Mailman 3 November 2015 - scikit-image

Re: skimage and opencv
by Emmanuelle Gouillart Sept. 22, 2016

Sept. 22, 2016

Hi FranÃ¯Â¿Â½ois, that's an excellent question, and not a troll :-). Opencv is a very powerful library, but it focuses primarily on computer vision (feature detection and extraction, classification, ...), as opposed to image processing in general (with other tasks such as denoising, segmentation, ...). The other big difference is that skimage builds on numpy ndarrays, and uses the full power of the numpy API (including of course the basic facilities for processing arrays as images that come with numpy), as well as some of scipy functions (you could have added scipy.ndimage to your list -- a few functions in skimage are wrappers around scipy.ndimage, that exist for the sake of completeneness). One important consequence is that algorithms working for 3-d or even n-d images can be easily implemented in 3-d/n-d in skimage, whereas opencv is restricted to 2-D images (as far as I know). Thanks to the use of numpy arrays, the API of skimage is also quite pleasant for a numpy user, more than the API of opencv. A related difference is that skimage is written in python and cython, whereas opencv is a C++ library. The two libraries attract a different crowd of developers, and a Python/Cython toolkit based on numpy arrays is easier to develop and maintain inside the Scientific Python ecosystem. I'm sure that other devs/users will have things to add to this discussion! Cheers, Emmanuelle On Thu, Dec 27, 2012 at 02:06:08PM -0800, FranÃ¯Â¿Â½ois wrote: > Hi users and devs, > It came to my knowledge that another python library (based on C++ and C > codes) for image processing exists too : opencv > I understand that numpy intregrates some basic features and we need some > advanced features but I have the feeling that skimages is redoundant with > opencv in some ways. > What's the position of skimage about that? (Don't read this question as a > troll but like a real question). > I mean that similar features exist in both. Would not be possible to > reuse/integrate opencv or merge? what's the reason for keeping them apart? > My observation is there is 4 libraries to manipulate images: > * PIL > * numpy > * skimages > * opencv > That's a lot. > Cheers,

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Boundary/outline of objects in an Image
by ashz＠walla.co.il Feb. 15, 2016

Feb. 15, 2016

Hi, I try to focus on scikit-image instead of other non-open source software. Therefore, is there an example how to boundary/outline of objects in an Image? Something like this tutorial: http://www.mathworks.com/help/images/boundary-tracing-in-images.html Thanks

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New to Contributing
by Kshitij Saraogi Dec. 3, 2015

Dec. 3, 2015

Hello, I am Kshitij Saraogi, a second year under-graduate student at IIT Kharagpur. I find scikit-image really intriguing and I would like to contribute to it. I went through the Issues section on GitHub but I am having a hard time finding an issue to get started Also, while going through the list of "Requested Features", I read about Image Colorisation. I would like to work on this. So, I would really appreciate if someone can guide me. Thanks Kshitij Saraogi

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Radius-scale matching in Determinant of Hessian (DoH) blob detector
by Iakovos Halegoua Dec. 1, 2015

Dec. 1, 2015

Hello there, I'd like to point out to the documentation authors a minor error in the blob_doh <http://scikit-image.org/docs/dev/api/skimage.feature.html#blob-doh> description. In the Notes section it is stated that: "The radius of each blob is approximately sigma.". As I tested the DoH responses of synthetic blob images, it seemed that the radius of the blob is matched to the scale according to: r = sqrt(2) * s. Could you provide me with some reference on how you calculated the matching scale to be at r = s? I guess this is nothing serious, but it was a little confusing when I first encountered it :P

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Re: Clustering of an image by taking into account the spatial context of each pixel (besides its intensity)
by Hakim Benoudjit Nov. 25, 2015

Nov. 25, 2015

Thanks Juan, I think you're right. I might have to read the paper on SLIC algorithm to understand how to tune the "compactness" parameter. Le mardi 24 novembre 2015 01:11:45 UTC, Juan Nunez-Iglesias a écrit : > > Incidentally, it seems you are just doing SLIC on a non-RGB image... Which > SLIC supports. (skimage.segmentation.slic). The "compactness" parameter > changes the weighting of intensity and space. > > On Tue, Nov 24, 2015 at 11:19 AM, Hakim Benoudjit <h.ben...(a)gmail.com > <javascript:>> wrote: > >> Hi Jonas, >> >> Thanks for your response. >> That's exactly what I've tried this week-end, by adding the (x, y) to >> gray-level intensity and giving the matrix of 3-components vector as input >> to k-means. >> As for the normalization, I applied this formula to each column >> (intensity, x, y): (value - mean) / std_dev. >> But, even with this normalization step, adding the (x, y) coordinates >> will influence the pixels on the left (resp. right) to be grouped together >> (See http://imgur.com/HxfkRig and original image taken from >> http://uk.mathworks.com/help/images/texture-segmentation-using-gabor-filter… >> ). >> >> Maybe I will need to find another normalization to apply of the >> (intensity, x, y) space. >> >> Le lundi 23 novembre 2015 23:53:34 UTC, Jonas Wulff a écrit : >>> >>> Hi Hakim, >>> >>> Have you tried just adding the coordinates of a pixel to its features? >>> For each pixel, the features would then be R,G,B,X,Y. From your >>> description, that seems what you're looking for. >>> >>> So if you have an RGB image I (so that I.shape = (height,width,3)), you >>> can do: >>> >>> y,x = np.mgrid[:height,:width] >>> I_stacked = np.dstack((I,x,y)) >>> data = I_stacked.reshape((-1,5)) >>> >>> ... and then use "data" as input to your clustering algorithm. >>> >>> You might want to scale / normalize the coordinates to fit the general >>> range of your color values -- but in general, this should do what I think >>> you're looking for. >>> >>> Cheers, >>> -Jonas >>> >>> >>> >>> >>> >>> On Sat, Nov 21, 2015 at 2:23 AM, Hakim Benoudjit <h.ben...(a)gmail.com> >>> wrote: >>> >>>> Hi Juan, >>>> >>>> Thanks for your answer, this seems to be a nice algorithm for the >>>> denoising of speckle. >>>> But actually I'm looking for an image clustering (segmentation) >>>> technique instead (that would take into consideration the spatial context >>>> of pixels). >>>> >>>> Le samedi 21 novembre 2015 00:47:21 UTC, Juan Nunez-Iglesias a écrit : >>>>> >>>>> Hey Hakim, >>>>> >>>>> The right answer here depends on your ultimate goal. If you're after >>>>> denoising, non-local means denoising (recently added to skimage) sounds >>>>> like exactly what you're after. >>>>> >>>>> Juan. >>>>> >>>>> On Sat, Nov 21, 2015 at 11:28 AM, Hakim Benoudjit <h.ben...(a)gmail.com> >>>>> wrote: >>>>> >>>>>> Hi Stéfan, >>>>>> >>>>>> Thanks for your reponse. >>>>>> What I'm looking for is a *spatial criteria* that encourages the *clustering >>>>>> algorithm* (K-means or others) to group together similar *neighbouring >>>>>> pixels* inside the same cluster. This will help avoid having >>>>>> persistent noise inside a cluster. >>>>>> >>>>>> Le vendredi 20 novembre 2015 13:20:15 UTC, Hakim Benoudjit a écrit : >>>>>>> >>>>>>> Hi, >>>>>>> >>>>>>> Is there a clustering algorithm implemented in *scikit-image *that >>>>>>> perform the image clustering by taking into account the *spatial >>>>>>> context *of the clustered pixel (its neighbourhood), besides its *pixel >>>>>>> brightness*? >>>>>>> >>>>>>> For the time being, I'm clustering images by reshaping them as >>>>>>> vectors of pixels intensities distributions, and then performing the *K-means >>>>>>> *or *Gaussian mixture models* implemented in *scikit-learn*. But, >>>>>>> I'm looking for a image clustering technique implemented (or could be >>>>>>> implemented) in *scikit-image *that would consider the >>>>>>> neighbourhood of a pixel when classifying it. >>>>>>> >>>>>>> Thanks. >>>>>>> >>>>>> -- >>>>>> You received this message because you are subscribed to the Google >>>>>> Groups "scikit-image" group. >>>>>> To unsubscribe from this group and stop receiving emails from it, >>>>>> send an email to scikit-image...(a)googlegroups.com. >>>>>> To post to this group, send email to scikit...(a)googlegroups.com. >>>>>> To view this discussion on the web, visit >>>>>> https://groups.google.com/d/msgid/scikit-image/0aad2045-b9da-442c-97bc-06c5… >>>>>> <https://groups.google.com/d/msgid/scikit-image/0aad2045-b9da-442c-97bc-06c5…> >>>>>> . >>>>>> >>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>> >>>>> >>>>> -- >>>> You received this message because you are subscribed to the Google >>>> Groups "scikit-image" group. >>>> To unsubscribe from this group and stop receiving emails from it, send >>>> an email to scikit-image...(a)googlegroups.com. >>>> To post to this group, send email to scikit...(a)googlegroups.com. >>>> To view this discussion on the web, visit >>>> https://groups.google.com/d/msgid/scikit-image/a2895510-2490-4ccf-a70a-20d6… >>>> <https://groups.google.com/d/msgid/scikit-image/a2895510-2490-4ccf-a70a-20d6…> >>>> . >>>> >>>> For more options, visit https://groups.google.com/d/optout. >>>> >>> >>> -- >> You received this message because you are subscribed to the Google Groups >> "scikit-image" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to scikit-image...(a)googlegroups.com <javascript:>. >> To post to this group, send email to scikit...(a)googlegroups.com >> <javascript:>. >> To view this discussion on the web, visit >> https://groups.google.com/d/msgid/scikit-image/5e41ffef-c3a3-421f-b6e4-d556… >> <https://groups.google.com/d/msgid/scikit-image/5e41ffef-c3a3-421f-b6e4-d556…> >> . >> >> For more options, visit https://groups.google.com/d/optout. >> > >

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Re: Clustering of an image by taking into account the spatial context of each pixel (besides its intensity)
by Michael Aye Nov. 24, 2015

Nov. 24, 2015

As SLIC uses K-means where one has to provide a number of clusters, I wonder what a SLIC implementation with DBSCAN could do, considering that it is free from the burden of defining the number of clusters. One would have to come up with a method of constraining `eps` and `min_samples`, but maybe that could be quite powerful. On Monday, November 23, 2015 at 6:11:45 PM UTC-7, Juan Nunez-Iglesias wrote: > > Incidentally, it seems you are just doing SLIC on a non-RGB image... Which > SLIC supports. (skimage.segmentation.slic). The "compactness" parameter > changes the weighting of intensity and space. > > On Tue, Nov 24, 2015 at 11:19 AM, Hakim Benoudjit <h.ben...(a)gmail.com > <javascript:>> wrote: > >> Hi Jonas, >> >> Thanks for your response. >> That's exactly what I've tried this week-end, by adding the (x, y) to >> gray-level intensity and giving the matrix of 3-components vector as input >> to k-means. >> As for the normalization, I applied this formula to each column >> (intensity, x, y): (value - mean) / std_dev. >> But, even with this normalization step, adding the (x, y) coordinates >> will influence the pixels on the left (resp. right) to be grouped together >> (See http://imgur.com/HxfkRig and original image taken from >> http://uk.mathworks.com/help/images/texture-segmentation-using-gabor-filter… >> ). >> >> Maybe I will need to find another normalization to apply of the >> (intensity, x, y) space. >> >> Le lundi 23 novembre 2015 23:53:34 UTC, Jonas Wulff a écrit : >>> >>> Hi Hakim, >>> >>> Have you tried just adding the coordinates of a pixel to its features? >>> For each pixel, the features would then be R,G,B,X,Y. From your >>> description, that seems what you're looking for. >>> >>> So if you have an RGB image I (so that I.shape = (height,width,3)), you >>> can do: >>> >>> y,x = np.mgrid[:height,:width] >>> I_stacked = np.dstack((I,x,y)) >>> data = I_stacked.reshape((-1,5)) >>> >>> ... and then use "data" as input to your clustering algorithm. >>> >>> You might want to scale / normalize the coordinates to fit the general >>> range of your color values -- but in general, this should do what I think >>> you're looking for. >>> >>> Cheers, >>> -Jonas >>> >>> >>> >>> >>> >>> On Sat, Nov 21, 2015 at 2:23 AM, Hakim Benoudjit <h.ben...(a)gmail.com> >>> wrote: >>> >>>> Hi Juan, >>>> >>>> Thanks for your answer, this seems to be a nice algorithm for the >>>> denoising of speckle. >>>> But actually I'm looking for an image clustering (segmentation) >>>> technique instead (that would take into consideration the spatial context >>>> of pixels). >>>> >>>> Le samedi 21 novembre 2015 00:47:21 UTC, Juan Nunez-Iglesias a écrit : >>>>> >>>>> Hey Hakim, >>>>> >>>>> The right answer here depends on your ultimate goal. If you're after >>>>> denoising, non-local means denoising (recently added to skimage) sounds >>>>> like exactly what you're after. >>>>> >>>>> Juan. >>>>> >>>>> On Sat, Nov 21, 2015 at 11:28 AM, Hakim Benoudjit <h.ben...(a)gmail.com> >>>>> wrote: >>>>> >>>>>> Hi Stéfan, >>>>>> >>>>>> Thanks for your reponse. >>>>>> What I'm looking for is a *spatial criteria* that encourages the *clustering >>>>>> algorithm* (K-means or others) to group together similar *neighbouring >>>>>> pixels* inside the same cluster. This will help avoid having >>>>>> persistent noise inside a cluster. >>>>>> >>>>>> Le vendredi 20 novembre 2015 13:20:15 UTC, Hakim Benoudjit a écrit : >>>>>>> >>>>>>> Hi, >>>>>>> >>>>>>> Is there a clustering algorithm implemented in *scikit-image *that >>>>>>> perform the image clustering by taking into account the *spatial >>>>>>> context *of the clustered pixel (its neighbourhood), besides its *pixel >>>>>>> brightness*? >>>>>>> >>>>>>> For the time being, I'm clustering images by reshaping them as >>>>>>> vectors of pixels intensities distributions, and then performing the *K-means >>>>>>> *or *Gaussian mixture models* implemented in *scikit-learn*. But, >>>>>>> I'm looking for a image clustering technique implemented (or could be >>>>>>> implemented) in *scikit-image *that would consider the >>>>>>> neighbourhood of a pixel when classifying it. >>>>>>> >>>>>>> Thanks. >>>>>>> >>>>>> -- >>>>>> You received this message because you are subscribed to the Google >>>>>> Groups "scikit-image" group. >>>>>> To unsubscribe from this group and stop receiving emails from it, >>>>>> send an email to scikit-image...(a)googlegroups.com. >>>>>> To post to this group, send email to scikit...(a)googlegroups.com. >>>>>> To view this discussion on the web, visit >>>>>> https://groups.google.com/d/msgid/scikit-image/0aad2045-b9da-442c-97bc-06c5… >>>>>> <https://groups.google.com/d/msgid/scikit-image/0aad2045-b9da-442c-97bc-06c5…> >>>>>> . >>>>>> >>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>> >>>>> >>>>> -- >>>> You received this message because you are subscribed to the Google >>>> Groups "scikit-image" group. >>>> To unsubscribe from this group and stop receiving emails from it, send >>>> an email to scikit-image...(a)googlegroups.com. >>>> To post to this group, send email to scikit...(a)googlegroups.com. >>>> To view this discussion on the web, visit >>>> https://groups.google.com/d/msgid/scikit-image/a2895510-2490-4ccf-a70a-20d6… >>>> <https://groups.google.com/d/msgid/scikit-image/a2895510-2490-4ccf-a70a-20d6…> >>>> . >>>> >>>> For more options, visit https://groups.google.com/d/optout. >>>> >>> >>> -- >> You received this message because you are subscribed to the Google Groups >> "scikit-image" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to scikit-image...(a)googlegroups.com <javascript:>. >> To post to this group, send email to scikit...(a)googlegroups.com >> <javascript:>. >> To view this discussion on the web, visit >> https://groups.google.com/d/msgid/scikit-image/5e41ffef-c3a3-421f-b6e4-d556… >> <https://groups.google.com/d/msgid/scikit-image/5e41ffef-c3a3-421f-b6e4-d556…> >> . >> >> For more options, visit https://groups.google.com/d/optout. >> > >

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Re: Clustering of an image by taking into account the spatial context of each pixel (besides its intensity)
by Hakim Benoudjit Nov. 23, 2015

Nov. 23, 2015

Hi Jonas, Thanks for your response. That's exactly what I've tried this week-end, by adding the (x, y) to gray-level intensity and giving the matrix of 3-components vector as input to k-means. As for the normalization, I applied this formula to each column (intensity, x, y): (value - mean) / std_dev. But, even with this normalization step, adding the (x, y) coordinates will influence the pixels on the left (resp. right) to be grouped together (See http://imgur.com/HxfkRig and original image taken from http://uk.mathworks.com/help/images/texture-segmentation-using-gabor-filter…). Maybe I will need to find another normalization to apply of the (intensity, x, y) space. Le lundi 23 novembre 2015 23:53:34 UTC, Jonas Wulff a écrit : > > Hi Hakim, > > Have you tried just adding the coordinates of a pixel to its features? For > each pixel, the features would then be R,G,B,X,Y. From your description, > that seems what you're looking for. > > So if you have an RGB image I (so that I.shape = (height,width,3)), you > can do: > > y,x = np.mgrid[:height,:width] > I_stacked = np.dstack((I,x,y)) > data = I_stacked.reshape((-1,5)) > > ... and then use "data" as input to your clustering algorithm. > > You might want to scale / normalize the coordinates to fit the general > range of your color values -- but in general, this should do what I think > you're looking for. > > Cheers, > -Jonas > > > > > > On Sat, Nov 21, 2015 at 2:23 AM, Hakim Benoudjit <h.ben...(a)gmail.com > <javascript:>> wrote: > >> Hi Juan, >> >> Thanks for your answer, this seems to be a nice algorithm for the >> denoising of speckle. >> But actually I'm looking for an image clustering (segmentation) technique >> instead (that would take into consideration the spatial context of pixels). >> >> Le samedi 21 novembre 2015 00:47:21 UTC, Juan Nunez-Iglesias a écrit : >>> >>> Hey Hakim, >>> >>> The right answer here depends on your ultimate goal. If you're after >>> denoising, non-local means denoising (recently added to skimage) sounds >>> like exactly what you're after. >>> >>> Juan. >>> >>> On Sat, Nov 21, 2015 at 11:28 AM, Hakim Benoudjit <h.ben...(a)gmail.com> >>> wrote: >>> >>>> Hi Stéfan, >>>> >>>> Thanks for your reponse. >>>> What I'm looking for is a *spatial criteria* that encourages the *clustering >>>> algorithm* (K-means or others) to group together similar *neighbouring >>>> pixels* inside the same cluster. This will help avoid having >>>> persistent noise inside a cluster. >>>> >>>> Le vendredi 20 novembre 2015 13:20:15 UTC, Hakim Benoudjit a écrit : >>>>> >>>>> Hi, >>>>> >>>>> Is there a clustering algorithm implemented in *scikit-image *that >>>>> perform the image clustering by taking into account the *spatial >>>>> context *of the clustered pixel (its neighbourhood), besides its *pixel >>>>> brightness*? >>>>> >>>>> For the time being, I'm clustering images by reshaping them as vectors >>>>> of pixels intensities distributions, and then performing the *K-means >>>>> *or *Gaussian mixture models* implemented in *scikit-learn*. But, I'm >>>>> looking for a image clustering technique implemented (or could be >>>>> implemented) in *scikit-image *that would consider the neighbourhood >>>>> of a pixel when classifying it. >>>>> >>>>> Thanks. >>>>> >>>> -- >>>> You received this message because you are subscribed to the Google >>>> Groups "scikit-image" group. >>>> To unsubscribe from this group and stop receiving emails from it, send >>>> an email to scikit-image...(a)googlegroups.com. >>>> To post to this group, send email to scikit...(a)googlegroups.com. >>>> To view this discussion on the web, visit >>>> https://groups.google.com/d/msgid/scikit-image/0aad2045-b9da-442c-97bc-06c5… >>>> <https://groups.google.com/d/msgid/scikit-image/0aad2045-b9da-442c-97bc-06c5…> >>>> . >>>> >>>> For more options, visit https://groups.google.com/d/optout. >>>> >>> >>> -- >> You received this message because you are subscribed to the Google Groups >> "scikit-image" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to scikit-image...(a)googlegroups.com <javascript:>. >> To post to this group, send email to scikit...(a)googlegroups.com >> <javascript:>. >> To view this discussion on the web, visit >> https://groups.google.com/d/msgid/scikit-image/a2895510-2490-4ccf-a70a-20d6… >> <https://groups.google.com/d/msgid/scikit-image/a2895510-2490-4ccf-a70a-20d6…> >> . >> >> For more options, visit https://groups.google.com/d/optout. >> > >

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Re: Clustering of an image by taking into account the spatial context of each pixel (besides its intensity)
by Hakim Benoudjit Nov. 21, 2015

Nov. 21, 2015

Hi Juan, Thanks for your answer, this seems to be a nice algorithm for the denoising of speckle. But actually I'm looking for an image clustering (segmentation) technique instead (that would take into consideration the spatial context of pixels). Le samedi 21 novembre 2015 00:47:21 UTC, Juan Nunez-Iglesias a écrit : > > Hey Hakim, > > The right answer here depends on your ultimate goal. If you're after > denoising, non-local means denoising (recently added to skimage) sounds > like exactly what you're after. > > Juan. > > On Sat, Nov 21, 2015 at 11:28 AM, Hakim Benoudjit <h.ben...(a)gmail.com > <javascript:>> wrote: > >> Hi Stéfan, >> >> Thanks for your reponse. >> What I'm looking for is a *spatial criteria* that encourages the *clustering >> algorithm* (K-means or others) to group together similar *neighbouring >> pixels* inside the same cluster. This will help avoid having persistent >> noise inside a cluster. >> >> Le vendredi 20 novembre 2015 13:20:15 UTC, Hakim Benoudjit a écrit : >>> >>> Hi, >>> >>> Is there a clustering algorithm implemented in *scikit-image *that >>> perform the image clustering by taking into account the *spatial >>> context *of the clustered pixel (its neighbourhood), besides its *pixel >>> brightness*? >>> >>> For the time being, I'm clustering images by reshaping them as vectors >>> of pixels intensities distributions, and then performing the *K-means *or >>> *Gaussian mixture models* implemented in *scikit-learn*. But, I'm >>> looking for a image clustering technique implemented (or could be >>> implemented) in *scikit-image *that would consider the neighbourhood of >>> a pixel when classifying it. >>> >>> Thanks. >>> >> -- >> You received this message because you are subscribed to the Google Groups >> "scikit-image" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to scikit-image...(a)googlegroups.com <javascript:>. >> To post to this group, send email to scikit...(a)googlegroups.com >> <javascript:>. >> To view this discussion on the web, visit >> https://groups.google.com/d/msgid/scikit-image/0aad2045-b9da-442c-97bc-06c5… >> <https://groups.google.com/d/msgid/scikit-image/0aad2045-b9da-442c-97bc-06c5…> >> . >> >> For more options, visit https://groups.google.com/d/optout. >> > >

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Clustering of an image by taking into account the spatial context of each pixel (besides its intensity)
by Hakim Benoudjit Nov. 20, 2015

Nov. 20, 2015

Hi, Is there a clustering algorithm implemented in *scikit-image *that perform the image clustering by taking into account the *spatial context *of the clustered pixel (its neighbourhood), besides its *pixel brightness*? For the time being, I'm clustering images by reshaping them as vectors of pixels intensities distributions, and then performing the *K-means *or *Gaussian mixture models* implemented in *scikit-learn*. But, I'm looking for a image clustering technique implemented (or could be implemented) in *scikit-image *that would consider the neighbourhood of a pixel when classifying it. Thanks.

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Help wanted: implementation of 3D medial axis skeletonization
by Stefan van der Walt Nov. 19, 2015

Nov. 19, 2015

Hi all, I have been approached by a group that is interested in sponsoring the development of 3D skeletonization in scikit-image. One potential starting place would be: http://www.insight-journal.org/browse/publication/181 Is anyone interested in working on this? Please get in touch. Thanks! Stéfan

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