Metadata-Version: 1.1
Name: pyefd
Version: 1.0
Summary: Python implementation of "Elliptic Fourier Features of a Closed Contour"
Home-page: https://github.com/hbldh/pyefd
Author: Henrik Blidh
Author-email: henrik.blidh@nedomkull.com
License: MIT
Description: PyEFD
        =====
        
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        An Python/NumPy implementation of a method for approximating a contour with a Fourier series, as described in [#first]_.
        
        Installation
        ------------
        
        .. code:: bash
        
            $ pip install pyefd
        
        Usage
        -----
        
        Given a closed contour of a shape, generated by e.g. `scikit-image <http://scikit-image.org/>`_
        or `OpenCV <http://opencv.org/>`_, this package can fit a
        `Fourier series <https://en.wikipedia.org/wiki/Fourier_series>`_
        approximating the shape of the contour:
        
        .. code:: python
        
            from pyefd import elliptic_fourier_descriptors
            coeffs = elliptic_fourier_descriptors(contour, order=10)
        
        The coefficients returned are the ``a_n``, ``b_n``, ``c_n`` and ``d_n`` of
        the following Fourier series representation of the shape.
        
        The coefficients returned are by default normalized so that they are
        rotation and size-invariant. This can be overridden by calling:
        
        .. code:: python
        
            from pyefd import elliptic_fourier_descriptors
            coeffs = elliptic_fourier_descriptors(contour, order=10, normalize=False)
        
        Normalization can also be done afterwards:
        
        .. code:: python
        
            from pyefd import normalize_efd
            coeffs = normalize_efd(coeffs)
        
        To use these as features, one can write a small wrapper function:
        
        .. code:: python
        
            def efd_feature(contour):
                coeffs = elliptic_fourier_descriptors(contour, order=10, normalize=True)
                return coeffs.flatten()[3:]
        
        If the coefficients are normalized, then ``coeffs[0, 0] = 1.0``,
        ``coeffs[0, 1] = 0.0`` and ``coeffs[0, 2] = 0.0``, so they can be disregarded when using
        the elliptic Fourier descriptors as features.
        
        See [#first]_ for more technical details.
        
        Testing
        -------
        
        Run tests with:
        
        .. code:: bash
        
            $ python setup.py test
        
        or with `Pytest <http://pytest.org/latest/>`_:
        
        .. code:: bash
        
            $ py.test tests.py
        
        The tests includes a single image from the MNIST dataset of handwritten digits ([#second]_) as a contour to use
        for testing.
        
        Documentation
        -------------
        
        See `ReadTheDocs <http://pyefd.readthedocs.org/>`_.
        
        References
        ----------
        
        .. [#first] `Frank P Kuhl, Charles R Giardina, Elliptic Fourier features of a closed contour,
           Computer Graphics and Image Processing, Volume 18, Issue 3, 1982, Pages 236-258,
           ISSN 0146-664X, http://dx.doi.org/10.1016/0146-664X(82)90034-X. <http://www.sci.utah.edu/~gerig/CS7960-S2010/handouts/Kuhl-Giardina-CGIP1982.pdf>`_
        
        
        .. [#second] `LeCun et al. (1999): The MNIST Dataset Of Handwritten Digits <http://yann.lecun.com/exdb/mnist/>`_
        
        
        v1.0 (2016-04-19)
        =================
        - Deemed stable enough for version 1.0 release
        - Created documentation.
        
        v0.1.2 (2016-02-29)
        ===================
        - Testing with pytest instead of nosetests.
        - Added Coveralls use.
        
        v0.1.1 (2016-02-17)
        ===================
        - Fixed MANIFEST
        - Added LICENSE file that was missing.
        
        v0.1.0 (2016-02-09)
        ===================
        - Initial release
        
Keywords: elliptic fourier descriptors,fourier descriptors,shape descriptors,image analysis
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
