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        "author": "Henrik Blidh",
        "author_email": "henrik.blidh@nedomkull.com",
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            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "\nPyEFD\n=====\n\n[![Build Status](https://dev.azure.com/hbldh/github/_apis/build/status/hbldh.pyefd?branchName=master)](https://dev.azure.com/hbldh/github/_build/latest?definitionId=6&branchName=master)\n[![image](https://travis-ci.org/hbldh/pyefd.svg?branch=master)](https://travis-ci.org/hbldh/pyefd)\n[![Documentation Status](https://readthedocs.org/projects/pyefd/badge/?version=latest)](http://pyefd.readthedocs.org/en/latest/?badge=latest)\n[![image](http://img.shields.io/pypi/v/pyefd.svg)](https://pypi.python.org/pypi/pyefd/)\n[![image](http://img.shields.io/pypi/l/pyefd.svg)](https://pypi.python.org/pypi/pyefd/)\n[![image](https://coveralls.io/repos/github/hbldh/pyefd/badge.svg?branch=master)](https://coveralls.io/github/hbldh/pyefd?branch=master)\n\nAn Python/NumPy implementation of a method for approximating a contour with a Fourier series, as described in [1].\n\nInstallation\n------------\n\n```bash\npip install pyefd\n```\n\nUsage\n-----\n\nGiven a closed contour of a shape, generated by e.g. [scikit-image](http://scikit-image.org/) or\n [OpenCV](http://opencv.org/), this package can fit a \n [Fourier series](https://en.wikipedia.org/wiki/Fourier_series) approximating the shape of the contour.\n\n### General usage examples\n\nThis section describes the general usage patterns of `pyefd`.\n\n```python\nfrom pyefd import elliptic_fourier_descriptors\ncoeffs = elliptic_fourier_descriptors(contour, order=10)\n```\n\nThe coefficients returned are the `a_n`, `b_n`, `c_n` and `d_n` of the following Fourier series \nrepresentation of the shape.\n\nThe coefficients returned are by default normalized so that they are rotation and size-invariant. \nThis can be overridden by calling:\n\n```python\nfrom pyefd import elliptic_fourier_descriptors\ncoeffs = elliptic_fourier_descriptors(contour, order=10, normalize=False)\n```\n\nNormalization can also be done afterwards:\n\n```python\nfrom pyefd import normalize_efd\ncoeffs = normalize_efd(coeffs)\n```\n\n### OpenCV example\n\nIf you are using [OpenCV](http://opencv.org/) to generate contours, this example shows how to \nconnect it to `pyefd`.\n\n```python\nimport cv2 \nimport numpy\nfrom pyefd import elliptic_fourier_descriptors\n\n# Find the contours of a binary image using OpenCV.\ncontours, hierarchy = cv2.findContours(\n    im, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n# Iterate through all contours found and store each contour's \n# elliptical Fourier descriptor's coefficients.\ncoeffs = []\nfor cnt in contours:\n    # Find the coefficients of all contours\n    coeffs.append(elliptic_fourier_descriptors(\n        numpy.squeeze(cnt), order=10))\n```\n\n### Using EFD as features\n\nTo use these as features, one can write a small wrapper function:\n\n```python\nfrom pyefd import elliptic_fourier_descriptors\n\ndef efd_feature(contour):\n    coeffs = elliptic_fourier_descriptors(contour, order=10, normalize=True)\n    return coeffs.flatten()[3:]\n```\n\nIf the coefficients are normalized, then `coeffs[0, 0] = 1.0`, `coeffs[0, 1] = 0.0` and \n`coeffs[0, 2] = 0.0`, so they can be disregarded when using the elliptic Fourier descriptors as features.\n\nSee [1] for more technical details.\n\nTesting\n-------\n\nRun tests with with [Pytest](http://pytest.org/latest/):\n\n```bash\npy.test tests.py\n```\n\nThe tests include a single image from the MNIST dataset of handwritten digits ([2]) as a contour to use for testing.\n\nDocumentation\n-------------\n\nSee [ReadTheDocs](http://pyefd.readthedocs.org/).\n\nReferences\n----------\n\n[1]: [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)\n\n[2]: [LeCun et al. (1999): The MNIST Dataset Of Handwritten Digits](http://yann.lecun.com/exdb/mnist/)\n\n\n## v1.40 (2019-07-27)\n- Merged PR #4: Vectorized contour reconstruction function\n\n## v1.3.0 (2019-06-18)\n- Merged PR #2: Numpy vectorized efd\n- Moved from Travis CI to Azure Pipelines\n- Replaced rst with markdown\n\n## v1.2.0 (2018-06-14)\n- Updated setup.py\n- Updated numpy requirement\n- Added Pipfile\n- Ran Black on code\n- Testing on 3.6\n\n## v1.1.0 (2018-06-13)\n- New example for OpenCV\n- Updated documentation\n\n## v1.0 (2016-04-19)\n\n- Deemed stable enough for version 1.0 release\n- Created documentation.\n\n## v0.1.2 (2016-02-29)\n- Testing with pytest instead of nosetests.\n- Added Coveralls use.\n\n## v0.1.1 (2016-02-17)\n- Fixed MANIFEST\n- Added LICENSE file that was missing.\n\n## v0.1.0 (2016-02-09)\n- Initial release\n\n\n",
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