Hi Dan On Thu, Mar 31, 2011 at 1:02 AM, Dan Farmer <dfarmernv@gmail.com> wrote:
https://github.com/dfarmer/scikits.image/compare/master...dfarmer-filters-ca...
I read through your patch and made some preliminary comments.
Mostly just trying to follow procedure. I already mentioned my concerns in the previous thread. I made one stab at introduced a "None" default for the mask, but I got hung up and reverted it. The default I was going to propose was np.ones(img.shape,bool) (and after the fact I even noticed that's how it is used in one of the unit tests). But I started thinking that that could be quite wasteful of memory if you were working with large images (on my test use case with ~512x512 images it's about 300 KB for the "fake" mask).
It seems as though this specific implementation of the algorithms relies on creating the mask, so I don't think you can get away from it. The typical way to do it would be: def canny(..., mask=None, ...): if mask is None: mask = np.ones(x.shape, dtype=bool)
The problem I had was that if I don't allocate the emask array I get run-time errors starting at line 129 (in the diff of canny.py) because the arrays all have different lengths if they aren't logical_and'd with emask above.
Yes, I think the only way to avoid allocating the mask explicitly is to rewrite the algorithm in Cython, where you can modify behaviour inside the for-loop. Regards Stéfan