{"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":"import os\nimport cv2\nimport pickle\nimport numpy as np\nimport pandas as pd\n\ntrain_df = pd.read_csv(\"../input/sorghum-id-fgvc-9/train_cultivar_mapping.csv\")\nfilenames = train_df.iloc[:, 0].copy().values\n\ndef get_broken_images(path: str, names: np.ndarray) -> list:\n    broken_images = []\n    for name in names:\n        image = cv2.imread(os.path.join(path, name), cv2.IMREAD_COLOR)\n        if image is None:\n            broken_images.append(name)\n    \n    return broken_images\n\nbroken_images = get_broken_images(\"../input/sorghum-id-fgvc-9/train_images\", filenames)\n\nwith open(\"broken_image_names.pkl\", \"wb\") as fp: pickle.dump(broken_images, fp)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-03-22T11:30:55.692063Z","iopub.execute_input":"2022-03-22T11:30:55.692563Z"},"trusted":true},"execution_count":null,"outputs":[]}]}