{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Version control  \ni. script inspection  \nii. module encapsulation, workflow","metadata":{}},{"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 20GB 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for a, b, c in os.walk('/kaggle/input'):\n    for f in c:\n        print(a, b, f)\n# output\nprint(os.getcwd())","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# os, tifffile, json\n# create project folder\n!mkdir project\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.image import imread\n# 显示图像\n# for img in os.listdir('../input/hubmap-organ-segmentation/train_images'):\n#     show = imread(img)\n#     plt.imshow(show)\n# plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-09-10T23:47:19.765027Z","iopub.execute_input":"2022-09-10T23:47:19.766726Z","iopub.status.idle":"2022-09-10T23:47:19.793161Z","shell.execute_reply.started":"2022-09-10T23:47:19.766664Z","shell.execute_reply":"2022-09-10T23:47:19.791461Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nids = pd.read_csv('../input/hubmap-organ-segmentation/train.csv')['id']\nfig, ax = plt.subplots(1, 5, figsize=(20, 20))\nc = 0\nfor id in ids[:5]:\n    path = '../input/hubmap-organ-segmentation/train_images/%s.tiff' % id\n    img = imread(path)\n    ax[c].imshow(img)\n    c += 1\n# which organ?","metadata":{"execution":{"iopub.status.busy":"2022-09-11T00:25:54.690915Z","iopub.execute_input":"2022-09-11T00:25:54.691392Z","iopub.status.idle":"2022-09-11T00:26:02.839676Z","shell.execute_reply.started":"2022-09-11T00:25:54.691353Z","shell.execute_reply":"2022-09-11T00:26:02.838361Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 准备训练数据集","metadata":{},"execution_count":null,"outputs":[]}]}