{"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)\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns\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\n\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":"2023-03-08T10:15:32.584058Z","iopub.execute_input":"2023-03-08T10:15:32.584480Z","iopub.status.idle":"2023-03-08T10:15:33.179842Z","shell.execute_reply.started":"2023-03-08T10:15:32.584442Z","shell.execute_reply":"2023-03-08T10:15:33.178378Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"parquet_file = '/kaggle/input/asl-signs/train_landmark_files/16069/100015657.parquet'\nparquet_df = pd.read_parquet(parquet_file)\nface=parquet_df.groupby(['frame','type']).get_group((103,'face'))\n","metadata":{"execution":{"iopub.status.busy":"2023-03-08T10:13:58.213494Z","iopub.execute_input":"2023-03-08T10:13:58.213938Z","iopub.status.idle":"2023-03-08T10:13:58.262953Z","shell.execute_reply.started":"2023-03-08T10:13:58.213896Z","shell.execute_reply":"2023-03-08T10:13:58.261368Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(40,40))\nsns.scatterplot(x=face.x, y=-face.y)\nfor i in range(face.shape[0]):\n    plt.text(x=face.x.iloc[i],y=-face.y.iloc[i],s=face.landmark_index.iloc[i], \n          fontdict=dict(color='red',size=10))\n    \n\n","metadata":{"execution":{"iopub.status.busy":"2023-03-08T10:19:38.158367Z","iopub.execute_input":"2023-03-08T10:19:38.159274Z","iopub.status.idle":"2023-03-08T10:19:41.293753Z","shell.execute_reply.started":"2023-03-08T10:19:38.159227Z","shell.execute_reply":"2023-03-08T10:19:41.291878Z"},"trusted":true},"execution_count":null,"outputs":[]}]}