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    "info": {
        "author": "Donggun LEE",
        "author_email": "gnyotnu39@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License"
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        "description": "# Deep Geo\n- Easy Deep Learning\n- Copyright (c) 2019 [InfoLab](http://infolab.kunsan.ac.kr) ([Donggun LEE](http://duration.digimoon.net))\n- PIP : https://pypi.org/project/deepgeo/\n- Demo\n  - [deepGeo.io](http://deepGeo.io)\n  - [DeepGeo.io](http://duration.digimoon.net/dev/deepGeo/test/)\n- How to install\n  ```bash\n  pip install deepgeo\n  ```\n- other version\n  ```basha\n  # 0.0.1\n  pip install deepgeo==0.0.1\n\n  # 0.0.13\n  pip install deepgeo==0.0.13\n\n  # 0.1.0 : Support Yolo (Requirement OpenCV, Only Detect)\n  pip install deepgeo==0.1.0\n\n  # 0.2.0 : Support Extendsion Library\n  pip install deepgeo==0.2.0\n\n  # 0.2.1 : Support Font Management\n  pip install deepgeo==0.2.1\n  ```\n- requirement\n  - Python 3.6\n    ```bash\n    pip install exifread piexif pillow matplotlib scikit-image\n    ```\n- How to use\n  - font Management\n    ```python\n    from deepgeo import Font\n    # register\n    Font.register(\"D:/User/Downloads/font.ttf\",\"font\")\n\n    # delete\n    Font.delete(\"font\")\n    ```\n  - detect\n    ```python\n    from deepgeo import Image, Engine\n    path = \"D:\"\n\n    engine = Engine()\n    engine.add_model('maskrcnn_mscoco','maskrcnn', path+'/default_config.json')\n\n    image = Image(\"image.jpg\",path)\n    image = engine.detect('maskrcnn_mscoco', image)[0]\n    image.draw_annotations(image.get_annotation())\n    image.save(path+\"/\",\"test\",\"PNG\")\n    ```\n  - train\n    ```python\n    import sys,os,json\n    from deepgeo import Engine, Image\n\n    def init_loading_bar(max):\n    max_ = str(max)\n    sys.stdout.write(\"%s/0\" % max_)\n    sys.stdout.flush()\n    sys.stdout.write(\"\\b\")\n\n    def update_loading_bar(num):\n    sys.stdout.write(\"%s\" % str(num))\n    sys.stdout.flush()\n    sys.stdout.write(\"\\b\" * len(str(num)))\n\n    def final_loading_bar():\n    sys.stdout.write(\"\\n\")\n\n    def fjson_to_imgs(engine, dataset_name, path, image_path):\n    file_list = os.listdir(path)\n    file_list.sort()\n    init_loading_bar(len(file_list))\n    count = 0\n    for item in file_list:\n    count+=1\n    update_loading_bar(count)\n    if item.find('.json') is not -1:\n    data=None\n    with open(path+item) as data_file:    \n      data = json.load(data_file)\n    engine.add_data('mscoco',dataset_name,Image(data['uri'],image_path,data['annotations']))\n    del data\n    final_loading_bar()\n    del count\n    del file_list\n\n\n    engine = Engine()\n    engine.add_model('mscoco', 'maskrcnn', 'D:/test/config.json')\n    engine.add_dataset('mscoco','train','maskrcnn')\n    engine.add_dataset('mscoco','val','maskrcnn')\n    fjson_to_imgs(engine, 'train','D:/test/val2017/json/','D:/test/val2017/images/')\n    fjson_to_imgs(engine, 'val','D:/test/val2017/json/','D:/test/val2017/images/')\n\n    ## Training...\n    print(\" > STEP3 : Fine tune all layers\")\n    engine.set_config('mscoco',{\"EPOCHS\":160, \"LAYERS\":'all',\"LEARNING_RATE\":engine.get_config('mscoco',\"LEARNING_RATE\")/10})\n    engine.train('mscoco',\"train\",\"val\",None)\n\n    ... etc ...\n    ```\n\n\n",
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