The final example shows you how to mix NumPy operators with a tiny bit of filtering to get an interesting result. We start with a photo of a path in the forest:

im = mh.imread('forest') 

To split the red, green, and blue channels, we use the following code. The NumPy transpose method changes the order of axes in a multi-dimensional array:

r,g,b = im.transpose(2,0,1) 

Now, we filter the three channels separately and build a composite image out of it with mh.as_rgb. This function takes three two-dimensional arrays, performs contrast stretching to make each an 8-bit integer array, and then stacks them, returning a color RGB image:

r24 = mh.gaussian_filter(r, 24.) 
g24 = mh.gaussian_filter(g, 24.) 
b24 = mh.gaussian_filter(b, 24.) 
im24 = mh.as_rgb(r24, g24, b24) 

Now, we blend the two images from the center away to the edges. First, we need to build a weights array W, which will contain at each pixel a normalized value, which is its distance to the center:

h, w = r.shape # height and width 
Y, X = np.mgrid[:h,:w] 

We used the np.mgrid object, which returns arrays of size (h, w), with values corresponding to the y and x coordinates, respectively. The next steps are as follows:

Y = Y - h/2. # center at h/2 
Y = Y / Y.max() # normalize to -1 .. +1 
 
X = X - w/2. 
X = X / X.max() 

We now use a Gaussian function to give the center region a high value:

C = np.exp(-2.*(X**2+ Y**2)) 
 
# Normalize again to 0..1 
C = C - C.min() 
C = C / C.ptp() 
C = C[:,:,None] # This adds a dummy third dimension to C 

Notice that all of these manipulations are performed using NumPy arrays and not some mahotas-specific methodology:

ringed = mh.stretch(im*C + (1-C)*im24) 

Finally, we can combine the two images to have the center be in sharp focus and the edges softer: