Mailman 3 April 2011 - scikit-image

Re: "sudo easy_install scikits.image" fails
by Stéfan van der Walt April 13, 2011

April 13, 2011

Hi Jean-Patrick On Tue, Apr 12, 2011 at 1:16 PM, jip <jeanpatrick.pommier(a)gmail.com> wrote: > Dear all, > The installation of scikits.image fails on my ubuntu10.10 32bits > laptop as the following output shows. > thanks for yours advices to solve that problem. Thanks for the feedback. Guess it's time for release 0.3! Could you do me a favour and try to build the latest version from github? http://github.com/stefanv/scikits.image Regards Stéfan

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"sudo easy_install scikits.image" fails
by jip April 12, 2011

April 12, 2011

Dear all, The installation of scikits.image fails on my ubuntu10.10 32bits laptop as the following output shows. thanks for yours advices to solve that problem. regards Jean-Patrick Pommier sudo easy_install scikits.image Searching for scikits.image Reading http://pypi.python.org/simple/scikits.image/ Reading http://stefanv.github.com/scikits.image Reading http://github.com/stefanv/scikits.image Best match: scikits.image 0.2.2 Downloading http://pypi.python.org/packages/source/s/scikits.image/scikits.image-0.2.2.… Processing scikits.image-0.2.2.tar.gz Running scikits.image-0.2.2/setup.py -q bdist_egg --dist-dir /tmp/ easy_install-fktrwG/scikits.image-0.2.2/egg-dist-tmp-sKcnP4 Warning: Assuming default configuration (scikits/ {setup_scikits,setup}.py was not found) Appending scikits configuration to Ignoring attempt to set 'name' (from '' to 'scikits') Traceback (most recent call last): File "/usr/local/bin/easy_install", line 9, in <module> load_entry_point('distribute==0.6.15', 'console_scripts', 'easy_install')() File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/command/easy_install.py", line 1858, in main with_ei_usage(lambda: File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/command/easy_install.py", line 1839, in with_ei_usage return f() File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/command/easy_install.py", line 1862, in <lambda> distclass=DistributionWithoutHelpCommands, **kw File "/usr/lib/python2.6/distutils/core.py", line 152, in setup dist.run_commands() File "/usr/lib/python2.6/distutils/dist.py", line 975, in run_commands self.run_command(cmd) File "/usr/lib/python2.6/distutils/dist.py", line 995, in run_command cmd_obj.run() File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/command/easy_install.py", line 344, in run self.easy_install(spec, not self.no_deps) File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/command/easy_install.py", line 584, in easy_install return self.install_item(spec, dist.location, tmpdir, deps) File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/command/easy_install.py", line 614, in install_item dists = self.install_eggs(spec, download, tmpdir) File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/command/easy_install.py", line 804, in install_eggs return self.build_and_install(setup_script, setup_base) File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/command/easy_install.py", line 1081, in build_and_install self.run_setup(setup_script, setup_base, args) File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/command/easy_install.py", line 1070, in run_setup run_setup(setup_script, args) File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/sandbox.py", line 29, in run_setup lambda: execfile( File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/sandbox.py", line 70, in run return func() File "/usr/local/lib/python2.6/dist-packages/distribute-0.6.15- py2.6.egg/setuptools/sandbox.py", line 31, in <lambda> {'__file__':setup_script, '__name__':'__main__'} File "setup.py", line 80, in <module> File "/usr/lib/python2.6/dist-packages/numpy/distutils/core.py", line 150, in setup config = configuration() File "setup.py", line 34, in configuration File "/usr/lib/python2.6/dist-packages/numpy/distutils/ misc_util.py", line 852, in add_subpackage caller_level = 2) File "/usr/lib/python2.6/dist-packages/numpy/distutils/ misc_util.py", line 835, in get_subpackage caller_level = caller_level + 1) File "/usr/lib/python2.6/dist-packages/numpy/distutils/ misc_util.py", line 782, in _get_configuration_from_setup_py config = setup_module.configuration(*args) File "scikits/image/setup.py", line 8, in configuration File "/usr/lib/python2.6/dist-packages/numpy/distutils/ misc_util.py", line 852, in add_subpackage caller_level = 2) File "/usr/lib/python2.6/dist-packages/numpy/distutils/ misc_util.py", line 835, in get_subpackage caller_level = caller_level + 1) File "/usr/lib/python2.6/dist-packages/numpy/distutils/ misc_util.py", line 767, in _get_configuration_from_setup_py ('.py', 'U', 1)) File "scikits/image/opencv/setup.py", line 3, in <module> ImportError: No module named image._build

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Help request
by Alex April 12, 2011

April 12, 2011

Dear skikits-image members, I have some difficulty to identify specific objects from the grayscale image. For example, in this image https://picasaweb.google.com/lh/photo/FYZ1_F4OAoe920iYj8bk7A?feat=directlink one can see about 15 glass beads on the floor illuminated from the left side. Human eye identifies them easily, but I cannot find the efficient way to identify them in such an image. Using Matlab the approach was 1) crop out one of the beads as is from the given image, and then 2)using normalized cross-correlation (normxcorr2) of the cropped bead with the image to identify the 50 - 60% of beads. The problems are due to uneven background illumination, different size of the beads and different light pattern due to their angle in respect to the light source. I could define the the problem of looking a feature that has two not- connected bright regions, separated by some distance that could be known within some reasonable limits. Any suggestion is gratefully appreciated. If possible, the pseudo-code would be helpful as I'm not yet familiar with all the possibilities of this great skikit. Thanks, Alex

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Re: Help request
by Maël Primet April 12, 2011

April 12, 2011

For denoising, you can use high performance tvdenoise on my scikits fork at github.com (user maelp) but for some reason (mainly because my code is C) people in scikits don't want to merge my code to the main branch On Tue, Apr 12, 2011 at 08:47, Alex <alex.liberzon(a)gmail.com> wrote: > All is right. We think in the same direction. BUT, this will solve the > problem for the next experiment, hopefully. The question is what to do > with the given image? I am looking into things like: > a) multiscale cross correlation or other template matching > b) hit-and-miss morphology, again multiscale > > there is also a step of adaptive/smart background elimination as far > as I can understand. Simple median, gaussian, morphological top-hat > filters help, but not solve it completely. > > > Please, bring up your brilliant ideas. > > Thanks > Alex > > On Apr 12, 8:57 am, Maël Primet <mael.pri...(a)gmail.com> wrote: > > Hi Alex, > > > > why not using colored beads? and why using a focus that blurs some of the > > beads? > > you could indeed either do cross correlation using numpy, or try to > detect > > highlights that appear in pairs (but then when several beads are close > from > > each other, you need to find a way to connect the two highlights from the > > same bead, and not from two different beads) > > if your image comes from a movie, perhaps you could also track the beads > > from frame to frame and thus have a more precise idea of their locations > in > > the next frame >

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Re: Connected components labelling
by Stéfan van der Walt April 11, 2011

April 11, 2011

> > 2011/4/11 Stéfan van der Walt <stefan(a)sun.ac.za>: >> Hi Damian >> >> 2011/4/5 Damian Eads <eads(a)soe.ucsc.edu>: >>> Nice work. Connected components is a very useful algorithm. Can you >>> clarify your definition of neighbouring? Does it apply to pixels >>> diagonal to one another (8 connectivity) or not (4 connectivity)? >> >> Thanks for the comments! Currently, I use 8-connectivity. Should I >> make 4-connectivity an option, or simply document that? 2011/4/11 Damian Eads <eads(a)soe.ucsc.edu>: > No problem! :) Whether you should implement 4-connectivity as an > option is up to you. I just wasn't sure which one the implementation > was using so documenting it would help. :) Great, I updated the docstring and merged. I personally prefer 8-connectivity because it is slightly more rotationally invariant. Regards Stéfan

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Review: Canny
by Dan Farmer April 7, 2011

April 7, 2011

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 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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Connected components labelling
by Stéfan van der Walt April 5, 2011

April 5, 2011

Hi all, I've added connected components labelling to the morphology module under the 'ccomp' branch. I'd be glad if you could take a look and suggest improvements. https://github.com/stefanv/scikits.image/compare/master...ccomp Regards Stéfan

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