23_ContourDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(652, 1080)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x210b1a67240>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6tQ273c6qVau49dZbmThxImPGjGHUqFFs3bq1yuUqYTab6d+/P0uWLHFpa6D09HT+/ve/XzEoWF5eHh988EGZs0VZrVY6duzoVfe36mVw7927N7fffruni1Ep7du3Z+bMmXXil4aovpLxyoODg6u8jfPnz5cZyLTWnDlzxqlt7N69m0WLFnHmzBm01hQWFrJv3z7mzp3LuXPnHL6uogEJzWYzAwcOZMmSJS5vqWKz2cjMzCxz2blz5ygoKChzWYcOHbyqZ3iFwV0p1Vwp9a1S6oBSar9SaraRH6aU2qiUSjT+NjTylVLqFaVUklJqj1KqVt3qDw4O5rHHHqvWP40nmEwmZs6cWe/mgayvfHx8uPbaa6vVcSkmJqbMezU+Pj5ONzPctGlTmTMvJSYmsmPHjiqVy2KxMGjQIBYvXkyrVq2qtI3yhISEONxuu3btHA5p3LJlS68a592ZK/dCYK7W+lqgJ3C/Uqo9MB/YpLWOATYZzwGGADFGmg687vJSV8PYsWPp06ePp4tRJU2aNGHevHnVusEm6gY/P79qDxLWuXPnKybkMJlMDBkyhLi4OKe2YbPZyszXWjtcBjj8hWmxWBg8eDCLFy/m6quvdqoMlRUYGMgDDzxASEjIZflRUVHMmDHD4QiY4eHhNTaTVE2osIJJa30KOGU8zlJKHQCaASOAOGO1d4B/Aw8b+e/q4t9lPyilGiiloozteFTTpk156KGH6nRwvO2221i9ejVff/21p4si3CgwMLDagcbHx4fHH3+cTp06sW7dOgoLCxkwYADjx493uvXVjTfeyPLly8nLy7ssPyIiotITu1ssFoYMGcLTTz/t9tEYhw0bhr+/P++++y5paWk0b96cGTNmlDtGjZ+fX/0K7qUppVoCXYBtQGRJwNZan1JKlYy+0wxILvWyFCPPo8HdZDJx//3307ZtW08Wo9qCgoJISEjghx9+cFivKOq+Ro0aERYWVu3tBAYGcueddzJu3DiASt8w7NOnD3fffTdvv/32pSv1Bg0aMG/ePFq2bOn0diwWC8OGDWPhwoXV+kWSn59PUVERVqu13DHoTSYT8fHx9O/fn8LCQnx8fCocs97X15eoqCiUUm6dJrCmOH2mlVJBwCfAHK11Zjk39spacMU7pZSaTnG1TY0IDAykqKiIFStW1NQu3aawsJCmTZtKcPdiERERBAUFuWx7VW0F4uvry5NPPkl8fDz/+c9/sFqtDB48mM6dOzt9c9/Hx4dhw4bx1FNPVbn1T25uLu+99x5r167FZrPRtm1b5syZU2FPVrPZ7PR9C6UUzZs3x2w2l3kjuq5x6owrpXwoDuyrtdb/MLLPlFS3KKWigFQjPwUo/dUcDZz8/Ta11m8Cbxrbd/vXZFZWFk8++aS7d1NjvOHKQjjWoEGDcntO5+fnc/bsWSwWC40aNXJrEz5fX1/i4uKcrqcvzcfHh5EjR/LEE09UObDb7XZeeeUVnn/++UtBd9++ffz888988MEHLm1tExERgcVi8Yrg7kxrGQWsAg5orV8otegzYJLxeBLwaan8iUarmZ5ARm2ob4figOgtSXi34OBgh1fGu3btYuLEifTv359+/foxe/ZsUlNTy1zXk3x8fBg1ahQLFy6sVnv9EydO8Pbbb18RcJOSkli9enV1i3mZoKAgr2nr7sxR3ATcBexVSu028hKApcBHSqmpwHFgjLFsPTAUSAJygCkuLbEQ9YC/v3+ZwT05OZkZM2aQlJR0KW/NmjXYbDaWL1/usJlfTfP19WXYsGE8+eST1e58d/LkSdLT08tcdvjw4Wpt+/f8/f29Ztx8Z1rLfE/Z9egAA8pYXwP3V7NcQtRbSimHwX39+vUcOXLkivyvvvqKvXv30r1795ooYrl8fHwYPXo08+fPd0mv6vDwcAICAsocF8bVQ2L7+vp6TXCvlz1UhajNlFIO69uPHz9eZrVcbm4uJ09ecWvLI7p06UJCQoLLhsto0aIFo0ePvqK1S9OmTS+1AnIVs9nsNb3AvaNySQgvorV22EW+TZs2mEymK8ZNCQoKqjXTRLp6AC6LxcLjjz9OSEgIn3/+OTabjdatWzN37lyX99guLCy84r2tqyS4C1HLlPT+LOsKfejQoaxevZrt27dfyjOZTNx2221ePTRFSEgIjz32GLNnz6agoICgoCC33F/Iy8uT4C6EcB9HwT0iIoJVq1axfPlyNm/ejJ+fHyNHjmTy5MlV6nldVFREWloaWmsaNmxYqYlrcnNzOX/+PP7+/jRo0KDCTkLVpZS6YkgBV7PZbBQVFbl1HzVFgrsQtdCFCxccXkE2b96cRYsWkZubi9lsxs/Pr0r7OHr0KMuWLeO7777DbrcTGxvL448/XuHwu3a7na+//pqXX36ZxMREgoODGTt2LA888ECdG5Dv9zIyMhxWidU1EtyFqIXOnTtHXl6ewytpk8lUrdm50tPTeeCBB/jhhx8u5a1bt46UlBQ+/vjjckdH3LZtG/fdd9+l5onnz5/n+eefJy8vjyeeeMLtV/Du5GiY5Lqo7p4FIbxYeno6OTk5btv+li1bLqu3L7Fnzx42b97s8HVaa95///0r2p3b7XbWrl3r9DjxJbKzs/n888959tlnee+99yo1Q1Rpubm5bNy4kWeffZaVK1fy66+/VnobJePce0snQblyF6IWSk9PJzMz022zb504caLMumW73U5KSorD1xUWFjoMwBkZGWRkZDg9oXd6ejqzZs3iq6++unS13Lp1a1577TViY2Od2gbAxYsXmTdvHp988smlKpWrrrqKV155hb59+zq9nfz8fJKTk73mhqpcuQtRC2VmZpKcnFzxilXUqlWrMrvZWyyWcucytVgsDjsORUREEB4e7nQZPvroI9avX39ZNUhSUhILFiwgOzvb6e18/PHHfPzxx5fVlR8/fpwFCxZUanC93NzcKv9yqI0kuAtRC+Xm5l42xICr9erVi0GDBl3WYUcpRVxcHL1793b4OqUUU6ZMITo6+rJ8X19fpk6d6vRMRkVFRWzevLnMq+S9e/c6/cWmtWbz5s1l/go5ePBgpb4gz507V+lqpdpMqmWEqIUKCws5dOgQRUVFbukOHxgYyIsvvkj79u3ZsGEDdrud+Ph4pk+fXuFQwx07duTdd99l+fLl7Nu3j0aNGnHnnXdy2223Od27UynlcIAuk8lUqWN2dAPXZDJV6ubusWPHSEtLc3r92k6CuxC1kNaaw4cPk5WVRYMGDdyyj/DwcB555BFmzZoFQEBAgNPB+frrr2fFihVkZ2djtVor1T4eigPvLbfcwpdffnlF65TY2FinJ/RQSnHLLbfwxRdfXNGEsXPnzpWaGGT//v3k5uY6vX5tJ9UyQtRSR48e5fjx427fT2BgIIGBgZUeU8VkMhEcHFzpwF5i5MiR3HPPPZfaxpvNZrp168aCBQsq1XZ/+PDhTJs2jYCAgEvl6tChA88884zTE57k5uaya9cur+nABKBqQ7OfmpisQ4i6xmq1smzZMiZOnOj0a/Ly8tiyZQtbtmwhICCA+Ph4OnXqVGsHwyosLGT//v3s27ePiIgIYmNjq/RLpaCggJ9//pl9+/YRFhZGr169iIiIcPr1R44cYcyYMVVqQulhO7TWZQ4FKsFdiFpKKcW4ceN46aWXnLo6ttlsPProo5fGd4fiqpfFixczevToWhvga4N//OMf/OlPf6qL1TIOg7tUywhRS2mt2b17t9ND+W7atInVq1dfCuxQ3AJk8eLFXtUKxNUKCgrYsmXLZe+bN5DgLkQtdvz4cbZs2eLUups3by5zQosTJ05w4MABVxfNa5w6dYqtW7d6Tc/UEhLchajFcnJy+Prrr53q1ONoVEilVJVGjKwvvvvuu7pY114hCe5C1HI7duxwaq7QgQMHXmoxUlqbNm3o0KGDO4pW5128eJH169d7XZUMSHAXotZLTU1l3bp1FY5W2KdPH+bOnUtYWBhKKcxmM+3atWPx4sU0bNiwhkpbt+zdu7fMAdS8gbSWEaIOaNOmDe+9955TY60nJiayc+dOgoKC6NmzZ6WaBJYoLCxkx44dfPPNNwDExcURGxtb6d6ydrud/fv38/XXX5OTk0OvXr3o27dvldvGu1J+fj6PPPII77zzTl0eLEyaQgpRl/n6+jJv3jxmz57tluEISisqKuKFF17glVdeuVTXHxwczLx587j33nud7tJfMjzwwoULOX/+PAB+fn5MmzaNxx57zOMBfvv27UyePLnWTCxeRVVvCqmU8lNK/aiU+lkptV8ptdDIv1optU0plaiU+rtSytfItxrPk4zlLV15JELUR/n5+fzzn//kt99+c/u+du/ezWuvvXbZTdysrCxefPHFSrW6SU5OZtmyZZcCOxS3xV+5ciXff/+9S8tcWXl5eaxZs4bTp097tBzu5MxXcB7QX2t9HXA9cItSqiewDHhRax0DXACmGutPBS5orVsDLxrrCSGq6dChQ6xZs8bt08D98MMPZGVlXZF/4cIFduzY4fR29uzZU2b7epvN5nTzTnfZuXMnX331VV2ujqlQhcFdF7toPPUxkgb6A2uN/HeAkcbjEcZzjOUDlHSNE6LaCgoK+Pjjj9m9e7db92O1Wh0uq0yTSl9fX4e9YqtaJWOz2cjOzq7WGDAZGRmsWLHCq6/awcnWMkops1JqN5AKbASOAOla65Lb9ylAM+NxMyAZwFieATg3yLMQolwpKSmsWrWKixcvVrxyFfXt25cmTZpckd+0aVN69erl9Ha6dOlC69atr8gPCQlhwIABlSpTdnY2K1asYOjQocTFxTFz5kz27dtXqW1A8Q3eTz/9lG+++cbrOi39nlPBXWtdpLW+HogGYoFry1rN+FvWV/UV76JSarpSartSyjvbIQnhBna7na+//pp169a5rUohJiaGpUuX0rJlS0wmE0opmjVrxlNPPUWLFi2c3k5ERAR//etf6dix46WbwOHh4cyfP58uXbo4vR273c4LL7zA448/zu7duzly5AiffPIJkydPJjExsVLHlpSUxMqVKys101NdVenWMkqpJ4Ec4GGgida6UCnVC1igtR6slNpgPN6qlLIAp4EIXc6OpLWMEJXTvn17Xn/9dTp16uS2fZw5c4aff/4Zm83G9ddfT/Pmzas0+Nj58+f5+eefSU9Pp2PHjlxzzTWVmkTj+PHjxMfHc/bs2SuWPfjggzz++ONObScrK4vHHnuMDz74wJuG9nXYWqbCyTqUUhFAgdY6XSnlDwyk+Cbpt8DtwIfAJOBT4yWfGc+3Gsu/KS+wCyEq7+DBg7z00ks899xzbuugFBkZyaBBg6q9nbCwMPr161fl16empjqcC/XgwYNObaOoqIiPP/6Yf/7zn94U2MvlzNdnFPCtUmoP8BOwUWu9juIr9z8rpZIorlNfZay/Cmhk5P8ZmO/6YgtRv5VUz7z99tvk5eV5ujhuFRYWRmBgYJnLWrZs6dQ2fvjhB1577bUyWwF5qwqv3LXWe4ArKsi01kcprn//fb4NGOOS0gkhHMrOzuaNN96gWbNm3H777W7v3OQpV111FbfddhtvvfXWZfcZoqKimDBhQoWvT0xM5JlnnqmRPgK1icyhKkQddvbsWf7617/SpEkT+vbt65UTclgsFh599FGCg4P59NNPyc7Opl27djz00EN07Nix3NeeOXOGJUuWsH37dq9vHfN7MvxAHWA2m/H19cXPz4/g4GDCwsIICgrC398ff39/AgMDsVgs5OXlkZeXR1paGpmZmZw7d47s7GxsNhsFBQVe3WGjPlNK0bVrV5599tlKtUJxBbvdzuHDh9mxYwd+fn706NGD6Ohot+xLa01mZiZ5eXmEhoaW2x4fim/kPvXUU3z44YdljnPvJWRsmbrEbDYTFBREZGQkbdq0oVOnTrRv354WLVoQGRl56YNtsVgudRRRSqG1RmtNbm4uOTk5pKWlcfr0aX777TcSExP5+eefOXz4MGfPniU7O1uCvRdRShEbG8vSpUu57rrramSfhYWF/O1vf+PVV1/lwoULKKVo2bIlf/3rX6t1A9UVMjIyWLJkCe+++65XDudbigT32s5kMhEaGkrnzp3p168f3bp1o127dkRGRhIYGFippmNlsdvt5PEZCVoAABz8SURBVOTkcPr0aQ4cOMC2bdv497//zS+//EJGRoYEei9gMpmIjY1l2bJlbm0iWWLLli2MHz/+ijbjbdu25fPPPyc8PNztZSjL+fPnee6553jnnXfq4pyolSXBvbby9/fnmmuuYeDAgfzhD3+ga9euhIaGur3uVGtNeno6O3fu5IsvvmDTpk0kJibWh38Gr6aU4oYbbmDhwoXExsa69XO0YMECXnnllSvyfXx8+PDDDz1y9X7q1CkWL17M2rVrvb4VkaHq7dyFewQGBnL99dczfvx4hg0bRnR0NBZLzZ0OpRQNGzZkwIABxMXFkZyczLp16/j73//O7t273dq9XbiP1pqffvqJOXPm8NhjjzF48GC3fK601g4HMNNae6Qt+eHDh1myZAnr1693++BqdYFcudewgIAAYmNjufPOOxk6dChRUVG1poWD1prU1FS++uorVq5cyY4dO+RKvg6Lioriz3/+M+PHj3fYTrw6vvjiC6ZOnXrFzcoWLVqwfv16oqKiXL7PshQWFrJ161aeffZZtm3bVuGMVV5GqmU8zWw2ExMTw4wZM5gwYQKNGzeuNUH997TWnDhxgg8//JD/+Z//4dChQ/XtH8ZrhISEMGbMGO6//36nO/w4y2azsWDBgstuWkZERLBo0SJGjx5dI5/vjIwM1q5dy4oVKzh69Gi9a+6IBHfPCgsLY8SIEcyaNYtOnTrVmc4mRUVF7N27l5deeolPP/2U9PR0TxdJVIHFYqFr167MnTuXm2++2aUzIOXn5/Pf//6XLVu2EBQUxKBBg2jXrp3bA7vdbmffvn2sWLGCL7/8koyMDLfurxaT4O4JJpOJNm3a8NBDDzFu3DiCgoI8XaQqyczM5KOPPuLll1/ml19+kZY1dVTjxo0ZM2YMU6ZM4eqrr661vxwrkpaWxmeffcaqVas4dOhQvRkrxgEJ7jXNarXSv39/Hn/8cXr06FHtpoyeZrfb2blzJwsWLGDjxo3e3CnEq1ksFtq2bcvdd9/NiBEjCAsL83SRnHbx4kW+//573nvvPbZs2eJwMLF6RoJ7TQoODmbSpEk8/PDDbuut5ykpKSksWrSI999/X1rU1GEBAQH06NGDu+66i759+9KwYcNaeyWfnZ3Nrl27WLNmDd988w2pqan1sW7dEQnuNSUsLIxZs2YxZ84cQkNDPV0ct8jIyOC5557j1Vdfrc91nV4hMDCQLl26MHbsWAYMGEBkZGSt+JWptebChQts3bqVzz77jP/+97+cPn26vlfBlEWCe02IjIwkISGBe+65B39/f08Xx60uXrzIa6+9xnPPPUdaWpqniyOqyc/PjzZt2jBgwAAGDRpE+/btCQ4OrvFy5OXl8dtvv7Flyxa+/PJLdu3axfnz5+VK3TEJ7u4WGRnJM888w8SJE13aGqE2s9lsrFixgqeffprz5897ujjCBUwmEw0bNqRTp07ExcURGxtL69atadCggds6Q2VnZ3Pq1Cl2797Npk2b+PHHHzl9+rS3jwnjKhLc3SksLIyFCxcyffr0ehPYS9hsNl544QWWLVsmN7i8jNlsJiQkhKuuuoouXboQGxtLTEwMTZo0ISQkhICAgEtVOBXV15cMapefn09WVhapqan8+uuv7Nmzh127dpGUlERqaiq5ublylV45EtzdJSgoiISEBB588EH8/Pw8XRyPuHjxIs888wyvvPKK9Gj1UkopLBYLwcHBhISEEB0dTWRkJI0bNyYiIoLQ0FBCQ0MvDXJXWFhIQUEBhYWF5OTkcObMGU6ePElqairJycmXps6z2WzStLZ6JLi7g4+PD1OnTuXZZ5/1SP1kbZKWlsaf/vQnPvroI7npVY+UXLGbzWYsFgtms/nS8NN2u/3S38LCwktX78KlJLi7mlKK+Ph4Vq5cSfPmzT1dnFrh8OHDTJkyha1bt8o/sRA1w2Fw93ybpzoqJiaGBQsWSGAvpU2bNjz55JNe17ZfiLpIgnsVhISE8OCDDxIbe8X84PVev379mDlzJgEBAZ4uihD1mgT3SjKZTAwdOpQJEybUmQHAapKPjw/Tpk3j5ptvrrU9HoWoD5wO7kops1Jql1JqnfH8aqXUNqVUolLq70opXyPfajxPMpa3dE/RPaNly5Ze3fvUFRo3bsycOXNo0qSJp4siRL1VmSv32cCBUs+XAS9qrWOAC8BUI38qcEFr3Rp40VjPK/j6+vLHP/6Rbt26ebootd7NN9/M7bffLr9uhPAQp4K7UioauBVYaTxXQH9grbHKO8BI4/EI4znG8gHKS36ft2/fnrvuustlPfUKCwtJS0vj9OnTZGZm1nh7X7vdzoULFzh16hTp6ekubcJotVqZOnUq11xzjcu2KYRwnrNX7i8B84CS6NMISNdal0zPkwI0Mx43A5IBjOUZxvp1mp+fH3/84x9p1apVtbeVk5PDxx9/zMiRI4mNjaVbt2706tWLu+++m//85z9un/WooKCAr776ijvuuOPS/nv06MH48eP5/PPPXdbtu0OHDowZM6ZG54YVQhhKOhY4SsAwYLnxOA5YB0QASaXWaQ7sNR7vB6JLLTsCNCpju9OB7UbStT117txZJyUl6eo6efKkHjdunPb19S1zP8HBwfrRRx/V2dnZ1d5XWTIzM/Xs2bN1QEBAmfv38/PT06ZN02lpaS7Z3+7du3XLli09fv4kSfLStF07it2OFuj/C8JLKL4y/xU4DeQAq4FzgMVYpxewwXi8AehlPLYY66kK9uHpN6jc5OPjo5944gldWFhYrUCXlZWlR48erY1OWw6TxWJxyf5+Lz8/X8+ZM0ebzeZy928ymfSUKVN0bm5utfeZl5en7733Xm0ymTx+HiVJ8sJU9eCuLw/CccA64/HHwHjj8QrgPuPx/cAK4/F44CMntuvpN6jcdNVVV+kdO3ZUO9CtXLlSWywWp/bZoEEDvW3btmrvs7SNGzfqwMBAp/ZvtVr1J5984pL9fvvtt7px48YeP4+SJHlhchjcq9PO/WHgz0qpJIrr1FcZ+auARkb+n4H51diHxyml6NmzJ+3atavWdvLy8njvvfecrk9PT0/ngw8+qNY+S9Na895775Gdne3U+nl5ebzzzjsuqf/v0qWLtDASooZV6k6X1vrfwL+Nx0eBK7poaq1twBgXlK1WCAgI4NZbb612j8u0tDQOHDhQ8YqlbNu2DZvN5pLRJi9evMjOnTsr9Zpdu3aRmZlZ7Xk2Q0ND6devH5s2bZK5V4WoIdJDtQLNmzend+/e1d5OTk4OeXl5lXpNenp6pV/jiM1mq/SUeDk5OS6bJ7Vfv340btzYJdsSQlRMgnsFunTpQrNmzSpesQJ+fn74+PhU6jVBQUEum/zDarUSEhJS6de4arrANm3a0LFjR5dsSwhRMQnu5fD19eXGG2/EarVWe1vh4eGVrrfv1q2byyYACQoKokuXLpV6TceOHV02zEJwcDDdu3eXHqtC1BAJ7uVo2LAh3buXOVRypfn5+TF58mSnO/QEBQVxxx13uGzwLZPJxMSJE52+d+Dj48OkSZNc9stBKUX37t1ltEghaogE93JER0e7pEdqibFjxzJ06NAKA7bZbGbmzJnceOONLts3FI/3MmnSpEvzXjqilGLUqFGMHDmy3PUqq3379kRGRrp0m0IIBxy1kazJhOfbipaZxo4dq202m0vaepc4ceKEHjVqlMP27v7+/nrWrFk6IyPDpfstceHCBX3PPfdoPz+/Mvfv4+OjJ0yYoM+cOePyfWdmZurBgwd7/LxKkuRFyWE7dxn0wwGTyUTbtm1dVi1RomnTprz77rusXbuW999/n19++QWbzUZISAhdu3Zl2rRpxMfHV/rmq7MaNGjAq6++yq233spbb73F7t27ycrKwt/fn+uuu4677rqLESNGuKX6xN/fnxYtWrh8u0KIK0lwd8DX15eYmBi3TDgRFBTE5MmTueOOO8jMzOTixYuEhoYSEhJSIzccrVYrI0aM4NZbbyUzM5PMzEyCgoIIDQ1125cKFFc3tWzZEovF4vbB0YSo7yS4O2C1WomKinLrPnx9fQkPDyc8PNyt+3HEYrEQFhZW7U5KzlJK0aJFC3x8fCS4C+FmckPVAX9/fxo1qvMjFdc60dHRLmveKYRwTIK7A35+fjKVnhuEhITI+O5C1AAJ7g5YrVaX30wVxVVR8r4K4X4S3B2wWq1yhekGvr6+LunxK4QonwR3B3x9faWrvBvIlbsQNUOCuwN2u72kg5VwIbvdXuMTgQtRH0lwdyA/P5+ioiJPF8Pr5Ofnu2wYYyGEYxLcHcjPz5e22G6Qn58vE3YIUQMkuDuQl5dHbm6up4vhdXJzc+VLU4gaIMHdgezsbC5cuODpYnidjIwMqZYRogZIcHfAZrNx9uxZTxfD6yQnJ0twF6IGSHB3ID8/n+TkZE8Xw6vY7XZ+/fVXCgoKPF0UIbyeBHcHCgoKOHz4sDTbc6GioiKOHTsm76kQNcCp4K6U+lUptVcptVsptd3IC1NKbVRKJRp/Gxr5Sin1ilIqSSm1RynV1Z0H4C5aa5KSkrDZbJ4uitfIzs7m+PHjni6GEPVCZa7c+2mtr9dal0wqOh/YpLWOATYZzwGGADFGmg687qrC1rSkpCTOnTvn6WJ4jZSUFI4dO+bpYghRL1SnWmYE8I7x+B1gZKn8d42Z1X4AGiil3DswupucOXOGgwcPumx7drv9Uvv5+tgDdu/evfJlKUQNcXZkLA18rZTSwBta6zeBSK31KQCt9SmlVGNj3WZA6TuRKUbeKReVucZkZWXxww8/EB8f75IZmXbs2MFjjz2G2WzGarXi5+eHn58fQUFB3H///bRr184Fpa6dioqK2LZtm/QdEKKGOBvcb9JanzQC+EalVHmXs2VFwSsuUZVS0ymutqm1CgsL+eGHH8jOziYoKMgl2/vPf/5zRT2+UorWrVt7LLifOXOGxMTES182fn5++Pj44OPjQ0REhEum3ktPT2fHjh317teKEB7jaOZsRwlYADwEHAKijLwo4JDx+A1gQqn1L61XzjY9PYO4w9S8eXO9Y8cO7QonTpzQUVFRZe6nXbt2+ujRoy7ZT2UtWrRI+/n5aX9/fx0QEKADAwN1aGiobtmypT5w4IBL9vHNN9/o8PBwj59PSZK8LG3XDuJqhXXuSqlApVRwyWNgELAP+AyYZKw2CfjUePwZMNFoNdMTyNBG9U1dlJqayr/+9S+XXHFGRkbSt2/fMpcdPHiQu+++m8TExGrvpzIOHjzIW2+9hc1mIzc3l5ycHLKzs8nIyABwyfyuRUVFfPnll9LjV4ia5Cjq6/+7qm4F/Gyk/cCjRn4jilvJJBp/w4x8BfwNOALsBbo7sQ9Pf/uVm/r166fPnTvnkivYTZs26cDAQIf7atWqlV61apXOzMx0yf4csdvt+scff9Tdu3d3WJaJEyfqoqKiau8rOTlZd+3a1ePnUZIkL0wOr9wrXS3jjlQL3qByU6NGjfRXX31V7SCntdb5+fl65syZ2rg5XWayWCy6d+/e+q233tLJycm6sLDQJfvWWuuCggJ94MAB/fDDD5dbTRIcHKy/++47l+zzo48+0sHBwR4/j5IkeWGS4F6dZDKZ9OTJk3VOTo5Lgt2ZM2d03759ndpvVFSUHjFihH711Vf11q1b9ZkzZ3R+fr622+1O7auwsFBfuHBB79mzR69YsUKPHDlSN2rUqNz9KqX0rFmzdEFBQbWPNSsrS48dO7bcLzNJkiRVOTkM7kq7oC65uox//FqtWbNmrFmzhj59+rhke4mJidxxxx1s377dqfWVUlitViIiImjRogWtWrWiadOmhIWFERgYiK+vLyEhIQCkpaVx9uxZ0tLSOHz4MMeOHePEiRNkZ2c7de8gPj6e1atXExERUa1jBNi0aRN33HEHqamp1d6WEOIKO/T/dSy9nKOoX5MJz3/7VZjMZrOeOXOmttls1b6aLXH8+HE9evRobbFYqlU2pZRWSmmTyaRNJlOVr5JNJpMePny4Tk5Odsnx5eTk6MmTJ2uTyeTx8ydJkpcmqZZxRWrRooXesmWLSwJfiaysLL1s2TIdERHh0WMLDg7W8+fP1+np6S47to0bN+rIyEiPnzdJkrw4SXB3RTKZTHrChAk6IyPDZQFQ6+KWK7t379bjx4+v8RuPPj4+Oi4uTm/atMmlN27T0tL0qFGjpK5dkiT3JgnurkoNGzbU7777rtM3NCsjPz9fb9u2TU+fPl03adLErYExICBA9+vXT3/44Yc6KyvLpcdht9v1m2++qUNCQjx+viRJ8vIkwd2VqWfPnvrIkSMuDYilFRUV6SNHjujXX39dDxkyREdFRVW7Xt5kMumQkBB93XXX6Yceekhv2bLFZa1/fm/v3r36+uuv9/h5kiSpHiRpLeNKvr6+zJgxg8WLF7tkzJnyFBYWcvr0aXbv3s2uXbvYs2cPx44d48yZM2RmZmK32y+NMGm32zGZTFgsFoKDgwkLCyMiIoI2bdpw44030rVrV1q2bElgYKBLBkIrS2ZmJrNnz+b999+XibCFcD+HrWUkuFdRWFgYS5cu5e6778ZsNtfYfrXW2Gw2srKyyMnJwWazUVBQcOmv1WolJCSEkJAQAgMD8ff3r7HyFRUV8cYbb5CQkHBp+AIhhFtJU0h3pLZt27qsF2ddZ7fb9ddff62vueYaj58XSZLqUar6wGHCscTERJ566qkaH+yrNtq/fz9PPPEER48e9XRRhBDIBNnVYrfb+fe//81jjz1GSkqKp4vjMcePH+fxxx/np59+KvklJoTwMAnu1VRQUMCnn37KwoUL6+UUcqmpqSQkJLB+/XqKioo8XRwhhEGCuwvk5eXxwQcf8PTTT3P27FlPF6fGnDx5koSEBD755BPy8/M9XRwhRGmOKuNrMuH5mxIuSQEBAXratGkuG5ulNjty5IgeP368tlqtHn/fJUmqx0k6MdVUslqtety4cToxMdGNodWz9u7dq4cNG6Z9fHw8/n5LklTPkwT3mkw+Pj46Pj5ef/fddy4dr8XTCgoK9L/+9S/du3dvbTabPf4+S5IkSYJ7jSeTyaTbtWunV65c6fKxWzwhPT1dv/zyy7pVq1YyGJgkSbUnSXD3VGrUqJGePXu2PnLkiFsGG3M3u92uDxw4oKdNm6ZDQ0M9/n5KkiTpsiRjy3iSr68vXbt25b777mP48OGEhoZ6ukhOOX/+PGvXruX1119n//79FBQUeLpIQojLydgytUHDhg255ZZbuO+++7jhhhuwWq2eLlKZcnNz2bp1K6+99hqbNm0iMzPT00USQpRNgnttYTKZaN68OUOHDuWuu+6iS5cu+Pn5ebpYAOTk5PDjjz/y7rvvsmHDBk6fPo3dbvd0sYQQjlUvuCulGgArgY4U1/PcDRwC/g60BH4FxmqtL6jisWRfBoYCOcBkrfXOCrZfb4J7CbPZTFRUFIMHD2bcuHF069aNhg0bum0oXkfsdjtpaWn89NNPrFmzhn/961+kpqZKUBeibqh2cH8H+E5rvVIp5QsEAAnAea31UqXUfKCh1vphpdRQ4E8UB/cewMta6x4VbL/eBfcSJpOJsLAwOnXqxODBg4mPjycmJoagoCC3BXq73U5WVhYHDx5k48aNbNiwgV9++YULFy5QG37JCSGcVvXgrpQKAX4GWulSKyulDgFxWutTSqko4N9a67ZKqTeMx2t+v145+5CIAvj4+NCkSRM6d+5M9+7d6d69O+3bt6dJkyb4+flhMlVttAi73U5OTg6nTp1i//79/Pjjj+zcuZO9e/dy9uxZuVEqRN3lMLhbnHhxK+As8LZS6jpgBzAbiCwJ2EaAb2ys3wxILvX6FCPPYXAXxQoKCkhOTiY5OZkNGzYQGBhIkyZNiImJ4ZprruHqq6/mqquuIioqirCwMCwWC2azGbPZjNaaoqIiioqKKCwsJC0tjRMnTpCSksLRo0c5cuQIiYmJpKamkp2dLYN8CeHlnAnuFqAr8Cet9Tal1MvA/HLWL6su4Yorc6XUdGC6U6WshwoLC8nIyCAjI4NDhw6hlMJsNmO1WrFarfj6+l5Kfn5+aK3Jy8sjLy+P/Px88vPzLz0uKiqS6hYh6hlngnsKkKK13mY8X0txcD+jlIoqVS2TWmr95qVeHw2c/P1GtdZvAm+CVMs4Q2tNYWEhhYWFZGdne7o4QoharsJKXK31aSBZKdXWyBoA/AJ8Bkwy8iYBnxqPPwMmqmI9gYzy6tuFEEK4njNX7lDc+mW10VLmKDCF4i+Gj5RSU4HjwBhj3fUUt5RJorgp5BSXllgIIUSFpBOTEELUXQ5by8hMTEII4YUkuAshhBeS4C6EEF5IgrsQQnghCe5CCOGFJLgLIYQXkuAuhBBeSIK7EEJ4IQnuQgjhhSS4CyGEF5LgLoQQXsjZgcPc7SLFc7LWN+HAOU8XwgPkuOsXOW73aeFoQW0J7occDX7jzZRS2+W46w857vrF08ct1TJCCOGFJLgLIYQXqi3B/U1PF8BD5LjrFznu+sWjx10rJusQQgjhWrXlyl0IIYQLeTy4K6VuUUodUkolKaXme7o8rqSUaq6U+lYpdUAptV8pNdvID1NKbVRKJRp/Gxr5Sin1ivFe7FFKdfXsEVSdUsqslNqllFpnPL9aKbXNOOa/G/PxopSyGs+TjOUtPVnu6lJKNVBKrVVKHTTOey9vP99KqQeNz/c+pdQapZSft55vpdRbSqlUpdS+UnmVPr9KqUnG+olKqUnuKKtHg7tSygz8DRgCtAcmKKXae7JMLlYIzNVaXwv0BO43jm8+sElrHQNsMp5D8fsQY6TpwOs1X2SXmQ0cKPV8GfCiccwXgKlG/lTggta6NfCisV5d9jLwlda6HXAdxe+B155vpVQzYBbQXWvdETAD4/He8/0/wC2/y6vU+VVKhQFPAj2AWODJki8El9JaeywBvYANpZ4/AjziyTK5+Xg/BeIp7rAVZeRFUdzOH+ANYEKp9S+tV5cSEG18yPsD6wBFcWcOy+/PO7AB6GU8thjrKU8fQxWPOwQ49vvye/P5BpoByUCYcf7WAYO9+XwDLYF9VT2/wATgjVL5l63nquTpapmSD0aJFCPP6xg/P7sA24BIrfUpAONvY2M1b3k/XgLmAXbjeSMgXWtdaDwvfVyXjtlYnmGsXxe1As4CbxtVUiuVUoF48fnWWp8AngOOA6coPn87qB/nu0Rlz2+NnHdPB3dVRp7XNd9RSgUBnwBztNaZ5a1aRl6dej+UUsOAVK31jtLZZayqnVhW11iArsDrWusuQDb/9xO9LHX+2I3qhBHA1UBTIJDi6ojf88bzXRFHx1oj74Gng3sK0LzU82jgpIfK4hZKKR+KA/tqrfU/jOwzSqkoY3kUkGrke8P7cRMwXCn1K/AhxVUzLwENlFIlw12UPq5Lx2wsDwXO12SBXSgFSNFabzOer6U42Hvz+R4IHNNan9VaFwD/AG6kfpzvEpU9vzVy3j0d3H8CYow7674U34j5zMNlchmllAJWAQe01i+UWvQZUHKHfBLFdfEl+RONu+w9gYySn3t1hdb6Ea11tNa6JcXn8xut9Z3At8Dtxmq/P+aS9+J2Y/06eSWntT4NJCul2hpZA4Bf8OLzTXF1TE+lVIDxeS85Zq8/36VU9vxuAAYppRoav3wGGXmuVQtuTgwFDgNHgEc9XR4XH1tvin9u7QF2G2koxXWMm4BE42+Ysb6iuPXQEWAvxS0QPH4c1Tj+OGCd8bgV8COQBHwMWI18P+N5krG8lafLXc1jvh7YbpzzfwINvf18AwuBg8A+4D3A6q3nG1hD8b2FAoqvwKdW5fwCdxvvQRIwxR1llR6qQgjhhTxdLSOEEMINJLgLIYQXkuAuhBBeSIK7EEJ4IQnuQgjhhSS4CyGEF5LgLoQQXkiCuxBCeKH/D4HUL+C14YtuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread('DATA/internal_external.png',0)\n",
    "print(img.shape)\n",
    "plt.imshow(img, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding Contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "image, contours, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'list'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(image))\n",
    "print(type(contours))\n",
    "print(type(hierarchy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "652\n",
      "22\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(len(image))\n",
    "print(len(contours))\n",
    "print(len(hierarchy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hierarchy ## Array of 22 contour items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(652, 1080)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "external_contours = np.zeros(image.shape)\n",
    "external_contours.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x210b1a67c50>"
      ]
     },
     "execution_count": 18,
     "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(external_contours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x210b1ba1b70>"
      ]
     },
     "execution_count": 16,
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(len(contours)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x210b1d15b00>"
      ]
     },
     "execution_count": 21,
     "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": [
    "for i in range(len(contours)):\n",
    "    \n",
    "    #EXTERNAL\n",
    "    if hierarchy[0][i][3] == -1:\n",
    "        cv2.drawContours(external_contours, contours, i, 255, -1)\n",
    "        \n",
    "plt.imshow(external_contours, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(652, 1080)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x210b1c9dc88>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gSwAHADxr2ocC+BxAMYA1AKJMe7R5XWKmp7G4M0z3yV133XVBYW9VW1urEydO9Hs52dnZev78+XbLaWho0AcffNDy7bRJLutSyAYAN6vqGABjAUwRkfEAXgKwRFUzAFQBmGvmnwugSlXTASwx8xFRN3H33XcjOjq6XXtsbCwmTJjg93J+9KMfoVevXu3ao6KiMGvWLIjIZfWTLq3D4m7+2J4zLyNNFMDNANab9pUA7jTPZ5jXMNMnC48iUbdx6tSpoCynubnZ57Tq6urW/7X7LSIiAldeeSWysrIQExNzud2zPz/HxB0A8gGcQ8uZ+BUASjymDwaw3zzfD2CQx7SvAFzBYRmG6R7JysrS06dPtxtOqaio8PsG2gA0PT3d612bAh3eAaB9+vTRF154QY8fP6719fWal5en48aNs3xfdYEE5+cHACQB2Abgp2hf3AvM8wNoX9z7eFnWPAB7TKzeQQzDmIiI5uTk6MmTJ9sK8qlTp/Q3v/lNwL82OXnyZD1y5Ejbcqqrq3Xx4sUBfTAbFRXl9e5P33zzjY4dO9by/WVxgvfbMgCeAfA/AFQAcJq2CQByzfNcABPMc6eZT3jmzjDdKz/84Q914cKFunjxYh09enSnlzNw4EBduHCh/ulPf9LrrrtOHQ5HQO+fNGmS1tbWtivuqqorVqywfD9ZnMu6WqYvgCTzPBbADgDTAawDMNu0vw7gP8zz+QBeN89nA1jrxzqs3kEMw3TRzJ8/32thV1UtLCzs9M1CbBKfxd2JjqUCWCkiDrR8ALtWVbeISCGAf4jI/wTwBYC3zPxvAVglIiUAKtFS4ImIOuXQoUOor6/3+iFqSUkJampqLOhVNxDosEwoAuv/+jEM00UTHR2tGzdubHfWXlpa6veNtG0c/p47wzDdN6mpqbpq1SqtqalRl8ulhYWFeuONN1rery4Q3maPiILP4XDgxz/+McaNGwdVxfbt21FQUIBQ1BWHw4HRo0cjISEBBQUFOHv2bNDX0Q3xt2UYhgluHA6H5uTkaE1NTdtQyenTp/Wuu+7qFjfotkk4LMMwTHBzzz33eP3tmFOnTunw4cMt718PCW+zR0TBExERgVmzZnn97Zh+/fohMzPTgl6RJxZ3IgqYiCAlJcXn9K7wWV5Px+JORAFrbm7GihUrvP442MmTJ7Fnzx4LekWeWNyJqFPef/99rF69Gk1NTW1tlZWVePTRR1FSUmJhzwjgbfaI6DL06tUL06dPxwMPPICysjKsWbMGn3zyCYdlwsfnpZAs7kRE3ZfP4s5hGSIiG2JxJyKyIRZ3IiIbYnEnIrIhFnciIhticScisiEWdyIiG2JxJyKyIRZ3IiIbYnEnIrIhFnciIhticScisiG/i7uIOETkCxHZYl4PFZHPRaRYRNaISJRpjzavS8z0tNB0nYiIfAnkzP1xAAc9Xr8EYImqZgCoAjDXtM8FUKWq6QCWmPmIiCiM/CruIjIIwO0A/mZeC4CbAaw3s6wEcKd5PsO8hpk+2cxPF0lISEBaWhrS0tIwZMgQREZGhnX9UVFRGDJkSFsf4uPjw7p+IgohX3fO9gxaivR1ACYB2ALgCgAlHtMHA9hvnu8HMMhj2lcAruhg+VbfQTys6d27tz777LNaUFCgNTU1WlNTo2fOnNHt27frfffdp5GRkSFdf2Jios6dO1d37NihZ8+ebevDl19+qTk5OdqrVy/L9xHDMH5lj8+66kdhnw7gL+b5JLQU975oX9wLzPMDaF/c+3hZ7jwAe0ys3kFhy6hRo3Tz5s3qdrvVm7q6Ol25cqUmJSWFZP0pKSm6YcMGbW5u9rr+xsZGXb58uSYkJFi+rxiG6TCXVdxfBFAG4BsAJwHUAlgNoAKA08wzAUCueZ4LYIJ57jTzCc/cW87Yd+zY4bWoenK73bp8+XJ1Op1BXX9CQoJu2LDB5x8Wz/WvWLFC4+PjLd9nDMNcMp0v7hcV4UkAtpjn6wDMNs9fB/Af5vl8AK+b57MBrPVjuVbvoLBk4cKFHRbWVnV1dXrvvfcGdf1z5szRpqYmv9bvdrv18ccft3yfMQxzyYSkuA8DsAtACVoKfbRpjzGvS8z0YSzu0KSkJD18+LBfhbXVRx99FLSz98jISN26dWtA6z9w4ACHZximayc4xT1U6QI7KOSZPHmyNjY2BlRcy8vLtX///kFZ/6BBg7SysjKg9Tc0NGh2drbl+45hGJ/xWdz5DdUwSUhIgNPpDOg9DocDwbqKVEQQERHY4Y6KikJCQkJQ1k9E4cXiHiZVVVVwuVwBvaexsRFutztEPepYfX09qqqqLFs/EXUei3uY7N+/H+Xl5QG95/PPP0dFRUVQ1v/dd9+hoKAgoPcUFRUF/B4i6hpY3MOksrISq1ev9vtMvLGxEW+99VbQztxra2vx6quvoqmpya/53W43/vnPf+L8+fNBWT8RhZnVH6b2lA9UAWifPn10586dHX6Q2dzcrG+88YbGxMQEdf29evXSt99+2+cXmDzt2LEjZF+kYhgmaOHVMl0lw4YN0507d/ossK2FPS4uLiTrj4+P1+XLl/u83r65uVl37NihP/jBDyzfVwzDdBgW966UlJQUff7557WwsFAbGhpUVbWmpkYPHjyov/3tb0NW2FsTHx+vCxYs0EOHDmlNTY2qtlz2WFBQoL///e81OTnZ8n3EMIxf8VncxRRXS4mI9Z2wQEJCAq699lokJSXh66+/xrfffouampqwrT8xMRFDhw7FkCFDUFlZifz8fJw7dy5s6yeiy7ZXVbO8TWBxJyLqvnwWd14tQ0RkQyzuREQ2xOJORGRDLO5ERDbE4k5EZEMs7kRENsTiTkRkQyzuREQ2xOJORGRDLO5ERDbE4k5EZEMs7kRENsTiTkRkQ34VdxH5RkQKRCRfRPaYthQR+VhEis1jsmkXEXlVREpEZJ+IZIZyA4iIqL1AztxvUtWxHj8vuQhAnqpmAMgzrwFgKoAMk3kAXgtWZ4mIyD/Oy3jvDACTzPOVAP4vgIWm/R1t+aH4nSKSJCKpqnricjpqByKChIQEiEhb2/nz5/2+aTURkb/8Le4K4P+Ym2q8oapvAujfWrBV9YSI9DPzDgRQ6vHeMtPW44v7LbfcgjfffBMOh6Otbd++fTh9+jReeeUV5OfnW9g7IrITf4v7Dap63BTwj0Xk0CXmFS9t7e60JCLz0DJs02MMHDgQQ4YMuaBt0KBBAIDKykp8+eWXsOLOWMOGDcPEiRMREfH9KF1RURG+/fZbHDt2DG63O+x9IqLL1ImbWf8ewH8HUAQg1bSlAigyz98AcJ/H/G3z9fQbZI8ZM6bthtQXO3HihKanp4e9TyKiS5cubdef+vp6/e677/SGG26wfL8xDOMzPm+Q3eEHqiISJyIJrc8B3ApgP4BNAOaY2eYA2GiebwLwK3PVzHgAZzne3qKoqAgFBQVepw0YMABvvfUWRo8efcEZdKiNGTMG99xzT7v26OhoREZG4uzZs2HrCxEFkR9n6sMAfGlyAMDTpr0PWq6SKTaPKaZdACwD8BWAAgBZfqzD6r9+Ycujjz6qLpfL69m7qmpVVZU+99xz2r9//5D2IzIyUn/2s5/poUOHfPYlLy9Po6OjLd9nDMP4jM8z94CHZUKRLrCDwpaYmBjdtGmTz4LaqqSkRBcvXqzXXHON9u7dO2jrT0hI0Jtuukk3b96s9fX1Ptfvcrl0+vTplu8vhmEuGZ/FXdSCD/AuZq7C6TFSU1Pxr3/9C9dff32H8547dw5lZWXIzc3F/v378dlnn6GiogIVFRUAALfb7fVD2IiICIgIYmJiMGDAAFxzzTWYMGECpk6dioyMDERHR19yvWvWrMFDDz2E+vr6zm0kEYXDXv3+u0cXYHG3SHZ2Nt577722q2X8VVdXh4qKCpSVlQFouZTy22+/vWCemJgYTJw4EVFRUYiLi8OwYcMQGxt7wSWYl1JQUIC7774bJSUlAfWNiMLOZ3G3fEimpw3LeGb48OG6devWDodowsXtdmt+fr6OGDHC8n3DMIxf6fzVMhQ6RUVF+OUvf4k///nPqKurs7QvtbW1WLZsGW677TYcOnSprzEQUbfgq+qHM7D+r5+lcTqdmp2drZ988onW1taG8Ny8PbfbrQUFBTp16lSNiIiwfF8wDBNQeLVMd0hsbKzeeOON+v7774e8yLtcLt23b58+8cQT2rdvX8u3nWGYToVXy3QnkZGR+MlPfoLbbrsNd9xxh19Xt/jD5XLh/Pnz2LJlC9atW4etW7fi/PnzQegxEVmEV8t0V8nJybjqqquQnZ2Nm2++GcnJyRg1ahQiIyMRFxfn9dusqopz5861PR4+fBi7du3C5s2bUVZWhqNHj/L3YojsgcXdDkQEDocD/fv3h9PpRFZWFuLj49vN53K5sGvXLjQ2NqKhoQGnT59mMSeyJxZ3IiIb8lnceSkkEZENsbgTEdkQizsRkQ2xuBMR2RCLOxGRDbG4ExHZEIs7EZENsbgTEdkQizsRkQ2xuBMR2RCLOxGRDbG4ExHZkF/FXUSSRGS9iBwSkYMiMkFEUkTkYxEpNo/JZl4RkVdFpERE9olIZmg3gYiILubvmft/AvhIVUcAGAPgIIBFAPJUNQNAnnkNAFMBZJjMA/BaUHtMREQd6rC4i0gigBsBvAUAqupS1TMAZgBYaWZbCeBO83wGgHfM3dx2AkgSkdSg95yIiHzy58x9GIDTAJaLyBci8jcRiQPQX1VPAIB57GfmHwig1OP9ZaaNiIjCxJ/i7gSQCeA1Vb0WwHl8PwTjjXhpa3czDhGZJyJ7RGSPXz0lIiK/+VPcywCUqern5vV6tBT7U63DLeax3GP+wR7vHwTg+MULVdU3VTXL111EiIio8zos7qp6EkCpiAw3TZMBFALYBGCOaZsDYKN5vgnAr8xVM+MBnG0dviEiovBw+jnfbwCsFpEoAF8DeBgtfxjWishcAEcB3Gvm/S8A0wCUAKg18xIRURjxBtlERN0Xb5BNRNSTsLgTEdkQizsRkQ2xuBMR2RCLOxGRDbG4ExHZEIs7EZENsbgTEdkQizsRkQ2xuBMR2RCLOxGRDfn7w2Ghdg5AkdWdsMAVACqs7oQFuN09C7c7dIb4mtBVintRT/xddxHZw+3uObjdPYvV281hGSIiG2JxJyKyoa5S3N+0ugMW4Xb3LNzunsXS7e4SN+sgIqLg6ipn7kREFESWF3cRmSIiRSJSIiKLrO5PMInIYBHZJiIHReSAiDxu2lNE5GMRKTaPyaZdRORVsy/2iUimtVvQeSLiEJEvRGSLeT1URD4327zG3I8XIhJtXpeY6WlW9vtyiUiSiKwXkUPmuE+w+/EWkQXm3/d+EXlPRGLserxF5G0RKReR/R5tAR9fEZlj5i8WkTmh6KulxV1EHACWAZgKYBSA+0RklJV9CrImAE+q6kgA4wHMN9u3CECeqmYAyDOvgZb9kGEyD8Br4e9y0DwO4KDH65cALDHbXAVgrmmfC6BKVdMBLDHzdWf/CeAjVR0BYAxa9oFtj7eIDATwWwBZqjoagAPAbNj3eK8AMOWitoCOr4ikAHgGwPUAxgF4pvUPQlCpqmUBMAFArsfrxQAWW9mnEG/vRgC3oOULW6mmLRUt1/kDwBsA7vOYv22+7hQAg8w/8psBbAEgaPkyh/Pi4w4gF8AE89xp5hOrt6GT250I4MjF/bfz8QYwEEApgBRz/LYAuM3OxxtAGoD9nT2+AO4D8IZH+wXzBStWD8u0/sNoVWbabMf89/NaAJ8D6K+qJwDAPPYzs9llf7wC4CkAbvO6D4AzqtpkXntuV9s2m+lnzfzd0TAApwEsN0NSfxORONj4eKvqMQB/AnAUwAm0HL+96BnHu1Wgxzcsx93q4i5e2mx3+Y6IxAP4J4AnVLX6UrN6aetW+0NEpgMoV9W9ns1eZlU/pnU3TgCZAF5T1WsBnMf3/0X3pttvuxlOmAFgKIArAcShZTjiYnY83h3xta1h2QdWF/cyAIM9Xg8CcNyivoSEiESipbCvVtUNpvmUiKSa6akAyk27HfbHDQB+LiLfAPgHWoZmXgGQJCKtP3fhuV1t22ym9wZQGc4OB1EZgDJV/dy8Xo+WYm/n4/0zAEdU9bSqNgLYAELvz60AAAFXSURBVOAn6BnHu1Wgxzcsx93q4r4bQIb5ZD0KLR/EbLK4T0EjIgLgLQAHVfV/e0zaBKD1E/I5aBmLb23/lfmUfTyAs63/3esuVHWxqg5S1TS0HM+tqno/gG0AZprZLt7m1n0x08zfLc/kVPUkgFIRGW6aJgMohI2PN1qGY8aLSC/z7711m21/vD0EenxzAdwqIsnmfz63mrbg6gIfTkwDcBjAVwCetro/Qd62bLT8d2sfgHyTaWgZY8wDUGweU8z8gparh74CUICWKxAs347L2P5JALaY58MA7AJQAmAdgGjTHmNel5jpw6zu92Vu81gAe8wxfx9Ast2PN4BnARwCsB/AKgDRdj3eAN5Dy2cLjWg5A5/bmeML4BGzD0oAPByKvvIbqkRENmT1sAwREYUAizsRkQ2xuBMR2RCLOxGRDbG4ExHZEIs7EZENsbgTEdkQizsRkQ39f+ogAmhpQpOeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "internal_contours = np.zeros(image.shape)\n",
    "print(internal_contours.shape)\n",
    "\n",
    "for i in range(len(contours)):\n",
    "    \n",
    "    #EXTERNAL\n",
    "    if hierarchy[0][i][3] != -1:\n",
    "        cv2.drawContours(internal_contours, contours, i, 255, -1)\n",
    "        \n",
    "plt.imshow(internal_contours, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(652, 1080)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x210b21fe3c8>"
      ]
     },
     "execution_count": 28,
     "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": [
    "internal_contours = np.zeros(image.shape)\n",
    "print(internal_contours.shape)\n",
    "\n",
    "for i in range(len(contours)):\n",
    "    \n",
    "    #EXTERNAL\n",
    "    if hierarchy[0][i][3] == 4:\n",
    "        cv2.drawContours(internal_contours, contours, i, 255, -1)\n",
    "        \n",
    "plt.imshow(internal_contours, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(652, 1080)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x210b224b198>"
      ]
     },
     "execution_count": 29,
     "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": [
    "internal_contours = np.zeros(image.shape)\n",
    "print(internal_contours.shape)\n",
    "\n",
    "for i in range(len(contours)):\n",
    "    \n",
    "    #EXTERNAL\n",
    "    if hierarchy[0][i][3] == 0:\n",
    "        cv2.drawContours(internal_contours, contours, i, 255, -1)\n",
    "        \n",
    "plt.imshow(internal_contours, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}