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        "author": "Chris Pickett",
        "author_email": "chris.pickett@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Image Recognition"
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        "description": "=========\nPhotoHash\n=========\n\n.. image:: https://travis-ci.org/bunchesofdonald/django-hermes.svg?branch=master\n    :target: https://travis-ci.org/bunchesofdonald/photohash\n\nThis was mainly created just for my own use and education. It's a perceptual\nhash algorithm, used to find if two images are similar.\n\nInstallation\n============\n\n::\n\n    pip install PhotoHash\n\n\nUsage\n=====\n\naverage_hash\n------------\nReturns the hash of the image using an average hash algorithm. This algorithm\ncompares each pixel in the image to the average value of all the pixels.::\n\n    import photohash\n    hash = photohash.average_hash('/path/to/myimage.jpg')\n\ndistance\n--------\nReturns the hamming distance between the average_hash of the given images.::\n\n    import photohash\n    distance = photohash.distance('/path/to/myimage.jpg', '/path/to/myotherimage.jpg')\n\nis_look_alike\n-------------\nReturns a boolean of whether or not the photos look similar.::\n\n    import photohash\n    similar = photohash.is_look_alike('/path/to/myimage.jpg', '/path/to/myotherimage.jpg')\n\nis_look_alike also takes an optional tolerance argument that defines how strict\nthe comparison should be.::\n\n    import photohash\n    similar = photohash.is_look_alike('/path/to/myimage.jpg', '/path/to/myimage.jpg', tolerance=3)\n\nhash_distance\n-------------\nReturns the hamming distance between two hashes of the same length::\n\n    import photohash\n    hash_one = average_hash('/path/to/myimage.jpg')\n    hash_two = average_hash('/path/to/myotherimage.jpg')\n    distance = photohash.hash_distance(hash_one, hash_two)\n\nhashes_are_similar\n------------------\nReturns a boolean of whether or not the two hashes are within the given tolerance. Same as\nis_look_alike, but takes hashes instead of image paths::\n\n    import photohash\n    hash_one = average_hash('/path/to/myimage.jpg')\n    hash_two = average_hash('/path/to/myotherimage.jpg')\n    similar = photohash.hashes_are_similar(hash_one, hash_two)\n\nhashes_are_similar also takes the same optional tolerance argument that is_look_alike does.",
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