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@@ -0,0 +1 @@ +/mahotas-1.4.13.tar.gz diff --git a/python-mahotas.spec b/python-mahotas.spec new file mode 100644 index 0000000..a87a6ce --- /dev/null +++ b/python-mahotas.spec @@ -0,0 +1,1184 @@ +%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. + + +[](https://coveralls.io/github/luispedro/mahotas?branch=master) +[](http://opensource.org/licenses/MIT) +[](https://pepy.tech/project/mahotas/month) +[](https://anaconda.org/conda-forge/mahotas) +[](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 +[](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. + + +[](https://coveralls.io/github/luispedro/mahotas?branch=master) +[](http://opensource.org/licenses/MIT) +[](https://pepy.tech/project/mahotas/month) +[](https://anaconda.org/conda-forge/mahotas) +[](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 +[](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. + + +[](https://coveralls.io/github/luispedro/mahotas?branch=master) +[](http://opensource.org/licenses/MIT) +[](https://pepy.tech/project/mahotas/month) +[](https://anaconda.org/conda-forge/mahotas) +[](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 +[](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 @@ -0,0 +1 @@ +a07199155370e1ed5d39c1597bafa1e8 mahotas-1.4.13.tar.gz |