{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ed340255-812c-98bb-8f2b-8768a90dd7a5"},"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)\nimport glob\nfrom PIL import Image, ImageDraw, ImageFilter\nimport matplotlib.pyplot as plt\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%matplotlib inline\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ada3dc9d-f30e-25c5-0c97-42732de88885"},"outputs":[],"source":"train = pd.read_csv('../input/Train/train.csv')\ntrain1 = glob.glob('../input/Train/*.jpg')\ntrain2 = glob.glob('../input/TrainDotted/*.jpg')\nsubmission = pd.read_csv('../input/sample_submission.csv')\nplt.rcParams['figure.figsize']=(18.0,7.0)\nim1 = Image.open(train1[1])\nim1"}],"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}