Python – Converting 2D Numpy array of grayscale values to a PIL image

numpypythonpython-imaging-library

Say I have a 2D Numpy array of values on the range 0 to 1, which represents a grayscale image. How do I then convert this into a PIL Image object? All attempts so far have yielded extremely strange scattered pixels or black images.

for x in range(image.shape[0]):
    for y in range(image.shape[1]):
        image[y][x] = numpy.uint8(255 * (image[x][y] - min) / (max - min))

#Create a PIL image.
img = Image.fromarray(image, 'L')

In the code above, the numpy array image is normalized by (image[x][y] – min) / (max – min) so every value is on the range 0 to 1. Then it is multiplied by 255 and cast to an 8 bit integer. This should, in theory, process through Image.fromarray with mode L into a grayscale image – but the result is a set of scattered white pixels.

Best Answer

I think the answer is wrong. The Image.fromarray( ____ , 'L') function seems to only work properly with an array of integers between 0 and 255. I use the np.uint8 function for this.

You can see this demonstrated if you try to make a gradient.

import numpy as np
from PIL import Image

# gradient between 0 and 1 for 256*256
array = np.linspace(0,1,256*256)

# reshape to 2d
mat = np.reshape(array,(256,256))

# Creates PIL image
img = Image.fromarray(np.uint8(mat * 255) , 'L')
img.show()

Makes a clean gradient

vs

import numpy as np
from PIL import Image

# gradient between 0 and 1 for 256*256
array = np.linspace(0,1,256*256)

# reshape to 2d
mat = np.reshape(array,(256,256))

# Creates PIL image
img = Image.fromarray( mat , 'L')
img.show()

Has the same kind of artifacting.

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