Mailman 3 February 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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Improving HoG
by Martin Savc Nov. 17, 2015

Nov. 17, 2015

I've been implementing my own HoG transform looking at different sources. While the implementation in scikit-image seems to lack certain features (multiple normalization schemes, general block overlap, Gaussian block window, trillinear interpolation/weighting of bin assignments,...) these don't seem to be that important, at least when applied to my current problem (eye blink analysis). Most of these would increase complexity, giving the implementation a complicated look, with little gain. I've also been looking into some practical improvements - integral histogram, separating the cell-block histogram feature to use it with other dense feature transforms such as LBP, a HoG visualization function that would render the visualization at higher resolutions that the original image. Would any of these be welcomed additions to scikit-image? Regards, Martin Savc PuppySaturation

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Problems with coordinate ranges when doing conversion from LCH to LAB to RGB
by Matteo Nov. 16, 2015

Nov. 16, 2015

A while ago I wrote a post on my blog with code on how to import in Python a colour palette in an ASCII file and convert it to a Matplotlib colormap: mycarta.wordpress.com/2014/04/25/convert-color-palettes-to-python-matplotli… Following that post I wrote a tutorial on a geoscience magazine on how to evaluate and compare colormaps using Python: wiki.seg.org/wiki/How_to_evaluate_and_compare_color_maps In the accompanying notebook I show how to convert a 256x3 RGB colormap to a 256x256x3 RGB image, then convert it to CIELAB using scikit-image's rgb2lab, then plot the 256x1 lightness (L) array to evaluate the colormaps perceptually. You can read the relevant extract of the notebook using this nbviewer link: http://nbviewer.ipython.org/urls/dl.dropbox.com/s/54zoax2qesb71wn/evaluate_… In that case scikit-image worked really well for me. Now I am trying to follow up with a new tutorial and I run into problems with the color space conversions. You can follow what I am trying to do in this other notebook extract: http://nbviewer.ipython.org/urls/dl.dropbox.com/s/noli66nzrlk0676/make_colo… The goal of this new tutorial is to show how to build colormaps from scratch using perceptual principles. I design a color palette in LCH (polar version of CIELAB) by keeping Chroma and Lightness fixed and interpolating Hue around the circle, then convert to LAB, then to RGB. As far as I know the code I wrote should work, but the result is a black colormap. I am thinking I got wrong one or more of the ranges for the LCH coordinates. I assumed L between (0,1), Ch between (0,1), and H between (0, 2*pi). Is that wrong, and if that's the case, what are the ranges? Many of them are not stated clearly in the documentation in here: http://scikit-image.org/docs/dev/api/skimage.color.html Is it possible to update the documentation to clearly state all ranges for all colour spaces. Thanks for your help. Matteo

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Finding pairs of images (homologous chromosomes)
by Jean-Patrick Pommier Nov. 15, 2015

Nov. 15, 2015

Dear All, I am trying to make pairs of images from the following set of images (chromosomes sorted by size after rotation). The idea is to make a feature vector for unsupervised classification (kmeans with 19 clusters) >From each chromosome an integral image was calculated: plt.figure(figsize = (15,15)) gs1 = gridspec.GridSpec(6,8) gs1.update(wspace=0.0, hspace=0.0) # set the spacing between axes. for i in range(38): # i = i + 1 # grid spec indexes from 0 ax1 = plt.subplot(gs1[i]) plt.axis('off') ax1.set_xticklabels([]) ax1.set_yticklabels([]) ax1.set_aspect('equal') image = sk.transform.integral_image(reallysorted[i][:,:,2]) imshow(image , interpolation='nearest') Then each integral image was flatten and combined with the others: Features =[] for i in range(38): Feat = np.ndarray.flatten(sk.transform.integral_image(reallysorted[i][:,:,2])) Features.append(Feat) X = np.asarray(Features) print X.shape The X array contains *38* lines and 9718 features, which is not good. However, I trried to submit these raw features to kmeans classification with sklearn using a direct example <http://scikit-learn.org/stable/modules/neighbors.html> : from sklearn.neighbors import NearestNeighbors nbrs = NearestNeighbors(n_neighbors=*19*, algorithm='ball_tree').fit(X) distances, indices = nbrs.kneighbors(X) connection = nbrs.kneighbors_graph(X).toarray() Ploting the connection graph shows that a chromosomes is similar to more than one ... - Do you think that integral images can be used to discriminate the chromosomes pairs? - If so, how to reduce the number of features to 10~20? (to get a better discrimination) Thanks for your advices. Jean-Patrick

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Pattern and object recognition with COSFIRE
by Almar Klein July 6, 2015

July 6, 2015

Hi all, Just wanted to mention an algorithm which I was very impressed by and which might be an awesome addition to scikit-image. The algorithm is called COSFIRE (Combination of Shifted Filter Responses). I saw a talk by Geore Azzopardi, who presented the use of the algorithm for object and patter recognition. I was very impressed with the quality of segmentation of the shown examples, but also with the wide variety of image data (e.g. retina images, traffic signs, hand-written characters, and more). The filter is inspired by how the brain processes visual information, and according to the author the algorithm is actually quite simple. The algorithm was the topic of his PhD and he's going to continue working on it in his new job. The code (in Matlab) is available, so it might be worth a shot to try and port it to Python. Some links: - PhD thesis: http://www.cs.rug.nl/~george/phd-thesis/ - Matlab code: nl.mathworks.com/matlabcentral/fileexchange/37395 - Paper in MIA: http://www.medicalimageanalysisjournal.com/article/S1361-8415%2814%2900136-… - Almar

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ANN: imageio v1.0
by Almar Klein July 3, 2015

July 3, 2015

Hi all, I'm pleased to announce version 1.0 of imageio - a library for reading and writing images. This library started as a spin-off of the freeimage plugin in skimage, and is now a fully-fledged library with unit tests and all. Imageio provides an easy interface to read and write a wide range of image data, including animated images, volumetric data, and scientific formats. It is cross-platform, runs on Python 2.x and 3.x, and is easy to install. Imageio is plugin-based, making it easy to extend. It could probably use more scientific formats. I welcome anyone who's interested to contribute! install: pip install imageio website: http://imageio.github.io release notes: http://imageio.readthedocs.org/en/latest/releasenotes.html Regards, Almar

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Re: Pattern and object recognition with COSFIRE
by George Azzopardi June 26, 2015

June 26, 2015

Dear all, I am pleased to see interest in the COSFIRE approach that I started during my PhD studies. The COSFIRE approach is a trainable pattern recognition approach which can be applied to several applications, including feature detection, object recognition and localization, image classification, contour detection and vessel segmentation. The selectively for a pattern of interest is automatically configured in a training process. The method involves several computations that are independent of each other, and thus it can be easily implemented using parallel programming (e.g. on a GPU). The original paper (http://www.cs.rug.nl/~george/articles/PAMI2013.pdf) combines information about the contours of the concerned pattern. We now have another paper which is currently being reviewed for CVPR2015 where we show that by adding colour information COSFIRE filters become even more robust. Please feel free to send me other ideas on how this work can be developed further. I would be very happy and available to work with an undergraduate or a postgraduate student (or any other person) to have this parallel implementation in Python. I see that you already added it to the Requested-features page. You can also add my contact details (geazzo@gmail) there for the interested readers. All my papers can be freely downloaded from my website: http://www.cs.rug.nl/~george/research-activities/ regards, George On Tuesday, 16 December 2014 15:22:45 UTC+1, Stefan van der Walt wrote: > > On Tue, Dec 16, 2014 at 1:57 PM, Pratap Vardhan <prat...(a)gmail.com > <javascript:>> wrote: > > I found few copies of the paper hosted by universities. I haven't > checked if > > these are the actual pre-prints - However, by the citation it looks like > it. > > Thanks! I've added it to the list: > > https://github.com/scikit-image/scikit-image/wiki/Requested-features > > Stéfan >

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regionprops - displaying region properties
by ciaran.robb＠googlemail.com April 7, 2015

April 7, 2015

Hello everyone, I have recently been attempting to modify some existing skimage code to display regionprops for a labeled image (e.g. area or eccentricity) I initially tried to translate a vectorized bit of old matlab code I had, but gave up on that and decided to alter the existing label2rgb skimage function I am attempting to change each label value to it's area property value similar to the label2rgb "avg" function. so I have: labels = a labeled image out = np.zeros_like(labels) #a blank array labels2 = np.unique(labels) #a vector of label vals out = np.zeros_like(labels) Props = regionprops(labels, ['Area']) bg_label=0 bg = (labels2 == bg_label) if bg.any(): labels2 = labels2[labels2 != bg_label] out[bg] = 0 for label in labels2: mask = (labels == label).nonzero() color = Props[label].area out[mask] = color but the "out" props image does not correspond to the correct area values? Can anyone help me with this? It also throws the following error: "list index out of range" It would certainly be useful to have a way to view the spatial distribution of label properties in this way - perhaps in a future skimage version?

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Example: Scikit-image and trackpy (bubble tracking in foams)
by François Boulogne March 25, 2015

March 25, 2015

Hi, Just for your information, I wrote an example based on my research to show the use of scikit-image with trackpy for feature detection and tracking. http://nbviewer.ipython.org/github/soft-matter/trackpy-examples/blob/master… Hopefully, this might be helpful for future workshops or presentations. Best, -- François Boulogne. http://www.sciunto.org GPG: 32D5F22F

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Building 0.11dev - issues
by Benjamin Cichy March 3, 2015

March 3, 2015

Hi all, I am attempting this install on Windows with >pip install . as per the instructions. The last compiler I have access to, is Visual Studio 10, so according to scikit-learn, and digging through the compiler script, it should be the last one recognized for .dll compilation before Python 3.5. I have the following errors right at the end of the build. This is under a fresh Anaconda install, with all the packages updated. creating build\temp.win-amd64-3.4\Release\skimage\_shared C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -IC:\Anaconda3\lib\core\include -IC:\Anaconda3\include -IC:\Anaconda3\include /Tcskimage\_shared\geometry.c /Fobuild\temp.win-amd64-3.4\Release\skimagej Found executable C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\BIN\ amd64\cl.exe C:\Anaconda3\lib\site-packages\setuptools-12.2-py3.4.egg\setuptools\dist.py: 282: UserWarning: Normalizing '0.11dev' to '0.11.dev error: Command "C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\BIN\amd64\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -IC-packages\numpy\core\include -IC:\Anaconda3\include -IC:\Anaconda3\include /Tcskimage\_shared\geometry.c /Fobuild\temp.win-amd64-3.4ared\geometry.obj" failed with exit status 2 geometry.c C:\Anaconda3\include\pyconfig.h(68) : fatal error C1083: Cannot open include file: 'io.h': No such file or directory ---------------------------------------- Rolling back uninstall of scikit-image Command "C:\Anaconda3\python.exe -c "import setuptools, tokenize;__file__='C:\\cygwin64\\tmp\\pip-jkks4ooy-build\\setup.py';execenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record C:\cygwin64\tmp\pip-xqr2l87y-recor --single-version-externally-managed --compile" failed with error code 1 in C:\cygwin64\tmp\pip-jkks4ooy-build Any suggestions? -Ben

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Re: Distinguishable shades of grey
by Juan Nunez-Iglesias Feb. 24, 2015

Feb. 24, 2015

> I reckon it would be similar to the middle colormap here http://earthobservatory.nasa.gov/blogs/elegantfigures/files/2013/08/three_p… (from the elegant figures block series linked above), which I've always found quite attractive. <3 ! It's gorgeous! I don't like the idea floating around later in the thread to compress the lightness range. At least for images, that's a terrible idea, limiting your dynamic range for no good reason. For scatterplots and other things with light backgrounds, I understand the logic, but perhaps a different range for different uses is warranted... Will comment something more complete to the mpl list directly in the morning... Thanks for the update and good night! On Tue, Feb 24, 2015 at 11:48 PM, Thomas Caswell <tcaswell(a)gmail.com> wrote: > Sorry, that is what I get for skimming emails too late at night :) > They are coming along, probably with a new color map rather than an > existing one. See > http://matplotlib.1069221.n5.nabble.com/release-strategy-and-the-color-revo… > Any feed back or suggestions on test patterns would be great. > -- > 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. > For more options, visit https://groups.google.com/d/optout.

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Re: Distinguishable shades of grey
by Thomas Caswell Feb. 24, 2015

Feb. 24, 2015

Sorry, that is what I get for skimming emails too late at night :) They are coming along, probably with a new color map rather than an existing one. See http://matplotlib.1069221.n5.nabble.com/release-strategy-and-the-color-revo… Any feed back or suggestions on test patterns would be great.

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Distinguishable shades of grey
by Juan Nunez-Iglesias Feb. 24, 2015

Feb. 24, 2015

>From this paper: http://rsif.royalsocietypublishing.org/content/early/2012/09/22/rsif.2012.0… The following quote (emphasis mine): Humans possess three cone visual pigments for conveying colour information that is said to allow humans to be able to detect approximately 10 million unique colours [8,9] but only distinguish about 30 shades of grey [10]. (Let's ignore recent movies of dubious merit in this discussion. =P) In the new MPL imshow plugin (which I recently wrote), we switch from grayscale to cubehelix when the dynamic range is too low to be displayed on a common monitor (1/255): https://github.com/scikit-image/scikit-image/blob/master/skimage/io/_plugin… Maybe the threshold should be when the difference is imperceptible to most humans (1/30)? Juan.

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ANN: SciPy Latin América 2015 - Call for Proposals
by Raniere Silva Feb. 20, 2015

Feb. 20, 2015

*Call for Proposals* *SciPy Latin América 2015*, the third annual Scientific Computing with Python Conference, will be held this *May 20-22* in *Posadas, Misiones, Argentina*. SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conferences allows participants from academic, commercial, and governmental organizations to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. *Proposals are now being accepted for SciPy Latin América 2015*. Presentation content can be at a novice, intermediate or advanced level. Talks will run 30-40 min and hands-on tutorials will run 100-120 min. We also receive proposal for posters. For more information about the different types of proposal, see below the "*Different types of Communication*" section. *How to Submit?* 1. Register for an account on http://conf.scipyla.org/user/register 2. Submit your proposal at http://conf.scipyla.org/activity/propose *Important Dates* - *April 6th*: Talks, poster, tutorial submission deadline. - *April 20th*: Notification Talks / Posters / Tutorial accepted. - *May 20th-22nd*: SciPy Latin América 2015. *Different types of Communication* *Talks*: These are the traditional talk sessions given during the main conference days. They're mostly 30 minutes long with 5 min for questions. If you think you have a topic but aren't sure how to propose it, contact our program committee and we'll work with you. We'd love to help you come up with a great proposal. *Tutorials*: We are looking for tutorials that can grow this community at any level. We aim for tutorials that will advance Scientific Python, advance this community, and shape the future. They're are 100-120 minutes long, but if you think you need more than one slot, you can split the content and submit two self-contained proposals. *Posters*: The poster session provides a more interactive, attendee-driven presentation than the speaker-driven conference talks. Poster presentations have fostered extensive discussions on the topics, with many that have gone on much longer than the actual "session" called for. The idea is to present your topic on poster board and as attendees mingle through the rows, they find your topic, read through what you've written, then strike up a discussion on it. It's as simple as that. You could be doing Q&A in the first minute of the session with a group of 10 people. *Lightning Talks*: Want to give a talk, but do not have enough material for a full talk? These talks are, at max, 5 minute talks done in quick succession in the main hall. No need to fill the whole slot, though! -- *The SciPy LA 2015 Program **Committee* Q29udGVudC1UeXBlOiBhcHBsaWNhdGlvbi9wZ3Atc2lnbmF0dXJlDQoNCi0tLS0tQkVHSU4gUEdQ IFNJR05BVFVSRS0tLS0tDQpWZXJzaW9uOiBHbnVQRyB2Mg0KDQppUUljQkFFQkNBQUdCUUpVNTVy ekFBb0pFSHZNY3ZwVXBYWE5Ic01QLzN3Tk13TUk3YmdaN3F0SjVGamFtOXJJDQo1czlFTHZHQ0Nt dWVLKytHZytvcE9MblJySUoyT1JUS1dmVm1PV3lHQzA3M2F2d0o5Ykgra3luQkI5L3RzTFJ3DQo5 UUlpSmxFU0xWM2toSXErS1hBdDNERXlWWjVTY2VWQWsxaHhETk1WT2NEeE1wYTFRL3JRSUtzSk9B SE0ySXd6DQpnZnlRR1BjQ2JaYnBOdzNsQWFLQmMrcG53K0VVa01oOHNSNXhZUjM0bWsweE41K3Iy bHBTWDdyZjl3NkphN296DQpoWVlKTFpuTU54c3dBUUg3Tm10eDQvVEJLMmN0VU1zT2JmdXU3dkFl elZRa05QVjNqdGRNeFFzMWRNUUpRQUlYDQpzWm9zNlV1bmlSckJHZktLUldVcUZyRTROK3MzeXZK ekFTck1yMWlNZlQxSGpZcEdsUHNUUkhIWTljRUhyOVZSDQpEKzZGaCtHL3hzMFNERU9SbFVjbnFN N3VwNjNmNGNkUHI4YTd6M1ArUmNoaFdMTnE3QXpOR3NHYzNFS2U4REhqDQpmdGdCNFI3TmwyVGFM M3UzNVpFY1ozM3VaSDd4NnJlUk50Q1g2bVRxMnRYUXRidEovUTJ2KzZwb3U3b1lTdFZQDQpnQUlN OE1tZUZ0YThqZ084a2dBQWJuanpCSWJXNlphU0EyZGYzOUtDdDhsRWdmNXRBRFJlZDlQeGM3OFF2 STVBDQpHd014cXlGdnVGc1Bid1lNM2xvZDJjNlFRL1ViWEVKcWgvMTRUSlBXSFRza1NNT1V3aXBE dlpWbzQvSVFWWEdRDQpYdnR3SDU2OWJ2UGd2VU5QMWp3eWl5UjZTYVR5Qnk5ZTBlNHRabWVUTjZn Z0xXZnhScVdaRmZJMnNYMm1DN0orDQpienEvVmF1UzU2SGlPcVNXazh2Kw0KPUREQ0oNCi0tLS0t RU5EIFBHUCBTSUdOQVRVUkUtLS0tLQ0K

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Re: Equivalent of watershed for cutting connected components of an image of particles?
by Georges H Feb. 19, 2015

Feb. 19, 2015

I second the post of Juan regarding the watershed for non sparse data. As for clearing image borders you have a function from the segmentation module here : http://scikit-image.org/docs/dev/api/skimage.segmentation.html#clear-border On Thursday, 19 February 2015 01:04:10 UTC+1, Adam Hughes wrote: > > Hi, > > In ImageJ, one can select watershedding to break up connected regions of > particles. Are there any examples of using watershed in this capacity in > scikit image? All of the examples I see seem to use watershedding to do > segmentation, not to break connected particles in an already-segmented > black and white image. > > Also, is there a straightforward way to remove particles on a the edge of > an image? Sorry, googling is failing me, but I know this is possible. > > Thanks >

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Google Summer of Code and NumFOCUS
by Raniere Silva Feb. 19, 2015

Feb. 19, 2015

Hi, NumFOCUS has promotes and supports the ongoing research and development of open-source computing tools including scikit-image. This year NumFOCUS want to try be a Google Summer of Code "umbrella" mentoring organization, Umbrella organizations are mentoring organizations accepted into the Google Summer of Code program that have other open source organizations working "under" them. Sometime organizations that work very closely or have very similar goals or communities may get put together under an "umbrella." Google stills expects all organizations under the umbrella, whether accepted into the program under their title or not, to adhere to all the rules and regulations of the program. From https://www.google-melange.com/gsoc/document/show/gsoc_program/google/gsoc2… To help promote and support scikit-image. We encourage scikit-image to apply to Google Summer of Code under your own title and will be very happy if you can also do with us. If you are interested, please check https://github.com/swcarpentry/gsoc2015 and https://github.com/swcarpentry/gsoc2015/blob/master/CONTRIBUTING.md. If you have any question, please email me directly. Thanks in advance, Raniere Q29udGVudC1UeXBlOiBhcHBsaWNhdGlvbi9wZ3Atc2lnbmF0dXJlDQoNCi0tLS0tQkVHSU4gUEdQ IFNJR05BVFVSRS0tLS0tDQpWZXJzaW9uOiBHbnVQRyB2Mg0KDQppUUljQkFFQkNBQUdCUUpVNWRO dkFBb0pFSHZNY3ZwVXBYWE5JaVFQLzFjWmxrcE01SDVvTnBlamVIZE1ReHpMDQpPeFp5T21NOVda cHVLM2VMVVU1SXBmZi94QmpCcHZwZkEzL3lwNElFYmxkVExOMlhRQktlVFcxYk40U3dSSDJNDQpU RCthV3J4ZkMxYURjTDdwNjhIVDRNaHo2ZXBOTDFJN1lJZ3QyM0JTek1PZDFZTTl0Q0NvaVhoaEZC UmQ4dG4vDQpPYTUvVFVjMnBKSFBmMkpYcFZBM1JHNTYvb1Z4b25HTXkxNVBKNGhpVy9sY0hrZmpT cTVZTnJOcUdrbGZUUTAzDQppbFR5RHdoZVJCTWJxaTliU0h3RzZ2cjVTUkRkVlY0MHYyUTEwRWc0 R2NjKzZsclJqYUw1T1BoZFYyWHJieENnDQpsM0RBWFJnbTJDUDExZ3VyWWc5QjFGb3dMVzl6ZnBy eTR3Z3lCSkRHQXdxMzJlOVBjdWdmV0prVG9nZFdLbkVEDQp6MGZod21BMklnTnR2eFVweTlBK0U0 eXJnUTZOaHF0cURDNGxJbVZES0JaaVQvb3Y0c25EQ2FRTEZYOFJvcDZwDQovOFZxdEVFT0NKRCtJ NlZ0NThtSGszNFJVaUcwUWppYytjMWpzb1NFQlVQeUh1Z3drc2Z1cldJMCtrZXpITDlvDQpWQnhh ZEdiYjhpS2UwSThKdVVpK2V3OVNManczVVhoVGMvZzBkQTd5R2dVSnROZW5ZVVZ6QmphSlZZNW5U RVhwDQpJL2hTWWlmSzZCR3hFa0ZYVVNmWHh4MHFWNHhCY1ZwaDVLck1yVHViWmZxY2FXV2luZjhB ZE5BQkRqb3RRb2RrDQppV1NWUGNoS2RiQy9LOHVBL1dtZ0tveEQ5Y0JNcTZhRkI5VnFjWWcxTUpj ODNPMmFUNUNqdmtwUW5YM0kzeHZnDQp4T1BOZkpzYml1cm81UHE2VDk3Mg0KPWFWd3YNCi0tLS0t RU5EIFBHUCBTSUdOQVRVUkUtLS0tLQ0K

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Re: Equivalent of watershed for cutting connected components of an image of particles?
by Juan Nunez-Iglesias Feb. 19, 2015

Feb. 19, 2015

Hey Adam, I'm *guessing* the IJ method is: 1. compute the thresholded background image (ie background labeled "True") 2. compute the Euclidean distance transform (scipy.ndimage.distance_transform_edt) 3. compute the local maxima (find_local_max) and set them as seeds 4. compute watershed, using the foreground as mask. All of those functions are available in scipy/scikit-image. If you get good results, a gallery example of this would certainly be appreciated! =) However, my experience with such methods is that they only work well for reasonably sparse, perfectly spherical particles. As to removing particles on the edge, I would use a bool mask with only the edges selected, then np.unique(), then remove them manually in a for loop. I agree that it's a bit laborious... Perhaps a separate function to do this could be added to the API... On Thu, Feb 19, 2015 at 11:04 AM, Adam Hughes <hughesadam87(a)gmail.com> wrote: > Hi, > In ImageJ, one can select watershedding to break up connected regions of > particles. Are there any examples of using watershed in this capacity in > scikit image? All of the examples I see seem to use watershedding to do > segmentation, not to break connected particles in an already-segmented > black and white image. > Also, is there a straightforward way to remove particles on a the edge of > an image? Sorry, googling is failing me, but I know this is possible. > 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+unsubscribe(a)googlegroups.com. > For more options, visit https://groups.google.com/d/optout.

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Equivalent of watershed for cutting connected components of an image of particles?
by Adam Hughes Feb. 19, 2015

Feb. 19, 2015

Hi, In ImageJ, one can select watershedding to break up connected regions of particles. Are there any examples of using watershed in this capacity in scikit image? All of the examples I see seem to use watershedding to do segmentation, not to break connected particles in an already-segmented black and white image. Also, is there a straightforward way to remove particles on a the edge of an image? Sorry, googling is failing me, but I know this is possible. Thanks

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Re: Last blockers for 0.11 release
by Johannes Schoenberger Feb. 17, 2015

Feb. 17, 2015

Excellent work @everyone! This will be a huge release. > On Feb 16, 2015, at 8:16 PM, Stéfan van der Walt <stefanv(a)berkeley.edu> wrote: > > Hi everyone > > We're overdue for the 0.11 release, and there are very few issues left > to close. I'd appreciate your feedback on this one: > > https://github.com/scikit-image/scikit-image/pull/1248 (peak detectors) > > And help in fixing: > > https://github.com/scikit-image/scikit-image/issues/1048 (doctest > failures on Mac OSX) > > I think this Sunday is a reasonable target for 0.11. Please get any > additional documentation updates in before then. > > 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. > For more options, visit https://groups.google.com/d/optout.

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Re: PeerJ Staff Picks 2015
by Johannes Schoenberger Feb. 17, 2015

Feb. 17, 2015

Awesome news! > On Feb 16, 2015, at 11:31 PM, Tony Yu <tsyu80(a)gmail.com> wrote: > > > On Mon, Feb 16, 2015 at 10:23 PM, Stefan van der Walt <stefanv(a)berkeley.edu> wrote: > Hi everyone > > Juan just informed me that our scikit-image paper appears on the > 2015 PeerJ Staff Picks: > > https://peerj.com/collections/13-peerjpicks2015/ > > Congratulations team! > > Stéfan > > > Sweet! > > > > -- > 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. > For more options, visit https://groups.google.com/d/optout.

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PeerJ Staff Picks 2015
by Stefan van der Walt Feb. 17, 2015

Feb. 17, 2015

Hi everyone Juan just informed me that our scikit-image paper appears on the 2015 PeerJ Staff Picks: https://peerj.com/collections/13-peerjpicks2015/ Congratulations team! Stéfan

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Last blockers for 0.11 release
by Stéfan van der Walt Feb. 17, 2015

Feb. 17, 2015

Hi everyone We're overdue for the 0.11 release, and there are very few issues left to close. I'd appreciate your feedback on this one: https://github.com/scikit-image/scikit-image/pull/1248 (peak detectors) And help in fixing: https://github.com/scikit-image/scikit-image/issues/1048 (doctest failures on Mac OSX) I think this Sunday is a reasonable target for 0.11. Please get any additional documentation updates in before then. Stéfan

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Re: Install difficulties - No module named _hough_transform
by Johannes Schoenberger Feb. 14, 2015

Feb. 14, 2015

Just to make sure: Do you try to import skimage from within the scikit-image source directory? > On Feb 14, 2015, at 12:21 PM, James Jackson <james.a.f.jackson.2(a)googlemail.com> wrote: > > Just tinkering, I wondered if this was related to having multiple versions of Python installed. When auto-config / build scripts are being run I always have a niggling feeling of unease in a multiple-version environment. Looking at the multi-version support in OS X, I came across this nugget to set a global default version to execute: > > sudo defaults write /Library/Preferences/com.apple.versioner.python Version 2.7 > > It looks like the modules are now importing properly. Perhaps worth adding to the install page as a Mac OS X note? > > Yours, > James. > > On Sat, Feb 14, 2015 at 6:17 PM, James Jackson <james.a.f.jackson.2(a)googlemail.com> wrote: > Josh, > > Thanks for the reply - I've just installed cython using the requirements file so should be up-to-date. Looking back at my install log, it seems I've got the latest (0.22). > > Collecting cython>=0.21 (from -r requirements.txt (line 1)) > Downloading Cython-0.22-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (3.7MB) > > Looking in the site-packages directory I do see a _hough_transform library: > > James-Jacksons-MacBook-Air:transform jamesjackson$ pwd > /Library/Python/2.7/site-packages/skimage/transform > James-Jacksons-MacBook-Air:transform jamesjackson$ ll | grep hough > -rwxr-xr-x 1 root wheel 659580 14 Feb 09:41 _hough_transform.so > -rw-r--r-- 1 root wheel 5566 14 Feb 09:41 hough_transform.py > -rw-r--r-- 1 root wheel 5044 14 Feb 09:41 hough_transform.pyc > > So something funny is clearly going on here with it not being picked up... > > Yours, > James. > > On Sat, Feb 14, 2015 at 5:51 PM, Josh Warner <silvertrumpet999(a)gmail.com> wrote: > Hi James, > > These are Cython modules that aren't being found or are not properly compiling. Please let us know what version of Cython you are running. > > Rest assured the modules are there. These are not external dependencies. I suspect an old version of Cython is to blame. > > > Regards, > > > > On Saturday, February 14, 2015 at 7:35:31 AM UTC-7, james.a.f...(a)googlemail.com wrote: > > Hi, > > > > > > I'm trying to install skimage, and having installed the dependencies (from requirements.txt in the source release), and then using pip to install skimage itself, I am having problems importing the transform library: > > > > > > > > Python 2.7.2 (default, Oct 11 2012, 20:14:37) > > [GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on darwin > > Type "help", "copyright", "credits" or "license" for more information. > > >>> import skimage.data > > >>> import skimage.transform > > Traceback (most recent call last): > > File "<stdin>", line 1, in <module> > > File "skimage/transform/__init__.py", line 1, in <module> > > from ._hough_transform import (hough_ellipse, hough_line, > > ImportError: No module named _hough_transform > > > > > > This is on Mac OSX 10.8.4 with Python 2.7.2. Is this an external library that needs installing from somewhere else? I have already had to install tifffile separately to get skimage.data working (perhaps should be added to requirements.txt?). I've tried a variety of approaches, but haven't had any success. > > > > > > Any and all advice welcome! > > > > > > Yours, > > James. > > -- > You received this message because you are subscribed to a topic in the Google Groups "scikit-image" group. > To unsubscribe from this topic, visit https://groups.google.com/d/topic/scikit-image/anOjoI4jW-w/unsubscribe. > To unsubscribe from this group and all its topics, send an email to scikit-image+unsubscribe(a)googlegroups.com. > 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+unsubscribe(a)googlegroups.com. > For more options, visit https://groups.google.com/d/optout.

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Re: Install difficulties - No module named _hough_transform
by James Jackson Feb. 14, 2015

Feb. 14, 2015

Just tinkering, I wondered if this was related to having multiple versions of Python installed. When auto-config / build scripts are being run I always have a niggling feeling of unease in a multiple-version environment. Looking at the multi-version support in OS X, I came across this nugget to set a global default version to execute: sudo defaults write /Library/Preferences/com.apple.versioner.python Version 2.7 It looks like the modules are now importing properly. Perhaps worth adding to the install page as a Mac OS X note? Yours, James. On Sat, Feb 14, 2015 at 6:17 PM, James Jackson < james.a.f.jackson.2(a)googlemail.com> wrote: > Josh, > > Thanks for the reply - I've just installed cython using the requirements > file so should be up-to-date. Looking back at my install log, it seems > I've got the latest (0.22). > > Collecting cython>=0.21 (from -r requirements.txt (line 1)) > Downloading > Cython-0.22-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl > (3.7MB) > > Looking in the site-packages directory I do see a _hough_transform library: > > James-Jacksons-MacBook-Air:transform jamesjackson$ pwd > /Library/Python/2.7/site-packages/skimage/transform > James-Jacksons-MacBook-Air:transform jamesjackson$ ll | grep hough > -rwxr-xr-x 1 root wheel 659580 14 Feb 09:41 _hough_transform.so > -rw-r--r-- 1 root wheel 5566 14 Feb 09:41 hough_transform.py > -rw-r--r-- 1 root wheel 5044 14 Feb 09:41 hough_transform.pyc > > So something funny is clearly going on here with it not being picked up... > > Yours, > James. > > On Sat, Feb 14, 2015 at 5:51 PM, Josh Warner <silvertrumpet999(a)gmail.com> > wrote: > >> Hi James, >> >> These are Cython modules that aren't being found or are not properly >> compiling. Please let us know what version of Cython you are running. >> >> Rest assured the modules are there. These are not external dependencies. >> I suspect an old version of Cython is to blame. >> >> >> Regards, >> >> >> >> On Saturday, February 14, 2015 at 7:35:31 AM UTC-7, >> james.a.f...(a)googlemail.com wrote: >> > Hi, >> > >> > >> > I'm trying to install skimage, and having installed the dependencies >> (from requirements.txt in the source release), and then using pip to >> install skimage itself, I am having problems importing the transform >> library: >> > >> > >> > >> > Python 2.7.2 (default, Oct 11 2012, 20:14:37) >> > [GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on >> darwin >> > Type "help", "copyright", "credits" or "license" for more information. >> > >>> import skimage.data >> > >>> import skimage.transform >> > Traceback (most recent call last): >> > File "<stdin>", line 1, in <module> >> > File "skimage/transform/__init__.py", line 1, in <module> >> > from ._hough_transform import (hough_ellipse, hough_line, >> > ImportError: No module named _hough_transform >> > >> > >> > This is on Mac OSX 10.8.4 with Python 2.7.2. Is this an external >> library that needs installing from somewhere else? I have already had to >> install tifffile separately to get skimage.data working (perhaps should be >> added to requirements.txt?). I've tried a variety of approaches, but >> haven't had any success. >> > >> > >> > Any and all advice welcome! >> > >> > >> > Yours, >> > James. >> >> -- >> You received this message because you are subscribed to a topic in the >> Google Groups "scikit-image" group. >> To unsubscribe from this topic, visit >> https://groups.google.com/d/topic/scikit-image/anOjoI4jW-w/unsubscribe. >> To unsubscribe from this group and all its topics, send an email to >> scikit-image+unsubscribe(a)googlegroups.com. >> For more options, visit https://groups.google.com/d/optout. >> > >

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Install difficulties - No module named _hough_transform
by james.a.f.jackson.2＠googlemail.com Feb. 14, 2015

Feb. 14, 2015

Hi, I'm trying to install skimage, and having installed the dependencies (from requirements.txt in the source release), and then using pip to install skimage itself, I am having problems importing the transform library: Python 2.7.2 (default, Oct 11 2012, 20:14:37) [GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import skimage.data >>> import skimage.transform Traceback (most recent call last): File "<stdin>", line 1, in <module> File "skimage/transform/__init__.py", line 1, in <module> from ._hough_transform import (hough_ellipse, hough_line, ImportError: No module named _hough_transform This is on Mac OSX 10.8.4 with Python 2.7.2. Is this an external library that needs installing from somewhere else? I have already had to install tifffile separately to get skimage.data working (perhaps should be added to requirements.txt?). I've tried a variety of approaches, but haven't had any success. Any and all advice welcome! Yours, James.

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