{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"06fe685e-7ab3-f716-c562-19b8fa23a502"},"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":"aee30549-58c2-b1f5-bcc7-2abc8c76e3ef"},"outputs":[],"source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom skimage.io import imread, imshow\nimport cv2\n\n%matplotlib inline\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objs as go\nimport plotly.tools as tls\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input/train\"]).decode(\"utf8\"))"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"d4b94d16-0db6-3a58-1793-e89e155b8420"},"outputs":[],"source":"from glob import glob\nbasepath = '../input/train/'\n\nall_cervix_images = []\n\nfor path in sorted(glob(basepath + \"*\")):\n    cervix_type = path.split(\"/\")[-1]\n    cervix_images = sorted(glob(basepath + cervix_type + \"/*\"))\n    all_cervix_images = all_cervix_images + cervix_images\n\nprint(all_cervix_images[1])\n\nall_cervix_images = pd.DataFrame({'imagepath': all_cervix_images})\nall_cervix_images['filetype'] = all_cervix_images.apply(lambda row: row.imagepath.split(\".\")[-1], axis=1)\nall_cervix_images['type'] = all_cervix_images.apply(lambda row: row.imagepath.split(\"/\")[-2], axis=1)\nall_cervix_images.head()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"05f651dc-364b-29ea-440a-41c206da7a07"},"outputs":[],"source":"fig = plt.figure(figsize=(12,8))\n\ni = 1\nfor t in all_cervix_images['type'].unique():\n    ax = fig.add_subplot(1,3,i)\n    i+=1\n    f = all_cervix_images[all_cervix_images['type'] == t]['imagepath'].values[0]\n    plt.imshow(plt.imread(f))\n    plt.title('sample for cervix {}'.format(t))"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"583823d9-f919-ec48-4078-455aa651f579"},"outputs":[],"source":"# load the image and show it\nf = all_cervix_images[all_cervix_images['type'] == \"Type_1\"]['imagepath'].values[0]\nprint(f)\nimage = cv2.imread(f)\ncv2.imshow(\"original\", image)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"6487140b-d8f7-7972-470c-3c35b69205ac"},"outputs":[],"source":""}],"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}