Mailman 3 December 2012 - 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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Re: Making algorithms at least 3D, preferably nD
by jeff witz April 26, 2013

April 26, 2013

Hello, I think you must all pay attention to standards if you want to act on nD images. For structured data files there is a library that hab been used for decade and which is very efficient, it's netcdf (http://www.unidata.ucar.edu/software/netcdf/), there is already tools in python to deal with it. This library allows one to deal with data in // which is important when one deals with big datas. The last version is based on hdf wich is the tools used on HPC. If your data are not stored in a standard format, the library ITK has provide a binding called SimpleITK that allows one to read every format ITK konws. It is important to take into account the fact that the operation on pixel depend on the grid spacing, as for exampe the finite difference gradient which is wrong when one chooses a step of one because the file as not been read correctly. As the 3D datas often comes from the measurement X-Ray Tomography, MRI, Echography, it is important to consider the device information. The format dicom as been made in this specific purpose there is a python binding called pydicom. I know that scikit team does'nt want to have to much dependances, but for nD case I think it is important to use existing libraries. Best regards Le vendredi 30 novembre 2012 05:45:49 UTC+1, Juan Nunez-Iglesias a écrit : > > Hey Guys, > > I mentioned this briefly at SciPy, but I would like to reiterate: a lot of > data is 3D images these days, and more and more data is being generated > that is multi-channel, 3D+t. Therefore, it would be awesome if scikit-image > started making more of an effort to support these. In the best case, the > dimension of the underlying array can be abstracted away — see here<https://github.com/jni/ray/blob/develop/ray/morpho.py#L328>for example, the functions juicy_center (which extracts the centre of an > array, along all dimensions), surfaces (grabs the "border" arrays along > each dimension), hollowed (zeroes-out the centre), and more. Otherwise, > writing a 3D function that gracefully degrades to 2D when one of the > dimensions is 1 is also possible. > > In general, the amount of additional effort to make code 3-, 4- or n- > dimensional is relatively low when you write the algorithm initially, > relative to refactoring a whole bunch of functions later. I'll try to > fiddle with whichever code I need, but in the meantime, what do you think > about adding a paragraph or a sentence about this issue in the scikit-image > contribute <http://scikit-image.org/docs/dev/contribute.html> section, so > that people at least have this in mind when they are thinking of writing > something new? > > Thanks, > > Juan. >

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Re: skimage and opencv
by François Jan. 5, 2013

Jan. 5, 2013

Many thanks for your responses. I agree with the fact that methods implemented in python are definitely easier to read. This a major advantage for education purpose. On the skimage home page, there is a 'Related project' box. Don't you think that it would be interesting to add these explanations on a dedicated page? Even the overview (see http://scikit-image.org/docs/dev/overview.html) is very light on the topic.

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Re: the memory issue
by Emmanuelle Gouillart Dec. 31, 2012

Dec. 31, 2012

Hi Vicky, the random walker algorithm has a quite complex api, because it can use several modes (the 'mode' keyword argument) to solve the linear system of the algorithm - 'bf' (for brute force): an LU decomposition of the matrix is computed. This is very costly in memory, and only recommended for small images. - 'cg' (for conjugate gradient). This mode needs much less memory, I suggest that you use it to test whether the algorithm gives good results for your problem. However, this mode is quite slow. - 'cg_mg': conjugate gradient with multigrid acceleration. This mode is much faster, but you'll need to install the module pyamg to use it. I use the cg_mg mode on tomography data (arrays of 500*500*20 float32 numbers, typically). Hope this helps, Emmanuelle On Mon, Dec 31, 2012 at 11:00:42AM -0800, vicky Liau wrote: > Hi everyone, > I use scikit-image to implement image segmentation for vegetation > classification. The target imagery is about 1GB, and I renewed my laptop > memory to 12GB (Window7 64 bit OS, CPU i7-2860QM) to read in imagery.Ã¯Â¿Â½ > However, when I tried to implement random walker segmentation, ipython > shows memory errors.Ã¯Â¿Â½ > Also, I shrink the imagery to 188 MB, and implement the same algorithm. > Ipython still shows errors as following.Ã¯Â¿Â½ > RuntimeError Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½Traceback (most recent call > last) > C:\Dropbox\open_codes\RS_codes\<ipython-input-80-737c60591471> in > <module>() > ----> 1 labels = random_walker(newarray3, markers, beta=10, mode='bf') > C:\Python27\lib\site-packages\skimage\segmentation\random_walker_segmentation.py > c in random_walker(data, labels, beta, mode, tol, copy, multichannel, > return_ful > l_prob, depth) > Ã¯Â¿Â½ Ã¯Â¿Â½ 396 Ã¯Â¿Â½ Ã¯Â¿Â½ if mode == 'bf': > Ã¯Â¿Â½ Ã¯Â¿Â½ 397 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ X = _solve_bf(lap_sparse, B, > --> 398 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ return_full_prob=return_full_prob) > Ã¯Â¿Â½ Ã¯Â¿Â½ 399 Ã¯Â¿Â½ Ã¯Â¿Â½ # Clean up results > Ã¯Â¿Â½ Ã¯Â¿Â½ 400 Ã¯Â¿Â½ Ã¯Â¿Â½ if return_full_prob: > C:\Python27\lib\site-packages\skimage\segmentation\random_walker_segmentation.py > c in _solve_bf(lap_sparse, B, return_full_prob) > Ã¯Â¿Â½ Ã¯Â¿Â½ 418 Ã¯Â¿Â½ Ã¯Â¿Â½ """ > Ã¯Â¿Â½ Ã¯Â¿Â½ 419 Ã¯Â¿Â½ Ã¯Â¿Â½ lap_sparse = lap_sparse.tocsc() > --> 420 Ã¯Â¿Â½ Ã¯Â¿Â½ solver = > sparse.linalg.factorized(lap_sparse.astype(np.double)) > Ã¯Â¿Â½ Ã¯Â¿Â½ 421 Ã¯Â¿Â½ Ã¯Â¿Â½ X = np.array([solver(np.array((-B[i]).todense()).ravel())\ > Ã¯Â¿Â½ Ã¯Â¿Â½ 422 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ for i in range(len(B))]) > C:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.pyc in > factori > zed(A) > Ã¯Â¿Â½ Ã¯Â¿Â½ 274 > Ã¯Â¿Â½ Ã¯Â¿Â½ 275 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ # Make LU decomposition. > --> 276 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ umf.numeric( A ) > Ã¯Â¿Â½ Ã¯Â¿Â½ 277 > Ã¯Â¿Â½ Ã¯Â¿Â½ 278 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ def solve( b ): > C:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\umfpack\umfpack.pyc > in > numeric(self, mtx) > Ã¯Â¿Â½ Ã¯Â¿Â½ 401 > Ã¯Â¿Â½ Ã¯Â¿Â½ 402 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ if self._symbolic is None: > --> 403 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ self.symbolic( mtx ) > Ã¯Â¿Â½ Ã¯Â¿Â½ 404 > Ã¯Â¿Â½ Ã¯Â¿Â½ 405 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ indx = self._getIndx( mtx ) > C:\Python27\lib\site-packages\scipy\sparse\linalg\dsolve\umfpack\umfpack.pyc > in > symbolic(self, mtx) > Ã¯Â¿Â½ Ã¯Â¿Â½ 384 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ if status != UMFPACK_OK: > Ã¯Â¿Â½ Ã¯Â¿Â½ 385 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ raise RuntimeError('%s failed with %s' % > (self.funs.symbolic > , > --> 386 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ > Ã¯Â¿Â½umfStatus[status] > )) > Ã¯Â¿Â½ Ã¯Â¿Â½ 387 > Ã¯Â¿Â½ Ã¯Â¿Â½ 388 Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ Ã¯Â¿Â½ self.mtx = mtx > RuntimeError: <function umfpack_di_symbolic at 0x000000015E72F9E8> failed > with U > MFPACK_ERROR_out_of_memory > What can I do for that? Just want to test whether this algorithm works for > my thoughts.Ã¯Â¿Â½ > Thanks!Ã¯Â¿Â½

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Re: skimage and opencv
by Stéfan van der Walt Dec. 28, 2012

Dec. 28, 2012

Hi Francois On Thu, Dec 27, 2012 at 2:06 PM, François <francois.boulogne(a)gmail.com> wrote: > 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? Thanks for your question--it's a good one. There are several reasons for a separate project, some of them already highlighted by Emmanuelle. A few years ago, we had to decide whether we wanted to build on top of OpenCV, and the general consensus was that it is too heavy a dependency. We have three aims with `skimage`: 1. Provide a highly Pythonic interface to building blocks for reproducible image processing reseach 2. Have simple, well-written and clear implementations of common algorithms for use in education 3. Provide tools for solving industry problems efficiently I think OpenCV does number 3 particularly well, while some projects such as SimpleCV take on number 1. But when it comes to education and reproducible research, I believe `skimage` still holds a significant advantage, in terms of the transparency and clarity of the code, as well as the examples provided. `skimage` plays particularly well with the scipy stack of tools, and I find that the pipelining tools (dtype conversion, color space handling, exposure adjustment, etc.) significantly improve my research productivity. Your comments and criticisms are most welcome; we are always keen to hear good feedback! Regards Stéfan

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Re: canvastools subpackage for scikit-image viewer
by Stéfan van der Walt Dec. 28, 2012

Dec. 28, 2012

Hi Tony On Wed, Dec 26, 2012 at 10:41 PM, Tony Yu <tsyu80(a)gmail.com> wrote: > I just added another tool: A canvas painter. It's a bit less responsive than > I'd like, but I think it's as fast as it'll get (it's limited by > matplotlib's blitting support for images). Check out the watershed demo in > the `viewer_examples/plugins` folder, which uses the paint tool to generate > markers for watershed segmentation. Finally, a path to that FFT filter painter I've had in mind! This is very exciting. By the way, it took me a while to figure out to press "enter" to run the segmentation, so how about printing some instructions to the terminal? Thanks again, Stéfan

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Re: canvastools subpackage for scikit-image viewer
by Tony Yu Dec. 27, 2012

Dec. 27, 2012

On Sun, Dec 23, 2012 at 3:34 AM, Stéfan van der Walt <stefan(a)sun.ac.za>wrote: > Tony and Steven, > > On Fri, Dec 14, 2012 at 11:11 AM, Steven Silvester > <steven.silvester(a)gmail.com> wrote: > > Great, I'll dig into your PR this weekend. I was thinking that each > plugin could be registered as a dock window, and be switchable via a View > menu. > > Thanks for working on this--it turns out to be a very powerful > teaching and demonstration tool! > > Cheers > Stéfan > > I'm glad you like it. I just added another tool: A canvas painter. It's a bit less responsive than I'd like, but I think it's as fast as it'll get (it's limited by matplotlib's blitting support for images). Check out the watershed demo in the `viewer_examples/plugins` folder, which uses the paint tool to generate markers for watershed segmentation. Best, -Tony

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Re: Testing
by Stéfan van der Walt Dec. 26, 2012

Dec. 26, 2012

Hi Vikram On Wed, Dec 26, 2012 at 11:36 AM, Vikram Kamath <vikram.kmth(a)gmail.com> wrote: > The problem I'm having is - I've installed scikit-image and I've also forked > the repo. I've created a branch and I'm tinkering with it. How do I run > tests to ensure that the code, after my modification actually works? First, you need to build the extension modules (type "make" in the repository root). Then, you can add that path to your PYTHONPATH: export PYTHONPATH=$PYTHONPATH:/path/to/skimage Then run "nosetests skimage" or import skimage skimage.test() > Also, is there an IRC channel for scikit-image? . Googling for it doesn't > show any relevant results and joining #scikit-image takes me to an empty > channel. Most of our communication takes place through the mailing list, although you may receive general installation help in #scipy on Freenode. Good luck! Stéfan

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Re: "No definition found"
by Stéfan van der Walt Dec. 23, 2012

Dec. 23, 2012

On Thu, Nov 29, 2012 at 6:43 PM, Juan Nunez-Iglesias <jni.soma(a)gmail.com> wrote: > I'm getting this message when trying to find the definition header for > skimage.segmentation.slic: > > In [12]: %pdef segmentation.slic > No definition header found for segmentation.slic > > In [15]: %pdef segmentation.join_segmentations > segmentation.join_segmentations(s1, s2) > > Anyone know why this is? Is this a general feature of Cython functions? Not sure--but this also shows that IPython does not handle unicode characters in a docstring correctly! Would you like to file a bug report? Stéfan

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Re: canvastools subpackage for scikit-image viewer
by Steven Silvester Dec. 23, 2012

Dec. 23, 2012

Great, I'll dig into your PR this weekend. I was thinking that each plugin could be registered as a dock window, and be switchable via a View menu.

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Re: High order statistics/Gary level runlength matrices statistics
by Dan Dec. 23, 2012

Dec. 23, 2012

Is this interesting option going to be implmented in the future release? El miércoles, 28 de noviembre de 2012 05:21:35 UTC-5, Dan escribió: > > Dear all, > > Is Scikit-image able to provide High order statistics/Gary level runlength > matrices statistics values(e.g., Short Run Emphasis, Long Run Emphasis, > Gray-Level Nonuniformity, Run Length Nonuniformity, Run Percentage, etc)?. > > Is there a way to calculate it using Scikit-image? > > Thanks a lot in advance.

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Re: Fourier transform upsample/shift?
by Schönberger Johannes Dec. 23, 2012

Dec. 23, 2012

Hi, I see you haven't benchmarked against the skimage.transform.* functions, those should be a lot faster compared to `ndimage.map_coordinates`. If your implementation provides a different kind of interpolation, I suggest to integrate them into the current functions and add a new parameter `mode="fourier"`. I'm not sure if there is an equivalent for your functions in all cases (see `transform/_warps.py`). Thoughts? Am 23.12.2012 um 08:30 schrieb Adam <keflavich(a)gmail.com>: > Benchmark tests created: > https://github.com/keflavich/image_registration/blob/master/examples/benchm… > https://github.com/keflavich/image_registration/blob/master/examples/benchm… > > They ought to be more extensive, but give the general results: > -fourier-based shifting is a little faster (maybe 50%), but follows > the same power-law > -fourier-based upsampling is marginally slower, but my test results so > far are questionable because of machine variability > > Of course, with fftw3, one can increase the number of processes used > for the fft, which may speed things up. > > On Sat, Dec 22, 2012 at 8:31 PM, Stéfan van der Walt <stefan(a)sun.ac.za> wrote: >> On Sat, Dec 22, 2012 at 5:24 PM, Adam Ginsburg <keflavich(a)gmail.com> wrote: >>> Reasonable idea. I think they are faster for at least some cases, but they >>> also behave a little differently than other interpolation techniques. So >>> put them all in a "fourier_interpolation.py" file, then put them somewhere >>> in transforms? >> >> Yes, I think some benchmarks would be interesting. Otherwise, we may >> want to add sinc interpolation to ndimage and see if that yields the >> same results. >> >> Thanks! >> Stéfan >> >> -- >> >> > > -- > Adam > > -- > >

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Re: Fourier transform upsample/shift?
by Adam Dec. 23, 2012

Dec. 23, 2012

Benchmark tests created: https://github.com/keflavich/image_registration/blob/master/examples/benchm… https://github.com/keflavich/image_registration/blob/master/examples/benchm… They ought to be more extensive, but give the general results: -fourier-based shifting is a little faster (maybe 50%), but follows the same power-law -fourier-based upsampling is marginally slower, but my test results so far are questionable because of machine variability Of course, with fftw3, one can increase the number of processes used for the fft, which may speed things up. On Sat, Dec 22, 2012 at 8:31 PM, Stéfan van der Walt <stefan(a)sun.ac.za> wrote: > On Sat, Dec 22, 2012 at 5:24 PM, Adam Ginsburg <keflavich(a)gmail.com> wrote: >> Reasonable idea. I think they are faster for at least some cases, but they >> also behave a little differently than other interpolation techniques. So >> put them all in a "fourier_interpolation.py" file, then put them somewhere >> in transforms? > > Yes, I think some benchmarks would be interesting. Otherwise, we may > want to add sinc interpolation to ndimage and see if that yields the > same results. > > Thanks! > Stéfan > > -- > > -- Adam

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Re: Fourier transform upsample/shift?
by Adam Ginsburg Dec. 23, 2012

Dec. 23, 2012

Reasonable idea. I think they are faster for at least some cases, but they also behave a little differently than other interpolation techniques. So put them all in a "fourier_interpolation.py" file, then put them somewhere in transforms? On Saturday, December 22, 2012 1:41:22 PM UTC-7, Stefan van der Walt wrote: > > Hi Adam > > On Fri, Dec 21, 2012 at 8:01 PM, Adam Ginsburg <kefl...(a)gmail.com<javascript:>> > wrote: > > > https://github.com/keflavich/image_registration/blob/master/image_registrat… > > > https://github.com/keflavich/image_registration/blob/master/image_registrat… > > > https://github.com/keflavich/image_registration/blob/master/image_registrat… > > (docs here: > > https://image-registration.readthedocs.org/en/latest/fourier_tools.html) > > > These look interesting; do they provide any real speed-up over the > existing routines? If so, how about using a `fourier_` prefix and > implement them all in the same file? > > Regards > Stéfan >

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Re: Fourier transform upsample/shift?
by Stéfan van der Walt Dec. 22, 2012

Dec. 22, 2012

Hi Adam On Fri, Dec 21, 2012 at 8:01 PM, Adam Ginsburg <keflavich(a)gmail.com> wrote: > https://github.com/keflavich/image_registration/blob/master/image_registrat… > https://github.com/keflavich/image_registration/blob/master/image_registrat… > https://github.com/keflavich/image_registration/blob/master/image_registrat… > (docs here: > https://image-registration.readthedocs.org/en/latest/fourier_tools.html) These look interesting; do they provide any real speed-up over the existing routines? If so, how about using a `fourier_` prefix and implement them all in the same file? Regards Stéfan

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Re: problem with skimage.measure.perimeter (version 0.7.1)
by Schönberger Johannes Dec. 22, 2012

Dec. 22, 2012

See https://github.com/scikit-image/scikit-image/pull/402.

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Re: problem with skimage.measure.perimeter (version 0.7.1)
by Johannes Schönberger Dec. 22, 2012

Dec. 22, 2012

Off course, you are right. I'll quickly fix this bug! Thanks for reporting. 2012/12/18 Patrice Munger <patricemunger(a)gmail.com>: > Comparing the code in regionprops.py with the reference > http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc, > should line 450 be (1 + sqrt(2) )/ 2: (13, 23) rather than 1 + sqrt(2) / 2: > (13, 23) ? > > -- > >

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Re: Stable vs Dev version, document image enhancement
by Marianne Corvellec Dec. 20, 2012

Dec. 20, 2012

On December 14, Tony S Yu wrote: > It might be a good idea for us to point to the stable version instead of > the development version by default. +1 For both "Gallery" and "Documentation". On Monday, December 17, 2012 12:57:12 AM UTC+1, Juan Nunez-Iglesias wrote: > > Perhaps you can add a "coming soon" link to the dev gallery? > +1 Would act as a teaser. :) "Coming soon" link on the "Gallery" page of stable version pointing to "Gallery" page of dev version. On December 14, Stefan van der Walt wrote: > The development gallery is useful as an advertisement, but we could maybe make the link to the other versions clearer? +1 I think the above could resolve the issue. The "Versions" box on the right-hand side is visible and great, but again, imho it makes more sense to point to the stable version by default. Marianne

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Re: Segmentation and Granulometry with scikit-image
by Dan Dec. 20, 2012

Dec. 20, 2012

El lunes, 17 de diciembre de 2012 05:23:00 UTC+1, Tony S Yu escribió: > > > > On Sun, Dec 16, 2012 at 10:41 AM, Dan <dk...(a)walla.co.il <javascript:>>wrote: > >> Hi, >> >> I installed Windows binaries (kindly provided by Christoph Gohlke)- file >> name:scikit-image-0.7.2.win32-py2.7.exe >> >> Thanks >> >> El domingo, 16 de diciembre de 2012 10:36:13 UTC-5, Dan escribió: >> >>> Hi, >>> >>> The output: >>> Python 2.7.3 (default, Apr 10 2012, 23:31:26) [MSC v.1500 32 bit >>> (Intel)] on win32 >>> Type "copyright", "credits" or "license()" for more information. >>> >>> import skimage >>> >>> print skimage.__version__ >>> 0.7.2 >>> >>> print skimage.__file__ >>> C:\Python27\lib\site-packages\**skimage\__init__.pyc >>> >>> >>> >>> Thanks >>> >> > > Hmm... that all looks good to me. > > Maybe something went wrong during the install? This is bizarre, because > `_skeletonize` is a regular python file (not cython, so it's not a build > issue). If you look in > > "C:\Python27\lib\site-**packages\skimage\morphology\" > > > is there a "_skeletonize.py" and does it like the original<https://github.com/scikit-image/scikit-image/blob/v0.7.2/skimage/morphology…> (i.e. > does it have a `skeletonize` function)? > > -Tony > > >>> El domingo, 16 de diciembre de 2012 09:41:35 UTC-5, Tony S Yu escribió: >>>> >>>> On Sat, Dec 15, 2012 at 6:45 PM, Dan <dk...(a)walla.co.il> wrote: >>>> >>>>> Hi, >>>>> >>>>> Thanks for the rapid respnse, I am using 0.7.2 on windows. >>>>> >>>>> The error is now: >>>>> >>>>> Traceback (most recent call last): >>>>> File "C:/Users/DK/PProjects/**skimage_seg/scikit_seg.py", line 3, >>>>> in <module> >>>>> import skimage.morphology as morph >>>>> File "C:\Python27\lib\site-**packages\skimage\morphology\__**init__.py", >>>>> line 7, in <module> >>>>> from ._skeletonize import skeletonize, medial_axis >>>>> ImportError: cannot import name skeletonize >>>>> >>>>> Any idea why?. >>>>> >>>>> Thanks >>>>> >>>> >>>> >>>> Hi Dan, >>>> >>>> How did you install scikit-image? Also, what's the output of the >>>> following? >>>> >>>> >>>> import skimage >>>> print skimage.__version__ >>>> print skimage.__file__ >>>> >>>> >>>> -Tony >>>> >>>> >>>> >>>>> El sábado, 15 de diciembre de 2012 16:24:31 UTC-5, Tony S Yu escribió: >>>>> >>>>>> On Sat, Dec 15, 2012 at 1:58 PM, Dan <dk...(a)walla.co.il> wrote: >>>>>> >>>>>>> Dear Tony, >>>>>>> >>>>>>> Thanks for the script. >>>>>>> >>>>>>> I was running the script and I got this error: >>>>>>> Traceback (most recent call last): >>>>>>> File "C:/Users/DK/PProjects/**skimage**_seg/scikit_seg.py", line >>>>>>> 18, in <module> >>>>>>> img_background = morph.reconstruction(img-h, img) >>>>>>> AttributeError: 'module' object has no attribute 'reconstruction' >>>>>>> >>>>>>> Process finished with exit code 1 >>>>>>> >>>>>>> Any reason why? >>>>>>> >>>>>>> Thanks a lot, >>>>>>> Dan >>>>>>> >>>>>> >>>>>> Hi Dan, >>>>>> >>>>>> Which version of scikit-image are you running? You'll need at least >>>>>> 0.7 (the latest stable release). You can check the version with the >>>>>> following shell command: >>>>>> >>>>>> $ python -c "import skimage; print skimage.__version__" >>>>>> >>>>>> -Tony >>>>>> >>>>> -- >> >> >> > Dear Tony, > > The files were a match. I uninstalled python and the other libraries, > installed them again and it works fine now. I do not what was the problem. > > Thanks a lot for the help. > > Dan

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Re: import of skimage.morphology.selem.disk fails
by Parand Darugar Dec. 18, 2012

Dec. 18, 2012

Yes indeed, a git pull and rebuild has fixed the issue. Much thanks! Best, Parand On Monday, December 17, 2012 1:37:35 PM UTC-8, Tony S Yu wrote: > > > > On Mon, Dec 17, 2012 at 3:08 AM, Guillaume Gay < > guil...(a)mitotic-machine.org <javascript:>> wrote: > >> Le 17/12/2012 09:03, Stéfan van der Walt a écrit : >> >> Hi Prashand >>> >>> On Sun, Dec 16, 2012 at 11:50 PM, Parand Darugar <dar...(a)gmail.com<javascript:>> >>> wrote: >>> >>>> "from skimage.morphology.selem import disk" fails with the message >>>> "ImportError: cannot import name convex_hull_image". Subsequent >>>> attempts to >>>> import result in message "ImportError: cannot import name _template". >>>> >>> No problems on this end. Could you please send usyour build log? >>> >>> Thanks >>> Stefan >>> >>> Hi >> >> I had very similar import problems when I checked out the source from >> github last friday, that got sorted out by pulling it again sunday, >> so maybe it is enough for you to update your repository and build again... >> >> Guillaume >> >> > I think Guillaume is correct here. The error looks like a circular import > that was fixed in PR #394<https://github.com/scikit-image/scikit-image/pull/394>. > Does updating fix the issue? > > -Tony > >

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Re: import of skimage.morphology.selem.disk fails
by Guillaume Gay Dec. 17, 2012

Dec. 17, 2012

Le 17/12/2012 09:03, Stéfan van der Walt a écrit : > Hi Prashand > > On Sun, Dec 16, 2012 at 11:50 PM, Parand Darugar <darugar(a)gmail.com> wrote: >> "from skimage.morphology.selem import disk" fails with the message >> "ImportError: cannot import name convex_hull_image". Subsequent attempts to >> import result in message "ImportError: cannot import name _template". > No problems on this end. Could you please send usyour build log? > > Thanks > Stefan > Hi I had very similar import problems when I checked out the source from github last friday, that got sorted out by pulling it again sunday, so maybe it is enough for you to update your repository and build again... Guillaume

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import of skimage.morphology.selem.disk fails
by Parand Darugar Dec. 17, 2012

Dec. 17, 2012

Hello, "from skimage.morphology.selem import disk" fails with the message "ImportError: cannot import name convex_hull_image". Subsequent attempts to import result in message "ImportError: cannot import name _template". System: Python 2.7.2 on OS X 1.8.2 , scikit-image latest version from git Installed via "python install setup.py" The install seemed to work fine - I can run most other scripts. Full error log here: http://pastebin.com/hD8ishuH Any ideas? Best, Parand

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Re: Segmentation and Granulometry with scikit-image
by Dan Dec. 17, 2012

Dec. 17, 2012

Hi, The output: Python 2.7.3 (default, Apr 10 2012, 23:31:26) [MSC v.1500 32 bit (Intel)] on win32 Type "copyright", "credits" or "license()" for more information. >>> import skimage >>> print skimage.__version__ 0.7.2 >>> print skimage.__file__ C:\Python27\lib\site-packages\skimage\__init__.pyc >>> Thanks El domingo, 16 de diciembre de 2012 09:41:35 UTC-5, Tony S Yu escribió: > > On Sat, Dec 15, 2012 at 6:45 PM, Dan <dk...(a)walla.co.il <javascript:>>wrote: > >> Hi, >> >> Thanks for the rapid respnse, I am using 0.7.2 on windows. >> >> The error is now: >> >> Traceback (most recent call last): >> File "C:/Users/DK/PProjects/skimage_seg/scikit_seg.py", line 3, in >> <module> >> import skimage.morphology as morph >> File "C:\Python27\lib\site-packages\skimage\morphology\__init__.py", >> line 7, in <module> >> from ._skeletonize import skeletonize, medial_axis >> ImportError: cannot import name skeletonize >> >> Any idea why?. >> >> Thanks >> > > > Hi Dan, > > How did you install scikit-image? Also, what's the output of the following? > > > import skimage > print skimage.__version__ > print skimage.__file__ > > > -Tony > > > >> El sábado, 15 de diciembre de 2012 16:24:31 UTC-5, Tony S Yu escribió: >> >>> On Sat, Dec 15, 2012 at 1:58 PM, Dan <dk...(a)walla.co.il> wrote: >>> >>>> Dear Tony, >>>> >>>> Thanks for the script. >>>> >>>> I was running the script and I got this error: >>>> Traceback (most recent call last): >>>> File "C:/Users/DK/PProjects/**skimage_seg/scikit_seg.py", line 18, >>>> in <module> >>>> img_background = morph.reconstruction(img-h, img) >>>> AttributeError: 'module' object has no attribute 'reconstruction' >>>> >>>> Process finished with exit code 1 >>>> >>>> Any reason why? >>>> >>>> Thanks a lot, >>>> Dan >>>> >>> >>> Hi Dan, >>> >>> Which version of scikit-image are you running? You'll need at least 0.7 >>> (the latest stable release). You can check the version with the following >>> shell command: >>> >>> $ python -c "import skimage; print skimage.__version__" >>> >>> -Tony >>> >>

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Filter out large segments
by Parand Darugar Dec. 17, 2012

Dec. 17, 2012

Hello, Not sure if it's appropriate to ask algorithm questions on this mailing list, please let me know if it's not. I'd like to filter out large contiguous regions out of my image - I only care about smaller items. Following the Image Segmentation example/tutorial, I'm able to find and label the large segments using an Otsu threshold and ndimage.label. Now I'd like to replace these large segments with "white", where "white" is the max value find in my grayscale image (basically I'd like to blank them out). I'm not quite following how this is done in the tutorial: >>> label_objects, nb_labels = ndimage.label(fill_coins) >>> sizes = np.bincount(label_objects.ravel()) >>> mask_sizes = sizes > 20 >>> mask_sizes[0] = 0 >>> coins_cleaned = mask_sizes[label_objects] How does the coins_cleaned = mask_sizes[label_objects] line clean the image? Generally speaking, given the labeled segments (label_objects from the tutorial), how would I apply operations to the original image - for example: set all of the pixels in "image" that are parts of objects labeled 3 and 9 to 0.577 . Also, how do I get smarter about how the system works so I don't ask elementary questions? Is there a good high level tutorial that'll familiarize me with the computation model for skimage / ndimage ? Best, Parand

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Re: Stable vs Dev version, document image enhancement
by Schönberger Johannes Dec. 16, 2012

Dec. 16, 2012

Maybe the "Gallery" link in the navigation should point to the dev gallery and the "Documentation" link to the stable docs?

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