K-means Python code analyse

[Alex]

Can please anyone explaine me what do each of these code lines and how k-means algorithm works?


from scipy.cluster.vq import *
from scipy.misc import imresize
from pylab import *
from PIL import Image
steps = 500 
im = array(Image.open("empire.jpg"))
dx = im.shape[0] / steps
dy = im.shape[1] / steps
# compute color features for each region
features = []
for x in range(steps): 
    for y in range(steps):
        R = mean(im[x*dx:(x+1)*dx,y*dy:(y+1)*dy,0])
        G = mean(im[x*dx:(x+1)*dx,y*dy:(y+1)*dy,1])
        B = mean(im[x*dx:(x+1)*dx,y*dy:(y+1)*dy,2])
        features.append([R,G,B]) 
features = array(features,"f") # make into array
# cluster
centroids,variance = kmeans(features,3)
code,distance = vq(features,centroids)
# create image with cluster labels
codeim = code.reshape(steps,steps)
codeim = imresize(codeim,im.shape[:2],interp="nearest")
figure()
imshow(codeim)
show()