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        "description": "==============================================\nsearchcolor - Extract colors from web searches\n==============================================\n\n|version| |github| |python35| |license| |format|\n\n.. |version| image:: https://img.shields.io/pypi/v/searchcolor.svg\n    :target: https://pypi.python.org/pypi/searchcolor\n.. |python35| image:: https://img.shields.io/badge/Python-3.5-brightgreen.svg\n    :target: https://www.python.org/\n.. |license| image:: https://img.shields.io/badge/License-MIT-blue.svg\n    :target: https://github.com/Tathorack/searchcolor/blob/master/LICENSE.md\n.. |github| image:: https://img.shields.io/github/tag/Tathorack/searchcolor.svg\n   :target: https://github.com/Tathorack/searchcolor\n.. |format| image:: https://img.shields.io/pypi/format/searchcolor.svg\n    :target: https://pypi.python.org/pypi/searchcolor\n\n---------------------------------------------------------------------------------------\nThis module uses imagecolor with PIL(Pillow) to extract colors from web image searches.\n---------------------------------------------------------------------------------------\n\nAvailable functions\n===================\naverage_image_url(url, name)\n============================\nAverages a single image from a url into RGB color values. Returns a dictionary with the following keys: ``name``, ``red``, ``green``, ``blue``\n\n* ``url`` - image url.\n* ``name`` - name to return. Generally passed from the function that generates the url.\n* ``timeout`` - ``requests`` timeout in seconds.\n* ``max_size`` - maximum size of image to fetch in MB.\n\n\n\\_image_search_average(url_list, max_threads=20)\n================================================\nAverages all urls in a list into a singular RGB average.\n\n* ``url_list`` - path to directory\n* ``max_threads`` - max processes to spawn.\n* ``timeout`` - ``requests`` timeout in seconds. This gets passed to ``average_image_url``\n* ``max_size`` - maximum size of image to fetch in MB. This gets passed to ``average_image_url``\n\ngoogle_average(search_term, num_results, api_key, cse_id, max_threads=20)\n=========================================================================\nDoes a Google image search and averages all the images into a singular RGB search average. Returns a dictionary with the following keys: ``name``, ``red``, ``green``, ``blue``\n\n* ``search_term`` - Google image search term.\n* ``num_results`` - Number of results to include.\n* ``api_key`` - Google API key.\n* ``cse_id`` - Google CSE ID.\n* ``max_threads`` - max processes to spawn. This gets passed to ``\\_image_search_average``\n* ``timeout`` - ``requests`` timeout in seconds. This gets passed to ``average_image_url``\n* ``max_size`` - maximum size of image to fetch in MB. This gets passed to ``average_image_url``\n\nFuture work\n===========\n* add more information to readme\n* build offline tests\n\nTests\n=====\nTesting is done with pytest_\n\n.. _pytest: http://docs.pytest.org/en/latest/\n\nCurrently tests only the google api. Requires the enviroment variables GOOGLE_SEARCH_API and GOOGLE_SEARCH_CSE to be set to a valid google custom search api and id\n\nRun with ``python3 setup.py test``\n\n\n",
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