{"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":"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","execution":{"iopub.status.busy":"2022-05-31T04:13:47.448438Z","iopub.execute_input":"2022-05-31T04:13:47.449144Z","iopub.status.idle":"2022-05-31T04:14:12.364460Z","shell.execute_reply.started":"2022-05-31T04:13:47.449083Z","shell.execute_reply":"2022-05-31T04:14:12.363404Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pydicom\nimport os\nfrom PIL import Image\n\nimport torch\nimport torch.nn as nn\nfrom torch.optim import Adam\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms , models\nfrom torchvision.datasets import ImageFolder\nfrom torch.autograd import Variable","metadata":{"execution":{"iopub.status.busy":"2022-05-31T04:14:12.366026Z","iopub.execute_input":"2022-05-31T04:14:12.366602Z","iopub.status.idle":"2022-05-31T04:14:14.732677Z","shell.execute_reply.started":"2022-05-31T04:14:12.366566Z","shell.execute_reply":"2022-05-31T04:14:14.730439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = os.listdir(\"/kaggle/input/rsna-pneumonia-detection-challenge/stage_2_train_images\")","metadata":{"execution":{"iopub.status.busy":"2022-05-31T04:14:14.734429Z","iopub.execute_input":"2022-05-31T04:14:14.734954Z","iopub.status.idle":"2022-05-31T04:14:14.752774Z","shell.execute_reply.started":"2022-05-31T04:14:14.734922Z","shell.execute_reply":"2022-05-31T04:14:14.750842Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/rsna-pneumonia-detection-challenge/stage_2_train_labels.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-05-31T04:14:14.754606Z","iopub.execute_input":"2022-05-31T04:14:14.754984Z","iopub.status.idle":"2022-05-31T04:14:14.831287Z","shell.execute_reply.started":"2022-05-31T04:14:14.754953Z","shell.execute_reply":"2022-05-31T04:14:14.830482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.mkdir(\"./train\")\nos.mkdir(\"./train/have\")\nos.mkdir(\"./train/have_not\")\nfor i in train:\n  ds = pydicom.dcmread(r\"/kaggle/input/rsna-pneumonia-detection-challenge/stage_2_train_images/\" + i)\n  name = i.split(\".\")[0]\n  img = Image.fromarray(ds.pixel_array)\n  con = df[df.patientId == name].values[0][5]\n  if con == 0:\n    img.save(\"./train/have/\" + name + \".jpeg\")\n  else:\n    img.save(\"./train/have_not/\" + name + \".jpeg\")","metadata":{"execution":{"iopub.status.busy":"2022-05-31T04:14:14.832693Z","iopub.execute_input":"2022-05-31T04:14:14.833311Z","iopub.status.idle":"2022-05-31T04:30:01.352833Z","shell.execute_reply.started":"2022-05-31T04:14:14.833277Z","shell.execute_reply":"2022-05-31T04:30:01.350630Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!tar -zcvf all_images.tar.gz train","metadata":{"execution":{"iopub.status.busy":"2022-05-31T04:30:31.399937Z","iopub.execute_input":"2022-05-31T04:30:31.401081Z","iopub.status.idle":"2022-05-31T04:32:04.582164Z","shell.execute_reply.started":"2022-05-31T04:30:31.401022Z","shell.execute_reply":"2022-05-31T04:32:04.580978Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!rm -r train","metadata":{"execution":{"iopub.status.busy":"2022-05-31T04:32:04.584071Z","iopub.execute_input":"2022-05-31T04:32:04.584426Z","iopub.status.idle":"2022-05-31T04:32:06.383649Z","shell.execute_reply.started":"2022-05-31T04:32:04.584390Z","shell.execute_reply":"2022-05-31T04:32:06.382559Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}