histrogram_equalization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img = cv2.imread('c:/tower.jpg',cv2.IMREAD_GRAYSCALE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x80398d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.16260000e+04,   6.51800000e+03,   3.21700000e+03,\n",
       "          2.60200000e+03,   2.88300000e+03,   3.00900000e+03,\n",
       "          3.40600000e+03,   3.87900000e+03,   4.05400000e+03,\n",
       "          4.28700000e+03,   4.31600000e+03,   3.99900000e+03,\n",
       "          3.81800000e+03,   3.54400000e+03,   3.19800000e+03,\n",
       "          2.84000000e+03,   2.65700000e+03,   2.31600000e+03,\n",
       "          2.08800000e+03,   1.88900000e+03,   1.64900000e+03,\n",
       "          1.41100000e+03,   1.22800000e+03,   1.11100000e+03,\n",
       "          9.60000000e+02,   9.06000000e+02,   7.91000000e+02,\n",
       "          7.23000000e+02,   6.31000000e+02,   6.28000000e+02,\n",
       "          5.84000000e+02,   5.49000000e+02,   4.68000000e+02,\n",
       "          4.52000000e+02,   4.40000000e+02,   4.54000000e+02,\n",
       "          4.27000000e+02,   3.95000000e+02,   4.05000000e+02,\n",
       "          3.63000000e+02,   3.03000000e+02,   3.07000000e+02,\n",
       "          3.09000000e+02,   2.75000000e+02,   2.69000000e+02,\n",
       "          2.71000000e+02,   2.71000000e+02,   2.33000000e+02,\n",
       "          2.29000000e+02,   2.11000000e+02,   2.37000000e+02,\n",
       "          2.17000000e+02,   3.57000000e+02,   9.48000000e+02,\n",
       "          1.28400000e+03,   1.56300000e+03,   2.30600000e+03,\n",
       "          3.21400000e+03,   4.06200000e+03,   4.94500000e+03,\n",
       "          5.02800000e+03,   4.78400000e+03,   5.06400000e+03,\n",
       "          4.82000000e+03,   4.48500000e+03,   4.94200000e+03,\n",
       "          5.98200000e+03,   7.03500000e+03,   6.64200000e+03,\n",
       "          6.04100000e+03,   5.71000000e+03,   5.79000000e+03,\n",
       "          5.82800000e+03,   5.84000000e+03,   5.18600000e+03,\n",
       "          4.84800000e+03,   4.31800000e+03,   4.02900000e+03,\n",
       "          3.55100000e+03,   2.86300000e+03,   2.35400000e+03,\n",
       "          1.91300000e+03,   1.50400000e+03,   1.17000000e+03,\n",
       "          8.69000000e+02,   6.02000000e+02,   4.75000000e+02,\n",
       "          3.65000000e+02,   3.38000000e+02,   3.38000000e+02,\n",
       "          3.35000000e+02,   3.26000000e+02,   3.82000000e+02,\n",
       "          3.76000000e+02,   3.80000000e+02,   4.24000000e+02,\n",
       "          4.10000000e+02,   4.38000000e+02,   4.21000000e+02,\n",
       "          4.37000000e+02,   4.62000000e+02,   4.82000000e+02,\n",
       "          5.04000000e+02,   5.19000000e+02,   5.73000000e+02,\n",
       "          6.17000000e+02,   5.97000000e+02,   6.59000000e+02,\n",
       "          5.85000000e+02,   5.88000000e+02,   6.81000000e+02,\n",
       "          6.38000000e+02,   6.76000000e+02,   6.81000000e+02,\n",
       "          7.28000000e+02,   7.15000000e+02,   7.97000000e+02,\n",
       "          7.98000000e+02,   8.22000000e+02,   8.50000000e+02,\n",
       "          8.79000000e+02,   9.63000000e+02,   9.45000000e+02,\n",
       "          9.96000000e+02,   1.00500000e+03,   1.03300000e+03,\n",
       "          1.08800000e+03,   9.63000000e+02,   9.76000000e+02,\n",
       "          9.63000000e+02,   8.96000000e+02,   8.70000000e+02,\n",
       "          7.49000000e+02,   6.95000000e+02,   6.19000000e+02,\n",
       "          5.41000000e+02,   4.50000000e+02,   3.65000000e+02,\n",
       "          3.15000000e+02,   3.22000000e+02,   3.21000000e+02,\n",
       "          3.15000000e+02,   2.89000000e+02,   2.97000000e+02,\n",
       "          3.07000000e+02,   3.01000000e+02,   3.20000000e+02,\n",
       "          3.50000000e+02,   3.52000000e+02,   3.25000000e+02,\n",
       "          2.97000000e+02,   3.11000000e+02,   3.32000000e+02,\n",
       "          3.40000000e+02,   3.64000000e+02,   3.60000000e+02,\n",
       "          3.63000000e+02,   3.50000000e+02,   3.72000000e+02,\n",
       "          3.50000000e+02,   3.68000000e+02,   3.05000000e+02,\n",
       "          3.13000000e+02,   3.44000000e+02,   3.25000000e+02,\n",
       "          3.63000000e+02,   3.13000000e+02,   3.24000000e+02,\n",
       "          3.35000000e+02,   3.41000000e+02,   3.46000000e+02,\n",
       "          3.52000000e+02,   3.52000000e+02,   3.91000000e+02,\n",
       "          3.50000000e+02,   3.42000000e+02,   3.74000000e+02,\n",
       "          3.54000000e+02,   3.40000000e+02,   4.00000000e+02,\n",
       "          3.70000000e+02,   4.03000000e+02,   3.73000000e+02,\n",
       "          3.88000000e+02,   3.80000000e+02,   4.05000000e+02,\n",
       "          4.49000000e+02,   4.08000000e+02,   4.09000000e+02,\n",
       "          3.77000000e+02,   3.90000000e+02,   4.25000000e+02,\n",
       "          3.85000000e+02,   3.65000000e+02,   3.76000000e+02,\n",
       "          3.23000000e+02,   3.78000000e+02,   3.44000000e+02,\n",
       "          3.44000000e+02,   3.31000000e+02,   3.32000000e+02,\n",
       "          2.74000000e+02,   2.64000000e+02,   2.52000000e+02,\n",
       "          2.50000000e+02,   2.08000000e+02,   2.02000000e+02,\n",
       "          2.08000000e+02,   1.80000000e+02,   2.01000000e+02,\n",
       "          2.14000000e+02,   1.80000000e+02,   1.55000000e+02,\n",
       "          1.43000000e+02,   1.33000000e+02,   1.14000000e+02,\n",
       "          1.08000000e+02,   1.37000000e+02,   9.50000000e+01,\n",
       "          1.24000000e+02,   1.10000000e+02,   7.10000000e+01,\n",
       "          7.60000000e+01,   7.70000000e+01,   5.20000000e+01,\n",
       "          4.90000000e+01,   4.60000000e+01,   5.10000000e+01,\n",
       "          4.90000000e+01,   5.80000000e+01,   4.60000000e+01,\n",
       "          3.70000000e+01,   4.00000000e+01,   2.10000000e+01,\n",
       "          3.60000000e+01,   2.70000000e+01,   2.40000000e+01,\n",
       "          1.70000000e+01,   2.10000000e+01,   2.50000000e+01,\n",
       "          1.80000000e+01,   1.90000000e+01,   2.70000000e+01,\n",
       "          1.80000000e+01,   1.70000000e+01,   1.10000000e+01,\n",
       "          2.00000000e+01,   2.30000000e+01,   1.80000000e+01,\n",
       "          1.80000000e+01,   1.30000000e+01,   2.30000000e+01,\n",
       "          1.70000000e+01,   1.80000000e+01,   3.90000000e+01,\n",
       "          4.30000000e+01]),\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,  255.        ]),\n",
       " <a list of 256 Patch objects>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.hist(img.ravel(),256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a21d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hist,bins = np.histogram(img.flatten(),256,[0,256])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cdf = hist.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cdf_normalized = cdf * hist.max()/ cdf.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xaf7f4e0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(cdf_normalized, color = 'b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.16260000e+04,   6.51800000e+03,   3.21700000e+03,\n",
       "          2.60200000e+03,   2.88300000e+03,   3.00900000e+03,\n",
       "          3.40600000e+03,   3.87900000e+03,   4.05400000e+03,\n",
       "          4.28700000e+03,   4.31600000e+03,   3.99900000e+03,\n",
       "          3.81800000e+03,   3.54400000e+03,   3.19800000e+03,\n",
       "          2.84000000e+03,   2.65700000e+03,   2.31600000e+03,\n",
       "          2.08800000e+03,   1.88900000e+03,   1.64900000e+03,\n",
       "          1.41100000e+03,   1.22800000e+03,   1.11100000e+03,\n",
       "          9.60000000e+02,   9.06000000e+02,   7.91000000e+02,\n",
       "          7.23000000e+02,   6.31000000e+02,   6.28000000e+02,\n",
       "          5.84000000e+02,   5.49000000e+02,   4.68000000e+02,\n",
       "          4.52000000e+02,   4.40000000e+02,   4.54000000e+02,\n",
       "          4.27000000e+02,   3.95000000e+02,   4.05000000e+02,\n",
       "          3.63000000e+02,   3.03000000e+02,   3.07000000e+02,\n",
       "          3.09000000e+02,   2.75000000e+02,   2.69000000e+02,\n",
       "          2.71000000e+02,   2.71000000e+02,   2.33000000e+02,\n",
       "          2.29000000e+02,   2.11000000e+02,   2.37000000e+02,\n",
       "          2.17000000e+02,   3.57000000e+02,   9.48000000e+02,\n",
       "          1.28400000e+03,   1.56300000e+03,   2.30600000e+03,\n",
       "          3.21400000e+03,   4.06200000e+03,   4.94500000e+03,\n",
       "          5.02800000e+03,   4.78400000e+03,   5.06400000e+03,\n",
       "          4.82000000e+03,   4.48500000e+03,   4.94200000e+03,\n",
       "          5.98200000e+03,   7.03500000e+03,   6.64200000e+03,\n",
       "          6.04100000e+03,   5.71000000e+03,   5.79000000e+03,\n",
       "          5.82800000e+03,   5.84000000e+03,   5.18600000e+03,\n",
       "          4.84800000e+03,   4.31800000e+03,   4.02900000e+03,\n",
       "          3.55100000e+03,   2.86300000e+03,   2.35400000e+03,\n",
       "          1.91300000e+03,   1.50400000e+03,   1.17000000e+03,\n",
       "          8.69000000e+02,   6.02000000e+02,   4.75000000e+02,\n",
       "          3.65000000e+02,   3.38000000e+02,   3.38000000e+02,\n",
       "          3.35000000e+02,   3.26000000e+02,   3.82000000e+02,\n",
       "          3.76000000e+02,   3.80000000e+02,   4.24000000e+02,\n",
       "          4.10000000e+02,   4.38000000e+02,   4.21000000e+02,\n",
       "          4.37000000e+02,   4.62000000e+02,   4.82000000e+02,\n",
       "          5.04000000e+02,   5.19000000e+02,   5.73000000e+02,\n",
       "          6.17000000e+02,   5.97000000e+02,   6.59000000e+02,\n",
       "          5.85000000e+02,   5.88000000e+02,   6.81000000e+02,\n",
       "          6.38000000e+02,   6.76000000e+02,   6.81000000e+02,\n",
       "          7.28000000e+02,   7.15000000e+02,   7.97000000e+02,\n",
       "          7.98000000e+02,   8.22000000e+02,   8.50000000e+02,\n",
       "          8.79000000e+02,   9.63000000e+02,   9.45000000e+02,\n",
       "          9.96000000e+02,   1.00500000e+03,   1.03300000e+03,\n",
       "          1.08800000e+03,   9.63000000e+02,   9.76000000e+02,\n",
       "          9.63000000e+02,   8.96000000e+02,   8.70000000e+02,\n",
       "          7.49000000e+02,   6.95000000e+02,   6.19000000e+02,\n",
       "          5.41000000e+02,   4.50000000e+02,   3.65000000e+02,\n",
       "          3.15000000e+02,   3.22000000e+02,   3.21000000e+02,\n",
       "          3.15000000e+02,   2.89000000e+02,   2.97000000e+02,\n",
       "          3.07000000e+02,   3.01000000e+02,   3.20000000e+02,\n",
       "          3.50000000e+02,   3.52000000e+02,   3.25000000e+02,\n",
       "          2.97000000e+02,   3.11000000e+02,   3.32000000e+02,\n",
       "          3.40000000e+02,   3.64000000e+02,   3.60000000e+02,\n",
       "          3.63000000e+02,   3.50000000e+02,   3.72000000e+02,\n",
       "          3.50000000e+02,   3.68000000e+02,   3.05000000e+02,\n",
       "          3.13000000e+02,   3.44000000e+02,   3.25000000e+02,\n",
       "          3.63000000e+02,   3.13000000e+02,   3.24000000e+02,\n",
       "          3.35000000e+02,   3.41000000e+02,   3.46000000e+02,\n",
       "          3.52000000e+02,   3.52000000e+02,   3.91000000e+02,\n",
       "          3.50000000e+02,   3.42000000e+02,   3.74000000e+02,\n",
       "          3.54000000e+02,   3.40000000e+02,   4.00000000e+02,\n",
       "          3.70000000e+02,   4.03000000e+02,   3.73000000e+02,\n",
       "          3.88000000e+02,   3.80000000e+02,   4.05000000e+02,\n",
       "          4.49000000e+02,   4.08000000e+02,   4.09000000e+02,\n",
       "          3.77000000e+02,   3.90000000e+02,   4.25000000e+02,\n",
       "          3.85000000e+02,   3.65000000e+02,   3.76000000e+02,\n",
       "          3.23000000e+02,   3.78000000e+02,   3.44000000e+02,\n",
       "          3.44000000e+02,   3.31000000e+02,   3.32000000e+02,\n",
       "          2.74000000e+02,   2.64000000e+02,   2.52000000e+02,\n",
       "          2.50000000e+02,   2.08000000e+02,   2.02000000e+02,\n",
       "          2.08000000e+02,   1.80000000e+02,   2.01000000e+02,\n",
       "          2.14000000e+02,   1.80000000e+02,   1.55000000e+02,\n",
       "          1.43000000e+02,   1.33000000e+02,   1.14000000e+02,\n",
       "          1.08000000e+02,   1.37000000e+02,   9.50000000e+01,\n",
       "          1.24000000e+02,   1.10000000e+02,   7.10000000e+01,\n",
       "          7.60000000e+01,   7.70000000e+01,   5.20000000e+01,\n",
       "          4.90000000e+01,   4.60000000e+01,   5.10000000e+01,\n",
       "          4.90000000e+01,   5.80000000e+01,   4.60000000e+01,\n",
       "          3.70000000e+01,   4.00000000e+01,   2.10000000e+01,\n",
       "          3.60000000e+01,   2.70000000e+01,   2.40000000e+01,\n",
       "          1.70000000e+01,   2.10000000e+01,   2.50000000e+01,\n",
       "          1.80000000e+01,   1.90000000e+01,   2.70000000e+01,\n",
       "          1.80000000e+01,   1.70000000e+01,   1.10000000e+01,\n",
       "          2.00000000e+01,   2.30000000e+01,   1.80000000e+01,\n",
       "          1.80000000e+01,   1.30000000e+01,   2.30000000e+01,\n",
       "          1.70000000e+01,   1.80000000e+01,   3.90000000e+01,\n",
       "          4.30000000e+01]),\n",
       " array([   0.,    1.,    2.,    3.,    4.,    5.,    6.,    7.,    8.,\n",
       "           9.,   10.,   11.,   12.,   13.,   14.,   15.,   16.,   17.,\n",
       "          18.,   19.,   20.,   21.,   22.,   23.,   24.,   25.,   26.,\n",
       "          27.,   28.,   29.,   30.,   31.,   32.,   33.,   34.,   35.,\n",
       "          36.,   37.,   38.,   39.,   40.,   41.,   42.,   43.,   44.,\n",
       "          45.,   46.,   47.,   48.,   49.,   50.,   51.,   52.,   53.,\n",
       "          54.,   55.,   56.,   57.,   58.,   59.,   60.,   61.,   62.,\n",
       "          63.,   64.,   65.,   66.,   67.,   68.,   69.,   70.,   71.,\n",
       "          72.,   73.,   74.,   75.,   76.,   77.,   78.,   79.,   80.,\n",
       "          81.,   82.,   83.,   84.,   85.,   86.,   87.,   88.,   89.,\n",
       "          90.,   91.,   92.,   93.,   94.,   95.,   96.,   97.,   98.,\n",
       "          99.,  100.,  101.,  102.,  103.,  104.,  105.,  106.,  107.,\n",
       "         108.,  109.,  110.,  111.,  112.,  113.,  114.,  115.,  116.,\n",
       "         117.,  118.,  119.,  120.,  121.,  122.,  123.,  124.,  125.,\n",
       "         126.,  127.,  128.,  129.,  130.,  131.,  132.,  133.,  134.,\n",
       "         135.,  136.,  137.,  138.,  139.,  140.,  141.,  142.,  143.,\n",
       "         144.,  145.,  146.,  147.,  148.,  149.,  150.,  151.,  152.,\n",
       "         153.,  154.,  155.,  156.,  157.,  158.,  159.,  160.,  161.,\n",
       "         162.,  163.,  164.,  165.,  166.,  167.,  168.,  169.,  170.,\n",
       "         171.,  172.,  173.,  174.,  175.,  176.,  177.,  178.,  179.,\n",
       "         180.,  181.,  182.,  183.,  184.,  185.,  186.,  187.,  188.,\n",
       "         189.,  190.,  191.,  192.,  193.,  194.,  195.,  196.,  197.,\n",
       "         198.,  199.,  200.,  201.,  202.,  203.,  204.,  205.,  206.,\n",
       "         207.,  208.,  209.,  210.,  211.,  212.,  213.,  214.,  215.,\n",
       "         216.,  217.,  218.,  219.,  220.,  221.,  222.,  223.,  224.,\n",
       "         225.,  226.,  227.,  228.,  229.,  230.,  231.,  232.,  233.,\n",
       "         234.,  235.,  236.,  237.,  238.,  239.,  240.,  241.,  242.,\n",
       "         243.,  244.,  245.,  246.,  247.,  248.,  249.,  250.,  251.,\n",
       "         252.,  253.,  254.,  255.,  256.]),\n",
       " <a list of 256 Patch objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.hist(img.flatten(),256,[0,256], color = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 256)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.xlim([0,256])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xaf7f8d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.legend(('cdf','histogram'), loc = 'upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa67a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cdf_m = np.ma.masked_equal(cdf,0)\n",
    "cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())\n",
    "cdf = np.ma.filled(cdf_m,0).astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img2 = cdf[img]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img3 = cv2.imread('c:/tower.jpg',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "equ = cv2.equalizeHist(img3) #histrogram equalization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = np.hstack((img3,equ)) #stacking images side-by-side"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(res.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('res.png',res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_stack = cv2.imread('res.png',cv2.IMREAD_GRAYSCALE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xbc37c50>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.imshow(img_stack, cmap = 'gray', interpolation = 'bicubic')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAB7CAYAAAB6i0uBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX9H75JNPPt3m9L81ve9wspyi0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf7f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAs2nS2sLTD+TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8123668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist,bins = np.histogram(img3.flatten(),256,[0,256])\n",
    "\n",
    "cdf2 = hist.cumsum()\n",
    "cdf_normalized2 = cdf * hist.max()/ cdf.max()\n",
    "\n",
    "plt.plot(cdf_normalized2, color = 'b')\n",
    "plt.hist(img3.flatten(),256,[0,256], color = 'r')\n",
    "plt.xlim([0,256])\n",
    "plt.legend(('cdf2','histogram'), loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cdf_m2 = np.ma.masked_equal(cdf,0)\n",
    "cdf_m2 = (cdf_m2 - cdf_m2.min())*255/(cdf_m2.max()-cdf_m2.min())\n",
    "cdf2 = np.ma.filled(cdf_m,0).astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img4 = cdf2[img3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}