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        "author": "Simon Waloschek",
        "author_email": "simon.waloschek@posteo.de",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
        ],
        "description": "dollarpy\r\n========\r\n\r\n*dollarpy* is a python implementation of the `$P Point-Cloud\r\nRecognizer <http://depts.washington.edu/aimgroup/proj/dollar/pdollar.html>`__.\r\n\r\n|License|\r\n\r\n|Python Compatibility| |PyPI Version| |Format|\r\n\r\n|Build Status| |Coverage Status|\r\n\r\nFrom the website:\r\n\r\n    The $P Point-Cloud Recognizer is a 2-D gesture recognizer designed\r\n    for rapid prototyping of gesture-based user interfaces. In machine\r\n    learning terms, $P is an instance-based nearest-neighbor classifier\r\n    with a Euclidean scoring function, i.e., a geometric template\r\n    matcher.\r\n\r\n    $P is the latest in the dollar family of recognizers that includes\r\n    $1 for unistrokes and $N for multistrokes. Although about half of\r\n    $P\u2019s code is from $1, unlike both $1 and $N, $P does not represent\r\n    gestures as ordered series of points (i.e., strokes), but as\r\n    unordered point-clouds. By representing gestures as point-clouds, $P\r\n    can handle both unistrokes and multistrokes equivalently and without\r\n    the combinatoric overhead of $N. When comparing two point-clouds, $P\r\n    solves the classic assignment problem between two bipartite graphs\r\n    using an approximation of the Hungarian algorithm.\r\n\r\nVatavu, R. D., Anthony, L., & Wobbrock, J. O., `\"Gestures as Point\r\nClouds: A $P Recognizer for User Interface\r\nPrototypes\" <http://faculty.washington.edu/wobbrock/pubs/icmi-12.pdf>`__,\r\nin: *Proceedings of the 14th ACM International Conference on Multimodal\r\nInteraction (ICMI)*, Santa Monica, LA, USA, 2012, pp. 273-280.\r\n\r\nInstallation\r\n------------\r\n\r\n*dollarpy* can be installed using pip:\r\n\r\n::\r\n\r\n    pip install dollarpy\r\n\r\nUsage\r\n-----\r\n\r\n*dollarpy* is used in 3 steps:\r\n\r\n.. code:: python\r\n\r\n    from dollarpy import Recognizer, Template, Point\r\n\r\n    # Define 'Template' gestures, each consisting of a name and a list of 'Point' elements.\r\n    # These 'Point' elements have 'x' and 'y' coordinates and optionally the stroke index a point belongs to.\r\n    tmpl_1 = Template('X', [\r\n        Point(0, 0, 1),\r\n        Point(1, 1, 1),\r\n        Point(0, 1, 2),\r\n        Point(1, 0, 2)])\r\n    tmpl_2 = Template('line', [\r\n        Point(0, 0),\r\n        Point(1, 0)])\r\n\r\n    # Create a 'Recognizer' object and pass the created 'Template' objects as a list.\r\n    recognizer = Recognizer([tmpl_1, tmpl_2])\r\n\r\n    # Call 'recognize(...)' to match a list of 'Point' elements to the previously defined templates.\r\n    result = recognizer.recognize([\r\n        Point( 31, 141, 1),\r\n        Point(109, 222, 1),\r\n        Point( 22, 219, 2),\r\n        Point(113, 146, 2)])\r\n    print(result)  # Output: ('X', 0.733770116545184)\r\n\r\n.. |License| image:: https://img.shields.io/pypi/l/dollarpy.svg\r\n   :target: https://www.gnu.org/licenses/lgpl.html\r\n.. |Python Compatibility| image:: https://img.shields.io/pypi/pyversions/dollarpy.svg\r\n   :target: https://pypi.python.org/pypi/dollarpy/\r\n.. |PyPI Version| image:: https://img.shields.io/pypi/v/dollarpy.svg\r\n   :target: https://pypi.python.org/pypi/dollarpy/\r\n.. |Format| image:: https://img.shields.io/pypi/format/dollarpy.svg\r\n   :target: https://pypi.python.org/pypi/dollarpy/\r\n.. |Build Status| image:: https://img.shields.io/travis/sonovice/dollarpy.svg\r\n   :target: https://travis-ci.org/sonovice/dollarpy\r\n.. |Coverage Status| image:: https://img.shields.io/codecov/c/github/sonovice/dollarpy.svg\r\n   :target: https://codecov.io/gh/sonovice/dollarpy",
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        "home_page": "https://github.com/sonovice/dollarpy",
        "keywords": "gesture",
        "license": "LGPLv3+",
        "maintainer": "",
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        "name": "dollarpy",
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        "summary": "A python imlementation of the $P Point-Cloud Recognizer",
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