Finding Blank Regions In Image

This question is somewhat language-agnostic, but my tool of choice happens to be a numpy array. What I am doing is taking the difference of two images via PIL: img = ImageChops.dif

Solution 1:

I believe scipy's ndimage module has everything you need...

Here's a quick example

import numpy as np
import scipy as sp
import scipy.ndimage.morphology

# The array you gave above
data = np.array( 
        [
           [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], 
        ])


# Fill holes to make sure we get nice clusters
filled = sp.ndimage.morphology.binary_fill_holes(data)

# Now seperate each group of contigous ones into a distinct value# This will be an array of values from 1 - num_objects, with zeros# outside of any contigous object
objects, num_objects = sp.ndimage.label(filled)

# Now return a list of slices around each object#  (This is effectively the tuple that you wanted)
object_slices =  sp.ndimage.find_objects(objects)

# Just to illustrate using the object_slicesfor obj_slice in object_slices:
    print data[obj_slice]

This outputs:

[[1]][[1 1 1]
 [1 1 1]][[1 1 1 1]
 [1 0 0 0]
 [1 0 0 1]][[1]][[0 1 1 0]
 [1 0 0 1]
 [0 1 1 0]][[0 0 1 0 0]
 [0 1 1 1 0]
 [1 1 1 1 1]
 [0 1 1 1 0]
 [0 0 1 0 0]]

Note that the "object_slices" are basically what you originally asked for, if you need the actual indicies.

Edit: Just wanted to point out that despite it appearing to properly handle the edge case of

[[1 1 1 1]
 [1 0 0 0]
 [1 0 0 1]]

it actually doesn't (Thus the extra lone [[1]]). You can see this if you print out the "objects" array and take a look at objects 3 & 4.

[[1 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0][0 0 0 0 0 0 3 3 3 3 0 0 0 2 2 2 0 0 0 0][0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0][0 0 0 0 0 0 3 0 0 4 0 0 0 0 0 0 0 0 0 0][0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0][0 0 0 0 0 5 5 0 0 0 0 0 0 0 6 0 0 0 0 0][0 0 0 0 5 5 5 5 0 0 0 0 0 6 6 6 0 0 0 0][0 0 0 0 0 5 5 0 0 0 0 0 6 6 6 6 6 0 0 0][0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 0 0 0 0][0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0]]

Hope that helps!

[1]

Solution 2:

You could look for connected components in the image and then determine the bounding boxes of these components.

Solution 3:

A clustering package (ie this) should be able to most of the work ( find connected pixels ). Finding the bounding box for a cluster is trivial then.