{"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","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport gc\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\nfrom tqdm import tqdm\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense\nfrom tensorflow.keras.layers import GlobalMaxPooling2D\nimport openslide\nfrom openslide import OpenSlide\nimport cv2","metadata":{"execution":{"iopub.status.busy":"2022-09-11T12:26:59.009259Z","iopub.execute_input":"2022-09-11T12:26:59.009734Z","iopub.status.idle":"2022-09-11T12:27:08.297353Z","shell.execute_reply.started":"2022-09-11T12:26:59.009623Z","shell.execute_reply":"2022-09-11T12:27:08.295819Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv('../input/mayo-clinic-strip-ai/train.csv')\ntest_df  = pd.read_csv('../input/mayo-clinic-strip-ai/test.csv')","metadata":{"execution":{"iopub.status.busy":"2022-09-11T12:30:27.030033Z","iopub.execute_input":"2022-09-11T12:30:27.030791Z","iopub.status.idle":"2022-09-11T12:30:27.058828Z","shell.execute_reply.started":"2022-09-11T12:30:27.030752Z","shell.execute_reply":"2022-09-11T12:30:27.057794Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-09-11T12:30:36.405007Z","iopub.execute_input":"2022-09-11T12:30:36.405784Z","iopub.status.idle":"2022-09-11T12:30:36.440735Z","shell.execute_reply.started":"2022-09-11T12:30:36.405734Z","shell.execute_reply":"2022-09-11T12:30:36.439801Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nsample_train = train_df[:4]\n\nfor i in range(4):\n    slide = OpenSlide(sample_train.loc[i, \"file_path\"])\n    region = (0, 0)\n    size = (10000, 10000)\n    region = slide.read_region(region, 0, size)\n    plt.figure(figsize=(8, 8))\n    plt.imshow(region)\n    plt.show() ","metadata":{"execution":{"iopub.status.busy":"2022-09-11T12:31:08.685215Z","iopub.execute_input":"2022-09-11T12:31:08.685623Z","iopub.status.idle":"2022-09-11T12:31:09.100382Z","shell.execute_reply.started":"2022-09-11T12:31:08.685589Z","shell.execute_reply":"2022-09-11T12:31:09.098961Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}