pip install numpy
pip install scipy

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
img=np.array(Image.open('d:/lena.jpg')) #打开图像并转化为数字矩阵
plt.figure("dog")
plt.imshow(img)
plt.axis('off')
plt.show()

print img.shape 
print img.dtype 
print img.size 
print type(img)

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
img=np.array(Image.open('d:/ex.jpg'))

#随机生成5000个椒盐
rows,cols,dims=img.shape
for i in range(5000):
  x=np.random.randint(0,rows)
  y=np.random.randint(0,cols)
  img[x,y,:]=255
  
plt.figure("beauty")
plt.imshow(img)
plt.axis('off')
plt.show()

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
img=np.array(Image.open('d:/pic/lena.jpg').convert('L'))

rows,cols=img.shape
for i in range(rows):
  for j in range(cols):
    if (img[i,j]<=128):
      img[i,j]=0
    else:
      img[i,j]=1
      
plt.figure("lena")
plt.imshow(img,cmap='gray')
plt.axis('off')
plt.show()

img[i,:] = im[j,:] # 将第 j 行的数值赋值给第 i 行
img[:,i] = 100 # 将第 i 列的所有数值设为 100
img[:100,:50].sum() # 计算前 100 行、前 50 列所有数值的和
img[50:100,50:100] # 50~100 行，50~100 列（不包括第 100 行和第 100 列）
img[i].mean() # 第 i 行所有数值的平均值
img[:,-1] # 最后一列
img[-2,:] (or im[-2]) # 倒数第二行