Mailman 3 October 2017 - scikit-image

Community guidelines
by Stefan van der Walt 25 Oct '17

25 Oct '17

Hi, everyone SciPy just merged their Code of Conduct documents after a long discussion [0]: https://github.com/scipy/scipy/blob/master/doc/source/dev/conduct/code_of_c… https://github.com/scipy/scipy/blob/master/doc/source/dev/conduct/report_ha… I have been hesitant to include anything similar for scikit-image before (other than http://scikit-image.org/community_guidelines.html ), because I did not feel comfortable with the aloof policing tone typically employed in similar documents elsewhere. But, I think the SciPy documents make an important statement about our vision for an inclusive community, and does so in a friendly but serious way. How would you feel about adopting these documents for scikit-image? Best regards Stéfan [0]: https://github.com/scipy/scipy/pull/7963

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Re: [scikit-image] Removing interference pattern noise
by Imanol Luengo Muntión 13 Oct '17

13 Oct '17

Hi Kesavan, I believe your problem is called flat field correction: https://en.m.wikipedia.org/wiki/Flat-field_correction Not sure if scikit-image has something already implemented, but the formula is simple and shouldn't be too complicated. Best, Imanol On Oct 13, 2017 8:17 AM, "Kesavan Subburam" <pskeshu(a)gmail.com> wrote: Hi all, I'm trying to remove interference pattern noise from my optics in my data, and to that effect, I have imaged the pattern by capturing an image without any sample (pattern.jpg). I was wondering what would be the straight forward way to remove such pattern noise. I don't suppose a simple subtraction would suffice. I have attached a sample image here. I have compressed it because of the mailing list limitations on file sizes. Thank you, -- Kesavan _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

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Removing interference pattern noise
by Kesavan Subburam 13 Oct '17

13 Oct '17

Hi all, I'm trying to remove interference pattern noise from my optics in my data, and to that effect, I have imaged the pattern by capturing an image without any sample (pattern.jpg). I was wondering what would be the straight forward way to remove such pattern noise. I don't suppose a simple subtraction would suffice. I have attached a sample image here. I have compressed it because of the mailing list limitations on file sizes. Thank you, -- Kesavan

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Re: [scikit-image] Using the flooding watershed
by Thomas Walter 09 Oct '17

09 Oct '17

Hello Alex, the ultimate erosion coincides with the local maxima of the distance map (or the local minima of the inverse distance map). In skimage, you can get these by: from skimage.morphology import extrema local_maxima = extrema.local_maxima <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morpho…>(distance_map) peak_local_max is not equivalent to the UEP, but can be preferable in certain situations. I did not check your example in practice as I do not have the image, but I was a bit confused by the inversions, as you start by applying the threshold the other way around. Best, Thomas. P.S.: The ImageJ description is not correct, which I have always found disturbing. The Watershed algorithm is of course not limited to applications to the inverse distance map. On 10/5/17 3:22 PM, Alexandre Fioravante de Siqueira wrote: > Dear all, > I am trying to write a that mimics the behavior of ImageJ's flooding > watershed. This is how they describe it > (https://imagej.nih.gov/ij/docs/menus/process.html#watershed): > > /Watershed > / > /Watershed segmentation is a way of automatically separating or > cutting apart particles that touch. It first calculates the Euclidian > distance map (EDM) and finds the ultimate eroded points (UEPs). It > then dilates each of the UEPs (the peaks or local maxima of the EDM) > as far as possible - either until the edge of the particle is reached, > or the edge of the region of another (growing) UEP. Watershed > segmentation works best for smooth convex objects that don't overlap > too much. > / > > I tried to implement that (more or less), using the local minima (as > pointed in https://www.mathworks.com/help/images/ref/bwulterode.html). > Of course, there is something wrong :) > > from scipy.ndimage import binary_fill_holes > from scipy.ndimage.morphology import distance_transform_edt > from skimage.feature import peak_local_max > from skimage.filters import threshold_otsu > from skimage.io <http://skimage.io> import imread > from skimage.measure import label > from skimage.morphology import disk, watershed > from skimage.util import invert > > import matplotlib.pyplot as plt > > image = imread('K90_incid4,5min_1.bmp') > img_bin = binary_fill_holes(image < threshold_otsu(image)) > > inv_bin = invert(img_bin) > inv_dist = distance_transform_edt(inv_bin) > inv_peak = peak_local_max(-inv_dist, indices=False) > markers = label(peak) > labels = watershed(-distance, markers, mask=img_bin, watershed_line=True) > > plt.imshow(labels, cmap='nipy_spectral'), plt.show() > > We don't have a function for UEPs in skimage yet, right? > How could I implement this flooding watershed? > Thank you very much. Kind regards, > > Alex > > -- > -------------------------------------------------- > Dr. Alexandre 'Jaguar' Fioravante de Siqueira > Grupo de Cronologia - Sala 13 > Departamento de Raios Cósmicos e Cronologia - DRCC > Instituto de Física "Gleb Wataghin" - IFGW > Unicamp - Universidade Estadual de Campinas > Rua Sérgio Buarque de Holanda, 777 > Cidade Universitária Zeferino Vaz - CEP 13083-859 > Campinas - SP - Brazil > > Phone: +55(19)3521-5362 > Lattes curriculum: 3936721630855880 > <http://lattes.cnpq.br/3936721630855880/> > ORCID: 0000-0003-1320-4347 <http://orcid.org/0000-0003-1320-4347> > Personal site: programmingscience.org <http://www.programmingscience.org/> > Github: alexandrejaguar <http://www.github.com/alexandrejaguar/> > Skype: alexandrejaguar > Twitter: alexdesiqueira <http://www.twitter.com/alexdesiqueira/> > -------------------------------------------------- > > > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image -- Thomas Walter 27 rue des Acacias 75017 Paris

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Re: [scikit-image] scikit-image Digest, Vol 13, Issue 2
by Alexandre Fioravante de Siqueira 05 Oct '17

05 Oct '17

Hi all, Thomas, thank you for your help, and sorry for not sending the image. It follows attached. I applied the threshold that way because I'm interested in the "black spots" in the image. Thank you again. I'll try to run the code using your insights. Kind regards, Alex PS.: Could you tell a little more about the errors in ImageJ's description? Thank you for that! On Thu, Oct 5, 2017 at 11:00 AM, <scikit-image-request(a)python.org> wrote: > Send scikit-image mailing list submissions to > scikit-image(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scikit-image > or, via email, send a message with subject or body 'help' to > scikit-image-request(a)python.org > > You can reach the person managing the list at > scikit-image-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-image digest..." > > Today's Topics: > > 1. Using the flooding watershed (Alexandre Fioravante de Siqueira) > 2. Re: Using the flooding watershed (Thomas Walter) > > > ---------- Forwarded message ---------- > From: Alexandre Fioravante de Siqueira <siqueiraaf(a)gmail.com> > To: scikit-image(a)python.org > Cc: > Bcc: > Date: Thu, 5 Oct 2017 10:22:39 -0300 > Subject: [scikit-image] Using the flooding watershed > Dear all, > I am trying to write a that mimics the behavior of ImageJ's flooding > watershed. This is how they describe it (https://imagej.nih.gov/ij/ > docs/menus/process.html#watershed): > > > *Watershed* > > *Watershed segmentation is a way of automatically separating or cutting > apart particles that touch. It first calculates the Euclidian distance map > (EDM) and finds the ultimate eroded points (UEPs). It then dilates each of > the UEPs (the peaks or local maxima of the EDM) as far as possible - either > until the edge of the particle is reached, or the edge of the region of > another (growing) UEP. Watershed segmentation works best for smooth convex > objects that don't overlap too much.* > > I tried to implement that (more or less), using the local minima (as > pointed in https://www.mathworks.com/help/images/ref/bwulterode.html). Of > course, there is something wrong :) > > from scipy.ndimage import binary_fill_holes > from scipy.ndimage.morphology import distance_transform_edt > from skimage.feature import peak_local_max > from skimage.filters import threshold_otsu > from skimage.io import imread > from skimage.measure import label > from skimage.morphology import disk, watershed > from skimage.util import invert > > import matplotlib.pyplot as plt > > image = imread('K90_incid4,5min_1.bmp') > img_bin = binary_fill_holes(image < threshold_otsu(image)) > > inv_bin = invert(img_bin) > inv_dist = distance_transform_edt(inv_bin) > inv_peak = peak_local_max(-inv_dist, indices=False) > markers = label(peak) > labels = watershed(-distance, markers, mask=img_bin, watershed_line=True) > > plt.imshow(labels, cmap='nipy_spectral'), plt.show() > > We don't have a function for UEPs in skimage yet, right? > How could I implement this flooding watershed? > Thank you very much. Kind regards, > > Alex > > -- > -------------------------------------------------- > Dr. Alexandre 'Jaguar' Fioravante de Siqueira > Grupo de Cronologia - Sala 13 > Departamento de Raios Cósmicos e Cronologia - DRCC > Instituto de Física "Gleb Wataghin" - IFGW > Unicamp - Universidade Estadual de Campinas > Rua Sérgio Buarque de Holanda, 777 > Cidade Universitária Zeferino Vaz - CEP 13083-859 > Campinas - SP - Brazil > > Phone: +55(19)3521-5362 <+55%2019%203521-5362> > Lattes curriculum: 3936721630855880 > <http://lattes.cnpq.br/3936721630855880/> > ORCID: 0000-0003-1320-4347 <http://orcid.org/0000-0003-1320-4347> > Personal site: programmingscience.org <http://www.programmingscience.org/> > Github: alexandrejaguar <http://www.github.com/alexandrejaguar/> > Skype: alexandrejaguar > Twitter: alexdesiqueira <http://www.twitter.com/alexdesiqueira/> > -------------------------------------------------- > > > ---------- Forwarded message ---------- > From: Thomas Walter <Thomas.Edgar.Walter(a)googlemail.com> > To: scikit-image(a)python.org > Cc: > Bcc: > Date: Thu, 5 Oct 2017 15:41:18 +0200 > Subject: Re: [scikit-image] Using the flooding watershed > Hello Alex, > > the ultimate erosion coincides with the local maxima of the distance map > (or the local minima of the inverse distance map). > > In skimage, you can get these by: > > from skimage.morphology import extremalocal_maxima = extrema.local_maxima <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morpho…>(distance_map) > > > peak_local_max is not equivalent to the UEP, but can be preferable in > certain situations. > > I did not check your example in practice as I do not have the image, but I > was a bit confused by the inversions, as you start by applying the > threshold the other way around. > > Best, > > Thomas. > > P.S.: The ImageJ description is not correct, which I have always found > disturbing. The Watershed algorithm is of course not limited to > applications to the inverse distance map. > > > On 10/5/17 3:22 PM, Alexandre Fioravante de Siqueira wrote: > > Dear all, > I am trying to write a that mimics the behavior of ImageJ's flooding > watershed. This is how they describe it (https://imagej.nih.gov/ij/ > docs/menus/process.html#watershed): > > > *Watershed * > > *Watershed segmentation is a way of automatically separating or cutting > apart particles that touch. It first calculates the Euclidian distance map > (EDM) and finds the ultimate eroded points (UEPs). It then dilates each of > the UEPs (the peaks or local maxima of the EDM) as far as possible - either > until the edge of the particle is reached, or the edge of the region of > another (growing) UEP. Watershed segmentation works best for smooth convex > objects that don't overlap too much. * > > I tried to implement that (more or less), using the local minima (as > pointed in https://www.mathworks.com/help/images/ref/bwulterode.html). Of > course, there is something wrong :) > > from scipy.ndimage import binary_fill_holes > from scipy.ndimage.morphology import distance_transform_edt > from skimage.feature import peak_local_max > from skimage.filters import threshold_otsu > from skimage.io import imread > from skimage.measure import label > from skimage.morphology import disk, watershed > from skimage.util import invert > > import matplotlib.pyplot as plt > > image = imread('K90_incid4,5min_1.bmp') > img_bin = binary_fill_holes(image < threshold_otsu(image)) > > inv_bin = invert(img_bin) > inv_dist = distance_transform_edt(inv_bin) > inv_peak = peak_local_max(-inv_dist, indices=False) > markers = label(peak) > labels = watershed(-distance, markers, mask=img_bin, watershed_line=True) > > plt.imshow(labels, cmap='nipy_spectral'), plt.show() > > We don't have a function for UEPs in skimage yet, right? > How could I implement this flooding watershed? > Thank you very much. Kind regards, > > Alex > > -- > -------------------------------------------------- > Dr. Alexandre 'Jaguar' Fioravante de Siqueira > Grupo de Cronologia - Sala 13 > Departamento de Raios Cósmicos e Cronologia - DRCC > Instituto de Física "Gleb Wataghin" - IFGW > Unicamp - Universidade Estadual de Campinas > Rua Sérgio Buarque de Holanda, 777 > Cidade Universitária Zeferino Vaz - CEP 13083-859 > Campinas - SP - Brazil > > Phone: +55(19)3521-5362 <+55%2019%203521-5362> > Lattes curriculum: 3936721630855880 > <http://lattes.cnpq.br/3936721630855880/> > ORCID: 0000-0003-1320-4347 <http://orcid.org/0000-0003-1320-4347> > Personal site: programmingscience.org <http://www.programmingscience.org/> > Github: alexandrejaguar <http://www.github.com/alexandrejaguar/> > Skype: alexandrejaguar > Twitter: alexdesiqueira <http://www.twitter.com/alexdesiqueira/> > -------------------------------------------------- > > > _______________________________________________ > scikit-image mailing listscikit-image@python.orghttps://mail.python.org/mailman/listinfo/scikit-image > > > > -- > Thomas Walter > 27 rue des Acacias > 75017 Paris > > > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image > > -- -------------------------------------------------- Dr. Alexandre 'Jaguar' Fioravante de Siqueira Grupo de Cronologia - Sala 13 Departamento de Raios Cósmicos e Cronologia - DRCC Instituto de Física "Gleb Wataghin" - IFGW Unicamp - Universidade Estadual de Campinas Rua Sérgio Buarque de Holanda, 777 Cidade Universitária Zeferino Vaz - CEP 13083-859 Campinas - SP - Brazil Phone: +55(19)3521-5362 Lattes curriculum: 3936721630855880 <http://lattes.cnpq.br/3936721630855880/> ORCID: 0000-0003-1320-4347 <http://orcid.org/0000-0003-1320-4347> Personal site: programmingscience.org <http://www.programmingscience.org/> Github: alexandrejaguar <http://www.github.com/alexandrejaguar/> Skype: alexandrejaguar Twitter: alexdesiqueira <http://www.twitter.com/alexdesiqueira/> --------------------------------------------------

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Using the flooding watershed
by Alexandre Fioravante de Siqueira 05 Oct '17

05 Oct '17

Dear all, I am trying to write a that mimics the behavior of ImageJ's flooding watershed. This is how they describe it ( https://imagej.nih.gov/ij/docs/menus/process.html#watershed): *Watershed* *Watershed segmentation is a way of automatically separating or cutting apart particles that touch. It first calculates the Euclidian distance map (EDM) and finds the ultimate eroded points (UEPs). It then dilates each of the UEPs (the peaks or local maxima of the EDM) as far as possible - either until the edge of the particle is reached, or the edge of the region of another (growing) UEP. Watershed segmentation works best for smooth convex objects that don't overlap too much.* I tried to implement that (more or less), using the local minima (as pointed in https://www.mathworks.com/help/images/ref/bwulterode.html). Of course, there is something wrong :) from scipy.ndimage import binary_fill_holes from scipy.ndimage.morphology import distance_transform_edt from skimage.feature import peak_local_max from skimage.filters import threshold_otsu from skimage.io import imread from skimage.measure import label from skimage.morphology import disk, watershed from skimage.util import invert import matplotlib.pyplot as plt image = imread('K90_incid4,5min_1.bmp') img_bin = binary_fill_holes(image < threshold_otsu(image)) inv_bin = invert(img_bin) inv_dist = distance_transform_edt(inv_bin) inv_peak = peak_local_max(-inv_dist, indices=False) markers = label(peak) labels = watershed(-distance, markers, mask=img_bin, watershed_line=True) plt.imshow(labels, cmap='nipy_spectral'), plt.show() We don't have a function for UEPs in skimage yet, right? How could I implement this flooding watershed? Thank you very much. Kind regards, Alex -- -------------------------------------------------- Dr. Alexandre 'Jaguar' Fioravante de Siqueira Grupo de Cronologia - Sala 13 Departamento de Raios Cósmicos e Cronologia - DRCC Instituto de Física "Gleb Wataghin" - IFGW Unicamp - Universidade Estadual de Campinas Rua Sérgio Buarque de Holanda, 777 Cidade Universitária Zeferino Vaz - CEP 13083-859 Campinas - SP - Brazil Phone: +55(19)3521-5362 Lattes curriculum: 3936721630855880 <http://lattes.cnpq.br/3936721630855880/> ORCID: 0000-0003-1320-4347 <http://orcid.org/0000-0003-1320-4347> Personal site: programmingscience.org <http://www.programmingscience.org/> Github: alexandrejaguar <http://www.github.com/alexandrejaguar/> Skype: alexandrejaguar Twitter: alexdesiqueira <http://www.twitter.com/alexdesiqueira/> --------------------------------------------------

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Re: [scikit-image] 回复：Re: Code for the short course 'Image Processing using Python', @ IAMG2017
by Juan Nunez-Iglesias 04 Oct '17

04 Oct '17

On 21 Sep 2017, 5:00 PM +1000, Stefan van der Walt <stefanv(a)berkeley.edu>, wrote: > Thanks, I never noticed! All the lessons were contributed for teaching purposes, so I don't imagine adding a license now would be a problem. CC-BY is fine, although we should also consider CC-0. Sure. How about CC0 + adding a note in the readme, “if you find these tutorials useful, we appreciate mentioning them in any derivative works.”

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