18_identifiyContours_byShape.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary packages\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11cb90208>"
      ]
     },
     "execution_count": 2,
     "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": [
    "#load images\n",
    "image = cv2.imread('images/someshapes.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "plt.imshow(gray, cmap =\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#method for display image\n",
    "def display_image(image, cmap=None):\n",
    "    fig = plt.figure(figsize = (12, 10))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.imshow(image, cmap) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#image thresholding \n",
    "ret, thresh = cv2.threshold(gray, 127, 255, 1)\n",
    "#extract Contours\n",
    "contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uukrSyfXLJ0u6MnP7e+rdO5ZK2upKQAAAk3NdLS666CK95ICXeI6mcw468CDNmTOnYbEZSjsAlFUzQwGvkvRuSf9jjLmnfttHJH1e0hXGmFMl/UrS2+r3LZd0gqR1krZJOqWtEQNAD3I9lk879TTPkXRHtsSD0g4AZTVjIm2tvUWT1z1L0msmebyV9P5ZxgUAPS+bTMZxrFtuuUU6xnNQXWBkNFod1eDgoJIkoZNHD8i+luGXO8PD/6M72HsBQJe5bhXZJFqSjj3mWF8hdZ1brZEkujcYYyjRKQgS6O5iDwYAXWStVRiGaU20VCvrcH2W+w3JV+8ggSsO/hfdQyINAF1kjGkoZxgZGWlIqvvJz372s/TvAQBl1J9DIADgibVWQRCkyePWrVu15x57eo7Kj0NecYguv+JyvfWtb/UdCtqgWq3qpj++XUmSKI7jdAItuiMIAu29996S9pckRVFEe8kuMEU4rTY8PGxXrVrlO4ymcLYEGJckTDDKwy1CEoahzJRzufuH3XHNLpRM9ixLkiQN7Q3RHW5RJ5LnmRljVltrh9uxLUo7AKDL3AfdyMiI50iKYcOGDZJEiUeJZSeNBkFAEu1BGIYk0R6QSANAF7mzgGEYau6cuZ6jKYa99txLUuPEwyiKfIUDAE0jkQaALkp7vFLS0cBovH0aPYkBlAWJNAB0URHmpRRVtsaWiWoAyoBEGgC6iAmGU7vgggskcbABoDxIpAHkxun36U2cPGet1YoVK/wEUwKnLzs97f5AMg2gDJje2aLsvj3b7gfoF+51nySJkoQlnqeTPdBwf7fjjj3OY0TFFwahrKxGR0c1NDTkOxwAmBafgC3KjpKQQKAfudc9La5mlv37BEGg++67z2M05UISDaAMyARblG3JZK3l9CP6TrZcwVpLm7JpRFGU/n0uvPBCvfzgl3uOqByMjOI49h0GAMyI0o4WDQwMKI7jdDSOBQTQb4wxiqJIYRgyKj2DMAzTv89pp57mOZpyWb58uU488UTfYQDAtEikc8i2ZaK8A/0ou3oWifT0kiTRbbfdJh3lO5JyedOJb2pYOtxaS1s8AIVDFggAbeASPffdGRkZ0R8d9UceIyuvxx9/PL1srSWJBlA4JNIA0AauK0d2hN4Yo53m7eQxqnLbZ+999NBDD0kSLfEAFBKJNAC0QRiG6QS5OI5VrVaZMNcGBy46ML3MnBQARUMiDQBt4Op3rbWqVCrasGGDKiHTUNoljmOFYUiXGACFQiINAG3gFqkxxsgYo+ft9zzfIfWM8847L62Pzk50BQDfSKQBoE1cF5/t27d7jqS3nL7sdBkZaqQBFA6JNAC0QTbJmzd3nsdIepfrYT4RCTYAX0ikAaBNrLUyoq92p6xZsyYt7cgmz/QyB+ALxWYA0AYtJ3PNPryVwVa3zbIM0Bq1FOuSxUvSRVqMqZV6kEQD8IkRaQBok5ZGo+0kX5Pd3oo8P1MyRiZtg0cSDcA3EmkAyMGtYujcfPPNHqPpLyzOAqAoSKQBoAUugXOt7tyiK8ccfYzPsPqKkUlLOwDAJxJpAGiBS57dpLcgCNJlrNE9Dz74oIwxGhsbYwVJAN4w2RAAWuT6RbvV9rLLWHdUdnJetjw4z4RE93PNTPhr9bkmli43+zzNbKfuoAMPkpXV4OBgixsFgPYhkQaAFkwsJ+h6uzuXkOatamg16Z74XNMlxFPF1uqfaLrtTLjN/T+YeAjAB0o7AKAFbglwaXxkuqvyJNB5csypEmY3ujyZqWLL032kCd/4xjca/h8A0G0k0gDQojiONTY2psCUYBeap6RCOX6mXbnsdNuZkMT/9V/9dbp0uGuJxwREAN1EaQcAtMAtAjI0OOQ7lN6VIyl3ZwdYpAVAN5VgOAUAiiUMQt8h9LbJFquZZpGaNWvWpCPRjEgD6CYSaQBAqQ0vGU5HocOwdpBDSzwA3UAiDQAtKEVddNZ0kwPb/Tzd2M4Uv0t26XDXlhAAOq1knwgA0F3ZUoEVK1b4C2Q2ulXtMFXCnqf93VS3z/C7WGsVhmGaVANAJzHZEACmEMexjDHpcuDHHXuc75BmL+9CLs2YbLEYd3sryfR025lGGISysoqiKF15EgA6iRFpAJhCEATp1yOPPNL5J5wpsW1H4jtx0l4nWuPNMDmw7dvJWLduHUk0gK4hkQaAKbjygIsuukgvOeAlnqNBMw7Y/wDfIQDoIyTSADAFN2HttFNP8xxJyeVdFAYACo5EGgAyspML4zjWTTfd5DGaEpqsFrrLSbSR0ejoaMNtcRwzARFA25FIA4DG+w67fsTu+rHHHOsrpHJyEwuzXx7MGZqTJs5RFNEOD0BHMCMDQN/LtkxzS02HYSjjKwssu4KUcbiDIjf5kFUPAbQbI9IA+p5rceeS6JGREcoAesADDzygKIrS6+7/CwDtwl4FQN+z1ioIAiVJoiRJtHXrVoUBpQBld/DLDtby5csbbmNUGkA7kUgD6HvGGEVRlCbUe+6xp++Q0CYnvekkWWvTRXVcuQcAtAOJNABovI52ZGTEcyRot40bN6ZnHACgnUikAfQ9d7o/DEPNnTPXczRot7323EtSrUY6WzMNALNFIg2g77lEmi4dvcuoVr7D8uEA2olEGkDfYwJaf6hUKvyvAbQViTSAvkfP6P5w/vnnk0gDaCsSaQB9ZeKEM2utVqxY4ScYdNWy9y1TtVpNr5NUA5gtEmkAfSXb/sy1RDvu2OM8RoRumjM0p+E6nTwAzAaJNIC+kk2kgyDQfffd5zEa+BDHcZpAs9ohgNlgDwKgr0RRlLZAu/DCC/Xyg1/uOSJ0WyWsde6gtAPAbJFIA+grYRimLdBOO/U0z9HAlyuvvJLRaACzNuNexBgzxxiz0hjzM2PM/caYM+u3v9AYc6cx5mFjzOXGmMH67UP16+vq97+gs78CALQmSRLdcsstvsOAR2/5k7f4DgFAD2jmcHxU0qutta+QdIik440xSyV9QdI51toDJD0j6dT640+V9Iy1dn9J59QfBwBdZ61VkiTpd2dkZER/dNQfeYwMRfDEE08oSRImHALIbcZE2tb8rn51oP5lJb1a0vfqt18s6c31yyfVr6t+/2tMdnYPAHRJkiQKgqBhgqExRjvN28ljVCiKvffaW+vWraPEA0BuTe09jDGhMeYeSZskXSvpEUlbrLVR/SHrJe1Tv7yPpF9LUv3+rZKeO8k2lxljVhljVm3evHl2vwUATCIMQ8VxLKnWqaFarabXAUla9JJF6eRTAGhVU4m0tTa21h4iaV9Jh0s6aLKH1b9PNvq8w9Roa+151tpha+3wwoULm40XAJqWJInCMJS1VpVKRRs2bEg7NgCOm3wKAK1q6XyWtXaLpBWSlkqab4xxe599JT1ev7xe0n6SVL9/F0lPtyNYAGhFEATpoivGGD1vv+f5DgkFdOGFF/oOAUBJNdO1Y6ExZn798lxJfyxpraQbJb21/rCTJV1Zv3xV/brq999gadYJwBNX/7p9+3bPkaCoTjv1NBkZWWsp/QHQkmbOZ+0l6WJjTKha4n2FtfZqY8wDki4zxnxG0t2S3CH9hZK+Y4xZp9pI9Ds7EDcAzMham040nDd3nudoUAbG1BLqKIo0MDDgOxwABTdjIm2tvVfSoZPc/qhq9dITbx+R9La2RAcAs2StVWDoyoCZrVy5UkcccYSstSTRAJrCpwuAnuVqo4FmLD1iqaTa64YSDwDNIJEG0NPMpI2EgMm5WukwDH2HAqAESKQB9Ay3iqFz8803e4wGZeXOYrjXUnblQ+bOA8gikQZQetmEJ3ta/pijj/EZFkrKncVwS8sHQZC+pigVApBFIg2g9Fxy4xbWCIJADz30kM+QUHI///nPFQRB2j7RlXpkR6cBgEQaQOm55MZ9v+CCC3TgogN9hoSSW/SSRZJqS8u7kWlpvC85AEgk0gB6gOv9607BL3vfMt8hoQe415QxJl0lEwCySKQBlJ4r7UiSRD/96U89R4NeEZhA1Wq1YTSaZBpAVjMrGwJA4cVxrCRJdNyxx/kOBT1kaHBIVrXJrHEc0xYPQANGpAH0hDAMNTQ45DsM9DBGpAFMRCINoPSstbQlQ8esWbNGURSxUiaAHZBIAyi9TZs2sYIhOmbJ4iX60Y9+pDiOSaQBNKBGGkDp7bnHnr5DQI9780lvTmulAcBhRBpA6bg61SiKNDY25jka9ItNmzalKxwCgMSINICScZ0TxsbGNDg4SEkHumaP3fdQYplsCGAcI9IASqlSqejcc8/1HQb6zLe//W3fIQAoEGOt/5qv4eFhu2rVKt9hACiJiV06JuvaQScPzFb2NRRFkSqVipIkYZlwoOSMMauttcPt2BZ7AwClMvHgnyQanZJdMdNdJokGkEWNNIBSmZggT5Ywk0SjnUieAUyFvQMAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0AAAAkAOJNAAAAJADiTQAAACQA4k0WmatnfT2JEkUx7GstUqSZMrHTHZZku666y6dddZZMlJbvj796U/ruuuu0/bt25UkSfp81tr0KxvH6OjolPHGcdzwHQD6VXbfHcdx/brRxo0bG+6b6rMC6CWmCC/04eFhu2rVKt9hoAlxHCsMw3RnGQS1Y7EkSSa9nOWS1yAI9POf/1xPPvmkXnXkkd0LXtI1P/mJFi5cqIMPPlgDAwOy1soYk353v597Xxhj0p+d6vcCABh94xtf1+mnny5jjKIoUqVS8R0UMCljzGpr7XA7ttV0VmCMCY0xdxtjrq5ff6Ex5k5jzMPGmMuNMYP124fq19fV739BOwJFMbjEMgiCNAHN3p4dsY3jWGeffXY6QhwYozAIZCQteslLup5ES9LrX/c6LT70UA0ODKQxZb+PjY01/E7ucrVaVRAEjEgDgKQoitLLbmDlr/7qr9P9ZnZAAuhlrQyv/b2ktZnrX5B0jrX2AEnPSDq1fvupkp6x1u4v6Zz649AjssmlG8XN7lCNMVq7dq2MpEoY6qMf+YinSPOZN3euAmN08803S6r9PkmSaGBgQJIUhqHP8ADAuyRJ0tHmJEm0cuXKhvuMMekX0OuaSqSNMftKeqOkC+rXjaRXS/pe/SEXS3pz/fJJ9euq3/8aw7upZ7idoytz+OEPf6iBSiUd0Q2DQAe/7GW+w5y1Y485Jh1Jd6PoF110kbZu3eo5MgAoBvc5sHTpK9Pbtm/f3nA/0OuaHZH+sqR/lOTeFc+VtMVa64Yi10vap355H0m/lqT6/Vvrj29gjFlmjFlljFm1efPmnOGjHZqZHOImELr777nnHhlJbzrxxG6EWAinvve9mr/LLvr+978vSQ0TK6eayMipTQC9xs0VsdbqwQcfbLjvpptuSu9jTgn6wYyvcmPM/5K0yVq7OnvzJA+1Tdw3foO151lrh621wwsXLmwqWHRGEARpeYbrvCE11jtv27ZNl1xySTryvGTxYh+hFsJb//RP09KVwBht2LBBQRCkpS4T68WziTYA9JIDDzyo4foJJ7xRURQxiIC+0czh4qskvckY80tJl6lW0vFlSfONMW5K7r6SHq9fXi9pP0mq37+LpKfbGDM6wO30XNcKabwe+Ec/+pH+YKeddPJ73uMtviLbZ++9d5ioKI3//YIgaBjBAYBeMNWckTVr1qSDC0CvmzGRttb+k7V2X2vtCyS9U9IN1tq/kHSjpLfWH3aypCvrl6+qX1f9/hss76bCcztEY2q9QLP9mP/XG9/oM7TSmDM0lI7YT2zn6HpUM10AQK87/PAjFMcx+zv0hdkUMP1/ks4wxqxTrQb6wvrtF0p6bv32MyR9eHYhotNcLVuSJNq4caP23GMP3yGV3uGHHaYVK1ZRCR11AAAgAElEQVSk14eGhiTV2ugBQJm5UrXf/e530z6OdqHoByzI0sOydbnZGl43adCNQt9zzz069JBDfIXZF6686iqdeOKJDSM02X7V2cVe3P8JAIptuv2UZRErFJaXBVlQPq421+3IXA/oIAjSJHrlypUk0V1w0pvepMsvvzy97iZ3Zv8n7sDHGKOxsTEvcQJAM7Zt2zbt/U8++SRJNPoCr/I+4EagXaP8p59+Oq1/PuLww32H1zf+7J3vTP/u69atS5ckt9YqjuOGLh+Dg4NeYwWA6cybt9O09++2G9240B9IpPuAKxVwo57PXbDAd0h976ADD9T27dsVhmF6hmBgYCBtlUe7PABlN9OoNdALSKR7nGtnNzY2lq7Sh2KYN3eujKTHHnssPWMQBIEqlQptowAUlitNm8lMo9ZALyCr6mFRFCkMQy1fvlxDlAoU1gue/3ydddZZDfWEU/VnBQDfKpXKzA8C+gSJdI/IlgK4y5/97GdlJL3xhBM8RYVmfeqTn0zrp7MmLvLCSDWAbnL7nOxnzOc///kWttC4V2Mfhl5D+7se4CarhWGY7qQ2b95MP+iSunHFCh111FHpqE+1WlUYhg3LjwNAN8VxrCAIFMexKpWBln7W2tq8jzAMaYmHQqD9HRrUdmyVdEJhGAQk0SV23LHHaqBS0caNGyWpIYkuwoEvgP6RXXfAGKNLL70013bcvot9GHoNiXQPcCOXmzdvnrY9Psplzz320PXXX68gCNIkmhFpAN3kFvOy1iqKIp188l+2vA1jTDqJmvkf6DUk0j3AWisjafeF9O3sNX/8mtfISNq6dSst8QB0nVswylqbe5Lhl7/8ZVWr1TZHBhQDiXSBuVEAd3kit4BHwChlz5u/yy4NrwH3vweATnJnwYwxuQ/m/+EfPpiWH7LvQq8hkS6wbF3sZKf0K2FIKUcfGahU0v83o9MAuiF7AL9ixYrc2/nWt74lidae6D107Si4yWY4u9tIovvTb599VjvvvHN6PYoi+roC6JgkSeoDO7Mbe7M2YZ4HCoGuHX3CJcwTSzxCkui+9pydd5YRs+ABdF61WlUQBLr11ltnvS3OpKEXkUiXgBsNGBsb05NPPuk7HBTEfffdpziONTDQWk9XAGiW278cddQfzXpbYRg2vbw4UBYk0gXiRhbTSYT15vdhGCqOYw0NDtKZA6k/fPnLVakvwuNGelzPVwAomptvvpnFWNBzeEUXgFuZ0JVwuNWf3EpQ1lotX77cd5goqKC+EI80PkGVmfEAiuboo48hkUbP4RVdAC75cQtvSLXRaLfDCYzRm0480WeIKDhXM++6vDAzHkA7xHGsZ599tm3be+SRR9q2LaAISKQLII7jtIxDUrqCVBzHuuCCCzxHh7LIJtOjo6NeYwHQG8Iw1OWXX9627b34xfu3bVtAEdD+rgBcjWt2FDGOY1UYVUQOcZIoiiINDg76DgVAydW6R7X7s8h/3oH+Rvu7HmOMSZPoKIoURZH+53/+x3NUKKuvf/3rJNEA2uK//uu/fIcAFBoj0gVjrWXJb7RFYm3aAzYMQxZCADCtyRYAUwdWLXj66ae0YMGC9PpUq/cCncKIdI/Jtizbvn2752jQK37zm99ocHCQTh4AmjJxYK1TC6hce+21Dc/lJkkDZUQiXRDGGBlJO82b5zsU9Ij99t1XRkrLhujkAWAq2dar2d70nfCOd7xTv/vd7yQpbfXKiDTKikS6ANiBoJN+//vfp5cZlQYwmWzrVXf9gQce6NjzrVy5kpIO9AQS6QJYuXJlB6rQgJo/2GknnX322ZIYlQYwNTcK7crBDj745R17rte85o9ljEnrsjs5Ag50Eol0l7mdRbZn9NIjjvAZEvrARz/ykfQ1N/EDi9pEABP3A7/4xS86/pxuXzT5JEegHHjldlkQBA0rz33+85/3HBH6RSUMFcdxWpPocGoVgBuFdvuGF77wRR1/TpJn9AJexR64xOXOO+/UR/7pnzxHg37yL//yL6pUKukHGDXTAJxun536xS9+QWtOlB6JdJdVq1VJtVNZlHSg2/7pwx9Ol5+XajXTlHYAkGojxN1Mai+99FJJtL9DuZFId1mlUpEkfeELX/AcCfrVQKWisF7m0ek2VwDKJUkSjY2NdeW5Pvaxj+s3v/mNJErMUF4k0h789Kc/paQDXp199tnpKVU6eQCQakl0GIa64YYbuvacExdnAcqGJcI7KI7jhgb3LmHhuBtF8MyWLXrOc54jiUk/QL9zuUBtZLjbn1JWY2NjGhwc7PLzol+xRHhJZE+fu8vLly/3HRYgSdp1/nwFQUASDSCtU7755pu7/twk0Siziu8AelkURWlNtLVWlTCUTjjBc1TAOCPJ/zkpAL65Xs5HH31M1597cHCQVQ5RWgxFdVClUklPl0VR5DkaYHJFKO8C4J+vdpjXXnstSTRKi0S6w9zO4eqrr/YcCTC5gA8wAJK3icevfe3rvDwv0A4k0l2wbds2veVP/sR3GMCU7rjjjvQyI9RA78u2vRwbG1MQBPr1r3/tLZ6f/exn6b6HfRDKhK4dHZQkiYwxjPihFKyU9pWmJR7Qu7L1yI21yb4/q2za7YqaaXQSXTtKgh0ByuTxxx+XpLRlI4DeNNnn0qZNmzxEsqO0Tawx7IdQCiTSHRTHsffje6BZ++y9t1asWCGJvtJAP7HWavfd9/AdhqTxifmuiwhQdLxKO+jcc8/1HQLQklcfd5wkahSBXuc6dLgSxCJ4+umn05axjEijLEik2ySbeLg3/4f+z//xFQ6QmytJyr6mrbUk10APyZZQFMWCBc9VFEWKokjGmELFBkyFRLoNskf0URQpCAJt27bNc1RAPvfee2/DSFB26WCSaaD83Gi0tVZBEHjrHz2ZOI5VqVRUrVZJpFEKJNJt4hIPN9t4p3nzPEcE5HPIK16h+++/vyGBJokGekcYhrU5PMbUu2QUZ5HjoaE5kmoLmhUpwQemQiLdBkEQKAiC9PT3D37wA98hAbPyhy9/+aQ1iowQAeVnrVUYhoqiqLCtLo0xhY0NyCKRbrMgCFh8BT0hO2t+srppAOXk3svGGH3mM5/xHc6UmGyIMiCRbhN3mgzoFVu2bNHY2JgkPtCAXuNGfD/2sY/7DmUHbn/DZyrKgES6DbKnx3jbo1fs9tznasuWLQ2TaUmogd5Q5PdyEBSvowgwFRLpNqhUKkqSRD/+8Y99hwK01R67756WeLB0ONAbXLeOIpdqFTk2IItEuk2CINAbjj/edxhA27kDRVYZA3qDm0j83e9+13coUzr//PN9hwA0xRThqG94eNiuWrXKdxizxkko9CK3h6hWqxoYGPAaC4B2KvanVpLEHMCjI4wxq621w+3YFq/QNlm/fr3vEICOuOyyy2StJYkGeoS1VsuXL/cdxoxIolEGvErb5Fvf+pbvEICO+LN3vlNJkrA4AkolSZJJ62ybPQs70+PcugF5ZFcWbOa5Zvvem7h9Y4xOOOGNs9pmt8327Hmz/6/s3zrPhMxm44yiqOHx7fj94AeJdIsmvuittbr33nv18Y99zGdYQEd98YtfZKIhSiFJkrTTTLbbTHalzqlEUaRqtaooimZMorILFrWacGXfS9baGX8+DMP0Mf2UMF1zzTU7/I2r1WpL28gm0M10AXH/m07PC3H/03Z0JqH9rl/USLfIvblcM3up6FVmQHtEcUwyjdLI7qObMdkCRNM91iXqcf190cpnqUv2mymXijPvuziu1QzPNmmq/a5leS9bRVGUe9Jz9n/ZzN9vYtLdyv914nabeQ269rmuk0oe7mCM/XPz2lkjXWnHRvqJe6E3vMk4EkQfCMOw4UMdKJKxsTENDg4qSRI99dRTevDBB9PT9Nn99VQrdEZRpMMOO0y77LLLjCt5ZhNulwS1ktyGYZiOSM6UPGXfb+5yqwNg7ndxydovfvELvfjFLW3Cm9WrV2vJkiU7HOg0y43WhmHYsKLjVLJnMfIctGQT8enijKJIAwMDSpJElUpFN910U0vP4+I75phj0tch/CCRblF2B5ue2uMFjD7BzhpFNTg4mCaLa9as0etfn78dqcttZ8qh3P15x1KCQDrrrDP1iU98sumf+f73v6fddtut6ce7JOuII47Q4OCg4jjW5Zdfro98JE/E3bdkybCk2gHAfffdp02bNrW8H7rlllv00Y9+TJ/97FlKkkSf/OSnZvyZs8/+dK5VH7OvhemOkdzJiMHB2vdjj235qer8VxX0u6ZKO4wxv5T0rKRYUmStHTbGLJB0uaQXSPqlpLdba58xtcO3r0g6QdI2SX9prV0z3fbLVNohKT3NZK3VVVddpTefdJLvkICuiDOjQrM53Qp0B2cL0Zu2b9+moaEh9r05+Wp/d5y19pDME39Y0vXW2gMkXV+/LklvkHRA/WuZpK+3I9AiccmDJJJo9JUgCNLT5e5gsshLDaPfWd1xx+2+gwDa6s4779DcuXPTFWfh12wOZU6SdHH98sWS3py5/du25g5J840xe83ieQrJGKOnn37adxhAV1111VVpbacrb6pUqBBDcQ0PDytJYl1zzY99hwLMykUXXagkiXXEEUdIGp88GccxCbVHzSbSVtJPjDGrjTHL6rftYa19QpLq33ev376PpF9nfnZ9/bYGxphlxphVxphVmzdvzhe9R8YYXXfddb7DALrqpDe9SZLSSThF6PoDTCcMQwVBoFe/+tW+QwFyW7fuYZ1yyilpKUd24rcxhhIPj5r9y7/KWrtYtbKN9xtjjp7msZMVpe3waWutPc9aO2ytHV64cGGTYRRHkiR65zve4TsMoOvWrVvXMCJNMo2icq9N1yFBsqpWx/wGBbTgzjvvUBRVtf/++zf0xM62XKSHtF9NJdLW2sfr3zdJ+i9Jh0va6Eo26t831R++XtJ+mR/fV9Lj7QrYN/fC5egP/eq2225raAvFThxF5V6brvzI9W7etu33PsMCmnb44Yenr9/smUDX1q9arTKg4dmM2aAxZidjzM7usqTXSbpP0lWSTq4/7GRJV9YvXyXpPaZmqaStrgSkF2T7cQL96OT3vCe9TF0eii57oOcGQObNmyfJavPmTVP8FOBXHEeSJu957fpiS0oX9WFAw59mZgntIem/6v+kiqR/t9b+2Bhzl6QrjDGnSvqVpLfVH79ctdZ361Rrf3dK26P2jEUp0O/c4hcTV/kEysJaq4ULFyqOI4UhE2ZRHEkSKwwDco2SmHHvYa19VNIrJrn9KUmvmeR2K+n9bYmugNzKUOecc450xhm+wwG8yC5+QZkTyiZb319LVNwZRg4I4c/27ds0MDCgSqW2TyWJLgc+AVvkZsf+b5Jo9DFXl0cSjTJyq9K60iRXqnfXXSt9hoW+ZjV37tyGdqKUzpUDn4I5jI0x6xv97ZZbbkkvR1HkMRIgn2ydqStNWrJkiSSrSy75jsfI0F+sso3NsvOvGKgoB/5LOTz88MO+QwC8cu+BJEk4/YhSmljX78o94jjWu971Lj3xRM80m0JBPfjg2h3OijDfpHxIpHNYvny57xAAr05ftixNPNjxoxc01kxLe+65pySrlSvv9BsYes5tt90qyWrRokXpMt9xHPsOCzmZIrRxGx4etqtWrfIdRtNIG4D6CUk6dqCHuNdztVpN24rVOifQ1QPtsXnzJrlF6FzyzFm97jPGrLbWDrdjW4xIoz2Mafya7nHdjAkd89BDD7EQAHqKOyh0SbTk6lQb61jLoNldMrpj06aNkmotF93KsJLSciImFpYXiTRmzxjJ2savqfbc092HUrn//vvZ+aNnRFGkOI53ODCMoii9rVod0z//8xd8hNc0lzhPtktm1+uL1e67797QMtRNdg2CIP2OcqK0Iwf2RSXhPk3QMfx10Y9qpR+DvsPYQTO7PHaL3TM6OqKhoSHfYWASlHYAKIQiHIgDfpTztc9btjuWL/8RSXSfIJFGfpwn7HvuVCXQT8ZrqMtXO43O+uxnP6NqdUwnnHCC71DQJSTSyGc25wcnS8An3tbMpMXZFv0xE2fWaH+HfpTt/Zskie6//z7PEdXk2SVPnJQ409t54uOaefv3y672oYce1Ec/+tGGyarofSTSLVq/fr3vEPqDmyEz0/15Pzkm20av7+U7wBjDyoboO0EQKIqidKnxgw46SJLV6tXFmevT7O5s4qTEmXaprex6p9rV9ppbb71F1iZatGiRpNoBFn2h+weJdIs2bdrkO4Te1cxQSN6fzT6uV/fmnlQq9NhFf4njOH3dVyoVBUEga60OOeQQ/fa3Wz1HV9POTh15ttFPu9ojjzwybQXqOr+EYUhXoz5BIt2iBx980HcIvWu6ve5Me+Vm99jt2AZS1EejH022gIZrZzZnzhxJtt432K+Jo8DdTIj7YddgbSLJpgl0tqWdJFra9QmGklr07LPP+g4BszHTJ0k/7P3byI3CUCcN1AwO1tri7bbbbqpNRCzOe4PdW/tUq2MaGKiV9pAw9zf++y36q9NP9x1CMZR1YZXJCgJ7vYCvg5IkIYkGJjGeXFklSXHqZXm7zs6TT26WZJlQiBSJdIs+deaZvkMoF/baPY0kGmjkyp1cRw836ezHP/5vn2GhDeI40m677Za2/XRn4yhx628k0i361Cc/6TuE4mhm59GuHcxMI+CtTDZE2/AhAozLdvJw7w1XT3388cfL2kTnnPOljj1/J3ZvZT352E7WJorjSGEYpgdGrvUnpW0gkcbstCO5bVa7JhtOFRc7w5bxIQKMc508XFmHS6LddWOMPvjBD+rBB9d25PlnM7Yx3du4n4+V77nn7oYBgzAMG/Z51EeDVwBmJ9uDeWLX/U7ufWfT4X+ymLO3A0AHHXjggZKsVq68s2PP0coiK9KOu8VmttvL/vu/lytJYh1yyCFKkoQ2n5gSrwy0RytJ6GSPbTWJbUdZCYkzAA/cmZwlS5Z08Dk683MT7+/F3egzzzyt44+fny445ZJozsBhMoxIAwDQZXEc12tuI9Xa5MG3hx/+uSSrXXfdNV1MpVKppBNHXf07kEUiDQBAF7mFOyRlVsCzOvPMT3mNq9+96EUvaqiFlsZbfLqRaCZXYyISaQCzwgcLMDtuwtrHP/5xjY6OeI6m/7izAhMnEkqNE0WlyVe1RH8jkQaQW3akBkB+cRwrCIJ6osbBaTecd96/SaIXNGaHRLpF//rFL/oOASgMPnyA9gjDUNVqNXNgamUt9bidYm2iZcuWpRMIGRBAXiTSLVq4cKHvEAAAPWhgYGCHRT/Wrn3Ac1S9yDYsnJMkCYMCyI1EukUve9nLfIcAAOhBLoF2dbjWWi1atEiS1X//93KPkfWGJImVLZsJgkBjY2MKw5BEGrmRSLdo11139R0CUBicDgXaZ7yDR40xRkEQKEkSveENb9DWrVs8RlduGzY8kf4ts0nz4OCgx6jQC0ikW7RgwQLfIQCFwkgO0D4Tl5y21qa37bLLLjr//PN8hFVyNi3LjKJo0gEAlvpGXrxyWjR//nzfIQCFwqg00DkT+xe/733vE109mjM2Nqo4jjQ6OpqWyzACjXYjkW6Rq2EDAKAbrLXpZ8/o6KgkaWRku+66a6XPsArtuuuuTSdvDg0NpbfzGY52I5FuEc3YgUaUdgCdZa1VpVKRVOvsIUlz5szRYYcd5jOsQnvVq16lKIoaDkKSJOEzHG1HIt0ikgagESM8QOfEcdxQPhUEgaIoUpIkuummmzxGVmxXX321BgYGGnpEu78d0E4k0i2iHhSoWb1mjSQm6QCdNNWy1dVqVcccc6yfoErgbW97ezoaHQSBrLXp4itAO/EJ2CJGpIGal770pZI4uAS6LQgC3XDDDb7DKDxjTNoj2o1MU9qBdiORbhFJA1DjJvBwcAl0l7VWb3jDCb7DKLxnnnlGktLRaHcZaCcS6RZZa3Xpv/+77zAA77J1hwC6h2SwOT/4wQ8k1fZR2WXXgXbiE7BFxhi97nWv8x0G4NVPrr02XYGNyTtAd42MjPgOoRROOeW92rJli6IoSruecBCCdiORbpG1VrvttpvvMACv/vAP/zCdAe8+oAB0xw9/+EPfIZTGlVde2bCPyi7BDrSDKcLR2fDwsF21apXvMJoWRZEGSB7Qx6ykarWqgYEBJUlCeQfQQe49FsexRkdHNW/eTr5DKhk6dqCRMWa1tXa4Hdvi0y+HSqWiG2680XcYgDfW2nRhCJJooHOiKErfY2EY6uqrr/YcUTmRRKNT+ARskZuwcNRRR3mOBPDHGNOwWhiAzqhUKhobG0uvv/3t7/AYTTk98cQT1EajY0ikW+R6ULrROKBfuREeRnqAzhocHFSSJCSDOf3nf/6njDEc9KMjSKQBtMStaCjVRqNJpIHOcvW9zz77rO9QSun97/9bbdiwgTI0dASvKgAtWbx4saRabTQfTEDnuVX5vve97/kOpbSuuuoq3yGgR/Ep2KI4jtPTa6tWr/YcDdBdXzn3XN8hAH3FlSNs2bJF733vqZ6jKa9ly06XRB9ptB+JdIuyI3BLlizxGAnQfSecwLLEQDe50im3Sh9mh1I0tBuJdItcTagbJTj/ggs8RwR0z/777+87BKAv/eVfnuI7hNL71a9+5TsE9CAS6Ra5rh2uOf7xxx/vOSKgOy659FJOiwJdliQJ3Sba5JJLLmEfhrZjZcOc3EpT1loFnCpCH7ASq4MBHmzcuFF77LGn7zB6wq9+9Zj222+/hv0Y+7X+w8qGHrkDjyAIFEURbz70jTiOeb0DHnz/+9/3HULPeN7zni9rbfpZ7j7HizCoiHIikW5RNpGoVCqy1uo/LrvMY0RAd4RhyIcN0GVPPfWU/uZv3u87jJ4SBEH6We4+xxkkQF4k0jm4ejU38fBtb3ub54iAzrr1ttsYkQY8uOKKK3yH0HPciLQbGGBEGrNBIt0ia2060dC1wguCQCvvustzZEBnnHnWWTr88MMVhqHiOPYdDtBX/vqv/8Z3CD3n/vvvTxe5cRgkQF4k0i1ybzbXvcPdduihh/oKCeiok08+WZVKRVLj6x4Ayujgg1++w22MSCMvEuk2IcFAr3r+85+fXo6iyGMkQH957LHHfIfQs+6+++70MmVrmA0S6TbhTYheZa1VHMey1qYj0wDaKzv3RpKq1ar+4z/+w2dILYmiqj73ubN9h9G0Qw9dLKmWRIdhyCABcmsqkTbGzDfGfM8Y86AxZq0x5pXGmAXGmGuNMQ/Xv+9af6wxxpxrjFlnjLnXGLO4s79CMSRJopHRUd9hAG215u67ZYxRGIYNK3oCaK+Jc282btyoD3/4nzxHNbMNG55Qrcu89KEPfUhPP/2U34Ba4BoGSJxVRn7Njkh/RdKPrbUHSnqFpLWSPizpemvtAZKur1+XpDdIOqD+tUzS19sacQG5xVkqlYq+dM45vsMB2mZi7b/7kAfQftkzm/vuu5/HSJq35557qlqtqlKpKAxDLViwQC6xLgP2aZitGV9BxpjnSDpa0oWSZK0ds9ZukXSSpIvrD7tY0pvrl0+S9G1bc4ek+caYvdoeeYEEQZCO1H3wgx/0HA3QHu6jMDsKTdcOoHPcarllEMeR3F7ClXy5A4Ha72D1zDNPe4quOStXrkz/3mX5u6N4mjkUe5GkzZK+aYy52xhzgTFmJ0l7WGufkKT6993rj99H0q8zP7++flsDY8wyY8wqY8yqzZs3z+qX8M21xAN6Ufa1zelPoHPcKnvFL6GyO3SuynLXd911VxV5dHrp0lemf28+w5FXM6+ciqTFkr5urT1U0u81XsYxmclm3e3wTrLWnmetHbbWDi9cuLCpYIvKNXN3I9Obn3zSd0jArHyPJYkBb9auXes7hEk99dSTSpLWzkrFcayxsVE9++xvOxTV7KxYscJ3CCi5ZhLp9ZLWW2vvrF//nmqJ9UZXslH/vinz+Gxx176SHm9PuMXkVkhyCfVuu+3mOyRgVv70T//UdwhAX0mSRJVKRVEU6WUvO9h3OJOw2mmnnVoeuQ3DUIODg9p55511zjlf6lBs+R177HF03cKszPiOsNZukPRrY8yi+k2vkfSApKsknVy/7WRJV9YvXyXpPfXuHUslbXUlIL0qO9Pane6q0koHJWVFvSDQba5rx8qVK32H0qBW51zbH8yZM2dW2/rABz7Q8oh2NxS/lAZF1uyh5d9JutQYc6+kQySdLenzkl5rjHlY0mvr1yVpuaRHJa2TdL6knl/f1E20cMmHG1kAyopJhUB3WVurOz7yyFf5DiV15pmf0q677pp+tuXZL2Qn84VhWJ9Qmej8889ra6yzEYYh+zzkZoow8jQ8PGxXrVrlO4y2u/vuu7WYpcNRIv/8L/+iD33oQ2mpEoDuiaJIlcqA7zDqbEcn4SVJopGREc2bt1NHtt+K5ct/pBNOOEGS0n2fa+nHfrA3GWNWW2uH27Etpql20MQevEDRufaNnOoEuq8IZzLPPvuzkqzGxsY6lkS7yflDQ0MqQlePE054o6IoSpcKT5JEAwMDJNFoCol0B8VxXIBdBNCcpL4UeHa1LwDdc/vtt3t9/meeeVof+chHJEmDg4MdmyuRXU2wdtButX37to48V7Ouv/56hWGoarXasDYEMBMS6Q6y1vJmRGkYYzQ0NFSqRSGAXvLKVx7p8dmt5s+fL0kaHR2t3dKh/YD7XHQj09ZazZ07V5dc8p2OPF8zXv/64yVJAwO10ppy9PNGEZBId1gQBIxKo/Cyr1HXzhFA99x4441ennfLlmcURVVJUrVa+14rudhxoZV2cdttXAlR+vM//3P5LvVwybMxhkVa0BReJR2UrXcbqR/hA0Wzes2a9LIr6yhCrSbQT4477tVdf85zz/2K5s+fn77fBwcHJTUmk53gthvV28S61n/jZ8OsvvvdKzry3DNxyTODCWgWiXQHZd+I7nQRUDSLFy9OLzMCA3TXqKdBljiO9IEPfKB+ubH1W7f2A9kD9jAMG7oFnRzKwH8AABU3SURBVHjiiRodHelKHM4VV1yRfm4zTwTN4lOzC9yR9hMbNvgOBWjwb+edl57OBdB9Q0NDXa3FPe+8f5O1SZq4xnGcLiTmm1sdWKot/lIbIe/eyPDb3/6ONIYkSaiRRlNIpDvI7RDc6ao999xTYyQtKIg1d9+tU089lbMlgCdxHKtareonP/lJV55vZGS73ve+96WfTVEUpQl1EbjEXtKE7kFW1nYnqb3kkkvSZJozdGgGC7J0WPZU1ejoqIaGhsQJIxSB/3c+0J9cv2L3pS58KsRxlLabG39eFX7xpdHRUQ0ODipJEl177bU6/vg3dOFZx0ekmS/Sm1iQpSTcDsudHnIjfyQw8O3Z3/1OEhNqAB/CMEyT2Wuuuaajz7Vt2+8bRnODIJAxJp3oV7TyBbdPcnENDQ2lHTSOP/54JUl3lvKmaweaxaukg9ybMPs9SRJZa/XU00/7DA197Lbbb9fcuXMbTu8C8MP1L+6ESy75jubNmydjzA510G6ktSj10Y4bHZ+YxDbebvWjH13d8VhIpNEMXiVd5kYDFixYoC/88z/7Dgd9aHh4uOEUL6cuge7qzkQ2q3e9610dfg4/4jjWG9/4RnXq/O7FF1/cke2iN5FId5kbkZakM844w3M06DdPPvVUWmJUtJEooF8EQdCx99/ll1+mJIl7+kyT+9vVumu0v9Tj5JP/koWp0DQS6S5zI9JOXLD6NPQuK+m5z30u7e4Az1yCdvnll7d1u2Njo3rLW94iqVa60avJdLa+OwiC+sqM7U16L7300kJPwkRxkEh3Wba9TxiGCoJAZ551lueo0E8GBgZkrVUURYrjeIfFGAB0lkvQ3vGOd7Zxq1aVSkUDAwNpbW+vlm1NrO9212+99Za2Pce73/2etm0LvY1Eusuykz7czvQTn/iEntmyxWdY6GF333NPOlaTXbWrUqkoDENKPIAuS5KkbaPR2dHYfp8cd+SRR0qy+vSn2zc4NbG8o2hdTuAffaQLwK0sxUkkdEKS6RMbRVHPjlIB5TL7Pf411/xYr3/96wu1OmFRrFmzRosXL5nlVsY7G7Hf7C30ke4x7tR6nCRatXq152jQK/7///t/ZaV00kwcx3wYAAXQnlFNq9e//vWSmDic5QYHFy9eLGsTnX32Z3Nv62tf+1q6KMvE/taAw4h0QWRXl2JkGu0Q1Uep3GhK0VcwA/pLvvfizTffpCOPPDI9KHYT7jAuu6+rXZ7N32c8gebv3DsYke5B7oAmjmNZSV/+ylf8BoRSy5ZzZMs6APh30UUX5fq5JIl19NFHN5xZCoKANm2TiOM4bTcbx1HD6o6t+Nd//VdJ1J9jaoxIF0D2SNcdSVerVQ3W+/0Crdg+MqI5c+ZIGn9tMZoCFEme0egdS7Q4y7QjVy8+2T7vzjvv1BFHLM2xVZv+ralH7w2MSPeY7Jvd7RQHBgY6tGYTetlvn302XXBF2nGZegDdNXGw6pvf/GarW5BUS6AnrkRKEr0jl+ROts8bHh6WZPWlL32x5e1OPMMHOHy6Fty27dt9h4CS+MUvf6mdd96Z0RKgQCYmXqec8t6mf/a2226VVEvG3Sgr8gvDUHEc64wzztBdd63MtQ0GJTARr4gCq1armjt3riIWzMAMfvvss3r+858viVpooEiyCx41mwjXOk1YHX744Y0T0Y0hmc7J/R/cGYLDDjtMktXXvvbVGX/2zDPPTH+2COWwKBZqpAsu27+SE0qYzFi1Ov4a4bQjUCguER5PiKd/j95++206/PDDFQTBhM4Thn7GbeL+nuN11M3sN/3nSmgfaqT7hDud546CraTvfu97vsNCgSTWNtREF+HAGMC47HvyG9/4xpSP+9znzpa1iY444ghZaycdeSaJbg93gDLe8cQqSWY688sgBSbHiHTJWGsVMOoIMT4ClIEbRa6Nfk43f4F3tG8PP/ywDjjgJdM8gv9Rr2BEuo8lSSIr6TePP+47FHjygyuvpG4eKAmXRH/mM5+Z9P7aSCgJWhHsv//+kqy+/e2Lp3xMtVrtXkAoBUakS4xx6f7k3rE71l4CKJrxuuYd36OrV6/SkiVLVK1WG0q04Ee2Bn3t2rU66KCXTniE/3wJ7cGINGSt1dPPPOM7DHTZgw89lI6IFOEgGMD0KpXKpJ10nn32tzrkkEMkiZaVBZFd6Oaggw7a4f4HHniArinYAYl0SVlrteuuuypOEj3085/7Dgcd9u3vfEfVKNKiRYt2WHCA0WiguNykcefBB9dKsmnPdw6Ii2e8zZ1tWFr8pS99GX2ksQNeESWVXbFu//331/aREc8RoVMefOghvfvd705HS9yS3xKj0kDRGWP02GOPpdcPPPBASY09pUnOiiXbetAYo7Gx0fS+W2+91VdYKCjevT0gCALNmTNHVmJ0uodc+u//rsRaLVq0aLwFYj1xZjQaKI97771Xbqlvh/dweQwODkqyeuyxX2rTpk2+w0HBMNmwR4yNjWlwcFBRFGmAXqM9wUoaGRnRnDlzJNVW5qKWEiiX7PvWHQwzAl0+biIik7t7A5MNsYPBwcH0DV6NIuYWl5iVFNdP+7qZ/GNjYyTRQAm5xVXiOJYxhiSsZKy1qtZXj42iiP8fdkAi3SPcrPAwDNOEK7FW5371qz7DQou2/va3iuM4HbFyk5FqpxbFjHGgZCqVioIgoJSjpIwxGhgYUBzHrCyJSZFI94gwDHfYQRtj9Hd/93eykm67/XY/gWFGX/3a12RVO/B5znOek1m2tiY7sZBTwkC5ZOc2uOsoH84IYip8KveIbBKdvex22kuXLtWZZ53V9bgws7/927+VpIZZ4tn/oduBM5IFlI97P2ff3wB6B4l0j8vuvD/xiU/ISrr+hhv8BgV9+StfURTHtXn8jFABAFBKFPz0uJGREQ0MDKTlAkmS6Nhjj9Xvt23TTvPm+Q6vL23ctEm7/s3fKAxDJUlCuQYAACVFIt3D4jjWnDlz0tni0vgSqK7vtCRxorE7Etc2affd09tIogEAKC8+xXuYm6QWhmGaTLvuHtnV8aI41patW73F2eseefRRxUkiY0xaxuG+R1FEaQcAACVFIt3DXA9iaXzCWrZ9T7bF2i677KI4SWRVq9/F7Ll1zF70ohft0Poqe4aAyUcAAJQTiTQkKa3VjeNYf//3f6/EWl30zW/6DquUtmzdyoI4AAD0ARJppEm0tVZhGKblHyeffHLaWYKkemqf+/zn9cvHHkv/VrvssovvkAAAQBeQSCOtl56s3MCVhJzy/9q73xi5rvKO499nd2sbuyr5Q0DETpW4tkwRVoO7oi6tKhRTSihyeBHUIFJbriv3BRL/WqVppYgmCKlICLeoVSRDoElVpQlpSixUtbVCpPKGCCeB2CVFMSZNDGbj1k6omgp2d56+uGfWs7Nj787J7s565/uRRjv33HtnzniPz/505tx79u1jcmqKuz75yYHVcyV6+tgxbrvtNjZt2jQzfWN6etp5z5IkDQGDtC6oc4W9VqvF6Ogod9xxBwlMt1q0hjAs3nvffUxNTzPdajHdarF9+/Y5K5d1L6giSZJWJ4O0mJ6enhWa28Ew27drY26oHhkZae5CAUMxH/iRw4eZbrXYs2cPo6OjjIyMzJoO0x7Fb98JRZIkrX4Gac1Zgrp7Sdu29tSFXvc+bmXOjNImcPbcOR548MGlrfgSOPT5z/O9kydJYHJqauYz7d69G5i7CmGvfyPvDS1J0nCIlTCXc3x8PI8ePTroauhVaI9etzrul9werT158iS/sHnzoKt4Ud/69rfZtm0b69atY3JykpGREVcelCRpFYqIJzJzfDFey4SgRdFqtcjMmSkfnRcvbt68eWYKSHukt3P7RxMTfPbgwSWr25133cXTx46RNCPnne/dfmzfvr1Z7TGTsbGxWeHZ6RqSJKkXR6S1qNoBunvKQ3fQBpicnGRsbGzW3Oz2NBOAiYkJjh8/ztq1a3nuuef43Vtvndn3Z3feCTTTUjZs2MCOHTuYnp7m6quvZsuWLUAz7aLz/dqjy1NTUzPvGxEzc8Q769zrc7Tr64WEkiRduhZzRNogrUXXeZFip3aQbYfUqampWasvdp/f/Trtttoe8e68ALL7/bpfo/O1LjRdo3t6Snewht7zwyVJ0qVjMYP02PyHSP250Iht98WKvUJ05/ndr9O53b3k9nyv0evcC53Ta78BWpIkdTMdSJIkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVZg3SEfEtoj4VsfjxxHx0Yi4IiKORMSz5efl5fiIiM9FxImIeDoidiz9x5AkSZKW17xBOjO/m5nXZ+b1wC8DrwD/CNwOPJqZW4FHyzbAjcDW8jgA3L0UFZckSZIGqd+pHbuA72XmfwI3AfeW8nuB95XnNwH3ZeMbwGUR8cZFqa0kSZK0QvQbpG8B7i/P35CZpwHKz9eX8o3ACx3nnCpls0TEgYg4GhFHz5w502c1JEmSpMFacJCOiDXAbuDL8x3ao2zO8omZeSgzxzNz/KqrrlpoNSRJkqQVoZ8R6RuBJzNzomxPtKdslJ8vlvJTwDUd520CfvhqKypJkiStJP0E6Q9wfloHwGFgb3m+F3iko3xPuXvHTuDl9hQQSZIkabUYW8hBEbEe+E3gDzqK/xx4MCL2A88D7y/l/wS8BzhBc4ePfYtWW0mSJGmFWFCQzsxXgCu7yv6b5i4e3ccm8KFFqZ0kSZK0QrmyoSRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVWFCQjoiPRcS/R8TxiLg/ItZFxHUR8XhEPBsRD0TEmnLs2rJ9ouy/dik/gCRJkjQI8wbpiNgIfBgYz8y3AKPALcCngYOZuRU4B+wvp+wHzmXmFuBgOU6SJElaVRY6tWMMeE1EjAHrgdPADcBDZf+9wPvK85vKNmX/roiIxamuJEmStDKMzXdAZv4gIj4DPA/8H/CvwBPAS5k5VQ47BWwszzcCL5RzpyLiZeBK4L86XzciDgAHyuZPIuL4q/wsWn1eR1e7kbBdqDfbhXqxXaiXbYv1QvMG6Yi4nGaU+TrgJeDLwI09Ds32KRfZd74g8xBwqLzH0cwcX2CdNSRsF+rFdqFebBfqxXahXiLi6GK91kKmdrwT+H5mnsnMSeBh4O3AZWWqB8Am4Ifl+SngmlLRMeC1wNnFqrAkSZK0EiwkSD8P7IyI9WWu8y7gO8BjwM3lmL3AI+X54bJN2f+1zJwzIi1JkiRdyuYN0pn5OM1Fg08Cx8o5h4A/Bj4eESdo5kDfU065B7iylH8cuH0B9TjUf9U1BGwX6sV2oV5sF+rFdqFeFq1dhIPFkiRJUv9c2VCSJEmqYJCWJEmSKgw8SEfEuyPiu2VJ8YXMp9YqERHXRMRjEfFMWYL+I6X8iog4UpafP1JuwUg0PlfaytMRsWOwn0BLJSJGI+KpiPhq2b4uIh4vbeKBiFhTyteW7RNl/7WDrLeWTkRcFhEPRcR/lD7jV+0rFBEfK38/jkfE/RGxzv5iOEXEFyPixc51SWr6iIjYW45/NiL29nqvTgMN0hExCvw1zX2p3wx8ICLePMg6aVlNAX+Ymb8I7AQ+VH7/twOPluXnH+X8Bas3AlvL4wBw9/JXWcvkI8AzHdufBg6WNnEO2F/K9wPnMnMLcLAcp9XpL4F/zsw3Ab9E0z7sK4ZYRGwEPgyMZ+ZbgFHgFuwvhtXfAO/uKuurj4iIK4BPAL8CvA34RDt8X8igR6TfBpzIzJOZ+VPg72kWf9EQyMzTmflkef4/NH8YNzJ7mfnu5efvy8Y3aO5l/sZlrraWWERsAn4b+ELZDuAGmrsHwdw20W4rDwG7yvFaRSLi54DfoNwdKjN/mpkvYV+hZmG515R1K9YDp7G/GEqZ+W/MXbek3z7it4AjmXk2M88BR5gbzmcZdJCeWU686FxqXEOkfMX2VuBx4A2ZeRqasA28vhxmexkOfwHcBrTK9pXAS5k5VbY7f+8zbaLsf7kcr9VlM3AG+FKZ8vOFiNiAfcVQy8wfAJ+hWe/iNM3//yewv9B5/fYRffcdgw7SC1pOXKtbRPws8A/ARzPzxxc7tEeZ7WUViYj3Ai9m5hOdxT0OzQXs0+oxBuwA7s7MtwL/y8XXKLBdDIHylftNwHXA1cAGmq/su9lfqNuF2kLfbWTQQXpmOfGic6lxDYGI+BmaEP13mflwKZ5ofw1bfr5Yym0vq9+vAbsj4jmaqV430IxQX1a+uoXZv/eZNlH2v5a5X+3p0ncKOFUWCIPma/kd2FcMu3cC38/MM5k5CTwMvB37C53Xbx/Rd98x6CD9TWBrucJ2Dc1FAocHXCctkzI37R7gmcz8bMeuzmXmu5ef31Outt0JvNz+ykarQ2b+SWZuysxrafqDr2XmB4HHgJvLYd1tot1Wbi7HO8K0ymTmj4AXImJbKdoFfAf7imH3PLAzItaXvyftdmF/obZ++4h/Ad4VEZeXbzzeVcouaOArG0bEe2hGnEaBL2bmpwZaIS2biPh14Os0S8+358P+Kc086QeBn6fpKN+fmWdLR/lXNBP/XwH2ZebRZa+4lkVEvAP4o8x8b0RsphmhvgJ4Crg1M38SEeuAv6WZX38WuCUzTw6qzlo6EXE9zQWoa4CTwD6awSD7iiEWEXcCv0NzF6ingN+nmdNqfzFkIuJ+4B3A64AJmrtvfIU++4iI+D2aLALwqcz80kXfd9BBWpIkSboUDXpqhyRJknRJMkhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUoX/B1oiasZKFurxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for cnt in contours:\n",
    "    #get approximate polygons\n",
    "    approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt,True),True)\n",
    "    \n",
    "    if len(approx) == 3:\n",
    "        shpae_name =\"Triangle\"\n",
    "        cv2.drawContours(image,[cnt],0,(0,255,0),-1)\n",
    "        # Find contour center to place text at the center\n",
    "        M = cv2.moments(cnt)\n",
    "        cx = int(M['m10'] / M['m00'])\n",
    "        cy = int(M['m01'] / M ['m00'])\n",
    "        cv2.putText(image, shpae_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)\n",
    "        #print(\"Cx:\",cx)\n",
    "    elif len(approx) == 4:\n",
    "        x,y,w,h = cv2.boundingRect(cnt)\n",
    "        M = cv2.moments(cnt)\n",
    "        cx = int(M['m10'] / M['m00'])\n",
    "        cy = int(M['m01'] / M['m00'])\n",
    "        \n",
    "        # Check to see if 4-side polygon is square or rectangle\n",
    "        # cv2.boundingRect returns the top left and then width and \n",
    "        if abs(w-h) <= 3:\n",
    "            shape_name = \"Square\"\n",
    "            \n",
    "            # Find contour center to place text at the center\n",
    "            cv2.drawContours(image, [cnt], 0, (0, 125 ,255), -1)\n",
    "            cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)\n",
    "        else:\n",
    "            shape_name = \"Rectangle\"\n",
    "            \n",
    "            # Find contour center to place text at the center\n",
    "            cv2.drawContours(image, [cnt], 0, (0, 0, 255), -1)\n",
    "            M = cv2.moments(cnt)\n",
    "            cx = int(M['m10'] / M['m00'])\n",
    "            cy = int(M['m01'] / M['m00'])\n",
    "            cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)\n",
    "            \n",
    "    elif len(approx) == 10:\n",
    "        shape_name = \"Star\"\n",
    "        cv2.drawContours(image, [cnt], 0, (255, 255, 0), -1)\n",
    "        M = cv2.moments(cnt)\n",
    "        cx = int(M['m10'] / M['m00'])\n",
    "        cy = int(M['m01'] / M['m00'])\n",
    "        cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)\n",
    "        \n",
    "            \n",
    "    elif len(approx) >= 15:\n",
    "        shape_name = \"Circle\"\n",
    "        cv2.drawContours(image, [cnt], 0, (0, 255, 255), -1)\n",
    "        M = cv2.moments(cnt)\n",
    "        cx = int(M['m10'] / M['m00'])\n",
    "        cy = int(M['m01'] / M['m00'])\n",
    "        cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)\n",
    "    \n",
    "    #plt.imshow(image)\n",
    "display_image(image)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
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