
print(‘颜色聚类后图片内存大小：‘,sys.getsizeof(new_saber))
print(‘压缩后图片大小：‘,new_saber[::6,::6].size)
print(‘压缩后图片内存大小：‘,sys.getsizeof(new_saber[::6,::6]))
print(‘压缩后图片维度：‘,new_saber[::6,::6].shape)
print(‘压缩后图片数据结构：\n‘,new_saber[::6,::6])
img.imsave(‘C://Users/无语/new_saber.jpg‘,new_saber[::6,::6].astype(np.uint8))     # 保存压缩后图片

# 展示3张图片
plt.rcParams[‘font.sans-serif‘]=[‘SimHei‘]
plt.title(‘原图‘)
plt.imshow(saber)
plt.show()
plt.title(‘颜色聚类64种‘)
plt.imshow(new_saber.astype(np.uint8))
plt.show()
plt.title(‘降低分辨率‘)
plt.imshow(new_saber[::6,::6].astype(np.uint8))
plt.show()