{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"6ff9e850-b855-f984-a39d-4c64b82258e2"},"outputs":[],"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 in \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 \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"dd89bd67-0477-0ee4-f462-05fd1ad2ecd7"},"outputs":[],"source":"3"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"82a4c441-fdf5-8bcc-75b8-c4a847ea61ab"},"outputs":[],"source":"import cv2\nimg = cv2.imread(\"../input/train\", cv2.IMREAD_GRAYSCALE)\n\nimg=cv2.resize(img, (30, 20), interpolation=cv2.INTER_CUBIC) \nX.append(img)\n\nX = np.array(X).reshape(len(X), img_shape[0], img_shape[1], -1)\n\nX = X.astype(np.float32)"}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.0"}},"nbformat":4,"nbformat_minor":0}