{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "88169e18-abbb-d747-6517-dc29e4669152"
      },
      "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",
        "\n",
        "import numpy as np # linear algebra\n",
        "import 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",
        "\n",
        "from subprocess import check_output\n",
        "print(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": "29b43f92-b534-d607-e812-7bcc08db9ad8"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "c19c392b-a8cf-aa36-6533-01d844c7214a"
      },
      "outputs": [],
      "source": [
        "!ls ../input/train_sm/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "02a3e052-ae2b-4f0e-3ce3-9dc718a0dbba"
      },
      "outputs": [],
      "source": [
        "img = cv2.imread('../input/train_sm/set107_1.jpeg')\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "img2 = cv2.imread('../input/train_sm/set107_2.jpeg')\n",
        "img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "f92c0cef-4b67-eccb-c28c-bfcf4837af7b"
      },
      "outputs": [],
      "source": [
        "plt.imshow(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "638efd06-e863-41fd-fc7f-0d819311649c"
      },
      "outputs": [],
      "source": [
        "plt.imshow(img2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "76c069ce-83bf-e85e-214e-81b49c05f90c"
      },
      "outputs": [],
      "source": [
        "img.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "cb7f374f-1b2b-9876-91fc-259ef61cdfd8"
      },
      "outputs": [],
      "source": [
        "df_sub = pd.read_csv(\"../input/sample_submission.csv\")\n",
        "df_sub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "8080dd41-f4a7-7c3b-82f9-4184a320d4cb"
      },
      "outputs": [],
      "source": [
        "df_train = pd.read_csv(\"../input/train_sm/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "b207ab29-6192-239c-c0c8-d4848e79b5df"
      },
      "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": "3.5.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}