16_SortingContours.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load our image\n",
    "image = cv2.imread('images/bunchofshapes.jpg')\n",
    "#cv2.imshow('0 - Original Image', image)\n",
    "#cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a black image with same dimensions as our loaded image\n",
    "blank_image = np.zeros((image.shape[0], image.shape[1], 3))\n",
    "\n",
    "# Create a copy of our original image\n",
    "orginal_image = image\n",
    "\n",
    "# Grayscale our image\n",
    "gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find Canny edges\n",
    "edged = cv2.Canny(gray, 50, 200)\n",
    "#cv2.imshow('1 - Canny Edges', edged)\n",
    "#cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of contours found = 4\n"
     ]
    }
   ],
   "source": [
    "# Find contours and print how many were found\n",
    "contours, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n",
    "print (\"Number of contours found = \" + str(len(contours)))\n",
    "\n",
    "#Draw all contours\n",
    "cv2.drawContours(blank_image, contours, -1, (0,255,0), 3)\n",
    "#cv2.imshow('2 - All Contours over blank image', blank_image)\n",
    "#cv2.waitKey(0)\n",
    "\n",
    "# Draw all contours over blank image\n",
    "cv2.drawContours(image, contours, -1, (0,255,0), 3)\n",
    "#cv2.imshow('3 - All Contours', image)\n",
    "#cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12a32a7b8>"
      ]
     },
     "execution_count": 7,
     "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": [
    "#if want to plot then \n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#method of finding contour area of shapes\n",
    "#get contours from previous \n",
    "def get_contour_area(contours):\n",
    "    all_area =[]\n",
    "    for cnt in contours:\n",
    "        area = cv2.contourArea(cnt)\n",
    "        all_area.append(area)\n",
    "    return all_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20587.5, 22901.5, 66579.5, 90222.0]\n"
     ]
    }
   ],
   "source": [
    "print(get_contour_area(contours))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contor Areas after sorting\n",
      "[90222.0, 66579.5, 22901.5, 20587.5]\n"
     ]
    }
   ],
   "source": [
    "#after sort the area of shapes\n",
    "sorted_contours = sorted(contours, key = cv2.contourArea, reverse = True)\n",
    "print (\"Contor Areas after sorting\")\n",
    "print (get_contour_area(sorted_contours))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAACsCAYAAAB1sGcWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAGctJREFUeJzt3X+wXGd93/H39zm7V8KmiW0QqWt5ahM8dclMJRkNNaXTaUMgQBlMZmDGlNjCliMjO6lU0SF2mOlM/+hMKcUxdjMQB9uVjQkQIMXjoWWof8hJg/XLkvwjjrFKCFYgsZgEkolr3bvn+faP5zl7V1f3Snt17+6ec/bzmpHu2XPO7n737Dnffc5znvM85u6IiEh7hUkHICIio6VELyLSckr0IiItp0QvItJySvQiIi2nRC8i0nIjSfRm9k4ze97MjprZLaN4DxERGY6tdjt6MyuA7wBvB44B+4EPuvsfr+obiYjIUEZRon8zcNTdv+vus8AXgatG8D4iIjKEUST6i4AXBx4fy/NERGQCOiN4TVtk3in1Q2a2DdgGcO65577p8ssvH0EoIiLtdfDgwR+5+7ozrTeKRH8MuHjg8XrgBwtXcve7gLsANm/e7AcOHBhBKCIi7WVmfzbMeqOoutkPXGZml5rZDHA18OAI3kdERIaw6iV6d++Z2a8C3wQK4B53f3a130dERIYziqob3P0bwDdG8doiIrI8ujNWRKTllOhFRFpOiV5EpOWU6EVEWk6JXkSk5ZToRURaToleRKTllOhFRFpOiV5EpOWU6EVEWk6JXkSk5ZToRURaToleRKTllOhFRFpOiV5EpOWU6EVEWk6JXkSk5ZToRURaToleRKTllOhFRFpOiV5EpOWU6EVEWk6JXkSk5ZToRURaToleRKTllOhFRFpOiV5EpOWU6EVEWk6JXkSk5ZToRURaToleRKTllOhFRFpOiV5EpOWU6EVEWu6Mid7MLjazR83sOTN71sx25PkXmNm3zOyF/Pf8PN/M7A4zO2pmT5nZFaP+ECIisrRhSvQ94KPu/o+BK4GbzeyNwC3Aw+5+GfBwfgzwLuCy/G8b8JlVj1pERIZ2xkTv7j909yfz9N8CzwEXAVcBu/Nqu4H35emrgPs8eQI4z8wuXPXIRURkKMuqozezS4BNwF7gZ9z9h5B+DIDX5dUuAl4ceNqxPG/ha20zswNmduD48ePLj1xERIYydKI3s1cDXwV2uvvfnG7VReb5KTPc73L3ze6+ed26dcOGISIiyzRUojezLinJP+DuX8uz/7Kqksl/X8rzjwEXDzx9PfCD1QlXRESWa5hWNwbcDTzn7rcNLHoQ2JKntwBfH5h/bW59cyXwk6qKR0RExq8zxDpvBa4Bnjazw3nebwD/GfiymW0Fvg98IC/7BvBu4CjwMnDdqkYsIiLLcsZE7+5/yOL17gBvW2R9B25eYVwiIrJKdGesiEjLKdGLiLTcMHX0LeNABAripEMZoxCB0MMJmH7fRabK9CV6N8ym72NXud29XPqKi4i00hRmvOTtv/QoWASfhtKt4aHH//7qL6DaOpHpM32J3noAlCESYgGei7fmadryTbxnml74vKWma/Aa0Rxi+qqjpcobEZke05fo80fulinJxzDQO4MtY3o56074NQqHkHuhCG6qupH6qnZbK4EA3oMws4LXi7wyO8famYJ0Xa4kAJFiqs5tpzDRi0gd9YCOAXZqSWT3ph0EB3OjDE50ywk7EEkVkoGY50GPwNbDt4EF1g68js+VzBHoFkxVgUeJXkRqobMgwd+3YQchBObI1axAaZEeRscDYAScrpUAeP+am9Eh8oV/sotZn6WHccPTdwJQdAsKIEZXiV5EZHQcsFQSn+vBTLe/ZPfGnSetWXq+pmSx3xy646SGFNU6VdHci5OeOxciRoduft0C5wQFWw/fRgjVczyHk2KqYmsbJXoRGasqRYeBEvy9b9pFKEd7Z8vLZpwTI7+zcRczVrDl0Cfnq4lywp8zo3v6l2mkaTp7EZEaCG4nJfkHrvi3I0/yAOfEQGnwKpsj+Cz3btoxv9CMnpV0Txk5ox1UoheREXMi1cVTJ4RUvrxv40cpigLv9YjFXL8eflTmQpmaHccuBakK6P6NO5nrOdc/82k61kkl+wWxt6EqR4leREbLjWAlkUCwlOQ/v2Enbj16ZbpTe9RJHnLTYgCLlAPzi45xz8/dRCjW8OF8phGjE6xM9f7Nz/OquhGR0XKfZY6in+R/Z+MuyuDzibcGusU5eKfH/RtTdU66WBuI9QlxRZToRWSkrFhD14z7fu5XeeBNH2MtEdzoTTqwAbFzAmIXz9U5AFhoTYJsy+cQkdpx6KVKknsv347PFLxS9uZb3UwusFPFgkDEzQgYn9v00flFEwxrtdRqW4tIW6QLsHTTZcBy7aspiKkNfM3NFrA25lp8M4Knu3Z7NCD4JSjRi8iqi8w3ody9aQdr7BXmvKDXgDrvbhmZo+DuqvllMDqUje4MUIleRFaH9//rJ/l7N+5M9fFe0AE6DagIKUmthDpuKX4A60CpEr2ITDVnsFMCgPs37qBTJX6aWdcdgC/8011puoCm1t4o0YvIivUGB6jMI7idwOkx+vbxo3biRJ6w0Ng29Ur0IrJiHU+D2lQl3i9s2sVM2YEwN9nAVkGHyO4NuRXOIl0oN4ESvUxUpJmn9LJA1ftj7t5gziNexLHc8TpqEdJdslnpnmY2qBpHiV4myBrdkkEGWb9bgfuv2DXRSEbCA/dtSK1wihBS5rTmZHolepm40JzjRZZQRuj2u/xtX1qJFinNuHtTaoUTYX5c5gZo3zci9ZUTem9hj4Bm/YVxYD1pjirH7964kxjb+QUGYCb31xMijbowq0QvYxMtJfJOLgndcPAP2PZHj6Rl1YhDTms6kpom1YhNs0WZB/Zup/71pMIaVSBRopexMc/jdJpx077HmelFOkXBR/Y/Tqia55mqcpqsE0Oj6q6XK3rJ5zfkaxANKpAo0cvYmBnV0TEbndKgtNQGO68xMH6nNFGInUbVXS9XBMpQ5unm7KdK9DJSg00nqxbV2/fuoRjY8wqPbN+7B4ATlqpx6qj6LD1PP1eT+lcfA9dVSK1tQourbQCCF/0fstCgOkaNMCUj1b/1PUa6oWD7vkfpWXWDzfx6VXJfg+Vn1PcgqiK76z+9aazvG0KPG249Mtb3PL10Eb0aUCTGlPIj7S1BpmtI+dMVtsjQg/XU1u9DaqI66ENIN87EMj1e2Iuh0+PGfXvyo7om+XTnZ3UNIVg5nn9EQjFbyzOduOB2tzYneRisZmyWob8TMyvM7JCZPZQfX2pme83sBTP7kpnN5Plr8uOjefklowldmiNlxhv37aGqs1m445kVA0PL2UD6qE+JqYovhqrkauP5ZxDrWPftp36PbU7yAMGcrjWvW4flfC87gOcGHn8C+E13vwz4a2Brnr8V+Gt3fwPwm3k9mVKDaf10rWkCkZ45W594dOAZ1C+5yTyD6pvaXXXn23oR98DnNzTr8w6V6M1sPfCvgc/lxwb8PPCVvMpu4H15+qr8mLz8bXl9mUY5ud+4b89p28f3h5cL1S5Z3WU5qsBkdUzXoZ32TqNsWBvgYUv0twMfY/54fA3wY3evxvc9BlyUpy8CXgTIy3+S15dplH/jzzQQdPSC7sCPQn5y++sCGmwa72IOud99b1uiN7P3AC+5+8HB2Yus6kMsG3zdbWZ2wMwOHD9+fKhgpWnSrvBr337sjGsGS+3qldebxPtH+7R8bxFLyb5hrUiH+X7eCrzXzL4HfJFUZXM7cJ6ZVc0z1wM/yNPHgIsB8vKfBv5q4Yu6+13uvtndN69bt25FH0Lqa+u+PfSCMbPMAlD9W+BIrG5wY3q6mo75Zr6m1UafMdG7+63uvt7dLwGuBh5x9w8BjwLvz6ttAb6epx/Mj8nLH3FvSGNTWbH5Az4dCB1PrUZ6YfhUEIGuDyZ7qa1m5bsVq+rom/bDtpIzrl8HdpnZUVId/N15/t3Aa/L8XcAtKwtRmicd/R/Z/3i/O4OwjK5rA+kO2W6/eDBl2aQpnKmro4+xQzSnUzYr1S8r0bv7Y+7+njz9XXd/s7u/wd0/4O4n8vxX8uM35OXfHUXgUk+DO1S5ghO56jC64eAfADA7bRmlAcJAnwxbDt8+0VjGxhzc2PLUnZOOZFmm5RqKjEGqoUtH/va9e1a0c804/D+Dc+ZSyp8hNKoTqWmQvplp+04ivQamzeZFLPWTh9CsLlBt33v6NvPDiJaS/YkAN+/PpfrYrNPlaRNCAA/EFqeVYE6ncTX0SvSyCqKl9sVg3PjtR1dt4JDqdWKMbNv7GGuD+uCrkyp5lHlEKYuWv7PmJcKhxfk0HxvUxkSJXlbMB7qyGkWnTwb0gi7I1k3/HuZ8wf2XD38KaHeXuNGtXwAJSvQyTarOvm46+Id0usWqv37VAucj+x9DLXDqxJnDBjqjgw4lba63D8UcWw/lC88N6ntfiV7OSo/5bg2s6p+7VxL9TJ0dLF9VQvSB3i2lDoxu+jNffeNFLbtTXi15wMtcbbP6hZpRUaKXs9IBOsy3srlp3+PMndT2fTS270u9W7a40NhIRirdOrT6Ymx172fq56c5P2jt/UZktBzmcpK/cd8eXrY08Hfpoy3l9KoLsg27Bb31LH3v1xy5rZGtUoZV3S/QwVet0cE4KNHL2THoDpyir+l3aTfaonaIkRv3Pg6kwZn7KUUl/IkKOOTqm56XBKDwdpTuewZrYsC6+bHn5gcN2uea/y3IRFQ18dWg3uNSDIxqFAhpwHE/dWhCmYD8I3/dkTvpAXNGK0r3ayL8XRG5dn8qzRcRAtaofU6JXs5KJ5fmZ8e8s58w6JnzkW+nuvqZskyDjY83DFkgeuoPoRdTESCYU7i3IM2nm/YGe181S3X0nQadRirRy7KkAzdl91/59mMT2YG6Dr1OqhO2okOHVt+i0wipvrqkk1uibDn0aUJoVql3KTMO1+S6+ehOz1K1TZNaFynRy9AG6yW3/dEjdGc6Y9+BOkBpULif1Ge9Ev1kpTOqIpUBqpYpXiyr19LayDFX+1QRcuW8pwYHHWjc4GdNilUmLEC/tYt3CubK+tww0qTT6GkQ3bn28Kfodpv3ExwspupAD3jZ4988+UmAdD2ooZToZSiDPVNWVTZ12Hm27X88T9UhGqmE/MP7y/ub2X1xcHB6fPjp/5ZmeBz5PSKjpKNDhhIHm00W9ambLOJ8FU5VdkyXAxt8VLZAD8DTN7Ll8O2UnQJCSYj1v5t0ttcjErj2yB1pRlXIqc9uv2xK9LKkyEA9Zb7IduO+PbUq2cwODFEY8himHWjUXYtt1HFLN9TlLjGuP/ApYiwgrH4XGaulOkvtdDpsPXxbmunNq3pajBK9LGlh9czNTzwGpPbRdRE8EBxuyu35y1ySb0Nrj0YzTwUCL/o3Ul13+HZm52pUSljEnPv8aFk+16iWNaej5seypDlInVblnb3XKejFyIxTm9u/gxuG9398CgtEYr+d/2qKuVld9eO37TcOrfp7tEXECHnYPQx60ekEY+uzd3Dvxp21LWFef+TTaaIl9wBU6rq9pQZyS3Ug3wEbY2qzXpMkD4A5ZW7qtvWJdBNVxFY0Xu1SqoMlaqSrM0rbKiX5iKfxZT21W7nu8O2EqovfCTW/rL7BQGoy2Q3dflv5qnloXRocrIa2fA4ZhROzwOoMDTgOM1bwK/v20MFG2udZYakx5/j+OSXen26aQEz3X3inn0SvOXQnPXOiRWwCA3h0SHdYxx6U0fpNKGnQYCLLoaobWVJYswaoWQn+NKKVdHMJMYyqbrUa3rB6T5yA9QcuP9N0Fddi00s9L/WC7nmeNa501qPonwmWAL2Sbqdg66FUTXLPhh1j79k9AjNuXPPMfPPPMkbMoY2DmSnRy6mi08PpBNj+xGOnnPeFgTr6xaaru2cXmz7d84Z9jaVeDwJzBjfse4TPvdnAIz0zOvO3AKya+Y7V7KS/K5leannSvARfGbyTNAAUYb7kbNavF//vm3fhr8wSOqkrtK4H3OKK6sojgWgx7QMeoJgjnjCue/aO+ZVyLPVv+Hn2lOhlAeeVAGspuPb/PMz5M11eiSWYn9Qt69lOr8ZrLPV6PYzg0MmXkLFAxz0PXi61FKsv0vjwgdSkcfeGXYRQ4kQMT1U+ZzlsX9fmKAnpLCmUbHnyzoG39nxj1whKAjWjRC+nWJvT4n1vfduEI1k59WxZc0Y6eyzT1QcrjC1HbusvvmfTR+naLPFsB7SxV+Feck0euBwAj8RYVYcZ5AuybTaFx0AqGcwVJaHs9G+sGUfVwqReI5rPdy5lJac/SU07PuROzAb+NtEU7uCN08n1OgYnVekAXH/oU6esf//GnZQhVWaVpdMNPTwaRdHhQ0/edsr6fT5feg+p46b51kEtN33HQSwgQBED2HztXwFgAynwTNN28vOWmq7DaxRueMhdMnkx9H4dFvwVGRt3euTO6mKEYj5V9ZtBnkF0J0TSgCj9ZljtT+qLmb5En4u63/r9fzXhQCajtDmKqg5bpMY6nq+7hILgPSCA2yLXXAZuzMoPO+Sz0dD+aplhTF+iByIl1sR+ss+WpXoeD3N4PqMRqbtYXV9x6J/LLnZh3ec7HEstfPLFVYdg1uiqx9UyhYnesIb3RLd8VR1oF5v2PV4ao7+rnulYPWW5nfRHu7y2gYhI6ynRi4i0nBK9iNSKt7S/mUlSohcROY02/PAMlejN7Dwz+4qZ/YmZPWdmbzGzC8zsW2b2Qv57fl7XzOwOMztqZk+Z2RWj/Qgi0iY2yq5Hz0Ld4jkbw5boPw38L3e/HNgAPAfcAjzs7pcBD+fHAO8CLsv/tgGfWdWIRURkWc6Y6M3sp4B/AdwN4O6z7v5j4Cpgd15tN/C+PH0VcJ8nTwDnmdmFqx65iIgMZZgS/euB48C9ZnbIzD5nZucCP+PuPwTIf1+X178IeHHg+cfyvJOY2TYzO2BmB44fP76iDyEiIksbJtF3gCuAz7j7JuDvmK+mWcxiFVqnXM1w97vcfbO7b163bt1QwYqIyPINk+iPAcfcfW9+/BVS4v/Lqkom/31pYP2LB56/HvjB6oQrIiLLdcZE7+5/AbxoZv8oz3ob8MfAg8CWPG8L8PU8/SBwbW59cyXwk6qKR0RExm/Yvm5+DXjAzGaA7wLXkX4kvmxmW4HvAx/I634DeDdwFHg5rysiIhMyVKJ398PA5kUWnTIEkae7C25eYVwiIrJKdGesiEjLKdGLiLScEr2ISMsp0YuItJzVoWc2M/tb4PlJxzGk1wI/mnQQQ2hKnNCcWJsSJzQn1qbECfWM9R+6+xnvOK3LUILPu/tirXpqx8wONCHWpsQJzYm1KXFCc2JtSpzQrFgXUtWNiEjLKdGLiLRcXRL9XZMOYBmaEmtT4oTmxNqUOKE5sTYlTmhWrCepxcVYEREZnbqU6EVEZEQmnujN7J1m9nweY/Z0/dyPI5aLzezRPC7us2a2I8+v5fi4ZlbkwWAeyo8vNbO9Oc4v5U7oMLM1+fHRvPySMcfZiDGHzezf5e/9GTP7XTNbW5dtamb3mNlLZvbMwLxlb0Mz25LXf8HMtiz2XiOK9ZP5+3/KzH7fzM4bWHZrjvV5M/vFgfkjzQ2LxTmw7N+bmZvZa/PjiW7TFXP3if0DCuD/kkaxmgGOAG+cYDwXAlfk6b8HfAd4I/BfgFvy/FuAT+TpdwP/kzTYypXA3jHHuwv4AvBQfvxl4Oo8/Vlge56+Cfhsnr4a+NKY49wN3JCnZ4Dz6rZNSaOg/SnwqoFt+eG6bFPScJ5XAM8MzFvWNgQuIPU+ewFwfp4+f0yxvgPo5OlPDMT6xnzcrwEuzfmgGEduWCzOPP9i4JvAnwGvrcM2XfFnneibw1uAbw48vhW4ddIbZSCerwNvJ93MdWGedyGp3T/AbwMfHFi/v94YYltPGpT954GH8g74o4GDqb9t8077ljzdyevZmOL8qZxAbcH8Wm1T5ofAvCBvo4eAX6zTNgUuWZA8l7UNgQ8Cvz0w/6T1RhnrgmW/BDyQp0865qvtOq7csFicpMGVNgDfYz7RT3ybruTfpKtuhhpfdhLyqfgmYC8rHB93RG4HPgbE/Pg1wI/dvbdILP048/Kf5PXHYSRjDq82d/9z4L+Sxlb4IWkbHaSe27Sy3G1Yl+PtelLpGGoWq5m9F/hzdz+yYFGt4lyuSSf6ocaXHTczezXwVWCnu//N6VZdZN7I4zez9wAvufvBIWOZ5HYeyZjDqy3Xb19Fqj74B8C5wLtOE0st991sqdgmHrOZfRzoAQ9UsxZZbSKxmtk5wMeB/7DY4iXimfg2HcakE33txpc1sy4pyT/g7l/Ls+s2Pu5bgfea2feAL5Kqb24HzjOzqluLwVj6ceblPw381RjirN67CWMO/wLwp+5+3N3ngK8B/4x6btPKcrfhRI+3fKHyPcCHPNdznCamScT6s6Qf+iP52FoPPGlmf79mcS7bpBP9fuCy3LJhhnRR68FJBWNmBtwNPOfutw0sqtX4uO5+q7uvd/dLSNvsEXf/EPAo8P4l4qzif39efyylDm/OmMPfB640s3PyflDFWbttOmC52/CbwDvM7Px8BvOOPG/kzOydwK8D73X3lxd8hqtzK6ZLgcuAfUwgN7j70+7+One/JB9bx0iNM/6CGm7TZZn0RQLS1ezvkK6wf3zCsfxz0mnXU8Dh/O/dpLrXh4EX8t8L8voG/FaO/Wlg8wRi/pfMt7p5PekgOQr8HrAmz1+bHx/Ny18/5hg3Agfydv0fpNYJtdumwH8E/gR4Brif1BKkFtsU+F3StYM5UgLaejbbkFQ/fjT/u26MsR4l1WVXx9VnB9b/eI71eeBdA/NHmhsWi3PB8u8xfzF2ott0pf90Z6yISMtNuupGRERGTIleRKTllOhFRFpOiV5EpOWU6EVEWk6JXkSk5ZToRURaToleRKTl/j9lsvSPpQBxRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now print sorted shape \n",
    "\n",
    "for i, c in enumerate(sorted_contours):\n",
    "    if i == 0:\n",
    "        cv2.drawContours(orginal_image, [c], -1, (0,255,0), 5)\n",
    "        plt.imshow(orginal_image)\n",
    "    if i == 1:\n",
    "        cv2.drawContours(orginal_image, [c], -1, (255,0,0), 5)\n",
    "        plt.imshow(orginal_image)\n",
    "    if i == 2:\n",
    "        cv2.drawContours(orginal_image, [c], -1, (255,255,0), 5)\n",
    "        plt.imshow(orginal_image)\n",
    "    if i == 3:\n",
    "        cv2.drawContours(orginal_image, [c], -1, (0,255,255), 5)\n",
    "        plt.imshow(orginal_image)\n"
   ]
  },
  {
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}