On Mon, May 11, 2009 at 9:42 AM, Mario Ceresa <mario.cer...@torrescalla.it> wrote: > Hello Brent, > thanks for your response. I'll surely give a try to matplotlib soon. > > As for the rtree, the generated points are already within the bounding > box, so I thought it wouldn't help, would it? >
hi mario. ah, i missed that. you're correct, it wouldn't help. since you're already using numpy, i think nxutils will be fast and easy. > Mario > > On Mon, May 11, 2009 at 5:32 PM, Brent Pedersen <bpede...@gmail.com> wrote: >> On Mon, May 11, 2009 at 3:36 AM, Mario Ceresa >> <mario.cer...@torrescalla.it> wrote: >>> Hi everybody, >>> I'd like to remove all the pixeles from an image which are inside a >>> given polygon. The first idea that come into my mind was to generate >>> all the points which are in the bounding box and then check if they >>> actually are inside the polygon: >>> >>> b = poly.bounds >>> xl = b[2] - b[0] >>> yl = b[3] - b[1] >>> points = (Point(i[0]+b[0],i[1]+b[1]) for i in numpy.ndindex((xl,yl))) >>> ps = list((p.x,p.y) for p in points if poly.contains(p)) >>> >>> For this test b was (38.0, 1073.0, 679.0, 1977.0) which lead to 579464 >>> points to be checked. >>> >>> Actually this is quite slow so I cannot use it on larger images: >>> >>> >>> In [10]: %time a=list(iterops.contains(poly,points)) >>> CPU times: user 43.82 s, sys: 0.21 s, total: 44.03 s >>> Wall time: 44.10 s >>> >>> In [12]: %time a=list(iterops.disjoint(poly,points)) >>> CPU times: user 42.64 s, sys: 0.20 s, total: 42.83 s >>> Wall time: 42.89 s >>> >>> In [16]: %time a=[p for p in points if poly.contains(p)] >>> CPU times: user 42.45 s, sys: 0.16 s, total: 42.61 s >>> Wall time: 42.68 s >>> >>> In [18]: %time a=map(poly.contains,points) >>> CPU times: user 37.98 s, sys: 0.15 s, total: 38.12 s >>> Wall time: 38.21 s >>> >>> In [20]: %time a=map(poly.disjoint,points) >>> CPU times: user 37.91 s, sys: 0.20 s, total: 38.10 s >>> Wall time: 38.18 s >>> >>> Is there a better way to do the same? Do you happen to know if the new >>> prepared geometry could help speeding up a little? >>> >>> Thanks and regards, >>> >>> Mario >>> _______________________________________________ >>> Community mailing list >>> Community@lists.gispython.org >>> http://lists.gispython.org/mailman/listinfo/community >>> >> >> hi, i'd be interested to see how prepared geometries could speed that up. >> but i've found this: >> http://matplotlib.sourceforge.net/faq/howto_faq.html?highlight=nxutils#test-whether-a-point-is-inside-a-polygon >> to be _extremely_ fast. >> >> you could also stick your points in an rtree >> (http://pypi.python.org/pypi/Rtree/) and grab the points falling >> within the bounds, then do the contains() test >> on the points that pass that. >> >> -brent >> _______________________________________________ >> Community mailing list >> Community@lists.gispython.org >> http://lists.gispython.org/mailman/listinfo/community > _______________________________________________ > Community mailing list > Community@lists.gispython.org > http://lists.gispython.org/mailman/listinfo/community > _______________________________________________ Community mailing list Community@lists.gispython.org http://lists.gispython.org/mailman/listinfo/community
