Harris Corners.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenCV Version : 3.4.3 \n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "print(\"OpenCV Version : {} \".format(cv2.__version__))\n",
    "from harris_corners import harris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Harris Corners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = cv2.imread('img/chessboard.jpg')\n",
    "# create copy\n",
    "\n",
    "corners = harris(img)\n",
    "\n",
    "# display images\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.subplot(121), plt.imshow(img)\n",
    "plt.title(\"Input Image\"), plt.xticks([]), plt.yticks([])\n",
    "plt.subplot(122), plt.imshow(corners)\n",
    "plt.title(\"Harris Corners\"), plt.xticks([]), plt.yticks([])\n",
    "plt.show()\n",
    "\n",
    "cv2.imwrite(\"output/q1.png\", cv2.cvtColor(corners, cv2.COLOR_BGR2RGB))"
   ]
  }
 ],
 "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": "3.7.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
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