automatic import of python-mahotas

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+/mahotas-1.4.13.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-mahotas
+Version:	1.4.13
+Release:	1
+Summary:	Mahotas: Computer Vision Library
+License:	MIT
+URL:		http://luispedro.org/software/mahotas
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ca/d7/072f0bba098df3acc8d2db25ae6dcb3d46882d241369038a0f8fe64b6702/mahotas-1.4.13.tar.gz
+
+
+%description
+# Mahotas
+
+## Python Computer Vision Library
+
+Mahotas is a library of fast computer vision algorithms (all implemented
+in C++ for speed) operating over numpy arrays.
+
+![GH Actions Status](https://github.com/luispedro/mahotas/workflows/Python%20Package%20using%20Conda/badge.svg)
+[![Coverage Status](https://coveralls.io/repos/github/luispedro/mahotas/badge.svg?branch=master)](https://coveralls.io/github/luispedro/mahotas?branch=master)
+[![License](https://img.shields.io/badge/License-MIT-blue)](http://opensource.org/licenses/MIT)
+[![Downloads](https://pepy.tech/badge/mahotas/month)](https://pepy.tech/project/mahotas/month)
+[![Install with Conda](https://anaconda.org/conda-forge/mahotas/badges/downloads.svg)](https://anaconda.org/conda-forge/mahotas)
+[![Install with Anaconda](https://anaconda.org/conda-forge/mahotas/badges/installer/conda.svg)](https://anaconda.org/conda-forge/mahotas)
+
+Python versions 2.7, 3.4+, are supported.
+
+Notable algorithms:
+
+- [watershed](http://mahotas.readthedocs.io/en/latest/distance.html)
+- [convex points calculations](http://mahotas.readthedocs.io/en/latest/polygon.html).
+- hit & miss, thinning.
+- Zernike & Haralick, LBP, and TAS features.
+- [Speeded-Up Robust Features
+  (SURF)](http://mahotas.readthedocs.io/en/latest/surf.html), a form of local
+  features.
+- [thresholding](http://mahotas.readthedocs.io/en/latest/thresholding.html).
+- convolution.
+- Sobel edge detection.
+- spline interpolation
+- SLIC super pixels.
+
+Mahotas currently has over 100 functions for image processing and
+computer vision and it keeps growing.
+
+The release schedule is roughly one release a month and each release
+brings new functionality and improved performance. The interface is very
+stable, though, and code written using a version of mahotas from years
+back will work just fine in the current version, except it will be
+faster (some interfaces are deprecated and will be removed after a few
+years, but in the meanwhile, you only get a warning). In a few
+unfortunate cases, there was a bug in the old code and your results will
+change for the better.
+
+Please cite [the mahotas paper](http://dx.doi.org/10.5334/jors.ac) (see
+details below under [Citation](#Citation)) if you use it in a publication.
+
+## Examples
+
+This is a simple example (using an example file that is shipped with
+mahotas) of calling watershed using above threshold regions as a seed
+(we use Otsu to define threshold).
+
+    # import using ``mh`` abbreviation which is common:
+    import mahotas as mh
+
+    # Load one of the demo images
+    im = mh.demos.load('nuclear')
+
+    # Automatically compute a threshold
+    T_otsu = mh.thresholding.otsu(im)
+
+    # Label the thresholded image (thresholding is done with numpy operations
+    seeds,nr_regions = mh.label(im > T_otsu)
+
+    # Call seeded watershed to expand the threshold
+    labeled = mh.cwatershed(im.max() - im, seeds)
+
+Here is a very simple example of using `mahotas.distance` (which
+computes a distance map):
+
+    import pylab as p
+    import numpy as np
+    import mahotas as mh
+
+    f = np.ones((256,256), bool)
+    f[200:,240:] = False
+    f[128:144,32:48] = False
+    # f is basically True with the exception of two islands: one in the lower-right
+    # corner, another, middle-left
+
+    dmap = mh.distance(f)
+    p.imshow(dmap)
+    p.show()
+
+(This is under [mahotas/demos/distance.py](https://github.com/luispedro/mahotas/blob/master/mahotas/demos/distance.py).)
+
+How to invoke thresholding functions:
+
+    import mahotas as mh
+    import numpy as np
+    from pylab import imshow, gray, show, subplot
+    from os import path
+
+    # Load photo of mahotas' author in greyscale
+    photo = mh.demos.load('luispedro', as_grey=True)
+
+    # Convert to integer values (using numpy operations)
+    photo = photo.astype(np.uint8)
+
+    # Compute Otsu threshold
+    T_otsu = mh.otsu(photo)
+    thresholded_otsu = (photo > T_otsu)
+
+    # Compute Riddler-Calvard threshold
+    T_rc = mh.rc(photo)
+    thresholded_rc = (photo > T_rc)
+
+    # Now call pylab functions to display the image
+    gray()
+    subplot(2,1,1)
+    imshow(thresholded_otsu)
+    subplot(2,1,2)
+    imshow(thresholded_rc)
+    show()
+
+As you can see, we rely on numpy/matplotlib for many operations.
+
+## Install
+
+If you are using [conda](http://anaconda.org/), you can install mahotas from
+[conda-forge](https://conda-forge.github.io/) using the following commands:
+
+    conda config --add channels conda-forge
+    conda install mahotas
+
+### Compilation from source
+
+You will need python (naturally), numpy, and a C++ compiler. Then you
+should be able to use:
+
+    pip install mahotas
+
+You can test your installation by running:
+
+    python -c "import mahotas as mh; mh.test()"
+
+If you run into issues, the manual has more [extensive documentation on
+mahotas
+installation](https://mahotas.readthedocs.io/en/latest/install.html),
+including how to find pre-built for several platforms.
+
+## Citation
+
+If you use mahotas on a published publication, please cite:
+
+> **Luis Pedro Coelho** Mahotas: Open source software for scriptable
+> computer vision in Journal of Open Research Software, vol 1, 2013.
+> [[DOI](http://dx.doi.org/10.5334/jors.ac)]
+
+In Bibtex format:
+
+>   @article{mahotas,
+>       author = {Luis Pedro Coelho},
+>       title = {Mahotas: Open source software for scriptable computer vision},
+>       journal = {Journal of Open Research Software},
+>       year = {2013},
+>       doi = {http://dx.doi.org/10.5334/jors.ac},
+>       month = {July},
+>       volume = {1}
+>   }
+
+You can access this information using the `mahotas.citation()` function.
+
+## Development
+
+Development happens on github
+([http://github.com/luispedro/mahotas](https://github.com/luispedro/mahotas)).
+
+You can set the `DEBUG` environment variable before compilation to get a
+debug version:
+
+    export DEBUG=1
+    python setup.py test
+
+You can set it to the value `2` to get extra checks:
+
+    export DEBUG=2
+    python setup.py test
+
+Be careful not to use this in production unless you are chasing a bug.
+Debug level 2 is very slow as it adds many runtime checks.
+
+The `Makefile` that is shipped with the source of mahotas can be useful
+too. `make debug` will create a debug build. `make fast` will create a
+non-debug build (you need to `make clean` in between). `make test` will
+run the test suite.
+
+## Links & Contacts
+
+*Documentation*:
+[https://mahotas.readthedocs.io/](https://mahotas.readthedocs.io/)
+
+*Issue Tracker*: [github mahotas
+issues](https://github.com/luispedro/mahotas/issues)
+
+*Mailing List*: Use the [pythonvision mailing
+list](http://groups.google.com/group/pythonvision?pli=1) for questions,
+bug submissions, etc. Or ask on [stackoverflow (tag
+mahotas)](http://stackoverflow.com/questions/tagged/mahotas)
+
+*Main Author & Maintainer*: [Luis Pedro Coelho](http://luispedro.org)
+(follow on [twitter](https://twitter.com/luispedrocoelho) or
+[github](https://github.com/luispedro)).
+
+Mahotas also includes code by Zachary Pincus [from scikits.image], Peter
+J. Verveer [from scipy.ndimage], and Davis King [from dlib], Christoph
+Gohlke, as well as
+[others](https://github.com/luispedro/mahotas/graphs/contributors).
+
+[Presentation about mahotas for bioimage
+informatics](http://luispedro.org/files/talks/2013/EuBIAS/mahotas.html)
+
+For more general discussion of computer vision in Python, the
+[pythonvision mailing
+list](http://groups.google.com/group/pythonvision?pli=1) is a much
+better venue and generates a public discussion log for others in the
+future. You can use it for mahotas or general computer vision in Python
+questions.
+
+## Recent Changes
+
+### Version 1.4.13 (Jun 28 2022)
+
+- Fix freeimage testing (and make freeimage loading more robust, see #129)
+- Add GIL fixed (which triggered crashes in newer NumPy versions)
+
+### Version 1.4.12 (Oct 14 2021)
+
+- Update to newer NumPy
+- Build wheels for Python 3.9 & 3.10
+
+### Version 1.4.11 (Aug 16 2020)
+
+- Convert tests to pytest
+- Fix testing for PyPy
+
+### Version 1.4.10 (Jun 11 2020)
+
+- Build wheels automatically (PR #114 by [nathanhillyer](https://github.com/nathanhillyer))
+
+### Version 1.4.9 (Nov 12 2019)
+
+- Fix FreeImage detection (issue #108)
+
+### Version 1.4.8 (Oct 11 2019)
+
+- Fix co-occurrence matrix computation (patch by @databaaz)
+
+### Version 1.4.7 (Jul 10 2019)
+
+- Fix compilation on Windows
+
+### Version 1.4.6 (Jul 10 2019)
+
+- Make watershed work for >2³¹ voxels (issue #102)
+- Remove milk from demos
+- Improve performance by avoid unnecessary array copies in `cwatershed()`,
+  `majority_filter()`, and color conversions
+- Fix bug in interpolation
+
+### Version 1.4.5 (Oct 20 2018)
+- Upgrade code to newer NumPy API (issue #95)
+
+### Version 1.4.4 (Nov 5 2017)
+- Fix bug in Bernsen thresholding (issue #84)
+
+### Version 1.4.3 (Oct 3 2016)
+- Fix distribution (add missing `README.md` file)
+
+### Version 1.4.2 (Oct 2 2016)
+
+- Fix `resize\_to` return exactly the requested size
+- Fix hard crash when computing texture on arrays with negative values (issue #72)
+- Added `distance` argument to haralick features (pull request #76, by
+  Guillaume Lemaitre)
+
+### Version 1.4.1 (Dec 20 2015)
+
+-   Add `filter\_labeled` function
+-   Fix tests on 32 bit platforms and older versions of numpy
+
+### Version 1.4.0 (July 8 2015)
+
+-   Added `mahotas-features.py` script
+-   Add short argument to citation() function
+-   Add max\_iter argument to thin() function
+-   Fixed labeled.bbox when there is no background (issue \#61, reported
+    by Daniel Haehn)
+-   bbox now allows dimensions greater than 2 (including when using the
+    `as_slice` and `border` arguments)
+-   Extended croptobbox for dimensions greater than 2
+-   Added use\_x\_minus\_y\_variance option to haralick features
+-   Add function `lbp_names`
+
+### Version 1.3.0 (April 28 2015)
+
+-   Improve memory handling in freeimage.write\_multipage
+-   Fix moments parameter swap
+-   Add labeled.bbox function
+-   Add return\_mean and return\_mean\_ptp arguments to haralick
+    function
+-   Add difference of Gaussians filter (by Jianyu Wang)
+-   Add Laplacian filter (by Jianyu Wang)
+-   Fix crash in median\_filter when mismatched arguments are passed
+-   Fix gaussian\_filter1d for ndim \> 2
+
+### Version 1.2.4 (December 23 2014)
+
+-   Add PIL based IO
+
+### Version 1.2.3 (November 8 2014)
+
+-   Export mean\_filter at top level
+-   Fix to Zernike moments computation (reported by Sergey Demurin)
+-   Fix compilation in platforms without npy\_float128 (patch by Gabi
+    Davar)
+
+### Version 1.2.2 (October 19 2014)
+
+-   Add minlength argument to labeled\_sum
+-   Generalize regmax/regmin to work with floating point images
+-   Allow floating point inputs to `cwatershed()`
+-   Correctly check for float16 & float128 inputs
+-   Make sobel into a pure function (i.e., do not normalize its input)
+-   Fix sobel filtering
+
+### Version 1.2.1 (July 21 2014)
+
+-   Explicitly set numpy.include\_dirs() in setup.py [patch by Andrew
+    Stromnov]
+
+### Version 1.2 (July 17 2014)
+
+-   Export locmax|locmin at the mahotas namespace level
+-   Break away ellipse\_axes from eccentricity code as it can be useful
+    on its own
+-   Add `find()` function
+-   Add `mean_filter()` function
+-   Fix `cwatershed()` overflow possibility
+-   Make labeled functions more flexible in accepting more types
+-   Fix crash in `close_holes()` with nD images (for n \> 2)
+-   Remove matplotlibwrap
+-   Use standard setuptools for building (instead of numpy.distutils)
+-   Add `overlay()` function
+
+### Version 1.1.1 (July 4 2014)
+
+-   Fix crash in close\_holes() with nD images (for n \> 2)
+
+### 1.1.0 (February 12 2014)
+
+-   Better error checking
+-   Fix interpolation of integer images using order 1
+-   Add resize\_to & resize\_rgb\_to
+-   Add coveralls coverage
+-   Fix SLIC superpixels connectivity
+-   Add remove\_regions\_where function
+-   Fix hard crash in convolution
+-   Fix axis handling in convolve1d
+-   Add normalization to moments calculation
+
+See the
+[ChangeLog](https://github.com/luispedro/mahotas/blob/master/ChangeLog)
+for older version.
+
+
+## License
+[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas?ref=badge_large)
+
+
+
+
+%package -n python3-mahotas
+Summary:	Mahotas: Computer Vision Library
+Provides:	python-mahotas
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-mahotas
+# Mahotas
+
+## Python Computer Vision Library
+
+Mahotas is a library of fast computer vision algorithms (all implemented
+in C++ for speed) operating over numpy arrays.
+
+![GH Actions Status](https://github.com/luispedro/mahotas/workflows/Python%20Package%20using%20Conda/badge.svg)
+[![Coverage Status](https://coveralls.io/repos/github/luispedro/mahotas/badge.svg?branch=master)](https://coveralls.io/github/luispedro/mahotas?branch=master)
+[![License](https://img.shields.io/badge/License-MIT-blue)](http://opensource.org/licenses/MIT)
+[![Downloads](https://pepy.tech/badge/mahotas/month)](https://pepy.tech/project/mahotas/month)
+[![Install with Conda](https://anaconda.org/conda-forge/mahotas/badges/downloads.svg)](https://anaconda.org/conda-forge/mahotas)
+[![Install with Anaconda](https://anaconda.org/conda-forge/mahotas/badges/installer/conda.svg)](https://anaconda.org/conda-forge/mahotas)
+
+Python versions 2.7, 3.4+, are supported.
+
+Notable algorithms:
+
+- [watershed](http://mahotas.readthedocs.io/en/latest/distance.html)
+- [convex points calculations](http://mahotas.readthedocs.io/en/latest/polygon.html).
+- hit & miss, thinning.
+- Zernike & Haralick, LBP, and TAS features.
+- [Speeded-Up Robust Features
+  (SURF)](http://mahotas.readthedocs.io/en/latest/surf.html), a form of local
+  features.
+- [thresholding](http://mahotas.readthedocs.io/en/latest/thresholding.html).
+- convolution.
+- Sobel edge detection.
+- spline interpolation
+- SLIC super pixels.
+
+Mahotas currently has over 100 functions for image processing and
+computer vision and it keeps growing.
+
+The release schedule is roughly one release a month and each release
+brings new functionality and improved performance. The interface is very
+stable, though, and code written using a version of mahotas from years
+back will work just fine in the current version, except it will be
+faster (some interfaces are deprecated and will be removed after a few
+years, but in the meanwhile, you only get a warning). In a few
+unfortunate cases, there was a bug in the old code and your results will
+change for the better.
+
+Please cite [the mahotas paper](http://dx.doi.org/10.5334/jors.ac) (see
+details below under [Citation](#Citation)) if you use it in a publication.
+
+## Examples
+
+This is a simple example (using an example file that is shipped with
+mahotas) of calling watershed using above threshold regions as a seed
+(we use Otsu to define threshold).
+
+    # import using ``mh`` abbreviation which is common:
+    import mahotas as mh
+
+    # Load one of the demo images
+    im = mh.demos.load('nuclear')
+
+    # Automatically compute a threshold
+    T_otsu = mh.thresholding.otsu(im)
+
+    # Label the thresholded image (thresholding is done with numpy operations
+    seeds,nr_regions = mh.label(im > T_otsu)
+
+    # Call seeded watershed to expand the threshold
+    labeled = mh.cwatershed(im.max() - im, seeds)
+
+Here is a very simple example of using `mahotas.distance` (which
+computes a distance map):
+
+    import pylab as p
+    import numpy as np
+    import mahotas as mh
+
+    f = np.ones((256,256), bool)
+    f[200:,240:] = False
+    f[128:144,32:48] = False
+    # f is basically True with the exception of two islands: one in the lower-right
+    # corner, another, middle-left
+
+    dmap = mh.distance(f)
+    p.imshow(dmap)
+    p.show()
+
+(This is under [mahotas/demos/distance.py](https://github.com/luispedro/mahotas/blob/master/mahotas/demos/distance.py).)
+
+How to invoke thresholding functions:
+
+    import mahotas as mh
+    import numpy as np
+    from pylab import imshow, gray, show, subplot
+    from os import path
+
+    # Load photo of mahotas' author in greyscale
+    photo = mh.demos.load('luispedro', as_grey=True)
+
+    # Convert to integer values (using numpy operations)
+    photo = photo.astype(np.uint8)
+
+    # Compute Otsu threshold
+    T_otsu = mh.otsu(photo)
+    thresholded_otsu = (photo > T_otsu)
+
+    # Compute Riddler-Calvard threshold
+    T_rc = mh.rc(photo)
+    thresholded_rc = (photo > T_rc)
+
+    # Now call pylab functions to display the image
+    gray()
+    subplot(2,1,1)
+    imshow(thresholded_otsu)
+    subplot(2,1,2)
+    imshow(thresholded_rc)
+    show()
+
+As you can see, we rely on numpy/matplotlib for many operations.
+
+## Install
+
+If you are using [conda](http://anaconda.org/), you can install mahotas from
+[conda-forge](https://conda-forge.github.io/) using the following commands:
+
+    conda config --add channels conda-forge
+    conda install mahotas
+
+### Compilation from source
+
+You will need python (naturally), numpy, and a C++ compiler. Then you
+should be able to use:
+
+    pip install mahotas
+
+You can test your installation by running:
+
+    python -c "import mahotas as mh; mh.test()"
+
+If you run into issues, the manual has more [extensive documentation on
+mahotas
+installation](https://mahotas.readthedocs.io/en/latest/install.html),
+including how to find pre-built for several platforms.
+
+## Citation
+
+If you use mahotas on a published publication, please cite:
+
+> **Luis Pedro Coelho** Mahotas: Open source software for scriptable
+> computer vision in Journal of Open Research Software, vol 1, 2013.
+> [[DOI](http://dx.doi.org/10.5334/jors.ac)]
+
+In Bibtex format:
+
+>   @article{mahotas,
+>       author = {Luis Pedro Coelho},
+>       title = {Mahotas: Open source software for scriptable computer vision},
+>       journal = {Journal of Open Research Software},
+>       year = {2013},
+>       doi = {http://dx.doi.org/10.5334/jors.ac},
+>       month = {July},
+>       volume = {1}
+>   }
+
+You can access this information using the `mahotas.citation()` function.
+
+## Development
+
+Development happens on github
+([http://github.com/luispedro/mahotas](https://github.com/luispedro/mahotas)).
+
+You can set the `DEBUG` environment variable before compilation to get a
+debug version:
+
+    export DEBUG=1
+    python setup.py test
+
+You can set it to the value `2` to get extra checks:
+
+    export DEBUG=2
+    python setup.py test
+
+Be careful not to use this in production unless you are chasing a bug.
+Debug level 2 is very slow as it adds many runtime checks.
+
+The `Makefile` that is shipped with the source of mahotas can be useful
+too. `make debug` will create a debug build. `make fast` will create a
+non-debug build (you need to `make clean` in between). `make test` will
+run the test suite.
+
+## Links & Contacts
+
+*Documentation*:
+[https://mahotas.readthedocs.io/](https://mahotas.readthedocs.io/)
+
+*Issue Tracker*: [github mahotas
+issues](https://github.com/luispedro/mahotas/issues)
+
+*Mailing List*: Use the [pythonvision mailing
+list](http://groups.google.com/group/pythonvision?pli=1) for questions,
+bug submissions, etc. Or ask on [stackoverflow (tag
+mahotas)](http://stackoverflow.com/questions/tagged/mahotas)
+
+*Main Author & Maintainer*: [Luis Pedro Coelho](http://luispedro.org)
+(follow on [twitter](https://twitter.com/luispedrocoelho) or
+[github](https://github.com/luispedro)).
+
+Mahotas also includes code by Zachary Pincus [from scikits.image], Peter
+J. Verveer [from scipy.ndimage], and Davis King [from dlib], Christoph
+Gohlke, as well as
+[others](https://github.com/luispedro/mahotas/graphs/contributors).
+
+[Presentation about mahotas for bioimage
+informatics](http://luispedro.org/files/talks/2013/EuBIAS/mahotas.html)
+
+For more general discussion of computer vision in Python, the
+[pythonvision mailing
+list](http://groups.google.com/group/pythonvision?pli=1) is a much
+better venue and generates a public discussion log for others in the
+future. You can use it for mahotas or general computer vision in Python
+questions.
+
+## Recent Changes
+
+### Version 1.4.13 (Jun 28 2022)
+
+- Fix freeimage testing (and make freeimage loading more robust, see #129)
+- Add GIL fixed (which triggered crashes in newer NumPy versions)
+
+### Version 1.4.12 (Oct 14 2021)
+
+- Update to newer NumPy
+- Build wheels for Python 3.9 & 3.10
+
+### Version 1.4.11 (Aug 16 2020)
+
+- Convert tests to pytest
+- Fix testing for PyPy
+
+### Version 1.4.10 (Jun 11 2020)
+
+- Build wheels automatically (PR #114 by [nathanhillyer](https://github.com/nathanhillyer))
+
+### Version 1.4.9 (Nov 12 2019)
+
+- Fix FreeImage detection (issue #108)
+
+### Version 1.4.8 (Oct 11 2019)
+
+- Fix co-occurrence matrix computation (patch by @databaaz)
+
+### Version 1.4.7 (Jul 10 2019)
+
+- Fix compilation on Windows
+
+### Version 1.4.6 (Jul 10 2019)
+
+- Make watershed work for >2³¹ voxels (issue #102)
+- Remove milk from demos
+- Improve performance by avoid unnecessary array copies in `cwatershed()`,
+  `majority_filter()`, and color conversions
+- Fix bug in interpolation
+
+### Version 1.4.5 (Oct 20 2018)
+- Upgrade code to newer NumPy API (issue #95)
+
+### Version 1.4.4 (Nov 5 2017)
+- Fix bug in Bernsen thresholding (issue #84)
+
+### Version 1.4.3 (Oct 3 2016)
+- Fix distribution (add missing `README.md` file)
+
+### Version 1.4.2 (Oct 2 2016)
+
+- Fix `resize\_to` return exactly the requested size
+- Fix hard crash when computing texture on arrays with negative values (issue #72)
+- Added `distance` argument to haralick features (pull request #76, by
+  Guillaume Lemaitre)
+
+### Version 1.4.1 (Dec 20 2015)
+
+-   Add `filter\_labeled` function
+-   Fix tests on 32 bit platforms and older versions of numpy
+
+### Version 1.4.0 (July 8 2015)
+
+-   Added `mahotas-features.py` script
+-   Add short argument to citation() function
+-   Add max\_iter argument to thin() function
+-   Fixed labeled.bbox when there is no background (issue \#61, reported
+    by Daniel Haehn)
+-   bbox now allows dimensions greater than 2 (including when using the
+    `as_slice` and `border` arguments)
+-   Extended croptobbox for dimensions greater than 2
+-   Added use\_x\_minus\_y\_variance option to haralick features
+-   Add function `lbp_names`
+
+### Version 1.3.0 (April 28 2015)
+
+-   Improve memory handling in freeimage.write\_multipage
+-   Fix moments parameter swap
+-   Add labeled.bbox function
+-   Add return\_mean and return\_mean\_ptp arguments to haralick
+    function
+-   Add difference of Gaussians filter (by Jianyu Wang)
+-   Add Laplacian filter (by Jianyu Wang)
+-   Fix crash in median\_filter when mismatched arguments are passed
+-   Fix gaussian\_filter1d for ndim \> 2
+
+### Version 1.2.4 (December 23 2014)
+
+-   Add PIL based IO
+
+### Version 1.2.3 (November 8 2014)
+
+-   Export mean\_filter at top level
+-   Fix to Zernike moments computation (reported by Sergey Demurin)
+-   Fix compilation in platforms without npy\_float128 (patch by Gabi
+    Davar)
+
+### Version 1.2.2 (October 19 2014)
+
+-   Add minlength argument to labeled\_sum
+-   Generalize regmax/regmin to work with floating point images
+-   Allow floating point inputs to `cwatershed()`
+-   Correctly check for float16 & float128 inputs
+-   Make sobel into a pure function (i.e., do not normalize its input)
+-   Fix sobel filtering
+
+### Version 1.2.1 (July 21 2014)
+
+-   Explicitly set numpy.include\_dirs() in setup.py [patch by Andrew
+    Stromnov]
+
+### Version 1.2 (July 17 2014)
+
+-   Export locmax|locmin at the mahotas namespace level
+-   Break away ellipse\_axes from eccentricity code as it can be useful
+    on its own
+-   Add `find()` function
+-   Add `mean_filter()` function
+-   Fix `cwatershed()` overflow possibility
+-   Make labeled functions more flexible in accepting more types
+-   Fix crash in `close_holes()` with nD images (for n \> 2)
+-   Remove matplotlibwrap
+-   Use standard setuptools for building (instead of numpy.distutils)
+-   Add `overlay()` function
+
+### Version 1.1.1 (July 4 2014)
+
+-   Fix crash in close\_holes() with nD images (for n \> 2)
+
+### 1.1.0 (February 12 2014)
+
+-   Better error checking
+-   Fix interpolation of integer images using order 1
+-   Add resize\_to & resize\_rgb\_to
+-   Add coveralls coverage
+-   Fix SLIC superpixels connectivity
+-   Add remove\_regions\_where function
+-   Fix hard crash in convolution
+-   Fix axis handling in convolve1d
+-   Add normalization to moments calculation
+
+See the
+[ChangeLog](https://github.com/luispedro/mahotas/blob/master/ChangeLog)
+for older version.
+
+
+## License
+[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas?ref=badge_large)
+
+
+
+
+%package help
+Summary:	Development documents and examples for mahotas
+Provides:	python3-mahotas-doc
+%description help
+# Mahotas
+
+## Python Computer Vision Library
+
+Mahotas is a library of fast computer vision algorithms (all implemented
+in C++ for speed) operating over numpy arrays.
+
+![GH Actions Status](https://github.com/luispedro/mahotas/workflows/Python%20Package%20using%20Conda/badge.svg)
+[![Coverage Status](https://coveralls.io/repos/github/luispedro/mahotas/badge.svg?branch=master)](https://coveralls.io/github/luispedro/mahotas?branch=master)
+[![License](https://img.shields.io/badge/License-MIT-blue)](http://opensource.org/licenses/MIT)
+[![Downloads](https://pepy.tech/badge/mahotas/month)](https://pepy.tech/project/mahotas/month)
+[![Install with Conda](https://anaconda.org/conda-forge/mahotas/badges/downloads.svg)](https://anaconda.org/conda-forge/mahotas)
+[![Install with Anaconda](https://anaconda.org/conda-forge/mahotas/badges/installer/conda.svg)](https://anaconda.org/conda-forge/mahotas)
+
+Python versions 2.7, 3.4+, are supported.
+
+Notable algorithms:
+
+- [watershed](http://mahotas.readthedocs.io/en/latest/distance.html)
+- [convex points calculations](http://mahotas.readthedocs.io/en/latest/polygon.html).
+- hit & miss, thinning.
+- Zernike & Haralick, LBP, and TAS features.
+- [Speeded-Up Robust Features
+  (SURF)](http://mahotas.readthedocs.io/en/latest/surf.html), a form of local
+  features.
+- [thresholding](http://mahotas.readthedocs.io/en/latest/thresholding.html).
+- convolution.
+- Sobel edge detection.
+- spline interpolation
+- SLIC super pixels.
+
+Mahotas currently has over 100 functions for image processing and
+computer vision and it keeps growing.
+
+The release schedule is roughly one release a month and each release
+brings new functionality and improved performance. The interface is very
+stable, though, and code written using a version of mahotas from years
+back will work just fine in the current version, except it will be
+faster (some interfaces are deprecated and will be removed after a few
+years, but in the meanwhile, you only get a warning). In a few
+unfortunate cases, there was a bug in the old code and your results will
+change for the better.
+
+Please cite [the mahotas paper](http://dx.doi.org/10.5334/jors.ac) (see
+details below under [Citation](#Citation)) if you use it in a publication.
+
+## Examples
+
+This is a simple example (using an example file that is shipped with
+mahotas) of calling watershed using above threshold regions as a seed
+(we use Otsu to define threshold).
+
+    # import using ``mh`` abbreviation which is common:
+    import mahotas as mh
+
+    # Load one of the demo images
+    im = mh.demos.load('nuclear')
+
+    # Automatically compute a threshold
+    T_otsu = mh.thresholding.otsu(im)
+
+    # Label the thresholded image (thresholding is done with numpy operations
+    seeds,nr_regions = mh.label(im > T_otsu)
+
+    # Call seeded watershed to expand the threshold
+    labeled = mh.cwatershed(im.max() - im, seeds)
+
+Here is a very simple example of using `mahotas.distance` (which
+computes a distance map):
+
+    import pylab as p
+    import numpy as np
+    import mahotas as mh
+
+    f = np.ones((256,256), bool)
+    f[200:,240:] = False
+    f[128:144,32:48] = False
+    # f is basically True with the exception of two islands: one in the lower-right
+    # corner, another, middle-left
+
+    dmap = mh.distance(f)
+    p.imshow(dmap)
+    p.show()
+
+(This is under [mahotas/demos/distance.py](https://github.com/luispedro/mahotas/blob/master/mahotas/demos/distance.py).)
+
+How to invoke thresholding functions:
+
+    import mahotas as mh
+    import numpy as np
+    from pylab import imshow, gray, show, subplot
+    from os import path
+
+    # Load photo of mahotas' author in greyscale
+    photo = mh.demos.load('luispedro', as_grey=True)
+
+    # Convert to integer values (using numpy operations)
+    photo = photo.astype(np.uint8)
+
+    # Compute Otsu threshold
+    T_otsu = mh.otsu(photo)
+    thresholded_otsu = (photo > T_otsu)
+
+    # Compute Riddler-Calvard threshold
+    T_rc = mh.rc(photo)
+    thresholded_rc = (photo > T_rc)
+
+    # Now call pylab functions to display the image
+    gray()
+    subplot(2,1,1)
+    imshow(thresholded_otsu)
+    subplot(2,1,2)
+    imshow(thresholded_rc)
+    show()
+
+As you can see, we rely on numpy/matplotlib for many operations.
+
+## Install
+
+If you are using [conda](http://anaconda.org/), you can install mahotas from
+[conda-forge](https://conda-forge.github.io/) using the following commands:
+
+    conda config --add channels conda-forge
+    conda install mahotas
+
+### Compilation from source
+
+You will need python (naturally), numpy, and a C++ compiler. Then you
+should be able to use:
+
+    pip install mahotas
+
+You can test your installation by running:
+
+    python -c "import mahotas as mh; mh.test()"
+
+If you run into issues, the manual has more [extensive documentation on
+mahotas
+installation](https://mahotas.readthedocs.io/en/latest/install.html),
+including how to find pre-built for several platforms.
+
+## Citation
+
+If you use mahotas on a published publication, please cite:
+
+> **Luis Pedro Coelho** Mahotas: Open source software for scriptable
+> computer vision in Journal of Open Research Software, vol 1, 2013.
+> [[DOI](http://dx.doi.org/10.5334/jors.ac)]
+
+In Bibtex format:
+
+>   @article{mahotas,
+>       author = {Luis Pedro Coelho},
+>       title = {Mahotas: Open source software for scriptable computer vision},
+>       journal = {Journal of Open Research Software},
+>       year = {2013},
+>       doi = {http://dx.doi.org/10.5334/jors.ac},
+>       month = {July},
+>       volume = {1}
+>   }
+
+You can access this information using the `mahotas.citation()` function.
+
+## Development
+
+Development happens on github
+([http://github.com/luispedro/mahotas](https://github.com/luispedro/mahotas)).
+
+You can set the `DEBUG` environment variable before compilation to get a
+debug version:
+
+    export DEBUG=1
+    python setup.py test
+
+You can set it to the value `2` to get extra checks:
+
+    export DEBUG=2
+    python setup.py test
+
+Be careful not to use this in production unless you are chasing a bug.
+Debug level 2 is very slow as it adds many runtime checks.
+
+The `Makefile` that is shipped with the source of mahotas can be useful
+too. `make debug` will create a debug build. `make fast` will create a
+non-debug build (you need to `make clean` in between). `make test` will
+run the test suite.
+
+## Links & Contacts
+
+*Documentation*:
+[https://mahotas.readthedocs.io/](https://mahotas.readthedocs.io/)
+
+*Issue Tracker*: [github mahotas
+issues](https://github.com/luispedro/mahotas/issues)
+
+*Mailing List*: Use the [pythonvision mailing
+list](http://groups.google.com/group/pythonvision?pli=1) for questions,
+bug submissions, etc. Or ask on [stackoverflow (tag
+mahotas)](http://stackoverflow.com/questions/tagged/mahotas)
+
+*Main Author & Maintainer*: [Luis Pedro Coelho](http://luispedro.org)
+(follow on [twitter](https://twitter.com/luispedrocoelho) or
+[github](https://github.com/luispedro)).
+
+Mahotas also includes code by Zachary Pincus [from scikits.image], Peter
+J. Verveer [from scipy.ndimage], and Davis King [from dlib], Christoph
+Gohlke, as well as
+[others](https://github.com/luispedro/mahotas/graphs/contributors).
+
+[Presentation about mahotas for bioimage
+informatics](http://luispedro.org/files/talks/2013/EuBIAS/mahotas.html)
+
+For more general discussion of computer vision in Python, the
+[pythonvision mailing
+list](http://groups.google.com/group/pythonvision?pli=1) is a much
+better venue and generates a public discussion log for others in the
+future. You can use it for mahotas or general computer vision in Python
+questions.
+
+## Recent Changes
+
+### Version 1.4.13 (Jun 28 2022)
+
+- Fix freeimage testing (and make freeimage loading more robust, see #129)
+- Add GIL fixed (which triggered crashes in newer NumPy versions)
+
+### Version 1.4.12 (Oct 14 2021)
+
+- Update to newer NumPy
+- Build wheels for Python 3.9 & 3.10
+
+### Version 1.4.11 (Aug 16 2020)
+
+- Convert tests to pytest
+- Fix testing for PyPy
+
+### Version 1.4.10 (Jun 11 2020)
+
+- Build wheels automatically (PR #114 by [nathanhillyer](https://github.com/nathanhillyer))
+
+### Version 1.4.9 (Nov 12 2019)
+
+- Fix FreeImage detection (issue #108)
+
+### Version 1.4.8 (Oct 11 2019)
+
+- Fix co-occurrence matrix computation (patch by @databaaz)
+
+### Version 1.4.7 (Jul 10 2019)
+
+- Fix compilation on Windows
+
+### Version 1.4.6 (Jul 10 2019)
+
+- Make watershed work for >2³¹ voxels (issue #102)
+- Remove milk from demos
+- Improve performance by avoid unnecessary array copies in `cwatershed()`,
+  `majority_filter()`, and color conversions
+- Fix bug in interpolation
+
+### Version 1.4.5 (Oct 20 2018)
+- Upgrade code to newer NumPy API (issue #95)
+
+### Version 1.4.4 (Nov 5 2017)
+- Fix bug in Bernsen thresholding (issue #84)
+
+### Version 1.4.3 (Oct 3 2016)
+- Fix distribution (add missing `README.md` file)
+
+### Version 1.4.2 (Oct 2 2016)
+
+- Fix `resize\_to` return exactly the requested size
+- Fix hard crash when computing texture on arrays with negative values (issue #72)
+- Added `distance` argument to haralick features (pull request #76, by
+  Guillaume Lemaitre)
+
+### Version 1.4.1 (Dec 20 2015)
+
+-   Add `filter\_labeled` function
+-   Fix tests on 32 bit platforms and older versions of numpy
+
+### Version 1.4.0 (July 8 2015)
+
+-   Added `mahotas-features.py` script
+-   Add short argument to citation() function
+-   Add max\_iter argument to thin() function
+-   Fixed labeled.bbox when there is no background (issue \#61, reported
+    by Daniel Haehn)
+-   bbox now allows dimensions greater than 2 (including when using the
+    `as_slice` and `border` arguments)
+-   Extended croptobbox for dimensions greater than 2
+-   Added use\_x\_minus\_y\_variance option to haralick features
+-   Add function `lbp_names`
+
+### Version 1.3.0 (April 28 2015)
+
+-   Improve memory handling in freeimage.write\_multipage
+-   Fix moments parameter swap
+-   Add labeled.bbox function
+-   Add return\_mean and return\_mean\_ptp arguments to haralick
+    function
+-   Add difference of Gaussians filter (by Jianyu Wang)
+-   Add Laplacian filter (by Jianyu Wang)
+-   Fix crash in median\_filter when mismatched arguments are passed
+-   Fix gaussian\_filter1d for ndim \> 2
+
+### Version 1.2.4 (December 23 2014)
+
+-   Add PIL based IO
+
+### Version 1.2.3 (November 8 2014)
+
+-   Export mean\_filter at top level
+-   Fix to Zernike moments computation (reported by Sergey Demurin)
+-   Fix compilation in platforms without npy\_float128 (patch by Gabi
+    Davar)
+
+### Version 1.2.2 (October 19 2014)
+
+-   Add minlength argument to labeled\_sum
+-   Generalize regmax/regmin to work with floating point images
+-   Allow floating point inputs to `cwatershed()`
+-   Correctly check for float16 & float128 inputs
+-   Make sobel into a pure function (i.e., do not normalize its input)
+-   Fix sobel filtering
+
+### Version 1.2.1 (July 21 2014)
+
+-   Explicitly set numpy.include\_dirs() in setup.py [patch by Andrew
+    Stromnov]
+
+### Version 1.2 (July 17 2014)
+
+-   Export locmax|locmin at the mahotas namespace level
+-   Break away ellipse\_axes from eccentricity code as it can be useful
+    on its own
+-   Add `find()` function
+-   Add `mean_filter()` function
+-   Fix `cwatershed()` overflow possibility
+-   Make labeled functions more flexible in accepting more types
+-   Fix crash in `close_holes()` with nD images (for n \> 2)
+-   Remove matplotlibwrap
+-   Use standard setuptools for building (instead of numpy.distutils)
+-   Add `overlay()` function
+
+### Version 1.1.1 (July 4 2014)
+
+-   Fix crash in close\_holes() with nD images (for n \> 2)
+
+### 1.1.0 (February 12 2014)
+
+-   Better error checking
+-   Fix interpolation of integer images using order 1
+-   Add resize\_to & resize\_rgb\_to
+-   Add coveralls coverage
+-   Fix SLIC superpixels connectivity
+-   Add remove\_regions\_where function
+-   Fix hard crash in convolution
+-   Fix axis handling in convolve1d
+-   Add normalization to moments calculation
+
+See the
+[ChangeLog](https://github.com/luispedro/mahotas/blob/master/ChangeLog)
+for older version.
+
+
+## License
+[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fluispedro%2Fmahotas?ref=badge_large)
+
+
+
+
+%prep
+%autosetup -n mahotas-1.4.13
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-mahotas -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.13-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..e036bfb
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+a07199155370e1ed5d39c1597bafa1e8  mahotas-1.4.13.tar.gz