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.

This is easily done with PIL/Pillow. Create a new grayscale image of the proper size, and convert each array value to the range 0-255 and copy it in. I added a resize at the end to make the pixels easier to see.

from PIL import Image

array1 = [[0.48, 0.4,  0.56, 0.32, 0.52],
 [0.36, 0.56, 0.56, 0.68, 0.56],
 [0.32, 0.72, 0.88, 0.44, 0.56],
 [0.56, 0.64, 0.76, 0.52, 0.48],
 [0.32, 0.88, 0.56, 0.52, 0.4 ]]

width1 = 5
height1 = 5

im = Image.new('L', (width1, height1))
pix = im.load()
for y in range(height1):
    for x in range(width1):
        pix[x, y] = int(array1[y][x] * 255.9999)

im = im.resize((width1 * 10, height1 * 10), resample=Image.NEAREST)
im.save(r'c:\temp\temp.png')

Your array of "binary values" is an array of bytes?

If so, you can do (using Pillow) after resizing it:

from PIL import Image
im = Image.fromarray(arr)

And then im.show() to see it.

If your array has only 0's and 1's (1-bit depth or b/w) you may have to multiply it to 255

im = Image.fromarray(arr * 255)

Here an example:

>>> arr = numpy.random.randint(0,256, 100*100) #example of a 1-D array
>>> arr.resize((100,100))
>>> im = Image.fromarray(arr)
>>> im.show()

Edit (2018):

This question was written in 2011 and Pillow changed ever since requiring to use the mode='L' parameter when loading with fromarray.

Also on comments below it was said arr.astype(np.uint8) was needed as well, but I have not tested it

Two issues. Firstly, you are not converting to greyscale. Secondly, the whole point of numpy is to vectorise and avoid for loops because they are slow.

So starting with this image:

You want something like this:

#!/usr/local/bin/python3
import numpy as np
from PIL import Image

# Load coins and convert to greyscale
grey = np.array(Image.open('coins.png').convert('L'))

# Threshold at 128
thresholded=((grey>128)*255).astype(np.uint8)

# Save
Image.fromarray(thresholded).save('result.png')