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I have a graysacle png image and I want to extract all the connected components from my image. Some of the components have same intensity but I want to assign a unique label to every object. here is my image

enter image description here

I tried this code:

img = imread(images + 'soccer_cif' + str(i).zfill(6) + '_GT_index.png')
labeled, nr_objects = label(img)
print "Number of objects is %d " % nr_objects

But I get just three objects using this. Please tell me how to get each object.

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Where does the label function come from? – Janne Karila Jun 5 '13 at 10:45
    
Possible solution: stackoverflow.com/a/5304140/190597 – unutbu Jun 5 '13 at 10:52
    
I am using something similar actually. The label function is from scipy.ndimage But getting the result that I posted – Khushboo Jun 5 '13 at 11:45
up vote 7 down vote accepted

J.F. Sebastian shows a way to identify objects in an image. It requires manually choosing a gaussian blur radius and threshold value, however:

import scipy
from scipy import ndimage
import matplotlib.pyplot as plt

fname='index.png'
blur_radius = 1.0
threshold = 50

img = scipy.misc.imread(fname) # gray-scale image
print(img.shape)

# smooth the image (to remove small objects)
imgf = ndimage.gaussian_filter(img, blur_radius)
threshold = 50

# find connected components
labeled, nr_objects = ndimage.label(imgf > threshold) 
print "Number of objects is %d " % nr_objects

plt.imsave('/tmp/out.png', labeled)
plt.imshow(labeled)

plt.show()

enter image description here

With blur_radius = 1.0, this finds 4 objects. With blur_radius = 0.5, 5 objects are found:

enter image description here

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Hmm, I didn't try Gaussian blurring earlier. This method works better. Thanks :) – Khushboo Jun 5 '13 at 13:47

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