04-Contour-Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.pieriandata.com\"><img src=\"../DATA/Logo.jpg\"></a>\n",
    "*Copyright by Pierian Data Inc.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contour Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## External vs Internal Contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('../DATA/internal_external.png',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(652, 1080)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x148fd2879b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img,cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**findContours**\n",
    "\n",
    "function will return back contours in an image, and based on the RETR method called, you can get back external, internal, or both:\n",
    "\n",
    "* cv2.RETR_EXTERNAL:Only extracts external contours\n",
    "* cv2.RETR_CCOMP: Extracts both internal and external contours organized in a two-level hierarchy\n",
    "* cv2.RETR_TREE: Extracts both internal and external contours organized in a  tree graph\n",
    "* cv2.RETR_LIST: Extracts all contours without any internal/external relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "image, contours, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(contours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(contours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(hierarchy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 22, 4)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hierarchy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 4, -1,  1, -1],\n",
       "        [ 2, -1, -1,  0],\n",
       "        [ 3,  1, -1,  0],\n",
       "        [-1,  2, -1,  0],\n",
       "        [21,  0,  5, -1],\n",
       "        [ 6, -1, -1,  4],\n",
       "        [ 7,  5, -1,  4],\n",
       "        [ 8,  6, -1,  4],\n",
       "        [ 9,  7, -1,  4],\n",
       "        [10,  8, -1,  4],\n",
       "        [11,  9, -1,  4],\n",
       "        [12, 10, -1,  4],\n",
       "        [13, 11, -1,  4],\n",
       "        [14, 12, -1,  4],\n",
       "        [15, 13, -1,  4],\n",
       "        [16, 14, -1,  4],\n",
       "        [17, 15, -1,  4],\n",
       "        [18, 16, -1,  4],\n",
       "        [19, 17, -1,  4],\n",
       "        [20, 18, -1,  4],\n",
       "        [-1, 19, -1,  4],\n",
       "        [-1,  4, -1, -1]]], dtype=int32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hierarchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw External Contours\n",
    "\n",
    "# Set up empty array\n",
    "external_contours = np.zeros(image.shape)\n",
    "\n",
    "# For every entry in contours\n",
    "for i in range(len(contours)):\n",
    "    \n",
    "    # last column in the array is -1 if an external contour (no contours inside of it)\n",
    "    if hierarchy[0][i][3] == -1:\n",
    "        \n",
    "        # We can now draw the external contours from the list of contours\n",
    "        cv2.drawContours(external_contours, contours, i, 255, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x148fd34e748>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADtCAYAAACxtL7fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAE5dJREFUeJzt3X+spFd93/H3p15sWlJYm8TWdndbG7FKQJUAd0WXElUpJNR2o6z/AAkUySt3pf0nbcgPKTHtHyhS/yhSFROrlZUVJlkiCjgOqVcWglqLq/YfHHYLNYaF7JIf3tt1vET+kSZITVy+/WPOxcP6eu/svTN3njnP+yWN5nnOnHvvOc9zns+ce2bm3lQVkqR+/a1lN0CStFgGvSR1zqCXpM4Z9JLUOYNekjpn0EtS5xYS9EluS/KtJOeT3LOInyFJmk3m/T76JNcAfwj8FLAGfBn4QFV9Y64/SJI0k0XM6N8OnK+qP6qqvwY+DRxewM+RJM1gEUG/F7gwtb/WyiRJS7BrAd8zG5S9bH0oyTHgWNv9RwtohyT17s+r6kc2q7SIoF8D9k/t7wMuXl6pqo4DxwGS+Ad3JOnq/ekslRaxdPNl4ECSW5JcC7wfOLmAnyNJmsHcZ/RV9WKSfwV8AbgG+HhVfX3eP0eSNJu5v71yS41w6UaStuJMVR3crJKfjJWkzhn0ktQ5g16SOmfQS1LnDHpJ6pxBL0mdM+glqXMGvSR1zqCXpM4Z9JLUOYNekjpn0EtS5wx6SeqcQT9nQ/hroJI0zaBfAMNe0pAs4l8JjpYBr97NOsaTjf51tJbFoJ+Tyy+AqnKwa+U5eemDQb9Ahr1WkeHeH4N+Drww1APHcb98MXbBvHg0dFXlOO2cM/pt8gLRKnLcjosz+h3gRaUhcTyOz6ZBn+TjSS4leXKq7IYkjyY51+6vb+VJcl+S80meSHLrIhu/bF4wWjWO2XGaZUb/28Btl5XdA5yqqgPAqbYPcDtwoN2OAffPp5nDc7UXjBeYlsl1+HHbNOir6r8Dz15WfBg40bZPAHdOlX+iJr4E7E6yZ16NXXVeaFoGx522ukZ/U1U9DdDub2zle4ELU/XWWllXvHC0CpzFa92833Wz0aeDNhxpSY4xWd5ZKdu9cPwQlXaCAa9pW53RP7O+JNPuL7XyNWD/VL19wMWNvkFVHa+qg1V1cIttkLQBQ16X22rQnwSOtO0jwMNT5Xe1d98cAl5YX+LpwbwuIC9ELYpjSxvZdOkmyaeAnwB+OMka8GHg3wMPJjkKPAW8r1X/HHAHcB74LnD3AtosaQOG/EvWj4XLpBMZwuBIsvxGbGIRx8lBqHkZwnU8bZlj+/Jj0fl1dmaW5W8/GbtEQ7s4tZocRy/Z6Fh4fAz6mThQpNU29mvYoF+ysQ9AbY/j5yWbHYsxHyuDfhM7MTjGPAC1dY6biav5YNhYj5lBfwVjHRQaPsfmhMdhNga9pJW01ZAf45ODQf8KdnowjHHwaWscK7paBv2AeAFrM46RiXn8zakxMeg3MLZBIK2Kef5FzjFd5wb9ZZZ98pf98zVcYx8bY+//dhj0kgZvUSE/licPg37KUE76UNqh4RjzmBhz3+fFoJc0WIb8fBj0zdAG1NDao+UZ61jYqX6P4fga9AM2hgEobcSxP18GPQ4qaUiWcT32ngEG/cD1PgB1ZWM7/2Pr704ZfdCvwsBahTZK2+U4X5xRB70DSxqGIVyLQ2jDoow66FdJz4NQ4+bYXrzRBr2DS0M3hjE6hj4OwWiDfhV5Uagnjueds2nQJ9mf5LEkZ5N8PckHW/kNSR5Ncq7dX9/Kk+S+JOeTPJHk1kV34mo5wKTlGuo1ONR2bdcsM/oXgV+uqjcBh4CfS/Jm4B7gVFUdAE61fYDbgQPtdgy4f+6t3oZVP5Gr3n7JMbzzNg36qnq6qv5n2/4/wFlgL3AYONGqnQDubNuHgU/UxJeA3Un2zL3lklaOIb8cV7VGn+Rm4G3A48BNVfU0TJ4MgBtbtb3AhakvW2tlS9fLIOulHxoXx+3y7Jq1YpIfAn4P+IWq+oskr1h1g7KXneEkx5gs7WgLqoornAOtuN5Csbf+rJqZgj7Jq5iE/Cer6rOt+Jkke6rq6bY0c6mVrwH7p758H3Dx8u9ZVceB4+3778goMBilnWfIL98s77oJ8ABwtqp+feqhk8CRtn0EeHiq/K727ptDwAvrSzySxsWQH4ZsdiKS/DjwP4CvAd9rxf+GyTr9g8DfB54C3ldVz7Ynhv8I3AZ8F7i7qk5v8jMcDdKUVQ/IJCvbhxX7zf9MVR3crNKmQb8TDHrpBw3huhyrHoPeT8ZKUucMeknqnEEvSZ0z6CWpcwa9JHXOoJekzhn00gCt2Fv8NHAGvSR1zqCXpM4Z9JLUOYNekppeXxsx6CWpcwa9NFC9zi618wx6SeqcQS9J9P0blEEvSZ0z6KUB63mWOSS9H2eDXpI6Z9BLA9f7bFOLZ9BLGrUxPJEa9NIKGEMYaXEMeknq3KZBn+TVSf4gyf9K8vUkv9bKb0nyeJJzST6T5NpWfl3bP98ev3mxXZDGwVn9/I3lmM4yo/+/wLuq6i3AW4HbkhwCPgLcW1UHgOeAo63+UeC5qnojcG+rJ0lakk2Dvib+su2+qt0KeBfwUCs/AdzZtg+3fdrj785YnjYlrYwxxdJMa/RJrknyVeAS8CjwbeD5qnqxVVkD9rbtvcAFgPb4C8Dr59loaazGFE6LNLbjOFPQV9X/q6q3AvuAtwNv2qhau9/oCNblBUmOJTmd5PSsjZU0vpCatzEev6t6101VPQ/8N+AQsDvJrvbQPuBi214D9gO0x18HPLvB9zpeVQer6uDWmi5JmsUs77r5kSS72/bfBn4SOAs8Bry3VTsCPNy2T7Z92uNfrKqXzeglbd0YZ6XzMNbjtmvzKuwBTiS5hskTw4NV9UiSbwCfTvLvgK8AD7T6DwC/k+Q8k5n8+xfQbmn0kuAcanZjDXmADGGgJFl+I6QVNYRreBV0GvRnZln+9pOxkrrXacjPbJalGw3QZrO4sQ/sMXEJ58q8Fgz6QdvOxXulr3Xg98ew35hjfcKgH5iduFinf4YXQj8M+x/k2H6JQT8Ay7w4Df2+GPYTjuUf5IuxS1RVg7ooh9Yebc3YQ27s/d+IM/olGHqYrrfPC2Z1rZ+7oY+1eXPMbsyg30GrdtEZ+KtvLEs5jtErc+lmh6zyxbbKbVf/Idh7/+bBoN8BPQSl6/erLUmXgdhjnxbBoF+gHsOxt/6MTS/B2OsT16IY9AvScyD23LcxWPWQXOW2L4svxs7ZWEKwqrzgVtwqvTPHsbY9zui1ZasQENrckGf4Q27bKnFGP0djDD5n9v2YPo/LHsuOqfky6Odk2RfGMhn2/VlG6DuGFsegn4Mxh/w6w75fl5/XeY13x8vOMei3yZB/iWE/Dp7j1eOLsdtgyL+cx0QaHoNekjpn0G+RM9dX5rGRhsWg3wKDbHMeI2k4Zg76JNck+UqSR9r+LUkeT3IuyWeSXNvKr2v759vjNy+m6cthgElaNVczo/8gcHZq/yPAvVV1AHgOONrKjwLPVdUbgXtbPY2QT4rSMMwU9En2Af8C+FjbD/Au4KFW5QRwZ9s+3PZpj787nbwfy+CStIpmndF/FPgV4Htt//XA81X1YttfA/a27b3ABYD2+AutvkbIJ0dp+TYN+iQ/DVyqqjPTxRtUrRkem/6+x5KcTnJ6ppYumYG1dR47ablm+WTsO4GfSXIH8GrgtUxm+LuT7Gqz9n3AxVZ/DdgPrCXZBbwOePbyb1pVx4HjAElMAklakE1n9FX1oaraV1U3A+8HvlhVPws8Bry3VTsCPNy2T7Z92uNfLKd0krQ023kf/a8Cv5TkPJM1+Ada+QPA61v5LwH3bK+Jy+fz1PZ5DKXlyRAuwKEv3QzhGPWgkzdfSUNypqoOblbJT8ZuwpCfH4+ltBwGvSR1zqCXpM4Z9JLUOYP+ClxTltQDg147yidPaecZ9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucMeknqnEEvSZ0z6CWpcwa9JHXOoJekzs0U9En+JMnXknw1yelWdkOSR5Oca/fXt/IkuS/J+SRPJLl1kR2QJF3Z1czo/1lVvbWqDrb9e4BTVXUAONX2AW4HDrTbMeD+eTVWqy/Jspsgjc52lm4OAyfa9gngzqnyT9TEl4DdSfZs4+csjaEkqQezBn0B/zXJmSTHWtlNVfU0QLu/sZXvBS5Mfe1aK5MkLcGuGeu9s6ouJrkReDTJN69Qd6Np8Mv+I3R7wji2QV1J0hzNNKOvqovt/hLw+8DbgWfWl2Ta/aVWfQ3YP/Xl+4CLG3zP41V1cGrNX5K0AJsGfZLXJPm769vAe4AngZPAkVbtCPBw2z4J3NXefXMIeGF9iWcVuU4/Px5LaTlmWbq5Cfj9dpHuAv5zVX0+yZeBB5McBZ4C3tfqfw64AzgPfBe4e+6tliTNLFUvWz7f+UYky2/EJoZwnFaZs3lpIc7MsvztJ2MlqXMG/YyckW6dx05aLoNekjpn0GuhnM1Ly2fQXwVDS9IqMuivkmE/O4+VNAwGvSR1zqDfAmeqm/MYScNh0G+RQfbKPDbSsBj022CgSVoFBr3myic/aXgM+m0y2F7isZCGyaCfAwPOYyANmUE/J2MOujH3XVoFBv0cjTHwxthnadUY9HM2puAbU1+lVWbQL8AYAnAMfZR6Mcu/EtQWrAdhb/+ZyoCXVo8z+gXrKRh76os0Jgb9DughIHvogzRWLt3skFVdyjHgpdVn0O+wVQl8A17qx0xLN0l2J3koyTeTnE3yjiQ3JHk0ybl2f32rmyT3JTmf5Ikkty62C6spySDDdKjtkrR1s67R/wbw+ar6MeAtwFngHuBUVR0ATrV9gNuBA+12DLh/ri3uzFCCdSjtkDR/mwZ9ktcC/xR4AKCq/rqqngcOAydatRPAnW37MPCJmvgSsDvJnrm3vDPLCloDXurfLDP6NwDfAX4ryVeSfCzJa4CbquppgHZ/Y6u/F7gw9fVrrUwzWA/e6dsqfG9JwzVL0O8CbgXur6q3AX/FS8s0G9koPV72ymOSY0lOJzk9U0tHbKOA3spN0jjNEvRrwFpVPd72H2IS/M+sL8m0+0tT9fdPff0+4OLl37SqjlfVwao6uNXGS5I2t2nQV9WfAReS/GgrejfwDeAkcKSVHQEebtsngbvau28OAS+sL/FIknberO+j/9fAJ5NcC/wRcDeTJ4kHkxwFngLe1+p+DrgDOA98t9WVJC1JhvDBnSTLb4QkrZ4zsyx/+7duJKlzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucMeknqnEEvSZ0z6CWpcwa9JHXOoJekzg3ln4P/JfCtZTdiCX4Y+PNlN2IJ7Pe42O/F+QezVBpK0H9rjH+XPslp+z0e9ntchtRvl24kqXMGvSR1bihBf3zZDVgS+z0u9ntcBtPvQfzjEUnS4gxlRi9JWpClB32S25J8K8n5JPcsuz3zlGR/kseSnE3y9SQfbOU3JHk0ybl2f30rT5L72rF4Ismty+3B1iW5JslXkjzS9m9J8njr82fa/x8myXVt/3x7/OZltnu7kuxO8lCSb7bz/o7ez3eSX2zj+8kkn0ry6l7Pd5KPJ7mU5Mmpsqs+v0mOtPrnkhxZdLuXGvRJrgH+E3A78GbgA0nevMw2zdmLwC9X1ZuAQ8DPtf7dA5yqqgPAqbYPk+NwoN2OAffvfJPn5oPA2an9jwD3tj4/Bxxt5UeB56rqjcC9rd4q+w3g81X1Y8BbmByDbs93kr3AzwMHq+ofAtcA76ff8/3bwG2XlV3V+U1yA/Bh4B8Dbwc+vP7ksDBVtbQb8A7gC1P7HwI+tMw2Lbi/DwM/xeTDYXta2R4mnyMA+E3gA1P1v19vlW7APiYD/l3AI0CYfHBk1+XnHfgC8I62vavVy7L7sMV+vxb448vb3/P5BvYCF4Ab2vl7BPjnPZ9v4Gbgya2eX+ADwG9Olf9AvUXclr10sz5I1q21su60X1HfBjwO3FRVTwO0+xtbtV6Ox0eBXwG+1/ZfDzxfVS+2/el+fb/P7fEXWv1V9AbgO8BvtWWrjyV5DR2f76r638B/AJ4CnmZy/s4wjvO97mrP746f92UHfTYo6+5tQEl+CPg94Beq6i+uVHWDspU6Hkl+GrhUVWemizeoWjM8tmp2AbcC91fV24C/4qVf4zey8n1vSw6HgVuAvwe8hsmSxeV6PN+beaW+7vgxWHbQrwH7p/b3AReX1JaFSPIqJiH/yar6bCt+Jsme9vge4FIr7+F4vBP4mSR/AnyayfLNR4HdSdb/5MZ0v77f5/b464Bnd7LBc7QGrFXV423/ISbB3/P5/kngj6vqO1X1N8BngX/COM73uqs9vzt+3pcd9F8GDrRX6K9l8iLOySW3aW6SBHgAOFtVvz710Elg/ZX2I0zW7tfL72qv1h8CXlj/lXBVVNWHqmpfVd3M5Hx+sap+FngMeG+rdnmf14/Fe1v9lZzhVdWfAReS/GgrejfwDTo+30yWbA4l+TttvK/3ufvzPeVqz+8XgPckub79RvSeVrY4A3hh4w7gD4FvA/922e2Zc99+nMmvZE8AX223O5isSZ4CzrX7G1r9MHkX0reBrzF5J8PS+7GN/v8E8EjbfgPwB8B54HeB61r5q9v++fb4G5bd7m32+a3A6XbO/wtwfe/nG/g14JvAk8DvANf1er6BTzF5LeJvmMzMj27l/AL/sh2D88Ddi263n4yVpM4te+lGkrRgBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucMeknqnEEvSZ37/4f+15GEtGhBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(external_contours,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create empty array to hold internal contours\n",
    "image_internal = np.zeros(image.shape)\n",
    "\n",
    "# Iterate through list of contour arrays\n",
    "for i in range(len(contours)):\n",
    "    # If third column value is NOT equal to -1 than its internal\n",
    "    if hierarchy[0][i][3] != -1:\n",
    "        \n",
    "        # Draw the Contour\n",
    "        cv2.drawContours(image_internal, contours, i, 255, -1)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x148fd45c588>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_internal,cmap='gray')"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}