图像加法

import numpy as np
import cv2
img = cv2.imread('jk.jpg')
res = cv2.add(img, img)
cv2.imshow('Mypicture1',img)
cv2.imshow('MyPicture2',res)

cv2.waitKey(0)
cv2.destroyAllWindows()

res = cv2.add(src1, src2, dst=None, mask=None, dtype=None)

src1：第一幅图像

src2：第二幅图像

mask：图像掩膜

dtype：图像数据类型

2幅图像必须相同大小才可以相加。图像加法就是图像的像素值相加，如果a+b > 255，则c=255。最大的特色是图像会变得更亮。

import cv2
import numpy as np

img = cv2.imread('jk.jpg')
res1 = cv2.add(img,img)
res2 = img + img
cv2.imshow('MyPicture1',img)
cv2.imshow('MyPicture2',res1)
cv2.imshow('Mypicture3', res2)

cv2.waitKey(0)
cv2.destroyAllWindows()

使用数学符号（+）执行图象加法。

c= a+b   # 如果a+b<=255

c = mod((a+b), 256)     #如果a+b>255，相当于取256的余数

掩膜

import cv2
import numpy as np

img1 = np.ones((4,5), dtype=np.uint8)*8
img2 = np.ones((4,5), dtype=np.uint8)*9
mask = np.zeros((4,5), dtype=np.uint8)
mask[1:3,1:4] = 255
dst = np.random.randint(0,256,(4,5), np.uint8)
print('img1=\n',img1)
print('img2=\n',img2)
print('mask=\n',mask)
print('最初值 dst=\n',dst)
dst = cv2.add(img1,img2,mask=mask)
print('结果值 dst=\n',dst)

重复曝光技术

图像加权和

dst = saturate(src1 X alpha + src2 X beta + gamma)

src1和src2必须是大小相同的图像

dst = addWeighted(src1, alpha, src2, beta, gamma)

import cv2
import numpy as np

src1 = np.ones((2,3), dtype=np.uint8)*10
src2 = np.ones((2,3), dtype=np.uint8)*50
alpha = 1
beta = 0.5
gamma = 5
print(f'src1=\n{src1}')
print(f'src2=\n{src2}')
dst = cv2.addWeighted(src1,alpha,src2,beta,gamma)
print(f'dst=\n{dst}')

图像的位运算

cv2.bitwise_and()：相当于逻辑的and运算

cv2.bitwise_or()：相当于逻辑的or运算

cv2.bitwise_not()：相当于逻辑的not运算

cv2.bitwise_xor()：相当于逻辑的xor运算

dis = cv2.bitwise_and(src1, src2, mask=None)

import cv2
import numpy as np
src1 = np.random.randint(0,255,(3,5),dtype=np.uint8)
src2 = np.zeros((3,5),dtype=np.uint8)
src2[0:2,0:2] = 255
dst = cv2.bitwise_and(src1,src2)
print(f'src1=\n{src1}')
print(f'src2=\n{src2}')
print(f'dst=\n{dst}')

import cv2
import numpy as np

src1 =cv2.imread('jk.jpg')
src2 = np.zeros(src1.shape, dtype = np.uint8)
src2[30:260,70:260,:] = 255  #  这是三维数组
dst = cv2.bitwise_and(src1,src2)
cv2.imshow('Hung',src1)
cv2.imshow('Mask',src2)
cv2.imshow('Result',dst)

cv2.waitKey(0)
cv2.destroyAllWindows()

dst = cv2.bitwise_or(src1, src2, mask=None)

import cv2
import numpy as np

src1 = cv2.imread('jk.jpg')
src2 = np.zeros(src1.shape,dtype=np.uint8)

src2[30:260,70:260,:] = 255
dst = cv2.bitwise_or(src1,src2)
cv2.imshow('Hung',src1)
cv2.imshow('mask',src2)
cv2.imshow('Result',dst)

cv2.waitKey(0)
cv2.destroyAllWindows()

dst = cv2.bitwise_not(src, mask=None)

import cv2
import numpy as np

src = cv2.imread('forest.jpg')
dst = cv2.bitwise_not(src)
cv2.imshow('Forest',src)
cv2.imshow('Result',dst)

cv2.waitKey(0)
cv2.destroyAllWindows()

dst = cv2.bitwise_xor(src1,src2,mask=None)

只要不相同则返回1.

任意像素与白色像素值（11111111）执行xor运算时，结果是not运算的结果。

人以像素于黑色像素值（00000000）执行xor运算时，结果是原像素值。

import cv2
import numpy as np

src1 = cv2.imread('forest.jpg')
src2 = np.zeros(src1.shape, dtype=np.uint8)
src2[:,120:360,:] = 255
dst = cv2.bitwise_xor(src1,src2)
cv2.imshow('Forest',src1)
cv2.imshow('Mask',src2)
cv2.imshow('Forest xor operation', dst)

cv2.waitKey(0)
cv2.destroyAllWindows()

图像加密与解密

若C = A xor B

则A = B xor C

 将淹没图像B当作一个密钥图像，A于B的xor运算就可以为A图像加密，要解密就让密钥图像与C执行xor运算。

import cv2
import numpy as np

src = cv2.imread("forest.jpg")
key = np.random.randint(0,255,src.shape,dtype=np.uint8)
print(src.shape)
cv2.imshow('forest',src)
cv2.imshow('key',key)

img_encry = cv2.bitwise_xor(src,key)
img_decry = cv2.bitwise_xor(img_encry,key)
cv2.imshow('encrytion', img_encry)
cv2.imshow('decrytion',img_decry)

cv2.waitKey(0)
cv2.destroyAllWindows()