读取一幅灰度图像，分别实现空域和频域两种算法对图像进行水印嵌入和提取；显示水印嵌入前后的图像，并显示水印嵌入前后的图像psnr值，以及水印提取的nc值

空域算法：

水印嵌入：

1. 读取灰度图像

import cv2
import numpy as np

img = cv2.imread('lena.jpg', cv2.IMREAD_GRAYSCALE)

2. 设计水印，并将其转化为二值化图像

watermark = np.random.randint(0, 2, size=img.shape[:2], dtype=np.uint8)*255

3. 将水印嵌入到图像中

alpha = 0.1  # 水印强度
img_watermark = cv2.addWeighted(img, 1-alpha, watermark, alpha, 0)

4. 显示水印嵌入前后的图像，并计算PSNR值

psnr_before = cv2.PSNR(img, img)
psnr_after = cv2.PSNR(img, img_watermark)

cv2.imshow('original', img)
cv2.imshow('watermarked', img_watermark)
print('PSNR before:', psnr_before)
print('PSNR after:', psnr_after)

水印提取：

1. 从水印嵌入后的图像中提取水印

img_extract = img_watermark - img

2. 将提取出的水印二值化

_, watermark_extract = cv2.threshold(img_extract, 127, 255, cv2.THRESH_BINARY)

3. 计算NC值

nc = np.sum(watermark_extract == watermark) / (watermark.shape[0]*watermark.shape[1])
print('NC:', nc)

频域算法：

水印嵌入：

1. 读取灰度图像

import cv2
import numpy as np

img = cv2.imread('lena.jpg', cv2.IMREAD_GRAYSCALE)

2. 设计水印，并将其转化为二值化图像

watermark = np.random.randint(0, 2, size=img.shape[:2], dtype=np.uint8)*255

3. 将水印嵌入到图像中

alpha = 0.1  # 水印强度
dct = cv2.dct(np.float32(img)/255.0)
dct_watermark = dct + alpha*watermark
img_watermark = cv2.idct(dct_watermark)*255.0

4. 显示水印嵌入前后的图像，并计算PSNR值

psnr_before = cv2.PSNR(img, img)
psnr_after = cv2.PSNR(img, img_watermark)

cv2.imshow('original', img)
cv2.imshow('watermarked', img_watermark)
print('PSNR before:', psnr_before)
print('PSNR after:', psnr_after)

水印提取：

1. 从水印嵌入后的图像中提取水印

dct_extract = cv2.dct(np.float32(img_watermark)/255.0)
dct_watermark_extract = dct_extract - cv2.dct(np.float32(img)/255.0)

2. 将提取出的水印二值化

_, watermark_extract = cv2.threshold(dct_watermark_extract, 0, 255, cv2.THRESH_BINARY)

3. 计算NC值

nc = np.sum(watermark_extract == watermark) / (watermark.shape[0]*watermark.shape[1])
print('NC:', nc)