{"cells":[{"metadata":{"_uuid":"a757d912-a26c-47c5-af19-a94702a8ca94","_cell_guid":"ec8f240d-015e-4f78-bf72-16734f4332b4","trusted":true},"cell_type":"code","source":"%%time\nimport numpy as np \nimport pandas as pd \nimport os\nimport cv2\nfrom utilities_x_ray import read_xray\nfrom tqdm import tqdm\nimport shutil\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nprint('Preparing numpy files from dicom: ')\npath = '../input/vinbigdata-chest-xray-abnormalities-detection/train/'\nif os.path.isdir('./images/'):\n    shutil.rmtree('./images/')\n    \nos.mkdir('./images/')\nos.mkdir('./images/train/')\nos.mkdir('./images/test/')\nprint('Train set')\nfor img in tqdm(os.listdir(path)):\n    im_name = img.split('.dicom')[0]\n    image = read_xray(path+img)\n    image = cv2.resize(image,(256,256),cv2.INTER_AREA)\n    image = np.expand_dims(image,axis=2)\n    np.save(f'./images/train/{im_name}.npy',image)\n\npath = '../input/vinbigdata-chest-xray-abnormalities-detection/test/'\nprint('Test set')\nfor img in tqdm(os.listdir(path)):\n    im_name = img.split('.dicom')[0]\n    image = read_xray(path+img)\n    image = cv2.resize(image,(256,256),cv2.INTER_AREA)\n    image = np.expand_dims(image,axis=2)\n    np.save(f'./images/test/{im_name}.npy',image)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}