https://github.com/dfarmer/scikits.image/compare/master...dfarmer-filters-c…
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 …
[View More]working with large images (on my test use case with
~512x512 images it's about 300 KB for the "fake" mask).
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.
So anyway, I thought I would let you take a look at the code and
decide if you'd like me to try to figure out a way around that or
incorporate it as is (with no default) or add the np.ones default.
The tests pass and as I mentioned it has worked fine so far in
day-to-day use. Also regarded Ralf's comment to consolidate into one
filter.py file, I agree with that I just figured I would do that with
the next batch (in a separate commit). But again, let me know if I'm
not approaching this properly.
Thanks,
Dan
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Hi Group,
I merged Cell Profiler's Canny over last weekend and spent this week
"dogfooding" it on my own project, I had a few questions and ideas to
run by before I push it back to github.
1. Organization: In general the CP filter code is mostly in one file,
I tried to follow the example of the constant time median filter merge
that was already done. I just wanted to make sure that you guys want
one filter per file?
2. API: So I used the function in part of a project I've been doing
this …
[View More]semester and my call to canny looks like this: canny(img,
np.ones(img.shape, dtype=bool), 5, 0.005, 0.01), the np.ones bit is
because the Cell Profiler canny (as far as I can tell) requires a
binary mask, and the 0.005 and 0.01 are the low and high thresholds
for hysteresis thresholding. As you can image that took me quit a bit
of fiddling to get those values. It seems that since it's thresholding
the magnitude values you wind up with strange values. So my thoughts
were: can we add a default parameter for the mask so that end-users of
the library don't have to think about it unless they want to (I do
think it's a nice functionality to have), and secondly should anything
be done (like normalizing between 0 and 1) about the pixel values so
that there can be some intuition about threshold values?
3. As far as the pull request goes, do I just merge my local canny
branch back into master locally and then push that to github or do I
actually push the branch to github and issue a pull against that?
Thanks,
Dan
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Hello,
I've recently started using SciPy/NumPy for my computer vision research work
and I wanted to get involved with scikits.image to help build a useful
library and to learn more about NumPy and Cython programming. I looked at
the tasks list and it seems there is a range of work from fairly simple
integration work to some high-end plans (OpenCL backend, etc.). So I thought
I might start with e.g., merging some of the Cell Profiler code, but then I
saw that there are 24 forks of scikits.…
[View More]image on github and it's not clear to
me what is already being worked on. So I thought I would just ask if someone
could point me in the direction of some entry level work to do?
Actually I just thought of something else, what is required to update the
OpenCV bindings for OpenCV 2.2? I can't seem to use scikits.image at the
moment because it can't find the opencv that it's looking for (the default
Python bindings for 2.2 work, and native C++ code works too -- so it's
definitely installed correctly).
Thanks,
Dan
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Hi,
I wonder - what happened to 0.2.2? It's available on pypi, but it's
unavailable on github where latest seems to be 0.2.1. Was this release
to fix packaging maybe?
Andrzej.