Numpy array to PIL image format

Question

I'm trying to convert an image from a numpy array format to a PIL one. This is my code:

img = numpy.array(image)
row,col,ch= np.array(img).shape
mean = 0
# var = 0.1
# sigma = var**0.5
gauss = np.random.normal(mean,1,(row,col,ch))
gauss = gauss.reshape(row,col,ch)
noisy = img + gauss
im = Image.fromarray(noisy)

The input to this method is a PIL image. This method should add Gaussian noise to the image and return it as a PIL image once more.

Any help is greatly appreciated!

Is there something wrong with your code? What is the question? — AGN Gazer
– AGN Gazer, Commented Jun 12, 2018 at 16:55
Re-added information now, don't know why it didn't show up once the question was posted — Stefano Pozzi
– Stefano Pozzi, Commented Jun 12, 2018 at 16:57
No, the PIL-alternative is Pillow (while skimage is great, it has a different target-group). And the author really should read the close-vote and the first comment and react to those. The problem, whatever it is, is probably related to types (e.g. uint8 image; gaussian is obviously continuous/float_double resulting in a change of types). Hint: there are similar questions here! — sascha
– sascha, Commented Jun 12, 2018 at 16:59
I would suggest you follow the steps outlined in stackoverflow.com/a/10967471/8033585 except replace colormap step with "adding noise", e.g., add noise, rescale (0-255), convert to uint8, etc. — AGN Gazer
– AGN Gazer, Commented Jun 12, 2018 at 17:01

AGN Gazer · Accepted Answer · 2018-06-12 17:49:57Z

7

In my comments I meant that you do something like this:

import numpy as np
from PIL import Image

img = np.array(image)
mean = 0
# var = 0.1
# sigma = var**0.5
gauss = np.random.normal(mean, 1, img.shape)

# normalize image to range [0,255]
noisy = img + gauss
minv = np.amin(noisy)
maxv = np.amax(noisy)
noisy = (255 * (noisy - minv) / (maxv - minv)).astype(np.uint8)

im = Image.fromarray(noisy)

answered Jun 12, 2018 at 17:49

AGN Gazer

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