{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"pip install addict","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pip install yapf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pip install terminal\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pip install terminaltables\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pip install mmcv","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pip install pycocotools","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!cp -r /kaggle/input/mmdetectfcos /kaggle/working","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install -v -e /kaggle/working/mmdetectfcos","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nimport csv\nimport cv2\nimport json\nimport shutil\nfrom tqdm import tqdm\nimport numpy as np\n\ntrain_img_dir = '/kaggle/input/global-wheat-detection/test'\nannot_dst_path = '/kaggle/working/annot_coco_test.json'\njs = {'images': [], 'annotations': [], 'categories': [], }\n\nimg_name2id = {}\nimg_id = 0\nannot_id = 0\nimg_names = os.listdir(train_img_dir)\nfor img_name in tqdm(img_names):\n    img_path = os.path.join(train_img_dir, img_name)\n    img = cv2.imread(img_path)\n    height = img.shape[0]\n    width = img.shape[1]\n    img_name2id[img_name] = img_id\n    js['images'].append({'file_name': img_name, 'height': height, 'width': width, 'id': img_id})\n    js['annotations'].append(\n        {'segmentation': [], 'area': 1, 'iscrowd': 0, 'image_id': img_id, 'bbox': [1, 1, 1, 1],\n         'category_id': 0, 'id': annot_id})\n    img_id += 1\n    annot_id += 1\n\njs['categories'].append({'id': 0, 'name': 'wheat'})\nwith open(annot_dst_path, 'w') as f:\n    json.dump(js, f)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!python -u /kaggle/working/mmdetectfcos/setup.py","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}