基于dwt(离散小波变换)实现彩色图像水印嵌入部分_2.0

修改了尺寸变换导致的图像失真问题，同时简化了部分代码。

本次内容为验证嵌入流程，所以过程不算繁杂，关键参数和小波变换级数后期可作为密钥。

import cv2
import pywt
import numpy as np
from PIL import Image

def inches3(num,times):
    for i in range(times):
        num = round((num/2+1))
    return num

def Icm(Img_path):
    def maxRC():
        Img = cv2.imread(Img_path, 0)
        R = Img.shape[0]
        C = Img.shape[1]
        num = max(R, C)
        return num
    num = maxRC()
    rc_new = inches3(num, 2) # 3-1(3维小波-1维小波)
    return rc_new

Img_path = '100_10.bmp'
waterImg_path = 'new.bmp'
Img = cv2.imread(Img_path)
Img = Img[:, :, [2, 1, 0]] # 调整通道顺序
waterImg = cv2.imread(waterImg_path, 0) # 读取水印灰度图像

rcnew = Icm(Img_path) # 对应1920，此处值为482
waterImg_new = cv2.resize(waterImg, (rcnew, rcnew))

R = Img.shape[0]
C = Img.shape[1]
RC_new = max(R, C)
Img_new = cv2.resize(Img, (RC_new, RC_new))
(r, g, b) = cv2.split(Img_new)

# 原图像三级小波变换
coeffs1 = pywt.wavedec2(b, 'db2', level=3)
[ca, (ch3, cv3, cd3), (ch2, cv2, cd2), (ch1, cv1, cd1)] = coeffs1

# 水印图像一级小波变换
coeffs2 = pywt.wavedec2(waterImg_new, 'db2', level=1)
[cA, (cH1, cV1, cD1)] = coeffs2

# 自定义嵌入系数
a1 = 0.1
a2 = 0.2
a3 = 0.1
a4 = 0.1

# 嵌入部分
ca = ca + cA * a1
ch3 = ch3 + cH1 * a2
cv3 = cv3 + cV1 * a3
cd3 = cd3 + cD1 * a4

newImg = pywt.waverec2([ca, (ch3, cv3, cd3), (ch2, cv2, cd2), (ch1, cv1, cd1)], 'db2')

merged = np.ones(Img_new.shape, dtype=np.uint8)
merged[:, :, 2] = newImg
merged[:, :, 1] = g
merged[:, :, 0] = r

Img = Image.fromarray(merged)
Img = Img.resize((1920, 1080), Image.ANTIALIAS) #重新调整大小，抗锯齿
Img.save('test.png')

原图像为1920*1080 .bmp格式（从视频中抽取）

因为后期将用于视频文件处理，所以代码中的尺寸变换暂时只针对这一固定尺寸。

结果示例：

嵌入后

 

原图（.bmp格式无法上传，此处为截图）1920*1080

 

 水印图像（ 经过Arnold置乱处理）100*100

 

 Arnold阿诺德置乱（猫脸变换）图像盲水印注入预处理（python）

基于dwt(离散小波变换)实现彩色图像水印嵌入部分_1.0