12. Histogram Equalization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Histogram Equalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "from collections import Counter\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# data= []\n",
    "# n= int(input(\"Enter no. of gray levels: \"))\n",
    "# for i in range(n):\n",
    "#     gray_lvl, nos= map(int, input('Enter gray level and no. of pixel: ').split())\n",
    "#     data.append([gray_lvl, nos])\n",
    "# data= np.array(data)\n",
    "\n",
    "n= 8\n",
    "data= np.array([[0, 790],\n",
    " [1, 1023],\n",
    " [2, 850],\n",
    " [3, 656],\n",
    " [4, 329],\n",
    " [5, 245],\n",
    " [6, 122],\n",
    " [7, 81]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,  790],\n",
       "       [   1, 1023],\n",
       "       [   2,  850],\n",
       "       [   3,  656],\n",
       "       [   4,  329],\n",
       "       [   5,  245],\n",
       "       [   6,  122],\n",
       "       [   7,   81]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
    }
   ],
   "source": [
    "plt.bar(data[:, 0], data[:, 1], width= 0.1)\n",
    "plt.title('Original Histogram')\n",
    "plt.xlabel('Gray Levels')\n",
    "plt.ylabel('No. of pixels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.000e+00, 7.900e+02, 1.900e-01, 1.900e-01, 1.000e+00],\n",
       "       [1.000e+00, 1.023e+03, 2.500e-01, 4.400e-01, 3.000e+00],\n",
       "       [2.000e+00, 8.500e+02, 2.100e-01, 6.500e-01, 5.000e+00],\n",
       "       [3.000e+00, 6.560e+02, 1.600e-01, 8.100e-01, 6.000e+00],\n",
       "       [4.000e+00, 3.290e+02, 8.000e-02, 8.900e-01, 6.000e+00],\n",
       "       [5.000e+00, 2.450e+02, 6.000e-02, 9.500e-01, 7.000e+00],\n",
       "       [6.000e+00, 1.220e+02, 3.000e-02, 9.800e-01, 7.000e+00],\n",
       "       [7.000e+00, 8.100e+01, 2.000e-02, 1.000e+00, 7.000e+00]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_sum= np.sum(data[:, 1])\n",
    "cdf= 0\n",
    "table= []\n",
    "for row in data:\n",
    "    pdf= round(row[1]/total_sum, 2)\n",
    "    cdf+= pdf\n",
    "    r= list(row)\n",
    "    r.extend([pdf, cdf, round(cdf*(n-1), 0)])\n",
    "    table.append(r)\n",
    "table= np.array(table)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gray Levels</th>\n",
       "      <th>No. of pixels</th>\n",
       "      <th>PDF</th>\n",
       "      <th>CDF</th>\n",
       "      <th>(L-1)*CDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.65</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.89</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.95</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.98</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gray Levels  No. of pixels   PDF   CDF  (L-1)*CDF\n",
       "0          0.0          790.0  0.19  0.19        1.0\n",
       "1          1.0         1023.0  0.25  0.44        3.0\n",
       "2          2.0          850.0  0.21  0.65        5.0\n",
       "3          3.0          656.0  0.16  0.81        6.0\n",
       "4          4.0          329.0  0.08  0.89        6.0\n",
       "5          5.0          245.0  0.06  0.95        7.0\n",
       "6          6.0          122.0  0.03  0.98        7.0\n",
       "7          7.0           81.0  0.02  1.00        7.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.DataFrame(table, columns= ['Gray Levels', 'No. of pixels', 'PDF', 'CDF', '(L-1)*CDF'])\n",
    "df.head(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data=[0 for i in range(n)]\n",
    "for i in range(len(data)):\n",
    "    new_data[int(table[i,4])]+= data[i, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 790, 0, 1023, 0, 850, 985, 448]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_data=[]\n",
    "for i in range(n):\n",
    "    hist_data.append([i, new_data[i]])\n",
    "hist_data= np.array(hist_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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"
    }
   ],
   "source": [
    "plt.bar(hist_data[:, 0], hist_data[:, 1], width= 0.1)\n",
    "plt.title('Equalized Histogram')\n",
    "plt.xlabel('Gray Levels')\n",
    "plt.ylabel('No. of pixels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter image file name: ./Images/pebbles.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(569, 570, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_file= input(\"Enter image file name: \")\n",
    "img=cv2.imread(img_file)\n",
    "img= cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((569, 570), (569, 570), (569, 570))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img0= img[:, :, 0]\n",
    "img1= img[:, :, 1]\n",
    "img2= img[:, :, 2]\n",
    "img0.shape, img1.shape, img2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def histogram_equalization_img(img, L):\n",
    "    count= dict(Counter(img.ravel()))\n",
    "    data_img= []\n",
    "    for i in range(L):\n",
    "        try:\n",
    "            data_img.append([i, count[i]])\n",
    "        except:\n",
    "            data_img.append([i, 0])\n",
    "    data_img= np.array(data_img)\n",
    "    \n",
    "    total_sum= np.sum(data_img[:, 1])\n",
    "    cdf= 0\n",
    "    table_img= []\n",
    "    for row in data_img:\n",
    "        pdf= row[1]/total_sum\n",
    "        cdf+= pdf\n",
    "        r= list(row)\n",
    "        r.extend([pdf, cdf, round(cdf*(L-1), 0)])\n",
    "        table_img.append(r)\n",
    "    table_img= np.array(table_img)\n",
    "    \n",
    "    df_img= pd.DataFrame(table_img, columns= ['Gray Levels', 'No. of pixels', 'PDF', 'CDF', '(L-1)*CDF'])\n",
    "    \n",
    "    new_data_img= [0 for i in range(L)]\n",
    "    for i in range(len(data_img)):\n",
    "        new_data_img[int(table_img[i,4])]+= data_img[i, 1]\n",
    "    \n",
    "    hist_data_img=[]\n",
    "    for i in range(L):\n",
    "        hist_data_img.append([i, new_data_img[i]])\n",
    "    hist_data_img= np.array(hist_data_img)\n",
    "    \n",
    "    new_img= img.copy()\n",
    "    for i in range(len(data_img)):\n",
    "        new_img[img==i]= table_img[i,4]\n",
    "    return data_img, df_img, hist_data_img, new_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_img0, df_img0, hist_data_img0, new_img0= histogram_equalization_img(img0, 256)\n",
    "data_img1, df_img1, hist_data_img1, new_img1= histogram_equalization_img(img1, 256)\n",
    "data_img2, df_img2, hist_data_img2, new_img2= histogram_equalization_img(img2, 256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (30, 5))\n",
    "plt.bar(data_img0[:100, 0], data_img0[:100, 1], width= 0.1, color='red')\n",
    "plt.title('Original Histogram Red')\n",
    "plt.xlabel('Gray Levels')\n",
    "plt.ylabel('No. of pixels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (30, 5))\n",
    "plt.bar(data_img1[:100, 0], data_img1[:100, 1], width= 0.1, color='green')\n",
    "plt.title('Original Histogram Green')\n",
    "plt.xlabel('Gray Levels')\n",
    "plt.ylabel('No. of pixels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (30, 5))\n",
    "plt.bar(data_img2[:100, 0], data_img2[:100, 1], width= 0.1, color='blue')\n",
    "plt.title('Original Histogram Blue')\n",
    "plt.xlabel('Gray Levels')\n",
    "plt.ylabel('No. of pixels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gray Levels</th>\n",
       "      <th>No. of pixels</th>\n",
       "      <th>PDF</th>\n",
       "      <th>CDF</th>\n",
       "      <th>(L-1)*CDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.0</td>\n",
       "      <td>11619.0</td>\n",
       "      <td>0.035825</td>\n",
       "      <td>0.227780</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>0.017889</td>\n",
       "      <td>0.245670</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>5469.0</td>\n",
       "      <td>0.016862</td>\n",
       "      <td>0.262532</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4217.0</td>\n",
       "      <td>0.013002</td>\n",
       "      <td>0.275534</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.0</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>0.012271</td>\n",
       "      <td>0.287806</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>0.011590</td>\n",
       "      <td>0.299396</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>0.012438</td>\n",
       "      <td>0.311834</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.0</td>\n",
       "      <td>3943.0</td>\n",
       "      <td>0.012157</td>\n",
       "      <td>0.323991</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4085.0</td>\n",
       "      <td>0.012595</td>\n",
       "      <td>0.336586</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>0.013829</td>\n",
       "      <td>0.350415</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gray Levels  No. of pixels       PDF       CDF  (L-1)*CDF\n",
       "20         20.0        11619.0  0.035825  0.227780       58.0\n",
       "21         21.0         5802.0  0.017889  0.245670       63.0\n",
       "22         22.0         5469.0  0.016862  0.262532       67.0\n",
       "23         23.0         4217.0  0.013002  0.275534       70.0\n",
       "24         24.0         3980.0  0.012271  0.287806       73.0\n",
       "25         25.0         3759.0  0.011590  0.299396       76.0\n",
       "26         26.0         4034.0  0.012438  0.311834       80.0\n",
       "27         27.0         3943.0  0.012157  0.323991       83.0\n",
       "28         28.0         4085.0  0.012595  0.336586       86.0\n",
       "29         29.0         4485.0  0.013829  0.350415       89.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_img0.loc[20:].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Gray Levels</th>\n",
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       "      <td>11619.0</td>\n",
       "      <td>0.035825</td>\n",
       "      <td>0.227780</td>\n",
       "      <td>58.0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>0.017889</td>\n",
       "      <td>0.245670</td>\n",
       "      <td>63.0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>5469.0</td>\n",
       "      <td>0.016862</td>\n",
       "      <td>0.262532</td>\n",
       "      <td>67.0</td>\n",
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       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4217.0</td>\n",
       "      <td>0.013002</td>\n",
       "      <td>0.275534</td>\n",
       "      <td>70.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.0</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>0.012271</td>\n",
       "      <td>0.287806</td>\n",
       "      <td>73.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>0.011590</td>\n",
       "      <td>0.299396</td>\n",
       "      <td>76.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>0.012438</td>\n",
       "      <td>0.311834</td>\n",
       "      <td>80.0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.0</td>\n",
       "      <td>3943.0</td>\n",
       "      <td>0.012157</td>\n",
       "      <td>0.323991</td>\n",
       "      <td>83.0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4085.0</td>\n",
       "      <td>0.012595</td>\n",
       "      <td>0.336586</td>\n",
       "      <td>86.0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>0.013829</td>\n",
       "      <td>0.350415</td>\n",
       "      <td>89.0</td>\n",
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      "text/plain": [
       "    Gray Levels  No. of pixels       PDF       CDF  (L-1)*CDF\n",
       "20         20.0        11619.0  0.035825  0.227780       58.0\n",
       "21         21.0         5802.0  0.017889  0.245670       63.0\n",
       "22         22.0         5469.0  0.016862  0.262532       67.0\n",
       "23         23.0         4217.0  0.013002  0.275534       70.0\n",
       "24         24.0         3980.0  0.012271  0.287806       73.0\n",
       "25         25.0         3759.0  0.011590  0.299396       76.0\n",
       "26         26.0         4034.0  0.012438  0.311834       80.0\n",
       "27         27.0         3943.0  0.012157  0.323991       83.0\n",
       "28         28.0         4085.0  0.012595  0.336586       86.0\n",
       "29         29.0         4485.0  0.013829  0.350415       89.0"
      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_img1.loc[20:].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Gray Levels</th>\n",
       "      <th>No. of pixels</th>\n",
       "      <th>PDF</th>\n",
       "      <th>CDF</th>\n",
       "      <th>(L-1)*CDF</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.0</td>\n",
       "      <td>11619.0</td>\n",
       "      <td>0.035825</td>\n",
       "      <td>0.227780</td>\n",
       "      <td>58.0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>0.017889</td>\n",
       "      <td>0.245670</td>\n",
       "      <td>63.0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>5469.0</td>\n",
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       "      <td>0.262532</td>\n",
       "      <td>67.0</td>\n",
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       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4217.0</td>\n",
       "      <td>0.013002</td>\n",
       "      <td>0.275534</td>\n",
       "      <td>70.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.0</td>\n",
       "      <td>3980.0</td>\n",
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       "      <td>0.287806</td>\n",
       "      <td>73.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>0.011590</td>\n",
       "      <td>0.299396</td>\n",
       "      <td>76.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4034.0</td>\n",
       "      <td>0.012438</td>\n",
       "      <td>0.311834</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.0</td>\n",
       "      <td>3943.0</td>\n",
       "      <td>0.012157</td>\n",
       "      <td>0.323991</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4085.0</td>\n",
       "      <td>0.012595</td>\n",
       "      <td>0.336586</td>\n",
       "      <td>86.0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>0.013829</td>\n",
       "      <td>0.350415</td>\n",
       "      <td>89.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    Gray Levels  No. of pixels       PDF       CDF  (L-1)*CDF\n",
       "20         20.0        11619.0  0.035825  0.227780       58.0\n",
       "21         21.0         5802.0  0.017889  0.245670       63.0\n",
       "22         22.0         5469.0  0.016862  0.262532       67.0\n",
       "23         23.0         4217.0  0.013002  0.275534       70.0\n",
       "24         24.0         3980.0  0.012271  0.287806       73.0\n",
       "25         25.0         3759.0  0.011590  0.299396       76.0\n",
       "26         26.0         4034.0  0.012438  0.311834       80.0\n",
       "27         27.0         3943.0  0.012157  0.323991       83.0\n",
       "28         28.0         4085.0  0.012595  0.336586       86.0\n",
       "29         29.0         4485.0  0.013829  0.350415       89.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_img2.loc[20:].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (30, 10))\n",
    "plt.bar(hist_data_img0[:, 0], hist_data_img0[:, 1], width= 0.1, color= 'red')\n",
    "plt.title('Equalized Histogram Red')\n",
    "plt.xlabel('Gray Levels')\n",
    "plt.ylabel('No. of pixels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (30, 10))\n",
    "plt.bar(hist_data_img1[:, 0], hist_data_img1[:, 1], width= 0.1, color= 'green')\n",
    "plt.title('Equalized Histogram Green')\n",
    "plt.xlabel('Gray Levels')\n",
    "plt.ylabel('No. of pixels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (30, 10))\n",
    "plt.bar(hist_data_img2[:, 0], hist_data_img2[:, 1], width= 0.1, color= 'blue')\n",
    "plt.title('Equalized Histogram Blue')\n",
    "plt.xlabel('Gray Levels')\n",
    "plt.ylabel('No. of pixels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_img= np.array([new_img0.T, new_img1.T, new_img2.T]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(ncols= 2, figsize= (8, 4))\n",
    "fsize= 14\n",
    "\n",
    "ax[0].set_title(\"Original Image\", fontsize= fsize)\n",
    "ax[0].imshow(img)\n",
    "\n",
    "ax[1].set_title(\"After histogram equalization\", fontsize= fsize)\n",
    "ax[1].imshow(new_img)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}