{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"14039574-44d7-f363-50a5-a4dcaaf20c0c"},"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)\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n%matplotlib inline\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/train/Type_2\"]).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":"45483c50-9512-b210-6f45-23b589361d42"},"outputs":[],"source":"img = Image.open('../input/train/Type_1/0.jpg')\nimg.show()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"4ee8cf7e-0b19-2a9a-3194-e796607cec73"},"outputs":[],"source":"img = mpimg.imread('../input/train/Type_2/1098.jpg')\nplt.imshow(img)"}],"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}