Python PIL struggles with uncompressed 16-bit TIFF

Question

My system is Mac OS X v10.8.2. I have several 2560x500 uncompressed 16-bit TIFF images (grayscale, unsigned 16-bit integers). I first attempt to load them using PIL (installed via Homebrew, version 1.7.8):

from PIL import Image
import numpy as np

filename = 'Rocks_2ptCal_750KHz_20ms_1ma_120KV_2013-03-06_20-02-12.tif'
img = Image.open(filename)

# >>> img
# <PIL.TiffImagePlugin.TiffImageFile image mode=I;16B size=2560x500 at 0x10A383C68>

img.show() 

# almost all pixels displayed as white.  Not correct.  
# MatLab, EZ-draw, even Mac Preview show correct images in grayscale.

imgdata = list(img.getdata()) 

# most values negative:
# >>> imgdata[0:10]
# [-26588, -24079, -27822, -26045, -27245, -25368, -26139, -28454, -30675, -28455]

imgarray = np.asarray(imgdata, dtype=np.uint16) 

# values now correct
# >>> imgarray
# array([38948, 41457, 37714, ..., 61922, 59565, 60035], dtype=uint16)

The negative values are off by 65,536... probably not a coincidence.

If I pretend to alter pixels and revert back to TIFF image via PIL (by just putting the array back as an image):

newimg = Image.fromarray(imgarray)

I get errors:

File "/usr/local/lib/python2.7/site-packages/PIL/Image.py", line 1884, in fromarray
    raise TypeError("Cannot handle this data type")
TypeError: Cannot handle this data type

I can't find Image.fromarray() in the PIL documentation. I've tried loading via Image.fromstring(), but I don't understand the PIL documentation and there is little in the way of example.

As shown in the code above, PIL seems to "detect" the data as I;16B. From what I can tell from the PIL docs, mode I is:

*I* (32-bit signed integer pixels)

Obviously, that is not correct.

I find many posts on SX suggesting that PIL doesn't support 16-bit images. I've found suggestions to use pylibtiff, but I believe that is Windows only?

I am looking for a "lightweight" way to work with these TIFF images in Python. I'm surprised it is this difficult and that leads me to believe the problem will be obvious to others.

Answer 1

Try Pillow, the “friendly” PIL fork. They've somewhat recently added better support for 16- and 32-bit images including in the numpy array interface. This code will work with the latest Pillow:

from PIL import Image
import numpy as np

img = Image.open('data.tif')
data = np.array(img)

Answer 2

It turns out that Matplotlib handles 16-bit uncompressed TIFF images in two lines of code:

import matplotlib.pyplot as plt
img = plt.imread(filename)

# >>> img
# array([[38948, 41457, 37714, ..., 61511, 61785, 61824],
#       [39704, 38083, 36690, ..., 61419, 60086, 61910],
#       [41449, 39169, 38178, ..., 60192, 60969, 63538],
#       ...,
#       [37963, 39531, 40339, ..., 62351, 62646, 61793],
#       [37462, 37409, 38370, ..., 61125, 62497, 59770],
#       [39753, 36905, 38778, ..., 61922, 59565, 60035]], dtype=uint16)

Et voila. I suppose this doesn't meet my requirements as "lightweight" since Matplotlib is (to me) a heavy module, but it is spectacularly simple to get the image into a Numpy array. I hope this helps someone else find a solution quickly as this wasn't obvious to me.