{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np \nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport glob","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"def read_and_modify_dataframes(input_path):\n    train_paths = glob.glob(input_path + 'train/*/*/*/*')\n    test_paths = glob.glob(input_path + 'test/*/*/*/*')\n    \n    mapping = {}\n    for path in train_paths:\n        mapping[path.split('/')[-1].split('.')[0]] = path\n    train_df = pd.read_csv(input_path + 'train.csv')\n    train_df['image_path'] = train_df['id'].map(mapping)\n    uniques = train_df['landmark_id'].unique()\n    uniques_map = dict(zip(uniques, range(len(uniques))))\n    train_df['label'] = train_df['landmark_id'].map(uniques_map).astype(np.int32)\n    \n    mapping = {}\n    for path in test_paths:\n        mapping[path.split('/')[-1].split('.')[0]] = path\n    submission_df = pd.read_csv(input_path + 'sample_submission.csv')\n    # remember to remove 'image_path' and 'label' when submitting:\n    # ...\n    # submission_df = submission_df.drop('label', axis=1)\n    # submission_df = submission_df.drop('image_path', axis=1)\n    # submission_df.to_csv('submission.csv')\n    submission_df['image_path'] = submission_df['id'].map(mapping)\n    submission_df['label'] = -1\n    \n    return train_df, submission_df\n\ntrain_df, submission_df = read_and_modify_dataframes('../input/landmark-recognition-2020/')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.head(20)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_df.head(20)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ID = 0\nimage_path = train_df.iloc[ID].image_path\nlabel = train_df.iloc[ID].label\nlandmark_id = train_df.iloc[ID].landmark_id\n\n\nimage = Image.open(image_path)\nplt.figure(figsize=(10, 10))\nplt.imshow(image)\nplt.title('label:' + str(label) + ', landmark_id:' + str(landmark_id),\n          fontsize=20);","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}