{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import cv2\nimport gc\nimport io\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport time\nfrom tqdm import tqdm_notebook as tqdm\nimport pydicom\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\nimport multiprocessing\nimport warnings\nfrom glob import glob\nimport os\nimport imageio\nimport time\nwarnings.filterwarnings(\"ignore\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def read_xray(path):\n    voi_lut = True\n    fix_monochrome = True\n    dicom = pydicom.read_file(path)\n    basename = os.path.basename(path).split('.')[0]\n    if voi_lut:\n        data = apply_voi_lut(dicom.pixel_array, dicom)\n    else:\n        data = dicom.pixel_array\n               \n    # depending on this value, X-ray may look inverted - fix that:\n    if fix_monochrome and dicom.PhotometricInterpretation == \"MONOCHROME1\":\n        data = np.amax(data) - data\n        \n    data = data - np.min(data)\n    data = data / np.max(data)\n    data = (data * 255).astype(np.uint8)\n    \n    return basename, data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cpu_count = multiprocessing.cpu_count()\nimage_paths = \"../input/vinbigdata-chest-xray-abnormalities-detection/train\"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Process 100 images**"},{"metadata":{"trusted":true},"cell_type":"code","source":"start = time.time()\nimage_paths = glob(os.path.join(image_paths, '*.dicom'))[:10]\ntry:\n    pool = multiprocessing.Pool(processes = cpu_count)\n    for basename, image in pool.map(read_xray, image_paths):\n        imageio.imwrite(basename + '.png', image)\nfinally:\n    pool.close()\n    pool.join()\n\nprint(\"Time Execution : {}\".format(time.time() - start))","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}