Mailman 3 March 2016 - scikit-image

Re: skimage and opencv
by Emmanuelle Gouillart 22 Sep '16

22 Sep '16

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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Re: img_as_float
by Michael Aye 29 Apr '16

29 Apr '16

I am not opposed to supporting range preservation, but we do have to > think a bit about the implications: > > - what do you do when the data-type of values change? > What are the situations where they *have* to change? > - what do you do when your operation due to, e.g., rounding issues > push values outside the input range? > Return a new object instead of changing the original, maybe? > - what do you do when you need to know the full potential range of the > data? > > don't understand, do you mean the full potential range per data-type? isn't that defined by the data-type the input image has? The ``preserve_range`` flag has allowed us to do whatever we do > normally, unless the user gave explicit permission to change data > types, ranges, etc. It also serves as a nice tag for "I, the > developer, thought about this issue". > > And that's quite cool that that's offered, but the question is, I guess, which default is best and why? Which default setting would confuse the least new (and old) users? Michael

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Feature extraction for 3D model
by Zichao Li 29 Apr '16

29 Apr '16

I have about 1Gb 3d models contained in .obj file and now I'd like to do some feature extraction: color, texture, shape etc. I wonder how can i do it with scikit-image? I can't find relative explanation in documents. Thanks a lot!

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release: scikit-image 0.12 is out!
by Emmanuelle Gouillart 28 Apr '16

28 Apr '16

Announcement: scikit-image 0.12 =============================== The scikit-image team is very pleased to announce the release of version 0.12 of scikit-image. scikit-image is an image processing toolbox for Python and SciPy, that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more, for 2-D and 3-D (sometimes n-D) images. For more information, examples, and documentation, please visit our website: http://scikit-image.org and our example gallery http://scikit-image.org/docs/dev/auto_examples/ This release includes new features such as image inpainting, seam carving, image comparison metrics, and a parallelization framework based on dask. The release has also improved the support of 3-D images, with 3-D skeletonization, 3-D phantom data. and partial support of measure.regionprops for 3-D images. Also note that the handling of background pixels by measure.label, a function labeling connected components, has changed to be consistent with scipy.ndimage.label. For this release, we merged over 200 pull requests from 64 contributors, with bug fixes, cleanups, improved documentation and new features. Release notes are available on http://scikit-image.org/docs/0.12.x/release_notes_and_installation.html#rel… and include a more detailed list of changes, and the complete list of contributors to this release. The release can be downloaded on PyPi https://pypi.python.org/pypi/scikit-image or directly installed using pip pip install --upgrade scikit-image Please let us know any issues you might have on the issue tracker https://github.com/scikit-image/scikit-image/issues Many thanks to all the developers who made this release possible, and a warm welcome to our new contributors. Happy image processing! The scikit-image team

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Re: RGB and CIR visualization
by Jaime Lopez Carvajal 22 Mar '16

22 Mar '16

Hi Stefan, Accord to ArcMAp, the order of layers is 0,1,2,3 correspond to Red, Green, Blue, Infrared. This way, if I want to visualize a true color image, I just use layers [0,1,2], in that order, and for CIR image the order would be [4,1,2]. So the order you ask me is: layers [0,1,2,3] = layers [Red, Green, Blue, Infrared] Thanks, Jaime On Monday, March 21, 2016 at 8:23:53 PM UTC-4, stefanv wrote: > > Hi Jaime > > These are the layers coming out of the TIFF reader: > > https://gist.github.com/6881c3527b3099c49891 > > Can you recognize which ones are which? Do they look correct? Maybe > you can compare them against the ones you see in ArcMap. > > Thanks! > Stéfan > > On 21 March 2016 at 16:36, Jaime Lopez Carvajal <jalo...(a)gmail.com > <javascript:>> wrote: > > Hi Stefan, > > > > Sure, I attached a small fraction of original image, because Remote > Sensing > > images are very big. > > The TIFF image has 4 bands. > > > > Hope this helps, Jaime > > > > On Monday, March 21, 2016 at 7:23:53 PM UTC-4, stefanv wrote: > >> > >> Hi Jaime > >> > >> On 21 March 2016 at 16:02, Jaime Lopez Carvajal <jalo...(a)gmail.com> > wrote: > >> > Of course, I would like to visualize the image like this (attached > >> > image), > >> > that was obtained using ArcMap software, with the usual band order > for > >> > True > >> > Color (RGB) and Composite Infrared (CIR) in remote sensing > applications. > >> > The tiff image that I loaded has 4 bands, so I would think that the > >> > order > >> > would be: band0(Red), band1(Green), band2(Blue), band3(Infrarred), > >> > so if I want to visualize the true color image, I would need to use > this > >> > instruction: > >> > TrueColor_img = img11[:,:,(0,1,2)] > >> > >> I'll take a look. Can you please upload an example tiff? > >> > >> Thanks > >> Stéfan > > > > -- > > 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/eb050d76-64f7-4f24-a67f-aa2a…. > > > > > For more options, visit https://groups.google.com/d/optout. >

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Re: RGB and CIR visualization
by Christoph Gohlke 22 Mar '16

22 Mar '16

I think the issue might be that the bands contain uint8 data stored as uint16 (one extra bit seems used for GDAL's nodata) and the display function `show_img` does not handle this. In this case casting the RGB array to uint8 before display might work: show_img(img11a.astype('uint8')) Christoph On 3/21/2016 5:17 PM, Stéfan van der Walt wrote: > Hi Jaime > > These are the layers coming out of the TIFF reader: > > https://gist.github.com/6881c3527b3099c49891 > > Can you recognize which ones are which? Do they look correct? Maybe > you can compare them against the ones you see in ArcMap. > > Thanks! > Stéfan > > On 21 March 2016 at 16:36, Jaime Lopez Carvajal <jalopcar(a)gmail.com> wrote: >> Hi Stefan, >> >> Sure, I attached a small fraction of original image, because Remote Sensing >> images are very big. >> The TIFF image has 4 bands. >> >> Hope this helps, Jaime >> >> On Monday, March 21, 2016 at 7:23:53 PM UTC-4, stefanv wrote: >>> >>> Hi Jaime >>> >>> On 21 March 2016 at 16:02, Jaime Lopez Carvajal <jalo...(a)gmail.com> wrote: >>>> Of course, I would like to visualize the image like this (attached >>>> image), >>>> that was obtained using ArcMap software, with the usual band order for >>>> True >>>> Color (RGB) and Composite Infrared (CIR) in remote sensing applications. >>>> The tiff image that I loaded has 4 bands, so I would think that the >>>> order >>>> would be: band0(Red), band1(Green), band2(Blue), band3(Infrarred), >>>> so if I want to visualize the true color image, I would need to use this >>>> instruction: >>>> TrueColor_img = img11[:,:,(0,1,2)] >>> >>> I'll take a look. Can you please upload an example tiff? >>> >>> Thanks >>> Stéfan >> >> -- >> 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+unsubscribe(a)googlegroups.com. >> To post to this group, send email to scikit-image(a)googlegroups.com. >> To view this discussion on the web, visit >> https://groups.google.com/d/msgid/scikit-image/eb050d76-64f7-4f24-a67f-aa2a…. >> >> For more options, visit https://groups.google.com/d/optout. >

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Re: RGB and CIR visualization
by Jaime Lopez Carvajal 22 Mar '16

22 Mar '16

Hi Stefan, Sure, I attached a small fraction of original image, because Remote Sensing images are very big. The TIFF image has 4 bands. Hope this helps, Jaime On Monday, March 21, 2016 at 7:23:53 PM UTC-4, stefanv wrote: > > Hi Jaime > > On 21 March 2016 at 16:02, Jaime Lopez Carvajal <jalo...(a)gmail.com > <javascript:>> wrote: > > Of course, I would like to visualize the image like this (attached > image), > > that was obtained using ArcMap software, with the usual band order for > True > > Color (RGB) and Composite Infrared (CIR) in remote sensing applications. > > The tiff image that I loaded has 4 bands, so I would think that the > order > > would be: band0(Red), band1(Green), band2(Blue), band3(Infrarred), > > so if I want to visualize the true color image, I would need to use this > > instruction: > > TrueColor_img = img11[:,:,(0,1,2)] > > I'll take a look. Can you please upload an example tiff? > > Thanks > Stéfan >

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Re: RGB and CIR visualization
by Jaime Lopez Carvajal 21 Mar '16

21 Mar '16

Hi Stefan, Of course, I would like to visualize the image like this (attached image), that was obtained using ArcMap software, with the usual band order for True Color (RGB) and Composite Infrared (CIR) in remote sensing applications. The tiff image that I loaded has 4 bands, so I would think that the order would be: band0(Red), band1(Green), band2(Blue), band3(Infrarred), so if I want to visualize the true color image, I would need to use this instruction: TrueColor_img = img11[:,:,(0,1,2)] but as you can see, the obtained visualization is very different from natural color. I hope this helps, Jaime On Monday, March 21, 2016 at 6:23:20 PM UTC-4, stefanv wrote: > > Hi Jaime > > On 21 March 2016 at 13:44, Jaime Lopez Carvajal <jalo...(a)gmail.com > <javascript:>> wrote: > > I am loading a 4-band image using io.imread, but I am having problems to > > visualize the true color (R,G,B) and Composite Infrared (IR,R,G) images. > > I have try different band combinations, but I couldnt view the right > > visualization (see attached image) > > Any suggestion? > > Can you tell us what you expect to see? Can you also share an example > image? > > Thanks > Stéfan >

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RGB and CIR visualization
by Jaime Lopez Carvajal 21 Mar '16

21 Mar '16

Hi, I am loading a 4-band image using* io.imread*, but I am having problems to visualize the true color *(R,G,B)* and Composite Infrared *(IR,R,G)* images. I have try different band combinations, but I couldnt view the right visualization (see attached image) Any suggestion? Thanks in advance, Jaime

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Re: Scipy2016: call for proposals
by Nelle Varoquaux 21 Mar '16

21 Mar '16

I'm terribly sorry about this second email. The deadline for submitting talks and posters for scipy 2016 is this friday (friday 25th), and not next friday (april fools day). Thanks, Nelle On 21 March 2016 at 15:32, Nelle Varoquaux <nelle.varoquaux(a)gmail.com> wrote: > Dear all, > > This is a quick reminder that the deadline for submitting talks and > posters proposal is next friday. > > Thanks, > Nelle > > On 22 February 2016 at 10:15, Nelle Varoquaux <nelle.varoquaux(a)gmail.com> > wrote: > >> >> Dear all, >> >> SciPy 2016, the Fifteenth Annual Conference on Python in Science, takes >> place in Austin, TX on July, 11th to 17th. The conference features two days >> of tutorials by followed by three days of presentations, and concludes with >> two days of developer sprints on projects of interest to attendees. . >> >> The topics presented at SciPy are very diverse, with a focus on advanced >> software engineering and original uses of Python and its scientific >> libraries, either in theoretical or experimental research, from both >> academia and the industry. This year we are happy to announce two >> specialized tracks that run in parallel to the general conference (Data >> Science , High Performance Computing) and 8 mini-symposia (Earth and Space >> Science, Biology and Medicine, Engineering, Social Sciences, Special >> Purpose Databases, Case Studies in Industry, Education, Reproducibility) >> >> Submissions for talks and posters are welcome on our website ( >> http://scipy2016.scipy.org). In your abstract, please provide details on >> what Python tools are being employed, and how. The talk and poster >> submission deadline is March 25th, 2016, while the tutorial submission >> deadline is March, 21st, 2016. >> >> >> Important dates: >> >> Mar 21: Tutorial Proposals Due >> Mar 25: Talk and Poster Proposals Due >> May 11: Plotting Contest Submissions Due >> Apr 22: Tutorials Announced >> Apr 22: Financial Aid Submissions Due >> May 4: Talk and Posters Announced >> May 11: Financial Aid Recipients Notified >> May 22: Early Bird Registration Deadline >> Jul 11-12: SciPy 2016 Tutorials >> Jul 13-15: SciPy 2016 General Conference >> Jul 16-17: SciPy 2016 Sprints >> >> We look forward to an exciting conference and hope to see you in Austin >> in July! >> >> >> The Scipy 2016 >> http://scipy2016.scipy.org/ >> >> Conference Chairs: Aric Hagberg, Prabhu Ramachandran >> Tutorial Chairs: Justin Vincent, Ben Root >> Program Chair: Serge Rey, Nelle Varoquaux >> Proceeding Chairs: Sebastian Benthall >> >> >

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