Mailman 3 September 2017 - scikit-image

Re: [scikit-image] 回复：Re: Code for the short course 'Image Processing using Python', @ IAMG2017
by Juan Nunez-Iglesias Oct. 4, 2017

Oct. 4, 2017

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.”

2 2

回复：Re: Code for the short course 'Image Processing using Python', @ IAMG2017
by imagepy＠sina.com Sept. 21, 2017

Sept. 21, 2017

Hi Stefan: "segmentation lesson from the skimage_tutorial repo" did you means this?(https://github.com/scikit-image/skimage-tutorials/blob/master/lecture…, I am writing a simple imageprocessing book, It may be useful for me. Best regardsYXDragon----- 原始邮件 ----- 发件人：Stefan van der Walt <stefanv(a)berkeley.edu> 收件人：Alexandre Fioravante de Siqueira <siqueiraaf(a)gmail.com>, scikit-image(a)python.org 主题：Re: [scikit-image] Code for the short course 'Image Processing using Python', @ IAMG2017 日期：2017年09月21日 05点16分 Hi Alex On Tue, Sep 19, 2017, at 21:25, Alexandre Fioravante de Siqueira wrote: as I said in an old previous e-mail, I presented a short course using scikit-image for processing microscopy images at IAMG2017 (short course 8 at <http://iamg2017.com/courses/>). An alpha version of the codes is now available; please feel free to check it at <https://github.com/alexandrejaguar/lectures/tree/master/2017/iamg2017>. Thanks for sharing these! Since it is still very raw, I'd be glad to receive your comments, suggestions and ideas! I'd say 1) stick to gray, viridis, and other reliable colormaps and 2) plot the found features so that they are easier to observe (currently just pixels inside a big image). You may also want to take a look at the segmentation lesson from the skimage_tutorial repo, which has a few sketches to clarify the workings of watershed. Best regards Stéfan _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

3 2

回复：Re: my watershed
by imagepy＠sina.com Sept. 21, 2017

Sept. 21, 2017

add one:if scikit-image can implements a ridge filter. just like watershed mixed with the skeleton. make all region a same label, then do level loop, just stop when one pixel left. It's usful in geography and hydrology analysis. ----- 原始邮件 ----- 发件人：Juan Nunez-Iglesias <jni.soma(a)gmail.com> 收件人：scikit-image <scikit-image(a)python.org>, imagepy(a)sina.com, "Mailing_list_for_scikit-image_(http://scikit-image.org)" <scikit-image(a)python.org> 主题：Re: [scikit-image] my watershed 日期：2017年09月21日 13点48分 Hi Yan, Thanks for this. Simple functions are very good for one’s own images, as well as to understand an algorithm, but for a widely used library like scikit-image, flexibility and robustness are least as important as speed. In skimage, we aim to support floating point input images, for which your code won’t work. There is a lot of thought into edge cases going into the skimage implementation, which unfortunately could have an effect on performance. The question we need to solve isn’t “can we make a super-fast watershed implementation”, but, “can we make a flexible and robust implementation that is also fast?” Using Numba is certainly not off-limits, but any candidate implementation should at a minimum pass the skimage test suite. I once tried a level-by-level implementation of watershed, by the way. It has a fatal flaw, which is that valleys with no markers will never get labeled. Here’s a test case that works with skimage but fails in your implementation. (Note also that your implementation overwrites the seeds image, which is pretty crazy. =) Thanks again! Juan. In [44]: import watershed In [45]: from skimage import morphology In [46]: import numpy as np In [47]: image = np.array([1, 0, 1, 0, 1, 0, 1], dtype=np.uint8) In [48]: seeds = np.array([0, 1, 0, 0, 0, 2, 0]) In [49]: morphology.watershed(image, seeds) Out[49]: array([1, 1, 1, 1, 2, 2, 2], dtype=int32) In [50]: watershed.watershed(image, seeds) Out[50]: array([0, 0, 0, 0, 0, 0, 0]) In [51]: seeds Out[51]: array([0, 0, 0, 0, 0, 0, 0]) On 12 Sep 2017, 6:34 AM +1000, imagepy(a)sina.com, wrote: Hi: Now It supports nd. but less of test. this image, skimage cost 20s, mine less than 1s. It is wrriten with numba in 90 lines. did not support compactness, but it's simple, and fast. YXDragon _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

2 1

my watershed
by imagepy＠sina.com Sept. 21, 2017

Sept. 21, 2017

Hi:Now It supports nd. but less of test. this image, skimage cost 20s, mine less than 1s.It is wrriten with numba in 90 lines. did not support compactness, but it's simple, and fast. YXDragon

2 1

Re: [scikit-image] Code for the short course 'Image Processing using Python', @ IAMG2017
by Stefan van der Walt Sept. 20, 2017

Sept. 20, 2017

Hi Alex On Tue, Sep 19, 2017, at 21:25, Alexandre Fioravante de Siqueira wrote:> as I said in an old previous e-mail, I presented a short course using > scikit-image for processing microscopy images at IAMG2017 (short > course 8 at <http://iamg2017.com/courses/>).> An alpha version of the codes is now available; please feel free to > check it at > <https://github.com/alexandrejaguar/lectures/tree/master/2017/iamg2017> > . Thanks for sharing these! > Since it is still very raw, I'd be glad to receive your comments, > suggestions and ideas! I'd say 1) stick to gray, viridis, and other reliable colormaps and 2) plot the found features so that they are easier to observe (currently just pixels inside a big image). You may also want to take a look at the segmentation lesson from the skimage_tutorial repo, which has a few sketches to clarify the workings of watershed. Best regards Stéfan

1 0

Code for the short course 'Image Processing using Python', @ IAMG2017
by Alexandre Fioravante de Siqueira Sept. 20, 2017

Sept. 20, 2017

Dear all, as I said in an old previous e-mail, I presented a short course using scikit-image for processing microscopy images at IAMG2017 (short course 8 at <http://iamg2017.com/courses/> <http://iamg2017.com/courses/>). An alpha version of the codes is now available; please feel free to check it at <https://github.com/alexandrejaguar/lectures/tree/master/2017/iamg2017> <https://github.com/alexandrejaguar/lectures/tree/master/2017/iamg2017>. Since it is still very raw, I'd be glad to receive your comments, suggestions and ideas! Thank you in advance. 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/> ------------------------------------------------------------------------------

1 0

转发：some question about h_minma (need a paremeter, and can run faster)
by imagepy＠sina.com Sept. 10, 2017

Sept. 10, 2017

Sorry, May be I had send one, But I am not sure if I send suceessfully, because of the attachment is too large, so I send again, with a jpg format. Hi every one: As I mentioned befor, In the official example, use watershed to segment two circle, In this example, use pick_local_max to find the seed. But When I try on other binary images, there always are two local_max nearby, then the segment be too trivial. And I use the h_maxma and h_minma, I have some trouble: 1. when a region has more than one min value(same but not nearby), the result will mark them all. (I want to use them as seed to watershed, So I need any one, else the region will be broken, can we add a parameter to control?)2. h_minma run too slowly, (what more, the watershed run more slowly... so my program crashed! I had heard Juan will improve it in the next version) So I wrote h_minma/maxma and watershed myself (as a sub package of my project imagepy). My watershed now only support 2D, But the h_minma now is OK, works on nd. I had test one image: scikit-image's h_minma runs in 15s, and mine runs in 0.3s, 45times. I will attach my code and test image. If any one can have a look, and improve the h_minma function? I will write a issue on github soon. YXDragon

3 2

回复：Re: 转发：some question about_h_minma_(need_a_paremeter,_and_can_run_faster)
by imagepy＠sina.com Sept. 10, 2017

Sept. 10, 2017

Hi Stefan:OK, I would commen my code, and try to profile the skimage's code. then start a issue on github.I once tried to contribute to scikit-image. But give up, for I am averse to write cython. I prefer writting in pure C, and importting with ctypes, or writting pure python with numba (JNI recommend to me). So I agree with using numba to extend scikit-image(we discussed before).And I had wrote or improve some other function may be usful. I improved the medal axis, watershed, h_maxma/minma, and writed a ridge algrism(extract the ridge or river valley), and we can use them in imagepy interactively. I will attach some snapshot, please have a look.(I had wrapped many functions of scipy.ndimage and skimage in imagepy, and have built some user base in my country (China), they will feed back some questions to me, I think it's useful to imagepy and skimage) ----- 原始邮件 ----- 发件人：Stefan van der Walt <stefanv(a)berkeley.edu> 收件人：scikit-image(a)python.org 主题：Re: [scikit-image] 转发：some question about_h_minma_(need_a_paremeter,_and_can_run_faster) 日期：2017年09月10日 11点29分 Hi, YXDragon On Fri, Sep 8, 2017, at 21:25, imagepy(a)sina.com wrote: So I wrote h_minma/maxma and watershed myself (as a sub package of my project imagepy). My watershed now only support 2D, But the h_minma now is OK, works on nd. I had test one image: scikit-image's h_minma runs in 15s, and mine runs in 0.3s, 45times. I tested on my machine and can confirm the slowness. Would you be willing to help profile the code, to find out why it is so slow? Best regards Stéfan _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

1 0

回复：Re: 转发：some question about_h_minma_(need_a_paremeter,_and_can_run_faster)
by imagepy＠sina.com Sept. 10, 2017

Sept. 10, 2017

sorry! Perhaps I wrote mixed and disorderly, I want to say, I had improve or wrote some function, I want to contribute them when we support numba. ----- 原始邮件 ----- 发件人：Stefan van der Walt <stefanv(a)berkeley.edu> 收件人：scikit-image(a)python.org 主题：Re: [scikit-image] 转发：some question about_h_minma_(need_a_paremeter,_and_can_run_faster) 日期：2017年09月10日 11点29分 Hi, YXDragon On Fri, Sep 8, 2017, at 21:25, imagepy(a)sina.com wrote: So I wrote h_minma/maxma and watershed myself (as a sub package of my project imagepy). My watershed now only support 2D, But the h_minma now is OK, works on nd. I had test one image: scikit-image's h_minma runs in 15s, and mine runs in 0.3s, 45times. I tested on my machine and can confirm the slowness. Would you be willing to help profile the code, to find out why it is so slow? Best regards Stéfan _______________________________________________ scikit-image mailing list scikit-image(a)python.org https://mail.python.org/mailman/listinfo/scikit-image

1 0

Re: [scikit-image] HOGDescriptor
by Stefan van der Walt Sept. 10, 2017

Sept. 10, 2017

Hi Peter On Sat, Sep 9, 2017, at 07:28, Peter Balazovic wrote: > I see there is HOG descriptor implemented within scikit image, I > wonder how can I reused or compare exmples given > http://scikit-image.org/docs/dev/auto_examples/features_detection/plot_hog.…> with OpenCV where I can find > http://docs.opencv.org/3.1.0/d5/d33/structcv_1_1HOGDescriptor.html.> I want to intialize and compute HoG descriptor with both same > parametrs. How and which the hog paramerts from scikit relates to > OpenCV one? Anyone can help? The API reference for HoG might help: http://scikit-image.org/docs/dev/api/skimage.feature.html?highlight=hog#ski… Best regards Stéfan

1 0