Recognizer-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from keras.models import load_model\n",
    "model = load_model('./training_models/NN_digit.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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anXE+HdFwRywraVO+nsiZ3sDDzgAAYURq8L0jwAAAAMURYAAAdsGaKG0jwADg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\nAKC4/w/Phf7k/xUIUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e56736810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Points = np.load('./Points.npy')\n",
    "\n",
    "img = cv2.imread('./Data/S_03.jpg')\n",
    "\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "r, c = gray.shape\n",
    "out_row = 500\n",
    "gray = cv2.resize(gray, (int(out_row * float(c)/r), out_row))\n",
    "\n",
    "gray = cv2.GaussianBlur(gray, (3, 3), 0)\n",
    "\n",
    "im_th = cv2.adaptiveThreshold(gray, 255, 1, 1, 11, 2)\n",
    "\n",
    "plt.figure(num =0, figsize=(10, 10))\n",
    "plt.imshow(im_th, 'gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_biggest_contour(image):\n",
    "    \n",
    "    # Copy to prevent modification\n",
    "    image = image.copy()\n",
    "    contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    print len(contours)\n",
    "\n",
    "    # Isolate largest contour\n",
    "    try:\n",
    "        contour_sizes = [(cv2.contourArea(contour), contour) for contour in contours]\n",
    "        biggest_contour = max(contour_sizes, key=lambda x: x[0])[1]\n",
    "    except:\n",
    "        biggest_contour = 0\n",
    "        \n",
    "    return biggest_contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "2\n",
      "2\n",
      "3\n",
      "5\n",
      "4\n",
      "1\n",
      "1\n",
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      "2\n",
      "4\n",
      "3\n",
      "2\n",
      "1\n",
      "1\n",
      "1\n",
      "4\n",
      "4\n",
      "2\n",
      "2\n",
      "5\n",
      "1\n",
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "3\n",
      "3\n",
      "2\n",
      "2\n",
      "5\n",
      "1\n",
      "1\n",
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      "3\n",
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      "3\n",
      "1\n",
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      "2\n",
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      "3\n",
      "5\n",
      "2\n",
      "2\n",
      "0\n"
     ]
    },
    {
     "ename": "error",
     "evalue": "/build/opencv-SviWsf/opencv-2.4.9.1+dfsg/modules/imgproc/src/contours.cpp:1904: error: (-215) contour.checkVector(2) >= 0 && (contour.depth() == CV_32F || contour.depth() == CV_32S) in function contourArea\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-168089f75c9a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mroi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mINTER_AREA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m         \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontourArea\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfind_biggest_contour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m             \u001b[0mS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31merror\u001b[0m: /build/opencv-SviWsf/opencv-2.4.9.1+dfsg/modules/imgproc/src/contours.cpp:1904: error: (-215) contour.checkVector(2) >= 0 && (contour.depth() == CV_32F || contour.depth() == CV_32S) in function contourArea\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/FJTxi81ZfxSU8YvNfW/Z3CcokN7jDEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e54377390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e566fa690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e542c7550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e54242a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e541d6790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e54148f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e540ddd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e5405e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e427b1310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e4271fa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e426b5850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e42627f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e425bbd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e4253b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e4244e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e423c0a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e406a5850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e40618f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e405a9d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e4052e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e404872d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4k4QUGkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e403f7a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e4038a810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e402fce50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e40211c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e40194150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e40126050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e40096f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e40080090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2dfaa1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2df3d090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADKVJREFUeJzt3V+MXHXdx/HPR1Au0AsWY9Nsq1UhJKYX1W7Ic7ERRdpA\nIyneEJtA9gml64UkmhjyACZIePIk5kElhosmFRpbIlslYCjGaKUx4sUTw9Iofwq1aNbYZdtKKrEF\nkj7A14s51S3s/GaYOTNntt/3K5nszPmeP9+c9NNzzpyZ+TkiBCCf9zXdAIBmEH4gKcIPJEX4gaQI\nP5AU4QeSIvxAUoQfSIrwA0mdP8yN2ebjhMCARYS7ma+vI7/tq20fsv2S7dv6WReA4XKvn+23fZ6k\nP0raIOmIpKckbYmIg4VlOPIDAzaMI//lkl6KiD9HxGlJeyRt7mN9AIaon/CPS/rrotdHqmlnsT1t\ne9b2bB/bAlCzgb/hFxE7JO2QOO0HRkk/R/55SasXvV5VTQOwDPQT/qckXWr747Y/IOnLkvbW0xaA\nQev5tD8i3rR9i6RfSjpP0s6IeL62zgAMVM+3+nraGNf8wMAN5UM+AJYvwg8kRfiBpAg/kBThB5Ii\n/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeS\nIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBThB5LqeYhuSbI9J+mkpLckvRkRE3U0BWDw+gp/5fMR\n8UoN6wEwRJz2A0n1G/6QtM/207an62gIwHD0e9o/GRHztj8i6Ve2X4yIJxfPUP2nwH8MwIhxRNSz\nIvsuSaci4juFeerZGIC2IsLdzNfzab/tC21/6MxzSRslPdfr+gAMVz+n/Ssk/dT2mfU8FBG/qKUr\nAANX22l/VxvjtB8YuIGf9gNY3gg/kBThB5Ii/EBShB9IivADSdXxrT40bGxsrG3tyiuvLC574403\n1t3OWW699da2tfn5+eKyr732Wt3tYBGO/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFPf5l4HJycli\nfWZmpm1t1apVxWUffvjhYv3w4cPF+jXXXFOsHzp0qG1t//79xWVvuOGGYv3o0aPFOso48gNJEX4g\nKcIPJEX4gaQIP5AU4QeSIvxAUtznXwauuuqqYr10L7/0GQBJuvnmm4v1119/vVi/9957i/WHHnqo\nbW3Dhg3FZcfHx4t17vP3hyM/kBThB5Ii/EBShB9IivADSRF+ICnCDyTVcYhu2zslfVHS8YhYW00b\nk/RjSWskzUm6PiL+3nFjDNG9pDVr1hTrjz/+eLFeuh++cePG4rKzs7PFer/Wr1/f87b37NlTrG/Z\nsqWnns51dQ7R/UNJV79j2m2S9kfEpZL2V68BLCMdwx8RT0o68Y7JmyXtqp7vknRdzX0BGLBer/lX\nRMRC9fyopBU19QNgSPr+bH9EROla3va0pOl+twOgXr0e+Y/ZXilJ1d/j7WaMiB0RMREREz1uC8AA\n9Br+vZKmqudTkh6rpx0Aw9Ix/LZnJP2fpMtsH7G9VdK3JW2wfVjSVdVrAMtIx2v+iGh3M/ULNfeS\n1sUXX1ysr127tlhfWFhoWxv0ffxBuuKKK5pu4ZzGJ/yApAg/kBThB5Ii/EBShB9IivADSfHT3SPg\n4MGDxfr27duL9W3btrWt3XnnncVl77777mId5y6O/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFPf5\nR8Abb7xRrN9///3F+qZNm9rWOv289erVq4v1Th588MFi/aabbupr/RgcjvxAUoQfSIrwA0kRfiAp\nwg8kRfiBpAg/kBT3+ZeBAwcOFOvr1q1rW7vnnnuKy3Yawrtfpc8BTE1Nta1h8DjyA0kRfiApwg8k\nRfiBpAg/kBThB5Ii/EBSjojyDPZOSV+UdDwi1lbT7pK0TdLfqtnuiIifd9yYXd4Yhu6yyy7ra/lD\nhw4V6+vXr29b6zR8+Msvv1ysj4+PF+tZRYS7ma+bI/8PJV29xPR7I2Jd9egYfACjpWP4I+JJSSeG\n0AuAIernmv8W28/Y3mn7oto6AjAUvYZ/u6RPSlonaUHSd9vNaHva9qzt8gUegKHqKfwRcSwi3oqI\ntyX9QNLlhXl3RMREREz02iSA+vUUftsrF738kqTn6mkHwLB0/Eqv7RlJn5P0YdtHJH1L0udsr5MU\nkuYkfWWAPQIYgI7hj4ilfvj9gQH0ggZ0uk/fr23btvW8bKfxCtAfPuEHJEX4gaQIP5AU4QeSIvxA\nUoQfSKrjV3pr3Rhf6e3J7bffXqxv3bq1be2SSy6pu52zTEyUP7i5b9++trX5+fnistdee22xPjc3\nV6xnVedXegGcgwg/kBThB5Ii/EBShB9IivADSRF+ICmG6F4GLrjggmJ91apVbWtPPPFE3e2cpdNP\nf588ebJtrdNXdrmPP1gc+YGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKb7PvwxMTk4W6zMzM21rpc8A\ndOPFF18s1nfv3l2s33fffW1rp06d6qknlPF9fgBFhB9IivADSRF+ICnCDyRF+IGkCD+QVMf7/LZX\nS9otaYWkkLQjIr5ve0zSjyWtkTQn6fqI+HuHdXGffwDGxsba1s4/v7+fbDh9+nSx/uqrr/a1ftSv\nzvv8b0r6RkR8StJ/SPqq7U9Juk3S/oi4VNL+6jWAZaJj+CNiISIOVM9PSnpB0rikzZJ2VbPtknTd\noJoEUL/3dM1ve42kT0v6naQVEbFQlY6qdVkAYJno+oLQ9gclPSLp6xHxD/vflxUREe2u521PS5ru\nt1EA9erqyG/7/WoF/0cR8Wg1+ZjtlVV9paTjSy0bETsiYiIiyiM6AhiqjuF36xD/gKQXIuJ7i0p7\nJU1Vz6ckPVZ/ewAGpZtbfZOSfivpWUlvV5PvUOu6/yeSPirpL2rd6jvRYV3c6gMGrNtbfXyfHzjH\n8H1+AEWEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBTh\nB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQ6ht/2\natu/tn3Q9vO2v1ZNv8v2vO3fV49Ng28XQF0cEeUZ7JWSVkbEAdsfkvS0pOskXS/pVER8p+uN2eWN\nAehbRLib+c7vYkULkhaq5ydtvyBpvL/2ADTtPV3z214j6dOSfldNusX2M7Z32r6ozTLTtmdtz/bV\nKYBadTzt/9eM9gcl/UbS/0TEo7ZXSHpFUkj6b7UuDW7qsA5O+4EB6/a0v6vw236/pJ9J+mVEfG+J\n+hpJP4uItR3WQ/iBAes2/N28229JD0h6YXHwqzcCz/iSpOfea5MAmtPNu/2Tkn4r6VlJb1eT75C0\nRdI6tU775yR9pXpzsLQujvzAgNV62l8Xwg8MXm2n/QDOTYQfSIrwA0kRfiApwg8kRfiBpAg/kBTh\nB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkOv6AZ81ekfSXRa8/XE0bRaPa26j2JdFbr+rs7WPdzjjU\n7/O/a+P2bERMNNZAwaj2Nqp9SfTWq6Z647QfSIrwA0k1Hf4dDW+/ZFR7G9W+JHrrVSO9NXrND6A5\nTR/5ATSkkfDbvtr2Idsv2b6tiR7asT1n+9lq5OFGhxirhkE7bvu5RdPGbP/K9uHq75LDpDXU20iM\n3FwYWbrRfTdqI14P/bTf9nmS/ihpg6Qjkp6StCUiDg61kTZsz0maiIjG7wnb/qykU5J2nxkNyfb/\nSjoREd+u/uO8KCL+a0R6u0vvceTmAfXWbmTp/1SD+67OEa/r0MSR/3JJL0XEnyPitKQ9kjY30MfI\ni4gnJZ14x+TNknZVz3ep9Y9n6Nr0NhIiYiEiDlTPT0o6M7J0o/uu0Fcjmgj/uKS/Lnp9RKM15HdI\n2mf7advTTTezhBWLRkY6KmlFk80soePIzcP0jpGlR2bf9TLidd14w+/dJiPiM5KukfTV6vR2JEXr\nmm2Ubtdsl/RJtYZxW5D03SabqUaWfkTS1yPiH4trTe67JfpqZL81Ef55SasXvV5VTRsJETFf/T0u\n6adqXaaMkmNnBkmt/h5vuJ9/iYhjEfFWRLwt6QdqcN9VI0s/IulHEfFoNbnxfbdUX03ttybC/5Sk\nS21/3PYHJH1Z0t4G+ngX2xdWb8TI9oWSNmr0Rh/eK2mqej4l6bEGeznLqIzc3G5kaTW870ZuxOuI\nGPpD0ia13vH/k6RvNtFDm74+IekP1eP5pnuTNKPWaeD/q/XeyFZJF0vaL+mwpCckjY1Qbw+qNZrz\nM2oFbWVDvU2qdUr/jKTfV49NTe+7Ql+N7Dc+4QckxRt+QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU\n4QeS+if98h9fwXjrPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2debe150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2ddc2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2dd500d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2dcd4f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTRm/1JTxS00Zv9SU8UtNGb/U1P8BORqCdniJNi8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2dc61050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2dbe5ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2db66f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2dafae90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGb/UlPFLTRm/1NT/AgK7p4/HbBpxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2da06050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d98add0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d909e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d89ed50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d81ce50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d7afcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2ded73d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d6fb610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d67c190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d581a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nx+sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d5006d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d494a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d408c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d3a61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX2rq/wBnBJeuw6HeIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d30f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d2ad710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d2226d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d1b4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADItJREFUeJzt3WGIHPd5x/Hfz3YCxolBbpyzsNwqFYewOIRSDtMXIqhu\nFbsmRs4bE726UrkX7NhUUEONaxyBKQ4lSumrgIKFpJIqMthBQpRGqShxMCVIMpJsy1EsBQlJ6KRa\nCkR6pdp68uLmkrN9+9+93dmdvXu+H1hud57ZmYdBP83Mzs7+HRECkM8tTTcAoBmEH0iK8ANJEX4g\nKcIPJEX4gaQIP5AU4QeSIvxAUrcNcmW2+Toh0GcR4U7m62nPb/th2ydtn7L9XC/LAjBY7va7/bZv\nlfQrSeslnZd0SNLGiDhReA97fqDPBrHnf0DSqYj4dUTckPQjSRt6WB6AAeol/PdKOjfr9flq2sfY\nnrR92PbhHtYFoGZ9/8AvIrZJ2iZx2A8Mk172/Bck3Tfr9bJqGoAFoJfwH5I0avtLtj8r6RuS9tXT\nFoB+6/qwPyI+tP20pJ9IulXS9oh4t7bOAPRV15f6uloZ5/xA3w3kSz4AFi7CDyRF+IGkCD+QFOEH\nkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBTh\nB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkup6iG5Jsn1G0jVJH0n6MCLG62gKQP/1FP7K\nX0TEBzUsB8AAcdgPJNVr+EPSAdtHbE/W0RCAwej1sH9tRFyw/UVJP7X9y4h4Y/YM1X8K/McADBlH\nRD0LsrdIuh4R3y3MU8/KALQUEe5kvq4P+23fYfvzM88lfVXSO90uD8Bg9XLYPyLpx7ZnlvMfEfFf\ntXQFoO9qO+zvaGUc9gN91/fDfgALG+EHkiL8QFKEH0iK8ANJEX4gqTru6sMitnLlymL9wQcf7HrZ\nr776arF+5cqVrpeN9tjzA0kRfiApwg8kRfiBpAg/kBThB5Ii/EBS3NK7yC1ZsqRY37p1a7G+fv36\nYn3ZsmXz7mnG6dOni/Xdu3cX6y+99FKxfuPGjXn3tBhwSy+AIsIPJEX4gaQIP5AU4QeSIvxAUoQf\nSIrr/Ivcvn37ivVHH320WN+/f3+xvmvXrnn3NOPFF18s1sfGxor10dHRYv3UqVPz7mkx4Do/gCLC\nDyRF+IGkCD+QFOEHkiL8QFKEH0iq7XV+29slfU3S5YgYq6bdJWmPpOWSzkh6PCJ+03ZlXOcfuKmp\nqWJ9ZGSkWLc7umTcldWrVxfrx44dK9YPHDhQrD/00EPz7mkxqPM6/w5JD39i2nOSDkbEqKSD1WsA\nC0jb8EfEG5KufmLyBkk7q+c7JT1Wc18A+qzbc/6RiLhYPZ+SVD52BDB0eh6rLyKidC5ve1LSZK/r\nAVCvbvf8l2wvlaTq7+VWM0bEtogYj4jxLtcFoA+6Df8+SRPV8wlJe+tpB8CgtA2/7d2S/lfSStvn\nbW+S9B1J622/L+mvqtcAFpC25/wRsbFF6S9r7gVd2rJlS8va3XffXXzvU089VXM3WCj4hh+QFOEH\nkiL8QFKEH0iK8ANJEX4gKX66exFYsWJFy9qhQ4eK7213y++qVau66qkTK1euLNbffPPNYv3IkSPF\nOrf0lrHnB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkev4ZLzTv9OnTLWs3btwovvf+++8v1icmJor1\nnTt3Fusl169fL9bPnTvX9bLRHnt+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK6/yL3Lp164r1Z555\npljfsWNHT/WjR4+2rJ08ebL43jVr1hTr7YboRhl7fiApwg8kRfiBpAg/kBThB5Ii/EBShB9Iqu3v\n9tveLulrki5HxFg1bYukv5P0f9Vsz0fEf7ZdGb/bv+D0+rv9V65caVkbGRkpvvfYsWPFervr/Pxu\nf1kne/4dkh6eY/q/RsSa6tE2+ACGS9vwR8Qbkq4OoBcAA9TLOf/Tto/b3m57SW0dARiIbsP/fUkr\nJK2RdFHS1lYz2p60fdj24S7XBaAPugp/RFyKiI8i4qakH0h6oDDvtogYj4jxbpsEUL+uwm976ayX\nX5f0Tj3tABiUtrf02t4taZ2kL9g+L+nbktbZXiMpJJ2R9M0+9gigD9qGPyI2zjH5lT70giF04sSJ\nvi17dHS0p/dfu3atpk5y4ht+QFKEH0iK8ANJEX4gKcIPJEX4gaTa3tJb68q4pXfgnn322WK93W2x\nx48fr7Odj2l3y+7q1auL9XvuuadYv3Tp0rx7WgzqvKUXwCJE+IGkCD+QFOEHkiL8QFKEH0iK8ANJ\ncZ1/kZuamirWb7/99mL9iSeeKNbvvPPOYv3ll19uWbvllvK+p93w4Xv27CnWb968WawvVlznB1BE\n+IGkCD+QFOEHkiL8QFKEH0iK8ANJcZ1/kdu0aVOxvnnz5mJ9bGysWD979myxXvp57RdeeKH43r17\n9xbrmBvX+QEUEX4gKcIPJEX4gaQIP5AU4QeSIvxAUm2v89u+T9IuSSOSQtK2iPg323dJ2iNpuaQz\nkh6PiN+0WRbX+ReYJ598sljfv39/sX7u3Lk620EH6rzO/6Gkf4iIVZL+XNK3bK+S9JykgxExKulg\n9RrAAtE2/BFxMSLeqp5fk/SepHslbZC0s5ptp6TH+tUkgPrN65zf9nJJX5b0C0kjEXGxKk1p+rQA\nwAJxW6cz2v6cpNckbY6I39p/OK2IiGh1Pm97UtJkr40CqFdHe37bn9F08H8YEa9Xky/ZXlrVl0q6\nPNd7I2JbRIxHxHgdDQOoR9vwe3oX/4qk9yLie7NK+yRNVM8nJHELFrCAdHKpb62kn0t6W9LMbyE/\nr+nz/lcl/bGks5q+1He1zbK41Af0WaeX+rifH1hkuJ8fQBHhB5Ii/EBShB9IivADSRF+ICnCDyRF\n+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8k\nRfiBpAg/kBThB5Ii/EBShB9IivADSRF+IKm24bd9n+3/sX3C9ru2/76avsX2BdtHq8cj/W8XQF0c\nEeUZ7KWSlkbEW7Y/L+mIpMckPS7pekR8t+OV2eWVAehZRLiT+W7rYEEXJV2snl+z/Z6ke3trD0DT\n5nXOb3u5pC9L+kU16Wnbx21vt72kxXsmbR+2fbinTgHUqu1h/+9ntD8n6WeS/jkiXrc9IukDSSHp\nJU2fGvxtm2Vw2A/0WaeH/R2F3/ZnJO2X9JOI+N4c9eWS9kfEWJvlEH6gzzoNfyef9lvSK5Lemx38\n6oPAGV+X9M58mwTQnE4+7V8r6eeS3pZ0s5r8vKSNktZo+rD/jKRvVh8OlpbFnh/os1oP++tC+IH+\nq+2wH8DiRPiBpAg/kBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iq7Q94\n1uwDSWdnvf5CNW0YDWtvw9qXRG/dqrO3P+l0xoHez/+plduHI2K8sQYKhrW3Ye1LorduNdUbh/1A\nUoQfSKrp8G9reP0lw9rbsPYl0Vu3Gumt0XN+AM1pes8PoCGNhN/2w7ZP2j5l+7kmemjF9hnbb1cj\nDzc6xFg1DNpl2+/MmnaX7Z/afr/6O+cwaQ31NhQjNxdGlm502w3biNcDP+y3faukX0laL+m8pEOS\nNkbEiYE20oLtM5LGI6Lxa8K2vyLpuqRdM6Mh2f4XSVcj4jvVf5xLIuIfh6S3LZrnyM196q3VyNJ/\nowa3XZ0jXtehiT3/A5JORcSvI+KGpB9J2tBAH0MvIt6QdPUTkzdI2lk936npfzwD16K3oRARFyPi\nrer5NUkzI0s3uu0KfTWiifDfK+ncrNfnNVxDfoekA7aP2J5supk5jMwaGWlK0kiTzcyh7cjNg/SJ\nkaWHZtt1M+J13fjA79PWRsSfSfprSd+qDm+HUkyfsw3T5ZrvS1qh6WHcLkra2mQz1cjSr0naHBG/\nnV1rctvN0Vcj262J8F+QdN+s18uqaUMhIi5Ufy9L+rGmT1OGyaWZQVKrv5cb7uf3IuJSRHwUETcl\n/UANbrtqZOnXJP0wIl6vJje+7ebqq6nt1kT4D0katf0l25+V9A1J+xro41Ns31F9ECPbd0j6qoZv\n9OF9kiaq5xOS9jbYy8cMy8jNrUaWVsPbbuhGvI6IgT8kPaLpT/xPS/qnJnpo0defSjpWPd5tujdJ\nuzV9GPj/mv5sZJOkP5J0UNL7kv5b0l1D1Nu/a3o05+OaDtrShnpbq+lD+uOSjlaPR5redoW+Gtlu\nfMMPSIoP/ICkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJPU7cqc5EGlVcOgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d129c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2d0491d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2cfc0190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2cf51710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2cec56d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2ce57c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyvBLRRl+qSjDLxVl+KWi/h/SqSTR3SO2BQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2cdccc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2cd6d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2cce0190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2cc72710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKeOXmvo//M+a5ssYz7oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2cbe66d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2cb79c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npP4P+7hCj0C12+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2caeec10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2ca0f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyvBLTRl+qSnDLzVl+KWmDL/U1P8BSBemm13OMSkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2c985190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e2c915710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4k4QUGkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e54392090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S = np.zeros((9, 9), dtype = 'uint8')\n",
    "kk = 0\n",
    "kernel = np.ones((1,1),np.uint8)\n",
    "\n",
    "for i in range(0, 9):\n",
    "    for j in range(0, 9):\n",
    "        \n",
    "        y1 = int(Points[j+i*10][1] + 4)\n",
    "        y2 = int(Points[j+i*10 +10][1] + 4)\n",
    "        \n",
    "        x1 = int(Points[j+i*10][0] + 4)\n",
    "        x2 = int(Points[j+i*10+1][0] - 4)\n",
    "        kernel = np.ones((1,1),np.uint8)\n",
    "\n",
    "        x = x1\n",
    "        y = y1\n",
    "        w = x2 - x1\n",
    "        h = y2 - y1\n",
    "        \n",
    "        # cropped cell\n",
    "        cropped_cell = im_th[y:y+h, x:x+w]\n",
    "                \n",
    "        #Region of Interest\n",
    "        roi = cropped_cell\n",
    "        # Resize the image\n",
    "        roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)\n",
    "        \n",
    "        if(cv2.contourArea(find_biggest_contour(roi)) < 10):\n",
    "            S[i][j] = 0\n",
    "        else:\n",
    "            pass\n",
    "        \n",
    "        # Bottom\n",
    "        roi[24:, :] = 0\n",
    "        \n",
    "        # Left\n",
    "        roi[:, :3] = 0\n",
    "        \n",
    "        # Right\n",
    "        roi[:, 25:] = 0\n",
    "        \n",
    "        #Top\n",
    "        roi[:2, : ] = 0\n",
    "        \n",
    "        cv2.imwrite('./SudokuCells/cell_np'+str(i)+' '+str(j)+'.png', roi)      \n",
    "        roi = roi.flatten()\n",
    "        roi = roi[np.newaxis]\n",
    "        plt.figure(kk)\n",
    "        kk += 1\n",
    "        plt.imshow(roi.reshape((28, 28)), cmap='gray')\n",
    "        \n",
    "        if S[i][j] is not 0:\n",
    "            S[i][j] = model.predict_classes(roi,verbose=0)\n",
    "print S\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}