Implementation of Histogram_equalization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import  numpy  as np \n",
    "import pandas as pd\n",
    "import cv2 \n",
    "import os \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get unique value, their frequency and plot histogram\n",
    "def frequency_table_and_histogram(img):\n",
    "    ## calculate gray intensity frequency \n",
    "    image_information=np.unique(img, return_counts=True)\n",
    "    ## show the frequency using histogram \n",
    "    plt.bar(image_information[0],image_information[1], width=1)\n",
    "    plt.show()\n",
    "    ## return the frequncy information\n",
    "    return image_information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1 -Implement Histogram equalization - Gray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(448, 384)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## read image \n",
    "mp2 = cv2.imread('mp2.jpg',2)\n",
    "## show image\n",
    "cv2.imshow(\"mp2\",mp2)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "\n",
    "## check image shape\n",
    "mp2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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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DHh+j8/H5v+nMm94zUX907jJ5sJ3rY8CGXo9/Fnr9x9bL0fYHYmXX9l9vvb4G3Nbr8U+j3xvpTAEdBV5ujy39eH4v0WvPzq8/RyFJGrhpIknSOAwDSZJhIEkyDCRJGAaSJAwDSRKGgSQJ+F/jvPA08aD46AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## implement histogram equalization \n",
    "\n",
    "#read img\n",
    "mp2 = cv2.imread('mp2.jpg',2)\n",
    "\n",
    "\n",
    "def histogram_equalization(img,image_info):\n",
    "    ## calculate pdf ( the gray intensity frequncy/total pixel)\n",
    "    pdf=(image_info[1]/sum(image_info[1]))\n",
    "    ## transform to cdf \n",
    "    cdf = np.cumsum(pdf)\n",
    "    ## equalization ( =  the gray intensity cdf*255) and combined info\n",
    "    equalization_result=np.array([image_info[0],image_info[1],cdf*255])\n",
    "\n",
    "    for i in range(img.shape[0]):\n",
    "        for j in range(img.shape[1]):\n",
    "            ## find the frequncy of specific gray intensity \n",
    "            location = np.where(img[i,j] == equalization_result[0])[0][0]\n",
    "            ## replace original value to hist_equalization value\n",
    "            img[i,j] = equalization_result[2][location]\n",
    "    \n",
    "    ## show his equal result\n",
    "    cv2.imshow(\"his_equal_result\",img)\n",
    "    ## two second\n",
    "    cv2.waitKey(2000)\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "    return(img)\n",
    "\n",
    "\n",
    "info=frequency_table_and_histogram(mp2)\n",
    "mp2_his=histogram_equalization(mp2,info)\n",
    "mp2_his_info=frequency_table_and_histogram(mp2_his)\n",
    "\n",
    "#read img\n",
    "mp2 = cv2.imread('mp2.jpg',2)\n",
    "## opencv function -  equalizeHist\n",
    "mp2_using_opencv  = cv2.equalizeHist(mp2)\n",
    "numpy_horizontal = np.hstack((mp2,mp2_using_opencv,mp2_his))\n",
    "cv2.imshow(\"result\",numpy_horizontal)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24df572e1f0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## opencv function -  equalizeHist\n",
    "mp2_using_opencv  = cv2.equalizeHist(mp2)\n",
    "cv2.imshow(\"mp2\",mp2_using_opencv)\n",
    "cv2.waitKey(2000)\n",
    "cv2.destroyAllWindows()\n",
    "## see the histogram\n",
    "plt.hist(mp2_using_opencv.ravel(), 256, [0, 255],label= 'equalize image')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q2 -Implement Histogram equalization to R,G,B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read img\n",
    "mp2a = cv2.imread('mp2a.jpg',1)\n",
    "## show image\n",
    "cv2.imshow(\"mp2\",mp2a)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Implement histogram equalization to rgb\n",
    "def equlization(image_info):\n",
    "    ## calculate pdf ( the gray intensity frequncy/total pixel)\n",
    "    pdf=(image_info[1]/sum(image_info[1]))\n",
    "    ## transform to cdf \n",
    "    cdf = np.cumsum(pdf)\n",
    "    ## equalization ( =  the gray intensity cdf*255) and combined info\n",
    "    equalization_result=np.array([image_info[0],image_info[1],cdf*255])\n",
    "    return(equalization_result)\n",
    "\n",
    "def histogram_equalization_RGB(img,image_info_b,image_info_g,image_info_r):\n",
    "    equl_b=equlization(image_info_b)\n",
    "    equl_g=equlization(image_info_g)\n",
    "    equl_r=equlization(image_info_r)\n",
    "    \n",
    "\n",
    "    for i in range(img.shape[0]):\n",
    "        for j in range(img.shape[1]):\n",
    "            ## find the frequncy of specific gray intensity \n",
    "            location_b = np.where(img[i,j,0] == equl_b[0])[0][0]\n",
    "            location_g = np.where(img[i,j,1] == equl_g[0])[0][0]\n",
    "            location_r = np.where(img[i,j,2] == equl_r[0])[0][0]\n",
    "\n",
    "            ## replace original value to hist_equalization value\n",
    "            img[i,j,:] = [equl_b[2][location_b],equl_g[2][location_g],equl_r[2][location_r]]\n",
    "    \n",
    "    ## show his equal result\n",
    "    cv2.imshow(\"his_equal_result\",img)\n",
    "    ## two second\n",
    "    cv2.waitKey(2000)\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "    return(img)\n",
    "\n",
    "\n",
    "info_b =frequency_table_and_histogram(mp2a[:,:,0])\n",
    "info_g =frequency_table_and_histogram(mp2a[:,:,1])\n",
    "info_r =frequency_table_and_histogram(mp2a[:,:,2])\n",
    "mp2a_rgb=histogram_equalization_RGB(mp2a,info_b,info_g,info_r)\n",
    "\n",
    "#read img\n",
    "mp2a = cv2.imread('mp2a.jpg',1)\n",
    "## Using Opencv histogram equalization to rgb\n",
    "channels = cv2.split(mp2a)\n",
    "eq_channels = []\n",
    "# take cv2.equalizeHist() The function applies to each channel \n",
    "for ch in channels:\n",
    "    eq_channels.append(cv2.equalizeHist(ch))\n",
    "eq_image = cv2.merge(eq_channels)\n",
    "\n",
    "numpy_horizontal = np.hstack((mp2a,eq_image,mp2a_rgb))\n",
    "cv2.imshow(\"result\",numpy_horizontal)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 421.,  868., 1016.,  948., 1184.,  699.,  768.,  924.,  963.,\n",
       "        1041., 1182., 1273., 1319.,    0., 1434., 1503.,    0., 1588.,\n",
       "        1681.,    0., 1705.,    0., 1617., 1685.,    0., 1662., 1666.,\n",
       "           0., 1621., 1520.,    0., 1440., 1411.,    0., 1411., 1273.,\n",
       "        1179., 1145., 1081.,  949.,  908.,  871., 1563.,  680., 1390.,\n",
       "         678., 1241., 1176., 1136.,  610., 1101., 1202., 1218.,  615.,\n",
       "        1244., 1184.,  645., 1209.,  674., 1307.,  717., 1430.,  731.,\n",
       "        1557.,  761.,  744., 1507.,  727.,  828.,  829., 1627.,  843.,\n",
       "         877.,  866.,  850.,  959., 1906.,  913.,  877.,  906.,  958.,\n",
       "         998., 1025., 1065., 1136., 1281.,    0., 1403., 1679.,    0.,\n",
       "        1687.,    0., 1782.,    0., 1880., 1830.,    0., 1797.,    0.,\n",
       "        1769.,    0., 1783., 1540., 1468.,    0., 1491., 1422.,    0.,\n",
       "        1485., 1600.,    0., 1511., 1603.,    0., 1650.,    0., 1802.,\n",
       "        1839.,    0., 1782.,    0., 1845.,    0., 1851.,    0., 1950.,\n",
       "           0., 1907., 1878.,    0., 1950.,    0., 1932.,    0., 2060.,\n",
       "           0., 2189.,    0.,    0., 2304.,    0., 2498.,    0.,    0.,\n",
       "        2807.,    0.,    0., 3088.,    0.,    0., 3246.,    0.,    0.,\n",
       "        3359.,    0.,    0., 3351.,    0.,    0.,    0., 3361.,    0.,\n",
       "           0., 3561.,    0.,    0., 3057.,    0.,    0., 2871.,    0.,\n",
       "           0., 2738.,    0., 2414.,    0., 2356.,    0., 2261.,    0.,\n",
       "           0., 2383.,    0., 2544.,    0.,    0., 2845.,    0.,    0.,\n",
       "        3116.,    0.,    0., 3407.,    0.,    0.,    0., 3758.,    0.,\n",
       "           0.,    0., 3680.,    0.,    0., 3808.,    0.,    0.,    0.,\n",
       "        3934.,    0.,    0.,    0., 3676.,    0.,    0., 3521.,    0.,\n",
       "           0., 3049.,    0.,    0., 2643.,    0., 2355.,    0., 2308.,\n",
       "           0., 2092.,    0., 2097.,    0., 2205.,    0.,    0., 2285.,\n",
       "           0., 2365.,    0., 2519.,    0.,    0., 2414.,    0., 2266.,\n",
       "           0., 2102.,    0., 1861., 1502., 1271., 1079.,  910.,  724.,\n",
       "        1175., 1215.,  967.,  684.]),\n",
       " array([  0.        ,   0.99609375,   1.9921875 ,   2.98828125,\n",
       "          3.984375  ,   4.98046875,   5.9765625 ,   6.97265625,\n",
       "          7.96875   ,   8.96484375,   9.9609375 ,  10.95703125,\n",
       "         11.953125  ,  12.94921875,  13.9453125 ,  14.94140625,\n",
       "         15.9375    ,  16.93359375,  17.9296875 ,  18.92578125,\n",
       "         19.921875  ,  20.91796875,  21.9140625 ,  22.91015625,\n",
       "         23.90625   ,  24.90234375,  25.8984375 ,  26.89453125,\n",
       "         27.890625  ,  28.88671875,  29.8828125 ,  30.87890625,\n",
       "         31.875     ,  32.87109375,  33.8671875 ,  34.86328125,\n",
       "         35.859375  ,  36.85546875,  37.8515625 ,  38.84765625,\n",
       "         39.84375   ,  40.83984375,  41.8359375 ,  42.83203125,\n",
       "         43.828125  ,  44.82421875,  45.8203125 ,  46.81640625,\n",
       "         47.8125    ,  48.80859375,  49.8046875 ,  50.80078125,\n",
       "         51.796875  ,  52.79296875,  53.7890625 ,  54.78515625,\n",
       "         55.78125   ,  56.77734375,  57.7734375 ,  58.76953125,\n",
       "         59.765625  ,  60.76171875,  61.7578125 ,  62.75390625,\n",
       "         63.75      ,  64.74609375,  65.7421875 ,  66.73828125,\n",
       "         67.734375  ,  68.73046875,  69.7265625 ,  70.72265625,\n",
       "         71.71875   ,  72.71484375,  73.7109375 ,  74.70703125,\n",
       "         75.703125  ,  76.69921875,  77.6953125 ,  78.69140625,\n",
       "         79.6875    ,  80.68359375,  81.6796875 ,  82.67578125,\n",
       "         83.671875  ,  84.66796875,  85.6640625 ,  86.66015625,\n",
       "         87.65625   ,  88.65234375,  89.6484375 ,  90.64453125,\n",
       "         91.640625  ,  92.63671875,  93.6328125 ,  94.62890625,\n",
       "         95.625     ,  96.62109375,  97.6171875 ,  98.61328125,\n",
       "         99.609375  , 100.60546875, 101.6015625 , 102.59765625,\n",
       "        103.59375   , 104.58984375, 105.5859375 , 106.58203125,\n",
       "        107.578125  , 108.57421875, 109.5703125 , 110.56640625,\n",
       "        111.5625    , 112.55859375, 113.5546875 , 114.55078125,\n",
       "        115.546875  , 116.54296875, 117.5390625 , 118.53515625,\n",
       "        119.53125   , 120.52734375, 121.5234375 , 122.51953125,\n",
       "        123.515625  , 124.51171875, 125.5078125 , 126.50390625,\n",
       "        127.5       , 128.49609375, 129.4921875 , 130.48828125,\n",
       "        131.484375  , 132.48046875, 133.4765625 , 134.47265625,\n",
       "        135.46875   , 136.46484375, 137.4609375 , 138.45703125,\n",
       "        139.453125  , 140.44921875, 141.4453125 , 142.44140625,\n",
       "        143.4375    , 144.43359375, 145.4296875 , 146.42578125,\n",
       "        147.421875  , 148.41796875, 149.4140625 , 150.41015625,\n",
       "        151.40625   , 152.40234375, 153.3984375 , 154.39453125,\n",
       "        155.390625  , 156.38671875, 157.3828125 , 158.37890625,\n",
       "        159.375     , 160.37109375, 161.3671875 , 162.36328125,\n",
       "        163.359375  , 164.35546875, 165.3515625 , 166.34765625,\n",
       "        167.34375   , 168.33984375, 169.3359375 , 170.33203125,\n",
       "        171.328125  , 172.32421875, 173.3203125 , 174.31640625,\n",
       "        175.3125    , 176.30859375, 177.3046875 , 178.30078125,\n",
       "        179.296875  , 180.29296875, 181.2890625 , 182.28515625,\n",
       "        183.28125   , 184.27734375, 185.2734375 , 186.26953125,\n",
       "        187.265625  , 188.26171875, 189.2578125 , 190.25390625,\n",
       "        191.25      , 192.24609375, 193.2421875 , 194.23828125,\n",
       "        195.234375  , 196.23046875, 197.2265625 , 198.22265625,\n",
       "        199.21875   , 200.21484375, 201.2109375 , 202.20703125,\n",
       "        203.203125  , 204.19921875, 205.1953125 , 206.19140625,\n",
       "        207.1875    , 208.18359375, 209.1796875 , 210.17578125,\n",
       "        211.171875  , 212.16796875, 213.1640625 , 214.16015625,\n",
       "        215.15625   , 216.15234375, 217.1484375 , 218.14453125,\n",
       "        219.140625  , 220.13671875, 221.1328125 , 222.12890625,\n",
       "        223.125     , 224.12109375, 225.1171875 , 226.11328125,\n",
       "        227.109375  , 228.10546875, 229.1015625 , 230.09765625,\n",
       "        231.09375   , 232.08984375, 233.0859375 , 234.08203125,\n",
       "        235.078125  , 236.07421875, 237.0703125 , 238.06640625,\n",
       "        239.0625    , 240.05859375, 241.0546875 , 242.05078125,\n",
       "        243.046875  , 244.04296875, 245.0390625 , 246.03515625,\n",
       "        247.03125   , 248.02734375, 249.0234375 , 250.01953125,\n",
       "        251.015625  , 252.01171875, 253.0078125 , 254.00390625,\n",
       "        255.        ]),\n",
       " <BarContainer object of 256 artists>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Using Opencv histogram equalization to rgb\n",
    "channels = cv2.split(mp2a)\n",
    "eq_channels = []\n",
    "# take cv2.equalizeHist() The function applies to each channel \n",
    "for ch in channels:\n",
    "    eq_channels.append(cv2.equalizeHist(ch))\n",
    "eq_image = cv2.merge(eq_channels)\n",
    "\n",
    "cv2.imshow(\"mp2a\",eq_image )\n",
    "cv2.waitKey(2000)\n",
    "cv2.destroyAllWindows()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## see the Blue histogram\n",
    "plt.hist(eq_image[:,:,0].ravel(),256, [0, 255],label= 'Blue equalize image')\n",
    "## see the Blue histogram\n",
    "plt.hist(eq_image[:,:,1].ravel(),256, [0, 255],label= 'Red equalize image')\n",
    "## see the Blue histogram\n",
    "plt.hist(eq_image[:,:,2].ravel(),256, [0, 255],label= 'Green equalize image')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q2 -Implement Histogram equalization to HSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read img\n",
    "mp2a = cv2.imread('mp2a.jpg',1)\n",
    "mp2a = cv2.cvtColor(mp2a,cv2.COLOR_BGR2HSV)\n",
    "cv2.imshow(\"his_equal_result\",mp2a)\n",
    "## two second\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def equlization(image_info):\n",
    "    ## calculate pdf ( the gray intensity frequncy/total pixel)\n",
    "    pdf=(image_info[1]/sum(image_info[1]))\n",
    "    ## transform to cdf \n",
    "    cdf = np.cumsum(pdf)\n",
    "    ## equalization ( =  the gray intensity cdf*255) and combined info\n",
    "    equalization_result=np.array([image_info[0],image_info[1],cdf*255])\n",
    "    return(equalization_result)\n",
    "\n",
    "def histogram_equalization_HSV(img,image_info_V):\n",
    "    equl_V=equlization(image_info_V)\n",
    "    for i in range(img.shape[0]):\n",
    "        for j in range(img.shape[1]):\n",
    "            ## find the frequncy of specific gray intensity \n",
    "            location_v = np.where(img[i,j,2] == equl_V[0])[0][0]\n",
    "            ## replace original value to hist_equalization value\n",
    "            img[i,j,2] = equl_V[2][location_v]\n",
    "    \n",
    "    ## show his equal result\n",
    "    cv2.imshow(\"his_equal_result\",img)\n",
    "    ## two second\n",
    "    cv2.waitKey(2000)\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "    return(img)\n",
    "\n",
    "info_v =frequency_table_and_histogram(mp2a[:,:,2])\n",
    "\n",
    "mp2a_HSV=histogram_equalization_HSV(mp2a,info_v)\n",
    "eq_image = np.dstack((cv2.split(mp2a)[0],cv2.split(mp2a)[1],cv2.equalizeHist(cv2.split(mp2a)[2])))\n",
    "\n",
    "\n",
    "numpy_horizontal = np.hstack((mp2a,eq_image,mp2a_HSV))\n",
    "cv2.imshow(\"result\",numpy_horizontal)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "\n",
    "\n",
    "\n",
    "mp2a_HSV = cv2.cvtColor(mp2a_HSV,cv2.COLOR_HSV2BGR)\n",
    "eq_image = cv2.cvtColor(eq_image,cv2.COLOR_HSV2BGR)\n",
    "mp2a = cv2.imread('mp2a.jpg',1)\n",
    "numpy_horizontal = np.hstack((mp2a,eq_image,mp2a_HSV ))\n",
    "cv2.imshow(\"result\",numpy_horizontal)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "eq_image = np.dstack((cv2.split(mp2a)[0],cv2.split(mp2a)[1],cv2.equalizeHist(cv2.split(mp2a)[2])))\n",
    "\n",
    "\n",
    "cv2.imshow(\"mp2a\",eq_image )\n",
    "cv2.waitKey(2000)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q2 -Implement Histogram equalization to YCbCr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read img\n",
    "mp2a = cv2.imread('mp2a.jpg',1)\n",
    "mp2a = cv2.cvtColor(mp2a,cv2.COLOR_BGR2YCrCb)\n",
    "cv2.imshow(\"his_equal_result\",mp2a)\n",
    "## two second\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def equlization(image_info):\n",
    "    ## calculate pdf ( the gray intensity frequncy/total pixel)\n",
    "    pdf=(image_info[1]/sum(image_info[1]))\n",
    "    ## transform to cdf \n",
    "    cdf = np.cumsum(pdf)\n",
    "    ## equalization ( =  the gray intensity cdf*255) and combined info\n",
    "    equalization_result=np.array([image_info[0],image_info[1],cdf*255])\n",
    "    return(equalization_result)\n",
    "\n",
    "def histogram_equalization_YCrCb(img,image_info_Y):\n",
    "    equl_Y=equlization(image_info_Y)\n",
    "    for i in range(img.shape[0]):\n",
    "        for j in range(img.shape[1]):\n",
    "            ## find the frequncy of specific gray intensity \n",
    "            location_y = np.where(img[i,j,0] == equl_Y[0])[0][0]\n",
    "            ## replace original value to hist_equalization value\n",
    "            img[i,j,0] = equl_Y[2][location_y]\n",
    "    \n",
    "    ## show his equal result\n",
    "    cv2.imshow(\"his_equal_result\",img)\n",
    "    ## two second\n",
    "    cv2.waitKey(2000)\n",
    "    cv2.destroyAllWindows()\n",
    "    return(img)\n",
    "\n",
    "info_y =frequency_table_and_histogram(mp2a[:,:,0])\n",
    "mp2a_YCrCb=histogram_equalization_YCrCb(mp2a,info_y)\n",
    "\n",
    "\n",
    "#read img\n",
    "mp2a = cv2.imread('mp2a.jpg',1)\n",
    "mp2a = cv2.cvtColor(mp2a,cv2.COLOR_BGR2YCrCb)\n",
    "eq_image = np.dstack((cv2.equalizeHist(cv2.split(mp2a)[0]),cv2.split(mp2a)[1],cv2.split(mp2a)[2]))\n",
    "\n",
    "numpy_horizontal = np.hstack((mp2a,eq_image,mp2a_YCrCb))\n",
    "cv2.imshow(\"result\",numpy_horizontal)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "\n",
    "\n",
    "\n",
    "mp2a_YCrCb = cv2.cvtColor(mp2a_YCrCb,cv2.COLOR_YCrCb2BGR)\n",
    "eq_image = cv2.cvtColor(eq_image,cv2.COLOR_YCrCb2BGR)\n",
    "mp2a = cv2.imread('mp2a.jpg',1)\n",
    "numpy_horizontal = np.hstack((mp2a,eq_image,mp2a_YCrCb))\n",
    "cv2.imshow(\"result\",numpy_horizontal)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4ce0e62306dd6a5716965d4519ada776f947e6dfc145b604b11307c10277ef29"
  },
  "kernelspec": {
   "display_name": "Julia 1.7.2",
   "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}