from sklearn.datasets import load_sample_image#先导入数据包 china = load_sample_image("china.jpg")#从包中拿出china那那张图 print(china.shape) china
array([[[174, 201, 231], [174, 201, 231], [174, 201, 231], ..., [250, 251, 255], [250, 251, 255], [250, 251, 255]], [[172, 199, 229], [173, 200, 230], [173, 200, 230], ..., [251, 252, 255], [251, 252, 255], [251, 252, 255]], [[174, 201, 231], [174, 201, 231], [174, 201, 231], ..., [252, 253, 255], [252, 253, 255], [252, 253, 255]], ..., [[ 88, 80, 7], [147, 138, 69], [122, 116, 38], ..., [ 39, 42, 33], [ 8, 14, 2], [ 6, 12, 0]], [[122, 112, 41], [129, 120, 53], [118, 112, 36], ..., [ 9, 12, 3], [ 9, 15, 3], [ 16, 24, 9]], [[116, 103, 35], [104, 93, 31], [108, 102, 28], ..., [ 43, 49, 39], [ 13, 21, 6], [ 15, 24, 7]]], dtype=uint8)
import matplotlib.pyplot as plt#导入画图的包 plt.imshow(china)#imshow函数 plt.show()#显示
plt.imshow(china[:,:,0])#所有行,列,按第一的颜色排列 plt.show()
plt.imshow(china[:,:,0],plt.cm.gray)#添加灰色 plt.show()
import sys sys.getsizeof(china)
819968
image=china[::3,::3]#将间隔扩大为3*3倍 image.shape plt.imshow(image) plt.show()
x=image.reshape(-1,3)
from sklearn.cluster import KMeans#导入kmeans包 import numpy as np model=KMeans(n_clusters=64)#设中心点为64 b=model.fit_predict(x)#预测处理 a=model.cluster_centers_#求均值找中心点 new_image=a[b]#靠近的聚合 new_image=new_image.reshape(image.shape) plt.imshow(new_image.astype(np.uint8))#改成int plt.show()
import sys import matplotlib.image as img sys.getsizeof(new_image)
128
#图片保存 import matplotlib.image as img img.imsave('e://01.jpg',china)
sd=img.imread('e://01.jpg')#导入图片数据 sd.shape plt.imshow(sd) plt.show()
pb=8/20 pa=1/2 pba=7/10 pab=(7/10*1/2)/(8/20) print(pab) cc=1-pab print(cc)
Python 3.6.6 (v3.6.6:4cf1f54eb7, Jun 27 2018, 02:47:15) [MSC v.1900 32 bit (Intel)] on win32 Type "copyright", "credits" or "license()" for more information. >>> ================== RESTART: C:/Users/lenovo/Desktop/sff.py ================== >>> ================== RESTART: C:/Users/lenovo/Desktop/sff.py ================== 0.8749999999999999 >>> ================== RESTART: C:/Users/lenovo/Desktop/sff.py ================== 0.8749999999999999 0.1250000000000001 >>>