CV/2_bitwiseoperation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Demonstrate Bitwise Operations on Binary Images using OpenCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to make things easy\n",
    "def display(texts, img):\n",
    "    cv2.imshow(texts, img)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1 = cv2.imread('../imagedata/1bit1.png')\n",
    "img2 = cv2.imread('../imagedata/1bit2.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f67fee2dca0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f67fcd79370>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1 = cv2.imread('../imagedata/1bit1.png')\n",
    "img2 = cv2.imread('../imagedata/1bit2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image Opeation AND\n",
    "bitwiseAnd = cv2.bitwise_and(img1, img2, mask=None) #It creates a region on which the operation should be applied.\n",
    "display(\"AND\", bitwiseAnd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image Operation OR\n",
    "bitwiseOr = cv2.bitwise_or(img1, img2)\n",
    "display(\"OR\", bitwiseOr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image Operation Not\n",
    "bitswiseNot = cv2.bitwise_not(img2)\n",
    "display(\"NOT\", bitswiseNot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image Operation XOR\n",
    "bitwiseXor = cv2.bitwise_xor(img1,img2)\n",
    "display(\"XOR\", bitwiseXor)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.18 ('nmamit-4nm21ai051')",
   "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": "3.9.18"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "2bbd2131ad204facff535c253583183b3f61508666e3d45279c0a6271fa1d28c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}