Mailman 3 November 2016 - scikit-image

Re: [scikit-image] filtering image entry values based on a threshold
by Stefan van der Walt Nov. 22, 2016

Nov. 22, 2016

On Mon, Nov 21, 2016, at 19:49, wine lover wrote: > In one code segment, I once saw an approach of filtering image values > as follows using cv2. In Scikit image, if I want to realize the same > functionality, how to do it? > > import cv2 img = cv2.threshold(img, 0.5, 1., > cv2.THRESH_BINARY)[1].astype(np.uint8 How about: from skimage import img_as_float, img_as_ubyte img = img_as_float(img) img_thresholded = img_as_ubyte(img < 0.5) Stéfan

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Model PRs
by Stefan van der Walt Nov. 22, 2016

Nov. 22, 2016

Hi everyone I've started a new page on the wiki to track "Model PRs" (thanks, Juan, for the suggestion). These PRs are meant to illustrate how to perform certain common refactoring operations. I've added one example already, of how to deprecate modules that return XY formatted outputs to row-column format. If you have similar examples, please feel free to add them: https://github.com/scikit-image/scikit-image/wiki/Model-PRs Thanks Stéfan

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filtering image entry values based on a threshold
by wine lover Nov. 21, 2016

Nov. 21, 2016

Dear All, In one code segment, I once saw an approach of filtering image values as follows using cv2. In Scikit image, if I want to realize the same functionality, how to do it? import cv2 img = cv2.threshold(img, 0.5, 1., cv2.THRESH_BINARY)[1].astype(np.uint8 Thanks, huaiyang

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Contributing to scikit-image
by Sourav Singh Nov. 16, 2016

Nov. 16, 2016

Hello, I am a student from India who is interested in scikit-image project. I would like to contribute to scikit-image by adding COSFIRE and merging CellProfiler code. If possible, I would also like to become a long-term maintainer of the project. Regards, Sourav

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Re: [scikit-image] statistical region merging
by Stefanie Lück Nov. 16, 2016

Nov. 16, 2016

Hi Juan, thank you very much for this detailed explanation and your suggestions. I just had a quick try with the merge_hierarchical example. If I use the code which I attached the results look more like expected. In addition, I will test your others suggestions also in terms of speed (I have to segment many images). Thanks again for your effort! Stefanie 2016-11-15 23:55 GMT+01:00 Juan Nunez-Iglesias <jni.soma(a)gmail.com>: > Hi Stefanie, > > Wow, those results *do* look weird! =) Why are they not the same shape as > the image? Could you send us the complete code you used? > > In the meantime, there's a few things to change. `cut_threshold` is > probably the single most fragile algorithm you can use with a RAG. You want > to be using merge_hierarchical. You can do this either on a "color graph", > which merges regions according to color similarity (and is most similar to > the statistical region merging of Fiji): > http://scikit-image.org/docs/dev/auto_examples/ > segmentation/plot_rag_merge.html#sphx-glr-auto-examples- > segmentation-plot-rag-merge-py > > or on a *boundary graph*, which first detects "edges" in the image and > then merges regions progressively according to the mean edge value: > http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_rag_ > boundary.html#sphx-glr-auto-examples-segmentation-plot-rag-boundary-py > > http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_boundary_ > merge.html#sphx-glr-auto-examples-segmentation-plot-boundary-merge-py > > My intuition about this image is that your best choice is to use a sobel > filter: > http://scikit-image.org/docs/dev/auto_examples/edges/plot_ > edge_filter.html#sphx-glr-auto-examples-edges-plot-edge-filter-py > followed by compact watershed: > http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_compact_ > watershed.html#sphx-glr-auto-examples-segmentation-plot- > compact-watershed-py > then by region boundary merging: > http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_boundary_ > merge.html#sphx-glr-auto-examples-segmentation-plot-boundary-merge-py > > The edges look pretty sharp, too. You might even get good results with > Canny: > http://scikit-image.org/docs/dev/auto_examples/edges/plot_ > canny.html#sphx-glr-auto-examples-edges-plot-canny-py > > I hope all this helps! > > Juan. > > On 15 November 2016 at 7:11:14 pm, Stefanie Lück (luecks(a)gmail.com) wrote: > > Dear all, > > sorry about the worse problem description! I tried this example > <http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_rag_mean_c…> with > different parameters for SLIC and graph.cut_threshold but none of them > gave me satisfying results. > > I attached the original image, the ImageJ SRM (Q=25) output and the RAG > output (segmentation.slic(img, compactness=20; n_segments=400), graph.cut_threshold(labels1, > g, 10)) > > The results are quite different, obviously I am doing something wrong. My > aim is to segment each leaf separately. At the moment I am > using felzenszwalb, which gives me quite reasonable results. However I > would like to try everything possible and therefore I would appreciate some > tips. > > Thank you for the explanation of the algorithm, that was helpful. > > Best regards, > Stefanie > > > > > > 2016-11-15 1:51 GMT+01:00 Juan Nunez-Iglesias <jni.soma(a)gmail.com>: > >> Hi Stefanie, >> >> Sorry, these responses should all be CCd to the list, so that others can >> benefit from the discussion — my bad for dropping that thread. Could you >> please: >> >> - provide an example segmentation where skimage is doing worse than Fiji >> - provide the script and parameter settings for both >> >> Then we can help troubleshoot. I don’t know what you mean by the results >> “were very strange”, for example, so it’s hard to diagnose the problem. =) >> >> Starting to merge directly from pixels, as the Fiji plugin does, is >> expensive and can be error prone. SLIC is a fast, initial pixel merging >> step, from which we can merge regions according to various criteria. With >> the right parameters, SLIC + RAG mean color agglomeration should give quite >> similar results to Fiji’s approach… >> >> Juan. >> >> On 15 November 2016 at 2:39:45 am, Stefanie Lück (luecks(a)gmail.com) >> wrote: >> >> Hi Juan, >> >> thank you for your reply! I have seen and tested the RAG examples but I >> did not understand the SLIC step and the results were very strange... Is >> there any advantage? I am using SLIC anyway at the moment but >> the statistical region merging of ImageJ gives me better results. >> >> Thanks >> Stefanie >> >> >> >> 2016-11-14 13:44 GMT+01:00 Juan Nunez-Iglesias <jni.soma(a)gmail.com>: >> >>> Hi Stefanie! >>> >>> Have a look at skimage.future.graph! There are some relevant examples in >>> the gallery, too: >>> >>> http://scikit-image.org/docs/dev/auto_examples/#segmentation-of-objects >>> >>> The future.graph API is still experimental (that’s why it’s in >>> “future”), so we really appreciate any feedback you have about it! >>> >>> Juan. >>> >>> >>> >>> On 14 November 2016 at 8:21:12 pm, Stefanie Lück (luecks(a)gmail.com) >>> wrote: >>> >>> Hi! >>> >>> I am looking for a statistical region merging segmentation. Is there >>> anything like this in skimage? >>> >>> Thanks in advance, >>> Stefanie >>> _______________________________________________ >>> scikit-image mailing list >>> scikit-image(a)python.org >>> https://mail.python.org/mailman/listinfo/scikit-image >>> >>> >> >

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Re: [scikit-image] statistical region merging
by Juan Nunez-Iglesias Nov. 15, 2016

Nov. 15, 2016

Hi Stefanie, Wow, those results *do* look weird! =) Why are they not the same shape as the image? Could you send us the complete code you used? In the meantime, there's a few things to change. `cut_threshold` is probably the single most fragile algorithm you can use with a RAG. You want to be using merge_hierarchical. You can do this either on a "color graph", which merges regions according to color similarity (and is most similar to the statistical region merging of Fiji): http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_rag_merge.… or on a *boundary graph*, which first detects "edges" in the image and then merges regions progressively according to the mean edge value: http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_rag_bounda… http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_boundary_m… My intuition about this image is that your best choice is to use a sobel filter: http://scikit-image.org/docs/dev/auto_examples/edges/plot_edge_filter.html#… followed by compact watershed: http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_compact_wa… then by region boundary merging: http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_boundary_m… The edges look pretty sharp, too. You might even get good results with Canny: http://scikit-image.org/docs/dev/auto_examples/edges/plot_canny.html#sphx-g… I hope all this helps! Juan. On 15 November 2016 at 7:11:14 pm, Stefanie Lück (luecks(a)gmail.com) wrote: Dear all, sorry about the worse problem description! I tried this example <http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_rag_mean_c…> with different parameters for SLIC and graph.cut_threshold but none of them gave me satisfying results. I attached the original image, the ImageJ SRM (Q=25) output and the RAG output (segmentation.slic(img, compactness=20; n_segments=400), graph.cut_threshold(labels1, g, 10)) The results are quite different, obviously I am doing something wrong. My aim is to segment each leaf separately. At the moment I am using felzenszwalb, which gives me quite reasonable results. However I would like to try everything possible and therefore I would appreciate some tips. Thank you for the explanation of the algorithm, that was helpful. Best regards, Stefanie 2016-11-15 1:51 GMT+01:00 Juan Nunez-Iglesias <jni.soma(a)gmail.com>: > Hi Stefanie, > > Sorry, these responses should all be CCd to the list, so that others can > benefit from the discussion — my bad for dropping that thread. Could you > please: > > - provide an example segmentation where skimage is doing worse than Fiji > - provide the script and parameter settings for both > > Then we can help troubleshoot. I don’t know what you mean by the results > “were very strange”, for example, so it’s hard to diagnose the problem. =) > > Starting to merge directly from pixels, as the Fiji plugin does, is > expensive and can be error prone. SLIC is a fast, initial pixel merging > step, from which we can merge regions according to various criteria. With > the right parameters, SLIC + RAG mean color agglomeration should give quite > similar results to Fiji’s approach… > > Juan. > > On 15 November 2016 at 2:39:45 am, Stefanie Lück (luecks(a)gmail.com) wrote: > > Hi Juan, > > thank you for your reply! I have seen and tested the RAG examples but I > did not understand the SLIC step and the results were very strange... Is > there any advantage? I am using SLIC anyway at the moment but > the statistical region merging of ImageJ gives me better results. > > Thanks > Stefanie > > > > 2016-11-14 13:44 GMT+01:00 Juan Nunez-Iglesias <jni.soma(a)gmail.com>: > >> Hi Stefanie! >> >> Have a look at skimage.future.graph! There are some relevant examples in >> the gallery, too: >> >> http://scikit-image.org/docs/dev/auto_examples/#segmentation-of-objects >> >> The future.graph API is still experimental (that’s why it’s in “future”), >> so we really appreciate any feedback you have about it! >> >> Juan. >> >> >> >> On 14 November 2016 at 8:21:12 pm, Stefanie Lück (luecks(a)gmail.com) >> wrote: >> >> Hi! >> >> I am looking for a statistical region merging segmentation. Is there >> anything like this in skimage? >> >> Thanks in advance, >> Stefanie >> _______________________________________________ >> scikit-image mailing list >> scikit-image(a)python.org >> https://mail.python.org/mailman/listinfo/scikit-image >> >> >

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Re: [scikit-image] statistical region merging
by Stefanie Lück Nov. 15, 2016

Nov. 15, 2016

Dear all, sorry about the worse problem description! I tried this example <http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_rag_mean_c…> with different parameters for SLIC and graph.cut_threshold but none of them gave me satisfying results. I attached the original image, the ImageJ SRM (Q=25) output and the RAG output (segmentation.slic(img, compactness=20; n_segments=400), graph.cut_threshold(labels1, g, 10)) The results are quite different, obviously I am doing something wrong. My aim is to segment each leaf separately. At the moment I am using felzenszwalb, which gives me quite reasonable results. However I would like to try everything possible and therefore I would appreciate some tips. Thank you for the explanation of the algorithm, that was helpful. Best regards, Stefanie 2016-11-15 1:51 GMT+01:00 Juan Nunez-Iglesias <jni.soma(a)gmail.com>: > Hi Stefanie, > > Sorry, these responses should all be CCd to the list, so that others can > benefit from the discussion — my bad for dropping that thread. Could you > please: > > - provide an example segmentation where skimage is doing worse than Fiji > - provide the script and parameter settings for both > > Then we can help troubleshoot. I don’t know what you mean by the results > “were very strange”, for example, so it’s hard to diagnose the problem. =) > > Starting to merge directly from pixels, as the Fiji plugin does, is > expensive and can be error prone. SLIC is a fast, initial pixel merging > step, from which we can merge regions according to various criteria. With > the right parameters, SLIC + RAG mean color agglomeration should give quite > similar results to Fiji’s approach… > > Juan. > > On 15 November 2016 at 2:39:45 am, Stefanie Lück (luecks(a)gmail.com) wrote: > > Hi Juan, > > thank you for your reply! I have seen and tested the RAG examples but I > did not understand the SLIC step and the results were very strange... Is > there any advantage? I am using SLIC anyway at the moment but > the statistical region merging of ImageJ gives me better results. > > Thanks > Stefanie > > > > 2016-11-14 13:44 GMT+01:00 Juan Nunez-Iglesias <jni.soma(a)gmail.com>: > >> Hi Stefanie! >> >> Have a look at skimage.future.graph! There are some relevant examples in >> the gallery, too: >> >> http://scikit-image.org/docs/dev/auto_examples/#segmentation-of-objects >> >> The future.graph API is still experimental (that’s why it’s in “future”), >> so we really appreciate any feedback you have about it! >> >> Juan. >> >> >> >> On 14 November 2016 at 8:21:12 pm, Stefanie Lück (luecks(a)gmail.com) >> wrote: >> >> Hi! >> >> I am looking for a statistical region merging segmentation. Is there >> anything like this in skimage? >> >> Thanks in advance, >> Stefanie >> _______________________________________________ >> scikit-image mailing list >> scikit-image(a)python.org >> https://mail.python.org/mailman/listinfo/scikit-image >> >> >

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Re: [scikit-image] statistical region merging
by Juan Nunez-Iglesias Nov. 14, 2016

Nov. 14, 2016

Hi Stefanie, Sorry, these responses should all be CCd to the list, so that others can benefit from the discussion — my bad for dropping that thread. Could you please: - provide an example segmentation where skimage is doing worse than Fiji - provide the script and parameter settings for both Then we can help troubleshoot. I don’t know what you mean by the results “were very strange”, for example, so it’s hard to diagnose the problem. =) Starting to merge directly from pixels, as the Fiji plugin does, is expensive and can be error prone. SLIC is a fast, initial pixel merging step, from which we can merge regions according to various criteria. With the right parameters, SLIC + RAG mean color agglomeration should give quite similar results to Fiji’s approach… Juan. On 15 November 2016 at 2:39:45 am, Stefanie Lück (luecks(a)gmail.com) wrote: Hi Juan, thank you for your reply! I have seen and tested the RAG examples but I did not understand the SLIC step and the results were very strange... Is there any advantage? I am using SLIC anyway at the moment but the statistical region merging of ImageJ gives me better results. Thanks Stefanie 2016-11-14 13:44 GMT+01:00 Juan Nunez-Iglesias <jni.soma(a)gmail.com>: > Hi Stefanie! > > Have a look at skimage.future.graph! There are some relevant examples in > the gallery, too: > > http://scikit-image.org/docs/dev/auto_examples/#segmentation-of-objects > > The future.graph API is still experimental (that’s why it’s in “future”), > so we really appreciate any feedback you have about it! > > Juan. > > > > On 14 November 2016 at 8:21:12 pm, Stefanie Lück (luecks(a)gmail.com) wrote: > > Hi! > > I am looking for a statistical region merging segmentation. Is there > anything like this in skimage? > > Thanks in advance, > Stefanie > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image > >

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Re: [scikit-image] Fitting figures with missing parts
by Alexandre Fioravante de Siqueira Nov. 14, 2016

Nov. 14, 2016

Dear Jean, Viji and Kevin, thank you very much for your proposed solutions! Kevin's solution looks nice, although Jean's one is very good too. I'll try to apply them on my problem this week. After that I return to contact you with the results. Thanks again for your support, Alex 2016-11-10 17:49 GMT+01:00 Jean-Patrick Pommier < jeanpatrick.pommier(a)gmail.com>: > > the picture is in the previous notebook > > 2016-11-10 17:47 GMT+01:00 Jean-Patrick Pommier < > jeanpatrick.pommier(a)gmail.com>: > >> The attached notebook proposes a solution >> >> 2016-11-10 14:54 GMT+01:00 Viji C <hellomurasaki(a)gmail.com>: >> >>> >>> >>> ---------- Forwarded message ---------- >>> From: *Viji C* <hellomurasaki(a)gmail.com> >>> Date: Thursday, November 10, 2016 >>> Subject: [scikit-image] Fitting figures with missing parts >>> To: Alexandre Fioravante de Siqueira <siqueiraaf(a)gmail.com> >>> >>> >>> Hello Mr. Alex, >>> >>> Please try Fractal Analysis. I couldn't understand the morphology of the >>> contours exactly from your mail. But if the contours are self similar, then >>> Fractals can help you. FracLac is one of tool for Fractal Analysis. >>> >>> I'm newbie to Vision Research. Kindly get suggestions from experts >>> before proceeding to implement Fractals. >>> >>> Regards, >>> C.Viji >>> >>> >>> >>> On Wednesday, November 9, 2016, Alexandre Fioravante de Siqueira < >>> siqueiraaf(a)gmail.com> wrote: >>> >>>> Dear all, >>>> I have these contours from the same object. However, there was another >>>> object over it. Its contour was divided, and I don't have the "center" part. >>>> I'd like to use a tool and show that they're belong to the same part. I >>>> was trying to use an ellipse to do that, but the results are really bad. >>>> Could you help me with that? I appreciate any idea. >>>> Thank you very much! >>>> Kind regards, >>>> >>>> Alex >>>> >>>> -- >>>> ------------------------------------------------------------ >>>> ------------------ >>>> Dr. Alexandre 'Jaguar' Fioravante de Siqueira >>>> Office 306 - Institut für Geologie >>>> Fakultät für Geowissenschaften, Geotechnik und Bergbau >>>> Technische Universität Bergakademie Freiberg >>>> Bernhard-von-Cotta-Straße, 2 >>>> Freiberg (09599), Mittelsachsen - Sachsen - Deutschland >>>> Lattes curriculum: http://lattes.cnpq.br/3936721630855880/ >>>> Personal site: http://www.programandociencia.com/about/ >>>> Github: http://www.github.com/alexandrejaguar/ >>>> Skype: alexandrejaguar >>>> Twitter: http://www.twitter.com/alexdesiqueira/ >>>> ------------------------------------------------------------ >>>> ------------------ >>>> >>> >>> >>> _______________________________________________ >>> scikit-image mailing list >>> scikit-image(a)python.org >>> https://mail.python.org/mailman/listinfo/scikit-image >>> >>> >> >> >> -- >> http://dip4fish.blogspot.fr/ >> Dedicated to Digital Image Processing for FISH, QFISH and other things >> about the telomeres. >> > > > > -- > http://dip4fish.blogspot.fr/ > Dedicated to Digital Image Processing for FISH, QFISH and other things > about the telomeres. > -- ------------------------------------------------------------------------------ Dr. Alexandre 'Jaguar' Fioravante de Siqueira Office 306 - Institut für Geologie Fakultät für Geowissenschaften, Geotechnik und Bergbau Technische Universität Bergakademie Freiberg Bernhard-von-Cotta-Straße, 2 Freiberg (09599), Mittelsachsen - Sachsen - Deutschland Lattes curriculum: http://lattes.cnpq.br/3936721630855880/ Personal site: http://www.programandociencia.com/about/ Github: http://www.github.com/alexandrejaguar/ Skype: alexandrejaguar Twitter: http://www.twitter.com/alexdesiqueira/ ------------------------------------------------------------------------------

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statistical region merging
by Stefanie Lück Nov. 14, 2016

Nov. 14, 2016

Hi! I am looking for a statistical region merging segmentation. Is there anything like this in skimage? Thanks in advance, Stefanie

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