{"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\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\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","execution":{"iopub.status.busy":"2021-10-04T15:12:06.072922Z","iopub.execute_input":"2021-10-04T15:12:06.073476Z","iopub.status.idle":"2021-10-04T15:12:06.785623Z","shell.execute_reply.started":"2021-10-04T15:12:06.073436Z","shell.execute_reply":"2021-10-04T15:12:06.784593Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/aptos2019-blindness-detection/train.csv\")","metadata":{"execution":{"iopub.status.busy":"2021-10-04T15:12:06.787201Z","iopub.execute_input":"2021-10-04T15:12:06.787438Z","iopub.status.idle":"2021-10-04T15:12:06.797897Z","shell.execute_reply.started":"2021-10-04T15:12:06.787411Z","shell.execute_reply":"2021-10-04T15:12:06.796889Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head(2)","metadata":{"execution":{"iopub.status.busy":"2021-10-04T15:12:06.799404Z","iopub.execute_input":"2021-10-04T15:12:06.80037Z","iopub.status.idle":"2021-10-04T15:12:06.817367Z","shell.execute_reply.started":"2021-10-04T15:12:06.800295Z","shell.execute_reply":"2021-10-04T15:12:06.815782Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import cv2","metadata":{"execution":{"iopub.status.busy":"2021-10-04T15:12:06.81914Z","iopub.execute_input":"2021-10-04T15:12:06.820364Z","iopub.status.idle":"2021-10-04T15:12:06.824444Z","shell.execute_reply.started":"2021-10-04T15:12:06.820311Z","shell.execute_reply":"2021-10-04T15:12:06.82386Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for elem in df['id_code']:\n    path = os.path.join(\"/kaggle/input/aptos2019-blindness-detection/train_images/\",elem)\n    path += \".png\"\n    #print(path)\n    img = cv2.imread(path)\n    height, width, channels = img.shape\n    print(height, width, channels)","metadata":{"execution":{"iopub.status.busy":"2021-10-04T15:13:42.269784Z","iopub.execute_input":"2021-10-04T15:13:42.270727Z","iopub.status.idle":"2021-10-04T15:13:51.259285Z","shell.execute_reply.started":"2021-10-04T15:13:42.270682Z","shell.execute_reply":"2021-10-04T15:13:51.258432Z"},"trusted":true},"execution_count":null,"outputs":[]}]}