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        "author": "Anthony Blackshaw",
        "author_email": "ant@getme.co.uk",
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            "Development Status :: 4 - Beta",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 3.7",
            "Topic :: Database"
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        "description": "# H51 Python Library\n\nThe H51 Python library provides a pythonic interface to the H51 API. It includes an API client class, a set of resource classes and classes for configuring core analyzers and transforms.\n\n\n## Installation\n\n```\npip install h51\n```\n\n## Requirements\n\n- Python 3.7+\n\n\n# Usage\n\n```Python\n\nimport h51\n\n\nclient = h51.Client('your_api_key...')\n\n# Create an asset\nwith open('image.bmp') as f:\n    asset = h51.resources.Asset.create(client, f)\n\n# Analyze the image asset to find its dominant colours and focal point\nasset.analyze([\n    h51.transforms.images.DominantColours(),\n    h51.analyzers.images.FocalPoint()\n])\n\n# Create web optimized variations of the image\nh51.resources.Variation.create(\n    asset,\n    {\n        'x1': [\n            h51.transforms.images.AutoOrient(),\n            h51.transforms.images.FocalPointCrop(aspect_ratio=0.5),\n            h51.transforms.images.Fit(640, 640),\n            h51.transforms.images.Output('WEBP')\n        ],\n        'x2': [\n            h51.transforms.images.AutoOrient(),\n            h51.transforms.images.FocalPointCrop(aspect_ratio=0.5),\n            h51.transforms.images.Fit(1280, 1280),\n            h51.transforms.images.Output('WEBP')\n        ]\n    }\n])\n\n```",
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        "keywords": "api h51",
        "license": "MIT",
        "maintainer": "Anthony Blackshaw",
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