Python code to add random Gaussian noise on images

add_gaussian_noise.py

import cv2

def add_gaussian_noise(X_imgs):
    gaussian_noise_imgs = []
    row, col, _ = X_imgs[0].shape
    # Gaussian distribution parameters
    mean = 0
    var = 0.1
    sigma = var ** 0.5
    
    for X_img in X_imgs:
        gaussian = np.random.random((row, col, 1)).astype(np.float32)
        gaussian = np.concatenate((gaussian, gaussian, gaussian), axis = 2)
        gaussian_img = cv2.addWeighted(X_img, 0.75, 0.25 * gaussian, 0.25, 0)
        gaussian_noise_imgs.append(gaussian_img)
    gaussian_noise_imgs = np.array(gaussian_noise_imgs, dtype = np.float32)
    return gaussian_noise_imgs
  
gaussian_noise_imgs = add_gaussian_noise(X_imgs)

where did you use the defined 'var' and 'sigma'?

it shall be:
gaussian = np.random.normal(mean,sigma,(row,col, 1))
instead of line #12:
gaussian = np.random.random((row, col, 1)).astype(np.float32)

Also Note that this is not adding gaussian noise, it adds a transparent layer to make the image darker (as if it is changing the lighting)

Adding gaussian noise shall looks like so:

import numpy as np
import cv2
img = cv2.imread(img_path)
mean = 0
var = 10
sigma = var ** 0.5
gaussian = np.random.normal(mean, sigma, (224, 224)) #  np.zeros((224, 224), np.float32)

noisy_image = np.zeros(img.shape, np.float32)

if len(img.shape) == 2:
    noisy_image = img + gaussian
else:
    noisy_image[:, :, 0] = img[:, :, 0] + gaussian
    noisy_image[:, :, 1] = img[:, :, 1] + gaussian
    noisy_image[:, :, 2] = img[:, :, 2] + gaussian

cv2.normalize(noisy_image, noisy_image, 0, 255, cv2.NORM_MINMAX, dtype=-1)
noisy_image = noisy_image.astype(np.uint8)

cv2.imshow("img", img)
cv2.imshow("gaussian", gaussian)
cv2.imshow("noisy", noisy_image)

cv2.waitKey(0)

Depends what your goal is:
3 examples,

• gaussian noise added over image: noise is spread throughout
• gaussian noise multiplied then added over image: noise increases with image value
• image folded over and gaussian noise multipled and added to it: peak noise affects mid values, white and black receiving little noise
  in every case i blend in 0.2 and 0.4 of the image

import numpy as np
import cv2
import matplotlib.pyplot as plt

img = cv2.imread(img_path)[...,::-1]/255.0
noise =  np.random.normal(loc=0, scale=1, size=img.shape)

# noise overlaid over image
noisy = np.clip((img + noise*0.2),0,1)
noisy2 = np.clip((img + noise*0.4),0,1)

# noise multiplied by image:
# whites can go to black but blacks cannot go to white
noisy2mul = np.clip((img*(1 + noise*0.2)),0,1)
noisy4mul = np.clip((img*(1 + noise*0.4)),0,1)

noisy2mul = np.clip((img*(1 + noise*0.2)),0,1)
noisy4mul = np.clip((img*(1 + noise*0.4)),0,1)

# noise multiplied by bottom and top half images,
# whites stay white blacks black, noise is added to center
img2 = img*2
n2 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.2)), (1-img2+1)*(1 + noise*0.2)*-1 + 2)/2, 0,1)
n4 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.4)), (1-img2+1)*(1 + noise*0.4)*-1 + 2)/2, 0,1)


# norm noise for viz only
noise2 = (noise - noise.min())/(noise.max()-noise.min())
plt.figure(figsize=(20,20))
plt.imshow(np.vstack((np.hstack((img, noise2)),
                      np.hstack((noisy, noisy2)),
                      np.hstack((noisy2mul, noisy4mul)),
                      np.hstack((n2, n4)))))
plt.show()
plt.hist(noise.ravel(), bins=100)
plt.show()

skimage has a few nice noise functions easy to compare, i like poisson, looks closest to film grain

import skimage
import matplotlib.pyplot as plt
img_path="https://i.guim.co.uk/img/media/4ddba561156645952502f7241bd1a64abd0e48a3/0_1251_3712_2225/master/3712.jpg?width=1920&quality=85&auto=format&fit=max&s=1280341b186f8352416517fc997cd7da"
img = skimage.io.imread(img_path)/255.0

def plotnoise(img, mode, r, c, i):
    plt.subplot(r,c,i)
    if mode is not None:
        gimg = skimage.util.random_noise(img, mode=mode)
        plt.imshow(gimg)
    else:
        plt.imshow(img)
    plt.title(mode)
    plt.axis("off")

plt.figure(figsize=(18,24))
r=4
c=2
plotnoise(img, "gaussian", r,c,1)
plotnoise(img, "localvar", r,c,2)
plotnoise(img, "poisson", r,c,3)
plotnoise(img, "salt", r,c,4)
plotnoise(img, "pepper", r,c,5)
plotnoise(img, "s&p", r,c,6)
plotnoise(img, "speckle", r,c,7)
plotnoise(img, None, r,c,8)
plt.show()

skimage has a few nice noise functions easy to compare, i like poisson, looks closest to film grain

import skimage
import matplotlib.pyplot as plt
img_path="https://i.guim.co.uk/img/media/4ddba561156645952502f7241bd1a64abd0e48a3/0_1251_3712_2225/master/3712.jpg?width=1920&quality=85&auto=format&fit=max&s=1280341b186f8352416517fc997cd7da"
img = skimage.io.imread(img_path)/255.0

def plotnoise(img, mode, r, c, i):
    plt.subplot(r,c,i)
    if mode is not None:
        gimg = skimage.util.random_noise(img, mode=mode)
        plt.imshow(gimg)
    else:
        plt.imshow(img)
    plt.title(mode)
    plt.axis("off")

plt.figure(figsize=(18,24))
r=4
c=2
plotnoise(img, "gaussian", r,c,1)
plotnoise(img, "localvar", r,c,2)
plotnoise(img, "poisson", r,c,3)
plotnoise(img, "salt", r,c,4)
plotnoise(img, "pepper", r,c,5)
plotnoise(img, "s&p", r,c,6)
plotnoise(img, "speckle", r,c,7)
plotnoise(img, None, r,c,8)
plt.show()

awesome

This is so cool. Thank you so much.

Depends what your goal is:
3 examples,

• gaussian noise added over image: noise is spread throughout
• gaussian noise multiplied then added over image: noise increases with image value
• image folded over and gaussian noise multipled and added to it: peak noise affects mid values, white and black receiving little noise
  in every case i blend in 0.2 and 0.4 of the image

import numpy as np
import cv2
import matplotlib.pyplot as plt

img = cv2.imread(img_path)[...,::-1]/255.0
noise =  np.random.normal(loc=0, scale=1, size=img.shape)

# noise overlaid over image
noisy = np.clip((img + noise*0.2),0,1)
noisy2 = np.clip((img + noise*0.4),0,1)

# noise multiplied by image:
# whites can go to black but blacks cannot go to white
noisy2mul = np.clip((img*(1 + noise*0.2)),0,1)
noisy4mul = np.clip((img*(1 + noise*0.4)),0,1)

noisy2mul = np.clip((img*(1 + noise*0.2)),0,1)
noisy4mul = np.clip((img*(1 + noise*0.4)),0,1)

# noise multiplied by bottom and top half images,
# whites stay white blacks black, noise is added to center
img2 = img*2
n2 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.2)), (1-img2+1)*(1 + noise*0.2)*-1 + 2)/2, 0,1)
n4 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.4)), (1-img2+1)*(1 + noise*0.4)*-1 + 2)/2, 0,1)


# norm noise for viz only
noise2 = (noise - noise.min())/(noise.max()-noise.min())
plt.figure(figsize=(20,20))
plt.imshow(np.vstack((np.hstack((img, noise2)),
                      np.hstack((noisy, noisy2)),
                      np.hstack((noisy2mul, noisy4mul)),
                      np.hstack((n2, n4)))))
plt.show()
plt.hist(noise.ravel(), bins=100)
plt.show()

Thank you so much that was so helpful

Depends what your goal is:
3 examples,

• gaussian noise added over image: noise is spread throughout
• gaussian noise multiplied then added over image: noise increases with image value
• image folded over and gaussian noise multipled and added to it: peak noise affects mid values, white and black receiving little noise
  in every case i blend in 0.2 and 0.4 of the image

import numpy as np
import cv2
import matplotlib.pyplot as plt

img = cv2.imread(img_path)[...,::-1]/255.0
noise =  np.random.normal(loc=0, scale=1, size=img.shape)

# noise overlaid over image
noisy = np.clip((img + noise*0.2),0,1)
noisy2 = np.clip((img + noise*0.4),0,1)

# noise multiplied by image:
# whites can go to black but blacks cannot go to white
noisy2mul = np.clip((img*(1 + noise*0.2)),0,1)
noisy4mul = np.clip((img*(1 + noise*0.4)),0,1)

noisy2mul = np.clip((img*(1 + noise*0.2)),0,1)
noisy4mul = np.clip((img*(1 + noise*0.4)),0,1)

# noise multiplied by bottom and top half images,
# whites stay white blacks black, noise is added to center
img2 = img*2
n2 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.2)), (1-img2+1)*(1 + noise*0.2)*-1 + 2)/2, 0,1)
n4 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.4)), (1-img2+1)*(1 + noise*0.4)*-1 + 2)/2, 0,1)


# norm noise for viz only
noise2 = (noise - noise.min())/(noise.max()-noise.min())
plt.figure(figsize=(20,20))
plt.imshow(np.vstack((np.hstack((img, noise2)),
                      np.hstack((noisy, noisy2)),
                      np.hstack((noisy2mul, noisy4mul)),
                      np.hstack((n2, n4)))))
plt.show()
plt.hist(noise.ravel(), bins=100)
plt.show()

Is there a way to add noise to the bottom half of the image?

multiply it by a mask should do it.

…

On Fri, May 29, 2020 at 8:30 AM Kanishk Rana ***@***.***> wrote: ***@***.**** commented on this gist. ------------------------------ Depends what your goal is: 3 examples, - gaussian noise added over image: noise is spread throughout - gaussian noise multiplied then added over image: noise increases with image value - image folded over and gaussian noise multipled and added to it: peak noise affects mid values, white and black receiving little noise in every case i blend in 0.2 and 0.4 of the image import numpy as npimport cv2import matplotlib.pyplot as plt img = cv2.imread(img_path)[...,::-1]/255.0noise = np.random.normal(loc=0, scale=1, size=img.shape) # noise overlaid over imagenoisy = np.clip((img + noise*0.2),0,1)noisy2 = np.clip((img + noise*0.4),0,1) # noise multiplied by image:# whites can go to black but blacks cannot go to whitenoisy2mul = np.clip((img*(1 + noise*0.2)),0,1)noisy4mul = np.clip((img*(1 + noise*0.4)),0,1) noisy2mul = np.clip((img*(1 + noise*0.2)),0,1)noisy4mul = np.clip((img*(1 + noise*0.4)),0,1) # noise multiplied by bottom and top half images,# whites stay white blacks black, noise is added to centerimg2 = img*2n2 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.2)), (1-img2+1)*(1 + noise*0.2)*-1 + 2)/2, 0,1)n4 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.4)), (1-img2+1)*(1 + noise*0.4)*-1 + 2)/2, 0,1) # norm noise for viz onlynoise2 = (noise - noise.min())/(noise.max()-noise.min())plt.figure(figsize=(20,20))plt.imshow(np.vstack((np.hstack((img, noise2)), np.hstack((noisy, noisy2)), np.hstack((noisy2mul, noisy4mul)), np.hstack((n2, n4)))))plt.show()plt.hist(noise.ravel(), bins=100)plt.show() [image: image] <https://user-images.githubusercontent.com/40925114/57724968-81aa7e00-767b-11e9-9df4-94a89ca47c73.png> Is there a way to add noise to the bottom half of the image? — You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://gist.github.com/28f6a2df8e8d463c6ddd040f4f6a028a#gistcomment-3322834>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AJYHPOXTVKJASPCK7CLRZRTRT7IJXANCNFSM4HM42OEQ> .

I have an issue in line 5..
'str' object has no attribute 'shape'
how can i solve this issue?

I have an issue in line 5..
'str' object has no attribute 'shape'

Appears like the parameter you have sent is a string.
You can try reading an image file by using cv2.imread() and send that to the function

Depends what your goal is: 3 examples,

* gaussian noise added over image: noise is spread throughout

* gaussian noise multiplied then added over image: noise increases with image value

* image folded over and gaussian noise multipled and added to it: peak noise affects mid values, white and black receiving little noise
  in every case i blend in 0.2 and 0.4 of the image

import numpy as np
import cv2
import matplotlib.pyplot as plt

img = cv2.imread(img_path)[...,::-1]/255.0
noise =  np.random.normal(loc=0, scale=1, size=img.shape)

# noise overlaid over image
noisy = np.clip((img + noise*0.2),0,1)
noisy2 = np.clip((img + noise*0.4),0,1)

# noise multiplied by image:
# whites can go to black but blacks cannot go to white
noisy2mul = np.clip((img*(1 + noise*0.2)),0,1)
noisy4mul = np.clip((img*(1 + noise*0.4)),0,1)

noisy2mul = np.clip((img*(1 + noise*0.2)),0,1)
noisy4mul = np.clip((img*(1 + noise*0.4)),0,1)

# noise multiplied by bottom and top half images,
# whites stay white blacks black, noise is added to center
img2 = img*2
n2 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.2)), (1-img2+1)*(1 + noise*0.2)*-1 + 2)/2, 0,1)
n4 = np.clip(np.where(img2 <= 1, (img2*(1 + noise*0.4)), (1-img2+1)*(1 + noise*0.4)*-1 + 2)/2, 0,1)


# norm noise for viz only
noise2 = (noise - noise.min())/(noise.max()-noise.min())
plt.figure(figsize=(20,20))
plt.imshow(np.vstack((np.hstack((img, noise2)),
                      np.hstack((noisy, noisy2)),
                      np.hstack((noisy2mul, noisy4mul)),
                      np.hstack((n2, n4)))))
plt.show()
plt.hist(noise.ravel(), bins=100)
plt.show()

img = cv2.imread(img_path)[...,::-1]/255.0 in this line what why [...,-1]/255.0] is used? please explain this !!!

I have an issue in line 5.. 'str' object has no attribute 'shape' how can i solve this issue?

bro, could you explain this line, img = cv2.imread(img_path)[...,::-1]/255.0 in this line what why [...,-1]/255.0] is used?

Hi, Santykish,

• img[...,::-1] simply reverses the channel order from cv2 BGR to RGB, so it can be shown by matplotlib which is RGB
  theres many ways that can be done, that one is simply an indexing shortcut. You could use the cv2.cvtColor(img, cv2.COLOR_BGR2RGB) or you could open it with PIL.Image.open(), or you could display it with cv2 and never convert the colors.
• /255 - you want a float.

There are a ton of ways in which one can 'add noise', in fact the topic of noise is much greater than what this gist covers.
Do you need to maintain moments of the data? Do you want to denoise a sensor? or generate data from a distribution.

Having a hard time trying to adapt it to a similar problem. I'm firstly testing this noise function to add later a sepia effect, so this looks more like a vintage image, but did not manage to plot it properly yet. Code goes as follows

def old_photo(file_nm):
    img = cv2.imread("HT.jpg")[...,::-1]/255.0
    noise =  np.random.normal(loc=0, scale=1, size=img.shape)
    
    noised_img = np.clip((img*(1 + noise*0.4)),0,1)
    plt.imshow(noised_img)
       
if __name__ == "__main__":
    print(old_photo("HT.jpg"))

Do you know what can be going wrong here? I'm missing a point I guess

Having a hard time trying to adapt it to a similar problem. I'm firstly testing this noise function to add later a sepia effect, so this looks more like a vintage image, but did not manage to plot it properly yet. Code goes as follows

def old_photo(file_nm):
    img = cv2.imread("HT.jpg")[...,::-1]/255.0
    noise =  np.random.normal(loc=0, scale=1, size=img.shape)
    
    noised_img = np.clip((img*(1 + noise*0.4)),0,1)
    plt.imshow(noised_img)
       
if __name__ == "__main__":
    print(old_photo("HT.jpg"))

Do you know what can be going wrong here? I'm missing a point I guess

Your old_photo function is returning None. You need to do it in a jupyter notebook. Or you can save the noised_image. How come you're trying to print it? Even if you output the results, it'll be a numpy array.