{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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)\nimport PIL\nfrom tqdm import tqdm\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\n#for 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 5GB 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","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"DATA_PATH = '../input/siim-isic-melanoma-classification/'\nTRAIN_IMAGE_PATH= DATA_PATH+'jpeg/train/'\nTEST_IMAGE_PATH= DATA_PATH+'jpeg/test/'\nRESIZE_DIM = 128\n\ntrain = pd.read_csv(DATA_PATH+'train.csv')\n\ndef resize_image(path, image_name):\n    image = PIL.Image.open(os.path.join(path,image_name+'.jpg'))\n    image = image.resize((RESIZE_DIM,RESIZE_DIM), resample = PIL.Image.LANCZOS)\n    return image","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = np.empty((train.shape[0], RESIZE_DIM,RESIZE_DIM,3), dtype=np.uint8)\n\nfor idx,image_name in enumerate(tqdm(train.image_name)):\n    img_resized = resize_image(TRAIN_IMAGE_PATH,image_name)\n    x_train[idx,:,:,:] = np.array(img_resized)\n\nnp.save('train128.npy', x_train)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Not able to save the test images with the same kernel because of write restrictions (5GB).","execution_count":null},{"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}