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            "License :: OSI Approved :: Apache Software License",
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            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Image Recognition",
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        "description": "# Huasca\n\n##### Computer vision models OOB (out-of-the-bottle).\n\n                     __\n                    [__]\n       ___        .+'. '+.\n       )_(       /:;/ _.+'\\\n       + +       +:._   .++\n     .+'+'+.  _  |:._     |\n    /+::_..+_[_]_+:._CV   |\n    )_     /_   _\\:._     |\n    +;:    )_``'_(:._     +\n    +;::+..+;:.._++.____.+'\n    `+.._..`+...+'\n\nHuasca enables prototyping by prioritizing generalization and rapid development over accuracy.\n\n##### Step into the cellar and select a bottle of computer visions.\n\n  * Face detection & localization\n  * Face classification\n    * age\n    * gender\n  * Object detection & localization\n  * Object tracking\n  * Object classification w/o localization\n\nFace and object localization include convenient cropping and annotation methods to feed classifiers.\n\n## Roadmap\n\n  * v0.3.0 - reduce and combine models to save space\n  * v0.4.x - add style transfer\n  * v0.4.x - face recognition\n\n## Examples\n\n### Detection\n\nDetection results include:\n\n  * `boxes`: Boxes follow PIL format of (left, upper, right, lower)\n    * top-left corner is (0,0) and offsets go down/right from there (physics indexing)\n  * `scores`: confidence score for each detected object\n  * `labels`: label description of the object e.g. ['dog','person']\n  * `portraits`: cropped objects from base image (PIL.Image format)\n  * `base_image`: the source image (PIL.Image format)\n  * `annotated`: the source image with objects annotated (PIL.Image format)\n\n#### Face & Object Detection\n\n    # Get a PIL image from somewhere:\n    image = ...\n\n    # Use PIL image as input:\n    import huasca\n\n    faces   = huasca.detect.faces(image)\n    objects = huasca.detect.objects(image)\n\n    # Display the first face\n    faces.portraits[0].show()\n\n    # Check classes\n    print(objects.labels)\n\n    # Retrieve annotated & labeled version of either\n    faces.annotated.show()\n    objects.annotated.show()\n\n#### Face Demographics\n\n    # Get a PIL image of a face from face detector:\n    face = faces.portraits[0]\n\n    gender,age = huasca.classify.demographics(face)\n\n\n### Object Tracking\n\n    import huasca\n\n    data = json.load(json_data)\n    object_log = huasca.object_tracking.track_objects(data)\n    output_json = [obj.to_json() for obj in object_log]\n\n\n",
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