Ahh my blindness and apologies :)
The nice feeling of reinventing the wheel...
Probably I forgot to reshape the image data in the first place before
applying into ndimage.label().
However, this was a nice example to understand recursion, and get to know
some basics of computer vision and few lib
I think Zachary is right, ndimage does what you want:
In [48]: image = array(
[[0,0,0,1,1,0,0],
[0,0,0,1,1,1,0],
[0,0,0,1,0,0,0],
[0,0,0,0,0,0,0],
[0,1,0,0,0,0,0],
[0,1,1,0,0,0,0],
[0,0,0,0,1,1,0],
[0,0,0,0,1,1,1]])
In [57]: import scipy.ndimage as ndimage
In [58]: labels, num_found = ndimage.la
ndimage.label works differently than what I have done here.
Later using find_objects you can get slices for row or column basis. Not
possible to construct a dynamical structure to find objects that are in the
in both axis.
Could you look at the stackoverflow article once again and comment back?
I believe that pretty generic connected-component finding is already
available with scipy.ndimage.label, as David suggested at the
beginning of the thread...
This function takes a binary array (e.g. zeros where the background
is, non-zero where foreground is) and outputs an array where each
