{"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":"2022-08-11T21:06:44.204497Z","iopub.execute_input":"2022-08-11T21:06:44.204889Z","iopub.status.idle":"2022-08-11T21:09:59.659817Z","shell.execute_reply.started":"2022-08-11T21:06:44.204812Z","shell.execute_reply":"2022-08-11T21:09:59.658459Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from glob import glob\nimport matplotlib.pylab as plt\n\nimport pydicom as dicom\n\ntrain_images = glob(\"../input/rsna-2022-cervical-spine-fracture-detection/train_images/1.2.826.0.1.3680043.10001/*\")\n\nplt.style.use('default')\nfig, axes = plt.subplots(4,4, figsize=(12,12))\ntrain_images\nfor i, ax in enumerate(axes.reshape(-1)):\n    img_path = train_images[i]\n    img = dicom.dcmread(img_path)  \n    ax.imshow(img.pixel_array)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-11T21:27:55.274049Z","iopub.execute_input":"2022-08-11T21:27:55.274371Z","iopub.status.idle":"2022-08-11T21:27:57.073020Z","shell.execute_reply.started":"2022-08-11T21:27:55.274346Z","shell.execute_reply":"2022-08-11T21:27:57.072370Z"},"trusted":true},"execution_count":null,"outputs":[]}]}