Can I save a numpy array as a 16-bit image using "normal" (Enthought) python?

One alternative is to use pypng. You'll still have to install another package, but it is pure Python, so that should be easy. (There is actually a Cython file in the pypng source, but its use is optional.)

Here's an example of using pypng to write numpy arrays to PNG:

import png

import numpy as np

# The following import is just for creating an interesting array
# of data.  It is not necessary for writing a PNG file with PyPNG.
from scipy.ndimage import gaussian_filter


# Make an image in a numpy array for this demonstration.
nrows = 240
ncols = 320
np.random.seed(12345)
x = np.random.randn(nrows, ncols, 3)

# y is our floating point demonstration data.
y = gaussian_filter(x, (16, 16, 0))

# Convert y to 16 bit unsigned integers.
z = (65535*((y - y.min())/y.ptp())).astype(np.uint16)

# Use pypng to write z as a color PNG.
with open('foo_color.png', 'wb') as f:
    writer = png.Writer(width=z.shape[1], height=z.shape[0], bitdepth=16)
    # Convert z to the Python list of lists expected by
    # the png writer.
    z2list = z.reshape(-1, z.shape[1]*z.shape[2]).tolist()
    writer.write(f, z2list)

# Here's a grayscale example.
zgray = z[:, :, 0]

# Use pypng to write zgray as a grayscale PNG.
with open('foo_gray.png', 'wb') as f:
    writer = png.Writer(width=z.shape[1], height=z.shape[0], bitdepth=16, greyscale=True)
    zgray2list = zgray.tolist()
    writer.write(f, zgray2list)

Here's the color output:

and here's the grayscale output:

Update: I created a library called numpngw (available on PyPI and github) that provides a function for writing a numpy array to a PNG file. The repository has a setup.py file for installing it as a package, but the essential code is in a single file, numpngw.py, that could be copied to any convenient location. The only dependency of numpngw is numpy.

Here's a script that generates the same 16 bit images as those shown above:

import numpy as np
import numpngw

# The following import is just for creating an interesting array
# of data.  It is not necessary for writing a PNG file.
from scipy.ndimage import gaussian_filter


# Make an image in a numpy array for this demonstration.
nrows = 240
ncols = 320
np.random.seed(12345)
x = np.random.randn(nrows, ncols, 3)

# y is our floating point demonstration data.
y = gaussian_filter(x, (16, 16, 0))

# Convert y to 16 bit unsigned integers.
z = (65535*((y - y.min())/y.ptp())).astype(np.uint16)

# Use numpngw to write z as a color PNG.
numpngw.write_png('foo_color.png', z)

# Here's a grayscale example.
zgray = z[:, :, 0]

# Use numpngw to write zgray as a grayscale PNG.
numpngw.write_png('foo_gray.png', zgray)

This explanation of png and numpngw is very helpful! But, there is one small "mistake" I thought I should mention. In the conversion to 16 bit unsigned integers, the y.max() should have been y.min(). For the picture of random colors, it didn't really matter but for a real picture, we need to do it right. Here's the corrected line of code...

z = (65535*((y - y.min())/y.ptp())).astype(np.uint16)

You can convert your 16 bit array to a two channel image (or even 24 bit array to a 3 channel image). Something like this works fine and only numpy is required:

import numpy as np
arr = np.random.randint(0, 2 ** 16, (128, 128), dtype=np.uint16)  # 16-bit array
print(arr.min(), arr.max(), arr.dtype)
img_bgr = np.zeros((*arr.shape, 3), np.int)
img_bgr[:, :, 0] = arr // 256
img_bgr[:, :, 1] = arr % 256
cv2.imwrite('arr.png', img_bgr)
# Read image and check if our array is restored without losing precision
img_bgr_read = cv2.imread('arr.png')
B, G, R = np.split(img_bgr_read, [1, 2], 2)
arr_read = (B * 256 + G).astype(np.uint16).squeeze()
print(np.allclose(arr, arr_read), np.max(np.abs(arr_read - arr)))

Result:

0 65523 uint16
True 0