Edge Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Edge and Line Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x26749895d00>"
      ]
     },
     "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(\"home.png\", cv2.IMREAD_GRAYSCALE)\n",
    "img = cv2.normalize(img.astype('float'), None, 0.0, 1.0, cv2.NORM_MINMAX)\n",
    "plt.imshow(img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 307)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using inbuilt function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x219f6f4e940>"
      ]
     },
     "execution_count": 22,
     "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": [
    "kernal = np.ones((3, 3))\n",
    "img_bound = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernal) \n",
    "plt.imshow(img_bound, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using sobel filter for vertical edge and horizontal detection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x219f6fa8310>"
      ]
     },
     "execution_count": 23,
     "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": [
    "filter = np.array([[1,0,-1],[2,0,-2],[1,0,-1]])\n",
    "filtered_img = cv2.filter2D(img, -1, cv2.flip(filter, -1), borderType=cv2.BORDER_CONSTANT)\n",
    "plt.imshow(filtered_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x219f6ff58b0>"
      ]
     },
     "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": [
    "filter = np.array([[1,2,1],[0,0,0],[-1,-2,-1]])\n",
    "filtered_img = cv2.filter2D(img, -1, cv2.flip(filter, -1), borderType=cv2.BORDER_CONSTANT)\n",
    "plt.imshow(filtered_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using prewitt filter for vertical edge and horizontal detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x219f7044d90>"
      ]
     },
     "execution_count": 25,
     "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": [
    "filter = np.array([[1,1,1],[0,0,0],[-1,-1,-1]])\n",
    "filtered_img = cv2.filter2D(img, -1, cv2.flip(filter, -1), borderType=cv2.BORDER_CONSTANT)\n",
    "plt.imshow(filtered_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x219f81dc310>"
      ]
     },
     "execution_count": 26,
     "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": [
    "filter = np.array([[1,0,-1],[1,0,-1],[1,0,-1]])\n",
    "filtered_img = cv2.filter2D(img, -1, cv2.flip(filter, -1), borderType=cv2.BORDER_CONSTANT)\n",
    "plt.imshow(filtered_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2674aa0bf10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oYtXwGjSxangNHYpVKTVAKbVPKXVaKfWdUuoXtvJIpdQupdQ523tEi2NeVUrlK6XOKqUe6s4voHH34IxnbQL+SURGAZOAF5VSSTTnv9ojIsOBPba/sW37ATAamAu8rZTS8vNodBpn8mBdFZHjts81wGma0wX1SC4sDQ07LrVZlVKDgfHAEW7KhQW0zIVV3OKwdnNhaWi4gtNLBJVSwcB64JciUn2bvFRO5cJyJmlbe9TW1vIP//APjBkzhoULFzJ27FgMBkOv5MrS6Dmc8qxKKR+ahbpSRDbYijuVC0tE3hORZBFJjomJ6dCGPn36EB0dDUBjYyPr1q3j3/7t30hNTeW+++7jueeeY/369dy4caPHshBq9CzOpBZSwAfAaRH5W4tN9lxYf+LWXFirlFJ/A/rRRbmwhg0bxs9//nPeeustrl+/7hCkyWTixIkTnDhxgk8//ZS4uDhmz57NuHHjmDNnDklJST2SV9SbsFqtt5SJCN999x0mk+mWbXl5eezfv9+RXbC3cKYZMBVYCuQqpbJtZf9CD+fCUkrx61//miVLlnDo0CEyMjI4dOgQFy9edFxgs9nMpUuXWLFiBStWrCAkJITExERSU1OZO3cuycnJhIaG4uPj01lzPAYRoaqqirKyslblRqORPXv2YLG0vvT19fWsW7eO+vr6VuVWq5VTp061KVb5v2zovYozebAO0XY7FHohF1ZCQgIJCQksXbqUiooKsrKy2LlzJ7t27aKwsLDVQ3c1NTVkZmaSmZnJu+++S1hYGNOmTWP+/Pncf//9JCQkeH07V0R48sknb/kBWiwWbty40UtWdQ9e/QxWREQEc+bMYfbs2VitVs6ePcvevXs5fPgwBw4coKSkxPGckslkwmQysW7dOtavX09kZCSjRo1i9uzZzJkzx+nnmRobG2/xSi2xWCxkZGRQV1fX5nYR4fPPP+fKlVtT2p49e9YpG4KCgoiPj3fclsvLy506riNCQkJuaTIppZg8eTJRUVGtysePH98l53QF5QnuPTk5WTIzM7u0zuvXr/PVV19x6tQpNmzYwOnTp9tNxR4UFISIOLaPHDmSBQsWtLnv2bNnOX78eLvnFRFKS0tvuf26yjPPPMPy5cvb3f7hhx/y4osvtnnbBoiJiaGtjqu/vz+LFy8mICCgVbler2fu3LmEhobeckx0dDS+vr4ufoPbo5TKEpFkl465U8XaErPZzIkTJ/jiiy84fvw4hw4dory8vNOC6k5+/OMf895777W73Wq1cuzYMWpra9vcPmjQIAYNGtTmNk9os7sjVq9uBjiLj48PycnJJCcnIyJcvXqVPXv2cPr0aTZv3kx+fn6XPdYcFBRE//7tz4GEh4eTlpZ22xEKpRTf//73b3senU53V0Rhacld4Vlvh8lk4ujRo3z++eecOXPmtvvq9XoeeeQRIiMj290nOjqasWPHtrtdp9N1+S3VG9E8qxv4+/szffp0pk+f3tumaHSANlqu4TVoYtXwGjSxangNmlg1vAZNrBpegyZWDa/BI8ZZlVLXgTrAk1deROPZ9oHn29jSvkEi0vFC5hZ4hFgBlFKZrg4S9ySebh94vo2dtU9rBmh4DZpYNbwGTxJr+0uMPANPtw8838ZO2ecxbVYNjY7wJM+qoXFbNLFqeA29Llal1FxbALd8pdQrvWjHh0qpUqXUyRZlHhN8ztMD5Cml/JVSR5VSJ2z2/a7L7bM/ZtsbL0APFABDAF/gBJDUS7ZMB74HnGxR9hfgFdvnV4A/2z4n2Wz1AxJs30Hfzfb1Bb5n+xwC5Nns8AgbaX4COtj22YfmEFOTutK+3vasE4F8ETkvIo3AapoDu/U4InIAuPkxUY8JPiceHiBPmrE/EOZje0lX2tfbYvX0IG4eGXzOUwPkKaX0tkAopcAuEelS+3pbrE4FcfNAes3umwPk3W7XNsq61UYRsYjIOJrjm01USo25ze4u29fbYnUqiFsv0qngc11NdwTI6w5EpBLYT3Mw6S6zr7fFegwYrpRKUEr50hwxe0sv29QSe/A5uDX43A+UUn5KqQS6KPjc7XAiQF6v2qiUilFKhds+BwBzgDNdal9v9Lxv6kXOp7lnWwD8ay/a8RlwFTDT/Kt/DoiiOQT9Odt7ZIv9/9Vm81lgXg/Yl0rzbTIHyLa95nuKjcBY4FubfSeB39rKu8w+bbpVw2votmaApwz2a9w5dItntWVnyQMeoPmWegx4UkR6NxqthlfTXZ7VYwb7Ne4cuit8UFsDvq2iiLVMgAFM6CY7NDyXG+LiM1jdJdYOB3xF5D1si3GVUlov7+7joqsHdFczwNMH+zW8kO4Sq6cP9mt4Id3SDBCRJqXUz4CdNC8D/FBEvuuOc2ncPXjEpIDWZr0rcTmYcG+vDdDQcBpNrBpegyZWDa9BE6uG16CJVQNoziLj6QmZ7/hsLTqdjoEDBxIbG9tmNmiN5mtkMBgoLi7m8uXLHnud7mixxsbG8uKLL3LmzBmuXbuG2WzubZM8EqvVyqBBg+jfvz+7d++moqKit01qkztWrAaDgaVLl7Jjxw5OnTqF0WjEE8aUPZXs7GxefvllsrKyqKqq8kjveseKddy4cRiNRk6fPk11deuHQEeOHMmwYcNcrjMvLw+j0Uh1dTXV1dVERUUxceJE9Hq9S/VYLBays7MxGAzcuHGD+vp6hg0bxsiRI122CSA3Nxer1UpFRQVGo5GBAwcyZsztHixtprCwkPLychoaGigrK+Obb75h1qxZrFq1qt2kzL3JHTmDFRISwj//8z+zfPlyioqKaGpqQqfTYbVaGTp0KCNGjMDX19flDoVer2fo0KEcOXKEY8eOkZqaCkBgYKDLNup0OkaNGsXbb7+NXq9n/PjxBAQEuNXJ0el0jB49mg8++ACdTkdqaip1dXUd/oj0ej2DBw/m8OHDHD16lKamJl5++WW2bt1KXl5el+WzbQctHaZSisWLF3Po0CEuX76MxWIhODiY6Ohorl27RmVlJdOnT2f58uVcuHDBpczYer2eoKAgXnnlFXJycujTpw9FRUUcPHiw3VTq7aHT6aipqSE9PZ2VK1cyefJkVq5cycWLF13O1q3T6SgoKOAHP/gBK1euJDExkY8++oiioqIOv09ERAS//OUvKSgooKSkhI0bN/LQQw9RWlpKaWnpbY/vaTx7rMINEhMT6devH9988w0NDQ088MADPPvsszz66KOEh4dTVlbG3r17mThxIv7+/litVqdfZrOZyspKNm3axIIFC9i+fTv3338/ISEhLtVjtVppampiz5499O/fn8TERPbt28e0adMICAhwq67s7GwsFguhoaGUlJQwePDgDusym82UlpaSkZHBjBkzCAwMJC8vD4PBwIgRI/Dz8+vtf2cr7iix+vv789RTT7F27Vpqa2uJi4sjNTWVdevWUVBQwIwZM/D19eXgwYMMHTqUgQMHupWh+tixYyQkJCAinDx5khkzZhAcHOxyPRaLhffff5+HH36Yo0eP0q9fP4YMGeK2SDZu3MiiRYvYsmUL06ZNu2327pZkZGQwYsQI4uPjERE+/PBD5syZQ3R0NM3hCjyDO0qsS5Ys4ZtvvuHy5csopfjRj37E5s2bqaqqYtu2bYwcOZLIyEgaGhr49NNPmT9/PlFRUS6fx2KxsG3bNp577jm2bdvG6NGj6dOnDwaD662q0tJSLl68yIMPPsjHH3/MvHnziIiIcEskRUVFmEwmgoKCKC0tJSEhgaCgoA6PM5lMrFy5kgULFhAZGUlFRQXnzp1j7NixhISEuGxHd3HHiLV///706dOHo0ePYjKZmDdvHteuXaOwsBCj0UhjYyP79u1j7ty5hIeHc+HCBcf4or+/v8vny8rKorKykiFDhrB3716mTZvmVj0Aq1evJiUlBb1ez9WrV5k6dSqhoaEu1yMibN68mbS0NLZu3crUqVMJDw936tjz58+jlCIhIQGDwcC2bduYMmWKW3eM7uKOEKvBYOD5559n27ZtlJeXExQUREpKCnv37m01wP31118zZMgQYmObA9lt2rSJOXPmuNWbB9i8eTMPP/wwhw8fJi4ujmHDhrl1CzebzXz44Yc8+uijrF27luTkZGJiYlweEoNm72o2mwkJCaG8vJyhQ4c6ZZPVamXVqlWkp6cTHBxMZWUlBw4c4KGHHiIsLMxlO7qDO0KsCxcu5Pz585w7dw6z2cyPfvQj9u/fz9WrV1sNbpvNZj766CMWL15MYGAg58+fJz8/nylTprh1u7t27RoXL17kgQceYPXq1cybN4/o6Gi3vsP58+dpbGzknnvuYcWKFSxcuNCtJgo0/4jsbVd7s8KZIbErV65w8uRJUlNTCQkJISMjwzGz5ePj45YtXYnXizU+Pp7x48ezbds26urqmDp1Kr6+vuTm5rY5TnjhwgWMRiOjR4/G39+fzz//nJSUFGJiYtxqJ65bt4777ruPsrIyamtrSUhIcOsfKyJ88MEHLFiwgKKiIpRSbrcZi4qKqK+vJyQkhH379jFz5kynmwM7duxgypQphIeHYzab2bFjB7Nnz3a7idOVeLVY9Xo9S5cuZcOGDVRXVxMZGUlaWhqbN2+mvPzmINbNiAgbN25k4cKFBAYGUltbS1ZWFpMnT3brH2Iymfjyyy95+umn+fTTT3nooYeIi4tza3C/rq6OzMxMFi1axEcffcScOXMICwtz+Udkb7ump6dz6NAhkpKSiI6OdqpZUVNTw+bNm5k/fz7+/v4cO3YMpRT33HMPAQEBLn+nrsSrxTphwgRqa2spLi5GRHjiiSfYuXMnV69epampqd3jiouLOXr0KFOnTsXHx4fPP/+cIUOGuO0VDx06hJ+fHxERERw7dozJkye73c6z2xIREcGmTZuYP3++00NQLbGPDAwcOJD9+/czceJEp71rVlYWsbGxJCQkoNfrWbduHbNnz3a7WdJVeK1Yg4ODWbRoERs2bKCiooJ7770XPz8/cnNznZrX3r17NxMmTCAiIgKLxcKWLVuYOXOmW8KA5nbi4sWL2bdvH+PHj3e6nXgzFouF5cuXs3TpUrKzs+nbty/x8fFudba2bNlCeno6X3/9NcOHDycqKsopm5qamtiyZQsLFy4kICCAq1evUlJSwujRo90al+4qvFKs9jHUHTt2UFpair+/P+np6Wzfvp2qqiqnVlfV19dz9OhRpk+fTnBwMDk5OYSGhhIXF+fWeOn58+eprKxkwoQJrFq1irS0NLeFf+XKFQoLC5k0aRL/8z//Q3p6umMEwxWKi4upr69n0KBB7N+/n/vuu4+IiIiODwROnDhBWVkZ99xzDwaDgY8//pjU1FT69evXa4u0vVKsU6dORafTkZ2djdlsZuHCheTm5lJcXOzSmlW7F4yJaQ65tGXLFhYtWuTUQPrNWK1W1q5dy6xZsygoKCAgIIDBgwe7JXyA9evXM3v2bIxGI9euXWPcuHEuj72KCJs2bSI9PZ3Dhw+TmJhIVFSU0156y5YtzJ49m+joaBoaGjhw4ACTJ092ujnR1XidWKOiokhLS2PNmjUYjUaGDh1KUlIShw8fdnkxidFo5JNPPmHBggWEhYXx3XffcenSJZKTk90aDK+uriYnJ4cZM2bw/vvvk56eTlxcnMv1ANTW1rJ+/Xqef/55Vq1axZQpU4iOjnbZqxUXF1NXV8egQYPIyMhg0qRJTovt+vXrXLhwgUmTJuHj48OBAwdISkoiNjbWrWZJZ/E6saanp/Pll19SUVGBj48PzzzzDJs3b6asrMytxdVnz54lKCjIMSdvn1d3d5px27ZtpKSk4OPjQ15eHvfcc4/bdWVnZ9PQ0MCgQYNYu3YtixYtcqtpsXHjRp566imysrIYOnSo021XaJ5dGzduHNHR0ZjNZlasWOGYlu1pvEqsY8aMIT4+nszMTEwmE9OnT+e7777j4sWLLntVOyLCli1bHL3lsrIyTpw4wYwZM9wSWX19PZs2beKRRx5hx44dzJgxg9DQULfbeWvWrOGHP/whBQUF6PV6hgwZ4vKM25UrVzh48CCTJ092jAw423Y1m83s2rXLMYyWn5/vmKbu6c6W14g1MDCQxx9/nFWrVlFbW0tsbCyzZ89m//79VFZWdqruwsJCLBaLQwSEZPcAAA9ASURBVFDbtm1j1KhR9OvXz602p33l/uDBg9m0aRMPPvig2+28a9eu8dVXXzFz5kw+/fRT0tLSCA8Pd3nsNSMjg8mTJ5Odne0YGXD2Vn706FGGDh3q6FzZZ8iCg4N7dFWWV4hVKUV6ejrHjh3j0qVLWCwWFi9ezNatW53u/d8Oo9GI2Wx2jM02Njby5Zdfkpqa6pbILBYL69atY8GCBWRnZzNgwAD69Onjdjtv7969pKSkYDAYOHToEPPnz3fZ6xuNRo4cOcLUqVPZv3+/S23XxsZGPvjgA8d5z58/z4kTJ9yepnYXrxDrkCFDGDZsGF999RUNDQ2EhYURHh5Obm4u9fX13XLOw4cPM3DgQKdnfm7m+vXrFBUVMXnyZLZv305aWprbC2bq6+v59NNPef7558nIyHB77HXPnj3cd999ZGdnM3ToUJe+W1FREY2NjYwaNQpfX1927tzJxIkTCQ0N7THv6vFi1ev1fP/732f79u3U1dUBzSMChYWF3fpodVNTEytWrGDx4sVuL05Zs2YNM2fO5Ny5c1RXV3dqyvLs2bOcPXuWCRMmsH79eh555BGXRyxMJhPffPMNU6ZMYd++fdx3330uzbStW7eOtLQ0goKCqKmp4auvvmLu3Lk9tozQ48U6c+ZMysvLOXPmDA0NDQCMGjWKhoYGx9/dxYULF6itrSUxMdGtzoTRaGT37t0sXrzYMZsUFhbmdmdrx44dpKenO1Z7zZo1y2VvnZGRwbhx48jOziYxMZHY2Fin7bl8+TKHDx8mNTWV4OBgdu3aRZ8+fXqss9WhlUqpD5VSpUqpky3KIpVSu5RS52zvES22vWrLfXVWKfVQZ4zr06cPs2fPZuvWrd12u28Lg8FAbGwsIsLHH3/MnDlzHM9s+fr6uvQ6duwYISEhNDQ0sHfvXh5//HHGjBnjcj2+vr4YjUYOHDjAiBEjWLt2LePHj2fWrFlEREQ4XYfFYiErK4uBAweyfPlyHn/8ccfjPc68jhw54li7YLVa+fzzz5k5c6bTowud+r84sc9HwP8DPm5R9gqwR0T+ZEvI9grwslIqieaQ7KOBfsBupdQIEXHtcU0bc+bMYePGjVRWVvZogIoJEyYQFxfHnj17qKqq4uOPPyY5OZnhw4e7XJfFYuHUqVMopdi9eze1tbWMHTuWCRNcT1BjTwt56dIlzGYzb7zxBtOnT2fRokVOtxtFhLKyMioqKrh8+TJffvkl06ZNc2nU4+TJk46hwtzcXBYsWEDfvn0pLy/v1qZZhxaKyAFbfvuWLAJm2D6voDkD8su28tUi0gBcUErl05wT6xt3jGtqaqKsrIzGxkYMBgM+Pj494mETEhIICAhg1KhR5ObmUlBQwKVLl9y+1TU0NNDY2IiIcOTIEUeAC3ewWq2YTCbHtdm2bRt+fn4udXLMZjMNDQ1YrVYyMzPJzc11qbNm/z7Q/GPct28fo0aNcix+7y7cjRsQJyJXAUTkqlLKvsqiP3C4xX6XbGVuYbVamTx5MqNHj6ayspIbN25w6lT3JilUShESEkJOTg5PPPEEZrOZa9eudcqzd/XCZXdHFez4+/u7NORktVodjwvV1dU5nr7w8fEhIiKCwMBArFZrt9/9ujrIRYf5rxw7tk7a1iY7duwgJSWFxsZGYmJiaGxs7JE5aYPBQFNTE//93//NrFmzPOKRjt7EHpsgLy+PzMxMxxMZI0eOxN/fn4aGBg4ePNjdEVzcFmuJUqqvzav2BeyhO5zOf+VM0raamhp2794NQHJyco8+WqGUoqCggOrqao94pKM3sXvNBx98kNzcXEwmE0OGDOHIkSNUV1djMpmorKy87YL3rsBdsW4Bngb+ZHvf3KJ8lVLqbzR3sIYDRztrZG8hIh4XQqc30ev1iAgGg8Ex+9VRiKKupEOxKqU+o7kzFa2UugS8RrNI1yilngOKgMcAROQ7pdQa4BTQBLzo7kiAhsbNODMa8GQ7m2a3s/8fgD90xigNjbbw+BksDQ07mlg1vAZNrBpegyZWDa9BE6uG16CJVcNr0MSq4TVoYtXwGjSxangNmlg1vAZNrBpegyZWDa9BE6uG16CJVcNr0MSq4TVoYtXwGjSxejk+Pj4elbKyO7njUrh7MzNnzuTee++ltrbW6ceag4KCyMrKIjMzs9vDKfU2mlg9iJEjR7Jhwwbq6uqcEqvVamXIkCGMGTOGnJwcTawaPYfJZKK+vp6ysjKg+XHwm2MWWK1WLBYLfn5+mEwmrFYrer3eo1KtdxeaWD0Mu0cdNmwYycnJt4TbbGho4Pr165w5c4YzZ870hom9hiZWD+Wee+6hsLCQkydPtio3mUwMHz6chIQE8vLyesm63kETqweilKJfv34UFxffIlZojvuakpLSq9n+egNt6ErDa9DEquE1aGLV8Bo0sWp4DZpYNbwGTawaXoMmVg2vQROrhtegiVXDa3AmadsApdQ+pdRppdR3Sqlf2Mp7JHGbhoYdZzxrE/BPIjIKmAS8aEvOZk/cNhzYY/ubmxK3zQXeVkp1f4oVjTueDsUqIldF5Ljtcw1wmubcVotoTtiG7f1h22dH4jYRuQDYE7d1GVarFZ1Od1csi9P4P1xqs9oyDY4HjnBT4jagZeK24haHtZm4TSn1E6VUplIq01WjT5w4QUBAQI/kxNLwHJxedaWUCgbWA78UkerbeDWnErc5kwerJTqdzuFNy8rKEBGWLFnCuXPnKC0tpaCgAItFSwxzJ+OUWJVSPjQLdaWIbLAVdzpxmytYrVb69u2LwWCgoaGBL7/8ksjISAYNGkRKSgozZ86ktLSU48ePU1VVRWVlZWdPqdGCxMTEbk/K1hHO5MFSwAfAaRH5W4tNPZq4LTc3l4ceeoi0tDR27NjB9evXuX79OufPn8ff35+oqChiY2OZMmUKoaGhmEwmTp8+TUlJCUVFRT2aVbsnMRqNQPOdxxna2s/X15eRI0ei0+kceVntDBgwgNjYWJRSjuyCvYUznnUqsBTIVUpl28r+hR5O3NbQ0MDrr7/OvHnzWLp0KZWVlVRXV3P8+HFu3LhBYWEhFy9e5MyZM4SEhBAeHk5CQgJJSUn4+PhQUlLC+fPnKSwspLa2trPmdDnudhbz8/OZP38+AwcOdNQRFRVFUlKSI+W7HT8/P/r06UNTU9MtXrKxsZGKiopbHlasqqqiqKiIsrIyqqqqqK+vx8/Pzy1bO4szSdsO0XY7FHo4cZvJZGLnzp1ER0cTGRlJ3759WbhwIdCclvHy5cucO3eO6upqLl++TH5+PgEBAURHRxMXF0dSUhKTJk2irq6OU6dOUVJSwrVr1+w2d7W5TqOUYsKECej1eqdTu9sfErRarRw6dIgRI0YwevRooDmtenV1NaWlpQ7PC81e9cyZM5SXl1NfX+8oFxEsFgtms/mWdr/FYrnl2iilaGpqusULdzde91iL0WikqKiIoqIicnJyiImJISoqir59+3Lvvfcybdo0ysrKqKio4Pjx41RWVlJZWUlBQQH+/v5EREQQExPDkCFDSE5Oxmq1UlpaSkNDA0opRIQrV5qb2Pa/7QQEBODr69uusAMCAhg/fvwtx9nR6/XEx8ej0+kwm82ttjU2NvLtt99SU1PT4TUQEeLi4hy39KysLHJzcx2jI1ar1fFYdnu2BgUFtRpN0ev16PV6hg8f3uohRbu3joyMJDQ01FFeWlpKVVVVh7Z2JV4n1pZYrVZKSkooKSnh7NmzBAYGEhMTQ2RkJP369ePRRx+lsbGRmpoacnNzKSgo4MqVK1y5coW8vDzH/mFhYeTn5zuEcvbsWe6//35GjhzZ6nyBgYEopTAajW16FavVSl1dHaWlpe02NS5fvkxFRQU1NTWt6rALzP6jgfaFVlJSQmhoKFOmTOHy5cvteriwsDCGDx/eqswu8JvFKiIYjUaMRmMr72o2m7lx4wYlJSWOY61WKzdu3OD69ettnre7UJ7Q8XBm6MrF+tDr9cTExBAUFMSAAQMYNGgQAQEBXL16lfLycr799ltH5JObxREQEEBsbCyNjY2t6vX19cVsNlNbW9vmMJmI0NDQcNvbo/0cduG3hU6n46mnniIjI6PNBwYBIiIimDt3LsHBwW2eo66ujsrKylYeXEQwmUyUlJQ4JlbsNDU1UVNTQ01NzS3fTUS645afJSLJrhxwR4r1ZnQ6HaGhoURHRxMdHU3//v2JjY3FaDRSUVFBTk4ORUVFTv9DYmNj24wvZRefn58fY8aMabPnbfdmISEh7bZPm5qaKCoq4vDhw46AF20RHBzc5hOuIoLZbKahoeGW5oYHoYnViXPh4+NDXFwcQUFBDBo0iPj4eEdb9HbXw94WbemNW2L3rJWVla06MC2325sJt5submpqory8nMrKyjt2yA1NrK6j1+sJDw93tF1vN4TU2NjIjRs3MBqN7Yq1qamJ+vr6Oz7uVBegiVXDa3BZrNriaw2vQROrhtegiVXDa9DEquE1aGLV8Bo8Zbr1BlBne/dUovFs+8DzbWxp3yBXD/aIoSsApVSmq0MZPYmn2weeb2Nn7dOaARpegyZWDa/Bk8T6Xm8b0AGebh94vo2dss9j2qwaGh3hSZ5VQ+O29LpYlVJzbTGx8pVSr/SiHR8qpUqVUidblHlMPC9PjzmmlPJXSh1VSp2w2fe7LrfPvj6zN16AHigAhgC+wAkgqZdsmQ58DzjZouwvwCu2z68Af7Z9TrLZ6gck2L6Dvpvt6wt8z/Y5BMiz2eERNtL8UGmw7bMPzVF7JnWlfb3tWScC+SJyXkQagdU0x8rqcUTkAFB+U3GvxfNqwz6Pizl2k30iIvYHz3xsL+lK+3pbrE7FxepFOhXPq7voyphjXWyX3hZbohTYJSJdal9vi9WpuFgeSK/ZfXPMsdvt2kZZt9ooIhYRGUdzyKiJSqkxt9ndZft6W6zdEherCymxxfGiJ+J5dcTtYo55io0AIlIJ7Kc5Pm+X2dfbYj0GDFdKJSilfGkOQryll21qiT2eF9waz+sHSik/pVQCXRTP63Y4EXOsV21USsUopcJtnwOAOcCZLrWvN3reN/Ui59Pcsy0A/rUX7fgMuAqYaf7VPwdE0RzV+5ztPbLF/v9qs/ksMK8H7Eul+TaZA2TbXvM9xUZgLPCtzb6TwG9t5V1mnzaDpeE19HYzQEPDaTSxangNmlg1vAZNrBpegyZWDa9BE6uG16CJVcNr0MSq4TX8f5A7bk0y9i+SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Canny_detector(img, weak_th = None, strong_th = None): \n",
    "       \n",
    "    img = cv2.GaussianBlur(img, (5, 5), 1.4) \n",
    "       \n",
    "    gx = cv2.Sobel(np.float32(img), cv2.CV_64F, 1, 0, 3) \n",
    "    gy = cv2.Sobel(np.float32(img), cv2.CV_64F, 0, 1, 3) \n",
    "      \n",
    "    mag, ang = cv2.cartToPolar(gx, gy, angleInDegrees = True) \n",
    "       \n",
    "    mag_max = np.max(mag) \n",
    "    if not weak_th:\n",
    "        weak_th = mag_max * 0.1\n",
    "    if not strong_th:\n",
    "        strong_th = mag_max * 0.5\n",
    "      \n",
    "    height, width = img.shape \n",
    "       \n",
    "    for i_x in range(width): \n",
    "        for i_y in range(height): \n",
    "               \n",
    "            grad_ang = ang[i_y, i_x] \n",
    "            grad_ang = abs(grad_ang-180) if abs(grad_ang)>180 else abs(grad_ang) \n",
    "               \n",
    "            if grad_ang<= 22.5: \n",
    "                neighb_1_x, neighb_1_y = i_x-1, i_y \n",
    "                neighb_2_x, neighb_2_y = i_x + 1, i_y \n",
    "              \n",
    "            elif grad_ang>22.5 and grad_ang<=(22.5 + 45): \n",
    "                neighb_1_x, neighb_1_y = i_x-1, i_y-1\n",
    "                neighb_2_x, neighb_2_y = i_x + 1, i_y + 1\n",
    "              \n",
    "            elif grad_ang>(22.5 + 45) and grad_ang<=(22.5 + 90): \n",
    "                neighb_1_x, neighb_1_y = i_x, i_y-1\n",
    "                neighb_2_x, neighb_2_y = i_x, i_y + 1\n",
    "              \n",
    "            elif grad_ang>(22.5 + 90) and grad_ang<=(22.5 + 135): \n",
    "                neighb_1_x, neighb_1_y = i_x-1, i_y + 1\n",
    "                neighb_2_x, neighb_2_y = i_x + 1, i_y-1\n",
    "              \n",
    "            elif grad_ang>(22.5 + 135) and grad_ang<=(22.5 + 180): \n",
    "                neighb_1_x, neighb_1_y = i_x-1, i_y \n",
    "                neighb_2_x, neighb_2_y = i_x + 1, i_y \n",
    "               \n",
    "            if width>neighb_1_x>= 0 and height>neighb_1_y>= 0: \n",
    "                if mag[i_y, i_x]<mag[neighb_1_y, neighb_1_x]: \n",
    "                    mag[i_y, i_x]= 0\n",
    "                    continue\n",
    "   \n",
    "            if width>neighb_2_x>= 0 and height>neighb_2_y>= 0: \n",
    "                if mag[i_y, i_x]<mag[neighb_2_y, neighb_2_x]: \n",
    "                    mag[i_y, i_x]= 0\n",
    "   \n",
    "    weak_ids = np.zeros_like(img) \n",
    "    strong_ids = np.zeros_like(img)               \n",
    "    ids = np.zeros_like(img) \n",
    "       \n",
    "    for i_x in range(width): \n",
    "        for i_y in range(height): \n",
    "              \n",
    "            grad_mag = mag[i_y, i_x] \n",
    "              \n",
    "            if grad_mag<weak_th: \n",
    "                mag[i_y, i_x]= 0\n",
    "            elif strong_th>grad_mag>= weak_th: \n",
    "                ids[i_y, i_x]= 1\n",
    "            else: \n",
    "                ids[i_y, i_x]= 2\n",
    "       \n",
    "    return mag \n",
    "   \n",
    "canny_img = Canny_detector(img) \n",
    "   \n",
    "# Displaying the input and output image   \n",
    "plt.figure() \n",
    "f, plots = plt.subplots(2, 1)  \n",
    "plots[0].imshow(img, cmap = 'gray') \n",
    "plots[1].imshow(canny_img, 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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}