Segmentation and Granulometry with scikit-image

[Dan]

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... at sun.ac.za<javascript:>
> > wrote:
>
>> On Wed, Sep 19, 2012 at 4:01 AM, ashz <ashz... at gmail.com <javascript:>> 
>> wrote:
>>
>> > Granulometry of Snowflakes:
>> > 
>> 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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