{"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\nfilePaths = []\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        filePaths.append(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":"2021-06-10T05:05:17.867643Z","iopub.execute_input":"2021-06-10T05:05:17.868002Z","iopub.status.idle":"2021-06-10T05:05:28.505153Z","shell.execute_reply.started":"2021-06-10T05:05:17.867972Z","shell.execute_reply":"2021-06-10T05:05:28.504267Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import re\nimport random\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport pydicom\n!conda install gdcm -c conda-forge -y\n!pip install pillow\n!pip install pylibjpeg\nimport gdcm\nimport pylibjpeg\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\nregex = '/train/'\ntrainPaths = []\ntestPaths = [] \nfor item in filePaths:\n    match = re.search(regex,item)\n    if match:\n        trainPaths.append(item)\n    else:\n        testPaths.append(item)\nprint(len(trainPaths),len(testPaths))    \ndetectionDf = pd.read_csv(filePaths[1])\ndef prepareData(filePath):\n    im = pydicom.read_file(filePath)\n    data = apply_voi_lut(im.pixel_array,im)\n    if im.PhotometricInterpretation == \"MONOCHROME1\":\n        data = np.amax(data) - data\n    data = data - np.min(data)\n    data = data / np.max(data)\n    data = (data * 255).astype(np.uint8)\n    print(data.shape)\n    return data","metadata":{"execution":{"iopub.status.busy":"2021-06-10T05:19:24.685773Z","iopub.execute_input":"2021-06-10T05:19:24.686118Z","iopub.status.idle":"2021-06-10T05:20:35.518459Z","shell.execute_reply.started":"2021-06-10T05:19:24.686090Z","shell.execute_reply":"2021-06-10T05:20:35.517567Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from matplotlib.patches import Rectangle\nimport json\npath = trainPaths[1906]\nprint(path)\ndata = prepareData(path)\nimg_id = os.path.basename(path)\nimg_id = img_id.replace('.dcm','_image')\nprint(img_id)\nbbox = detectionDf[detectionDf['id'] == img_id]['boxes']\nprint(bbox.iloc[0])\nplt.figure(1); plt.clf()\nplt.imshow(data)\nax = plt.gca()\nprint()\nif type(bbox.iloc[0]) == type(''):\n    box_val = eval(bbox.iloc[0])\n    for item in box_val:\n        x = item.get(\"x\")\n        y = item.get(\"y\")\n        w = item.get(\"width\")\n        h = item.get(\"height\")\n        rect = Rectangle((x,y),w,h,linewidth=1,edgecolor='r',facecolor='none')\n        ax.add_patch(rect)\nplt.pause(1)","metadata":{"execution":{"iopub.status.busy":"2021-06-10T05:20:54.377959Z","iopub.execute_input":"2021-06-10T05:20:54.378325Z","iopub.status.idle":"2021-06-10T05:20:54.428778Z","shell.execute_reply.started":"2021-06-10T05:20:54.378290Z","shell.execute_reply":"2021-06-10T05:20:54.426759Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}