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        "author": "John T. Leonard",
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        "description": "# JLpy Utils Package\nCustom modules/classes/methods for various data science, computer vision, and machine learning operations in python\n\n## Dependancies\n* General libraries distributed with Anaconda (pandas, numpy, sklearn, scipy, matplotlib, etc.)\n* image/video analysis:\n    * cv2 (pip install opencv-python)\n* ML_models sub-package dependancies:\n    * tensorflow or tensorflow-gpu\n    * dill\n\n## Installing & Importing\nIn CLI:\n```\n$ pip install -upgrade JLpy_utils_package\n```\nAfter this, the package can be imported into jupyter notebook or python in general via the comman:\n```import JLpy_utils_package as JLutils```\n\n## Modules Overview\nThere are several modules in this package:\n```\nJLutils.summary_tables\nJLutils.plot\nJLutils.img\nJLutils.video\nJLutils.ML_models\n```\n\n```JLutils.summary_tables``` and ```JLutils.plot``` probably aren't that useful for most people, so we won't go into detail on them here, but feel free to check them out if you're curious.\n\n### JLutils.img\nThe ```JLutils.img``` module contains a number of functions related to image analysis, most of which wrap SciKit image functions in some way. The most interesting functions/classes are the ```JLutils.img.auto_crop....``` and ```JLutils.img.decompose_video_to_img()```. \n\nThe ```auto_crop``` class allows you to automatically crop an image using countours via the ```use_countours``` method, which essentially wraps the function ```skimage.measure.find_contours``` function. Alternatively, the ```use_edges``` method provides cropping based on the ```skimage.feature.canny``` function. Generally, I find the ```use_edges``` runs faster and gives more intuitive autocropping results.\n\nThe ```decompose_video_to_img()``` is fairly self explanatory and basically uses cv2 to pull out and save all the frames from a video.\n\n### JLutils.video\n...\n\n### JLutils.kaggle\nThis module contains functions for interacting with kaggle. The simplest function is:\n```\nJLutils.kaggle.competition_download_files(competition)\n```\nwhere ```competition``` is the competition name, such as  \"home-credit-default-risk\"\n\n\n\n\n",
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