Metadata-Version: 1.1
Name: mahotas
Version: 0.9.6
Summary: Mahotas: Computer Vision Library
Home-page: http://luispedro.org/software/mahotas
Author: Luis Pedro Coelho
Author-email: luis@luispedro.org
License: MIT
Description: =======
        Mahotas
        =======
        Python Computer Vision Library
        ------------------------------
        
        This library of fast computer vision algorithms (all implemented in C++)
        operates over numpy arrays for convenience.
        
        Notable algorithms:
         - watershed.
         - convex points calculations.
         - hit & miss. thinning.
         - Zernike & Haralick, LBP, and TAS features.
         - freeimage based numpy image loading (requires freeimage libraries to be
           installed).
         - Speeded-Up Robust Features (SURF), a form of local features.
         - thresholding.
         - convolution.
         - Sobel edge detection.
         - spline interpolation
        
        Mahotas currently has over 100 functions for image processing and computer
        vision and it keeps growing.
        
        There is a `manuscript about mahotas <http://arxiv.org/abs/1211.4907>`__, which
        will hopefully evolve into a journal publication later.
        
        Examples
        --------
        
        This is a simple example of loading a file (called `test.jpeg`) and calling
        `watershed` using above threshold regions as a seed (we use Otsu to define
        threshold).
        
        ::
        
            import numpy as np
            from scipy import ndimage
            import mahotas
            import pylab
        
            img = mahotas.imread('test.jpeg')
            T_otsu = mahotas.thresholding.otsu(img)
            seeds,_ = ndimage.label(img > T_otsu)
            labeled = mahotas.cwatershed(img.max() - img, 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
        
            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 = mahotas.distance(f)
            p.imshow(dmap)
            p.show()
        
        (This is under ``mahotas/demos/distance.py``).
        
        How to invoke thresholding functions::
        
            import mahotas
            import numpy as np
            from pylab import imshow, gray, show, subplot
            from os import path
        
            photo = mahotas.imread('luispedro.org', as_grey=True)
            photo = photo.astype(np.uint8)
        
            T_otsu = mahotas.otsu(photo)
            thresholded_otsu = (photo > T_otsu)
        
            T_rc = mahotas.rc(photo)
            thresholded_rc = (photo > T_rc)
        
        
        Install
        -------
        
        You will need python (naturally), numpy, and a C++ compiler. Then you should be
        able to either
        
        Download the source and then run::
        
            python setup.py install
        
        or use one of::
        
            pip install mahotas
            easy_install mahotas
        
        You can test your instalation by running::
        
            python -c "import mahotas; mahotas.test()"
        
        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 compile. 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. The
        debug modes are pretty slow as they add many runtime checks.
        
        
        Contacts
        --------
        
        For bugfixes, feel free to use my email: luis@luispedro.org
        
        For more general with achieving certain tasks 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.
        
        Recent Changes
        --------------
        0.9.6 (December 02 2012)
        ~~~~~~~~~~~~~~~~~~~~~~~~
        - Fix ``distance()`` of non-boolean images (issue #24 on github)
        - Fix encoding issue on PY3 on Mac OS (issue #25 on github)
        - Add ``relabel()`` function
        - Add ``remove_regions()`` function in labeled module
        - Fix ``median_filter()`` on the borders (respect the ``mode`` argument)
        - Add ``mahotas.color`` module for conversion between colour spaces
        - Add SLIC Superpixels
        - Many improvements to the documentation
        
        0.9.5 (November 05 2012)
        ~~~~~~~~~~~~~~~~~~~~~~~~
        - Fix compilation in older G++
        - Faster Otsu thresholding
        - Python 3 support without 2to3
        - Add ``cdilate`` function
        - Add ``subm`` function
        - Add tophat transforms (functions ``tophat_close`` and ``tophat_open``)
        - Add ``mode`` argument to euler() (patch by Karol M. Langner)
        - Add ``mode`` argument to bwperim() & borders() (patch by Karol M. Langner)
        
        
        0.9.4 (October 10 2012)
        ~~~~~~~~~~~~~~~~~~~~~~~
        - Fix compilation on 32-bit machines (Patch by Christoph Gohlke)
        
        0.9.3 (October 9 2012)
        ~~~~~~~~~~~~~~~~~~~~~~
        - Fix interpolation (Report by Christoph Gohlke)
        - Fix second interpolation bug (Report and patch by Christoph Gohlke)
        - Update tests to newer numpy
        - Enhanced debug mode (compile with DEBUG=2 in environment)
        - Faster morph.dilate()
        - Add labeled.labeled_max & labeled.labeled_min (This also led to a refactoring
          of the labeled_* code)
        - Many documentation fixes
        
        
        0.9.2 (September 1 2012)
        ~~~~~~~~~~~~~~~~~~~~~~~~
        - Fix compilation on Mac OS X 10.8 (reported by Davide Cittaro)
        - Freeimage fixes on Windows by Christoph Gohlke
        - Slightly faster _filter implementaiton
        
        
        0.9.1 (August 28 2012)
        ~~~~~~~~~~~~~~~~~~~~~~
        
        - Python 3 support (you need to use ``2to3``)
        - Haar wavelets (forward and inverse transform)
        - Daubechies wavelets (forward and inverse transform)
        - Corner case fix in Otsu thresholding
        - Add soft_threshold function
        - Have polygon.convexhull return an ndarray (instead of a list)
        - Memory usage improvements in regmin/regmax/close_holes (first reported
          as issue #9 by thanasi)
        
        
        0.9 (July 16 2012)
        ~~~~~~~~~~~~~~~~~~
        - Auto-convert integer to double on gaussian_filter (previously, integer
          values would result in zero-valued outputs).
        - Check for integer types in (reg|loc)(max|min)
        - Use name `out` instead of `output` for output arguments. This matches
          Numpy better
        - Switched to MIT License
        
        See the ``ChangeLog`` for older version.
        
        *Website*: `http://luispedro.org/software/mahotas
        <http://luispedro.org/software/mahotas>`_
        
        *API Docs*: `http://packages.python.org/mahotas/
        <http://packages.python.org/mahotas/>`_
        
        *Mailing List*: Use the `pythonvision mailing list
        <http://groups.google.com/group/pythonvision?pli=1>`_ for questions, bug
        submissions, etc.
        
        *Author*: Luis Pedro Coelho (with code by Zachary Pincus [from scikits.image],
        Peter J. Verveer [from scipy.ndimage], and Davis King [from dlib])
        
        
Platform: Any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python
Classifier: Programming Language :: C++
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
