Mailman 3 December 2012 - scikit-image

Re: import of skimage.morphology.selem.disk fails
by Guillaume Gay 17 Dec '12

17 Dec '12

Le 17/12/2012 09:03, Stéfan van der Walt a écrit : > Hi Prashand > > On Sun, Dec 16, 2012 at 11:50 PM, Parand Darugar <darugar(a)gmail.com> wrote: >> "from skimage.morphology.selem import disk" fails with the message >> "ImportError: cannot import name convex_hull_image". Subsequent attempts to >> import result in message "ImportError: cannot import name _template". > No problems on this end. Could you please send usyour build log? > > Thanks > Stefan > Hi I had very similar import problems when I checked out the source from github last friday, that got sorted out by pulling it again sunday, so maybe it is enough for you to update your repository and build again... Guillaume

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import of skimage.morphology.selem.disk fails
by Parand Darugar 17 Dec '12

17 Dec '12

Hello, "from skimage.morphology.selem import disk" fails with the message "ImportError: cannot import name convex_hull_image". Subsequent attempts to import result in message "ImportError: cannot import name _template". System: Python 2.7.2 on OS X 1.8.2 , scikit-image latest version from git Installed via "python install setup.py" The install seemed to work fine - I can run most other scripts. Full error log here: http://pastebin.com/hD8ishuH Any ideas? Best, Parand

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Re: Segmentation and Granulometry with scikit-image
by Dan 17 Dec '12

17 Dec '12

Hi, The output: Python 2.7.3 (default, Apr 10 2012, 23:31:26) [MSC v.1500 32 bit (Intel)] on win32 Type "copyright", "credits" or "license()" for more information. >>> import skimage >>> print skimage.__version__ 0.7.2 >>> print skimage.__file__ C:\Python27\lib\site-packages\skimage\__init__.pyc >>> Thanks El domingo, 16 de diciembre de 2012 09:41:35 UTC-5, Tony S Yu escribió: > > On Sat, Dec 15, 2012 at 6:45 PM, Dan <dk...(a)walla.co.il <javascript:>>wrote: > >> Hi, >> >> Thanks for the rapid respnse, I am using 0.7.2 on windows. >> >> The error is now: >> >> Traceback (most recent call last): >> File "C:/Users/DK/PProjects/skimage_seg/scikit_seg.py", line 3, in >> <module> >> import skimage.morphology as morph >> File "C:\Python27\lib\site-packages\skimage\morphology\__init__.py", >> line 7, in <module> >> from ._skeletonize import skeletonize, medial_axis >> ImportError: cannot import name skeletonize >> >> Any idea why?. >> >> Thanks >> > > > Hi Dan, > > How did you install scikit-image? Also, what's the output of the following? > > > import skimage > print skimage.__version__ > print skimage.__file__ > > > -Tony > > > >> El sábado, 15 de diciembre de 2012 16:24:31 UTC-5, Tony S Yu escribió: >> >>> On Sat, Dec 15, 2012 at 1:58 PM, Dan <dk...(a)walla.co.il> wrote: >>> >>>> Dear Tony, >>>> >>>> Thanks for the script. >>>> >>>> I was running the script and I got this error: >>>> Traceback (most recent call last): >>>> File "C:/Users/DK/PProjects/**skimage_seg/scikit_seg.py", line 18, >>>> in <module> >>>> img_background = morph.reconstruction(img-h, img) >>>> AttributeError: 'module' object has no attribute 'reconstruction' >>>> >>>> Process finished with exit code 1 >>>> >>>> Any reason why? >>>> >>>> Thanks a lot, >>>> Dan >>>> >>> >>> Hi Dan, >>> >>> Which version of scikit-image are you running? You'll need at least 0.7 >>> (the latest stable release). You can check the version with the following >>> shell command: >>> >>> $ python -c "import skimage; print skimage.__version__" >>> >>> -Tony >>> >>

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Filter out large segments
by Parand Darugar 17 Dec '12

17 Dec '12

Hello, Not sure if it's appropriate to ask algorithm questions on this mailing list, please let me know if it's not. I'd like to filter out large contiguous regions out of my image - I only care about smaller items. Following the Image Segmentation example/tutorial, I'm able to find and label the large segments using an Otsu threshold and ndimage.label. Now I'd like to replace these large segments with "white", where "white" is the max value find in my grayscale image (basically I'd like to blank them out). I'm not quite following how this is done in the tutorial: >>> label_objects, nb_labels = ndimage.label(fill_coins) >>> sizes = np.bincount(label_objects.ravel()) >>> mask_sizes = sizes > 20 >>> mask_sizes[0] = 0 >>> coins_cleaned = mask_sizes[label_objects] How does the coins_cleaned = mask_sizes[label_objects] line clean the image? Generally speaking, given the labeled segments (label_objects from the tutorial), how would I apply operations to the original image - for example: set all of the pixels in "image" that are parts of objects labeled 3 and 9 to 0.577 . Also, how do I get smarter about how the system works so I don't ask elementary questions? Is there a good high level tutorial that'll familiarize me with the computation model for skimage / ndimage ? Best, Parand

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Re: Stable vs Dev version, document image enhancement
by Schönberger Johannes 16 Dec '12

16 Dec '12

Maybe the "Gallery" link in the navigation should point to the dev gallery and the "Documentation" link to the stable docs?

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Re: Segmentation and Granulometry with scikit-image
by Dan 16 Dec '12

16 Dec '12

Hi, Thanks for the rapid respnse, I am using 0.7.2 on windows. The error is now: Traceback (most recent call last): File "C:/Users/DK/PProjects/skimage_seg/scikit_seg.py", line 3, in <module> import skimage.morphology as morph File "C:\Python27\lib\site-packages\skimage\morphology\__init__.py", line 7, in <module> from ._skeletonize import skeletonize, medial_axis ImportError: cannot import name skeletonize Any idea why?. Thanks El sábado, 15 de diciembre de 2012 16:24:31 UTC-5, Tony S Yu escribió: > > On Sat, Dec 15, 2012 at 1:58 PM, Dan <dk...(a)walla.co.il <javascript:>>wrote: > >> Dear Tony, >> >> Thanks for the script. >> >> I was running the script and I got this error: >> Traceback (most recent call last): >> File "C:/Users/DK/PProjects/skimage_seg/scikit_seg.py", line 18, in >> <module> >> img_background = morph.reconstruction(img-h, img) >> AttributeError: 'module' object has no attribute 'reconstruction' >> >> Process finished with exit code 1 >> >> Any reason why? >> >> Thanks a lot, >> Dan >> > > Hi Dan, > > Which version of scikit-image are you running? You'll need at least 0.7 > (the latest stable release). You can check the version with the following > shell command: > > $ python -c "import skimage; print skimage.__version__" > > -Tony > > El lunes, 24 de septiembre de 2012 18:58:19 UTC-4, Tony S Yu escribió: >>> >>> >>> >>> On Fri, Sep 21, 2012 at 8:46 PM, Stéfan van der Walt <ste...(a)sun.ac.za>wrote: >>> >>> On Wed, Sep 19, 2012 at 4:01 AM, ashz <ashz...(a)gmail.com> wrote: >>>> >>>> > Granulometry of Snowflakes: >>>> > http://www.mathworks.com/**products/image/examples.html?** >>>> file=/products/demos/shipping/**images/ipexsnow.html<http://www.mathworks.com/products/image/examples.html?file=/products/demos/…> >>>> >>>> Tony, do you think the morphological reconstruction would work for >>>> this? >>> >>> >>> Yes and no. Morphological reconstruction (specifically, h-dome, a.k.a. >>> h-maxima or regional max) tries to enhance features (maxima) based on the >>> local region. So the h-dome of a dim snowflake and bright snowflake will >>> look similar if they have a similar peakiness. In the original example, dim >>> peaks are still much dimmer than the bright peaks, even after the adaptive >>> histogram equalization. >>> >>> Also, the h-dome of an image will tend to remove the corona-like >>> blurring around the larger snowflakes since the middle is so much brighter >>> than the surrounding region. I've attached an image of the results, along >>> with the code to generate it (although you can't run it without the >>> snowflakes image, which I probably shouldn't email). You'll notice that dim >>> peaks are much brighter than in the example, and the large snowflakes are >>> smaller (because of suppression of the corona region). These differences >>> alter the intensity curve quite a bit (I'm not sure what the "right" output >>> is, though). >>> >>> >>>> Otherwise perhaps one of the segmentation algorithms. >>>> Translating the example line-by-line is pretty easy, except that we do >>>> not have adaptive histogram equalization implemented yet. >>>> >>> >>> BTW, there's a pending PR to add adaptive histogram equalization<https://github.com/scikits-image/scikits-image/pull/242>. >>> (Although, I'm not sure how I feel about the large header file required for >>> that PR.) >>> >>> As for the original post about these Matlab examples: Thanks for >>> bringing this up, ashz! These are exactly the type of examples I'd like to >>> see in the user guide: Something that takes the user all the way from image >>> loading to data extraction. We'd probably want to write our own instead of >>> simply translating the Matlab examples. It'd be great if people on this >>> list could provide examples from their own research. >>> >>> Best, >>> -Tony >>> >>> >>> [image: Inline image 1] >>> >>> # Code to generate image above >>> # Note: this won't run without image from Matlab install: >>> # toolbox/images/imdemos/**snowflakes.png >>> >>> >>> from skimage import io >>> from skimage import img_as_float >>> import skimage.morphology as morph >>> from skimage import exposure >>> from scipy import ndimage >>> >>> >>> # snowflake image from a Matlab install >>> img = io.imread('snowflakes.png') >>> # convert from uint8 to float since we'll be subtracting (prevent >>> underflow) >>> orig = img_as_float(img) >>> >>> img = exposure.equalize(img) >>> img = ndimage.gaussian_filter(img, 1) >>> >>> # look for regional maxima greater than surrounding regions by h >>> h = 0.2 >>> img_background = morph.reconstruction(img-h, img) >>> img_hdome = img - img_background >>> img = exposure.rescale_intensity(**img_hdome) >>> >>> io.imshow(img) >>> # io.imsave('snowflakes_**enhanced.png', img) >>> io.show() >>> >>> >>> >>> -- >> >> >> > >

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Re: Segmentation and Granulometry with scikit-image
by Dan 15 Dec '12

15 Dec '12

Dear Tony, Thanks for the script. I was running the script and I got this error: Traceback (most recent call last): File "C:/Users/DK/PProjects/skimage_seg/scikit_seg.py", line 18, in <module> img_background = morph.reconstruction(img-h, img) AttributeError: 'module' object has no attribute 'reconstruction' Process finished with exit code 1 Any reason why? Thanks a lot, Dan El lunes, 24 de septiembre de 2012 18:58:19 UTC-4, Tony S Yu escribió: > > > > On Fri, Sep 21, 2012 at 8:46 PM, Stéfan van der Walt <ste...(a)sun.ac.za<javascript:> > > wrote: > >> On Wed, Sep 19, 2012 at 4:01 AM, ashz <ashz...(a)gmail.com <javascript:>> >> wrote: >> >> > Granulometry of Snowflakes: >> > >> http://www.mathworks.com/products/image/examples.html?file=/products/demos/… >> >> Tony, do you think the morphological reconstruction would work for >> this? > > > Yes and no. Morphological reconstruction (specifically, h-dome, a.k.a. > h-maxima or regional max) tries to enhance features (maxima) based on the > local region. So the h-dome of a dim snowflake and bright snowflake will > look similar if they have a similar peakiness. In the original example, dim > peaks are still much dimmer than the bright peaks, even after the adaptive > histogram equalization. > > Also, the h-dome of an image will tend to remove the corona-like blurring > around the larger snowflakes since the middle is so much brighter than the > surrounding region. I've attached an image of the results, along with the > code to generate it (although you can't run it without the snowflakes > image, which I probably shouldn't email). You'll notice that dim peaks are > much brighter than in the example, and the large snowflakes are smaller > (because of suppression of the corona region). These differences alter the > intensity curve quite a bit (I'm not sure what the "right" output is, > though). > > >> Otherwise perhaps one of the segmentation algorithms. >> Translating the example line-by-line is pretty easy, except that we do >> not have adaptive histogram equalization implemented yet. >> > > BTW, there's a pending PR to add adaptive histogram equalization<https://github.com/scikits-image/scikits-image/pull/242>. > (Although, I'm not sure how I feel about the large header file required for > that PR.) > > As for the original post about these Matlab examples: Thanks for bringing > this up, ashz! These are exactly the type of examples I'd like to see in > the user guide: Something that takes the user all the way from image > loading to data extraction. We'd probably want to write our own instead of > simply translating the Matlab examples. It'd be great if people on this > list could provide examples from their own research. > > Best, > -Tony > > > [image: Inline image 1] > > # Code to generate image above > # Note: this won't run without image from Matlab install: > # toolbox/images/imdemos/snowflakes.png > > > from skimage import io > from skimage import img_as_float > import skimage.morphology as morph > from skimage import exposure > from scipy import ndimage > > > # snowflake image from a Matlab install > img = io.imread('snowflakes.png') > # convert from uint8 to float since we'll be subtracting (prevent > underflow) > orig = img_as_float(img) > > img = exposure.equalize(img) > img = ndimage.gaussian_filter(img, 1) > > # look for regional maxima greater than surrounding regions by h > h = 0.2 > img_background = morph.reconstruction(img-h, img) > img_hdome = img - img_background > img = exposure.rescale_intensity(img_hdome) > > io.imshow(img) > # io.imsave('snowflakes_enhanced.png', img) > io.show() > > > >

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Re: Stable vs Dev version, document image enhancement
by Parand Darugar 15 Dec '12

15 Dec '12

Hi Tony, You're absolutely right, that was the issue. Thanks very much. Now to learn how to use scikit-image :-) Best, Parand On Friday, December 14, 2012 11:37:32 AM UTC-8, Tony S Yu wrote: > > > > On Fri, Dec 14, 2012 at 1:31 PM, Parand Darugar <dar...(a)gmail.com<javascript:> > > wrote: > >> Hi Tony, >> >> Installation steps: >> >> This is on OS X 10.8.2, Python 2.7.2: >> >> - I setup a virtualenv and activated that virtual env. >> - I cloned the scikit-image git repository >> - I installed: >> >> $ python ./setup.py install >> ... >> Installed >> /Users/parand/Projects/Receipts/pyenv/lib/python2.7/site-packages/scikit_image-0.8dev-py2.7-macosx-10.8-intel.egg >> Processing dependencies for scikit-image==0.8dev >> Finished processing dependencies for scikit-image==0.8dev >> >> $ python >> Python 2.7.2 (default, Jun 20 2012, 16:23:33) >> [GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on >> darwin >> >>> import skimage >> >>> skimage.__version__ >> '0.8dev' >> >>> import skimage.filter.rank as rank >> Traceback (most recent call last): >> File "<stdin>", line 1, in <module> >> File "skimage/filter/__init__.py", line 2, in <module> >> from .ctmf import median_filter >> File "skimage/filter/ctmf.py", line 15, in <module> >> from . import _ctmf >> ImportError: cannot import name _ctmf >> >> Best, >> >> Parand >> > > Hi Parand, > > If those commands are exactly as you copied them above, it looks like > you're running the python interpreter from within the root scikit-image > directory. Since python prepends the path of the working directory to the > python path, you're actually importing the skimage package from that > directory---not the one you just installed (i.e. the one in site-packages). > You can check which you're running with: > > python -c 'import skimage; print skimage.__file__' > > If you move up one directory and then start the python interpreter, does > the rank import work? > > Best, > -Tony >

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Re: Stable vs Dev version, document image enhancement
by Parand Darugar 14 Dec '12

14 Dec '12

Hi Tony, Installation steps: This is on OS X 10.8.2, Python 2.7.2: - I setup a virtualenv and activated that virtual env. - I cloned the scikit-image git repository - I installed: $ python ./setup.py install ... Installed /Users/parand/Projects/Receipts/pyenv/lib/python2.7/site-packages/scikit_image-0.8dev-py2.7-macosx-10.8-intel.egg Processing dependencies for scikit-image==0.8dev Finished processing dependencies for scikit-image==0.8dev $ python Python 2.7.2 (default, Jun 20 2012, 16:23:33) [GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on darwin >>> import skimage >>> skimage.__version__ '0.8dev' >>> import skimage.filter.rank as rank Traceback (most recent call last): File "<stdin>", line 1, in <module> File "skimage/filter/__init__.py", line 2, in <module> from .ctmf import median_filter File "skimage/filter/ctmf.py", line 15, in <module> from . import _ctmf ImportError: cannot import name _ctmf Best, Parand On Thursday, December 13, 2012 8:54:00 PM UTC-8, Tony S Yu wrote: > > On Thu, Dec 13, 2012 at 11:22 PM, Josh Warner <silvertr...(a)gmail.com<javascript:> > > wrote: > >> Hi Parand, >> >> Welcome! Most of the gallery examples work in the current release (0.7.2 >> I believe), but the rank filter is in a PR that was merged recently, and is >> presently only in the dev version. You may want the functionality in the >> rank filter for OCR work, where fast local operations like the otsu >> threshold could be very beneficial. >> >> I'll let other people answer the questions about specific references as >> I'm on the road, but I think most of what you need to do is available in >> scikit-image. >> >> Josh Warner >> > > Just wanted to add: The documentation for the last release can be found > here: > > http://scikit-image.org/docs/0.7.0/ > > > It might be a good idea for us to point to the stable version instead of > the development version by default. > > > > On Wednesday, December 12, 2012 7:00:24 PM UTC-6, Parand Darugar wrote: >>> >>> >>> <snip> > >> - My initial goal is image/document enhancement and cleanup in order to >>> improve OCR. The images are primarily captured via cell phone cameras and >>> scanners, so I'm looking to normalize and enhance the color, remove noise, >>> and straighten them. Any pointers to code, papers, or algorithms that may >>> be of interest would be appreciated. >>> >>> -- Error message -- >>> >>> import skimage.filter.rank as rank >>> Traceback (most recent call last): >>> File "<stdin>", line 1, in <module> >>> File "skimage/filter/__init__.py", line 2, in <module> >>> from .ctmf import median_filter >>> File "skimage/filter/ctmf.py", line 15, in <module> >>> from . import _ctmf >>> ImportError: cannot import name _ctmf >>> >> > This shouldn't happen. How did you install scikit-image? Are you sure that > you built the package after downloading. > > Cheers, > -Tony > >

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canvastools subpackage for scikit-image viewer
by Steven Silvester 14 Dec '12

14 Dec '12

Tony, I'm going all in on this with you. I have been hacking at matplotlib recently as well, and have demonstrated the following in PySide and PyQt4: - right click menu with grid toggle and colormap selection, and simplified uimenu creation - roi with stats overlay (mean, std, min, max) - cross section slice view - scroll zoom - improved colorbar placement I also wrote a tool in enaml that allows you to pick a skimage package and function from dropdowns. It then displays the docstring and allows you to write the function call inline, and safely evaluates it. Thus far, it has all been proof of concept and for use at work, but I'd like to start integrating these features into your viewer. I will start a repo on my github to demonstrate the features prior to integration. Thoughts?

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