在python中为RGB图像添加噪声

Question

我需要在 python 中为多个彩色图像（文件格式为 ppm；来源：http://benchmark.ini.rub.de/Dataset/GTSRB_Final_Training_Images.zip）添加噪声。噪声输出图像应该仍然是彩色的。

我尝试了以下方法：

from scipy import misc
import numpy as np
import cv2
import imageio

# Read image ('00000_00030.ppm') from file system
image  = misc.imread('./00000/00000_00030.ppm', mode="RGB")

# Add noise to the input image
noised_image = image + 3 * image.std() * np.random.random(image.shape)

# Plot original and noisy images
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
f, axarr = plt.subplots(2, 2)

axarr[0, 0].imshow(image)
axarr[0, 0].set_title('Original image')

axarr[0, 1].imshow(noised_image)
axarr[0, 1].set_title('Noised image')

plt.show() 

# Save noised image to file system
saved_image = cv2.imwrite("./noised.ppm", noised_image)

但首先问题是在 jupyter notebook 中无法正确绘制噪声图像（见图 1）：

figure 1

第二个问题是 RG 通道（红色和绿色）丢失（在保存的文件中）：

figure 2

那么如何在噪声图像中保留所有 RGB 颜色？

搜索了很长时间后，我得到了解决方案 - 保存的文件现在保留了所有 RGB 颜色（参见以下代码中的第 8 行；参见图 3）：

from scipy import misc
import numpy as np
import cv2
import imageio

# Read image ('00000_00030.ppm') from file system
# image  = misc.imread('./00000/00000_00030.ppm', mode="RGB")
image = cv2.imread('./00000/00000_00030.ppm',1)

# Add noise to the input image
noised_image = image + 3 * image.std() * np.random.random(image.shape)

# Plot original and noisy images
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
f, axarr = plt.subplots(2, 2)

axarr[0, 0].imshow(image)
axarr[0, 0].set_title('Original image')

axarr[0, 1].imshow(noised_image)
axarr[0, 1].set_title('Noised image')

plt.show() 

# Save noised image to file system
saved_image = cv2.imwrite("./noised1.ppm", noised_image)

Figure 3

但绘制的数字仍然是错误的：

Figure 4

这是最终代码，用于在 python 中为 RGB 图像添加噪声，并正确绘制它们：

from scipy import misc
import numpy as np
import cv2
import imageio

# Read image ('00000_00030.ppm') from file system
# image  = misc.imread('./00000/00000_00030.ppm', mode="RGB")
image = cv2.imread('./00000/00000_00030.ppm',1)

# Add noise to the input image
noised_image = image + 3 * image.std() * np.random.random(image.shape)

RGB_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Plot original and noisy images
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
f, axarr = plt.subplots(2, 2)

axarr[0, 0].imshow(RGB_image)
axarr[0, 0].set_title('Original image')

axarr[0, 1].imshow(noised_image)
axarr[0, 1].set_title('Noised image')

plt.show() 

# Save noised image to file system
saved_image = cv2.imwrite("./noised1.ppm", noised_image)

Answer 1

这将获取给定图像的像素值，并开始将您作为输入提供的噪声编码到像素中的最低有效位。图像输出会略有不同。

def asciiToBin(ascii):
    return ''.join(str(bin(ord(byte)))[2:].zfill(8) for byte in ascii)

def hide(img, data, outName):
    dataBin = asciiToBin(data)
    pixels, mode = list(img.getdata()), img.mode
    newPixels = []

    for i in range(len(dataBin)):
        newPixel = list(pixels[i])
        newPixel[i%len(mode)] = setLSB(newPixel[i%len(mode)], dataBin[i])
        newPixels.append(tuple(newPixel))

        newData = newPixels + pixels[len(newPixels):]

        img.putdata(newData)
        img.save(outName, "PNG")

   def setLSB(target, value):
        binary = str(bin(target))[2:]
        if binary[-1] != value:
            binary = binary[:-1] + value
        return int(binary, 2)