histo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 - 5.1 : 7.0\n",
      "5.1 - 10.2 : 37.0\n",
      "10.2 - 15.299999999999999 : 127.0\n",
      "15.299999999999999 - 20.4 : 414.0\n",
      "20.4 - 25.5 : 1053.0\n",
      "25.5 - 30.599999999999998 : 3360.0\n",
      "30.599999999999998 - 35.699999999999996 : 6556.0\n",
      "35.699999999999996 - 40.8 : 5885.0\n",
      "40.8 - 45.9 : 7182.0\n",
      "45.9 - 51.0 : 15884.0\n",
      "51.0 - 56.099999999999994 : 33573.0\n",
      "56.099999999999994 - 61.199999999999996 : 15345.0\n",
      "61.199999999999996 - 66.3 : 13665.0\n",
      "66.3 - 71.39999999999999 : 14265.0\n",
      "71.39999999999999 - 76.5 : 15631.0\n",
      "76.5 - 81.6 : 18137.0\n",
      "81.6 - 86.69999999999999 : 21706.0\n",
      "86.69999999999999 - 91.8 : 25622.0\n",
      "91.8 - 96.89999999999999 : 28453.0\n",
      "96.89999999999999 - 102.0 : 29611.0\n",
      "102.0 - 107.1 : 36060.0\n",
      "107.1 - 112.19999999999999 : 29628.0\n",
      "112.19999999999999 - 117.3 : 30009.0\n",
      "117.3 - 122.39999999999999 : 30368.0\n",
      "122.39999999999999 - 127.49999999999999 : 35407.0\n",
      "127.49999999999999 - 132.6 : 49035.0\n",
      "132.6 - 137.7 : 49456.0\n",
      "137.7 - 142.79999999999998 : 47894.0\n",
      "142.79999999999998 - 147.89999999999998 : 51543.0\n",
      "147.89999999999998 - 153.0 : 63727.0\n",
      "153.0 - 158.1 : 150205.0\n",
      "158.1 - 163.2 : 214865.0\n",
      "163.2 - 168.29999999999998 : 342376.0\n",
      "168.29999999999998 - 173.39999999999998 : 383838.0\n",
      "173.39999999999998 - 178.5 : 223312.0\n",
      "178.5 - 183.6 : 188078.0\n",
      "183.6 - 188.7 : 195591.0\n",
      "188.7 - 193.79999999999998 : 176106.0\n",
      "193.79999999999998 - 198.89999999999998 : 159412.0\n",
      "198.89999999999998 - 204.0 : 200340.0\n",
      "204.0 - 209.1 : 535729.0\n",
      "209.1 - 214.2 : 683538.0\n",
      "214.2 - 219.29999999999998 : 517362.0\n",
      "219.29999999999998 - 224.39999999999998 : 215093.0\n",
      "224.39999999999998 - 229.49999999999997 : 43204.0\n",
      "229.49999999999997 - 234.6 : 5140.0\n",
      "234.6 - 239.7 : 1066.0\n",
      "239.7 - 244.79999999999998 : 247.0\n",
      "244.79999999999998 - 249.89999999999998 : 49.0\n",
      "61.199999999999996\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "from cv2 import data, exp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "image = cv2.imread('./factures/invoice1.jpg')\n",
    "image = cv2.imread('./resulTreat/invoice3testfjnzjdzjkdnzdjndjzdnjzndzjknd.jpg')\n",
    "image = cv2.resize(image, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "hist,bins = np.histogram(image.flatten(),256,[0,256])\n",
    "\n",
    "cdf = hist.cumsum()\n",
    "cdf_normalized = cdf * hist.max()/ cdf.max()\n",
    "\n",
    "plt.plot(cdf_normalized, color = 'b')\n",
    "n,modes,p = plt.hist(image.flatten(), 50, (0, 255))\n",
    "plt.xlim([0,256])\n",
    "plt.show()\n",
    "\n",
    "x = 12\n",
    "t=n[x:-10].argmin() + x\n",
    "for i in range(0,len(n)-1):\n",
    "    print(str(modes[i])+\" - \"+str(modes[i+1])+\" : \"+str(n[i]))\n",
    "print(modes[t])\n"
   ]
  }
 ],
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