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        "author": "Austin Blanton",
        "author_email": "imaus10@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "# thumbor_extras\n\nSome useful extensions to [thumbor](https://thumbor.readthedocs.io/en/latest/index.html) - extra filters and detectors.\n\n```\npip install thumbor_extras\n```\n\n## Detectors\n\nAdd these detectors to your `thumbor.conf`:\n\n```\nDETECTORS = [\n    'thumbor_extras.detectors.dnn_face_detector',\n    'thumbor_extras.detectors.dnn_object_detector'\n]\n```\n\n### dnn_face_detector\n\n![example of face detector on a lemur](images/face_lemur.jpg)\n\n```\nhttp://localhost:8888/unsafe/smart/filters:draw_center_of_mass():draw_focal_points(2,.25)/https://farm2.static.flickr.com/1098/998604755_c2155161b3.jpg\n```\n\nAn improved face detector that uses a deep neural network. More specifically, it uses the [SSD object detection model](https://arxiv.org/abs/1512.02325), trained specifically on faces. According to [the OpenCV repo](https://github.com/opencv/opencv/blob/master/samples/dnn/face_detector/how_to_train_face_detector.txt), it was trained with \"some huge and available online dataset.\" :/\n\nFor more information on how the normal thumbor face detector compares to this one in both accuracy and efficiency, see [this comparison script](https://github.com/imaus10/compare_face_detection).\n\n### dnn_object_detector\n\n![example of object detector on two cats](images/object_cats.jpg)\n\n```\nhttp://localhost:8888/unsafe/smart/filters:draw_center_of_mass():draw_focal_points(2)/https://farm2.static.flickr.com/1308/562554257_53f458e042.jpg\n```\n\nThe full SSD object detection model, trained on the [COCO dataset](http://cocodataset.org). It was built as a [MobileNet](https://arxiv.org/abs/1704.04861), a class of efficient, light weight deep neural network models meant to work well in mobile and embedded applications.\n\n## Filters\n\nAdd these filters to your available filters in `thumbor.conf`:\n\n```\nFILTERS = [\n    ...other filters,\n    'thumbor_extras.filters.draw_center_of_mass',\n    'thumbor_extras.filters.draw_focal_points'\n]\n```\n\n### draw_center_of_mass\n\nDraws a circle at the calculated center of mass, according to the focal points.\n\nUsage: `draw_center_of_mass([r, g, b])`\n\nExamples:\n- `draw_center_of_mass()`: draws a circle with default red color.\n- `draw_center_of_mass(0,0,255)`: draws a blue circle.\n\n### draw_focal_points\n\nDraws a box around the focal points, for displaying the results of the detectors on a given image. Takes optional arguments for box color and line width.\n\nUsage: `draw_focal_points([line_width, label_height_percentage, show_heatmap, show_labels, show_rainbow, r, g, b])`\n\nArguments:\n\n- `line_width`: the width of the box lines\n- `label_height_percentage`: the height of the class label as a percentage of the box\n- `show_heatmap`: make the box more opaque for higher-confidence detections, more transparent for less confident detections\n- `show_labels`: print the class label at the top of the box\n- `show_rainbow`: use a different color for each focal point box; pass false to provide a single color RGB value for all boxes\n- `r`: R component of RGB color of box, default 0\n- `g`: G component of RGB color of box, default 255\n- `b`: B component of RGB color of box, default 0\n\nExamples:\n- `draw_focal_points()`: draws boxes with defaults (each box a different color, with opacity according to the detection confidence).\n- `draw_focal_points(5,0,false,false,false,255,0,0)`: draws solid red boxes with 5 pixel line width, and no class labels.\n\n## TODO\n\n- Choose thresholds empirically\n\n## Development\n\nBecause I will definitely forget. Python development can be a pain.\n\n```\n# -- ENV --\n# use python 2 - thumbor doesn't support 3 yet.\nvirtualenv thumbor_env --python=/path/to/python2\nsource thumbor_env/bin/activate\n\n# -- DEVELOP --\npip install -e .\n\n# -- TEST --\n# i don't know why setup.py can't install pytest...but it fails without manual install.\n# it could be a python2 / mac thing.\npip install pytest\npython setup.py test\n\n# -- DISTRIBUTE --\npython setup.py sdist\n# make a wheel from the source tarball (to use manifest correctly)\npip wheel --no-index --no-deps --wheel-dir dist dist/*.tar.gz\npip install twine\n# make sure pip URLs are configured per https://truveris.github.io/articles/configuring-pypirc/\npython -m twine upload --repository pypi dist/*\n```\n\n\n",
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