{"cells":[{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_dir = '/kaggle/input/landmark-retrieval-2020/test/'\ntrain_dir = '/kaggle/input/landmark-retrieval-2020/train/'\nindex_dir = '/kaggle/input/landmark-retrieval-2020/index/'\ntrain = pd.read_csv('/kaggle/input/landmark-retrieval-2020/train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"landmark_list = train['landmark_id'].unique()\nprint('There are ' + str(len(landmark_list)) + ' locations in train data')\nprint(landmark_list)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def print_landmark(df, num):\n    folder = []\n    for i in range(3):\n        folder.append(df['id'][num][i])\n        \n    _path = train_dir + '{}/{}/{}/'.format(folder[0], folder[1], folder[2]) \n    file_name = df['id'][num] + '.jpg'\n    image_path = _path + file_name\n    \n    img = Image.open(image_path, 'r')\n    plt.figure()\n    plt.title('landmark_id: ' + str(df['landmark_id'][num]))\n    plt.imshow(img)\n    print(file_name)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print_landmark(train, 2)","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}