Geometric mean filter of different sizes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-27-7cc0c2ffb581>:29: RuntimeWarning: overflow encountered in long_scalars\n",
      "  temp = temp * img[i+l, j+k]\n"
     ]
    },
    {
     "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",
    "# 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 = 1\n",
    "        for l in range(-1, 2):\n",
    "            for k in range(-1, 2):\n",
    "                temp = temp * img[i+l, j+k]\n",
    "        temp = np.power(temp, 1/9)\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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-0cd7ae901ac3>:27: RuntimeWarning: overflow encountered in long_scalars\n",
      "  temp = temp * img[i+l, j+k]\n"
     ]
    },
    {
     "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 5X5 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",
    "# 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 = 1\n",
    "        for l in range(-2, 3):\n",
    "            for k in range(-2, 3):\n",
    "                temp = temp * img[i+l, j+k]\n",
    "        temp = np.power(temp, 1/25)\n",
    "        \n",
    "        img_new[i, j]= temp \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": 31,
   "metadata": {},
   "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 9X9 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",
    "# 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 = 1\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 = np.power(temp, 1/81)\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
}