Arithmatic filter of different sizes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#applying 3X3 mask\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt \n",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "#creating the image\n",
    "img = np.zeros([230, 233])\n",
    "img = img.astype(np.uint8) \n",
    "for i in range(10, 220):\n",
    "    j = 0\n",
    "    while j <= 192:\n",
    "        for k in range(j+0, j+16):\n",
    "            img[i, k] = 0\n",
    "        for k in range(j+17, j+24):\n",
    "            img[i, k] = 255\n",
    "        j += 24\n",
    "\n",
    "m, n = img.shape \n",
    "\n",
    "img_new = np.zeros([m, n]) \n",
    "\n",
    "\n",
    "# Convolve the 3X3 mask over the image \n",
    "for i in range(1, m-1): \n",
    "    for j in range(1, n-1): \n",
    "        temp = 0\n",
    "        for l in range(-1, 2):\n",
    "            for k in range(-1, 2):\n",
    "                temp = temp + img[i+l, j+k]\n",
    "        temp = temp/9\n",
    "        \n",
    "        img_new[i, j]= temp\n",
    "    \n",
    "img_new = img_new.astype(np.uint8) \n",
    "\n",
    "\n",
    "# Display the images\n",
    "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) \n",
    "plt.show()\n",
    "\n",
    "# Display the images\n",
    "plt.imshow(cv2.cvtColor(img_new, cv2.COLOR_BGR2RGB)) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#applying 5X5 mask\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt \n",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "m, n = img.shape \n",
    "\n",
    "img_new = np.zeros([m, n]) \n",
    "# Convolve the 5X5 mask over the image \n",
    "for i in range(3, m-3): \n",
    "    for j in range(3, n-3): \n",
    "        temp = 0\n",
    "        for l in range(-2, 3):\n",
    "            for k in range(-2, 3):\n",
    "                temp = temp + img[i+l, j+k]\n",
    "        temp = temp/25\n",
    "        \n",
    "        img_new[i, j]= temp \n",
    "\n",
    "img_new = img_new.astype(np.uint8) \n",
    "\n",
    "\n",
    "# Display the images\n",
    "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) \n",
    "plt.show()\n",
    "\n",
    "# Display the images\n",
    "plt.imshow(cv2.cvtColor(img_new, cv2.COLOR_BGR2RGB)) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#applying 9X9 mask\n",
    "\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt \n",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "m, n = img.shape \n",
    "img_new = np.zeros([m, n]) \n",
    "# Convolve the 9X9 mask over the image \n",
    "for i in range(4, m-4): \n",
    "    for j in range(4, n-4): \n",
    "        temp = 0\n",
    "        for l in range(-4, 5):\n",
    "            for k in range(-4, 5):\n",
    "                temp = temp + img[i+l, j+k]\n",
    "                \n",
    "        temp = temp/81\n",
    "                \n",
    "        \n",
    "        img_new[i, j]= temp \n",
    "\n",
    "img_new = img_new.astype(np.uint8) \n",
    "\n",
    "\n",
    "# Display the images\n",
    "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) \n",
    "plt.show()\n",
    "\n",
    "# Display the images\n",
    "plt.imshow(cv2.cvtColor(img_new, cv2.COLOR_BGR2RGB)) \n",
    "plt.show()"
   ]
  },
  {
   "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
}