{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
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  "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\n\nimport time\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom PIL import Image\nfrom PIL import ImageFilter\nimport multiprocessing\nimport random; random.seed(2016);\nimport cv2\nimport re\nimport os, glob\n\nsample_sub = pd.read_csv('../input/sample_submission.csv')\ntrain_files = pd.DataFrame([[f,f.split(\"/\")[3].split(\".\")[0].split(\"_\")[0],f.split(\"/\")[3].split(\".\")[0].split(\"_\")[1]] for f in glob.glob(\"../input/train_sm/*.jpeg\")])\ntrain_files.columns = ['path', 'group', 'pic_no']\ntest_files = pd.DataFrame([[f,f.split(\"/\")[3].split(\".\")[0].split(\"_\")[0],f.split(\"/\")[3].split(\".\")[0].split(\"_\")[1]] for f in glob.glob(\"../input/test_sm/*.jpeg\")])\ntest_files.columns = ['path', 'group', 'pic_no']\nprint(len(train_files),len(test_files),len(sample_sub))\ntrain_images = train_files[train_files[\"group\"]=='set107']\ntrain_images = train_images.sort_values(by=[\"pic_no\"], ascending=[1]).reset_index(drop=True)\nplt.rcParams['figure.figsize'] = (12.0, 12.0)\nplt.subplots_adjust(wspace=0, hspace=0)\ni_ = 0\na = []\nfor l in train_images.path:\n    im = cv2.imread(l)\n    plt.subplot(5, 2, i_+1).set_title(l)\n    plt.hist(im.ravel(),256,[0,256]); plt.axis('off')\n    a.append([im.mean(),im.max(),im.min()])\n    plt.subplot(5, 2, i_+2).set_title(l)\n    plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB)); plt.axis('off')\n    i_ += 2\nprint(a)\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."
 }
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