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.

You can use np.asarray() to get the array view, then append a new axis with None/np.newaxis and then use type conversion with copy set to False (in case you were converting from same dtype to save on memory) -

im = np.asarray(PIL.open(path))
im_out = im[None].astype(dtype=int, copy=False)

This appends new axis at the start, resulting in (1,H,W) as the output array shape. To do so, for the end to get an array shape of (H,W,1), do : im[...,None] instead of im[None].

A simpler way would be -

im_out = np.asarray(img, dtype=int)[None]

If the input is already in uint8 dtype and we want an output array of the same dtype, use dtype=np.uint8 and that should be pretty fast.

You want to use the mode 'L' instead of 'LA' as the parameter to the convert() method. 'LA' leaves an alpha channel and then the numpy.asarray doesn't work as you intended. If you need the alpha channel, then you will need a different method to convert to a numpy array. Otherwise, use mode 'L'.

When you are creating the numpy array using the image data from your Pillow object, be advised that the default precision of the array is int32. I'm assuming that your data is actually uint8 as most images seen in practice are this way. Therefore, you must explicitly ensure that the array is the same type as what was seen in your image. Simply put, ensure that the array is uint8 after you get the image data, so that would be the fourth line in your code¹.

arr = np.array(img.getdata(), dtype=np.uint8) # Note the dtype input

^{1. Take note that I've added two more lines in your code at the beginning to import the necessary packages for this code to work (albeit with an image offline).}