Hi,
I need some help regarding the following piece of code
If arr_1 is an image, I have used the prewitt edge detection technique.
edge_prewitt = prewitt(arr_1)
Is any thresholding applied here?
Dear all,
I’ve just released scikit-image 0.18.1, a small bug fix release on top of 0.18.0. Specifically:
- Fix indexing error for labelling in large (>2GB) arrays (#5143, #5151)
This was caused by a cdef in Cython using long rather than size_t for an indexing integer, which failed for big images in 32-bit platforms. Thanks to Riadh for the fix!
- Only use retry_if_failed with recent pooch (#5148)
Even though we added pooch 1.3 as an optional dependency, if the user had an *existing* pooch installation and didn’t request pooch from us, we would happily use their old pooch, but ask it to do new tricks that it didn’t know yet. This caused an error on importing skimage, because data/__init__ is executed by default. (We should fix this, but that was a bigger undertaking — I think this bandaid already makes things much better.)
Enjoy! 😊
Juan.
Some methods, like Li, have some special handling for nan values, but that is not uniform across all our methods, as you have seen. 😂 I would suggest:
```python
t = threshold_otsu(image[np.isfinite(image)])
```
That will only pass the non-nan, non-inf pixels to Otsu to do the calculation.
Hope this helps!
Juan.
> On 18 Dec 2020, at 7:19 am, Laurynas Gedminas <laurynasgedminas(a)gmail.com> wrote:
>
> Hello,
> I have NDVI image, its not a square, and has some None values,
> I am trying to create otsu threshold, but getting error:
> autodetected range of [nan, nan] is not finite
>
> how to go around that?
>
> <image.png>
>
> Thank you,
Dear skimagers,
I’ve just pushed scikit-image 0.18.0 release candidate 2 to PyPI. I think this is the real one! If I don’t hear objections by tomorrow morning (~24h from the timestamp on this email), I’ll tag and (finally!) release 0.18.0. 🎉
Please try this release candidate out by using `pip install -U --pre scikit-image`. Since we have started adding further dependencies to our documentation examples, in order to run some of the examples you will need to `pip install -U --pre scikit-image[docs]`. Or, if you only want access to our new example datasets, you can use `pip install -U --pre scikit-image[data]`. Finally, if you want to systematically test this release on your machine, you can use `pip install -U --pre scikit-image[test]`, then run `pytest --pyargs skimage`.
You can find the release notes below.
Happy image analysis!
Juan.
Announcement: scikit-image 0.18.0
=================================
We're happy to announce the release of scikit-image v0.18.0!
scikit-image is an image processing toolbox for SciPy that includes algorithms
for segmentation, geometric transformations, color space manipulation,
analysis, filtering, morphology, feature detection, and more.
This release of scikit-image drops support for Python 3.6 in accordance with
the `NEP-29 Python and Numpy version support community standard
<https://numpy.org/neps/nep-0029-deprecation_policy.html>`_: Python 3.7 or
newer is required to run this version.
For more information, examples, and documentation, please visit our website:
https://scikit-image.org
New Features
------------
- Add the iterative Lucas-Kanade (iLK) optical flow method (#4161)
- Add Feret diameter in region properties (#4379, #4820)
- Add functions to compute Euler number and Crofton perimeter estimation (#4380)
- Add a function to compute the Hausdorff distance (#4382)
- Added 3D support for many filters in ``skimage.filters.rank``.
- An experimental implementation of trainable pixel segmentation, aiming for
compatibility with the scikit-learn API, has been added to
``skimage.future``. Try it out! (#4739)
- Add a new function ``segmentation.expand_labels`` to dilate labels while
preventing overlap (#4795)
- It is now possible to pass extra measurement functions to
``measure.regionprops`` and ``regionprops_table`` (#4810)
- Add rolling ball algorithm for background subtraction (#4851)
- New sample images have been added in the ``data`` subpackage: ``data.eagle``
(#4922), ``data.human_mitosis`` (#4939), ``data.cells3d`` (#4951), and
``data.vortex`` (#5041). Also note that the image for ``data.camera`` has
been changed due to copyright issues (#4913).
- ``skimage.feature.structure_tensor`` now supports 3D (and nD) images as input
(#5002)
- Many thresholding methods can now receive a precomputed histogram as input,
resulting in significant speedups if multiple methods are tried on the same
image, or if a fast histogram method is used. (#5006)
- ``measure.regionprops`` now supports multichannel intensity images (#5037)
Documentation
-------------
- Add an example to the flood fill tutorial (#4619)
- Docstring enhancements for marching cubes and find_contours (#4641)
- A new tutorial presenting a cell biology example has been added to the
gallery (#4648). Special thanks to Pierre Poulain and Fred Bernard
(Université de Paris and Institut Jacques Monod) for scientific review of
this example!
- Improve register rotation example with notes and references (#4723)
- Add versionadded for new scalar type support for "scale" param in
``transform.AffineTransform`` (#4733)
- New tutorial on `visualizing 3D data <https://scikit-image.org/docs/dev/auto_examples/applications/plot_3d_image_…>`_ (#4850)
- Add example for 3D adaptive histogram equalization (AHE) (#4658)
- Automatic formatting of docstrings for improved consistency (#4849)
- Improved docstring for ``rgb2lab`` (#4839) and ``marching_cubes`` (#4846)
- Improved docstring for ``measure.marching_cubes``, mentioning how to decimate a
mesh using mayavi (#4846)
- Document how to contribute a gallery example. (#4857)
- Fix and improve entropy example (#4904)
- expand the benchmarking section of the developer docs (#4905)
- Improved docstring for ``util.random_noise`` (#5001)
- Improved docstrings for ``morphology.h_maxima`` and ``morphology.h_minima``
(#4929).
- Improved docstring for ``util.img_as_int`` (#4888).
- A new example demonstrates interactive exploration of regionprops results
using the PyData stack (pandas, seaborn) at
<https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_regionpro…>`_
(#5010).
- Documentation has been added to explain
`how to download example datasets <https://scikit-image.org/docs/dev/install.html#downloading-all-demo-datasets>`_
which are not installed with scikit-image (#4984). Similarly, the contributor
guide has been updated to mention how to host new datasets in a gitlab
repository (#4892).
- The `benchmarking section of the developer documentation <https://scikit-image.org/docs/dev/contribute.html#benchmarks>`_
has been expanded (#4905).
- Added links to the image.sc forum in example pages (#5094, #5096)
- Added missing datasets to gallery examples (#5116, #5118)
- Add farid filters in __all__, to populate the documentation (#5128, #5129)
- Proofread gallery example for rank filters. (#5126, #5136)
Improvements
------------
- float32 support for SLIC (#4683), ORB (#4684, #4697), BRIEF (#4685),
``pyramid_gaussian`` (#4696), Richardson-Lucy deconvolution (#4880)
- In ``skimage.restoration.richardson_lucy``, computations are now done in
single-precision when the input image is single-precision. This can give a
substantial performance improvement when working with single precision data.
- Richardson-Lucy deconvolution now has a ``filter_epsilon`` keyword argument
to avoid division by very small numbers (#4823)
- Add default level parameter (max-min) / 2 in ``measure.find_contours`` (#4862)
- The performance of the SLIC superpixels algorithm
(``skimage.segmentation.slice``) was improved for the case where a mask
is supplied by the user (#4903). The specific superpixels produced by
masked SLIC will not be identical to those produced by prior releases.
- ``exposure.adjust_gamma`` has been accelerated for ``uint8`` images by using
a look-up table (LUT) (#4966).
- ``measure.label`` has been accelerated for boolean input images, by using
``scipy.ndimage``'s implementation for this case (#4945).
- ``util.apply_parallel`` now works with multichannel data (#4927).
- ``skimage.feature.peak_local_max`` supports now any Minkowski distance.
- We now use sparse cross-correlation to accelerate local thresholding
functions (#4912)
- ``morphology.convex_hull_image`` now uses much less memory by checking hull
inequalities in sequence (#5020)
- Polygon rasterization is more precise and will no longer potentially exclude
input vertices. (#5029)
- Add data optional requirements to allow pip install scikit-image[data]
(#5105, #5111)
- OpenMP support in MSVC (#4924, #5111)
- Restandardize handling of Multi-Image files (#2815, #5132)
- Consistent zoom boundary behavior across SciPy versions (#5131, #5133)
API Changes
-----------
- ``skimage.restoration.richardson_lucy`` returns a single-precision output
when the input is single-precision. Prior to this release, double-precision
was always used.
- The default value of ``threshold_rel`` in ``skimage.feature.corner`` has
changed from 0.1 to None, which corresponds to letting
``skimage.feature.peak_local_max`` decide on the default. This is currently
equivalent to ``threshold_rel=0``.
- ``data.cat`` has been introduced as an alias of ``data.chelsea`` for a more
descriptive name.
- The ``level`` parameter of ``measure.find_contours`` is now a keyword
argument, with a default value set to (max(image) - min(image)) / 2.
- ``p_norm`` argument was added to ``skimage.feature.peak_local_max``
to add support for Minkowski distances.
- In ``measure.label``, the deprecated ``neighbors`` parameter has been
removed. (#4942)
Bug fixes
---------
- A bug in ``label2rgb`` has been fixed when the input image had np.uint8
dtype (#4661)
- Fixed incorrect implementation of ``skimage.color.separate_stains`` (#4725)
- Many bug fixes have been made in ``peak_local_max`` (#2592, #4756, #4760,
#5047)
- Fix bug in ``random_walker`` when input labels have negative values (#4771)
- PSF flipping is now correct for Richardson-Lucy deconvolution work in >2D (#4823)
- Fix equalize_adapthist (CLAHE) for clip value 1.0 (#4828)
- For the RANSAC algorithm, improved the case where all data points are
outliers, which was previously raising an error
(#4844)
- An error-causing bug has been corrected for the ``bg_color`` parameter in
``label2rgb`` when its value is a string (#4840)
- A normalization bug was fixed in ``metrics.variation_of_information``
(#4875)
- Euler characteristic property of ``skimage.measure.regionprops`` was erroneous
for 3D objects, since it did not take tunnels into account. A new implementation
based on integral geometry fixes this bug (#4380).
- In ``skimage.morphology.selem.rectangle`` the ``height`` argument
controlled the width and the ``width`` argument controlled the height.
They have been replaced with ``nrow`` and ``ncol``. (#4906)
- ``skimage.segmentation.flood_fill`` and ``skimage.segmentation.flood``
now consistently handle negative values for ``seed_point``.
- Segmentation faults in ``segmentation.flood`` have been fixed (#4948, #4972)
- A segfault in ``draw.polygon`` for the case of 0-d input has been fixed
(#4943).
- In ``registration.phase_cross_correlation``, a ``ValueError`` is raised when
NaNs are found in the computation (as a result of NaNs in input images).
Before this fix, an incorrect value could be returned where the input images
had NaNs (#4886).
- Fix edge filters not respecting padding mode (#4907)
- Use v{} for version tags with pooch (#5104, #5110)
- Fix compilation error in XCode 12 (#5107, #5111)
Deprecations
------------
- The ``indices`` argument in ``skimage.feature.peak_local_max`` has been
deprecated. Indices will always be returned. (#4752)
- In ``skimage.feature.structure_tensor``, an ``order`` argument has been
introduced which will default to 'rc' starting in version 0.20. (#4841)
- ``skimage.feature.structure_tensor_eigvals`` has been deprecated and will be
removed in version 0.20. Use ``skimage.feature.structure_tensor_eigenvalues``
instead.
- The ``skimage.viewer`` subpackage and the ``skivi`` script have been
deprecated and will be removed in version 0.20. For interactive visualization
we recommend using dedicated tools such as `napari <https://napari.org>`_ or
`plotly <https://plotly.com>`_. In a similar vein, the ``qt`` and ``skivi``
plugins of ``skimage.io`` have been deprecated
and will be removed in version 0.20. (#4941, #4954)
- In ``skimage.morphology.selem.rectangle`` the arguments ``width`` and
``height`` have been deprecated. Use ``nrow`` and ``ncol`` instead.
- The explicit setting ``threshold_rel=0` was removed from the Examples of the
following docstrings: ``skimage.feature.BRIEF``,
``skimage.feature.corner_harris``, ``skimage.feature.corner_shi_tomasi``,
``skimage.feature.corner_foerstner``, ``skimage.feature.corner_fast``,
``skimage.feature.corner_subpix``, ``skimage.feature.corner_peaks``,
``skimage.feature.corner_orientations``, and
``skimage.feature._detect_octave``.
- In ``skimage.restoration._denoise``, the warning regarding
``rescale_sigma=None`` was removed.
- In ``skimage.restoration._cycle_spin``, the ``# doctest: +SKIP`` was removed.
Development process
-------------------
- Fix #3327: Add functionality for benchmark coverage (#3329)
- Release process notes have been improved. (#4228)
- ``pyproject.toml`` has been added to the sdist.
- Build and deploy dev/master documentation using GitHub Actions (#4852)
- Website now deploys itself (#4870)
- build doc on circle ci and link artifact (#4881)
- Benchmarks can now run on older scikit-image commits (#4891)
- Website analytics are tracked using plausible.io and can be visualized on
https://plausible.io/scikit-image.org (#4893)
- Artifacts for the documentation build are now found in each pull request
(#4881).
- Documentation source files can now be written in Markdown in addition to
ReST, thanks to ``myst`` (#4863).
- update trove classifiers and tests for Python 3.9 + fix pytest config (#5052)
- fix Azure Pipelines, pytest config, and trove classifiers for Python 3.8 (#5054)
- Moved our testing from Travis to GitHub Actions (#5074)
- We now build our wheels on GitHub Actions on the main repo using
cibuildwheel. Many thanks to the matplotlib and scikit-learn developers for
paving the way for us! (#5080)
- Disable Travis-CI builds (#5099, #5111)
- Improvements to CircleCI build: no parallelization and caching) (#5097, #5119)
Other Pull Requests
-------------------
- Manage iradon input and output data type (#4298)
- random walker: Display a warning when the probability is outsite [0,1] for a given tol (#4631)
- MAINT: remove unused cython file (#4633)
- Forget legacy data dir (#4662)
- Setup longdesc markdown and switch to 0.18dev (#4663)
- Optional pooch dependency (#4666)
- Adding new default values to functions on doc/examples/segmentation/plot_ncut (#4676)
- Reintroduced convert with a strong deprecation warning (#4681)
- In release notes, better describe skimage's relationship to ecosystem (#4689)
- Perform some todo tasks for 0.18 (#4690)
- Perform todo tasks for 0.17! (#4691)
- suppressing warnings from gallery examples (#4692)
- release notes for 0.17.2 (#4702)
- Fix gallery example mentioning deprecated argument (#4706)
- Specify the encoding of files opened in the setup phase (#4713)
- Remove duplicate fused type definition (#4724)
- Blacklist cython version 0.29.18 (#4730)
- Fix CI failures related to conversion of np.floating to dtype (#4731)
- Fix Ci failures related to array ragged input numpy deprecation (#4735)
- Unwrap decorators before resolving link to source (sphinx.ext.linkcode) (#4740)
- Fix plotting error in j-invariant denoising tutorial (#4744)
- Highlight all source lines with HTML doc "source" links (sphinx.ext.linkcode) (#4746)
- Turn checklist boxes into bullet points inside the pull request template (#4747)
- Deprecate (min_distance < 1) and (footprint.size < 2) in peak_local_max (#4753)
- forbid dask 2.17.0 to fix CI (#4758)
- try to fix ci which is broken because of pyqt5 last version (#4788)
- Remove unused variable in j invariant docs (#4792)
- include all md files in manifest.in (#4793)
- Remove additional "::" to make plot directive work. (#4798)
- Use optipng to compress images/thumbnails in our gallery (#4800)
- Fix runtime warning in blob.py (#4803)
- Add TODO task for sphinx-gallery>=0.9.0 to remove enforced thumbnail_size (#4804)
- Change SSIM code example to use real MSE (#4807)
- Let biomed example load image data with Pooch. (#4809)
- Tweak threshold_otsu error checking - closes #4811 (#4812)
- Ensure assert messages from Cython rank filters are informative (#4815)
- Simplify equivalent_diameter function (#4819)
- DOC: update subpackage descriptions (#4825)
- style: be explicit when stacking arrays (#4826)
- MAINT: import Iterable from collections.abc (Python 3.9 compatibility) (#4834)
- Silence several warnings in the test suite (#4837)
- Silence a few RuntimeWarnings in the test suite (#4838)
- handle color string mapping correctly (#4840)
- DOC: Autoformat docstrings in ``io.*.py`` (#4845)
- Update min req for pillow due to CVE-2020-10379 and co. (#4861)
- DOC: First pass at format conversion, rst -> myst (#4863)
- Fixed typo in comment (#4867)
- Alternative wording for install guide PR #4750 (#4871)
- DOC: Clarify condition on unique vertices returned by marching cubes (#4872)
- Remove unmaintained wiki page link in contributor guidelines (#4873)
- new matomo config (#4879)
- Fix Incorrect documentation for skimage.util.img_as_int Issue (#4888)
- Minor edit for proper doc rendering (#4897)
- Changelog back-log (#4898)
- minor refactoring in phase_cross_correlation (#4901)
- Fix draw.circle/disk deprecation message, fixes #4884 (#4908)
- Add versionchanged tag for new opt param in measure.find_contours() (#4909)
- Declare build dependencies (#4920)
- Replace words with racial connotations (#4921)
- Fixes to apply_parallel for functions working with multichannel data (#4927)
- Improve description of h_maxima and h_minima functions (#4928) (#4929)
- CI: Skip doc build for PYTHONOPTIMIZE=2 (#4930)
- MAINT: Remove custom fused type in skimage/morphology/_max_tree.pyx (#4931)
- MAINT: remove numpydoc option, issue fixed in numpydoc 1.0 (#4932)
- modify development version string to allow use with NumpyVersion (#4947)
- CI: Add verbose option to avoid travis timeout for OSX install script (#4956)
- Fix CI: ban sphinx-gallery 0.8.0 (#4960)
- Alias for data.chelsea: data.cat() (#4962)
- Fix typo. (#4963)
- CI: Use Travis wait improved to avoid timeout for OSX builds (#4965)
- Small enhancement in "Contour finding" example: Removed unused variable n (#4967)
- MAINT: remove unused imports (#4968)
- MAINT: Remove conditional import on networkx (#4970)
- forbid latest version of pyqt (#4973)
- Remove warnings/explicit settings on feature, restoration (#4974)
- Docstring improvements for label and regionprops_label (#4983)
- try to fix timeout problem with circleci (#4986)
- improve Euler number example (#4989)
- [website] Standardize Documentation index page. (#4990)
- Proofread INSTALL file. (#4991)
- Catch leftover typos in INSTALL file. (#4992)
- Let tifffile.imread handle additional keyword arguments (#4997)
- Update docstring for random_noise function (#5001)
- Update sphinx mapping for sklearn and numpy (#5003)
- Update docstring slic superpixels (#5014)
- Bump numpy versions to match scipy (kinda) (#5016)
- Fix usage of numpy.pad for old versions of numpy (#5017)
- [MRG] Update documentation to new data.camera() (#5018)
- bumped plotly requirement for docs (#5021)
- Fix IndexError when calling hough_line_peaks with too few angles (#5024)
- Code simplification after latest numpy bump (#5027)
- Fixes broken link to CODE_OF_CONDUCT.md (#5030)
- Specify whether core dev should merge right after second approving review. (#5040)
- Update pytest configuration to include ``test_`` functions (#5044)
- MAINT Build fix for pyodide (#5059)
- reduce OSX build time so that Travis is happy (#5067)
- DOC: document the normalized kernel in prewitt_h, prewitt_v (#5076)
- Some minor tweaks to CI (#5079)
- removed usage of numpy's private functions from util.arraycrop (#5081)
- peak_local_max: remove deprecated `indices` argument from examples (#5082)
- Replace np.bool, np.float, and np.int with bool, float, and int (#5103, #5108)
- change plausible script to track outbound links (#5115, #5123)
- Remove Python 3.6 support (#5117, #5125)
- Optimize ensure_spacing (#5062, #5135)
52 authors added to this release [alphabetical by first name or login]
----------------------------------------------------------------------
A warm thank you to all contributors who added to this release. A fraction of contributors were first-time contributors to open source and a much larger fraction first-time contributors to scikit-image. It's a great feeling for maintainers to welcome new contributors, and the diversity of scikit-image contributors is surely a big strength of the package.
- Abhishek Arya
- Abhishek Patil
- Alexandre de Siqueira
- Ben Nathanson
- Cameron Blocker
- Chris Roat
- Christoph Gohlke
- Clement Ng
- Corey Harris
- David McMahon
- David Mellert
- Devi Sandeep
- Egor Panfilov
- Emmanuelle Gouillart
- François Boulogne
- Genevieve Buckley
- Gregory R. Lee
- Harry Kwon
- iofall (cedarfall)
- Jan Funke
- Juan Nunez-Iglesias
- Julian Gilbey
- Julien Jerphanion
- kalpana
- kolibril13 (kolibril13)
- Kushaan Gupta
- Lars Grüter
- Marianne Corvellec
- Mark Harfouche
- Marvin Albert
- Matthias Bussonnier
- Max Frei
- Nathan
- neeraj3029 (neeraj3029)
- Nick
- notmatthancock (matt)
- OGordon100 (OGordon100)
- Owen Solberg
- Riadh Fezzani
- Robert Haase
- Roman Yurchak
- Ronak Sharma
- Ross Barnowski
- Ruby Werman
- ryanlu41 (ryanlu41)
- Sebastian Wallkötter
- Shyam Saladi
- Stefan van der Walt
- Terence Honles
- Volker Hilsenstein
- Wendy Mak
- Yogendra Sharma
41 reviewers added to this release [alphabetical by first name or login]
------------------------------------------------------------------------
- Abhishek Arya
- Abhishek Patil
- Alexandre de Siqueira
- Ben Nathanson
- Chris Roat
- Clement Ng
- Corey Harris
- Cris Luengo
- David Mellert
- Egor Panfilov
- Emmanuelle Gouillart
- François Boulogne
- Gregory R. Lee
- Harry Kwon
- Jan Funke
- Juan Nunez-Iglesias
- Julien Jerphanion
- kalpana
- Kushaan Gupta
- Lars Grüter
- Marianne Corvellec
- Mark Harfouche
- Marvin Albert
- neeraj3029
- Nick
- OGordon100
- Riadh Fezzani
- Robert Haase
- Ross Barnowski
- Ruby Werman
- ryanlu41
- Scott Trinkle
- Sebastian Wallkötter
- Stanley_Wang
- Stefan van der Walt
- Steven Brown
- Stuart Mumford
- Terence Honles
- Volker Hilsenstein
- Wendy Mak
Hi Chien-Cheng,
The scikit-image release 0.18.0 is being prepared now (so hopefully it will
be out soon). I think installing the release candidate will be the simplest
way forward for you, until the official release 0.18.0 becomes available.
You can install the latest release candidate into your anaconda environment
with: pip install scikit-image==0.18.0rc2
Last, in case you are also interested to know what went wrong with trying
to install the development version, I suspect the problem you had earlier
is because of one of two possibilities:
1. You have not cloned the scikit-image repository from github, or
2. You have not changed directory in your terminal before running that
conda install command. You'll need to move to the location of the
scikit-image git repository with something like "cd
Documents/Github/scikit-image" or "cd Documents\Github\scikit-image" before
you can install it.
There are a few other pre-requisites for a development installation (like a
working C compiler, and a C++ compiler). These are listed as bullet points
under the heading "Installing scikit-image for contributors" on the page
you linked to: https://scikit-image.org/docs/dev/install.html#id4
I hope that helps,
Genevieve
On Fri, 11 Dec 2020 at 21:13, <scikit-image-request(a)python.org> wrote:
> Send scikit-image mailing list submissions to
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>
> Today's Topics:
>
> 1. Installation question about skimage developer version
> (Hung, C. (Chien-Cheng))
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 11 Dec 2020 10:01:12 +0000
> From: "Hung, C. (Chien-Cheng)" <c.hung(a)uu.nl>
> Subject: [scikit-image] Installation question about skimage developer
> version
> To: "scikit-image(a)python.org" <scikit-image(a)python.org>
> Message-ID: <AM6PR05MB4999E409E9F2D0A6FA8455808FCA0(a)AM6PR05MB4999.eur
> prd05.prod.outlook.com>
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>
> Dear scikit team,
>
> My name is Chien-Cheng and I am trying to use scikit-image to do some
> image segmentation.
> The method I choose is called "Training segmentation" (
> https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_trainable…
> ).
> Currently, I am trying to run the example of the training method and I got
> a few questions.
> The latest skimage version I have in the Anaconda is 0.17.2.
> But I found the training method seems only applicable for the developer
> version 0.19.0.dev0 since some of the attribute in the script is missing in
> the version of 0.17.2 (for example, there is an error called
> AttributeError: module 'skimage.feature' has no attribute
> 'multiscale_basic_features' after running the example script with 0.17.2).
> Also, I cannot find the example of the training method in the version of
> 0.17.2 so I figure the training methods require the developer version.
>
> I have tried installing the contributor/developer version in the conda
> environment by following the instruction (
> https://scikit-image.org/docs/dev/install.html#id6) but when I was
> running the third comment "conda install `for i in
> requirements/{default,build,test}.txt; do echo -n " --file $i "; done`. The
> console shows me the error below:
>
> [cid:image002.jpg@01D6CFE7.99D9EF80]
>
> I really don't know what the problem might be so I would like to turn to
> your help.
> There are two questions I have so far:
>
> 1. Do I need to install the developer version to use the training
> methods?
> 2. If so, did I install the developer version with the correct
> procedure? If yes, how to solve the issue I have above?
>
> Looking forward to your reply, thanks!
>
> Best regards,
> Chien-Cheng
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
Dear scikit team,
My name is Chien-Cheng and I am trying to use scikit-image to do some image segmentation.
The method I choose is called "Training segmentation" (https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_trainable…).
Currently, I am trying to run the example of the training method and I got a few questions.
The latest skimage version I have in the Anaconda is 0.17.2.
But I found the training method seems only applicable for the developer version 0.19.0.dev0 since some of the attribute in the script is missing in the version of 0.17.2 (for example, there is an error called AttributeError: module 'skimage.feature' has no attribute 'multiscale_basic_features' after running the example script with 0.17.2). Also, I cannot find the example of the training method in the version of 0.17.2 so I figure the training methods require the developer version.
I have tried installing the contributor/developer version in the conda environment by following the instruction (https://scikit-image.org/docs/dev/install.html#id6) but when I was running the third comment "conda install `for i in requirements/{default,build,test}.txt; do echo -n " --file $i "; done`. The console shows me the error below:
[cid:image002.jpg@01D6CFE7.99D9EF80]
I really don't know what the problem might be so I would like to turn to your help.
There are two questions I have so far:
1. Do I need to install the developer version to use the training methods?
2. If so, did I install the developer version with the correct procedure? If yes, how to solve the issue I have above?
Looking forward to your reply, thanks!
Best regards,
Chien-Cheng
Hello,
Although Smil is not part os Scikit-image, it may be of interest to some of you.
I'm glad to announce the release 0.11.0 of Smil, our Mathematical Morphology library.
Smil is a library completely written in C++ with a Python interface (thanks to Swig).
This is maintenance release with some few changes.
Smil is the successor of other Mathematical Morphology software we created since the 70s : Xlim, Xlim3D, Micromorph,
Morph-M and now Smil. Mathematical Morphology is a discipline created at our research department in the 60s by Georges
Matheron and Jean Serra.
The web site of the library is :
http://smil.cmm.mines-paristech.fr
You can have have a quick global view of what functions are available inside Smil here :
http://smil.cmm.mines-paristech.fr/publis/SmilQuickReference.pdf
Release notes here :
http://smil.cmm.mines-paristech.fr/doc/_release_notes.html
Feel free to make comments or give some feedback.
Best regards
--
---------------------------------------------------------------
Jose Marcio MARTINS DA CRUZ, Ph.D.
Ecole des Mines de Paris
Centre de Morphologie Mathématique
---------------------------------------------------------------
Spam : http://amzn.to/LEscRu ou http://bit.ly/SpamJM
---------------------------------------------------------------
Hello,
Although Smil is not part os Scikit-image, it may be of interest to some of you.
I'm glad to announce the release 0.11.0 of Smil, our Mathematical Morphology library.
Smil is a library completely written in C++ with a Python interface (thanks to Swig).
This is maintenance release with some few changes.
Smil is the successor of other Mathematical Morphology software we created since the 70s : Xlim, Xlim3D, Micromorph,
Morph-M and now Smil. Mathematical Morphology is a discipline created at our research department in the 60s by Georges
Matheron and Jean Serra.
The web site of the library is :
http://smil.cmm.mines-paristech.fr
You can have have a quick global view of what functions are available inside Smil here :
http://smil.cmm.mines-paristech.fr/publis/SmilQuickReference.pdf
Release notes here :
http://smil.cmm.mines-paristech.fr/doc/_release_notes.html
Feel free to make comments or give some feedback.
Best regards
--
---------------------------------------------------------------
Jose Marcio MARTINS DA CRUZ, Ph.D.
Ecole des Mines de Paris
Centre de Morphologie Mathématique
---------------------------------------------------------------
Spam : http://amzn.to/LEscRu ou http://bit.ly/SpamJM
---------------------------------------------------------------
