ContourDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2675de2f630>"
      ]
     },
     "execution_count": 3,
     "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": [
    "img=cv2.imread(\"../DATA/internal_external.png\",0)\n",
    "plt.imshow(img,cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "image,contours,hierarchy=cv2.findContours(image=img,mode=cv2.RETR_CCOMP,method=cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2675dec6b70>"
      ]
     },
     "execution_count": 5,
     "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,cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hierarchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "external_contours=np.zeros(image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#values with -1 are treated as external contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(len(contours)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(contours)):\n",
    "    #EXTERNAL\n",
    "    if hierarchy[0][i][3] == -1:\n",
    "        cv2.drawContours(external_contours,contours,i,255,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2675df28f28>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADtCAYAAACxtL7fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAE2lJREFUeJzt3W+MXNddxvHvg9OkJVDVoSQytkVcyQIcpP6zrJYgBBSIKajOm0hGKhgU5DcBtYBU2fQF4l1BCAFCQbKSgoFSywqFWJUotdwi3lRJ7bbQOI7Jti7xYhMXoULhRUrSHy/mWpk6G+/s7szOnXO/H2l17z17Z/acM+c8c/fMzG6qCklSu75t3hWQJM2WQS9JjTPoJalxBr0kNc6gl6TGGfSS1LiZBX2S/UkuJllKcmRWP0eSdHOZxfvok2wB/gX4SWAZ+Czwc1X19NR/mCTppmZ1Rb8PWKqqL1fVN4ATwIEZ/SxJ0k3MKui3A5fHjpe7MknSJrtlRvebFcq+ZY0oyWHgcHf49hnVQ5Ja9h9V9d2rnTSroF8Gdo4d7wCujJ9QVceAYwBJ/IM7krR2/zrJSbNauvkssDvJriS3AgeBUzP6WZKkm5jJFX1VvZjkV4C/B7YAH66q87P4WZKkm5vJ2yvXXAmXbiRpPc5V1d7VTvKTsZLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6KesD38NVJLGGfQzYNhL6pNZ/SvBQTLg1bpJx3iy0r+N1rwY9FNy4wSoKge7Fp4XL20w6GfIsNciMtzbY9BPgRNDLXAct8sXY2fMyaO+qyrHaeO8ot8gJ4gWkeN2WLyi3wROKvWJ43F4Vg36JB9Oci3JU2NldyQ5neTZbrt17HtHkywluZjkvllVvA+cMFo0jtlhmuSK/s+A/TeUHQHOVNVu4Ex3TJI9wEHgnu42DyfZMrXa9shaJ4wTTPPkOvywrRr0VfWPwH/eUHwAON7tHwfuHys/UVUvVNUlYAnYN6W6LjwnmubBcaf1rtHfVVVXAbrtnV35duDy2HnLXVlTnDhaBF7F67ppv+tmpU8HrTjSkhwGDk/558/cRieOH6LSZjDgNW69V/TPJ9kG0G2vdeXLwM6x83YAV1a6g6o6VlV7q2rvOusgaQWGvG603qA/BRzq9g8Bj4+VH0xyW5JdwG7gyY1VsT+mNYGciJoVx5ZWsurSTZKPAj8KvDHJMvBbwIeAk0keBJ4DHgCoqvNJTgJPAy8CD1XVSzOqu6QxhvzLrveFy6Qj6cPgSDL/SqxiFv3kINS09GEej5vn2L6xLxqfZ+cmWf72k7Fz1LfJqcXkOHrZSn1h/xj0E3GgSItt6HPYoJ+zoQ9AbYzj52Wr9cWQ+8qgX8VmDI4hD0Ctn+NmZC0fDBtqnxn0NzHUQaH+c2yO2A+TMeglLaT1hvwQnxwM+lex2YNhiINP6+NY0VoZ9D3iBNZqHCMj0/ibU0Ni0K9gaINAWhTT/IucQ5rnBv0N5v3gz/vnq7+GPjaG3v6NMOgl9d6sQn4oTx4G/Zi+POh9qYf6Y8hjYshtnxaDXlJvGfLTYdB3+jag+lYfzc9Qx8JmtXsI/WvQ99gQBqC0Esf+dBn0OKikPpnHfGw9Awz6nmt9AOrmhvb4D629m2XwQb8IA2sR6ihtlON8dgYd9A4sqR/6MBf7UIdZGXTQL5KWB6GGzbE9e4MNegeX+m4IY3QIbeyDwQb9InJSqCWO582zatAn2Znk00kuJDmf5H1d+R1JTid5tttuHbvN0SRLSS4muW+WDVgPB5g0X32dg32t10ZNckX/IvAbVfUDwDuAh5LsAY4AZ6pqN3CmO6b73kHgHmA/8HCSLbOo/Hos+gO56PWXHMObb9Wgr6qrVfW5bv/rwAVgO3AAON6ddhy4v9s/AJyoqheq6hKwBOybdsUlLR5Dfj7WtEaf5G7grcATwF1VdRVGTwbAnd1p24HLYzdb7srmrpVB1ko7NCyO2/m5ZdITk3wH8NfA+6vqv5O86qkrlL3iEU5yGDg86c/Xt6oqbvIYaMG1FoqttWfRTBT0SV7DKOQ/UlUf64qfT7Ktqq4m2QZc68qXgZ1jN98BXLnxPqvqGHCsu/9NGQUGo7T5DPn5m+RdNwEeBS5U1e+PfesUcKjbPwQ8PlZ+MMltSXYBu4Enp1dlSYvCkO+HSa7o7wV+Hvhiki90Zb8JfAg4meRB4DngAYCqOp/kJPA0o3fsPFRVL0295pJ6zZDvj/ThwdispRtpUfRhXg7Vgi3xnquqvaud5CdjJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeqmHFuwtfuo5g16SGmfQS1LjDHpJapxBL0mdVl8bMeglqXEGvdRTrV5davMZ9JLUOINekmj7NyiDXpIaZ9BLPdbyVWaftN7PBr0kNc6gl3qu9atNzZ5BL2nQhvBEatBLC2AIYaTZMeglqXGrBn2S1yZ5Msk/JTmf5Le78juSnE7ybLfdOnabo0mWklxMct8sGyANhVf10zeUPp3kiv4F4Mer6s3AW4D9Sd4BHAHOVNVu4Ex3TJI9wEHgHmA/8HCSLbOovCRpdasGfY38T3f4mu6rgAPA8a78OHB/t38AOFFVL1TVJWAJ2DfVWkvSBg3lah4mXKNPsiXJF4BrwOmqegK4q6quAnTbO7vTtwOXx26+3JVJ2qAhhdMsDa0fJwr6qnqpqt4C7AD2JfnBm5y+Ug/WK05KDic5m+TsZFWVBMMLqWkbYv+t6V03VfU14B8Yrb0/n2QbQLe91p22DOwcu9kO4MoK93WsqvZW1d511FuSNKFJ3nXz3Une0O2/DvgJ4BngFHCoO+0Q8Hi3fwo4mOS2JLuA3cCT0664NGRDvCqdhqH22y0TnLMNON69c+bbgJNV9fEknwFOJnkQeA54AKCqzic5CTwNvAg8VFUvzab60nAloeoVq6J6FUMNeYD0YaAkmX8lpAXVhzm8CBoN+nOTLH/7yVhJzWs05Cc2ydKNemi1q7ihD+whcQnn5pwLBn2vbWTy3uy2Dvz2GPYrc6yPGPQ9sxmTdfxnOBHaYdh/K8f2ywz6Hpjn5DT022LYjziWv5Uvxs5RVfVqUvatPlqfoYfc0Nu/Eq/o56DvYXq9fk6YxXX9sev7WJs2x+zKDPpNtGiTzsBffENZynGM3pxLN5tkkSfbItdd7Ydg6+2bBoN+E7QQlK7fL7YkTQZii22aBYN+hloMx9baMzStBGOrT1yzYtDPSMuB2HLbhmDRQ3KR6z4vvhg7ZUMJwapywi24RXpnjmNtY7yi17otQkBodX2+wu9z3RaJV/RTNMTg88q+HeOP47zHsmNqugz6KZn3xJgnw7498wh9x9DsGPRTMOSQv86wb9eNj+u0xrvjZfMY9BtkyL/MsB8GH+PF44uxG2DIv5J9IvWPQS9JjTPo18kr11dn30j9YtCvg0G2OvtI6o+Jgz7JliSfT/Lx7viOJKeTPNttt46dezTJUpKLSe6bRcXnxQCTtGjWckX/PuDC2PER4ExV7QbOdMck2QMcBO4B9gMPJ9kynepqkfikKPXDREGfZAfwM8AjY8UHgOPd/nHg/rHyE1X1QlVdApaAfdOp7nwZXJIW0aRX9H8AfAD45ljZXVV1FaDb3tmVbwcuj5233JVpgHxylOZv1aBP8rPAtao6N+F9rvRpilfM9iSHk5xNcnbC+50rA2v97Dtpvib5ZOy9wHuSvBt4LfD6JH8JPJ9kW1VdTbINuNadvwzsHLv9DuDKjXdaVceAYwBJTAJJmpFVr+ir6mhV7aiquxm9yPqpqnovcAo41J12CHi82z8FHExyW5JdwG7gyanXXJI0kY38rZsPASeTPAg8BzwAUFXnk5wEngZeBB6qqpc2XNM5culh4/w7ONL8pA8h1velmz70UQsMemnqzlXV3tVO8pOxqzDkp8e+lObDoJekxhn0ktQ4g16SGmfQ34RrypJaYNBrU/nkKW0+g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1LiJgj7JV5J8MckXkpztyu5IcjrJs91269j5R5MsJbmY5L5ZVV6StLq1XNH/WFW9par2dsdHgDNVtRs40x2TZA9wELgH2A88nGTLFOusBZZk3lWQBmcjSzcHgOPd/nHg/rHyE1X1QlVdApaAfRv4OXNjKElqwaRBX8Ank5xLcrgru6uqrgJ02zu78u3A5bHbLndlkqQ5uGXC8+6tqitJ7gROJ3nmJueudBn8iv8I3T1hHF7hXEnSFE10RV9VV7rtNeBvGC3FPJ9kG0C3vdadvgzsHLv5DuDKCvd5rKr2jq35S5JmYNWgT3J7ku+8vg/8FPAUcAo41J12CHi82z8FHExyW5JdwG7gyWlXfLO4Tj899qU0H5Ms3dwF/E03SW8B/qqqPpHks8DJJA8CzwEPAFTV+SQngaeBF4GHquqlmdRekrSqVL1i+XzzK5HMvxKr6EM/LTKv5qWZODfJ8refjJWkxhn0E/KKdP3sO2m+DHpJapxBr5nyal6aP4N+DQwtSYvIoF8jw35y9pXUDwa9JDXOoF8Hr1RXZx9J/WHQr5NB9ursG6lfDPoNMNAkLQKDXlPlk5/UPwb9BhlsL7MvpH4y6KfAgLMPpD4z6KdkyEE35LZLi8Cgn6IhBt4Q2ywtGoN+yoYUfENqq7TIDPoZGEIADqGNUism+VeCWofrQdjaf6Yy4KXF4xX9jLUUjC21RRoSg34TtBCQLbRBGiqXbjbJoi7lGPDS4jPoN9miBL4BL7VjoqWbJG9I8liSZ5JcSPLOJHckOZ3k2W67dez8o0mWklxMct/sqr+4kvQyTPtaL0nrN+ka/R8Cn6iq7wfeDFwAjgBnqmo3cKY7Jske4CBwD7AfeDjJlmlXvBV9Cda+1EPS9K0a9EleD/wI8ChAVX2jqr4GHACOd6cdB+7v9g8AJ6rqhaq6BCwB+6Zd8dbMK2gNeKl9k1zRvwn4KvCnST6f5JEktwN3VdVVgG57Z3f+duDy2O2XuzJN4Hrwjn8twn1L6q9Jgv4W4G3An1TVW4H/pVumeRUrpccrXnlMcjjJ2SRnJ6rpgK0U0Ov5kjRMkwT9MrBcVU90x48xCv7nk2wD6LbXxs7fOXb7HcCVG++0qo5V1d6q2rveykuSVrdq0FfVvwOXk3xfV/Qu4GngFHCoKzsEPN7tnwIOJrktyS5gN/DkVGstSZrYpO+j/1XgI0luBb4M/BKjJ4mTSR4EngMeAKiq80lOMnoyeBF4qKpemnrNJUkTSR8+uJNk/pWQpMVzbpLlb//WjSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1Li+/HPw/wEuzrsSc/BG4D/mXYk5sN3DYrtn53snOakvQX9xiH+XPslZ2z0ctntY+tRul24kqXEGvSQ1ri9Bf2zeFZgT2z0stntYetPuXvzjEUnS7PTlil6SNCNzD/ok+5NcTLKU5Mi86zNNSXYm+XSSC0nOJ3lfV35HktNJnu22W8duc7Tri4tJ7ptf7TcmyZYkn0/y8e64+TYDJHlDkseSPNM97u8cQtuT/Fo3xp9K8tEkr22x3Uk+nORakqfGytbcziRvT/LF7nt/lCQzrXhVze0L2AJ8CXgTcCvwT8CeedZpyu3bBryt2/9O4F+APcDvAke68iPA73T7e7o+uA3Y1fXNlnm3Y51t/3Xgr4CPd8fNt7lrz3Hgl7v9W4E3tN52YDtwCXhdd3wS+MUW2w38CPA24KmxsjW3E3gSeCcQ4O+An55lved9Rb8PWKqqL1fVN4ATwIE512lqqupqVX2u2/86cIHRpDjAKBDotvd3+weAE1X1QlVdApYY9dFCSbID+BngkbHiptsMkOT1jILgUYCq+kZVfY0BtJ3RZ3Jel+QW4NuBKzTY7qr6R+A/byheUzuTbANeX1WfqVHq//nYbWZi3kG/Hbg8drzclTUnyd3AW4EngLuq6iqMngyAO7vTWumPPwA+AHxzrKz1NsPoN9OvAn/aLVs9kuR2Gm97Vf0b8HvAc8BV4L+q6pM03u4xa23n9m7/xvKZmXfQr7Qu1dzbgJJ8B/DXwPur6r9vduoKZQvVH0l+FrhWVecmvckKZQvV5jG3MPq1/k+q6q3A/zL6Vf7VNNH2bk36AKPlie8Bbk/y3pvdZIWyhWv3BF6tnZve/nkH/TKwc+x4B6Nf+ZqR5DWMQv4jVfWxrvj57tc3uu21rryF/rgXeE+SrzBaivvxJH9J222+bhlYrqonuuPHGAV/623/CeBSVX21qv4P+BjwQ7Tf7uvW2s7lbv/G8pmZd9B/FtidZFeSW4GDwKk512lqulfSHwUuVNXvj33rFHCo2z8EPD5WfjDJbUl2AbsZvWizMKrqaFXtqKq7GT2en6qq99Jwm6+rqn8HLif5vq7oXcDTtN/254B3JPn2bsy/i9HrUa23+7o1tbNb3vl6knd0/fULY7eZjR68iv1uRu9G+RLwwXnXZ8pt+2FGv5L9M/CF7uvdwHcBZ4Bnu+0dY7f5YNcXF5nxK/Gb0P4f5eV33QylzW8BznaP+d8CW4fQduC3gWeAp4C/YPROk+baDXyU0esQ/8foyvzB9bQT2Nv11ZeAP6b78OqsvvxkrCQ1bt5LN5KkGTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklq3P8DIOrlCsxyPcAAAAAASUVORK5CYII=\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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "internal_contours=np.zeros(image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All the values which are not -1 are treated as internal contours "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(contours)):\n",
    "    #INTERNAL\n",
    "    if hierarchy[0][i][3] != -1:\n",
    "        cv2.drawContours(internal_contours,contours,i,255,-1)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1766cb0aac8>"
      ]
     },
     "execution_count": 24,
     "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(internal_contours,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "internal_contours=np.zeros(image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(contours)):\n",
    "    if (hierarchy[0][i][3]) == 4:\n",
    "        cv2.drawContours(internal_contours,contours,i,255,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1766aebdcf8>"
      ]
     },
     "execution_count": 38,
     "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(internal_contours,cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(contours)):\n",
    "    if (hierarchy[0][i][3]) == 0:\n",
    "        cv2.drawContours(internal_contours,contours,i,255,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1766ad44cf8>"
      ]
     },
     "execution_count": 45,
     "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(internal_contours,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EXTERNAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "image,contours,hierarchy=cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1, -1, -1, -1],\n",
       "        [ 2,  0, -1, -1],\n",
       "        [-1,  1, -1, -1]]], dtype=int32)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hierarchy # in External Retrieval mode values with hierarchy[0][i][3] as -1 gets returned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17674036a90>"
      ]
     },
     "execution_count": 272,
     "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": [
    "external_contours=np.zeros(image.shape)\n",
    "for i in range(len(contours)):\n",
    "        cv2.drawContours(external_contours,contours,i,255,-1)\n",
    "plt.imshow(external_contours,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1766ae71be0>"
      ]
     },
     "execution_count": 51,
     "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": [
    "nadia=cv2.imread(\"../DATA/nadia_murad.jpg\",0)\n",
    "plt.imshow(nadia,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FUNCTION TO FIND EXTERNAL AND INTERNAL CONTOURS\n",
    "def ext_int_con(image,contours,hierarchy):\n",
    "    ext_con=np.zeros(image.shape)\n",
    "    int_con=np.zeros(image.shape)\n",
    "    for i in range(len(contours)):\n",
    "        if hierarchy[0][i][3] == -1:\n",
    "            #EXTERNAL\n",
    "            cv2.drawContours(ext_con,contours,i,255,-1)\n",
    "        else:\n",
    "            #Internal\n",
    "            cv2.drawContours(int_con,contours,i,255,-1)\n",
    "\n",
    "    plt.subplot(121)\n",
    "    plt.title(\"External\")\n",
    "    plt.imshow(ext_con,cmap='gray')\n",
    "    plt.subplot(122)\n",
    "    plt.title(\"Internal\")\n",
    "    plt.imshow(int_con,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ext_int_con(image,contours,hierarchy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CANNY + CONTOUR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "nadia_blur=cv2.blur(nadia,(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "median=np.median(nadia_blur)\n",
    "lower=int(max(0,0.7*median))\n",
    "upper=int(min(1.3*median,255))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "out=cv2.Canny(nadia_blur,lower,upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17673952630>"
      ]
     },
     "execution_count": 155,
     "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(out,cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "image,contours,hierarchy=cv2.findContours(out,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ext_int_con(image,contours,hierarchy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "##ADAPTIVE THRESHOLD + CONTOURS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17673e39080>"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMUAAAD8CAYAAADHTWCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFwJJREFUeJztnW/sZkV1x7/HBcQWDbW7GiLQpQlpC6aC+wtCaIyF2lJKtG+aaNLGNCa8sQ2mTQzUpI0vSKxNGvqmTYjS0ogS4p9oiBEJ1bRNGmBXoC4uq0ht3UDdtY3R9oUWPH3x3MvOzs6dO3Pv/Dlz53ySX37Pc5/nuXfunfOdc+Y/MTMURTnNK2onQFGkoaJQFAsVhaJYqCgUxUJFoSgWKgpFscgmCiK6iYiOE9GzRHR7rusoSmooRz8FEe0D8A0AbwdwAsDjAN7NzF9PfjFFSUwuT3ENgGeZ+Tlm/jGA+wG8M9O1FCUp52Q67xsAfMd4fwLAW6a+vH//fj548GCmpCjKjiNHjnyPmQ/MfS+XKMhx7Iw4jYhuBXArAFx66aU4fPhwpqQoyg4i+veQ7+UKn04AuMR4fzGA580vMPPdzLzHzHsHDsyKV1GKkUsUjwO4nIguI6LzALwLwOczXUtRkpIlfGLmF4noDwA8BGAfgHuY+ekc11KU1OSqU4CZvwDgC7nOryi50B5tRbFQUSiKhYpCUSxUFIpioaJQFAsVhaJYqCgUxUJFoSgWKgpFsVBRKIqFikJRLFQUimKholAUCxXFxiByTXpUYlBRbAxdRX492eZTKGmYKvnV+POhohBISAjk+w4zn/W5iigcFUVhiMhptC58huz7veuzOREpp1FRFGQ0TFsYo1Ha783f2cfWGrIpEvO1CkRFkZy1rT+mQHIaqC3EkdzXbQFtfUoAEb3852I0MpexmcdCXqeGmV/+G+m9WVc9xQrmjMdl2KWMfQlmSDcVyvWAimKCuTBiq3G43QgwJ44thlsqCge+jO6hqTMmlNri/WudwkFMRkuNv311nBjMCrnUe02NiiICl5dwNZ9KMB5X2qaI8QYS7i03s6IgonuI6CQRHTWOvZaIHiaibw7/f8b47I5hS6/jRPQbuRJeGrsO4TK4ViunIZ2JW/CeoYR4ir8DcJN17HYAjzDz5QAeGd6DiK7AboXxK4ff/PWw1VfTxFSqWxPESEi6Q0OpVp/ByKwomPkfAfy3dfidAO4dXt8L4LeN4/cz84+Y+d8APIvdVl/NstVWphS07hGmWFqneD0zvwAAw//XDcdd23q9wXUCIrqViA4T0eFTp04tTEZaXL27I3OCmDKQrRlOTF2lVVJXtGe39Xr5oMCdjKYqlKGCCBmztBWmhomMx1ouDJaK4rtEdBEADP9PDsdnt/VqgZQhU8vGEUpIq1xLLBXF5wG8Z3j9HgCfM46/i4heSUSXAbgcwGPrkliP0Iyd+l7rxjGHeX9z4m+pcJjt0SaiTwJ4G4D9RHQCwJ8B+DCAB4jovQD+A8DvAAAzP01EDwD4OoAXAbyPmV/KlPYk2CFOq82qEvCFiy09z1lRMPO7Jz66ceL7dwK4c02iSpIis7ZcdwghdNJUK2iPtkHI4DfFTWgY1QIqigiWTA/dgpHE0vo9dykK3xzmJWGQr0LdU1i1lXvtUhQ2IYKYantvvU0+Fy0/ky5FkdobAP7wqWUDiWUL3kInGa1kyfDsLRiOj7E1qtVWuS49hUnufgnfnIvSHqQnj7WG7kWRipD5CLVXzihZarfcRNt1+JQyw2INbungQyU/6imwzhBDQ6HQSTn2uVosaVuna08Ry9xiZ2sIFUZLnqTVCndXojAzJ6aCncIoY37nm/+dMk2Km67CpyXGI8X47Ap6bytslKQrTxFLS25finhtWgyhVBQepGeiq//D/O/6jjJPV+HT1pnqKFTiUE+xQaZastRrhKGeYoKtlLD2POoa99Va73aXouix5NTWqnC6EUWMIWzVaHwdhKWvL5luRLFmbNKWqDkgsZXhK92IIjQTJGdWSiTUNaQ+625EsdWSfy01h7FLzZNuRKFME7PS35prtEIXopDqpqVRoum0hbzoQhSx86hbKtVS01qfQg5Ctve6hIi+TETHiOhpIrptON7dFl+90LswQjzFiwD+mJl/CcC1AN5Hu228NrnFV89ewqRnYYRs7/UCM391eP1DAMew251oU1t89Zj5c+QWhtQ1saLqFER0EMDVAB7Fyi2+SOD2XsrZ5BSGVG8ULAoiugDApwG8n5l/4Puq49hZMUmJ7b1iH7aGTm5SPxfp8z2CREFE52IniPuY+TPDYfFbfMU+cGklljTWPh/fItSSnn1I6xMB+BiAY8z8l8ZHXWzx1TIpVyLsqQ8jZJLR9QB+D8DXiOjJ4difYENbfAHySqsl+NKfYo507mckJZQK2d7rn+GuJwAb2eKrRZYYZyph5ECKIIBOerS3gBkK1WxAWBuOhYRhtT22iqIBlhiJb9++3EYdSs4VF9fQ9cIFIeHE0sxPmbEpViW0jblms6j0+luXnqKEEUjN9BTbAaRcO1fic+raU8whwUvkZG06U636J231wC49BZDHcEvOJvNVukuMKUpxr1JXGOnWU5ixtW+Vb0kl2IgrbVPiGJF4HzZSnrl6CuRvBclRck9VVqdKcFcFew1L7mnKu9UWgU23ngLwt4L4Mir2NylX3h7PEWqQrhU7anXi+da5tdNZUyjdegqTGCPzhSS+c6xtbTGNZS7MmPIg0pC6ooeKAmGGapa0rswcj9nhgf27pZjn950r5F5yGmKM6H3irVnx7l4UuVqhgNNhSoxHCTm3K832cd/3UtGKR4ql6zrFEnwV9DljX1IxjTUyXyVbCaN7TxFjMK6QyXceu66SysBjBweWjt2n0uQ7Lkm46ikwX9H2tdr4DD9HRofWJXKHTuZ1Q+855PoSxNG9KEIyak40Ejr/pMTyPtHa4nENWJSAhk+R7f3m73whjK+CLckActLq2LHuPQUQ3yEGuMOkufqD9CHTqfF5gpQtcqnp3lPEZo6vGdI1LHvueltnSb+J9mgLoUSnl6TSUBK1RWCjokCcsbt6rXNcZwu4jH1t61QJtE4xsGSQnXImMePHJD9DFYWSnBojcFOi4ZNBicFo0npv1+B6XiG9/ObnpteQ8lzUUxRk6/0Uc/dljx4O+U0oS5rVp1BRFGargnAx5TVS9mzH/D5UMCELLJ9PRI8R0VO0297rQ8PxTW7vlctopYQGKVlS2vsGOS7B7CNKRUid4kcAbmDmNwG4CsBNRHQtNrq9l0RaFJRt6Ha9wZ6wJWkWXsj2XszM/zO8PXf4Y2xsey8baYYotYI+NSbM/GzN8PnYYfIpCN20ZR/tluE/CeBhZt709l65Sqyl5zVLUYnCMPGV+j4BhZzT/M2Sv1CCRMHMLzHzVdjtSnQNEb3R83XX1c96Qlxge6+lpO5cSmXIUsKLpdheZC5kqhVqRfVTMPP3AXwFu7qC+O29lpBrYpBEka0lZtKQL6QKvU6pUCqk9ekAEV04vH4VgF8D8Ax0e68qSPMWcwZqjyC2f+vD/o3tKaY8x9pnFNJPcRGAe4cWpFcAeICZHySif8GGtvcCzu5ckmaAkvFVpkvNnZgTRvDYLAkZv7e3x4cPHy5yLd/ITdu1p342qYQmSbBThhbau13yPojoCDPvzX2vyx5tM0NKZk7p65ViaS+0lLqRTXeiMDPDZaCpQyjXNWIqqCZj2qUJKiRNrvBK2n2MdCcKIG7FiVTEiM03TmgLSBS2iQ4dH7ANzvYmS3G1nvjOa7fJT6VPEiGV2pStQ7lRUcBf6RtDljVG6fNM5n/X910dXq3guq8W0t9l+GQyJ4jxf0qP4bp+SFu/NCR7rzWIaJIlIq6RDp8gXPWLXGlcM2CuJr6GCon3Edok2334JGGgXYnxPLVpyat0KwpfJk15hZYythShQm5J8F2KYknfQUuZWptUfTu16FIUpcbi9EbIcwydP1GTLkUBLH/wKqB12MPAJdJ9k6ySli3UMbr1FFOkCgFqUDNdUp/JEtRTDIQMKU89sjNkDFRs/Uf6uKIW6F4USzubYkeG2vjGNi0d8xQ6+nZNfaoHwXUvCrPiNzdjbDxmDs3wGYnPs8z1k8yldyk9GPVauhcFkDckWetNpNBCU2oqVBQB5MzsFgxpbjLW1tDWJ8xPtJ/7fmmkGqTUdMWiojCYmmgU85ul14qhhihD5prM1aFaEY2KYiG9VHhdhjy38MDU/PLxM+ni0DrFwFSrUi/NkC6mmqunFh6Ye26tPEf1FAb2HOrQxQVyULs0Dem/cZX6kicZhaKewoEdGuTwFiG91VPCyG1wSwVp1jF881GkC0Y9xQS+tYnGTF8za880oNg4u5QXWWO8dqEynk+6IIAIUdBuj4oniOjB4f0mt/dyEdI+v8S4p1p0fH+5WVuat2D0c8R4itsAHDPed7e919zKH0sMItbopZS2rYRCSwjdyehiAL8F4KPG4U1v72UT06tbu5K8BLu/ISWpRxfnJtRT3AXgAwB+YhxLtr1XdKoLs8RYWjKC1EvsTNW1WnkmIZu23ALgJDMfCTyn687PetJsbO8VeN6qhM5rkBLehJJaEFO09FxCmmSvB/AOIroZwPkAXkNEH8ewvRczv0Ab2t7LZq50i50kJIk1gggNiaQ/AxchWwbfwcwXM/NB7CrQ/8DMv4sOtveaC5ummmvNJlupIUPtZtIlrXWlWNN592FsbHuvUCRmZAwSWo7ssVCSvEnXa8mG4Ot59tUz7OEiUjI+tSBS1ElKiZRaWkv20KFDtZMwSWgF2/ebmN/a30/plXIYn91E7RsP5TuHhAJjRMc+zWCX8D6vMdWp5ztfCUq1MNnX9A2VkYwIT9EKcyVeaMkeWvqvLUHtDrlcxmkXGmvGhElARTGDb7Tn1Hdd4lgShi3Fvn6JktoWQsvCUFFE4Iqf7eM+A3QJI3WdwRZDydBlqTCkCUdF4SB2TJNrmPQUMZ4nlBDPVIpYYUgTBKAV7UWYnXIhAwR9v596P8dcCFcTc66I6/mEetZaqCgc2BnpakVZm5m++kds6VmjNWvumi5hxDRC1BSLhk8zhGbO0swsMQcjNaHXXDJaVkKfhXqKGWL7FZbEyLWNICeuUDPn3I0UqKeYYUl8L6G0k8xUPUzKAEEVRWJUDGG4OvmkFCYqioSU7KBT8qGiWIka/jp8rXC1UFEsRFImto6EkMlERbGAkKEUKpgdMR2aMd/PiTbJRhA7lEJaCViLtf0apZ+jiiIA7XtYhoRSfwkqCg+1S6wtsLTHfqRGR5/WKRwsGXUqeeWOGuR4FqWer4rCYMkcCRP1JGeS4nlMzUHJKRANnwZSDWuWsmpHTVIb7FTrXq6wqntPkfrh9h5GlYr/p+odKejWU+SsREta56kkpe/ZbsJNJciuROEqTXKucNGTMGrea2pxdCGKkmJwTcHcujCkhItT4jA/CyF005ZvE9HXiOhJGvaToEa29yo910H6/OPUSJws5MrjGOHGVLR/lZmv4tNrcYre3qv0ci++yTJSStLUSBSEydI8X9P6JHZ7rxprH015CEkD3VIiXRAmsTYQKgoG8CUiOkJEtw7Hkm3vderUqeAEz7E0jkzN1NzkLdCSIJYQWtG+npmfJ6LXAXiYiJ7xfDd4ey8AdwPA3t7e6qcrRQzA6cq12QI1tRZSS0h6xjkJ8hTM/Pzw/ySAz2IXDn2Xdtt6gSpv77U0s3KW3GZpugWPsUYQrd1vyEaQP01Erx5fA/h1AEchZHuvNZmVs49i6n1rwpC+HE0OQsKn1wP47PBgzgHwCWb+IhE9jsrbe0l25770tDgUZLwfac85B7OiYObnALzJcfy/ANw48Zs7Ady5OnUeQgQhLX5vqWPPfr5L0yr5Hqdorke7xYk/UxVv8zMpuAqbNWmUdG+hiBfFVJgx97BLGFvoNeZ6uSUIwyeG2mkrjUhRzMXbsYYomdpNta5KdAsV65zPS5QoSg7cy8WSzDKFMb7PzRoxSBBN1iE7EoyOiFydezWSEkXq0qpUa9pUv0nINSWEekuhwH20RXmK1h526vTWbKoN9Q7j69byKgYR01EPHTrkbVbtiRKde7Eewvye+dut5o0IUfjYcolk4qpTSDA6l3C2Lg7xougFu+kzt9H5hOeai+I7x9ZQUQjG1TJU49q10lCL5kWx9YyqIQyfZ9hiuGTTvChstpZhrmEhOa4R+nkPPdxNi2ILnX1z5Bx2HjMyYKruEXqelmhaFMD2MgTwG31tj2H+bZXmROHLjK1k1JzQU7ZMzQ1WHI+bYZP5fouFUlOi2IrRpyB1PWM8hy9MGq+7dZoSBTA/o61F1pT4JTrRWn2uS2lOFCO2AbTsRdaGIWubbe0QquVnmYJmRWHTW2lmk6I/o3cxjDQlCt8qGT0xFSql6ujr3VuIF8VWM2et0doFgjl7L0UFfE2B03qeiRfF1rxBrpGwvgGFKfC1TrnS0jLiRTFiT3JpFVcImPp+UgvPnFTUusGH0IwoWmeul9osiXN0yNUKpVqkGVG0WskOHR9k9xbPnXPJcItaw0daQ7QoWs4gl8Gm7Hl2VbSnvt9KASKF0O29LiSiTxHRM0R0jIiuowLbe7Wamb5hEWuEYQti9Cp26LV0WEarzzs1oZ7irwB8kZl/Ebt1ZY9B+PZetXBN8rePp2Yu5GrZ49YgZCn+1wB4K4CPAQAz/5iZv49K23u1kMFT3mFN51rIOVL1UbTwjHMS4il+HsApAH9LRE8Q0Udpt09Fte29pLp5O353daiNpJjaGTo4MnZsldTnW4oQUZwD4M0A/oaZrwbwvxhCpQlcuexaAfBuZt5j5r0DBw54E9BCyeUSBDBvuGv7KWJ+O9ULPtUgMDeMfKuEiOIEgBPM/Ojw/lPYiaTY9l6tlVxToc5UU+oaYcRU5GObclt77qmYFQUz/yeA7xDRLwyHbsRulyIR23tJwPQSvtjfN1ttThgh8ybmBGHPmFti9D0IJXQt2T8EcB8RnQfgOQC/j52gim7v1YrrDjEcnzB800JjxiCNaF9FHGJWHQ9JR85mzTXYniKk9WnufHPfd4nC9f25c02Nk5L2jFNALa06fujQodpJWEWMZ4jpWJvzGnOoh1iG6GEerTEV0riaX0PGQq2pgIcKQkVzNlLCpx8COF47HQP7AXyvdiIGNC1ulqbl55jZ3/4PIeETgOMhsV4JiOiwpuVsekqLhk+KYqGiUBQLKaK4u3YCDDQtbrpJi4iKtqJIQoqnUBQxVBcFEd00zNB7loh8o29TXe8eIjpJREeNY9lnETrScQkRfXmYyfg0Ed1WMS3nE9FjRPTUkJYP1UqLcf59w1SFB4unxTVIrNQfgH0AvoXdnI3zADwF4IrM13wrdqN8jxrHPgLg9uH17QD+fHh9xZCmVwK4bEjrvkTpuAjAm4fXrwbwjeF6NdJCAC4YXp8L4FEA19ZIi5GmPwLwCQAPls6j2qK4DsBDxvs7ANxR4LoHLVEcB3CRYazHXekB8BCA6zKl6XMA3l47LQB+CsBXAbylVlqwm27wCIAbDFEUS0vt8Cloll4BVs0iXAsRHQRwNXYldJW0DOHKk9jNi3mYd/Nnaj2XuwB8AMBPjGPF0lJbFEGz9CqSPX1EdAGATwN4PzP/oFZamPklZr4Ku1L6GiJ6Y420ENEtAE4y85HQn6ROS21RJJ+lt5BiswhNiOhc7ARxHzN/pmZaRni3KMVXsFuJpUZargfwDiL6NoD7AdxARB8vmpYc8XFE7HgOdpOWLsPpivaVBa57EGfWKf4CZ1biPjK8vhJnVuKeQ9rK7d8DuMs6XiMtBwBcOLx+FYB/AnBLjbRY6XobTtcpiqWlqiiGm7oZu5aXbwH4YIHrfRLACwD+D7tS5r0Afha7it03h/+vNb7/wSFtxwH8ZsJ0/Ap2bv5fATw5/N1cKS2/DOCJIS1HAfzpcLx4Wqx0maIolhbt0VYUi9p1CkURh4pCUSxUFIpioaJQFAsVhaJYqCgUxUJFoSgWKgpFsfh/EyJdlBKr0cwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nadia_blur=cv2.blur(nadia,(5,5))\n",
    "ad_thresh=cv2.adaptiveThreshold(nadia_blur,161,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,8)\n",
    "plt.imshow(ad_thresh,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "image,contours,hierarchy=cv2.findContours(ad_thresh,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ext_int_con(image,contours,hierarchy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "##THRESHOLD + CONTOUR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret,thresh=cv2.threshold(nadia_blur,100,255,cv2.THRESH_BINARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x176743a7588>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMUAAAD8CAYAAADHTWCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEU1JREFUeJztnW3MJlV5x3+XC4gtGrS7GiLQpQlpC6aC2SCExlioLUWi/dJEExvTmPDFNpg2MVCTNn4gsTZp6Jc2IUpLI0qIL9EQIyVUo00a4FmBurisIrV1A3WxjbH2gxa8+mHmZueZvV/OvJyZc+b8f8mT577nmeee655z/c91Xuacy9wdIcRpXja3AUKkhkQhRAuJQogWEoUQLSQKIVpIFEK0iCYKM7vBzE6Y2dNmdmus6wgxNhZjnsLMDgDfAt4GnAQeBd7t7t8c/WJCjEysSHEV8LS7P+PuPwXuBd4Z6VpCjMpZkT739cD3Gu9PAm/edPLBgwf98OHDkUwRouLo0aM/cPdDu86LJQpbc2xfO83MbgZuBrj44ovZ29uLZIoQFWb27yHnxWo+nQQuary/EHi2eYK73+nuR9z9yKFDO8UrxGTEEsWjwKVmdomZnQO8C/hCpGsJMSpRmk/u/oKZ/SHwAHAAuMvdn4xxLSHGJlafAnf/IvDFWJ8vRCw0oy1EC4lCiBYShRAtJAohWkgUQrSQKIRoIVEI0UKiEKKFRCFEC4lCiBYShRAtJAohWkgUQrSQKIRoIVEI0SLaegoxHmb7l7wrfUJcJIoEaYug69/dXUIagEQxE7sce+zP3nY9CWY/EsVExBTBUFa2SRwVEsXIpOz8u5A4KiSKEchZCOsws6KFoSHZgSxNECvMbLHfbRcSRU9KcZoSvmMbiaIHpTlKad9XouhIaQ6yopTICBJFJ0pxim2UcA92isLM7jKzU2Z2rHHsNWb2oJl9u/796sbfbqtTep0ws9+OZfjUlOAMoiIkUvw9cEPr2K3AQ+5+KfBQ/R4zu4xqh/HL6//5mzrVV9ZIEPtZelNqpyjc/avAf7cOvxO4u359N/C7jeP3uvtP3P3fgKepUn1ly5ILfyhLvTd9+xSvc/fnAOrfr62Pr0vr9fp1H2BmN5vZnpntPf/88z3NiMtSC11sZ+yO9s60Xi8dTDyTUV9BlDYTvMSKo68ovm9mFwDUv0/Vx3em9cqBIQW9RCfZxdK+c19RfAF4b/36vcDnG8ffZWYvN7NLgEuBR4aZKHJgScLY+UCgmX0KeCtw0MxOAn8OfAS4z8zeB/wH8HsA7v6kmd0HfBN4AXi/u78YyfYoLKlwRT92isLd373hT9dvOP924PYhRok8WcrTtZrRbqAoMZwl3EOJQoxO7sKQKGpyL0gxHhIFEkQMcr6nEoUQLYoXRc41Wurkem+1cUEkmkOTuTpHqUgUI7NunL59rCSR5Dh3UXTzaS7ndPfsHKUkFClGpKujq4mVJhLFCIxR6y+5iZVbE6pYUQx1utiFvGSRpE6xoujLXDWeRDIdRXe0u5JSEyC3znpOIpYoAknVAXMTRw5IFAtB4hgP9SkCyMnZNMw7nCIjRSnOklr0yOW+FymKLqTkVH1ZwneYEomiECSMcCSKLSzNkVJrTqWKRFEgEsd2JIqCkTDWI1EUjqLGmUgUApguauQwLFucKHIolLlQxKgISe91kZl92cyOm9mTZnZLfby4FF+iDEIixQvAn7j7rwJXA++v03gVleKrFBQtwtJ7PefuX69f/w9wnCo7UZYpvkILXc5RLp36FGZ2GLgSeJiBKb5ySO8lyiRYFGZ2HvAZ4APu/qNtp645dka1m3p6r5KJHSVTH+wIEoWZnU0liHvc/bP14UWn+CqdkucvQkafDPg4cNzd/6rxJ6X4KoAShRGyyOha4PeBb5jZ4/WxP2XBKb5yZVezpEQH70NIeq9/Zn0/AZTiazambJe7e/L9gDEpbkY7Z8zspZ++/9+XsaNMyiLTGu0A1hXg1E2RPk60bq+o1eek0JRKdedAiWIL2xxx299iFPSuJkzINVfn9BVHKc2oIkURUrhDCj9WbTzW56VUO6cYLdSnyJgh/YuUSO07FBkplkDTkTY5VWo18DZSihgSRQC7CmuTU8Yq6NCatX3eWCkDYtXsqQwCFCuKkMLt8kRt87PmLtRNpOJ0qVOsKIYQ2kmfIko0R5RW71Nro3dl7qZU0R3trje+a8c2hnM2bV5n/zrRpJTbYmVPytGqaFHsojmD3Kd9HrPWHpJfb6hDTuHQcwpXouhBl4myWBN5fY6lUEPn0LQrvk8RozZPJQVYiuRgY/GRoqsgxmyGiDQpXhSb6LvBgYSSP8WLYpsTh7bBcxhRSY2U50yKF8U6ttX+OXQUU6Xd9NxVIc2FRBFIijWaiEPxo09dkDD6kdt9U6Qgv0ITcZEoxKTk0CeTKGoULeIQ8nxWyP9MiUTRYO7CyIXQ2j7X+6mOthiVbUIIWS2YAhKFGI1QQXT936lR86nFEhbpzEFKTj2UkA2WzzWzR8zsiTq914fr40rvVSAlVBghkeInwHXu/kbgCuAGM7sapfcSNZtGmFYCyqnpBGHpvdzdf1y/Pbv+cTJN7yWGEeLAXR72S00QEJ605UC9Df8p4EF3X3R6L/Urwtm04q+9oUJOBInC3V909yuoshJdZWZv2HJ69um9cixIMR6dRp/c/YfAV6j6CotM7yVBdCOl7f3HImT06ZCZnV+/fgXwm8BTFJDeSwIJo73bSWgejVTvb8jk3QXA3fUI0suA+9z9fjP7FxaW3ivVQsqFpdw/SyGEHTlyxPf29ma1Yep8E7kzlgCmvLdmdtTdj+w6T495sH+bxqXUdqmTckUjUXDmfqxtxtrbtMv4fYwdw0UYevZpAtZ1RHedH3IsV1IXuERRM4XThe4K0twyJ/eJMNg/mZe6IEDNJyDM2YY0oTb9X5ecFjk40zpy3FFRkaLFXDv+LSV/3RIoPlKsIsC6SNAM+SunzWEb+1TI9bsWHSk2OXmuhSnGoWhRbGrvzp1eagnkfP+KFUXfLfjV7l/+PShWFDnXZCIuxYqiSdfZ46XXlEPJvcKRKMi/EKekhApBouhJCc7RhyVUMMXPU8CZT8luKtiU1m5vsiOmU6by3WMjUdDfkUKGboeu0whxxCXUzikhUXRkzGiR00bFpUQJUJ/iJWIlJuybIDKl5JJdHlxcAooUAwmd/S7BmZaCIkUPSnLw0qIESBT76Pv4dknt7V0s4V5IFDVda8H2+UtwhjZ9o0Tu90KiaFBK8yCEEptNKySKFqEOEHtzgZxq2/YoWe4i0ujTyAyd0Nt03sSbhvW67pgrFOdEkaIHQ/dIHdp/iUmIIMaaiU+VYFHUOSoeM7P76/dFpvdqTvJ1dda2wzXXgG/7mYoxIlPOEWJFl0hxC3C88b7I9F6h+zCt+1vK68H71uyp2D8moZmMLgTeDnyscVjpvRqsq9VzaUJoi879hEaKO4APAj9rHFtseq8Q+nSWU2PdZOUYgsh9PXtI0pabgFPufjTwM7NP7zWEXGrZ0OZdiYQMyV4LvMPMbgTOBV5lZp+gTu/l7s/ZgtJ7DWXb/EUqTjelIHIcng1JGXybu1/o7oepOtD/5O7voYD0XpvYNEqTQ3NhLEHk5uhdGDJ59xEWlt6rL11mwed0JjWZwlB6rx60I0XfjdWmItZ67txGrZTeawJiru0eiyk3OMhNJJvQYx49aA459nW60JwYffspQ2wLZambxilSRCLU6ZuMlRtjju1vNjF3P6oPEkUPYtWAQ4Zux34IcUxyE4ZEMQFdZ3jHFF0qzpiTMNSnmJCpnSIXJ0wNiaIHQ9r+UzwOPuUj513XbufQ+VbzqSdDnS7GvrRzRoaU9tkdikQRmW2OOoYjpdBEai6WgvCViSnYvg41n2amT1MnpS011zFk84cUUKQYgV0r8Lpsq7mUWeHQKJjiqJQiRWRS3qQgNrlGDIlCiBYSxUBi1nJLihq7SGm4VqLoSWgh9inoksSQIhJFD6ao0UoVRgoRQ6NPHZi7sHJjyP2acy5DkSKAFGqvUpnjvitSbEFC6E+MHdinihqKFGsYMzKU2jeIwVSVlCJFA0WGcYh5H6dITyBRIDGMyZT3MlazqvjmkwQxHnPdy7EHQoqMFBLC+KRwT8eKHEVFijmGVlNwltik9h2HlnMRkSK1QlsSKd/bvpEjNGnLd83sG2b2uJnt1ceySO+VQqGlYEPJdI0cXZpPv+HuVzT24kw6vVdqs9Ap2TIWS/xOMKxPkWx6r6UWViqkVuGMTagoHPhHMztqZjfXx5JM75VyYaVsWyhL+A67CO1oX+vuz5rZa4EHzeypLecGp/cC7oRqK/5AOzZfNJPCSnFNcii53OOhBEUKd3+2/n0K+BxVc+j7VqX1wmZO75VbYeVmL+Rpc19CEkH+vJm9cvUa+C3gGImk98q1sHKxe+n9h3WENJ9eB3yuvjFnAZ909y+Z2aPMnN4r98JKvSmV+/3ty05RuPszwBvXHP8v4PoN/3M7cPtg67ZQaoFNQen3NrvHPJYWzlP7LqnZMwfJP+ZRQiGl0Iwq4T6HkqQoSiyguYRR4r3eRTKiUOFMuxZZ93szSeTRNrP5jUiMmOVSsCCC8mhn19EWIjYSRaKkukdtCi2L2EgUCZNSM6eZJCblhDFjIFEkTsrzMksVhkSRCWMLo2tG15KQKDJizi1kmq9TjVxjIVFkxtwOufT+BEgUWTLHPrfrrjm3QGORzIy26MYcj4UsVQRtFCkyZoz2/dKbQn2QKBZAKTX4VEgUC0HCGA+JYkH0bU6pCbUfiWKBKGoMQ6JYKBJGfySKBSNh9EOiWDgSRnckigKQMLohURSChBGORFEQ24ShYdnTSBSZIieOR2h6r/PN7NNm9pSZHTeza3JJ75UCYztwrGWhElpFaKT4a+BL7v4rVPvKHifx9F4pEbs9X8IahykJ2Yr/VcBbgI8DuPtP3f2HJJzea8lsc36JYxxCIsUvAc8Df2dmj5nZx+o8FaOl9xr0DRJEjpk3IaI4C3gT8LfufiXwv9RNpQ0Ep/dy9yMhO7blhLvPOvwZeu3Vw4PNhwg1bFsRIoqTwEl3f7h+/2kqkSST3islYjvW0M/f9CStBHGanaJw9/8Evmdmv1wfup4qS1ES6b1KZJMDD3Fs9UdOE7pG+4+Ae8zsHOAZ4A+oBDVreq/cWDndGLWyavZ4aNfxCRlTFKIX2nU8NVLfSCyFCjIFJAohWqSy79OPgRNzG1FzEPjB3EbUTGrLjii2hPvyiyEnpSKKE6nMV5jZnmw5k5JsUfNJiBYShRAtUhHFnXMb0EC2rKcYW5KYpxAiJVKJFEIkw+yiMLMb6hV6T5vZtqdvx7reXWZ2ysyONY5NvorQzC4ysy/XKxmfNLNbZrTlXDN7xMyeqG358Fy2ND7/QL1U4f7JbVk9CDbHD3AA+A7Vmo1zgCeAyyJf8y1UT/keaxz7KHBr/fpW4C/q15fVNr0cuKS29cBIdlwAvKl+/UrgW/X15rDFgPPq12cDDwNXz2FLw6Y/Bj4J3D91Gc0timuABxrvbwNum+C6h1uiOAFc0HDWE+vsAR4Arolk0+eBt81tC/BzwNeBN89lC9Vyg4eA6xqimMyWuZtPQav0JmDQKsKhmNlh4EqqGnoWW+rmyuNU62Ie9Gr9zFz35Q7gg8DPGscms2VuUQSt0puR6PaZ2XnAZ4APuPuP5rLF3V909yuoaumrzOwNc9hiZjcBp9z9aOi/jG3L3KJIZZXeLKsIzexsKkHc4+6fndOWFV5tSvEVqp1Y5rDlWuAdZvZd4F7gOjP7xKS2xGgfd2g7nkW1aOkSTne0L5/guofZ36f4S/Z34j5av76c/Z24Zxi3c/sPwB2t43PYcgg4v379CuBrwE1z2NKy662c7lNMZsusoqi/1I1UIy/fAT40wfU+BTwH/B9VLfM+4BeoOnbfrn+/pnH+h2rbTgC/M6Idv04V5v8VeLz+uXEmW34NeKy25RjwZ/XxyW1p2dUUxWS2aEZbiBZz9ymESA6JQogWEoUQLSQKIVpIFEK0kCiEaCFRCNFCohCixf8D31sWBKNwY5kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(thresh,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "image,contours,hierarchy=cv2.findContours(thresh,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ext_int_con(image,contours,hierarchy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret,thresh=cv2.threshold(nadia_blur,80,255,cv2.THRESH_BINARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17671ed07f0>"
      ]
     },
     "execution_count": 219,
     "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(thresh,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "image,contours,hierarchy=cv2.findContours(thresh,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ext_int_con(image,contours,hierarchy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Eyebrow is not detected as internal as it is a curve which is connected with the background curve of the image ,\n",
    "#so it is also detected as external contour,\n",
    "# whereas eyes, nose and mouth are not connected with background and are in foreground, thus treated as internal contour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixing the threshold for separation helps to find better results but sometimes may give wrong results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As when threshold value decreased for better separation between background and foreground objects, \n",
    "# along with eye, nose,lips, hairs too  come in internal contours  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret,thresh=cv2.threshold(nadia_blur,77,255,cv2.THRESH_BINARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "image,contours,hierarchy=cv2.findContours(thresh,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ext_int_con(image,contours,hierarchy)"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}