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        "author": "Henrik Blidh",
        "author_email": "henrik.blidh@nedomkull.com",
        "bugtrack_url": null,
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "SudokuExtract\n=============\n\n.. image:: https://travis-ci.org/hbldh/sudokuextract.svg?branch=master\n    :target: https://travis-ci.org/hbldh/sudokuextract\n.. image:: http://img.shields.io/pypi/v/sudokuextract.svg\n    :target: https://pypi.python.org/pypi/sudokuextract/\n.. image:: http://img.shields.io/pypi/dm/sudokuextract.svg\n    :target: https://pypi.python.org/pypi/sudokuextract/\n.. image:: http://img.shields.io/pypi/l/sudokuextract.svg\n    :target: https://pypi.python.org/pypi/sudokuextract/\n.. image:: https://coveralls.io/repos/github/hbldh/sudokuextract/badge.svg?branch=master\n    :target: https://coveralls.io/github/hbldh/sudokuextract?branch=master\n\nLibrary for extracting Sudokus from images using `scikit-image <http://scikit-image.org/>`_.\n\nRequirements\n------------\n\n* ``numpy>=1.9.2``\n* ``scipy>=0.15.1``\n* ``scikit-image<0.12.0``\n* ``Pillow>=3.1.0``\n* ``pyefd>=0.1.2``\n* ``dlxsudoku>=0.10.0``\n\nUsage\n-----\n\nInstall via `pip`::\n\n    $ pip install sudokuextract\n\nSudokuExtract is a tool for parsing Sudokus from images, this primarily\nto be able to send it forward to some kind of solver. It applies some\nimage analysis on the input image and then uses a K-Nearest Neighbours\nclassifier to determine which digits that are present in which box.\n\n``SudokuExtract`` can be used as a command line tool::\n\n    parse-sudoku -p /path/to/sudoku_image.jpg\n\nwhich prints the parsed Sudoku in the terminal. In can also be called\nwith an url to an image::\n\n    parse-sudoku -u http://www.domain.com/sudoku.jpg\n\nIt can also be used as a regular Python package::\n\n    In [1]: from sudokuextract.extract import extract_sudoku, load_image, predictions_to_suduko_string\n\n    In [2]: img = load_image('/path/to/sudoku_image.jpg')\n\n    In [3]: predictions, sudoku_box_images, whole_sudoku_image = extract_sudoku(img)\n\n    In [4]: print(predictions_to_suduko_string(predictions))\n    800603001\n    057401630\n    000000000\n    006109800\n    400000007\n    001805400\n    000000000\n    072504310\n    900302004\n\nThere are possibilities of using a classifier of your own creation when\npredicting digits; see the documentation for more details.\n\nTesting\n-------\n\nRun tests with `pytest <http://pytest.org/>`_::\n\n    $ py.test tests.py\n\n\nDocumentation\n-------------\n\nTBD.\n\nReferences\n----------\n\nThis library includes classifiers trained with data from the MNIST dataset\n(This data is also included in SudokuExtract).\n\n.. _1:\n\n\\[1\\] `LeCun et al. (1999): The MNIST Dataset Of Handwritten Digits <http://yann.lecun.com/exdb/mnist/>`_\n\nThe current parsing strategy for the `sudokuextract` package is\ninspired by this blog entry:\n\n.. _2:\n\n\\[2\\] `AI: SuDoKu Grabber with OpenCV <http://aishack.in/tutorials/sudoku-grabber-opencv-plot/>`_\n\n\nv0.8.6 (2016-04-16)\n===================\n- Changed the main function of `sudokuextract.extract` to print and\n  not return anything.\n\nv0.8.5 (2016-03-10)\n===================\n- Replace ndi.binary_fill_holes with a binary_erosion and increased number of blobs to test to 2.\n\nv0.8.4 (2016-03-10)\n===================\n- New classifiers.\n- New data with additional data of 1:s.\n\nv0.8.3 (2016-03-09)\n===================\n- Disabled the Corners parsing solution again.\n- Warping image now creates a much smaller image to prevent memory issues.\n- This also increased speed with a factor of at least 4.\n- ``apply_gaussian`` now applies a Gaussian on entire image first.\n- Testing can now use a tar-file of images that can be downloaded from the web.\n- Also removed lambda functions in favour of ``functools.partial``.\n\nv0.8.2 (2016-03-07)\n===================\n- Restricted scikit-image version to < 0.12.\n\nv0.8.1 (2016-03-06)\n===================\n- New classifiers with both SudokuExtract and MNIST data.\n- New data.\n- MNIST data stored separately from SudokuExtract data.\n- Number of Nearest Neighbours increased to 10 due to larger training data.\n- Several small bugfixes for new features added in v0.8.0.\n\nv0.8.0 (2016-03-05)\n===================\n- New classifiers with MNIST data.\n- New multi-attempt approach to Sudoku parsing.\n- Using DLXSudoku to attempt classification of correct parsing of Sudoku.\n- Removed a lot of deprecated code.\n\nv0.7.0 (2016-02-26)\n===================\n- Two different extraction methods:\n    * Local thresholding\n    * Adaptive thresholding for entire image\n- Refactored extensively and updated classifiers.\n\nv0.6.1 (2016-02-20)\n===================\n- Patch for tests in Python 3.\n\nv0.6.0 (2016-02-19)\n===================\n- Simplified blob extraction.\n- Added adaptive block_size for ``to_binary_adaptive``.\n- Added tests that fetch Sudokus from Xanadoku.\n\nv0.5.0 (2016-02-18)\n===================\n- Removed hard dependency on scikit-learn.\n- Included an own K-Nearest-Neighbors classifier as default.\n\nv0.4.0 (2016-02-17)\n===================\n- Initial release on PyPI",
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