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        "description": "# gamecam\nFilter out trail-camera images that don't contain an animal (eventually will be able to label exported images as well).\n\n## Motivation\nWhile working for the [Francis Ecology Lab](https://francisecologylab.wixsite.com/francislab), a problem was presented to me. In 2018, there were 800,000+ remote camera images collected for a single experiment. The majority turned out to be false triggers - photos of grass and bushes swaying.\n\nInstead of going through them one by one, I developed this program. Once coded, it only took a week to generate a data table with all the images that contained animals, labeled to genus or species, and that had detection numbers corresponding to one of two common schemes. The data table can be converted into observational units of detections instead of individual images.\n\n## Installation\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install gamecam.\n\n```bash\npip install gamecam-sdrabing\n```\n\n## Usage\n```python\nfrom gamecam import pyrcolate as pyr\n\n# creates data table from folder\njpg_data = pyr.construct_jpg_data(pyr.input_directory())\n\n# Cam objects store all relevant metadata\ncam = pyr.Cam(jpg_data)\n\nhelp(Cam.plot)                     # shows quick-guide for key bindings\ncam.plot()                         # interactive plot for image filtering\ncam.save(pyr.input_filename())     # creates a .sav file that can be loaded\ncam.export(pyr.input_directory())  # exports images and a .csv file to folder\n```\n\n## To-Do\nFinish gamecam.classipy, which will allow for rapid labeling of exported images.\n\n## Contributing\nThis is my first project, so I have no idea what I'm doing.\nIf you have suggestions or tips, please tell me!\n\n## License\n[MIT](https://choosealicense.com/licenses/mit/) \u00a9 Shane Drabing 2019\n\n",
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