{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":46105,"databundleVersionId":5087314,"sourceType":"competition"}],"dockerImageVersionId":30407,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install -q flatbuffers 2> /dev/null\n!pip install -q mediapipe 2> /dev/null","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-03-25T06:25:07.606171Z","iopub.execute_input":"2023-03-25T06:25:07.606745Z","iopub.status.idle":"2023-03-25T06:25:32.276535Z","shell.execute_reply.started":"2023-03-25T06:25:07.606666Z","shell.execute_reply":"2023-03-25T06:25:32.273887Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"import os\nimport cv2\nimport json\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport mediapipe as mp\nimport matplotlib.pyplot as plt\n\nfrom matplotlib import animation\nfrom pathlib import Path\nimport IPython\nfrom IPython import display\nfrom IPython.display import HTML\n\nimport mediapipe as mp\nfrom mediapipe.framework.formats import landmark_pb2\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\n# import os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n# for filename in filenames:\n# print(os.path.join(dirname, filename))\n","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:25:32.280156Z","iopub.execute_input":"2023-03-25T06:25:32.280613Z","iopub.status.idle":"2023-03-25T06:25:32.290448Z","shell.execute_reply.started":"2023-03-25T06:25:32.280567Z","shell.execute_reply":"2023-03-25T06:25:32.28888Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"class Cfg:\n RANDOM_STATE = 2023\n INPUT_ROOT = Path('/kaggle/input/asl-signs/')\n OUTPUT_ROOT = Path('kaggle/working')\n INDEX_MAP_FILE = INPUT_ROOT / 'sign_to_prediction_index_map.json'\n TRAN_FILE = INPUT_ROOT / 'train.csv'\n INDEX = 'sequence_id'\n ROW_ID = 'row_id'","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:25:32.292665Z","iopub.execute_input":"2023-03-25T06:25:32.293147Z","iopub.status.idle":"2023-03-25T06:25:32.31027Z","shell.execute_reply.started":"2023-03-25T06:25:32.293105Z","shell.execute_reply":"2023-03-25T06:25:32.308541Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"def read_index_map(file_path=Cfg.INDEX_MAP_FILE):\n \"\"\"Reads the sign to predict as json file.\"\"\"\n with open(file_path, \"r\") as f:\n result = json.load(f)\n return result \n\ndef read_train(file_path=Cfg.TRAN_FILE):\n \"\"\"Reads the train csv as pandas data frame.\"\"\"\n return pd.read_csv(file_path).set_index(Cfg.INDEX)\n\ndef read_landmark_data_by_path(file_path, input_root=Cfg.INPUT_ROOT):\n \"\"\"Reads landmak data by the given file path.\"\"\"\n data = pd.read_parquet(input_root / file_path)\n return data.set_index(Cfg.ROW_ID)\n\ndef read_landmark_data_by_id(sequence_id, train_data):\n \"\"\"Reads the landmark data by the given sequence id.\"\"\"\n file_path = train_data.loc[sequence_id]['path']\n return read_landmark_data_by_path(file_path)","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:25:32.313492Z","iopub.execute_input":"2023-03-25T06:25:32.315508Z","iopub.status.idle":"2023-03-25T06:25:32.327861Z","shell.execute_reply.started":"2023-03-25T06:25:32.315423Z","shell.execute_reply":"2023-03-25T06:25:32.326412Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"train_data = read_train()\ntrain_data.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:25:32.329397Z","iopub.execute_input":"2023-03-25T06:25:32.329946Z","iopub.status.idle":"2023-03-25T06:25:32.515853Z","shell.execute_reply.started":"2023-03-25T06:25:32.329891Z","shell.execute_reply":"2023-03-25T06:25:32.514784Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" path participant_id \\\nsequence_id \n1000035562 train_landmark_files/26734/1000035562.parquet 26734 \n1000106739 train_landmark_files/28656/1000106739.parquet 28656 \n100015657 train_landmark_files/16069/100015657.parquet 16069 \n1000210073 train_landmark_files/25571/1000210073.parquet 25571 \n1000240708 train_landmark_files/62590/1000240708.parquet 62590 \n\n sign \nsequence_id \n1000035562 blow \n1000106739 wait \n100015657 cloud \n1000210073 bird \n1000240708 owie ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pathparticipant_idsign
sequence_id
1000035562train_landmark_files/26734/1000035562.parquet26734blow
1000106739train_landmark_files/28656/1000106739.parquet28656wait
100015657train_landmark_files/16069/100015657.parquet16069cloud
1000210073train_landmark_files/25571/1000210073.parquet25571bird
1000240708train_landmark_files/62590/1000240708.parquet62590owie
\n
"},"metadata":{}}]},{"cell_type":"code","source":"sequence_id = 1000106739\ndata = read_landmark_data_by_id(sequence_id, train_data)\nprint(data)","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:25:32.517832Z","iopub.execute_input":"2023-03-25T06:25:32.518197Z","iopub.status.idle":"2023-03-25T06:25:32.557217Z","shell.execute_reply.started":"2023-03-25T06:25:32.518163Z","shell.execute_reply":"2023-03-25T06:25:32.55533Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stdout","text":" frame type landmark_index x y \\\nrow_id \n29-face-0 29 face 0 0.577776 0.509360 \n29-face-1 29 face 1 0.572079 0.470164 \n29-face-2 29 face 2 0.572135 0.483382 \n29-face-3 29 face 3 0.561517 0.437963 \n29-face-4 29 face 4 0.572215 0.459153 \n... ... ... ... ... ... \n39-right_hand-16 39 right_hand 16 0.448404 0.801023 \n39-right_hand-17 39 right_hand 17 0.236284 0.721779 \n39-right_hand-18 39 right_hand 18 0.294856 0.757523 \n39-right_hand-19 39 right_hand 19 0.340462 0.781477 \n39-right_hand-20 39 right_hand 20 0.381433 0.803320 \n\n z \nrow_id \n29-face-0 -0.050245 \n29-face-1 -0.075696 \n29-face-2 -0.044619 \n29-face-3 -0.050466 \n29-face-4 -0.078405 \n... ... \n39-right_hand-16 -0.106966 \n39-right_hand-17 -0.063194 \n39-right_hand-18 -0.088237 \n39-right_hand-19 -0.100694 \n39-right_hand-20 -0.106972 \n\n[5973 rows x 6 columns]\n","output_type":"stream"}]},{"cell_type":"code","source":"# CHANGE TO THE SIGN YOU WANT TO CREATE DATASET FOR\nsign = \"green\"\n# train_data.query('sign == \"listen\"')\nnum_of_files = len(train_data.query(f'sign == \"{sign}\"'))\nnum_of_files","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:25:32.558881Z","iopub.execute_input":"2023-03-25T06:25:32.559257Z","iopub.status.idle":"2023-03-25T06:25:32.578296Z","shell.execute_reply.started":"2023-03-25T06:25:32.559214Z","shell.execute_reply":"2023-03-25T06:25:32.576492Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"382"},"metadata":{}}]},{"cell_type":"code","source":"if not os.path.exists(sign):\n print(\"making directory\")\n os.makedirs(sign)","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:27:32.066644Z","iopub.execute_input":"2023-03-25T06:27:32.067172Z","iopub.status.idle":"2023-03-25T06:27:32.07577Z","shell.execute_reply.started":"2023-03-25T06:27:32.067129Z","shell.execute_reply":"2023-03-25T06:27:32.074043Z"},"trusted":true},"execution_count":43,"outputs":[{"name":"stdout","text":"making directory\n","output_type":"stream"}]},{"cell_type":"code","source":"# counter for videos - for first video it should be one \ncounter = 0\n\nfor video in range(0,num_of_files):\n link_to_video = f\"/kaggle/input/asl-signs/{train_data.query(f'sign == @sign')['path'].values[video]}\"\n\n\n data = pd.read_parquet(link_to_video)\n\n unique_frames = data[\"frame\"].nunique()\n unique_types = data[\"type\"].nunique()\n types_in_video = data[\"type\"].unique()\n # print(\n # f\"The file has {unique_frames} unique frames and {unique_types} unique types: {types_in_video}\"\n # )\n \n # counter for frames in video - for first frame it should be one \n frame_counter = 0\n \n for frame, group in data.groupby('frame'):\n \n# print(f\"Data for frame {frame}:\")\n\n # print(len(group.query('type == \"pose\"')))\n body_parts = ['pose','face', 'left_hand', 'right_hand']\n concate_frame_data = np.empty(0)\n\n for part in body_parts: \n frame_data = group.query(f\"type == '{part}'\")\n\n individual_frame = np.empty((0, 3))\n for index, row in frame_data.iterrows():\n individual_frame = np.append(individual_frame, [[row.x, row.y, row.z]], axis=0)\n\n flatten_data = individual_frame.flatten()\n\n if np.any(np.isnan(flatten_data)) == True:\n flatten_data = np.zeros(flatten_data.shape)\n\n concate_frame_data = np.append(concate_frame_data,flatten_data)\n\n # print(flatten_data.shape,np.any(np.isnan(flatten_data)),flatten_data)\n # print(concate_frame_data,concate_frame_data.shape)\n if not os.path.exists(f\"/kaggle/working/{sign}/{counter}\"):\n# print(f\"/kaggle/working/{sign}/{counter} path doesn't exist\")\n os.makedirs(f\"/kaggle/working/{sign}/{counter}\")\n \n \n path_to_save = f\"/kaggle/working/{sign}/{counter}/{frame_counter}\" \n# print(path_to_save)\n np.save(path_to_save,concate_frame_data)\n frame_counter += 1\n \n counter += 1\n# if counter == 4:\n# break","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:27:34.828843Z","iopub.execute_input":"2023-03-25T06:27:34.830101Z","iopub.status.idle":"2023-03-25T06:42:32.721471Z","shell.execute_reply.started":"2023-03-25T06:27:34.830045Z","shell.execute_reply":"2023-03-25T06:42:32.719722Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"code","source":"unique_frames = data[\"frame\"].nunique()\nunique_types = data[\"type\"].nunique()\ntypes_in_video = data[\"type\"].unique()\nprint(\n f\"The file has {unique_frames} unique frames and {unique_types} unique types: {types_in_video}\"\n)","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:44:05.360105Z","iopub.execute_input":"2023-03-25T06:44:05.360546Z","iopub.status.idle":"2023-03-25T06:44:05.373164Z","shell.execute_reply.started":"2023-03-25T06:44:05.360508Z","shell.execute_reply":"2023-03-25T06:44:05.371316Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stdout","text":"The file has 37 unique frames and 4 unique types: ['face' 'left_hand' 'pose' 'right_hand']\n","output_type":"stream"}]},{"cell_type":"code","source":"print(num_of_files)","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:44:07.869583Z","iopub.execute_input":"2023-03-25T06:44:07.870837Z","iopub.status.idle":"2023-03-25T06:44:07.877599Z","shell.execute_reply.started":"2023-03-25T06:44:07.870775Z","shell.execute_reply":"2023-03-25T06:44:07.875896Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"382\n","output_type":"stream"}]},{"cell_type":"code","source":"for frame, group in data.groupby('frame'):\n print(f\"Data for frame {frame}:\")\n \n # print(len(group.query('type == \"pose\"')))\n body_parts = ['pose','face', 'left_hand', 'right_hand']\n concate_frame_data = np.empty(0)\n \n for part in body_parts: \n frame_data = group.query(f\"type == '{part}'\")\n \n individual_frame = np.empty((0, 3))\n for index, row in frame_data.iterrows():\n individual_frame = np.append(individual_frame, [[row.x, row.y, row.z]], axis=0)\n\n flatten_data = individual_frame.flatten()\n\n if np.any(np.isnan(flatten_data)) == True:\n flatten_data = np.zeros(flatten_data.shape)\n \n concate_frame_data = np.append(concate_frame_data,flatten_data)\n\n# print(flatten_data.shape,np.any(np.isnan(flatten_data)),flatten_data)\n print(concate_frame_data,concate_frame_data.shape)\n \n np.save(\"0\",concate_frame_data)\n \n break","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:44:43.610567Z","iopub.execute_input":"2023-03-25T06:44:43.611119Z","iopub.status.idle":"2023-03-25T06:44:43.693798Z","shell.execute_reply.started":"2023-03-25T06:44:43.611077Z","shell.execute_reply":"2023-03-25T06:44:43.692422Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"Data for frame 0:\n[ 0.44963461 0.42114234 -1.80340421 ... 0. 0.\n 0. ] (1629,)\n","output_type":"stream"}]},{"cell_type":"code","source":"# after creation of folder don't view on kaggle, just add the folder name here.\n!zip -r green.zip /kaggle/working/green","metadata":{"execution":{"iopub.status.busy":"2023-03-25T06:44:46.282733Z","iopub.execute_input":"2023-03-25T06:44:46.283142Z","iopub.status.idle":"2023-03-25T06:45:05.550892Z","shell.execute_reply.started":"2023-03-25T06:44:46.283096Z","shell.execute_reply":"2023-03-25T06:45:05.548837Z"},"trusted":true},"execution_count":48,"outputs":[{"name":"stdout","text":" adding: kaggle/working/green/ (stored 0%)\n adding: kaggle/working/green/229/ (stored 0%)\n adding: kaggle/working/green/229/0.npy (deflated 50%)\n adding: kaggle/working/green/229/1.npy (deflated 50%)\n adding: kaggle/working/green/229/3.npy (deflated 50%)\n adding: kaggle/working/green/229/4.npy (deflated 50%)\n adding: kaggle/working/green/229/5.npy (deflated 48%)\n adding: kaggle/working/green/229/2.npy (deflated 50%)\n adding: kaggle/working/green/252/ (stored 0%)\n adding: kaggle/working/green/252/32.npy (deflated 48%)\n adding: kaggle/working/green/252/40.npy (deflated 48%)\n adding: kaggle/working/green/252/59.npy (deflated 48%)\n adding: kaggle/working/green/252/9.npy (deflated 50%)\n adding: kaggle/working/green/252/44.npy (deflated 48%)\n adding: kaggle/working/green/252/28.npy (deflated 48%)\n adding: kaggle/working/green/252/0.npy (deflated 48%)\n adding: kaggle/working/green/252/58.npy (deflated 48%)\n adding: kaggle/working/green/252/46.npy (deflated 48%)\n adding: kaggle/working/green/252/13.npy (deflated 50%)\n adding: kaggle/working/green/252/25.npy (deflated 48%)\n adding: kaggle/working/green/252/43.npy (deflated 48%)\n adding: kaggle/working/green/252/10.npy (deflated 50%)\n adding: kaggle/working/green/252/21.npy (deflated 48%)\n adding: kaggle/working/green/252/27.npy (deflated 48%)\n adding: kaggle/working/green/252/57.npy (deflated 48%)\n adding: kaggle/working/green/252/31.npy (deflated 48%)\n adding: kaggle/working/green/252/24.npy (deflated 48%)\n adding: kaggle/working/green/252/60.npy (deflated 48%)\n adding: kaggle/working/green/252/26.npy (deflated 48%)\n adding: kaggle/working/green/252/19.npy (deflated 48%)\n adding: kaggle/working/green/252/22.npy (deflated 48%)\n adding: kaggle/working/green/252/18.npy (deflated 48%)\n adding: kaggle/working/green/252/1.npy (deflated 48%)\n adding: kaggle/working/green/252/34.npy (deflated 48%)\n adding: kaggle/working/green/252/29.npy (deflated 48%)\n adding: kaggle/working/green/252/56.npy (deflated 50%)\n adding: kaggle/working/green/252/3.npy (deflated 48%)\n adding: kaggle/working/green/252/8.npy (deflated 50%)\n adding: kaggle/working/green/252/52.npy (deflated 48%)\n adding: kaggle/working/green/252/11.npy (deflated 50%)\n adding: kaggle/working/green/252/20.npy (deflated 48%)\n adding: kaggle/working/green/252/37.npy (deflated 48%)\n adding: kaggle/working/green/252/36.npy (deflated 48%)\n adding: kaggle/working/green/252/50.npy (deflated 48%)\n adding: kaggle/working/green/252/49.npy (deflated 48%)\n adding: kaggle/working/green/252/38.npy (deflated 48%)\n adding: kaggle/working/green/252/33.npy (deflated 48%)\n adding: kaggle/working/green/252/45.npy (deflated 48%)\n adding: kaggle/working/green/252/42.npy (deflated 48%)\n adding: kaggle/working/green/252/7.npy (deflated 50%)\n adding: kaggle/working/green/252/54.npy (deflated 50%)\n adding: kaggle/working/green/252/30.npy (deflated 48%)\n adding: kaggle/working/green/252/23.npy (deflated 48%)\n adding: kaggle/working/green/252/16.npy (deflated 48%)\n adding: kaggle/working/green/252/4.npy (deflated 48%)\n adding: kaggle/working/green/252/5.npy (deflated 48%)\n adding: kaggle/working/green/252/35.npy (deflated 48%)\n adding: kaggle/working/green/252/55.npy (deflated 50%)\n adding: kaggle/working/green/252/39.npy (deflated 48%)\n adding: kaggle/working/green/252/41.npy (deflated 48%)\n adding: kaggle/working/green/252/53.npy (deflated 48%)\n adding: kaggle/working/green/252/61.npy (deflated 48%)\n adding: kaggle/working/green/252/14.npy (deflated 48%)\n adding: kaggle/working/green/252/51.npy (deflated 48%)\n adding: kaggle/working/green/252/6.npy (deflated 50%)\n adding: kaggle/working/green/252/47.npy (deflated 48%)\n adding: kaggle/working/green/252/17.npy (deflated 48%)\n adding: kaggle/working/green/252/15.npy (deflated 48%)\n adding: kaggle/working/green/252/12.npy (deflated 50%)\n adding: kaggle/working/green/252/48.npy (deflated 48%)\n adding: kaggle/working/green/252/2.npy (deflated 48%)\n adding: kaggle/working/green/28/ (stored 0%)\n adding: kaggle/working/green/28/0.npy (deflated 48%)\n adding: kaggle/working/green/28/1.npy (deflated 50%)\n adding: kaggle/working/green/28/3.npy (deflated 48%)\n adding: kaggle/working/green/28/4.npy (deflated 48%)\n adding: kaggle/working/green/28/5.npy (deflated 48%)\n adding: kaggle/working/green/28/6.npy (deflated 48%)\n adding: kaggle/working/green/28/2.npy (deflated 48%)\n adding: kaggle/working/green/111/ (stored 0%)\n adding: kaggle/working/green/111/0.npy (deflated 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adding: kaggle/working/green/18/5.npy (deflated 48%)\n adding: kaggle/working/green/18/14.npy (deflated 48%)\n adding: kaggle/working/green/18/6.npy (deflated 48%)\n adding: kaggle/working/green/18/17.npy (deflated 48%)\n adding: kaggle/working/green/18/15.npy (deflated 48%)\n adding: kaggle/working/green/18/12.npy (deflated 48%)\n adding: kaggle/working/green/18/2.npy (deflated 48%)\n","output_type":"stream"}]},{"cell_type":"code","source":"# remove a existing folder. DON'T RUN, ONLY RUN WHEN YOU WANT TO DELETE A FOLDER\nimport shutil\nshutil.rmtree(\"/kaggle/working/yellow\")","metadata":{"execution":{"iopub.status.busy":"2023-03-25T05:53:07.674805Z","iopub.execute_input":"2023-03-25T05:53:07.67528Z","iopub.status.idle":"2023-03-25T05:53:07.703918Z","shell.execute_reply.started":"2023-03-25T05:53:07.675243Z","shell.execute_reply":"2023-03-25T05:53:07.70161Z"},"trusted":true},"execution_count":21,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_27/888811186.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# remove a existing folder. DON'T RUN, ONLY RUN WHEN YOU WANT TO DELETE A FOLDER\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshutil\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrmtree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/kaggle/working/yellow\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/opt/conda/lib/python3.7/shutil.py\u001b[0m in \u001b[0;36mrmtree\u001b[0;34m(path, ignore_errors, onerror)\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[0morig_st\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m \u001b[0monerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 486\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/shutil.py\u001b[0m in \u001b[0;36mrmtree\u001b[0;34m(path, ignore_errors, onerror)\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# lstat()/open()/fstat() trick.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 482\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 483\u001b[0;31m \u001b[0morig_st\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 484\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 485\u001b[0m \u001b[0monerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/kaggle/working/yellow'"],"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: '/kaggle/working/yellow'","output_type":"error"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}