Mailman 3 January 2019 - scikit-image

Scikit Blob detection, Not properly working on 3D image
by Francesco Alemanno 15 May '19

15 May '19

Hello everyone, recently (and for the first time) i had to deal with image processing, my problem is detecting sferical blobs in a 3D grayscale image shaped like this (20,1000,1000). So i looked inside scikit, and i found the function blob_dog&log&doh (the three main algorithms), the procedure is working perfectly for the X and Y coordinates (x,y in [0,1000]x[0,1000]) but failing miserably at identifying the correct Z coord (Z in [0,20]). I think this has to do with: the blobs occupy rougly 30x30 pixels in X,Y, but only 3/4 pixels in Z (since my 3D images have to be taken with a lower resolution in Z). Moreover the algorithms apparently use isotropic gaussian filtering so the variance is the same in all directions (at a given sigma scale) according to the algorithm (Unfortunately not according to my data). Is there any reasonable/sane way to fix for this? What do you guys suggest?

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ANN: SMIL - Small Morphological Image Library
by Jose-Marcio Martins da Cruz 25 Jan '19

25 Jan '19

Announcement: SMIL 0.9.1 ======================== Hi all, I'm pleased to announce SMIL - Simple Morphological Image Library - v. 0.9.1 SMIL is a library with all basic and some advanced mathematical morphology features which can be extended with plugins and user modules. Among its features it can handle 2D and 3D images and can handle data from/to NumPy data. It's been developed in C++ and has a Python interface thanks to Swig. SMIL is a product of CMM, the research Center of Mathematical Morphology of Mines-Paristech, where the discipline of Mathematical Morphology was created in the 60's by Jean Serra and Georges Matheron. SMIL is distributed with GPL license. We use SMIL in our research and teaching activities in the field and may, eventually, be used besides scikit-image. You can find SMIL - binaries and documentation - at our web site : http://smil.cmm.mines-paristech.fr or the source code at : https://github.com/ensmp-cmm/smil Thanks Jose-Marcio -- --------------------------------------------------------------- Jose Marcio MARTINS DA CRUZ, Ph.D. Ecole des Mines de Paris CMM - Centre de Morphologie Mathématique --------------------------------------------------------------- Spam : http://amzn.to/LEscRu ou http://bit.ly/SpamJM ---------------------------------------------------------------

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Using process pool in skimage
by Stefan van der Walt 24 Jan '19

24 Jan '19

Hi all, Inevitably, codes have to execute in parallel to make best use of modern processors. One such PR caught my eye today: https://github.com/scikit-image/scikit-image/pull/3670/files Do we have any precedent for how we handle parallel processing code? We have used dask in the past, but it is an optional dependency. We have Cython code that uses prange. It would be good to have a set of guidelines around which parallel paradigms we embrace. Best regards, Stéfan

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SciPy2019 submissions
by Stefan van der Walt 24 Jan '19

24 Jan '19

Hi everyone, There will be an Image Processing mini-symposium at SciPy2019 this year, and we'd love to see what you all have been working on! Please submit your talk proposals by *9 February* at http://conference.scipy.org. Best regards, Stéfan

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ANN: scikit-image 0.14.2
by Stefan van der Walt 18 Jan '19

18 Jan '19

Announcement: scikit-image 0.14.2 ================================= This release handles an incompatibility between scikit-image and NumPy 1.16.0, released on January 13th 2019. It contains the following changes from 0.14.1: API changes ----------- - ``skimage.measure.regionprops`` no longer removes singleton dimensions from label images (#3284). To recover the old behavior, replace ``regionprops(label_image)`` calls with ``regionprops(np.squeeze(label_image))`` Bug fixes --------- - Address deprecation of NumPy ``_validate_lengths`` (backport of #3556) - Correctly handle the maximum number of lines in Hough transforms (backport of #3514) - Correctly implement early stopping criterion for rank kernel noise filter (backport of #3503) - Fix ``skimage.measure.regionprops`` for 1x1 inputs (backport of #3284) Enhancements ------------ - Rewrite of ``local_maxima`` with flood-fill (backport of #3022, #3447) Build Process & Testing ----------------------- - Dedicate a ``--pre`` build in appveyor (backport of #3222) - Avoid Travis-CI failure regarding ``skimage.lookfor`` (backport of #3477) - Stop using the ``pytest.fixtures`` decorator (#3558) - Filter out DeprecationPendingWarning for matrix subclass (#3637) - Fix matplotlib test warnings and circular import (#3632) Contributors & Reviewers ------------------------ - François Boulogne - Emmanuelle Gouillart - Lars Grüter - Mark Harfouche - Juan Nunez-Iglesias - Egor Panfilov - Stefan van der Walt

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SciPy 2019 Call for Proposals
by Jill Cowan 07 Jan '19

07 Jan '19

SciPy 2019 CFP is open. Details are below. We would love to hear what you are working on. SciPy 2019, the 18th annual Scientific Computing with Python conference, will be held July 8-14, 2019 in Austin, Texas. The annual SciPy Conference brings together over 800 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The call for abstracts for SciPy 2019 for talks, posters and tutorials is now open. *The deadline for submissions is February 10, 2019.* Conference Website: https://www.scipy2019.scipy.org/ Submission Website: https://easychair.org/conferences/?conf=scipy2019 *Talks and Posters (July 10-12, 2019)* In addition to the general track, this year will have specialized tracks focused on: - Data Driven Discoveries (including Machine Learning and Data Science) - Open Source Communities (Sustainability) *Mini Symposia* - Science Communication through Visualization - Neuroscience and Cognitive Science - Image Processing - Earth, Ocean, Geo and Atmospheric Science There will also be a SciPy Tools Plenary Session each day with 2 to 5 minute updates on tools and libraries. *Tutorials (July 8-9, 2019)* Tutorials should be focused on covering a well-defined topic in a hands-on manner. We are looking for useful techniques or packages, helping new or advanced Python programmers develop better or faster scientific applications. We encourage submissions to be designed to allow at least 50% of the time for hands-on exercises even if this means the subject matter needs to be limited. Tutorials will be 4 hours in duration. In your tutorial application, you can indicate what prerequisite skills and knowledge will be needed for your tutorial, and the approximate expected level of knowledge of your students (i.e., beginner, intermediate, advanced). Instructors of accepted tutorials will receive a stipend. _______________ Jill Cowan Operations and Recruiting Manager | Enthought Tel +1 512 536 1057 jillc(a)enthought.com <jillc(a)enthought.com>

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Shape Index example not working
by Stefanie Lück 02 Jan '19

02 Jan '19

Hi everybody, first of all I wish you a happy new year :) I have a problem with the Shape Index <http://scikit-image.org/docs/dev/auto_examples/features_detection/plot_shap…>example. When I try with my image (shape (778, 1802, 3)) I am getting the following error message: Traceback (most recent call last): File "C:/Users/stefanie/PycharmProjects/BluVision/Testing/shape_index.py", line 16, in <module> s = shape_index(image) File "D:\Anaconda\lib\site-packages\skimage\feature\corner.py", line 419, in shape_index l1, l2 = hessian_matrix_eigvals(H) ValueError: too many values to unpack (expected 2) System: scikit-image 0.14.1 Windows 10 Python 3.6 Any suggestions? Thanks Stefanie

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