How to calculate dice coefficient for measuring accuracy of image segmentation in python

Question

I have an image of land cover and I segmented it using K-means clustering. Now I want to calculate the accuracy of my segmentation algorithm. I read somewhere that dice co-efficient is the substantive evaluation measure. But I am not sure how to calculate it. I use Python 2.7 Are there any other effective evaluation methods? Please give a summary or a link to a source. Thank You!

Edits: I used the following code for measuring the dice similarity for my original and the segmented image but it seems to take hours to calculate:

for i in xrange(0,7672320):
  for j in xrange(0,3):
    dice = np.sum([seg==gt])*2.0/(np.sum(seg)+np.sum(gt)) #seg is the segmented image and gt is the original image. Both are of same size

Answer 1

Please refer to Dice similarity coefficient at wiki

A sample code segment here for your reference. Please note that you need to replace k with your desired cluster since you are using k-means.

import numpy as np

k=1

# segmentation
seg = np.zeros((100,100), dtype='int')
seg[30:70, 30:70] = k

# ground truth
gt = np.zeros((100,100), dtype='int')
gt[30:70, 40:80] = k

dice = np.sum(seg[gt==k])*2.0 / (np.sum(seg) + np.sum(gt))

print 'Dice similarity score is {}'.format(dice)