Segmenting Images In Parallel With Python & Jug

On Friday, I posted an introduction to Jug. The usage was very basic, however. This is a slightly more advanced usage.

Let us imagine you are trying to compare two image segmentation algorithms based on human-segmented images. This is a completely real-world example as it was one of the projects where I first used jug [1].

We are going to build this up piece by piece.

First a few imports:

import mahotas as mh
from jug import TaskGenerator
from glob import glob

Here, we test two thresholding-based segmentation method, called method1 and method2. They both (i) read the image, (ii) blur it with a Gaussian, and (iii) threshold it [2]:

def method1(image):
    # Read the image
    image = mh.imread(image)[:,:,0]
    image  = mh.gaussian_filter(image, 2)
    binimage = (image > image.mean())
    labeled, _ = mh.label(binimage)
    return labeled@TaskGenerator
def method2(image):
    image = mh.imread(image)[:,:,0]
    image  = mh.gaussian_filter(image, 4)
    image = mh.stretch(image)
    binimage = (image > mh.otsu(image))
    labeled, _ = mh.label(binimage)
    return labeled

Just to make sure you see what we are talking about. Here is one possible input image:
What you see is cell nuclei. The very bright areas are noise or unusually bright cells. The results of method 1 can be seen as follows:
image_method1Each color represents a different region. You can see this is not very good as many cells are merged. The reference (human segmented image looks like this):


Running over all the images looks exactly like Python:

results = []
for im in glob('images/*.jpg'):
    m1 = method1(im)
    m2 = method2(im)
    ref = im.replace('images','references').replace('jpg','png')
    v1 = compare(m1, ref)
    v2 = compare(m2, ref)
    results.append( (v1,v2) )

But how do we get the results out?

A simple solution is to write a function which writes to an output file:

def print_results(results):
    import numpy as np
    r1, r2 = np.mean(results, 0)
    with open('output.txt', 'w') as out:
        out.write('Result method1: {}\nResult method2: {}\n'.format(r1,r2))


Except for the “TaskGenerator“ this would be a pure Python file!

With TaskGenerator, we get jugginess!

We can call:

jug execute &
jug execute &
jug execute &
jug execute &

to get 4 processes going at once.


Note also the line:


results is a list of Task objects. This is how you define a dependency. Jug picks up that to call print_results, it needs all the results values and behaves accordingly.

Easy as Py.


You can get the full script above including data from github



Tomorrow, I’m giving a short talk on Jug for the Heidelberg Python Meetup.

If you miss it, you can hear it in Berlin at the BOSC2013 (Bioinformatics Open Source Conference) in July (19 or 20).

[1] The code in that repository still uses a pretty old version of jug, this was 2009, after all. TaskGenerator had not been invented yet.
[2] This is for demonstration purposes; the paper had better methods, of course.
[3] Again, you can do better than Adjusted Rand, as we show in the paper; but this is a demo. This way, we can just call a function in milk