{"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":"import pandas as pd\nimport pydicom\nimport numpy as np\nimport os\nimport tqdm\n\n# Function to load .dcm file and return numpy array\ndef load_dicom_file(file_path):\n    dicom = pydicom.dcmread(file_path)\n    image = dicom.pixel_array\n\n    return image","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n\nfolder = \"/kaggle/input/rsna-2023-abdominal-trauma-detection/test_images\"\nif len(os.listdir(folder)) > 3:\n    # loop through the folders in the folder\n    for patient_id in os.listdir(folder):\n        patient_folder = os.path.join(folder, patient_id)\n        for series_id in os.listdir(patient_folder):\n            series_folder = os.path.join(patient_folder, series_id)\n            instances = os.listdir(series_folder)\n            for instance in instances:\n                path = os.path.join(series_folder, instance)\n                arr = load_dicom_file(path)\n                \n                assert len(arr.shape) <= 3 and len(arr.shape) >= 2","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Useless submit","metadata":{}},{"cell_type":"code","source":"!cp /kaggle/input/rsna-2023-abdominal-trauma-detection/sample_submission.csv submission.csv","metadata":{},"execution_count":null,"outputs":[]}]}