I have a picture like the below and I would like to count the number of bugs (continuous blobs of color/grey) that show up on it with Python. How could I do this best?

Bugs on a noisy background

I've so far looked at ImageChops, SciPy and PIL but I'm unsure what I can/should use...

I think I can use ndimage.gaussian_filter() and then scipy.ndimage.measurements.label() am just not sure yet how I use latter to count my blue dots in the gaussian-ized image...... it looks something like enter image description here

Okay,

With above image I now got this code:

#! /usr/bin/python

import numpy as np
import scipy
import pylab
import pymorph
import mahotas
from PIL import Image
import PIL.ImageOps
from scipy import ndimage

image = Image.open('bugs.jpg')   
inverted_image = PIL.ImageOps.invert(image)    
inverted_image.save('in_bugs.jpg')
dna = mahotas.imread('in_bugs.jpg')
#pylab.imshow(dna)
pylab.gray()
#pylab.show()
T = mahotas.thresholding.otsu(dna)
pylab.imshow(dna > T)
#pylab.show()
dnaf = ndimage.gaussian_filter(dna, 8)
T = mahotas.thresholding.otsu(dnaf)
pylab.imshow(dnaf > T)
#pylab.show()
labeled,nr_objects = ndimage.label(dnaf > T)
print nr_objects
pylab.imshow(labeled)
pylab.jet()
pylab.show()

the problem is, this returns me a number of 5 which isn'rt that bad but I need to have it more accurate, I want to see two. How can I do this? Will it help to blur the image before applying the gaussian filter?

Thanks for help!

Ron

python image-processing computer-vision
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