{"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 \"../input\n# Input data files are available in the read-only/\" 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":"!ls -al /kaggle/input/landmark-recognition-2020/","metadata":{"execution":{"iopub.status.busy":"2021-12-29T02:16:26.016209Z","iopub.execute_input":"2021-12-29T02:16:26.017253Z","iopub.status.idle":"2021-12-29T02:16:26.783158Z","shell.execute_reply.started":"2021-12-29T02:16:26.017209Z","shell.execute_reply":"2021-12-29T02:16:26.782026Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport matplotlib.pyplot as plt ","metadata":{"execution":{"iopub.status.busy":"2021-12-29T02:18:02.685407Z","iopub.execute_input":"2021-12-29T02:18:02.685765Z","iopub.status.idle":"2021-12-29T02:18:02.690908Z","shell.execute_reply.started":"2021-12-29T02:18:02.685729Z","shell.execute_reply":"2021-12-29T02:18:02.690246Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/landmark-recognition-2020/train.csv')\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2021-12-29T02:18:25.49182Z","iopub.execute_input":"2021-12-29T02:18:25.492588Z","iopub.status.idle":"2021-12-29T02:18:26.842405Z","shell.execute_reply.started":"2021-12-29T02:18:25.492543Z","shell.execute_reply":"2021-12-29T02:18:26.841219Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_ids = np.array(df['id'])\nprint(image_ids.shape)\nprint(image_ids[:3])\n\nlandmark_ids = np.array(df['landmark_id'])\nprint(image_ids.shape)\nprint(image_ids[:3])","metadata":{"execution":{"iopub.status.busy":"2021-12-29T02:20:36.207904Z","iopub.execute_input":"2021-12-29T02:20:36.208268Z","iopub.status.idle":"2021-12-29T02:20:36.261028Z","shell.execute_reply.started":"2021-12-29T02:20:36.20823Z","shell.execute_reply":"2021-12-29T02:20:36.260122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_id[0]","metadata":{"execution":{"iopub.status.busy":"2021-12-29T02:23:46.784391Z","iopub.execute_input":"2021-12-29T02:23:46.785189Z","iopub.status.idle":"2021-12-29T02:23:46.791961Z","shell.execute_reply.started":"2021-12-29T02:23:46.785145Z","shell.execute_reply":"2021-12-29T02:23:46.790822Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_id = '17660ef415d37059'\nfull_path = '/kaggle/input/landmark-recognition-2020/{}/{}/{}/{}/{}.jpg'.format('train',image_id[0],image_id[1],image_id[2],image_id)\nprint(full_path)","metadata":{"execution":{"iopub.status.busy":"2021-12-29T02:24:32.747233Z","iopub.execute_input":"2021-12-29T02:24:32.748478Z","iopub.status.idle":"2021-12-29T02:24:32.757145Z","shell.execute_reply.started":"2021-12-29T02:24:32.748398Z","shell.execute_reply":"2021-12-29T02:24:32.756027Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls  -al {full_path}","metadata":{"execution":{"iopub.status.busy":"2021-12-29T02:25:19.993337Z","iopub.execute_input":"2021-12-29T02:25:19.993647Z","iopub.status.idle":"2021-12-29T02:25:20.780575Z","shell.execute_reply.started":"2021-12-29T02:25:19.99361Z","shell.execute_reply":"2021-12-29T02:25:20.77938Z"},"trusted":true},"execution_count":null,"outputs":[]}]}