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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "facereg\n=======\n\n.. image:: https://img.shields.io/pypi/v/facereg.svg\n    :target: https://pypi.org/pypi/facereg/\n\n.. image:: https://img.shields.io/pypi/pyversions/facereg.svg\n    :target: https://pypi.org/project/facereg\n\n.. image:: https://travis-ci.org/verifid/facereg.svg?branch=master\n    :target: https://travis-ci.org/verifid/facereg\n\n.. image:: https://codecov.io/gh/verifid/facereg/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/verifid/facereg\n\n**facereg** is a module for face recognition with OpenCV and Deep Learning.\n\nFor now it can be used for just images. It is easy to use with a handy feature \nwhich downloads images from Google for you with given keywords to create dataset/s.\n\nUses two different technics **CNN** and **HoG** for recognition based on `dlib <http://dlib.net/>`_'s\nface recognition system with using `face_recognition <https://github.com/ageitgey/face_recognition>`_.\n**facereg** has totally three different layers and only recognizer has connection on encoder.\n\n|image_layers|\n\nPrerequisites\n=============\n\n* `CMake <https://cmake.org>`_\n* All dependencies are listed on ``requirements.txt`` and will be installed when you install with **pip**.\n\nInstallation\n============\n\n* Install module using ``pip``::\n\n    $ pip install facereg\n\n\n* Download the latest ``facereg`` library from: https://github.com/verifid/facereg and install module using ``pip``::\n\n    $ pip install -e .\n\n* Extract the source distribution and run::\n\n    $ python setup.py build\n    $ python setup.py install\n\nUsage\n=====\n\n* ``google_images``:\n\n.. code:: python\n\n    import os\n    from facereg import google_images\n\n    output_directory = os.getcwd() + '/datasets' # directory path where you want to save photos\n    image_paths = google_images.download('michael jordan', limit=3)\n\n* ``face_encoder``:\n\n.. code:: python\n\n    import os\n    from facereg import face_encoder\n\n    datasets_path = os.getcwd() + '/datasets'\n    encodings_path = os.path.dirname(os.path.realpath(__file__)) + '/encodings.pickle'\n    # these are default values for this method\n    face_encoder.encode_faces(datasets=datasets_path, encodings=encodings_path, detection_method='cnn')\n\n* ``recognize_faces``:\n\n.. code:: python\n\n    from facereg import recognize_faces\n\n    image_path = 'DIRECTORY PATH OF YOUR_IMAGE'\n    names = recognize_faces.recognize(image_path)\n    # returns found names from your datasets\n\nCLI Usage\n=========\n\n* Download images\n\n.. code::\n\n    # -d: keyword, -l: limit\n    $ python -m facereg -d 'michael jordan'\n    $ python -m facereg -d 'michael jordan' -l 5\n\n* Recognition\n\n.. code::\n\n    # -i: Directory path for image\n    $ python -m facereg -i tests/resources/michael_jordan.jpeg\n\nSample Result\n=============\n\n|image_sample|\n\n.. |image_layers| image:: https://raw.githubusercontent.com/verifid/facereg/master/resources/layers.png\n.. |image_sample| image:: https://raw.githubusercontent.com/verifid/facereg/master/resources/mj&sp.png\n\n\n",
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        "license": "MIT License",
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