Segmentation and Granulometry with scikit-image

[Dan]

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... at 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... at sun.ac.za>wrote:
>>>
>>> On Wed, Sep 19, 2012 at 4:01 AM, ashz <ashz... at 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/shipping/images/ipexsnow.html>
>>>>
>>>> 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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