---------------------------------------- > Date: Fri, 1 Jun 2012 18:50:15 +1000 > From: st...@pearwood.info > To: tutor@python.org > Subject: Re: [Tutor] Masked arrays & scipy.ndimage.<filters> > > Koen De Munter wrote: >> Dear tutors, >> >> Given an image, I want to generate another image with the mean values of >> the pixels in their neighbourhood, thereby ignoring some of the >> neighbouring pixels (e.g. the padded boundary). I hoped I could use >> masked arrays for that, but apparently, this does not work. > > Define "does not work". > > Do you get an exception? Your computer crashes? You get a result, but it's not > the result you were expecting? Something else? > > > >> -- >> def fnc(buffer) >> return numpy.mean(buffer) > >> mean = scipy.ndimage.generic_filter(masked_array, fnc, footprint = f, mode = >> 'constant', cval = 0.0) > > > scipy, even more than numpy, is a fairly specialised library. You may have > better luck asking this question on a dedicated scipy mailing list. > >> Is there a way to get around this issue, or do I have to accept the filter >> function can not handle masked arrays? > > What does the documentation for generic_filter say? > > What happens if you test it with a really small, simple example, say an array > of just five or six values? Can you show us what you expect to get and what > you actually get? > > > > -- > Steven > > _______________________________________________ > Tutor maillist - Tutor@python.org > To unsubscribe or change subscription options: > http://mail.python.org/mailman/listinfo/tutor
Hi Steven, Thanks for your reply. 1) "does not work" ------------------ With this I mean that each time, all pixels belonging to the neighbourhood are involved in the calculation of the mean value. My neighbourhood is a circle, defined in a binary square footprint array (1 = neighbourhood, 0 = not neighbourhood). With a masked array, I aim to command the filter method as follows: "ignore those pixels that are identified by the mask array as 'invalid'". But, it seems the generic_method doesn't work with masked arrays: it only takes account for the data part of a masked array, not the mask part. So: result is not what I expect to get. For the bigger story I can refer to the following link: http://stackoverflow.com/questions/10683596/efficiently-calculating-boundary-adapted-neighbourhood-average 2) A small example ------------------ d = array([[0, 0, 0, 0],[0, 1, 6, 0],[0, 0, 0, 0]]) #this represents an image array [1, 6], surrounded by zeroes d = d.astype(float) m = array([[1, 1, 1, 1],[1, 0, 0, 1],[1, 1, 1, 1]]) #the ones in the mask have a meaning of 'invalid' md = numpy.ma.masked_array(d, mask = m) f = array([[0, 1, 0],[1, 1, 1],[0, 1, 0]]) #small footprint def fnc(buffer): return numpy.mean(buffer) avg = scipy.ndimage.generic_filter(md, fnc, footprint = f, mode = 'constant', cval = 0.0) --> what I expect to get: array([[ 0. , 1. , 6. , 0. ], [ 1. , 3.5, 3.5, 6. ], [ 0. , 1. , 6. , 0. ]]) --> what I actually get: array([[ 0. , 0.2, 1.2, 0. ], [ 0.2, 1.4, 1.4, 1.2], [ 0. , 0.2, 1.2, 0. ]]) Footprint f has 5 elements. I expect this number to change according to the number of elements that should be ignored. But that doesn't. The code as I presented above always calculates the mean of the full 5 members of the footprint/neighbourhood. Regards, Koen _______________________________________________ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
