[Numpy-discussion] position of objects?

Bob Klimek klimek at grc.nasa.gov
Wed Jan 19 11:26:37 CST 2005

Peter Verveer wrote:

> The watershed_ift() is a somewhat  uusual implementation of watershed. 
> In principle it does the same as a normal watershed, except that it 
> does not produce watershed lines. I implemented this one, because with 
> the current implementation of binary morphology it is a bit cumbersome 
> to implement more common approaches. That will hopefully change in the 
> future.

Well, it might turn out to still be useful. From what I'm reading, 
watershed from markers can do some interesting things. See the library 

> The procedure you show below seems to be based on a normal watershed. 
> I am not completely sure how the Image-J implementation works, but one 
> way to do that would be to do a watershed on the distance transform of 
> that image (actually  you would use the negative of the distance 
> transform, with the local minima of that as the seeds). You could do 
> that with watershed_ift, in this case it would give you two  labeled 
> objects, that in contrast to your example would however touch each 
> other. To do the exact same as below a watershed is needed that also 
> gives watershed lines.

I'll give this procedure a try. Even if the labeled objects touch, some 
code could perhaps separate the objects by changing the touching pixels 
to 0.

> Prompted by your earlier questions about skeletons I had a look at 
> what it would take to properly implement skeletons and other 
> morphology based algorithms, such as watersheds, and I found that I 
> need to rethink and reimplement the basic morphology operations first. ...

Well, improving things is always good but from what I can see its not 
bad right now.

if you are going to be changing things, one minor suggestion from me 
would be to make indices of label() (and sum(), mean(), ...) and 
find_objects() the same. For example, in an image containg two objects, 
label() returns a list of three: 0, 1, and 2 where 0 is the background 
and the two objects are labeled 1 and 2. But find_objects() returns a 
list of two (indices in the list being 0 and 1). Its not a big deal but 
in a for-loop it gets a little messy. Also forces me to do things like 
the following example which requires the loop to start at 1 (to skip the 
background) and run the range to n+1 to capture the second object.

labeled, n = ND.label(binImage)
objList = ND.sum(binImage, labeled, range(n+1))
for i in range(1, len(objList)):
    print 'object %d pixels: %d ' % (i, objList[i])

On a different note, I came across a morphology library which looks very 


I've contacted one of the authors of the package (R. Lotufo) and he 
indicated that they are thinking about updating it to run under numarray 
probably in about 6 months. Perhaps you and them could join forces. The 
only potential problem I see is that their code is designed strictly for 
2D grayscale and binary images whereas you are trying to keep it general 
for any number of dimensions.


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