ISS296-PreProcessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import load_img \n",
    "from keras.preprocessing.image import img_to_array \n",
    "from keras.preprocessing.image import array_to_img\n",
    "import warnings\n",
    "import numpy as np\n",
    "import sys\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import os \n",
    "import cv2\n",
    "import tensorflow as tf \n",
    "from PIL import Image \n",
    "from numpy import asarray\n",
    "from tqdm import tqdm\n",
    "import argparse\n",
    "from __future__ import print_function\n",
    "from builtins import input\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = r\"C:\\Users\\rassa\\Documents\\Machine Learning A-Z  Udemy\\Arabic Handwritten Characters Dataset\"\n",
    "#np.set_printoptions(threshold=sys.maxsize)\n",
    "\n",
    "# using opencv library's imread\n",
    "cv2image = cv2.imread(DATADIR+ r\"\\Train Images 13440x32x32\\train\\id_1_label_1.png\", cv2.IMREAD_GRAYSCALE)\n",
    "print(cv2image)\n",
    "cv2image.shape\n",
    "cv2.imshow('cv2image', cv2image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "cv2image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using asarray from numpy\n",
    "img2 = Image.open(DATADIR+ r\"\\Train Images 13440x32x32\\train\\id_1_label_1.png\")\n",
    "\n",
    "asarrayimage = asarray(img2)\n",
    "print(asarrayimage)\n",
    "\n",
    "plt.imshow(asarrayimage)\n",
    "cv2.imshow('asarray', asarrayimage)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "asarrayimage.shape\n",
    "asarrayimage.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using keras image _to_array\n",
    "imgK = load_img(DATADIR+ r\"\\Train Images 13440x32x32\\train\\id_1_label_1.png\")\n",
    "imgKdata = img_to_array(imgK)\n",
    "print(imgKdata)\n",
    "plt.imshow(imgKdata)\n",
    "imgKdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we try to get the image from the csv file\n",
    "\n",
    "csvarray = pd.read_csv(DATADIR + r\"\\csvTrainImages 13440x1024.csv\", nrows = 1, header= None)\n",
    "\n",
    "csvarray.shape\n",
    "csvarray= csvarray.iloc[:,:].values.astype('uint8')\n",
    "csvarray.shape\n",
    "csvarray = csvarray.reshape([32, 32, 1])\n",
    "csvarray.shape\n",
    "\n",
    "print(csvarray)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kerasimgfromcsv = array_to_img(csvarray)\n",
    "plt.imshow(kerasimgfromcsv)\n",
    "cv2.imshow('example', csvarray)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no dice, still dont know how they did it...\n",
    "# let's just convert the pictures our own way...\n",
    "\n",
    "path = DATADIR+ r\"\\Train Images 13440x32x32\\train\"\n",
    "\n",
    "#returns a list containing all the directories in path (names of the images)\n",
    "imgFileNameList = os.listdir(path)\n",
    "\n",
    "\n",
    "print(imgFileNameList[0])\n",
    "imageIdTest = imgFileNameList[0].split('_')[1]\n",
    "print(imageIdTest)\n",
    "\n",
    "#it work! so now we use this label as our sorting criteria\n",
    "def getImgId(imgName):\n",
    "    return int(imgName.split('_')[1])\n",
    "\n",
    "imgFileNameList.sort(key=getImgId)\n",
    "print(imgFileNameList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_array = cv2.imread(os.path.join(path,imgFileNameList[0]) ,cv2.IMREAD_GRAYSCALE)\n",
    "cv2.imshow('test',img_array)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "img_array.shape\n",
    "img_array.reshape([32,32,1])\n",
    "\n",
    "# it works!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we make our new train_data\n",
    "train_data = []\n",
    "\n",
    "for img in tqdm(imgFileNameList):  # iterate over each image per dogs and cats\n",
    "            try:\n",
    "                img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array\n",
    "                train_data.append([img_array])  # add this to our training_data\n",
    "            except Exception as e:  # in the interest in keeping the output clean...\n",
    "                pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's have a look at the shape of our array\n",
    "train_data = np.asarray(train_data)\n",
    "print(train_data.shape)\n",
    "train_data = train_data.reshape([-1, 32, 32, 1])\n",
    "print(train_data.shape)\n",
    "np.set_printoptions(threshold=1000)\n",
    "print(train_data)\n",
    "#looks the same as train_data from our model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 13440/13440 [00:02<00:00, 4746.72it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 3360/3360 [00:00<00:00, 4798.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
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     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Let's put allwe need in one cell and test!\n",
    "DATADIR = r\"C:\\Users\\rassa\\Documents\\Machine Learning A-Z  Udemy\\Arabic Handwritten Characters Dataset\"\n",
    "train_data = []\n",
    "test_data = []\n",
    "trainPath = DATADIR + r\"\\Train Images 13440x32x32\\train\"\n",
    "testPath  = DATADIR + r\"\\Test Images 3360x32x32\\test\"\n",
    "\n",
    "def getImgId(imgName):\n",
    "    return int(imgName.split('_')[1])\n",
    "\n",
    "def makeData(data, directory, sortingCriteria):\n",
    "    imgFileNameList = os.listdir(directory)     #get list of directories in path (ie. list of image names)\n",
    "    imgFileNameList.sort(key = sortingCriteria) #sort list based on key\n",
    "    \n",
    "    for img in tqdm(imgFileNameList):  # iterate over the sorted list\n",
    "                try:\n",
    "                    img_array = cv2.imread(os.path.join(directory, img) ,cv2.IMREAD_GRAYSCALE)  # convert image to array\n",
    "                    data.append([img_array])  # add this to our data\n",
    "                except Exception as e:  # just in case there is a mistake.\n",
    "                    pass\n",
    "\n",
    "makeData(train_data, trainPath, getImgId)\n",
    "makeData(test_data, testPath, getImgId)\n",
    "\n",
    "# convert to ndarray and reshape\n",
    "train_data = np.asarray(train_data).reshape([-1, 32, 32, 1])\n",
    "test_data = np.asarray(test_data).reshape([-1, 32, 32, 1])\n",
    "print(train_data.shape)\n",
    "print(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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   ],
   "source": [
    "print(test_data.shape)\n",
    "print(train_data[0].shape)\n",
    "print(train_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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     ]
    }
   ],
   "source": [
    "print(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# more testing\n",
    "cv2.imshow('test', train_data[0])\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "#it works!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162.0\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162.0\n",
      " Basic Linear Transforms \n",
      "-------------------------\n",
      "* Enter the alpha value [1.0-3.0]: 3.0\n",
      "* Enter the beta value [0-100]: 0\n"
     ]
    }
   ],
   "source": [
    "# Now let's try to work with a sample to test\n",
    "\n",
    "# Read image given by user\n",
    "#parser = argparse.ArgumentParser(description='Code for Changing the contrast and brightness of an image! tutorial.')\n",
    "#parser.add_argument('9alb-Ba2-0.png', help='C:\\Users\\rassa\\Desktop\\Desktop2.0\\CNN', default='9alb-Ba2-0.png')\n",
    "#args = parser.parse_args()\n",
    "#r\"C:\\Users\\rassa\\Desktop\\Desktop2.0\\CNN\\9alb-Ba2-0.png\") #\n",
    "image = cv2.imread(r\"C:\\Users\\rassa\\Desktop\\Desktop2.0\\CNN\\9alb-Ba2-0.png\") #(cv.samples.findFile(args.input))\n",
    "#removethe three dimensions and turn image to gray scale\n",
    "im_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "#binarize the image using Otsu method to get the threshold, binarize to black and white(ie. 255)\n",
    "th, im_gray_th_otsu = cv2.threshold(im_gray, 128, 255, cv2.THRESH_OTSU)\n",
    "\n",
    "#print the threshold because I'm curious\n",
    "print(th)\n",
    "new_image = np.zeros(im_gray_th_otsu.shape, im_gray_th_otsu.dtype)\n",
    "alpha = 1.0 # Simple contrast control\n",
    "beta = 0    # Simple brightness control\n",
    "# Initialize values\n",
    "try:\n",
    "    alpha = float(input('* Enter the alpha value [1.0-3.0]: '))\n",
    "    beta = int(input('* Enter the beta value [0-100]: '))\n",
    "except ValueError:\n",
    "    print('Error, not a number')\n",
    "# Do the operation new_image(i,j) = alpha*image(i,j) + beta\n",
    "# Instead of these 'for' loops we could have used simply:\n",
    "# new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta)\n",
    "# but we wanted to show you how to access the pixels :)\n",
    "for y in range(im_gray_th_otsu.shape[0]):\n",
    "    for x in range(im_gray_th_otsu.shape[1]):\n",
    "        # the clip makes sure that the new values of the pixel are between 0 and 255\n",
    "        new_image[y,x] = np.clip(alpha*im_gray_th_otsu[y,x] + beta, 0, 255)\n",
    "\n",
    "#Show the images so we can compare them\n",
    "cv2.imshow('New Image', new_image)\n",
    "cv2.imshow('Original Image', image)\n",
    "cv2.imshow('Binarized image', im_gray_th_otsu)\n",
    "# Wait until user press some key\n",
    "cv2.waitKey(0) \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Enter the alpha value [1.0-3.0]: 1.5\n",
      "* Enter the beta value [0-100]: 0\n",
      "253.0\n"
     ]
    }
   ],
   "source": [
    "# Now let's try enhacing the edges first\n",
    "\n",
    "# Read image given by user\n",
    "image = cv2.imread(r\"C:\\Users\\rassa\\Desktop\\Desktop2.0\\CNN\\9alb-Ba2-0.png\") #(cv.samples.findFile(args.input))\n",
    "kernel0 = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])\n",
    "\n",
    "\n",
    "edge_enhance_img0 = cv2.filter2D(image, -1, kernel0)\n",
    "\n",
    "\n",
    "contrast_img = np.zeros(edge_enhance_img0.shape, edge_enhance_img0.dtype)\n",
    "\n",
    "alpha = 1.0 # Simple contrast control\n",
    "beta = 0    # Simple brightness control\n",
    "\n",
    "# Initialize values\n",
    "try:\n",
    "    alpha = float(input('* Enter the alpha value [1.0-3.0]: '))\n",
    "    beta = int(input('* Enter the beta value [0-100]: '))\n",
    "except ValueError:\n",
    "    print('Error, not a number')\n",
    "    \n",
    "# Do the operation new_image(i,j) = alpha*image(i,j) + beta\n",
    "# Instead of these 'for' loops we could have used simply:\n",
    "# new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta)\n",
    "# but we wanted to show you how to access the pixels :)\n",
    "for y in range(edge_enhance_img0.shape[0]):\n",
    "    for x in range(edge_enhance_img0.shape[1]):\n",
    "        for c in range(edge_enhance_img0.shape[2]):\n",
    "            # the clip makes sure that the new values of the pixel are between 0 and 255\n",
    "            contrast_img[y,x,c] = np.clip(alpha*edge_enhance_img0[y,x,c] + beta, 0, 255)\n",
    "        \n",
    "#removethe three dimensions and turn image to gray scale\n",
    "im_gray = cv2.cvtColor(contrast_img, cv2.COLOR_BGR2GRAY)\n",
    "#binarize the image using Otsu method to get the threshold, binarize to black and white(ie. 255)\n",
    "th, im_gray_th_otsu = cv2.threshold(im_gray, 128, 255, cv2.THRESH_TRIANGLE)\n",
    "\n",
    "resized = cv2.resize(im_gray_th_otsu, (28,28), interpolation = cv2.INTER_AREA)\n",
    "#print the threshold because I'm curious\n",
    "print(th)\n",
    "\n",
    "#Show the images so we can compare them\n",
    "cv2.imshow('Original Image', image)\n",
    "cv2.imshow('Edge Enhaced0 image', edge_enhance_img0)\n",
    "cv2.imshow('Contrast image', contrast_img)\n",
    "cv2.imshow('New Image', im_gray_th_otsu)\n",
    "cv2.imshow('resized Image', resized)\n",
    "\n",
    "\n",
    "# Wait until user press some key\n",
    "cv2.waitKey(0) \n",
    "cv2.destroyAllWindows()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's doing the contrast first\n",
    "\n",
    "# Read image given by user\n",
    "def preProcess(addr):\n",
    "    \n",
    "    image = cv2.imread(addr) #(cv.samples.findFile(args.input))\n",
    "    \n",
    "    contrast_img = np.zeros(image.shape, image.dtype)\n",
    "    \n",
    "    alpha = 1.0 # Simple contrast control\n",
    "    beta = 0    # Simple brightness control\n",
    "    \n",
    "    # Initialize values\n",
    "    try:\n",
    "        alpha = float(input('* Enter the alpha value [1.0-3.0]: '))\n",
    "        beta = int(input('* Enter the beta value [0-100]: '))\n",
    "    except ValueError:\n",
    "        print('Error, not a number')\n",
    "    \n",
    "    # Do the operation new_image(i,j) = alpha*image(i,j) + beta\n",
    "    # Instead of these 'for' loops we could have used simply:\n",
    "    # new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta)\n",
    "    # but we wanted to show you how to access the pixels :)\n",
    "    for y in range(image.shape[0]):\n",
    "        for x in range(image.shape[1]):\n",
    "            for c in range(image.shape[2]):\n",
    "                # the clip makes sure that the new values of the pixel are between 0 and 255\n",
    "                contrast_img[y,x,c] = np.clip(alpha*image[y,x,c] + beta, 0, 255)\n",
    "            \n",
    "    #removethe three dimensions and turn image to gray scale\n",
    "    im_gray = cv2.cvtColor(contrast_img, cv2.COLOR_BGR2GRAY)\n",
    "    #binarize the image using Otsu method to get the threshold, binarize to black and white(ie. 255)\n",
    "    th, im_gray_th_otsu = cv2.threshold(im_gray, 128, 255, cv2.THRESH_OTSU)\n",
    "    \n",
    "    if image.shape[0]>image.shape[1]*1.4:\n",
    "        resized = cv2.resize(im_gray_th_otsu, (32,int(32*image.shape[0]/image.shape[1])), interpolation = cv2.INTER_AREA)\n",
    "    elif image.shape[1]>image.shape[0]*1.4:\n",
    "        resized = cv2.resize(im_gray_th_otsu, (int(32*image.shape[1]/image.shape[0]),32), interpolation = cv2.INTER_AREA)\n",
    "    else:\n",
    "        resized = cv2.resize(im_gray_th_otsu, (32,32), interpolation = cv2.INTER_AREA)\n",
    "    \n",
    "    #print the threshold because I'm curious\n",
    "    print(th)\n",
    "    return [image, contrast_img, im_gray, im_gray_th_otsu, resized]\n",
    "\n",
    "def whiteToBlack(final_img, img ):\n",
    "    for y in range(img.shape[0]):\n",
    "        for x in range(img.shape[1]):\n",
    "            if img[y,x] < 125:     \n",
    "                final_img[y,x] = 255\n",
    "            else :\n",
    "                final_img[y,x] = 0\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Enter the alpha value [1.0-3.0]: 1.25\n",
      "* Enter the beta value [0-100]: 10\n",
      "212.0\n",
      "* Enter the alpha value [1.0-3.0]: 1.25\n",
      "* Enter the beta value [0-100]: 10\n",
      "205.0\n",
      "* Enter the alpha value [1.0-3.0]: 1.0\n",
      "* Enter the beta value [0-100]: 0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "Ba2LoImg = preProcess(r\"D:\\School\\MedTech\\Sophomore Pre-Eng\\ISS296\\CNN\\images\\9alb-Ba2-0.png\")\n",
    "kalbWordLoImg = preProcess(r\"D:\\School\\MedTech\\Sophomore Pre-Eng\\ISS296\\CNN\\images\\9alb.png\")\n",
    "sha3alWordLoImg  = preProcess(r\"C:\\Users\\rassa\\Documents\\Machine Learning A-Z  Udemy\\ifnenit-database-demo\\ifnenit-database-demo\\data\\set_a\\bmp\\ae07_050.bmp\")\n",
    "\n",
    "final_Ba2img = np.zeros(Ba2LoImg[4].shape, Ba2LoImg[4].dtype)\n",
    "final_9albWordImg = np.zeros(kalbWordLoImg[4].shape, kalbWordLoImg[4].dtype)\n",
    "final_sha3alWordImg = np.zeros(sha3alWordLoImg[4].shape, sha3alWordLoImg[4].dtype)\n",
    "\n",
    "whiteToBlack(final_Ba2img, Ba2LoImg[4])\n",
    "whiteToBlack(final_9albWordImg, kalbWordLoImg[4])\n",
    "\n",
    "\n",
    "\n",
    "#Show the images so we can compare them\n",
    "#cv2.imshow('New Image', im_gray_th_otsu)\n",
    "#cv2.imshow('Original Image', image)\n",
    "#cv2.imshow('Contrast image', contrast_img)\n",
    "#cv2.imshow('resized Image', resized)\n",
    "#cv2.imshow('final Image', final_img)\n",
    "#plt.imshow(im_gray_th_otsu)\n",
    "\n",
    "# Wait until user press some key\n",
    "#cv2.waitKey(0) \n",
    "#cv2.destroyAllWindows()\n",
    "\n",
    "#this one gives the best results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce60a6ef70>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "whiteToBlack(final_sha3alWordImg, sha3alWordLoImg[4])\n",
    "\n",
    "def paddImg(img):\n",
    "    paddedimg  = np.zeros((img.shape[0]+2, img.shape[1]+2), img.dtype)\n",
    "    for y in range(1, paddedimg.shape[0]-1):\n",
    "        for x in range(1, paddedimg.shape[1]-1):\n",
    "                paddedimg[y,x] = img[y-1,x-1]\n",
    "    return paddedimg\n",
    "\n",
    "final_sha3alWordImg = paddImg(final_sha3alWordImg)\n",
    "plt.imshow(final_sha3alWordImg, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(32, 32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(final_Ba2img)\n",
    "final_Ba2img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_Ba2img.reshape([32,32,1])\n",
    "final_Ba2img.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(final_Ba2img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce613d9880>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Ba2LoImg[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(808, 722, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Ba2LoImg[1])\n",
    "Ba2LoImg[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61800cd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Ba2LoImg[2], cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61863910>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Ba2LoImg[3], cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce618c5370>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPrUlEQVR4nO3dfYxUVZrH8e+jNiqjsCqNtoK2o5iIRJFUkOhmguvuyBoTwUQdTUYSzDAmY1wTN4Gwibp/qOy6vsVsSJoVh1lcB7JI1I2Z0ZBVnLhhLV1BkJERggxrp7tRxpcogvazf9Ql2+A91dV166Xh+X2STledU6fuk5v+9a26p+pcc3dE5Nh3XLsLEJHWUNhFglDYRYJQ2EWCUNhFglDYRYI4ochgM5sDPAkcD/yLuy+t9vgJEyZ4d3d3kU2KSBW7du1i7969ltdXd9jN7Hjgn4G/AvYAb5nZi+7+fmpMd3c35XK53k2KyDBKpVKyr8jL+JnAh+6+090PAL8GbijwfCLSREXCfg7wxyH392RtIjIKFQl73vuC73321swWmlnZzMoDAwMFNiciRRQJ+x5g8pD7k4CPj3yQu/e4e8ndS52dnQU2JyJFFAn7W8AUMzvfzMYAPwFebExZItJodZ+Nd/dvzewu4LdUpt5WuPvWhlUmIg1VaJ7d3V8GXm5QLSLSRPoEnUgQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQCrtIEAq7SBAKu0gQha4IY2a7gC+A74Bv3T19JXgRaZi+vr7c9oMHDybHFAp75mp339uA5xGRJtLLeJEgiobdgVfM7G0zW9iIgkSkOYq+jL/K3T82s4nAq2b2e3ffMPQB2T+BhQDnnntuwc2JSL0KHdnd/ePsdz+wDpiZ85gedy+5e6mzs7PI5kSkgLrDbmY/MLNTD90GfgxsaVRhItJYRV7GnwmsM7NDz/Nv7v6bhlQlIgwODib7pk6dmtv+2WefJcfUHXZ33wlcVu94EWktTb2JBKGwiwShsIsEobCLBKGwiwTRiC/CiEgTVJt6+/LLL0c8Rkd2kSAUdpEgFHaRIBR2kSAUdpEgdDZeZJQ64YR0PKdMmZLbvmPHjuQYHdlFglDYRYJQ2EWCUNhFglDYRYJQ2EWC0NSbyFFo4sSJue27d+9OjtGRXSQIhV0kCIVdJAiFXSQIhV0kCIVdJIhhp97MbAVwPdDv7tOyttOB1UA3sAu42d33Na9MERlq2rRpue1bt25NjqnlyP5LYM4RbYuB9e4+BVif3ReRUWzYsGfXW//0iOYbgJXZ7ZXA3MaWJSKNVu979jPdvRcg+53/cR4RGTWafoLOzBaaWdnMygMDA83enIgk1Bv2PjPrAsh+96ce6O497l5y91JnZ2edmxORouoN+4vA/Oz2fOCFxpQjIs1Sy9Tbc8BsYIKZ7QHuB5YCa8zsDmA3cFMzi2ylAwcOJPvuvPPO3Pb+/uQLG3p6epJ9Z599du2FBZC6pBHAxo0bk32lUinZN378+EI1jVYzZszIbX/ppZeSY4YNu7vfmui6pqaqRGRU0CfoRIJQ2EWCUNhFglDYRYJQ2EWC0IKTR3j44YeTfc8888yIn2/79u3JPk29He6+++5L9j3++OPJvssuuyzZt2HDhtz2cePG1V7YKDRp0qTc9o6OjuQYHdlFglDYRYJQ2EWCUNhFglDYRYJQ2EWCCDn19v777yf7HnzwwRE/37333pvsmz179oifL6rjjqvv2LNp06Zk30MPPZTbvnTp0rq2NVpccsklue0nn3xycoyO7CJBKOwiQSjsIkEo7CJBKOwiQYQ8G79q1apk38GDB5N9Z511Vm57PWfw5fsuvPDChj/nI488ktt+7bXXJsdcffXVDa+j0bq6unLb9UUYEVHYRaJQ2EWCUNhFglDYRYJQ2EWCqOXyTyuA64F+d5+WtT0A/Aw4dFnWJe7+crOKbLTe3t66xt16a/7FcU488cQi5Uhm7NixDX/OwcHB3PYbb7wxOeb1119P9l166aWFa2qXWo7svwTm5LQ/7u7Ts5+jJugiUQ0bdnffAHzaglpEpImKvGe/y8w2m9kKMzutYRWJSFPUG/ZlwAXAdKAXeDT1QDNbaGZlMysPDAykHiYiTVZX2N29z92/c/dBYDkws8pje9y95O6lzs7OeusUkYLqCruZDf0U/jxgS2PKEZFmqWXq7TlgNjDBzPYA9wOzzWw64MAu4OfNK7HxUtMxwxk/fnyDK5Ghdu7cWde4amvXTZ48Obf9o48+So6ptm7g4sWLk3233XZbsi91uaZ6ffXVV7nt1f62hw27u+dNLj9dc1UiMiroE3QiQSjsIkEo7CJBKOwiQSjsIkGEXHDyjDPOqGvcm2++mdvu7skxZlbXtkaL/fv3J/teeeWVZN/evXtz26tdqmnZsmW1FzbEzJnJz3Txxhtv5LavXbs2OWbBggXJvkWLFiX7qk3LjRs3Ltl30kkn5bZXm0b7/PPPc9sPHDiQHKMju0gQCrtIEAq7SBAKu0gQCrtIEAq7SBBWbdqo0UqlkpfL5ZZtL2XLlvQ3cqt94+mTTz7JbZ81a1ZyTLUFCqt9v7/a9GC1KZnUAiGffppeWazat802b9484m01wwknpGeJ16xZk+ybN2/eiLe1Y8eOZN+SJUuSfS+88EKy75tvvhlxHfVy99z5Xh3ZRYJQ2EWCUNhFglDYRYJQ2EWCCHk2vpq+vr5k3xNPPJHbvmrVquSYPXv2FC3pqHTRRRfltl955ZXJMVdccUWy7/rrr0/2NXp9t3p9/fXXyb7UTA7Avn37ctur/e2k1ta75ZZb2Lp1q87Gi0SmsIsEobCLBKGwiwShsIsEobCLBDHs1JuZTQZ+BZwFDAI97v6kmZ0OrAa6qVwC6mZ3z59DyBwNU2/1qPbFlA8++CDZt23btmRftWmXatM4Y8eOzW2v9qWb8847L9lX7Qs5Y8aMSfalpt6qfaFFiiuVSpTL5bqn3r4F7nX3i4FZwC/MbCqwGFjv7lOA9dl9ERmlhg27u/e6+zvZ7S+AbcA5wA3AyuxhK4G5TapRRBpgRO/ZzawbuBzYCJzp7r1Q+YcATGx4dSLSMDWH3cxOAdYC97h7/qLV+eMWmlnZzMqtXOxARA5XU9jNrINK0J919+ez5j4z68r6u4D+vLHu3uPuJXcvVTtJJCLNNWzYrXJJk6eBbe7+2JCuF4H52e35QHpNHhFpu1rmQa4Cfgq8Z2bvZm1LgKXAGjO7A9gN3NSUCo8Cxx2X/p958cUX19Un0mjDht3dfwekLlh2TWPLEZFm0SfoRIJQ2EWCUNhFglDYRYJQ2EWCUNhFglDYRYJQ2EWCUNhFglDYRYJQ2EWCUNhFglDYRYJQ2EWCUNhFglDYRYJQ2EWCUNhFgtC1eI4x27dvz20///zzk2M6OjqaVY6MIjqyiwShsIsEobCLBKGwiwShsIsEobCLBDHs1JuZTQZ+BZwFDAI97v6kmT0A/Aw4dGnWJe7+crMKlf939913J/ueeuqp3Pa5c+cmx6xbt65oSXIUqGWe/VvgXnd/x8xOBd42s1ezvsfd/Z+aV56INEot13rrBXqz21+Y2TbgnGYXJiKNNaL37GbWDVwObMya7jKzzWa2wsxOa3RxItI4NYfdzE4B1gL3uPvnwDLgAmA6lSP/o4lxC82sbGblgYGBvIeISAvUFHYz66AS9Gfd/XkAd+9z9+/cfRBYDszMG+vuPe5ecvdSZ2dno+oWkREaNuxmZsDTwDZ3f2xIe9eQh80DtjS+PBFplFrOxl8F/BR4z8zezdqWALea2XTAgV3Az5tQX1j79u1L9i1fvnzEz3fw4MEi5cgxoJaz8b8DLKdLc+oiRxF9gk4kCIVdJAiFXSQIhV0kCIVdJAgtODlKrV69Otm3f//+ET/fggULipQjxwAd2UWCUNhFglDYRYJQ2EWCUNhFglDYRYLQ1Nso9dprr9U1rru7O7d9zpw59RcjxwQd2UWCUNhFglDYRYJQ2EWCUNhFglDYRYLQ1NsotWjRomTfuHHjkn233357bvvYsWML1yRHNx3ZRYJQ2EWCUNhFglDYRYJQ2EWCGPZsvJmdBGwATswe/+/ufr+ZnQ6sBrqpXP7pZndPX7NIRuTyyy9P9vX09LSwEjlW1HJk/wb4C3e/jMrlmeeY2SxgMbDe3acA67P7IjJKDRt2r/gyu9uR/ThwA7Aya18JzG1GgSLSGLVen/347Aqu/cCr7r4RONPdewGy3xObVqWIFFZT2N39O3efDkwCZprZtFo3YGYLzaxsZuWBgYE6yxSRokZ0Nt7d/wS8BswB+sysCyD73Z8Y0+PuJXcvdXZ2FqtWROo2bNjNrNPM/iy7fTLwl8DvgReB+dnD5gMvNKlGEWmAWr4I0wWsNLPjqfxzWOPu/2Fm/wWsMbM7gN3ATU2sU0QKGjbs7r4Z+N6kr7t/AlzTjKJEpPH0CTqRIBR2kSAUdpEgFHaRIBR2kSDM3Vu3MbMB4KPs7gRgb8s2nqY6Dqc6Dne01XGeu+d+eq2lYT9sw2Zldy+1ZeOqQ3UErEMv40WCUNhFgmhn2EfLciuq43Cq43DHTB1te88uIq2ll/EiQbQl7GY2x8w+MLMPzaxta9eZ2S4ze8/M3jWzcgu3u8LM+s1sy5C2083sVTP7Q/b7tDbV8YCZ/W+2T941s+taUMdkM/tPM9tmZlvN7G+y9pbukyp1tHSfmNlJZvbfZrYpq+Pvs/Zi+8PdW/oDHA/sAH4IjAE2AVNbXUdWyy5gQhu2+yNgBrBlSNs/Aouz24uBf2hTHQ8Af9vi/dEFzMhunwpsB6a2ep9UqaOl+wQw4JTsdgewEZhVdH+048g+E/jQ3Xe6+wHg11QWrwzD3TcAnx7R3PIFPBN1tJy797r7O9ntL4BtwDm0eJ9UqaOlvKLhi7y2I+znAH8ccn8PbdihGQdeMbO3zWxhm2o4ZDQt4HmXmW3OXuY3/e3EUGbWTWX9hLYuanpEHdDifdKMRV7bEXbLaWvXlMBV7j4D+GvgF2b2ozbVMZosAy6gco2AXuDRVm3YzE4B1gL3uPvnrdpuDXW0fJ94gUVeU9oR9j3A5CH3JwEft6EO3P3j7Hc/sI7KW4x2qWkBz2Zz977sD20QWE6L9omZdVAJ2LPu/nzW3PJ9kldHu/ZJtu0/McJFXlPaEfa3gClmdr6ZjQF+QmXxypYysx+Y2amHbgM/BrZUH9VUo2IBz0N/TJl5tGCfmJkBTwPb3P2xIV0t3SepOlq9T5q2yGurzjAecbbxOipnOncAf9emGn5IZSZgE7C1lXUAz1F5OXiQyiudO4AzqFxG6w/Z79PbVMe/Au8Bm7M/rq4W1PHnVN7KbQbezX6ua/U+qVJHS/cJcCnwP9n2tgD3Ze2F9oc+QScShD5BJxKEwi4ShMIuEoTCLhKEwi4ShMIuEoTCLhKEwi4SxP8B8/w22E6EwJQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Ba2LoImg[4], cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce6191c5e0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(final_Ba2img, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from tkinter import Tk     # from tkinter import Tk for Python 3.x\n",
    "#from tkinter.filedialog import askopenfilename\n",
    "\n",
    "#Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\n",
    "#filename = askopenfilename() # show an \"Open\" dialog box and return the path to the selected file\n",
    "#print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def blackToWhite(img, finalImg):\n",
    "    for y in range(img.shape[0]):\n",
    "        for x in range(img.shape[1]):\n",
    "            if img[y,x] == 0:     \n",
    "                finalImg[y,x] = 255\n",
    "            else :\n",
    "                finalImg[y,x] = 0\n",
    "\n",
    "\n",
    "\n",
    "dictionary = [3,6,7,12,24,28,14,224,225,216,143,15,30,48,52,96,112,54,240,227,131,135,99,56,60,120,62,129,192,248,251,195,141]\n",
    "\n",
    "def skeletonize(img):\n",
    "    for c in range(1, img.shape[1]-1):\n",
    "        for r in range(1, img.shape[0]-1):\n",
    "            if img[r,c]==0:\n",
    "                b = calculateB(img, r, c)\n",
    "                #print(str([r,c]) + \": b = \" + str(b) )\n",
    "                if classifyB(b):\n",
    "                    img[r,c] = 150\n",
    "    for c in range(1, img.shape[1]-1):\n",
    "        for r in range(1, img.shape[0]-1):\n",
    "            if img[r,c]==150:\n",
    "                #print(True)\n",
    "                img[r,c] = 255\n",
    "    \n",
    "\n",
    "def calculateB(img, r, c):\n",
    "    numbers = [int(img[r-1,c]),int(img[r-1,c+1]),int(img[r,c+1]),int(img[r+1,c+1]),int(img[r+1,c]),int(img[r+1,c-1]),int(img[r,c-1]),int(img[r-1,c-1])]\n",
    "    #numbers = [int(img[c,r+1]),int(img[c+1,r+1]),int(img[c+1,r]),int(img[c+1,r-1]),int(img[c,r-1]),int(img[c-1,r-1]),int(img[c-1,r]),int(img[c-1,r+1])]\n",
    "    #print(numbers)\n",
    "    b = 0\n",
    "    for i in range(0,8):\n",
    "        if numbers[i]<140:\n",
    "            b+=2**i\n",
    "    #print(b)\n",
    "    return b\n",
    "\n",
    "def classifyB(b):\n",
    "    for i in dictionary:\n",
    "        if int(b) == int(i):\n",
    "            print(b)\n",
    "            return True\n",
    "    return False \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce6198e100>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_Ba2imgBtW = np.zeros(Ba2LoImg[4].shape, Ba2LoImg[4].dtype)\n",
    "\n",
    "\n",
    "blackToWhite(final_Ba2img, final_Ba2imgBtW)\n",
    "plt.imshow(final_Ba2imgBtW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "30\n",
      "30\n",
      "14\n",
      "30\n",
      "30\n",
      "15\n",
      "28\n",
      "30\n",
      "216\n",
      "7\n",
      "135\n",
      "135\n",
      "14\n",
      "28\n",
      "30\n",
      "30\n",
      "3\n",
      "60\n",
      "28\n",
      "60\n",
      "48\n",
      "96\n",
      "60\n",
      "60\n",
      "60\n",
      "62\n",
      "28\n",
      "54\n",
      "28\n",
      "14\n",
      "30\n",
      "28\n",
      "30\n",
      "30\n",
      "30\n",
      "30\n",
      "54\n",
      "24\n",
      "28\n",
      "30\n",
      "30\n",
      "248\n",
      "141\n",
      "7\n",
      "135\n",
      "135\n",
      "135\n",
      "135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce619e6100>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_Ba2imgBtW)\n",
    "plt.imshow(final_Ba2imgBtW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61a37430>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_Ba2imgBtW)\n",
    "plt.imshow(final_Ba2imgBtW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61a8d6a0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_Ba2imgBtW)\n",
    "plt.imshow(final_Ba2imgBtW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61ae2bb0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_Ba2imgBtW)\n",
    "plt.imshow(final_Ba2imgBtW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61b435e0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_Ba2imgBtW)\n",
    "plt.imshow(final_Ba2imgBtW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61ba0100>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_Ba2imgBtW)\n",
    "plt.imshow(final_Ba2imgBtW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61befb80>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_Ba2imgBtW)\n",
    "plt.imshow(final_Ba2imgBtW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "final_9albWordImg.shape\n",
    "print(final_9albWordImg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 50)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_9albWordImg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 50)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kalbWordLoImg[4].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61c54ca0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(kalbWordLoImg[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce60a520d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(kalbWordLoImg[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61abc160>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Thv+dWqOOV0Dp4NXGz+sciFcA2tI7bdyeY45l5vLdTSQVHLtTXKzfjnvO2FbzAbfj+/TdJ8dwpE+nHV34Ooy2IoqcBmG2nxRH6jqFenR3DT/bxldjiV6aXCt9cBz2I85PC8I5l8LxuR+/EsOg/1bLjUj10PkZ7fMUKpVqIMv/pngDfWOBrmM5Zk6XxG052GRaldYfizfzUm91l5H/k8WwzdUylv0crUbcj3XsZXcDbjMq98XxMhC4jwaATVbqYcXI+DIyt3Wdflfv0lkx7adPn9YN7MYumz5IQCgvwcZpNzP1tOng2Gy4Be02l9zb4rXNJ50uyef25k/+d2lzTh1xnGmHNtD2X6y/BuCpx7n0XHOOMgdHoN3SVqtAF1xc6eU65cYw/gYSzRiPGN9VDMZGydIc0IbJui3t09ptzh8ZV4ZLB27XN4bK/poDG9xS+FZUXn8ETI218fgqv9lkXTkWPx/p3nJ6k76N38Dmegy+rWzbVn98JOUoVdDsg0HJq1t+XqXoaDNtjmde3EmSlagBy30SqKx/6pCrN35udWnXvqbND3PvR4X1W7BuqynbrD/7TbaZc/dlX/EGbGRZYdZRIfiTYBwFkOuWGwH8LnQ8Rn1G8Zdpq0V/G5qNc+W8LCvAbP35++rOhFWgcR9kLu2ZAhcCQ2N9lMXtEQQIGEx9NYBoDp/rWt6yOXDTdwNUO0PT/arEqb2x2MaSwnpHY0ndBjxmiB5bzq9eEMf6q748V6uVVNhrC3gEtCYf+3YgJsP3pn/ON4JD/1+Bv5k5x9pkJGjSPvxwnVcAlN3H/eqL6JG41YKrdddy/cl6UE+s+7KBhOVGAn9jZqvJpEFQeQT8KH+VdycYO5KvGNjKAQnEPO46bcxHwOm6Dk5H+wG8jmN0IDRAm12YAUU//D3cVSDgWAg4bSysw3MzfTXnAOTx21FaH+0ldQ00VnMSOUwYGgttgNICM+1ytZq1vbV9MoOq9RBm7XbbPLmN9Ml79ZvuPCaeYx2Oo/2n/aaf1dyw7ZZqMemhH3nsTvf6umZT9oUVsUibJCDe86Bcjei8bLmRwL8yEp6b6SkFO4jzhas+mkM3wOXnI0NbsU/314KBQZnG43SAx+5rCJxsxw7n5WhjZTTuFmgM6tadgwYdqemcDuBxNjDndwOCdcHSztv2GqNs9Qy8vIPEsjTm1mzu6DwZcxt/000jPW7fum83F+Q70xBNfy1grNj1apWfQpKx8j/rzfsSrMc9EB5LyY+dUJfcuI8sfAU3/aGRrKYTB+e2N2Xdfsnn+FfFt0Q2xfgd1VctC61wM3qeuyrl4MlcFYNe5CGQNIdxXeuiyeaA0YzuiP1Z5tXYkx5oY2vtt+DQCp3dr1R2sLIuVs515CAGRZcGrGadZsdOXRAomLP1yopte+XJ8/6B+kYyGgg2/dH+nNJrdu65XQXt5hsrGQz4KWb5+dxK9lw8L26PY+N8uD6DRFst0HdTx7/NwBfuNYLEOc05/1Z1VlJ37969RL7eSrkxwM9ouVrerfL1NoyWDpjpy8kjZtRSKTnfmNVVk9HYW2PCR9eaIbW2VmzXoNAChINMSgCH8vCzN7T8w99t7OnHQfCIwR4FKgcIf27Me9tO8/st5cHP/HESA2OTx/Pna13avonnpJEA93sULD2+Iz/jtc13Ut+fPcYV883x9Bng422oR/4cfazSIwRurmCePXt2KfXrje/Uz59/tMUPVfHlePSH/Odv667m/WicTrldh3SuypXAv23bn5iZ3z0zn933/Z8/P/Y1M/NnZuYbZubnZub37fv+j8/PfWRmvmdmns7MH9z3/S9dR5AozCxp5jS/ZabvZeHKSHI9Pxt4W+6OS7nGwq6rfBt9Ppsdpu8WDFaB4+gaj92rKP/nmBwA2OZqNZTCDU8zZMpr1mhm5PlcBW4HRO8LWFZeQ5aVY7ymrQo9j22PwzrzuZm+cbuyC7fX9HLV3Fl3GR8BrhEGt7NKnaYP56StO/ptSpuHo/0dzzkDeb4b6POf56kP6sD1/HDbw4cPZ9suvyY7m7L7vs+DBw8uggLltI2a+KQdBiKD/pH/XVWuw/j/LzPzH8zMf4xjH56ZH9n3/fu3bfvw+ffv27btm2bmAzPzzTPzG2bmh7dt+037vj+dKwongcXso9W5iu0fsR07sI2dTtpAx58bMK+AIDLnuN++acAxYzxikW3MZKy5poErjatt7FF3DfQ4NgfVFTvm5xYoqCvrsumcwMm5zMqF+vIYHYyOisHVc9KOcwzWrVdATmXmGo7NqQjqz6tag2ULLCtbbePkKqHZolcEOe8VgGXhA2EkeW0lwnkwoAfsnzx5Uhm86+cagjyvyXv5WQL0fFUDbeq11167AH/LHTvL3oVBfxV4j+bnOuVK4N/3/T/ftu0bdPj9M/Md558/PjN/ZWa+7/z4D+z7/nBmfnbbtk/NzLfPzI9e1Y8HnMl2/tGGRmNflQbAXGKSKThlQRlsqDnu92tzyWngdqrKjmYQzHG2cd1iELXTs44BiHU9ft/j7D4JSmYo/O5reIyM0CBp4Eg9Anq7Da4FhDbuBqAr8uB5bKsDsmhfy7uJ+C58M28CU8a6eh+O5eGxpnvLRF238UaW9O+AfKRP2keTJefYplfzlI2MPueePHlycSwgHiB/+vTpPH78+KJNBwTOWcaaa1M/P2iT/5lHBoB79+7Nw4cP5+zsbO7du3dxPuOhLTi9Rl/Lec8Bz79KedUc/zv3ff/MzMy+75/Ztu3rz4+/e2b+Kup9+vzYpbJt24dm5kMzM+95z3vmvK0LwCSwUzGrp/saOyIYNHbIsmLINkLIX6/lNTTk5kStTRp6k3fFxqIjjp3skYXf2ZfHy2PNqS1X023G08As/VMm68p6aAB7xF7TxxHguDggUGYCX5Oh6TVtNpuxXt2vc7/sv8lm/VEH1mUD99iMyQDty+mX9ju4HmuTl4GJ19LfLX9LUzKNM/P8TpyA/NOnT+fJkycX51mX3z1+BnwC/8ycBA/ex79tz9M+GcNrr712kl7iL8kR1JlOpg/62ZLmy69avtCbu02q6mH7vn9sZj42M/Nt3/Zt+8wLh2vLqZnTZd/MZdCigvh/Vcy0W2plxcgNNkesvdWh/I0V+frGQtnmVde1sTe22kpjzQY9jstBcOby5lXT0xFwOlD4OPttY23B2dfz3Irdz1x+rW/OreyDjtyAkXIwx2xHp57TXgr3bsgs0+aKQXoeeA3BOX26bcpBVutAEPl9njoP8JERE3CtL6ZfqLeAfY4H+An4M3OxMkg96ppvOn3y5MmJTjKuBJjo5e7duycAn7t6nj59Ovfv318SFDN96orzx+OrtPZ1y6sC/y9t2/au/Tnbf9fMfPb8+Kdn5j2o98bM/OJ1G12xxpnOaH2+lRULbecMAmaZDWQMsATT9lj/0RLvSL589q18q+Wgc+qtPRpiCyCroOf84yqIEATN7JoM1KfBiWywzQH7vM4KpZ1rjLwx6Nbmahycn4Cn00dkhTNzAUQev1dODSD5m7qUwzLlzytnBjRex3fDXGXDHiePk/RYx83HmPLNOeor99kH4APUjx8/PgF+Mvkc27bt5D59Az8ZP/XJ9E90mL6T6qG8ueaI2GQMnAvrm98TKF+1vCrwf3JmPjgz33/+/wdx/E9t2/ZH5vnm7ntn5sdepuEG/j5mpR29gbC1vzrWnJp920AbkNLY22ZOYz9MJa3Gv2rHY7pKxpmec14FsqNjGa8Bl99TCHZmfU2/lq2txrgcnnnx6H0LXgY96m0F5tapr/H3o0BmpydosD7TEhkTgX/m9GEh25h12mTlZ4KtU0GNkKyImcmA03qNRDSCkBKWzX4J5AHkfH706NFJMMhnpns4zhyjjjOO1GlzTd+mv/v2U7fjOUmdph+nWg3wK5x4mXKd2zn/9DzfyP26bds+PTP/u3kO+J/Ytu17ZubnZ+a7zwX6iW3bPjEzPzkzT2bme/dr3NFzfu0lZk2lpLQXY63SP2w3nzW2anRWKB2CDm75LLuvuwrYXezILQC08TU2RQbmvQ/qy/sZqdPuX/d36ybHfCus9Wyjp7zRfwOyBoqU3fpqujoCsiPbaW20NleBwEQhDPXx48cnoBSAi0z5f//+/Usg1Mbo9Gfkc7BerTqp10Y60vaK4duvKZPbPtJxUjf7vtcUzuPHjy8AP3/RaQgBN30jN2WKLXlVTWJhX2wEJYH57t27c/fu3ZNNeLZHe+DYTaaonxx/q/n+69zV8/sXp75zUf+jM/PRVxFmBQopq9QIjdsgxknO02+tXztr+m7HfU1jr8yrtiVdM/TUPWKTDfAtW763dIsd2ozEQdfyGJRXjG0Fmg3sfV3bt2mg4EB8tO/gPldtsqzALHpz+20Du11rPZCVBsACUgEsgnwCd+4Rz/HWB+0w8hDYmt04WJmZpl4bj/Xe5saA1UhN9vOoA4I89cWA6Y3bHDc20Lb5F6A2EOfuHdoj/Thy37t3b+7fv38C+AwGxjPfEUgdOp2duYt+3kq5MU/uzpzmBlNsYAR/7vS3jclVEHCf6acZpxn8ivUcAR0DQgPsdq5FdwaRFXs2cPtzS5lQFvZpQ6WRNx2QfXPcBs8jBhwZ3Z51TeBq4MXPDiAEI7bvdvjLUG2sKS3d5QDZ5EphSuLRo0fz8OHDk7dlcrXFHLJ1yNSX5zn1ct53srTx53tbUTngtVuh6Sve4yDDdpDgraoEcW7aPnny5EJPjx49OknvRP+pz7RN+vaLBWdOU2gcx927dy8YPH3QzwgE8O/evTv379+fe/fuzYMHDy6uN9uP3pyuy6Yw7Ta68YNgr1puBPAz2l/lKHbABvhHO97NiXPNqg8zoJXTGWzNZK674crP3gxsxtB0xLoNaFfs3EHGzMc6sezUBWViP238q4BiUHDwyTkDyCqge4xHsrTXXXt+W+Bo9uVAQzCbmRMmSzbrjcQ7d+7M3bt358GDByfg6XG3jdicJ+NPXdoY2/HcrcbHYNPITgu81hfnkIGJ9+E/evTo5O/hw4cnaaAU3onjNFeYNmWk3eaWTO6p5I8BhKuKAPbdu3fntddem/v371+A/7179y7An/rKfFrvtjHPQcO8ly03AvhXhQZChdDQ25ONKV56p7g9G3sDKzNF14tcnLwWka9yKMtk+W0Y7Scr2Y/bI1toTr5ipTS4Vb7ewMlgyM+r4NP0Q1BaBYHUa32TBFg+9rmyk9VcUb52zkGzATHbzV/A//HjxxdMNsB3dnZ2ASJpk8CRB4ryOQDmcTQ7T7lz587FbYqUraUk3J7/N3CPPC2lSRmYtokOwvCZEmNen0/bZg7YJ4NijiUlY1Y/8+JHiqLH6D36nXkelO7du3dhc3fv3r14YCv/0zYBu6Wem43Yx3JtC84vU24M8DeA9eA8UIM+wXLmch52ZYRtWew+j5TMSNzaaSDeWCaX3A2Qm+O1ANMCV2N1HOOKvbsf9ud+8p1g1AB/5vJmY9Mv57IVB7XWFnV2pMOj+V0BG8dNHTvI2v44z2SknJ/Hjx/Pm2++eZLGCBvNxm5SBK+99trFZwYFymcWaXvlKiTneX9+6viBSOuv+UDq0x9X+zGsR/0wICYoPnr06NItnSu78kZyAP7evXsXgE1WnyAbwPZPHSbFExmTsrlz5848ePDg5Gld2wz1FF20YLry67cK+jM3DPhTzB4NRHbelpdrZQUu/L9i6QYggrPB10yS9dqGDcd8NI5VYFmBeZPX55oDMwg5bZb6YWOr823erJsmZ9vDaIGyXev2j1YUbdXhdjwnzQ4JpC4r+Zo9sc3olumMZ8+ezb17907ALf2GUfIhrhYITayuktN1GchZz3bAlSHbZ57fY2fQ8/XpI6Cf9A5TQLRZthU2nkCZ/9TZnTt35v79+yerxJwjW8/1vEsocr3++usz82KfwCsJAjz1yXSiN2xpE07vrvY1r1tuDPC3/DfLkcMfscLrlhUApP0WGAwAPL9iGq0O+2kBxTpxXnmlM4IMnd6Bsl1PA3Mblou5UAbBtinrcbdAYAZpAGn1WLyvsbrexQFmBforXR+tmlZzyb4IeLkvPd/3fZ/Pf/7zc+/evYvAYJBJ+uH+/fuXxnl2djaPHz++ACI+oNRIgwPadYNbmyOuBKMLbs5nfE0OMuoAP9NASf1YXgIpV0gJlGT4AejUcYCI/gLceUI389XurkogcTBxaXZB2/Ec5JpGHl6m3BjgXzmxjx1t3F7VVjtPBa/OrdjRahXA826bhUyJDsp6+U5HiWGaJR2xNLbpe/KbnGw/cvC2wpz38jfnDJrUn4NO+myG33TMPiJbZGksnzazYv6eLzJI65PXN2BvoB85VoV7AuwjG4feSGSO+/HjxyergaQdcudKctUBpbRhMsE5irxkl7Zv6t1j4RxwE9q20W6qyBiYs2/Bm9elD4Ms8+wr0DfDn5mT42w7f8nPrzDAgG99R6+Zm1aH+qSu+P9Vy40AfjtKA8oYrjdDjtIN1+2bfZglUr4mW4vUbttjIwMiwDnv2YBvldZaMfYmu4GY/dHRCa7W88pYHRgoWwPAq4J0q7OaM47BwbAxKAf9FK/w3G5z4oyrBXcHFl7jMYTJE3zffPPNk/PJaT969Gju3bt3cvdPbIpPo3rsjYwEWJjnZ+qm6dyB3nNAnXjFyLlc+R3nz/M+c/pD5wFjgi1XQ87je8OVx9M37/xhGibH+AAhC69xisb+wcDpANt0w3Nf8oy/McxmtB4ol8Iv008D5Nanj7O0lYcDQAtIZt6px/d7OD1kwGmsh3LxOoNNnJg5em5Kzpw+Hc0xcTPdjtp05HZWwarpyYGDgLUCV7NKgxoDQhuHbW+1AbnSPY83OTl/7MPHuVFoOdJ+5jZ/uYuH4G+22XyMdkpg8Wp0Bb5OOdpG27xbz24/3/lMAvUVX8k4swmb8wH4/EAKmT2ZPx+qMljHZvjf4+D8tEDK80+ePDlJdc28uGvI9kHd0F7ty1cRpqNyI4CfBtAcJsXR71WXOjZSy5E6bQlLQ2e95iAtUNnJG0NqctmwPJ4WLHitN77aOQOnx7mSvZW2D8H2Vikiyk9g4phWQbClZijLvp+myVjX85rPbR5WKxenOAxwK2YYMMsxMtGM3a9wCJiE7WezMymftNVWGSubt+75R/uxLls7LcA1n3GqhzqPXgjmMy9uLHj99ddPdDgzF7e7BtzJ/FOP99SnHgNlxuY5amOzLleYZPsyQTPgew4dwN9quRHAP3M5/dAGZzbC427D163qNYBPHRvhirkcyUUmH8NYgXAKl9rO3UcWM6IGrnTaphsao4MXx9/G2VYTDRTTNv/z2tWc2MidVlgF7RZ4HVCuA8oGKbfdQNS6sk7aOTNO2xnTFlwB7fuLu0r4hGoYv9MC1iGBhCkd68xAQxY88+J3ZFl3FUxau06F0VZyLg+sZY/p/v37JwE86RmCONM6jcFTt6lP26W9Ut5mO8236KsukcN40YC9Af1Kzy9Tbgzwz3Rm2JzAxQo4SrHYeQ24DeDdTgO7thxb5XwpZ4v27XtjyE1W99cc2ODeQNSAxs9NZveROsyNNiNe6Xq18kjxC7PM8Nv4W/G1vG61sljpqrFXX+O2+AI2s/z2QkLqn5u5GXcAn+OyLtucNRLCWyU5vm17kd92UORDZL5mxVhXup2Zi5TNkydP5rXXXpuHDx9eyMY9spm5AHsGUT6QlfbiT5wj6sg2xRQsn1HJ+FLH2OTvDGhthZn0FYP3aqWwstvrlhsB/G0QBrsUsx46zCrnv1KQQXEVWVe59ybvign6+gYWrW3K0OpT9hVrpsGlfQMTc7qUjd89HgLGkUNzo9Hteo6PmD/HEgf25hjbIOvluJ2Scf8G9tb26hgLQa/N2cyLPHE2bO/fvz+PHj26JHOuM1BFv9HFvr94DsApAsrEsds+0ob1kmCUvu0Ptqv0xXw89bXye/fLfP3MXNxOSQBPf0njhHTwHG317Oz0ltbV/mKupa7bCvBV0s7UFe3saLVAPX3JM/7m7I098T8d4jqbuwSrlMbwV8w49e3EvM7j8cQ6arOs9iyuCkr+bBnYH1cYvoaOQX1RdoMD52k1hy6rcRyNhcctP2UzU3OQXOVKbV/pL/+Zj6U8Ptb2GJoePaaZubij5NGjRxeyB/TM1rmpmz75QBNfBRAQ98u9GghzjgyM+W+m38ZvHZklr+bPcx/2e3Z2duleecpP34nOWM9AmrFRDw50TBFxzAR9+wr1wrlJaUHXdp+AZZ0yLdXw52XLjQD+mdP8a2NKKyc9Yltkq5ycFdi4rZazb45CGS2XDZwslUzDch21YcfMZxpGW0rGSfzyMbNLg3tKmCTngfXcHpmh9Z858vXW52pePTauIBp459rVXPP7CqRWAa7psbVrh+VcUu+cCxKTBjB5QRnf8BmZVjnk5MUd6L2abmNqOvCYGjg2O286YmEabNu2i4eiLGdAlg9h2X/4Gwetj+gqfkQdzczFZ7edMXGc3pOZmZM9Cc5Fxudj1F8L2C1T8DLlRgB/M2gfZ2kOlvo53tILK0Bs17eSifOErByB9cxKuGSMfLmmMQWnJwhyBveZvnHc9h+88duYjAObgZd16OBkLm157PGw2Ln8venb31fBdMW2eI313oBqFUBsF0e2xrE7H017cT9h9vnMoGyGn2OrVcRRQEv/LTXSXjGwIkbUC+u3/bgU3sWTurlllXKwPwZMror2fb94qI3H+ZBYbDt6zxPQZ2dnl17KRl26/1bos3zSeOZ0X6QFh+bn18lyHJUbAfwpK5aSc02pBlReZ0WmHZ4nYPk6p5t4/WqiGxO247rtxrB4fQMOHzcDaE54FBTYXz47NWQgcJ455ww6bK/1x6Dg1V4zfgZw62SlpxYQOOcOgOzX55r+m12swL8FT9pJgLlt7BEECV587zzZf2O/M6d7ZfYRjt1zRT0k+HB+WHe155WxcD6tb85nUj5h3d47sO+acFE3z549u3j3ETfGqaO0/+jRo5OnfrftxRs6834f6oT+RdZvRp80En3GK6KriN1bLTcC+M1yPOjGTqgoFoNGU2jrewXsbGdlxO3axkopx2r8rt/ktHHTUOgYBqHWF9u3PjPmBmYeR9t8duBsm40NUKjPzF+bb4//OnokKPHOIMrkFBXb4vk23y1Ir3Rt+XL87OzsJE/N3DIDAgEwQJ+neak/6jjBwMGHqQ4HxRaErQvOh+dgFTQdIGzXrOtAnwAZPThwZdxJg+VV0/w5RrJ/t3v37t1LD8VRzvv371+sAkho7D/8zyDc9Ol9jkY4VqTtZct1fnP3PTPzH8/Mf2tmns3Mx/Z9//e3bfuamfkzM/MNM/NzM/P79n3/x+fXfGRmvmdmns7MH9z3/S8d9WHm1NiUB3vdQXv1QINv4MzPzZBbHdaNkTQmYbCOfK0PBy8ztzZO6+6IsbYxWz+Wp7HUfKZ8LTi1/smGWlC2Hgy0vqWT7TY7cTur/Rtu0OW7V42rILOSnTrMWDmvOX8EaK1Nvo/Hm74EU8rI1y0T1Ehu3L91w8/eO4mMbR48hxx78wWvKp3jb+0H0PN20+yD+L3+BH+DbnTCn1Fs7/JhsM54uGI7spsV4SDxsN+9LMlYlesw/icz87/Z9/1vbtv2a2bmb2zb9kMz86/PzI/s+/7927Z9eGY+PDPft23bN83MB2bmm2fmN8zMD2/b9pv2l/jR9dWmH+scsTsrstXhd/fVgIb1rmJ6lq8xUjqHAcjteUzXAR6PpbXfnNG3q7Edjp2phHznD1VYBqdVwmLtcKljXbvdNnaDYgvOq3M+5s3iBoJu1+fp9Lx+td/EdASvC7AHwChjwJspDQJ/2m/7Rvzv4ET9t+DJVYgDvAOZVw8Ovra5FixT6C8eU8YZhh9gf/jw4QXY57XOTIdZP3zDZmw8TwSbNEQXMy/2aNq8X5WOzrjpE5QrQaC9NuVVynV+bP0zM/OZ88//bNu2n5qZd8/M+2fmO86rfXxm/srMfN/58R/Y9/3hzPzstm2fmplvn5kfvUZfM3P5gROeXzFH128sO9/pdOw3bZElm6EbMAzcqZPJ49O1bawu14nmlClGah20AJbjftKSpaViVqsSstXojA+ekLl4bC2YRzYGRKaHqJc23273qE7bh6AsGZOPWV9cedB+/eCag2Zk8+Zi2+Bdsey0TbBLAMjGZBujx542WoDnGKhPAzzTX/a9Iz8ms7Veo8t85r5GK9zkJuPPX37YhvriqzBi62Trb7755sXPJ77jHe+4kP3x48cXQYu3q3LFwPdhefzUA69rq9hGEN4K6M+8ZI5/27ZvmJlvnZm/NjPvPA8Ks+/7Z7Zt+/rzau+emb+Kyz59fsxtfWhmPjQz8573vIfHK8MgAK0GbaNrxujifK3zcK3NBoQ0HDqPWZE/83qnZVrgaUHJdalLXufg5XG0zTKOm/XYDvsy6Lke6x7p0YxvBTocu++WMFhbh54vAy5lavsT1tlV4/Jn2xmBkn/J+QdMohMC5gq425xwPrk567QK6xm8eJcQ58A2yT5bsGWf7Xjm1ekvp1HC7vOrXGH6Bv38elfAf9/3k1dfR9f5nrt43vGOd8znPve5i187S0A20ckxMvNnz05fK8H/JmcrvGjXvq2MP2Xbtn9uZv7TmflD+77/04NO24lLtGrf94/NzMdmZr71W791x/HqXD4v2U7O51h75N2AHuMNaHijJ2024Daw0xgdlV2/te82fC3/28EoYwMps7Mmg+u3PmZOmVd0zOcIVis2z58DmMfRPq/m3cvpBkAcm/VpYF7N98qe2lx7vkwyKGtskXbgTVT2Y9ANSOXnAK1nA2nab+kS2lHzxbbRaj14jLmW51bA5kIgdZpn3/cTwI8OPve5z50Aff6S6nn27NkJa18RtrOzs4ufwUzQZc7faTHKluu9J0HfaOlBB/7U8cN5q5XPdcq1njPetu3ePAf9P7nv+587P/xL27a96/z8u2bms+fHPz0z78Hlb8zML17Vh/OfZhKtkJG0SOqn8VZ9cqKb4fI4o30mhSyvyUuj8OP1HrdBe7Wsa07WWGaTxcaX/qnDFYjaqMkor2KADSBZnJcmAJnBruanAbRZsNtvumwB2mUFYO7ToH3VU7RcMXgvZKXn6I/pjuT8LVvG5juGKLvJAe08xasMykAdxOabzAY56zZtRNYmJ398/dGjR5dAP8w/uX7qhukx7o8wHTTzIo2UdkIUM76rQJj+z+8cd7Mf4lueQ/B1r1KuBP7teet/fGZ+at/3P4JTn5yZD55//uDM/CCOf2Dbtgfbtn3jzLx3Zn7sGv2csIyUBqY0QjsBleeHK1Zgwce3G/g0WfnfbTcWwDFaVjslZVgB0xFDZSHorICKDuzz/rOOIzPH7etbnwas5jgGoKuANqUxeAazNlc83oAv9na0CmAwT39OGZGQpD2DIFM7lLnl/20nzS6s71zrIM5CGzbzX9Vd+UXGdARUzd4beXC/Afyw/oCy8/sEdr7J1CSMey4Zl39U3aurFrgoY9ujacVBwHPKvpo+XqZch/H/tpn5H8/Mb9+27W+d//2umfn+mfkd27b9zMz8jvPvs+/7T8zMJ2bmJ2fmL87M9+7XuKNnZbA+N3M5PWHgssGbjTvf7fZcmhE2J2iTsQoI+e4+zaBicC11ZPkIoA24ctyOSgBiO80BDdgp7aVeHFMLZn7vS2PybMPztNKHmSmPpy5Jg+fc9pH2aGcMBmalq8DXWK3nIHLx4SC2uyIlDmC5V70RGoI+bZqyJ/dvGwjoNTBj+43kWMZGtJzzd7BnfwHfAD9z+mH8ubuHdu/9AvfT7JDH+DsIXiE0UkTMYOBnQG12kjmxPlc49TLlOnf1/L+m5+1nZr5zcc1HZ+ajLyNIi2IcIAdNQ3MUNDjToHO+KZ6GPdPvr6eBUm624TotwDQgbU62KlcFGOqLum2AyOBpnfo7DXcVYLw5xT4JAJyLFZO23tqYfc4gw6B/1XzaCanHFqCb3i03wYUysN18p76iF6djGlhR9hQCEo9xk9dzzHYNfmGubXWXdptd0uabv638JP15dUTfJfDzR9h5K2eCAgHXeqScq9IyAUytJVBGbt6dxh91Sd8pIQzpw3ZBnTKwOpC+bHn5d4m+jcUgb0c0ALGu668c08zZLCTOehRhVwHK11i+9GdQamDrfpxzb7K4TzP4Zngpvq2wMZCjwnv4m37a8RVrb4DmwOj2M2/t2ga4Kzk4/66zCs50dMvVGKztnPbm1SmBwfl1gmD+J5XBwtRT+nUalKXtYXE8rEf9Wo/831asqyAY8EwQ5A/LzMxJWoe5/QB+wD7/oys+qOYVFvW4CvAMprRJ64UA3ezAerZvUD5jEFdrR2mjq8qNAX476VXsgf9TGjA08F4ZHes4DUOZqHDmCZ1qoZGkDcraGGuMvR3nuz3MolvA8vKejkVdxyitN84Fc5XO/85MvZvEY2Y5CtKU00GL5yzfdQJO+2wnth0etdFWMdG352dlY2w34MQXqQWoA8SxubY5a322QOPNVwMX26KsHA/Hn//NLpqfHTH9VvyAV+R59uzZyZO5CQj8z5L2ecu29wgtf+pwQ557Aiu9ZFWcefMtv9aF7dplFTRftdwY4D9y7hxrBre6ngbXjK2lH3gtZWBp7IdG0BzPRsJrVsBn55w53URtwZFgTR2ZWVovZhaUkfUJOC5hIq2tNlcuLcjSMdtS2xtxbV9i5SAtSFC+Bu6U00G0jbPVX92MQMbf/gxSXGGsbLHZXAp15zmlvaz8JMf49GsjWfm+8l3aIfWQ69pdR7m7Jmy/pXaY2+fYmTJLf0zFrFI6BG3m9FnPc3Qd2zBhWBX7zFsF/xsD/DYG3tFg1tFA07llG3JTrNMnzVjNipnXs5PxMXDeSuclJI2h7SVYHk9yWx42Bs1iRzfgHOmJY/CdJC0Yue02X9TDUUD3I+oGu+ZYdFjmQ9uYGzNtemjg32SmzXB+nWtv7TA/nPfC3Lt37wRQXJ8bl5lj2l/69i2H3nvwnHBsBF6CY4DZQWi1Kkh9t9HITyMiHM++7xf37huM84AW6zOAMHXWUmv2Cc4h02skaLYXB82ZFz8NyTrUe/r1OE1irhMorio34u2cM6cT3pY7K1aac43BeRllZ/dkt2vdZjPSFajRKFZsmLLEWBujboC5Cgw0oHy/6hUFNqyW92QAbMXBp/3gi9kdvzt90/KcDoDMh1oXzZEt/yrokVCYXKS0lYuv9/jZZ46nnfy2LG/lzGc/LRrb4pO8Ab179+6dsP0270xDUN6weLZtgCJ4W/dNv34NAe2R11G2Nqf7vl9i+WT7uZOJgdKgn2PZjM3DbOnDv1XAMcQWHFgJ0g6utNHUMUml/mkfJlnW71vJ8d8Y4J+5DAQrgLfj2NFT6HQNcFpxfbdncMtfGJWdm0BuRzPokMm7HpkJ5YlR5bvvfogMRwyWYzLIOljOXL51szkwc5rOz3r8je0QMDzuthx3sKNeCbBmrxzbinxwns2+LL+DxIpEcAwEFPZPhukUhfVP8HeqhysfbnJ6HtJ3HhQy8FhvfhDNgTbfj2Sm/g3SuTZ2HsBtgM9fISNZ8bMPAXvqkufPzk7fvzPzYo/B44hunjx5cvHTkPRX2qP9mPNMG2h6Zl8ptuWXLTcK+NsmTj4fRcHGWFmsQLPt1h+vJRNNn3YwRn1OfNoLEDYGwj5XbJjO3dqnbtyWjxn0Waw76yLOlM3cVtxmio219cW5oZy8tgGSWe2KRBBMeMx35ThFY1vLsatsKP0wEHIOKUdAJ+fI+Lnha52Q9ZP5v/baaydpkfv371+8q7+l7kxmOG7LbBvibblHZCz9cF5840GuM8Pm07NM6eSeegZOkqmMc+b5j9rzjbLO7/MaF+uJD4VFzozXKype54BHUmJfdgaAcq787DrlxgB/BkrgJ4jmGIuBvgUBOmdjvUftUo6U5BR5b/Aq30cH5tja7XlHIOqVRAtGjS0YHN2Oge7oFRcGnAaqWUKbZTNQUQ9OlawCcq5r7Nlja0BvPZGFmWEeAZeDkdtudkbZW1BmHd62uG3b3Lt3bx49enRiN2anSSGwnXwPUKZwlcFjacMysZ32RteW1mjBjzrg99gF9dp8Iteu7tX3JmvGlD0S2tudO3cuXsOQW5CdpnF6ZmUfDDZcJXiO/ZI5F/s0A2nkY3CgL71quTHA78G1iGZ2t2LNrG9wYZsGsNYP/2eiM+lefsawMmnPnj27MLwwN/aROn7Aw87j4Gfm4IDJa91fM5YELerTAS/jTyFI2WHZp8Eoc2K9t3vSm5xOna3A2EGgkYIVyFD+BtS8hk7rtlLaMj6FwJLzsZeMkWw/m76PHj260Nu2vXiDZXTE1w2nDn+6kPZnwGs+0+Y4/TF/Tb1Yh05ltkAQneR4/M1P5+bVDKmbQECd5XXK/m3iyMDVFXP4XgVFFtvrvj9fSZlk+oEspo9sO80OoiuTvesQxeuWGwP8BnWzPLORFCuQEb85vFnEzGkAaG3NvJhoGmLAPga4bdvF8jNyhgUnSNy9e/dENrPi1YQy10vjXAU96okO1ZaTqWOW3gwz57ysZjFLYvueY+qfAZVzu/q/Aqimg9V3g5TPWTcGhHY9deh+KbvzxmmTG7nUBwMaX9Gc1YJJge8uczDnPDFlkcL5ZlBiWy0F1HToz9Srde00G/2MJCuFr6eIrPfv35/79+/PgwcPLtq7f//+yUvacsfUo0ePTgC6+VWCRnTPMfD1DUmlkbw20LY9+Hv7vNLjq5QbA/xmrSuGNbN+Z7XB0wzCS/EGjOzLQEWjC5C3W+fYf1IfAX0aw+PHj08CgZ2ITrcKfNbPimG15aHBie07MFL/NrzGfJ2CaLonW3dgtR7pPFnGGyw8HoIHx2Vgtr20YGVWutIZ9dE+U3fNXrN/EvBPuoK/NdBSD0zF8KVi7Jc2ajCiTCxmoaxjltpKbI+2bN9q9k3bS2qHT+KGfCUYZFxZWd+7d29ee+21uXfv3qXVadrOe/gD6EkBUfacz3w0wkh/ZSDNSiNjagTC4211bEv051ctNwb4Z05TOFSADYNKXjkxmQ/Bhn04TUCwMmgRQJxnJPhz9cAlMBkRDYXjoNyRh8V1Xdo4DdYcY1s1rAzScrjfBop25hUoM4hGJ/kjQPlOi9VrB/zdS/frAhcd3OTA4+HmJu2qASkBZdUn+6Atpw6/ewU5M/Po0aN58ODBJXbM8RDIeYvoil16M7TJTXBagZ1z12mPNnt2dnbyugX+SHqO5b/7T4rn/v37J6kyYwtX4Y8ePbqwL9/RlHaZzs0qnnP57Nnzjeawfs5jm1vjT0pbPbVA0NKh1y03Dvj5wx4GCht7Cg2M9b28WgWImctKzLXMPZPpxwj9AxCUkxteYW5mXmYK/nEH6qClglZA1vRB5p36rR6vZwCjHCsZeZ6Pxqc/16cTx9GZnuBcBqD4w+IEIm9+ekzss9lCisHWuWlfS3LSGJ1tLX14lRDd0fa5mRdZ/JwHwbPNP20t8nGz0QE/372CMuNvMvCzCRb131hsro1N7Pt+8iZMgj1v5aRd8cG3s7MXbzlNvzNzsSLwMxNMv5GURB+sx6CdoJ8HtBrhaStPk47mez5n8H/VcmOAv7GZxh5YrIxmaEcMiv22QlY+MxegRMMj8895Om2A/MGDByfAT5C20Xt8diQHQRuS23F7vMZ9k/nxGuab2zyQqbE0RmP2yqV8nD265J5GZKNMtBE66Eq/BFWyd+vBwMzvTs804uFUl+fLc7DSUZujyMAHo8hiPT76B//Cbp0K8bwSzI5SP7zONsDxekXf2K39jD+YwttX01/sI7n9sP4EgthZ+s5npob2fb/4Va6Z09/pMEMPuzdYNz00P3ax7TadNiL8quXGAH9jZS3irZQXI2gPF1FJDCLMI7sfAnTL7ZuNhO3z4ZDUT942QOaloPvm5POvMWaOsS0BG7NqunSKy+1yI9Hg4KC8WoG1PrnqiY7iUAy6YVRmtLxdj6sTyk6QcH68ydU+Ux+sH50ZyK5qx2DouTPApt7du3cvQCtP6FrnBCwyYhYCOc/Zdtq1rMu3sq5soAVWB2XrK/+ZTo0seQlb6nA1GIKVVA9fkxDi8PTp04u7fZK+Culx+oiyeDXg1YlXTyQo1Esbs//TdldB4MsC+FcMq52buZy3ZWGEXt1tsEpVmBX59i+CP/N6vGc6db0kJ0B4LJSTstGgXKyrBkxkhVe1ZxlWAaYtW1mXt9Wx2OijJ75w6+HDhxfOHbaXTfE8IZm7MHibIvtvD86YLVsWj3HlrNbJ0Zx4nqmr5ugM/A1oOC6ydc9vnrxtgdfFAd8B1wGKfeY6jsdAybG1Y62PyMANf6dS6YPbtl0AOV9xkUCQtjOmAD7foc8VQMZJ3RmA6bMzcxKYcr6RrqNiX7Kt+Xx7tct1y40BfjshJ4q3qjm/nLr53hyvAWSKnYYG3ZyBv9XJdE8DRPfRZOb47RQtuK1kJeslcDeG0VYOZmerNJQZNWWJvEfAm/ads2VA5at2932/eIgpuf379+/Pvu/z2muvnfwGaUpLNyVQeFzUtwHI8jcgbYzM5Yj5W758DnBwleO2CC4tH2+/4OrKsjhF2jaquddgPTf9NRBc6ZR2y3FSfpIv2gd1E0Am088x7pckgD19+nQePHhwAfbuNzZPu01aiHI2jEp/njvOH/Vgf6W/Nf29lXJjgH/msiMY5BqY8Hu7l7kprTmEj9HIWp6ROUjnArlRGxZBp2lA0QC6gbM/rwJXYxgEZ+r7aB7anonzyzzvgE1ZHDgIINE3X8IVfc+8YGkMSk7xHAVb21ADnaaPxmSPwH7F7tr8NrANqfD4qEvuHTHoUwavTA2QlpdtZy6uE9w4Pxznyl+bXlKPadrIv2L+Jllh/PyjrvzuqKSDaJcMdp43BjIGJz/RnPbaPtNKN62vVme1Yn2VcuVbfrZte23bth/btu1vb9v2E9u2/dvnx79m27Yf2rbtZ87//zpc85Ft2z61bdtPb9v2O68rDB3QS8iZ01z0zOVHra0IA2eONePjf08ynYc/9eY39JFx8EnLMBDu+rOuHbextVY4dgYc6siF8lJnPGaws3MyPWD2776Pgp3HxvnkLys9fPjw5O/NN9+8CBIJxAwSHqOZagPBlc4sY4CBY2fx3LkN64p1M4d8tsPz1AKc2WLa9g+Mc2XVxkww8cZt6gacabN+z9GKnboO2zZwUidMaXglHv9hqub+/fsnc27WzTt/uEfEVZb1Etm8oc72STbJ/K2HfD4KMNTHyi/fVuCfmYcz89v3ff8XZuZbZua7tm37LTPz4Zn5kX3f3zszP3L+fbZt+6aZ+cDMfPPMfNfM/NFt214qGcXI2wbu/ylWXCt2wAZemWCCPu8lDsjwejpxIj6ZBYHft301p7EzRy+UPcXyr9gA2bIBmWzPBhp9UAaucjwnDGYBHbMljpfOnDpMoz19+vQC9B89ejRvvvnmxc/u5XbaBnAcQ8tl878DsPVrHZIhttICHoHChGbVJ/vjXgYDMeeH50JU2L7bNXBRpgbEsRGz5JynDbZgugrAlt324OOUibaZ9vkG0pyLHyZA5Hue8G23YzZf8Fg4Jyu9tRSW54p653mPwyveVylXXrk/L/+/86/3zv/2mXn/zHz8/PjHZ+b3nn9+/8z8wL7vD/d9/9mZ+dTMfPt1hOHmqAef4/m/Yqwr8I/yDVY0KP7lmAGIG7kEmLRDxtZuK+PENXBo7HCVK2Sf1Jt10/pxesrBoumR1xM0PD9HKw6WVYBme07/vPnmm5d+Yo/7LW3OGdTYL/XYVgmWNXXNxuzkq3bcBnXFuWSOmnek5P+KjVMu3iHVWD5XK145U3de1Vj2tmJZEQ+2y++2f9sUCQnrclWT47711GnW1eo71+aYg7P1Zaxo7J8Bsq1uWnB03j9tce6+EOVaIWPbtjvbtv2tmfnszPzQvu9/bWbeue/7Z84F+8zMfP159XfPzC/g8k+fH7uqj0uD5UQ3NsLjLSiwvr+TKRj8Dez588+8EWgIrrmVLIyC4O+Xbq1kdqG8rrsCewYMnycgHS35fcxBxyDY2jRDMXDRMdw3HS6OxFRPNoPb3PFOC8+Tgy/3ZRgwIp9lXgVMl0Zi2Lbb4Hh9txKD16oPEpyADvUy8yLot3lLG5TX55xSdFsGzFVbHD/HyHNpjyDtFWJ8iwH8iLVv23bhl0zFrti3Azz3GXi8rf6tpzY2yptr2WercxVeXFWuBfz7vj/d9/1bZuaNmfn2bdv++YPqbe17Scpt2z60bduPb9v247/8y798whjOz19iS2boPGemlePN8Ah+q9WEHx7hhi5TPQSOAHuMKg+TxLAYACiPAwAnfAXCbTw+T73RYe1cjcWuHMcg1vplOihz5XlobJuriJaXZ/rt0aNHJ2keMn5vTMaZVnqm3fC6Vscs13bL61mHbZLVuQ51RNCjfrgHYFm5H8Vg2HzDxIdyWf+08yOicmQrDu7WPeukH95y6bozPTg6oLld3u7pjeDol9e11VJKWzlZP0zDNf+mj7cfjmlY0ILoy5SXShLt+/5PZuavzPPc/S9t2/aucyHeNc9XAzPPGf57cNkbM/OLpa2P7fv+vn3f3/d1X/d1J0rmRKdwAmZOUw10WoIGjYDGsNo8NbiQMTKl4KVz5E2+MBtMMaywfy49+QbGkwkBOHvZ19gXx+1AYCCn0bX8dOvDfXl14ODK79yU47ga4LR3wJgNEtwyF0798HW93Nz00tzjav02vVPHrGf9tUDi9o8CT+qRMJj0MO3T7nahrnwnWguQKS2F0cgE5WSg4BhWqS6TBANi6q3a4vmsbHyHjTeFqePIzJU40z/0PWIP2+BnvlYiMjEVHJ/jyqQRN5Ig6sp6afp4mXKdu3p+/bZtX33++fWZ+Zdn5u/NzCdn5oPn1T44Mz94/vmTM/OBbdsebNv2jTPz3pn5sav6aZPe2G4DsuaANhhHzTaBmSSmVPggEZ8qJaOamQtg93tC+Eh4nJdyxEDaeLnsM1DY8ZyPTbs5n2OUnbqhfnmNv7eAvHpHjm/P43/OsZkWA6MdjoGY72bnD29n3vLfummM0HbWUgZHjkpHbqkc6t4MPI5uGWYuP7TFHDXnLHUIPAZzAh7Hw3bYz1WAQ/ZvP1z5L3VpBpv2zJDp25QpeuWGs/26fbfdcbPexzhnsSNigufYxNAy2H/y2fMfTKDeW8roVct17uN/18x8fHt+Z87ZzHxi3/e/sG3bj87MJ7Zt+56Z+fmZ+e5zoX5i27ZPzMxPzsyTmfnefd/XP+10UOyYnDCDT4qZCtvh57Z5R+fk3SH+gWeyDEZyb+IS8PlKZsrnYBXDS/uW38wr/70Eb0C8Sg9Eb63t9n3bthO2GFnZPnPlBBG+C6gxv+gs70wh+Kev3LqXOllV5TXXOf/06dOL1xpQ722sBimDHutYd27Xm5GcW/ZpIObx6CQ68kZk7Mv36eeYmTT111Y9ZrkrgGJ9jt06MtliPRfbLeec1zN45Rqy/Ab8sdW2ouSK/M0337w4xgCQskqPJu2Ya/iSxRTfjWV9WXeNwBrLHCBftlwJ/Pu+/52Z+dZy/Fdm5jsX13x0Zj76ssLYuBjJU5rSaAgrwJ+5/FIuG0nqRKlO7TAoxDjTH+/YCev3vcKNFXEczv16zC4ep/cdrgoMK/0xqHE+0g43DemoBDfeV01G5PpmoI2FcXzUE/dgHj16NHfv3r0IBuyTwaY9Cctx8ThlpT4ZlG0/nI/olw9b5Xi+Myi2ecl487+BEueYY/DDhZGdemlzTL24bc5X2rDOWrBptmYdBaQd7FKXvzxm4sS2nWvnONl+ZKNdpd+VL6YenyBmiie/yHX//v2TuW8+3ubdhMGk17p81XJjntxdAY6jfwDlKgbnto8AlQwnDD8TyjSC2VIMPymcljO0IWVMKTFKT7AZKMdB2WngDnwtv0pDNJAegVmbLzOxo5wjxxg5cm0LhuzDgWLf94uUW8bDn+Zj8GDO1mNzULENUUe2M+vH1zb2TB3FLkhsDLS8P51janZMebwSyB9z+9GpQW4VhDjmfd8vAqjr8DqPmYE+vkRA47UtxcVCX4wemaLlvLabKSiLCQpl5ZgcpGN3WS3MzMUvfuVHXoIDtCG3w1VLI7Q852D9quXGAH+KGYeNsIGfl9YpzhETqOz0ZPppi3npMEsbBu8MaC+JWrF9y0fDz/fG6lZR3sBKJkvj8RI+xw2ANroU90FQI1CzZIXEdpnuieO1MTV2nhLHC3hlyZ2UT159kHf70MGsT7NaykC9mP2TjOR7mxf2w/+NwHBTkGzfTM8Ay/opvgXY/mGGaVmoN4/NQcg241Slg33u2LFNpE7K6iYBkjXf0cWX+7UVR9poaRiurBzsU6K37P3x5xtzvpG5vG+KY6HPsfB66mVFDF+m3Bjgj6GY8bcAkPr5fwT2M/0FVmnLuer8f/bs2cmPOlOWtMU7AQL8AbEAPg2Vbcy8WHbSSNo4j4CYddtnAn0LONah+3X6w7LSYB1Yw7a8ZI+D+PF3P+MQhzf7TTt27gD+nTt3Ll64RTDYtu3kGoO9Qd365Dza/tocWr+r9JlZXcZOgKKeCFCNnXMcnus27w50Jig8R5tudkj7sW+24LPSWc4lgLNtPpTF6/j8xv379y+tImiXbazRO1cbbcVH0OdPZcYGkybO3X2ZK6b3XDx+1vM8NFL4suXGAD/ZB/OPbelDUDJQHa0YGrvJtWT4+Ww2bgZApprJpVFyOcy+VhHbzrYCewINZSPz5DXUnzeejsDKn5PL9OsB2C4dhoBvp7Ecnis/RRlGz7tZwp7C7JnjZ9Cg4/E/A2NjV9ZDSgM+HmtBgP1Rd+7HdkH5ecx25X0AHgsjJtFYFZ7nnLVgwHE618//9G33wzYdGAOqZObcfCWBypw+ePDggoXTPvy7DV6ZMJCmn3v37p089LbSQYA+80u5IxdXECs9NYxqAM+g/qrlxgC/gYARry2bnJ+mghzpWVaRkkBKcPL31GVukE8B+q4AOv6KsXsCG6DkuMfaAoWB3nJQP24zY+P7yFkv4/Dy1E47s/6NVrPOFTu00/jlWV6hPXny5MJZ44x2ZjubA+gqLcC6XEFQz21O2I7nYgXABBjn9wle0U9y+pSHfTJQe+XUbiWmjzGQt5y+06a0kfTTxkcZqFvOVdI36ddpVM7L06cv3ud07969C5KQvkgE6Md+rTf9tq2WOV5u8PK8VyjBh7YXZltbyWGycVUAv6rcCOBvzHTm8rtAzByco6QRU3Ft8txvcsMNiOgEAbOkdnjXDo0shmawSVtZGnJiLSd10AIWwSSlOTHHYuZtx5u5/M4kgr6ZvVkcC0G3BR+27QDhB9xakKE+44DOtWYuTAbaMroxuRbQqJc2b9Qt7YJ21dJK1GdkDHP0apKBzDYQ1slzXME2ZtmCkufN52LHPGa9etXA8bY0YuYq4wyIx+eSVjk7O7v43dxnz55dPCH/6NGj+fznPz93796dhw8fnmx0szBYB/xpRznnmyE4P6nvvYnoIPL5WZJnz56drEBWOvHn5tuvWm4E8Ke0pY2d0cfynU64ArZVn/nviEu2z754B4BZSIySPwRhZ5+ZS0bkPhqYOJBFTp93MUi3MaVERgfVGGyTj05EuZju4XUtUOUaB+ccbysffmcaKst8fnZutaUTG7lwP83OKDv3jJjesfzNrn2N54arH8rpFELGlwCQ761fj5kAx3n3cxmRh+M5sj0W1o+ttVUgV9Rk7DwWspcAeefOnYtnbhII8owHA9XMnDD2jDmrDIJsxu1VYVaVDtYcd27rJB6FVNDPaAcmwNSl7fFVy40Afg8wk57PrmPDuY4C6Eyp33L4fAlbzjXgdlqHy3GCmcGhBTeXxrTbeQeRtjJoAE89OCgwL9x0zrueqNM2NgPkikHa0FOP33kbr9MOBCguu/kj29kHaIGKbTkYHxGGRjTYB6/1U9ScE4Ov2wmgebw5HlAiS+U8Oj3B+Wt+kWISYNm9AjRJafbYxs7+WxAMkcrdWdmvSPqEr0vgfg/z6lylc6z8tTc+tGnfWtlBMgCxPaehUji3Lei7/YZtrNtWoi9TbgTwG+Do3E0xDTxWkbABka9bgR/P0dkc1cny+dKn1QSbYe/7fil/bSa6Ysn53gyhpWXIqhvQWUYHPh4zkz3K5+Y8c8y53oGozSmfSN33yz/vmDYJbp///OdP+mHhE77WT5x5BVrpn2BuACS5MGCzv1XgaJ+pCzP/jN/zm3H7QcTIzsDtgOjx2p5aqobXXEVCfI1tKbrjPtqTJ0/m/v37J/s5+376rpxt2+bNN9+8IHJ5WWKeso1c3Avy8zqN9dMueZ7sPZ/5bi7OJVdLDV+opyMdvhXQn7khwJ9iBtgiZDOYHG+AT9BqhmnQZ7qAxz3ZXnbbGdvYzOIZ7JrsNA6Cpo2O17iYRTeDWQHVis23vjx3TjMwcDonmvE15p9NPuZa7927dyI35WV7cfS8AmKlrwQA7z2QgFhXBoYUtmEQJbttgWCl19hX2udGLnXP77br9M0/s9/mU2ynpS4zb55/1iPQ0t5JRFZ2mbtrcn1kz+2aSaUwoDk/HzbuX9uKLTx8+PDiOj6x316J4JUwjz99+vRipfHgwYN58ODByaufqRemz6h741Z0lJWs23nVciOAvxn+CqioIC/XzZKsRB9nPjbXkTXZabjxxAe0Uqfd0UPgNuM2qHtFQhl8K57Huvpu9k49r9gm2w+QNSNbGV5rg/3z+jirgxqdJXrJUj/HnBtO/YB8nuRtzsrUD0GRt6AStFg3tuegStDJWBw0PccGQs6xGbyfc2AqpOnB8qefAFRsKikxzl3mizLYJ1eBzz7KsXOOTThWbXifgikZzt3Mad4+cx/7yiYx5zEPZvoVLXwdu+fe+o4ec98+X9fy2muvXexHcL4YRG0jTT/Zp0rA/rJh/A2sc7yxmxXQub1WzNhSf+by2ydjOGaw2TRMW3TGI0ZIZ0gdgv6KvTWHS3F+uIF2Y2LNgVeBtn1mOy14t/lMHTLv5kzt3L7vFz+QHSeKQ4QR+Zrolvdax0mZ6vFGr9N5HnvTmdkY0yORmauoVW49/bYAwL650oz8K6C3Tbc5MQHIH9OuJi++ZddkwiBOuVafbZvRY/L8vLeexIF3/OQ8fZXzGptI4Mg7dvgQGAMLx0e2Ht3zqf0HDx7Ma6+9Nq+99tqcnb34+VX+YAztqvkV9dV8mmN5lXJjgN+Rz4CS4mV3ruV3HvM5/m8gZ2Wu8nTO9bcgMnO6NKTR2ZlWTmumkTaPnKUFn6Zv649z0IJVc4RmxJ4HLvFbvt1zYdZMYDbg8TxlyF8Ym/PHnIMU37pH4DN7NTixb+u/gZn1ZXttANBYN+eYevezBkzxEMA4RyY/JikG/QRdB35f42BgZusA0WwoffE5jddee+2SHfHVKh6f/Yi2xiDg+fWql+OK3dy9e/citfPgwYNLP7va2D7HZ+zjmJoOSSpepdwY4Kei6XQudojGluxwLKu2V8DrjZ4UTmbajUxkZJSZzH4lA9uwk7RAtaqbdhqL5mdv0PEz56OVVYCwXuI4qzseGsimJD/PtIP3VQxmvHOFAYCOnGDRAj3v1DDR4LzxfPomGWi2SYCMHC1nb9Bp6UPr33sp7I91Iyv3ShzwbaPsP/3kFkrbhG3bQb8FBfZDkOb4ssIO66csDoDepOXTvSYiaSefCej2SadLA/qvvfba3Lt3b15//fV5/fXXL72h1+kdB8uGdanL/jynr1puDPBzgHa2FeDR2ZoDteKUglmeQY73CifS2klXTmsQWzEoXmvmE5mtJ/93oVFZtqt0ZABY9WeW5/5SyLxaoAkwt6dIcz5gz9v0uGwmsz87OzsBegaDO3fuXLzaN29VzSZhzvOOn6ZjB9ymO+9BGLAjF/XV2LPth2kVB1imkfg/x8n6eX41ty3AMEikzWafvp7HVoQm80gAp89w/GTveTsm8WDf94u7dPgCN/bJcfM8dZCHxlLPK8fIwJ9bffDgwckv7xH8qTPaUNOnQd+4d5UfH5UbA/wz67dsujTWREPwLYMzc8mh3C+djABvAKIMZJ9HwYDHec4rAvbhwHDEipt+GiA5T9uc1Etdrwpynfcl8kdG73w2b3trQdYgkbEHiHOcIMCnOGfmIoAE/K13/jLXa6+9Nvu+X1yb/QPODfXH4w3QXN9zxnYMivnO20QNitFprmOgpJ5ThzpLW2a2lNFMPIVpNcpgPXm1twri/Gzga/q0fzW7IwDnAa7YQ+bdz0JY/86fU/crOZnfT17/wYMHJ7egkiwGL9wW26RuWLgP9Vby+zM3BPibQRF0eMxLe7MIsnOCTAO6mdPbuho7bmkOMirKw37dHsfpnCHlZDG4GIRZxwbZSmOx1C3lNDvhfd+NOTGlY6M0k6UeGxvlMY8rbcSZ/MMqAc44fNrzSiqbvWarCQLcuLdOKTfnxHZw5OCN4DjYNpB0vtg6Y5tJr2V+mo00xp4xeJ4ta5uXFhDtxwRBE56VPgzyeesl++fm6ptvvnmxustxEwGmcqIfPrk7c/pyNmMFN3izAg2zD/snOfWzOtSbU9AZM8G+3V76quVGAP/MGqxmTpd1NMzmVKzXcrQ2Yi9785mMk/KtmHTaIqtgPy3QUKamgxbY2jEHrFU7ZlJHTs/vLUiRcWZc0ZtZk+Vx+oM6bEwmdQLGfAoz72iJbNn4S508mZl2eJ739ieg5VZH5mXJEFtpQEs9rIB/BYwmHQR3gzjb96pixQrT39OnTy9+OMR+Y5+Lzpxqae06N05wv8o2V/89nvjR/fv3Lxh3XoWcVF6A2E/20mb5MsKk/PgEcMYT/bdnH3IbJ4MP77byKsipz7bX1OpyPt4q678RwN8mm+dm+qYRi0GWE0OWuwLwGHsMaGYulo4rR2wOx8nk2DiW9rk5BcfWZG6spAWKVRutbs77uQGz3BxbzcHqu8dOMHMahTr27YoB9jCpOEQcPe9QSR06ePpl/tc6SfHvKsxMzUOnMKhzWc/0I9l0Sx1xZcanS2N3nBvOmW3b80c7Czixrjc3c8zpHDNVjoH9W6+rYhuLz4aB5zPngSSL5wK2fNKX+uOzFrRp3hDgF/3xdljiQNpgYCROeUXSsOBIL5wvE1V/ftlybeDfnv/Y+o/PzD/c9/13b9v2NTPzZ2bmG2bm52bm9+37/o/P635kZr5nZp7OzB/c9/0vvaxgV7HgnFuxXl9rY3RqhVGZddO+74X2qiIyMMfawNLBp7GmFUN3vZnL6YUW/Niu9z0M/HSytH9UqGvLsAqyKS1lZAM/soMwujy9yTHwR3HefPPNiwCegBDGyDkjkwq7Sz8BSzIv6rbZT8ZB/XpcfnAtn3mvemQm6Dcm6AfIuJLIi8hoU2SjbY7aiuWIxBC47aP+vEptWG++ltcxzUoSkGAR4M+8R3+eZ+qW8x0iQXm4YsiYs+LgeBiIPL8pWUkYw4g/jSA6Vfwq5WUY/78xMz81M7/2/PuHZ+ZH9n3//m3bPnz+/fu2bfummfnAzHzzzPyGmfnhbdt+077vVyaoyB68pOFAaRAtT5gSZXpjkHVpiPlupyZDIMtuy3PXYzvu05PqnDeB1M5H3bANj43txsCbnmxkBnzr3nfg0OB9jzF1mDbIXgNsYeAMngRmvrYghU9Ls1/PeZ7CJAiGUc68+HnGAH6YNtsOk6ReyeYYLE1GIqftxkzbgTkvD8sqJjri085MVXll8uDBgxPyQYZqUsL5pR3Qj5pt53jrh2Nt522L0RGDU6sTnTn1wvbJ9PnMgYlP7CM6i6yULzqLPAT66N665Hf6QCMObQwmZSZGb3uqZ9u2N2bmX5uZj87Mv3l++P0z8x3nnz8+M39lZr7v/PgP7Pv+cGZ+dtu2T83Mt8/Mjx71wQFGSYyWq/daNFaQzy2Hnf9WKGVwH7ye9f2EH4+vmIxlNQtaFQcF6qbJ7natO+ugpVpy3nsS/M824gxmpS7pj29EJJgxrcES1u38acCIwMwgkTs68jOacXTOF1MKuT7j4FOZvDtk5sUPfFAvTBGkLt8aaoCO7tvdPE+fPn/VAF8gFqDhH1ckTdfsm/ZEP3Gwoq0a0Dj3aY8rDAYM++XKlqILnvd1JB1tleXAxDZ8iy/P5Y4u2l4C8ZtvvnkC+vadVeBsqyn6VsMb6956zDn+OtirlOsy/n9vZv6tmfk1OPbOfd8/cy7IZ7Zt+/rz4++emb+Kep8+P3ZStm370Mx8aGbmjTfeyLFLCklpiloZJPqoQWHm8iuDaTBOnxh0nz59evKOHuf8DfR0smb4q+KAYDCns9kQmwO7Tgs4KwNsTzby/FUG7nwqAT+M1m9H9LKe/Tol5bQFgwHv3snmH5k/55WrjYB2dJ1b8/jj8bzbye/937bt4gfgWViX+jc75h/fHUO2ztRDY8YGjMiV4MMgZmJjEkZA41xEXu+F2IbYpgmaV4lk/s1H2b9tPnNFlk9b4bhYeCdYvmfDOKtA+3qCSfaC2E8bv/V2VXFbJGpu92XKlcC/bdvvnpnP7vv+N7Zt+45rtNlGdUnCfd8/NjMfm5n5lm/5lv38WPpkvUvMnHVspAbv1F2BXkCFTyGSSXLJmn54rzUZWOq1Vyy3cTGQNVld19fRUcmEWM9Oxz6Y5246zvhWbTU50xYNM9/TFwGM4M/8a9pjoMlcZX5yJwUZOZ2bc0gHOjt78WtradPAH93kHFcAaSuriwASWT3bps5jP5/73OcurmUAZhB3OszvjCd4ezOePuC/RiSo77axz+c2mPp0UDfLX/ktz3Ol1chXI3AtAFCXYejc1OfDmrb3fX+RNoq+uTnMucw5EgMGMOqSZKT5CutwLI1E+rmNt1Kuw/h/28z8nm3bftfMvDYzv3bbtv9kZn5p27Z37c/Z/rtm5rPn9T89M+/B9W/MzC8edXDEUHneUZpAYyN2W+1zuyYTTIc1mIaB5T9zrQwi7dY3MlnmKmkgbdJXq4YG1AafpgM7pBmEDdHj53eOz0tiAhh1t+/7CZjxVbjUL+Uji49uj26bIyBkE44/yJ5gyfl69uzZBctLG8z10zYYaPkCv1a8QnAuP+21/RindVowpn2wP9sWr2deOte3VQjB3Tn4jMlgdASAK5Lm85xHEhtf32w28nJlRbloUyROedd/bhzgXUG0tdhoNpPffPPNecc73nGJXFJW6tCkjeN0MCBexE48lpctVwL/vu8fmZmPnCv0O2bmf7vv+x/Ytu3fnZkPzsz3n///wfNLPjkzf2rbtj8yzzd33zszP3ZFHycKWTGXFhDy3azB9RujJitjrthszcz/2bNnF0YRkAuY8IGZXGtA54R6bCkNsDk+BxMGFALASt/Wk/+zLa9oCDxsLyUPzaSQSact/jGIsi/O7cyLXz3jnPG+aQYDAidtJk9VRv9h6ZGRqY+wRaZwojOmkBLsV8VsuNmzgS16y/noKraW9jIW7hvEVqkvHo/OCUacC+prRbYiM+904jVmuA6IThetiJ/TM67Lcw58TNkZLLmCoTxPn754joO3dXLFyP6ZjuI4Oae53vPueeF1DS+iDwezVylv5T7+75+ZT2zb9j0z8/Mz893nwv3Etm2fmJmfnJknM/O9+zXu6KExecAtetrYVoo4MkAbtRnizOkvP9lZAvwBOgLbzJzcAZLrCKqRL3K13GAYlo+vvrcgyrHZSAn0liX98i4Y3k3C68gKGSDSHsGF4J7NNKcxCJYG+dyq6ad2M3e5jiw8n3M+D37lbpmAOMFv5kXQyucAh4GeG6uRl0BuOyW5MMOP/LyegZNznXE6xZU2yIzdHoNFjnGuWzsMfNGfSRN1wcBDu3e6qBEVp5ZcbPfxZ66qXN8b6+w346LMrR9+9mrKfk3dpA8TgdR34PTnFXa8Snkp4N/3/a/M87t3Zt/3X5mZ71zU++g8vwPopcpqgr38tPGsGHMLDFZ06nAZloniSoC/32r2l6WgDYrvBycQzJzm665KnRytBgj0OeelJA0sujBza2kHAj2B29dQv80BfT0BLukeAh/lYwDmC6/u379/6Y6aFLNEBvLGPDnn3MhjMLAdGVQ8TxwT58xB/Ijtef/F80WbNBjwXnTOUfTPu5p4nEGZQG/wct6bq11ez1Wx7ZR26e+Ry8fa3FF/Lgb6tGtySRmav6V91zERSn3bZgtuDcC9WmrnVmN9mXIjntyd6ZsZ+c6JI7uk4XoCs2xnm6vlpANADDq3TGVpxc2cmTm5tY/LwrBY/izczOmbIxk4UnwXw2pF0gIeN5s5RrfvQsdgGofyELDJ8HOsyUuQI8NkmoFAQ9BniU7zpkOmdch0qUvOfQMdzj+fwuVT25mjfE+71idXiayXcXA/IUDIeWnB3/0RYCMng2N7+piFIMR5zKqFuf6swghyTNuF0QfoUxhc0z/v1Amxyue0a3brYECf9PhWKbTIaTLS5tDnyKopR+aQJI66yzX0Gc67ZXQwp9wkbpSTOlwFw+uWGwP8NnyDGr87n5/rDPheTnrS2V/OkQEG/AlyZPu5LvI9fPjwQob79++fPDREQ7dsLo0dtJSP6zsgBBRokKm/CprZaM2rD6J/BjumgNIeDZrBI+cI8r77hbolkFJvAZr82AVfpUH5G7MneMaBkvrhvATs8z/zH4ZM/bd0WtpjWizjaOkxvw6EY6ft5+EyprfoJ7ynO8CcYJDvjQDkOgbzjI227gfnolvvH3gOOBYC4MxcbIrSp3lNC+j+7EDS5oXjsq8RkDP/3riOnPzM9nkjA+3DKwcS1nxv4yFJMetvePeq5UYAPyfHyxlOYIpZbbuuseHWZ5ySRkKwiWMx3US2ReCfmQvmv+/7pVfBmrnw3uAU54obk+Z4fdysgEttpmmsi/xvKRkGAN5t01gzwSM58fTdXhvA8RukAjr5kYukd5g+M6h5NUV9RTdkkjmX/2mfY2a6KfpjQOVKjnNgMEofZvBkebTf/F4Abcd7FbHH1CXYM9fNa2lvBNmMN+OjDREgGVyaDo9KdMhbb32bJX0m+oicXhH6obUQv9iyn1Noc0Aw5urH6VySB9Yn0Yqd8/37KfTXdpxyNDLH/18WwM8BOAjkGNlki/r57jouDiZUoCM43+8SkAkTdpQ3EIdN2YBmXhjaw4cPL85lzL5FqzEqyt5YRT6nLt802PRAhppzeTAoIOA8MJ2erNXFex8zpykS6p/pmfzxJ+yow9Sz7Ti95/klg2MKhKyP4J9xB3Q4vyQABoWs+rgJHEbJ1QZL7JYpkjwoSFlpMyY8DKKUjzbNgEQ7ouwkCtRdrrVtp3hMAW37Zm6PJOA5KLTg7KDq+W3z7tUE6zOgkNzxuow9duDgkGto//ls0mlCsiK9bGNFblcZgOuUGwH8HjwdnJ8NLC1AsDD606itQP4nE2E7/pk652rpKHkCNawthQ4b0KJ8bNdA7wBhfdhpzTid/rLhkMnOzAmzT5vtISvKQjDKMevKOiNji4Pxrh1/N0uzrXBPhnpp7PvZs9P7vOnICQ4cd65ZOTWZq1kc7/Si/KnDucw4GQjMhKlDz4fvSiJBih1RTgZ82zOBPO1mNeHVaQJnildbtH1eHzBNYHHePNdwFZM+qT+CuvVNLOCDdxkPZU5qj6uQ9Oc5ZrBKYVrTpJKl2abbiE6az72VciOAP6UBw0x/dW2++xgLI3ljBXaeGFYAiEYSpwsTjvM4PxrA5N0hZkWRjfLz1k+PzYySwGKjIFjbac0ymqHF4ZnnZTBpfVqf1DNTBgaCtBuHStDNk7j8zzp+Bz/7NSO0/ts8pDhoJSgwQGcZT+aecaR9pwUjBzfDG2NN4U0EZNpc4TSyE9Dkb9L6z/7iY7Q52y0DlFc3AfK26stYbH+0wbOzs4vXWzgdl+v5I+qUxWkS+oqf0Cbgh+3TFxOE8gqR9MH8PgGct39ylUXgT78JRk5nxk7o3/b/ZqNvpdwY4KciCGpt+Uan4PX5890xOW+nc98pkSEOF6NMXf/gg9snazBjoAMSvLjhybF6mcrzdLyW/2Rd5wpjiGSEdEbftcBr3DbBkqBOh/LYZubiLp04EwGOwJ9jYf9MBbV2OR9tE5i5b/9PgHzw4MFJvjsBnT+4YXbqObZO2zytHNm6Yy7erJPzSqZIsPUth2mDQEV5zs5e3NiQ6z2nnPNW2EfGxPQRQS924FRV+ozPrWyOL9/L3XS0BY5j216sxJm+Cukg/uT63EbM77mGacjYim+19X4BdUT7oW040EYXxLdXLTcG+BnxzPobeJiJZLKYC6WRJ9I7iHi1QDC2kedYHvuP3I7YvIe7RW2yXYIImVCOOe0U2cmM6Fzsj+Mz2CQwWSY6nsErf2bVbINAZb1lRRWn4Lt24oxkYAy8cbqcy39u5FnXDvY5z2vSJtMP0RdZJ+eMDpg7bqhbFjL+o3opBCH+P2LgTnGQ7bd5Dxj5tRjeaKePpA9vGJtMcQ7su9QvAxbToTOXH46j7PRfy8I0ZdMpGTef0aDN8XZMkqvYbD5n3ExZPXny5IIw8OVtkdErDcrEIMH5iPwMDNTBq5YbAfw06rZsczDgdTMvlGEmxGvTnq93MKGxOuKGEZJxxpGyPCQDJFhwiZn6My/ypZE/cma1wI2uFAYZ9pHCvhgcOB4y+hgwg5HBnkwwcnHuPI80cgY3LpVTJ3frMLC6Td5lRccxW6XdRL90aBKKnDcDnZlLd3KRadmeHCRchysPHvMctkBPkHF6i6kBp6QYpNxvQCaMnkBHIOIfbasRMPqOV55mvV6t2N+Zg6c9Msg0Juy0CYMigwHJUvMt/o+tcu8oqb6AvecjvpuVQK5ppMnzZ5C3TTBgfskD/8zpQx8pHKSB2Ndm4h01ObFmgjyWz96k4QSFefodLbm1LUbLl49RrrRDYCZwNdmT9yRjdapileJJ4S2VZjUGQjsv+40ufTcMGZM3InkNwZub6Gkny2cuv70JTnbL1AfZEQO6g1aALzIbYDi26JZBzEDYbIT9pi3qPKDLec5qlTozyEZmrnC5WuEcOiCy77RFoGHab0W0qAPaij8b2GmfJBe223YNSV2IFpm3gx0DJd+Smxx9AJh6tvyxqchAe+QvcjF4EvTzW8++Gyy/EU3fY7+cVwdU6uK6fn9Ubgzwt42hltPLcTsWQSDXzPQHOsyWUuy4vN2MS2SzUDIOBoCZy5uqkbflyulwvgOg3boVZ29BjGyHhtRYDuXirYYOmgRUp3Wii8boMx7m8WnYM3OS1tn3/YL1uy6ZIx2OztlAiw7F4GtSwMJ5IOmwLXH+ch31yzEwT+z6Dlhkh9R75jCrEpOVlkZIPa5AyXzTBp/mZWAgO7eMHCtJjle7DkaW2Wx75nRFzDQh/YHkxKtWsuVml9QB7Y+rIV7DzWL/IDvtMsceP3588oP2LedvMDexoa1Qh2+l3Bjgn+lPsc7MJUPzObNWTigVbAUaVLx0CvhnwriiIODFIPkKV94rnnx6gkdb8hK8LYfzsB4v5Y2McRbeLUQHpvExFWGjpI4yR3QGAj6ZP9kWnYJs38txbpi5X97K6NUIPzt428mp/8yv9UjbIvA2O+N3zhP1GPlXDttSRflOPTb/IJhRt+6LxMKsO3oJWLlOG3P69DWUiX0zwBjEWpCmPpl24222DhguBFu37UCa/rhC5VzkczaOw/bj02mLq8OVn1IW2rpX/JaZch+1e51yY4CfDkzWYgNl/QZKKVSqwd7gcNRG+iJIEcAJ/mSfMfRnz56d3AJIQ6ERMw3TlqB2sshltjVz+kMyBs+ZF7cqGtyjl4ypgSp1y+U3v6fflTyUi0E0x/wa5DbndKoW2KMPM9Lor7VLu2PO3HbCPgmoZmjUYSMtkctP1Fo2zq2DgetHlswFwZ7ATJmZIiFLZ9BjsPdKZGWPHhPbJemYmRM2T/1Spshp/a0A1vPlVTNXRiRGTi/ah+jHSfW6NGLp77YHBzG3cTTWly03Avib09LQCOJmR1GEDcwKXwUOTyzP53/LM5KRhdHnc+7fT5qH32depGj4dK/ZYupk/DRIvxrAaRe2R+Ni4CCDpj74febFw0RtpZS2covl2dmLd+DwdsvI5RRN6nGe89mg6TkiG6O8zW4aGHHucy0duLFWryhtK65P3a/kJbDx7i6vWlYg0Fi5x+rgyLQL779Pn94IZV/53FZllJd+Q8JlHdjnvQJjGyFIrL9i++wzsvDnUjMn+c62GMD5P+NKGsi/HxG7YuqHNm87Xn33MaevvhDlRgA/iwHaRkDDZbHx01iagTbW2Nq1A8c42QeNL4bw7NmziwdpAvp+4Zs35KyHvCyN52PAZlleWubWSI6b+pu5nOukQ7dzznGbxc/MpZ9BtO65h2DgIvBFvnwmS22gfRXAsLSH5Ti/LCQNR8xrFaRaUGBfnqMU5uOph+b8DkjUQ8sdeyxJD6a/3I3CFAbJx9G4CHQNlCm7bbPZMc85mKROm5+0xTSOx8zg28CfuuMeVNrKBi7JG2Xl6tn7gSz0MZI+E7FcvyKrL1NuFPCbCawciADmJScNvgFFcyQ7rVkWjbUtjdk/00GJ/AHrOBeNsUX0fL9///5J/W3bTt59YyCeOX1mgd8NlqzPtAYDAB13ZXS+DTPXZ6XQAogB18F1xbToUHRkzzf7XREElmYPvI75eduLmTntkwy2BQjKnHMENKeOnApqeuKYV+3NvEhvkO27H94hEz2wH6cdrZ/oIv+tH+vZn8/OXtw1x1Qn6zSbbL6fYixwvyQ4JkD8H2IXGULkeJt25t9pV/pBSxO11Sbloc28arlRwE+WPnP5TZOp04zGx/jfRsH+eD3rtqhqB8tEMNrHkRj9A/hhu2T6rEOn43nqhncbGJgNyLmOwcAGw98aCIg7v+7NK6+8uILIn1+qZtCnzj0nTvswoPI4x75iQSu23AJQu879NZJB/TfmyT4sj/t3YPAct/r2BZYmPwPozGne2teanbItzlXm3f4XvRDMGLxXeyj5H3CN3Gb+9u0EtPia/Yism3seKQw0uY5jM5tPHe+PrIiOP1OO6Mp7Ec0+OLZXKTcG+Bsg8Dgn2ZOQYrBgW2ZqrZ/GRBubIjjOnD6gkusb68+1fBAof77Pn3J6leDxciwGYgN0xkn5DcB+ktK/8engSyMm8zeLXzlBvmdj23PT5rOx24xrBYQcc2ubsqQ0efK9pU9WBITXGFBX9sxzDbg9F77O+iGAmjnTTmZObx+mrB6zA56Bj/7i+ad98kYHjtmpEc+3gzMBn3ozEVrp1n7O1KRti6s5+zTn3MGc8kQOYgVlbYHfaatXKdcC/m3bfm5m/tnMPJ2ZJ/u+v2/btq+ZmT8zM98wMz83M79v3/d/fF7/IzPzPef1/+C+73/pqP2mrJn+5BpLW1rTGBw8uPnCCeEyN/Vt4GbBdBLezmaA8O1xHCNlJGugXvjf+U0yhdRrgLzvL+6Lz3Ut124wcJsNUOmsYXYGhMZ63Y912uaNdSxXcwamsLhi4Lw2sD0iC/xsoLBuDCpetTQQNwg3WY+AhX1R19azZeH98q7LuWDq0H658h9exzZTbNcrG6adOVBah+nTenXhqsO69Qqm+UfaoExeOUW3lOk6gZp643zRj1+1vAzj/x/s+/7L+P7hmfmRfd+/f9u2D59//75t275pZj4wM988M79hZn5427bftB/84LoZlidpZRBH17A+i5ku22d/dmqf8/Ws7z49WR5bjCp5woypbWpxHDZ2LkvtXOyTgLkKZgZ+MkDKZQAg4NtJ0/dKhyt5eZ3Pr0A8gM+2r2JJnCcey1iaozkVuWqXcjkYOpBRFpKUq3TX7I7z2FI0LJ7P1b4G5fYYWsDidatrW67b/mv57QNHsu/75XfhzFwG4rS9Wumx7yaXbShycSVi3bCNRgKyrxdiSJ991fJWUj3vn5nvOP/88Xn+I+zfd378B/Z9fzgzP7tt26dm5ttn5kePGlsxn1UA4ASY8XsiHDjcFgvZisHPMq5Ax/2xvZYvDGgHrJpszMM6v8g2mzF5U6yNm21SbwF9r2paUKVeDHBtXo90yLGxLz5AtwoOZq2se90A4msbsbB9rIIY66wCXBujA/1q5WubpG8QpBpoNFmtL9owr2EdrqZp3zlvUtF0dpVvtZWtz3s1z/oZewtoKazDzW3brdtKP84eeKx+ZmCFHQwg9L2jW45fplwX+PeZ+cvbtu0z83/e9/1jM/POfd8/cy7kZ7Zt+/rzuu+emb+Kaz99fuy4g4Vj5NzMZQNnKshBI8WTxYmwobAfKnm1oqAs7VyuM5g77WDHoWE4KHCzdwVELc3i8acez1FnBAzfWug0AudmNQ8rcGwMyI7G9lZBxfPHNujoHnva90rMsjR9N3CxPJalteW+DM6rsV7VjuuvVoG0yZn+2w1NH74RIZ/bqtapUxfPbZOvBRXaZOtjRQTy37qijhyAaV+NnB3t1eQa+jBTN2yTAdtBk3K/lXJd4P9t+77/4jm4/9C2bX/voG6T6BKab9v2oZn50MzMG2+8sTSKpuwcv+qYwXWm31YGmS6BZztn+Y4AYVWff04/sW8HplUevO138LiNm7pp7dGhmjO2pbAd17owGPh6p5244W0dkPk3YsC229POlstpoRY0LK8f5ms69cZ/mxNfQ7to51uAYwn4UJ6Vza/GejRvjTA4/2xdr4JkC0qeK8+//YCB6Ko0p/3Hdt50ufJn7pXxocr02VKDzvVbTtsr5fGeRcOj65a+dlTZ9/0Xz/9/dmb+/DxP3fzStm3vOhf4XTPz2fPqn56Z9+DyN2bmF0ubH9v3/X37vr/v677u6y4GTaa7Agof83kCiCP/EYCnPg11peDGeFy3AUwM0wDbAh8B8ezsxYMgfEqWD0qlboCMzD99866BlLRl5pFzkTHnvaKwvqif6MSvgPBYZ06fYo0jEZTsDEfsKsc4Vm6eN0DytQ1EGstNXc8bdcX6Tg+0TdUmXwNsX9P8hDJlPr1nlGto19x3Wvmar+Hnq+SnHTRwdZ9tX4vXO+jwmMGf/tFISIrtblXf/Zsw5Nr0S/C2r7Ri+Vc6u265Evi3bfuqbdt+TT7PzL8yM393Zj45Mx88r/bBmfnB88+fnJkPbNv2YNu2b5yZ987Mj11HmMYM2ufUbdc/e/bs4lFqAxj/s9gpVwDT+mtOFGdmGx6Xj7dAcjS5jUEeGXvq0VgJAhwDAX7mlGlQRr8wjoysMZbI1Ix8BQrWUws2ZlYNCHzNtm2Xnqfwhh2X5HRksme2z37y2WyVY3B7LAbQpse2muP4HZxXvydAWTPeXOPgZ3DjeNMf3wllH/J32gL7pC1ZL069cp7sZ7ZrH+eDhrThZiexXeqOdmP7auSzzRGfWG/kqBGkVYC4brlOquedM/Pnzzu/OzN/at/3v7ht21+fmU9s2/Y9M/PzM/Pd54P7iW3bPjEzPzkzT2bme/eDO3pcViyubey4rjcDeZ7X0ZHTduqunMkycWJyDV+wxj6bDDTgFC/xWC8GSINpxYaTtjJmg4xzpmyHxm5AY73U8X34nBOO3bI1w24O0OaT+jpKoRgIHQhW+xUNvNkX63l/JueYLmt3orTxePxt09/64NjZl4mEGWcLSu36VRAngWhptfz5lRxuy7rgvNomnJpr8nk+Oc8Ebl5vGze79hzSflpbLch7v6ytDpqskbf54cuWK4F/3/d/MDP/Qjn+KzPznYtrPjozH31VoQzENsKVQ642QQwKBpuW3+M1BF2ezzlPFK9vwYJG11iG22hg0By1BS8bPR3VcrXS5IrszHGnnVXAa3pb9d32ZlbjYvstsLVjbe5XOqXe3IYDDOXk2Fuu1224nQba7fr4hsfWAkHadZ8M2L6pYLV6DPh6/tsegnXafNU68GrHhKGNLYXgumo/15hMZf+oEQm34bZsR5bRgZeyRve2eZaVbl+l3Jgnd4+ArDHd1GmM1Y5n5/PkGISOImozwtUxT+TM5YfNVqDkcdIQDd5kEjzvNujINsgGYr6e31d3PK0C8CqArALeqr510ticZW5zdaRzyuwx2TYM7g0wWvC2HLyO1/r6pqfVuGY6qfH47EMkCF6xsR/+toFltkwtgDe5PJ5GsBz0m28fAaiDOuusiMCKGKwwY6WzJkvTc9r069o9h69arrW5+6tVaOAGNUfVFINEUg5ug3W9FCegminYQFph3xzLKlCxT8tvZ2Y5yu3lOoIUg6YBibI0gFy1n/GuAiqd5Aj8jwIOdWr9tPlvm3QrUuAxGlRXNnjkvJSJ83sdcpFr24Z70yN1YrZN0LEu/df0mjbzsjESCROWXMMf/Wl6oOzU62pc/t8C7cycsHLasvszAWgytMDv757L7Av4HUc5ftQW27TMtFnKzAfNjrDouuVGAb9zXTM9peLJY11vRqUYdGlQNNzUpdIbILWJ9CSuWM4RiFEX/uOY7QheojcZ8r+NjUHDsjRwTZ+8tqV9XOxwDur57xw6x9kYl9tPXbd/BERs/6g+ZVqBVFvtuZ5lYNvUA9tq+vUcO7XUyATr2p642dny1rYv3qliosMXnl1FLqwX68HBzW8LJUDaP+w3tEETQZcWpCxL2vBdUEcB13qhLL7ZwPK81XJjgP/IINp5GuiRU6S0OxBYN38taLQJaA7r85xoG52NwGOYubyhnc9mpTbaNvYVA6HxNl0aIHLMbND9OkBwDARjvgp6FXg4ztV4V6vC64B+G6/1ZEDz3Sis70DV7v5x+2w7dchquRptY4te0w7vdGl2Z3ldVnNq+dsmt4mV/af5mOemAXYjP5yHVSBmn01e98dx27Ysb0vPNT/w3LE4CKzkbLp61XJjgH+m59JSGtD5u43BUXl1DQ1gZi4ZUzNit2sjZNtmSenDy0IadDO8FcuL/GYVSXnZqCOrAxP13QDziLG1N46yv9XylHOVvo4YGMfa5oug6XMuHmOzhQbsM/1ujwZOmT+Op8nTwMfBmDLx/9FGcJsLlyNdN4LV2s4cO4XJuc35FlS4wvB4U9e/GsegSN2mPd49Y71zhdLGZz9fYcZKfw2nUqdhkdPTjSysyNurlBuxuduAkudWzs9lsMGS1zajJxjZQNotd022nF/JbZldvzn/TL+Nq8nPdl3aRpCZvx3L4NRkmOkrIAcw/n6q72JoZTVWBw07fY45IDiIGVib3uysjem5fgsaPtfsxuea/t22Qe/ITxpJoF5bAEobfh987uBqgMxrV3cBtblhfYN1+jQQtzbZl+cux/iOK455BdQt+DuVZX2yUO+Ue+V/27bV9wK1tls/r1JuBONvyqBS2q49jST1zWBTvKHKaxj1j+Sx47GuHb4BqOs76pO1OE/o/lcOe5XOPDYaZAsq0V1rp5WrWMoKCO1QR0vrVQqDY1jl11c5U/fb9NzmsYFyuzMj17M95tHbXK4CJNvNuF8G5Bsh8LzyAazIatlt7x4n+ydwroCybSAfBW/K0QKebbptjkZ/9D3qMXXTnsmN56PNke2IsjkI2j4tA3X5VkB/5oYAv4snvg0yRtLuwvBnpxPS5hGDSbnKAW3ozWBdt02+ZfPk0jhXd214vOmz6arpNOyIxasf6oT/DX68Lhtxq5TBkQOzX+t3dc0qiLM/j9ulzRvb48N6BNSrcv/53Oah3bV1HSJhXTQSYuBtrNJzkjH5B855e7XHlmubTbq0IGR/WNlAk7kx8pWuOGfUQ/Nh1mkb59QB9dxIo481+dx+IxLs+1XKjQH+Iya0UkhKi4ZUMqN26jejXUXxxmDYt3ObZB1ul8ZjOemMjQmujlsmth3Qbdc1pm/DXTlSwJ3O035GL/8ZfI/moYFRm48GXCv22ZxtpcOVfhvYtGV8Cz7uL8Vs3blogwllpCwtNWngTF0HAgbdBtZpm7LaPxuo0y+aHTU7t/171dqCnO3J+lmlCSm37ZLXrzbUj2Rq7bB4P2NmTvbjSCIsB/u+agV7VG4E8B85IY+vQCbnWsBYRenUycvAPGkG5BUYEkAbcK0YWgO01G8g8zKR3sBngDGwsN9W3+1GTy3dlnreuPb4Vrpt8jedN335uiZ3019z5hZEfNygYkdtoE75co37WpERy81UTCvtJgeTJMq/IjSruToaY+q3vD//pw+mY9v4Hbx5l1ObH4+p2QT7awTiKlzKdZzHdgdd6ra7m2wLrM9+2Tf3JF+13Ajgp+JmXhilDWYFqC3Sk9mQdRioWvTNuSPwsJM2gGZd1+EEUlaP7agPG30DviMHcn8MogYkpzX8wBFlcJ8GvQauq2Dn4G1waHPPOqvSgNSlgcqRjCub3PfLKTS24faO7Jx13AfrEnBp262fpouVPgmYq58R5Hh9TQtu1lmKMaD5qn0nxw3ER/bn8bqw79YW9ch3FVknDjK2/7TL23its5WuXrbcCOBfGWBbijUA4//G1JxvZnHUN4O5yjAaC2tBqC0ZLeMKPNsqhMZrfbl96sWbyjyWtnw7pRka++Bv7Po6Ao/P2QHb8pi68Pw6uLWxUt9Oo+Q6liNwtx00sD26rv3EXwsonAOXVaB3Hc/zUS6dulkFyvZCNPblz7691HZ1HcBqwOwVjv3URIV1G8Y0UtQ+u07TRfDJb6y1vlr2oT2F3IgLv69WFtctN+J2zua4nNxMFoE459oEcGJbjtLtkh1ZJk9im1Q7ks+x7+bYZiBsb5XnawGPrMTAlz7a8r8ZuOfjZfqgvA1QvFnogGJ5mqxOM61Ka8vnV2zQefsVyK+CsUGe9Vd9rupQlmavHCvtpgGobYfXU79Hemvypq7z+gxGLbBaB6vA5nZWvuAVztHdQqu54o/KX2f+3F7qruzG9rsiAJS5rV5etdwYxt8Alg5o4+WdI60O20p7M+tbDt0+6zYwbPKujLblPpsO/LkZ+ion2Zgkv4eNRB7Kthpba7PJxus4b81AW+BZlRZgrNf22TZEIGxEoeWLm27a2Hi89e++Vu3ZLlmHel7pzoCwCkKsvxpH5GHwSLsr/dnXaHcrAFwFUvpqA1KPh3PLoNgwg/3ah6IP3856VK4KWEdkwTc5cDM9bazSYy0Yv0y5EcCfwsFclRO9aufcE+DJWX02WJlFteOUvU0MX+TUnGemb/6FeRgU/Ocx+JZCX982xVa3Eh4tzw0eDWjsuLzDatVm/h+xpav6sS5XDDYyrfYEjphiA0HWbct66z71va/CMR3Z6AoADGqrPSwCPG0lOnG6tY2dcq1sounRRGKV7mS//iW39keZPB8Oiv4ceZgp8GrLc+Tglu/cX3Sd2KT90AF+tWp+q+XGAL+NJcdscDm/cjzfWul2Gkg2NtT647lmnCvW49uzGiMgUHMsDdCONi5jZAk2q/ZZN22vfiijXetxZHxt7siirFfW54ZcY3tHxQ7EzyYArf5KPwSCtk/j69p8+a/1fZSOSFvt1sjmCwQQy5Gx2NaaPAb9I//jNW314rx/Kw14PYa2EnE5mnfXaX5tndF2V/slDljNXlq2wZvXPJdAt8KuI11eVW5Ejn/mMjOYuRy1m3Pn2qbUVR/57H7ZNh3tiFWtgJJ9tIDQ6rp+lq0GpyPw4jtqVjJkFeFVhu/EcH/bts2TJ0+WANbumU693DbLc17NWA+eD7bXxuXxNvBcATevjf54hwZTi629xiY5Bs9z69MkgXUbsfC5ZotOiVJuy7dt2yX7yWcSKus6dvT06dML8pA++d3jbT5lAI581jePU0a2Q7vk68l57QpTjnxuRcgcADjvXD21wNn8wISi6eRVy41i/G3DauVIvpZMZQVCKwCfuZxqmTl9qGIF7my/ycylYqt/ZDQeGx2MwG22lfOPHz+eR48ezZMnT05WAHyhWpPPY2ksrAU3G/Iq0Pr+cLIeA31jQQ2wV/LbeXxuFSBftt9mP7YRL+P5+ciGok/K2dKObpf7Om38JDg5v7rr6ygVRh00O/DLAlP8Lvumt9XcrXzId/iknxTb2ioAr4iFj61SpGwjgWe1CucYmg2tsGdl89cpN4bxz3SGu2IKRxM3c/kn1Y6AOn3PHDNwg1y7e6GBVzOqZqAtwLDYCHh9AD2g3vLLM3Pp9kvrojlac2g7mWVqvxGbfs2QPTd0nKMA4/ltjK7NDb+zUE47b9MFQZlMnctzfretOAXo0vSS/3fv3r3EYt3uKnBErmZvBCuDGn3EG6jWnW9IOBqLSUybP5NCYoX9h77nlRvltcxNtmYjbqPhSZuTlb3SD4kNnrcvZL7/Wox/27av3rbtz27b9ve2bfupbdt+67ZtX7Nt2w9t2/Yz5/9/Hep/ZNu2T23b9tPbtv3OlxFopfTGbtBfNRQDTK5rG8M0Uk5QAxyDiJfFrV//93nLw2NNXubDnzx5Mo8ePZrHjx/PkydPLv09fvz4gm2Y/XtsdLD2AzUcb9ppOmqvaeZ4yDTbmFtAas62ylcbbFx4n3eTkW01m6NMvruI9mCCwrGt9HtUaHMOupaVn83Y7ScOCKsVhvtzcHCfLVC7Ttusb/Nvm+G8NUBOO77Zoem02YD9zXn7VV32m2s5JvfFsXpvxW22469Srpvq+fdn5i/u+/7fmec/vP5TM/PhmfmRfd/fOzM/cv59tm37ppn5wMx888x818z80W3b7tRWF8WgzvwiN1paxMz1BjJ+JyuJkTiHmTacCmlRt92ueZTv9gZYq9v0YacnuD99+vTi86NHjy4CAQNCVgRtZdAcg5u9DnL8ztde2EF9S+QKJHx9vjewWrGjjIdzvQLHFZizDtsyAFqmBuyUgXboXK/7pYzNHhhwDX4t0LC9dm1LV1gP/PMthtbxKmDa3lapLx4z+Wr+12RoumtEznNAnbR5aGNqwdH+wmuoo9WYKZvrNrt52XIl8G/b9mtn5l+amT9+3vGjfd//ycy8f2Y+fl7t4zPze88/v39mfmDf94f7vv/szHxqZr79OsKsHJ65af5gAUGGim6RtTlU2px54UxedtmZVsbqQJPrDfKpQ+cxmHpSCZ5k+mT4jx8/vgD65PUfPXo0Dx8+nIcPH54Eg+Rdnzx5cgmIaLwrp7bcnjueW7EXX9dWSC2oW98EZtuN5W7stIFlk88BbeWATpHEZprM1I3n9ggIqR8ep85b/ZnLT+FaVq4ErG+PxQG76Y/kinU9B34NeevXq2nWa9dYB+38EYA2+3DwNNlxXY75qqCTkhTeqo8vRLlOK79xZv7RzPxH27b9F9u2/bFt275qZt657/tnzgfymZn5+vP6756ZX8D1nz4/dlK2bfvQtm0/vm3bj//Kr/zKciLP615M/opB0vHbj603A7BBEoRW7VsmytIAyisOTqj78TGPM6DA/wF9M3wGAG/ycqMpd+lw3DQ2y9HG6rufVrptOrQ+DWJHgafpx8GjzXcDHqYcmv21diw39ZFiFujAumq3/R7DCjDSrlcorLNa2TUAbHbm4ORrvLlNmewrbQy2kRYk27jd1upWbh6jvn38KEhY1qONaWOBg2PDnTZ+P0vRbOdVynWA/+7MfNvM/If7vn/rzPw3c57WWZQm0aWwtu/7x/Z9f9++7+/72q/92hxbMjBHO09QDPLs7PLLozzJBDr26eDBXHQznnxvLKMZQmOCzv+xmF2x7YB2QP3p06cXrP/hw4fz+PHjefPNN5dpn+T9o4eWLmvpBM8H6zVdszg/akegM6WNlm6ys/i/Acvt23l8bBX80q5tq/XHOT4iKUfO7PMGjbQfWzG7phxtfjiPnM+rQCx1Vrd3st7MnOwpeUVNmfb9xQq8rTioO86Fg6b1yeDR5LPds5+VLa8CZq61fbFN64xtOOCvglVW/K9argP8n56ZT+/7/tfOv//ZeR4IfmnbtnedC/+umfks6r8H178xM794HWGshBQ7V46dDOTcUJK+mDlNN6xSCaxnAzLw0rlbeiTfm8G0DT8Gljb2Blr5C2sPiw+wB/Q///nPXwB9ggH3ABg8yPop44qd0bHyAyscn5nQCmhbfx53Sye1vhyUeI66a2NoS3Q7da5ppMRg6fEZcCgvAXsV4JtPNNbpO22oC163ApbIelW6z+2sApQ3vSlPxub5iy2twJLymd3nfNpZzWEbw+q2VweYNhb6pYttehW82eZVRDPl7t1XvynzSuDf9/2/nplf2LbtN58f+s6Z+cmZ+eTMfPD82Adn5gfPP39yZj6wbduDbdu+cWbeOzM/dtTHKlqTiSwHoJwrgX0FXDPrF4itNt4snz/zPO9PdvRnXTJmg5mDDY0sgM8gEGb/6NGjefPNNy/YPkE/K4O009JKdvTm3FfNnc+3+j7edOt5aLlr9kH5CaiN/VPvK0LhsTVSkH4aCJlJtr0dAzvrtNWCSYqZsdswGBPo0kYDLtpf2ztoc+Zxt+CzAkC25fllnznH3wBuc2QQpt6cOjmyJx7jHK9uDHFwsH7Splc2K7J0lT5etVw3ZPyvZuZPbtt2f2b+wcz8T+d50PjEtm3fMzM/PzPffS7UT2zb9ol5HhyezMz37vt++cU7i2Kj4fEjBkIlHV3Pz5zA1vcq6hogj6Kyiw2+BYamCzpiQD95ezL5MHw6Nft48uTJBUtv+nv2rD8Bad3acAm4DdipyxXwO1izXZ9f6dZOZqBMaUDN70kX8jW71odXFPnf7klvAfDs7OzkSWjWbYEv+qX86W9VP8c81tXe04pwGPjYbpsXz7Ptxz64aodjOvJT13FuvAWflLZ/4DG3MVCWVjIvrc4qhel+Vnj2Vsu1gH/f9781M+8rp75zUf+jM/PRlxHkiB3k+JHTNyCNUXk5bIBqmzQGoRYMaNhkcU02Gzv7XU1uy/dxUzcAHMYf0E9guHPnzsVvw2bs/qGIlbPSIFc6bzle6rzNWZxpJYN1z3ZTt4EKr/V4mqwNYNm3V0O5hg9NHQW3Jg/HQv00W7sOwDi4e1yrNtp8Nl0TuEwKDNotWK9kpi5W87Vqr9lKSvuJ0XYX0tnZ2clvJtNuZ05v7mjYkeMG9pWcTe8rgsL5tMwkJXk9xquWG/HKhhUjWkXTnGspIE+kQZ6MxqmD5iA2JL9szMZqUGr5/xxPabnOI1nSZjZo83/14BaDQdry8n0FMDQ8L28td1YSBlGP23Pb5tH6afZB+aijtHnExmgjq0CX8wycdG7eummbPJKX47lqddCC5wq4DRgrwF8F+JnTJ975rIfnrBE09tv0b99obbkvk6UjHRg8Wxosn+PHbXV7lW1RZsrqtA03tFeBoPmd8cr/GfBftdwI4J85ZYIrw/d/D/wqBmg2s3I4OjZfccC2bcAEAAaWlVyWe3Uu16VNbuiyHwcAHm/g3lhO+mobazTgpntvphlYmiO7P+t3VY5uu1wFbQegNgduw/KxrRbkKV8bWwuIDlaUfaWTZrcNfJv8nIsWUBvrtf1aP/zzuQb+HJvTVOzHMjd/P/Jfy2U9W7ar/HUVbFYA3Oa52Tv9yjo7IjBHmHFVuTHA7wlqkdK5OAMmd/TzvU18M0Yr2BPBKN76tpOQAdoAeD2NkUacOuy3GRmf2iU7s/6a0/A82St1yLFEtpWhsy+DbvSYN3KuwC2fV7paBcmm/9Y2x7Nyqlb/OmTC4OtN1lU/foPlqh/Pidsx8B0FJOqy+Yb3aviZL11z2u6ors/NrN9KmeLXWnC81F2Tg3VJ4pqtea+q9eWA4GDT9sdWwdN12k0CLahHZy2ovky5McB/ZMgzc0kp3uRqTIYT2lIuBh+DuhWe/o4chiXGQOa9Yk/tDpu2ZMz3tDlz+S2HOc+A4xQMHSHH79y5c5GuaazLuqD+XTwWymaHoc5XYJP557VXMV8HXPbJ/RMGTMvb5sZ53zb3VwEI+3K6yP3QRjwXTVdps90p0krzh8ZAHagiU2zGAezIRzg3loXl7t27l4Ji09GKFFp3rb9GQhpgN0Zuva+C1Er/bJd4wWDmP5OAVyk3BvjtTC5mmman7dY3f84k+docayDsAJJjZsY0ckZvAhflOXI26yNANTMnKZ4cdwoozmg2cVUAoAx0JK8GzNJaUKVDMFBbf7zDiA6zen86ncuATNktnx3UY0q7LR9rx+ZY+KoPy8Finc4cB80VYHkOGrhFRuvC7VK21XjZr21kNdYVUK/0kmLdU37n67nH5DlpYyFxzHcHCtpUA3jK2WRu/z1ek61V4bX04bcK+Ck35rXMUbrvOvEdIqmbkuUVDb5FXPbTHJj95TqzNrbnu4Uiv1M27sdj43GPgRttM3OJ4TOfT72QGW/bNnfv3p179+5dsKezs7O5e/fupVsWCRht7HE0yt1YqHWdeitHWrEtGz+PMbhQ76u7TCgr9d9s6yp2/PTp07l79+6loNGCemRz7nzVfhtfrjN5sP3aJo9smTJS/qaHlf02Gaxf27WBnnVW4HoE0Ct2zf7THse08mvrm3PBequUL9u46jwDWAsE1Av7u8pGr1NuBOO3sTUDuGrSGvNrDM8RmnX93RNIlrDaCGqMk+dZvAcxc3llQJnN6lven8xg30/vtLl79+7JeS/RZ14El6tYXQt6Bhb/b4DV9NICp+eSZWUbdubWR0vVcK4tTxuv+z1i9ZZzZevtmFeiCf4EwwbyTB9YFrNzjoH/KXPacn9MSbJ+S9OxbZ63PVNOy7eaH+rHY6BOmp/bHxupaQH/CJO8klitEpqO2+rgiDhct9wI4KeBxJgbe+JkWClkFE2pZqruO/2nEHAN9pSrlSMn3Lat3n/Lifcf323Pl6s9fPjw4rONOP3kfxj+nTt35v79+1XuBAHqorGx6Jolj483J/XxFVtp15HFWi8zHSzZ56qPVfBo1zUHtH2u+m1BgXUIBCsAanPL4vdK2S/oQ02OI/KTOiYkR7pd6YMBwXPYgk8r7fq00coK4Ju9tGCy8nHiwQq885lBdxVELLPtkfo/svnrlhsB/CkrQF8Z42pC27Vs/6piJzPrYttmuiwNXGbmUgqnyW2j8i2afvUCr+FL6pLmCegn3eOnd73kdv47ffE7yyqn2/TJwjotb21A8nUcd9tHaU7XZDDIktWyjttIMOYxy84SXbSUFAmQAX8FLl6ZrMbooOL5avI0/diGZ/rdc40skVTYJlY6a7ZjO2hBzW2zju3JgamNazWXnoNW2H+zH8vS0tV8lxCvf9VyY3L8Mz36tdu0osQVC+GkN8dqym8MtzlcY30NkGgUDTRaHxxf2uIynYEmn/N6BoN2nDksPuz/7t27J/l938Vjh6EzZP9lxa7shP7Mcbc7h1bzS7n83UHJOqc+jvTegk30w7Zsc5bNc73aLEwd53hNNNyXdbQC6iOgW/mDr6OMBn7bDD8zddOAfLUazvmj8TQ7cdDhNZmDRsKsw7Ozs5PbR2k/1p+Bm8HM417ZefTYbNepuZYefSvgf6OAf+byxoiddubyo+QuR4q2ITZH9rXstxltk6cFGYK3QaABG3P4vFc/7P/hw4cnQMy7Y2LIZPjJ8dt5VumBpsfGyn1XzrNnzy7dvWDgtI6uCtZNN83wV+eaHawC9irn3drw/9hIW30YqNrdTCviQTkoZ0DWK8R8doCcOZ3DlR9dddx/ljGvRDD40meO5HKgXvkn54rXHW1yc+wG9xak0wZf89AITNMVb1ZZ/ei88SPXeS/ANtF0ct1yY1I9HHjLnTndYsfNuVXbPu/rzbLovF56uZ+VPG7faaEWONxu/viAFnOiNg4GAW7e+q85nvVD51ltSFnOtBN9+DpuHjswNL0ZXDiXtpX2nW2u7MDXG8w4ttZGA1ePw9+z4mqbeM253QcDx2pD1HcT0TYY8NMufyTehfXMnj3HBlDL5rlsZIrAHLLjZy8oD/XnOeS8Uj7q2vr32CyfA0h7mR9tlPPF/jkm6pHXeBzs91XLjWH8TanNQV2Oou3R9yMmSgNaMY0V02kG7Oua3HaUsHxey9s38xOKMWjf9550zrZtF58DBGbqNErKQibJu6J4PqW9Jtf6ZJ92QgNzAq9ZcnsRV5tbEgSf8/4Fnc12QbmtE49vRUYMRAYbkgyPlwBim2FagDI3oGJ96m3bnr+xta0EvcmYwheEHQVvgnLst62IVuSjrUxoFywONp4P2jwDDQlUCxScE7ZzNGaWthJbBRAzeuMI57bdqPIy5cYw/pnLimO0bIBJBbVo72N0El9H57djH7F1MsXUsdHYgDJxzTDZ1sxc+kH1sH/2NXMKqHEMbubev39/7t+/f/KIOwPBUaBqxmgAXYEvQazpswFAc37OH485mAVg2m+4ur3V3LLQLmbm0h1nbew+1sAzbTpIpR5/G9dteU5mXsw/N/4dMBqQt7RUG8dKBpYW/HlTAsfTmKtltb/yWreR/4202Q7dR/RH+2uvd6DebWPeI/Rnz9sqqLQx8jxXcSubvU65McB/lTKaw7eBtzz3apOwGVU+2yhaHs5Gwc+efDMxMwz/TjDb5zKXjmSnDZCenZ1dPLBF8M+5fKeOrnrFq+uvQO8qBpTieWGaYaXnlmJgn5SPc0J7aUDW2Ku/+78JQgN3A2+ra5mpO/bn4/zsu7OObq3l3WCrILIaEz/zFmPqJPXcnoGV83yVnowNLaDlv+tbDyvfvSqlk9KIluey4cJVMltvuc54Zmx61XKjUj02xrasa0DjiTYocYPF9doyvLEiGndzRoOGd+dXgMj/jvIOCHE2/lg6g2HSOffu3bsA/vv3718AwbNnzy6xMjsh9zUowwoEaIycN4Opjbrpw6DPugQ5rhbsZA3YObaV89Fm0odvoVvtxbCvdveICUYjOLSFpt/2eeUXzR8ayGbcXuFS7tSPH3JuuX9g8Pd4KDN9lrlxzgH7Xfmu59vjbOka+ksD+IYj9M2Z01RXWzFZrqaThg3+fxXO+PzLlBvD+GfWSrludDNYE7gaUDXAXrEVpgQaY/A4WsqoAW2r6/RH+k2On7dw2kECcGnbm7wG/DYHR8ZoPTU98DjltPP5umboDaRTVoBFHTZAYl3fadLAoOVpW8AhoPIz61m3tnWzuZbntgxuz38+Z/22406xzvRXe9hG932vq8eWnlnJwrkLsDo1Sd2wOICviId1S59wmtB+v7oxoul7pr+ahW23NJjtntd/IcqNYvwzl1+Q1qKgGZrZCZmMAb8BVXPOFTg1xbfAYTBpS8nImcCyAkb20cadMfgJXW7w5lja8NK7jXHFPKwrAoQDivXe8pON1RFgVn1T3y39RNkaaLvP6N6BtwGHN77z33ZMGRrZaOfYzxHLbWPgccro/myDq2BjcGQxe049p4F4bfOf5pPUIWWkrKt0b2vXY2df1nkyBL4ltRWuergabbI0gmi9ZKPdq03Wyf9W57rlSsa/bdtv3rbtb+Hvn27b9oe2bfuabdt+aNu2nzn//+twzUe2bfvUtm0/vW3b77yOIC062rCacdsgzKII5J5gGifZn9M/DTx8PnVaOqEt9WgwKdyUowHxwSkyJwc2LsEdBO7du3fC+lcbu40VtfQWc/TO13M+2ZZBzHVXd5aYEZmhObi20jaRrYMGVOzHryNuY+WctQDoPldBsgEldeE5od2m+BZDkg+OjfPHNsiYW1+NjRpM/UfdmUkfBeQWeN1nSttoPWqvzV/aacGDOuDrMo7qNt9q/Sd4cHWevvb9+R5fytv604v7vv/0vu/fsu/7t8zMvzgzn5uZPz8zH56ZH9n3/b0z8yPn32fbtm+amQ/MzDfPzHfNzB/dtu1KCe3UqzrnfdTzV0XmxqZsJI3h5XP7aUGe9x0VZmjNyAkABovInfrenG0bZgF5gr2vM0CuDNwy8pw3pVagtQIxH6dcLagdMccmj8eyAmqzcBKFXLfK4TbG5rHnGNsxuzeY+PpVv6zPTduXCX5sPzbGwteAr1Zfq0IiZblW9tZYrIMPQZdjzp+DEvte3SBAGVqwyPkWFG1fHH8jrNaFbSh7cpTJ4515mxm/ynfOzN/f9/2/mpn3z8zHz49/fGZ+7/nn98/MD+z7/nDf95+dmU/NzLdf1XAzVhvNysnaSiD/W06QzKO1bQcwkzcQtT+D4mq8LfoHwMlkzVKzaRuAd4qnfU8bDmCrgNoYhx0sxauVfDZ4s+1Wb3Wd2arBnoHQzmp5W4CwPtgOg8CKXLQUlvun/VAe2nFLLTiAtnE0O+K+j3XKwlVdW2Vbp14htDHzHAkW05seb2vTQOyxtlUHGTjn3vVt09SN7S3y+ViuaSvjlV5c9v3FA2qcJ8v8soH3qLxsSx+YmT99/vmd+75/Zmbm/P/Xnx9/98z8Aq759Pmxa5Wm7MbCVwyCRtZSB3buHG8MzXLlXMoREKxWBStjaCsDB5/crUNGH3Zw9+7defDgwbz++usnQJ/0DnWzSjV4tdIcYjVGyxr95H8LAAYkt22ANnityEL+E2RY2q14BlOPhe034LWsrZ22WuH1rZ02tnaLnwGa82cAboE2OiCo0wab/5FANPvmapP+RL+kbF7t5DufNCcgtxSjdWU98XoHM+uK/eVzSp6kT/tNrxxLwx3KtyJjli2lrTBeplx7c3fbtvsz83tm5iNXVS3HLlnxtm0fmpkPzcy88cYbzysVYzRLTPFGSlNm/tMxVkt+g4Ajt8HQbIv/2+Qejcm5WI7v7Oz57Zkpz549m/v3759clzdw3r9//yIo8B7+9tQuxx/DM5AdARU/57tzjgaimRd5ZwdlMlQWphgMPq0Nj4t1VsHHc9IcdUUg2jkDmWWlDR3Zrfvn55WNehWxCpSp6zZX1zAI7PvpO2hYXI/jzXG+88ZjbH7T9sg8Ps9t08OK9NGXjsjZqrBvrjaajbluw7x85pPO+eEfH3/V8jKM/1+dmb+57/svnX//pW3b3nUu5Ltm5rPnxz89M+/BdW/MzC+6sX3fP7bv+/v2fX/f137t1855O5fA1cBucLaS261TV7HDlOaEjS2wnpd4K4NkX16KGkgC0sn5J60Ttv/aa6/N66+/Pu94xzvm7t2781Vf9VXz+uuvz4MHDy6e0E1A4J091E/bCHRAayDsMdr4mq45R+6fbTVgbH1Yn26DOd489ey5bcFtRSJWtmOwNJC2DfS2UrWumywtYDMA+BqnS1ou2+3zwa9Vaq0RqFxPX0kJwDPtRNBvQM9rj9J3/N5SdSsdr0gd+6We2zXW3cxlm3b9pkvLS73zjrz0wzz/W0n9vMyVv39epHlmZj45Mx88//zBmflBHP/Atm0Ptm37xpl578z82HU6sDF5c4iFCjZbzXnXtbPbWBrYtf7MLlqwclsEPfbfcom+hsw9QWDbnqd+vvqrv3pef/31ef311y+9d5/LR28EO4XGsawYVss/e/yNiTbQs7PkMwsdOtfR2A26+UxwyefVqoogt8rrcs5XgTx1VwFmBZzU+4o8eC+EpCA2ZPvk9xaA2vwkfUF9et5XtprjTv0Y4OjL/Ez/oF7pr/ZpzwP1SX35u9u23R3tWzjg+P9KxyuZCeweY/stbKbPjoLmVeVaqZ5t294xM79jZv4XOPz9M/OJbdu+Z2Z+fma++1zAn9i27RMz85Mz82Rmvnff9yu3nzMofqdhO4qnTgo3XswAeD0npjEJsyf3YyOiXAQn9uE6DchWRhOjyB06jx8/vgD2BnZ+A2cLgKtASF3OzMWtYy0YNJbJ8wYfOgffQ2P9sC07hOeB8tt2WBoDzPg4lnbXi1c5PM/rOBavRnMNz60AiWNivRZojkhGq9dWHw7sWfkxoFiXzK1fxx/sj80nHLyop6Q2XJpOPFer+v6dhW3rKddGYjznKd78bSTH7VKG6CGA3/adbHOvWq4F/Pu+f25mvlbHfmWe3+XT6n90Zj76MoIYkFlikD7XHIN3wtiwvTzKMU8kJ/AoODRZiy4ugf7R+Al6DVw5NqYwCOgJAHTQVX4/n9vy9s6dOye/FmZHs/PTYJsDr9JMK31QPgNcAxzrvenc4+AcczOR/Rj8j4KeA/7MXNKhbYhgclUxqFhnbWXc0jYtGDJvzPb5IBPnkH65em4hn5vPrACNMq3qpqz2iii3gdIA3lI07pdzn/EbU4gfq70Wyhn5uWJ08DABSN/8/yrlxjy521hOop8nroExP7ut5ugrECFwrVjeUWmBZmVYPm/maEfj63OfPHky9+7dO2FLZmL5yw+wNCNcsZQGYC2ArUCPY2AbGfMqeHq+bPRtNeXP/n5VHZ/3SopymXExsHHs7Zo2VurDIGK9+rOJj23U9raayxZUW8CzrXkuTaD41+Q0+MY+WFZptHaThv2Xc2gZmj5X+07X8elma2nTc+px8N0/7GcV9ByEX6XcKODf9/1kI6MBbz6njr87srKOFbWKwlcFFQPXKijke35NxxGb9cx46IjO57otvzQrn50PPNJnMzJ/Xq260t+KeRkwOb5VoM1xz22b36s2f1fH2LYdzQGYY8yfQZ/t8jzld+Ai6PFarzqpx1W/1AkDGL+v5tlg7ODnubFcJi3so8npzedcFxvzavLILh1Q0n7kDrC2Fb991iuDFcY4yKxsm3Zs/XrzlntRlNvE6a0w/ZQbA/wzc2lSZtYsMsXfWVZOyeuaQTt/xzZWO/4taFgup0dWTtKMzIARQ06u3yyvbeq2QjCxnDFQs7w2NuqHY8h/A8PKeZs+myNdVY6AsQGSc9rt+raZ7HHz2sjK1FFLczWw4nHLbPBphKSNvQUhB4e0aUZv2c1aG2FpvmCd8zduKYfH1MZu3eR6PwhFm7E9Nv00fbk0/PA42I7167E2uzNRTeqKc/Oq5cYAP5XDd7MfMYgV+M70lMJVE+12uMxcsSXL7kls0b4FlnZ9MySfi5M6ZcQAsGJLBgkaZ46t3gfCsVAHLAwg1i3Z0lWgz/YaMLb6BjTPl1mzZWnBiLlug5uvWfVv2/De0nXA3vPX9L0CK39nH76bpQXr1i7Z9NH82nYbiTiqvyIRHA/fZWN7to0abJu8rQ/rvtlXm3P7Wv6id6/8eB39e0VoXqbcGOCn0VyX0bVJaYbJwkjJfJ03ldIHv68AqTmoz3njpq0cmgwGf7JH/0qTA+YRWBw5ssGJ8nEzvBU7WBtH+87PBhHKynExuF8176tgms92qqsC1NE5OmrrbwUezaGPgIbXtN+csB02AKFsZ2dnJ8C5YsVZxbS8eyMmlP9o3ilru846cd3IbPxgnZZzz3k/x3AdLDEJaYSTfdnmVnoI0XCKinb/VsqNeR//CohWUXVm/eZLfibItY24OENzvBapW1+Wj334jY4Bg7zQLWBOkHW0b8ZNp4sxnZ2d3t+d/00PHMMR4yIruSogp51mlKug1oDN8jhtRZ2wrevmPh14PVbq7Sg1Y9kbODNllnnn+asKx9yCl3Vi0PQYrQcWM37bC1d3Oc6c/Gr118C7bc5SZ7kuMjUfbEGE+1pNFu8dmKS0Pto54gZloczWI3VhGVYvXMvxJ0+enOwFvNU8/40B/pme06TD8zgZjNtYgUFLWzgy+1fv085qYzjnzOa4KdOMgeDCPGdjYc1IbeT+a8yVoGZntO4JVvu+nwQw6nXFUj0OtkUdGMya7luarYFs061toTnMinm7r+bkHnNzco+ZY/O56+iyyWuZDDxNXo+fhGC1V2O7mDn1GfeZ8zOXn8S1781cfnGaA3Ijeqy7IoKsw9XYqp3YTwsUnAf6FLHAOm664X+v3tMWg1jT1auWGwX8rYQlcUltgJy5/PImXk8gYxspPLb6kWU7NtnJEfhd5disyzoNCNudEA4GHA+vdXuWx22bEbdXTrPwlQ9HueZ2bfpPf9Fr6tMprDPKfcTiXYdBYAX6Bri2Mc+g5uCeuk6J5LNBhZ8NHpH1qhUu9WOm3oKXP1tftq88HMj+8ryHWTzTggZX6nSlmxbQeNyB3AGgBRB/Z730YV+gLmgz/lF7zo+DMDGp4RfPtfQl7Tbf+frmly03CvhtcFQOlzdWGqMjJ4epnQbOdl4b2AooCaLNmdyfWdMq8KyMzH37vdxJ8dAhcrwZfdpxf6vgcxRI2zgpm8e+Kg0EG0i3QNLmoQVJym/HdBC0HihT5qCxRgOnQaU5O2W3zbbglXq2J9aj/XqvgXrzmD1Oz1vmgkSkEQXbeNND84c2l81G7Rvsq6XTmm3zXNpgutS40AC8tc02rd/r2CLf/Mm+PEdvhfHfmM3dmcv51FVEnHnOMp48eXIpb99ysqvUh+vw2pVSVwDJfgwobROI5+0M19EBjaaBjPXQgDfHeO/wqh+2f3RX0oqdNTlozOyrLW05L9lc5Lk2n60N68E6tG5Zh5/N/lr7TS+tbgsMHo9lcOqRq6wWFFZyWv8zswxqtHP6Xfr2xibnrAVE2ul1A6jnsrXp/Zmju5Oaj7RgszpnW2Q7LvQdFmNOI505nvJlk+M34M2cKsKTnFvrVqmZ/Oe5lqdvzMNBwsWO0FiIz69YcwtEjel72cw26agce2PJ1o3PN4a37/tJcAj4Mr0TAHBuPv85buu/MVH+2fnaK4Gtz6tYWAMapw6ck25jSD3r0rbl+fHcO/ibUVOfLf/s8XOM19GLbSeA2QIc56pt5Lc+2M91SAnnIp9NKlow5vUrP20Eh77PAMI2Vis8jq21uQrmrLfvl/fROBZ//7K6q6dFbxttM9iZ04lwtDabvE4b/k+HtBwrdkmAbk5jINi2F/lsgyUNyCsItk+5+AMWPufxN92yeIwNsPmfwXq1MU49NOdvTsO6dAgC5coZrUcDbPpx6ozjd1utj9VmtNNPq/9NxwRN6zGyHLH06IhtrQCX7XsOmz3yOgMoZUlpZMz1ndP22Jt+2fdRwPf4SWScw2fw9V5ZPvOOnNbPkQ7YdsOWVfmyYfztzpgUK7uxoRXbaQC5Yn0zlw3Yk2wgizzefWd9BzSO2fKQ2TRg9dKYDtQCWnNUyrliIzF89m/AbQDMdh0sOQ8cD0sbewMo20QD4hWwrYCL8vIVxUdB32DDu6qoPwe7o2LAd/22qmq22WyIc0a5jvZQAm62Zc/72dnZyY0Y1Kt10PSS77YZytHkbQHYAOwnhB00m45sw2TkJn4rkG/j8/HHjx/XgM72qc/VDSUvU24M8M/0KEbFrwDU39vkeunYrlsZoY3RLIByrtqwE1LOZuS8fgU01gXBhu88srOscsDs27eS8TqO2334f2OZHqvH0j577E1XDkjUYbObtuHGfhxYDOrWhZ1/dStuC5TWKesQdFcgczR2jsfjbIHX/sLg5zZdeKtkdOb6Kzuw3E6ztf4I7JSB7ZpYeb4T6NmvS0svEoD5LI6DahtvAqRfOc2xthWOb3J51XIjgL8ZbEufNBBYsdy00SaaRkmjaL856/p20tXdP3bMlQy8hudoAI2BNEcxWOWzb0Nsn2cup06ocxqoQYj1fDcJ6zbg5oagWSfH2oDHeuSYGwC2VwezH46VOqB+MkanIlb7O6196q7N1yrwHY1x9fu3rQ3P7ZFdut8GRtEJ9RM9Np9sOlqNm/7bUmUO0g6qlLMFbMps28/nXG/7Ifu2Xuy3xA8/rNWIaKtjzGmB8LrlRtzVY4Y+c/rbrD43c/kuHNYx2NqYG2C1tjxhBqHGvtlmwGaVMokxr9pp4O5+aPheYjd5mwy+dqU3g3Zz6LYJ11YHHjfbYP9pk3Nn8LZcbSM9n1u/7u9Iz6s5OgpMbewpDWR8fbNx31nTfqzEMhiIaNtNX7Ql20jqrgJg7ve37lY+m2v50kGPfUV6oo/mg9bxilA6aNH2aMP2J+vAOjWGtKwGA6VLXsXusdivXrZsLbr/apdt2/7ZzPz0F1uOt6l83cz88hdbiLep3I7tS7Pcju1Ls7Sx/bf3ff/1L9vQjWD8M/PT+76/74stxNtRtm378duxfemV27F9aZbbsV2v3Igc/225LbflttyWX71yC/y35bbcltvyFVZuCvB/7IstwNtYbsf2pVlux/alWW7Hdo1yIzZ3b8ttuS235bb86pWbwvhvy225LbfltvwqlS868G/b9l3btv30tm2f2rbtw19seV6mbNv2nm3b/p/btv3Utm0/sW3bv3F+/Gu2bfuhbdt+5vz/r8M1Hzkf609v2/Y7v3jSX69s23Zn27b/Ytu2v3D+/ctibNu2ffW2bX9227a/dz5/v/XLaGz/63N7/Lvbtv3pbdte+1Id27Ztf2Lbts9u2/Z3ceylx7Jt27+4bdv/+/zc/3FrDwP8KpfF2P7dc5v8O9u2/flt274a575wY2tP+f1q/c3MnZn5+zPzG2fm/sz87Zn5pi+mTC8p/7tm5tvOP/+amfkvZ+abZuZ/PzMfPj/+4Zn5d84/f9P5GB/MzDeej/3OF3scV4zx35yZPzUzf+H8+5fF2Gbm4zPzPz//fH9mvvrLYWwz8+6Z+dmZef38+ydm5l//Uh3bzPxLM/NtM/N3ceylxzIzPzYzv3Vmtpn5v8/Mv3pDx/avzMzd88//zts1ti824//2mfnUvu//YN/3RzPzAzPz/i+yTNcu+75/Zt/3v3n++Z/NzE/Nc8d7/zwHljn//3vPP79/Zn5g3/eH+77/7Mx8ap7r4EaWbdvemJl/bWb+GA5/yY9t27ZfO8+d7o/PzOz7/mjf938yXwZjOy93Z+b1bdvuzsw7ZuYX50t0bPu+/+cz8//R4Zcay7Zt75qZX7vv+4/uz5HyP8Y1X7TSxrbv+1/e9z2/eP9XZ+aN889f0LF9sYH/3TPzC/j+6fNjX3Jl27ZvmJlvnZm/NjPv3Pf9MzPPg8PMfP15tS+18f57M/NvzQyfDf9yGNtvnJl/NDP/0Xka649t2/ZV82Uwtn3f/+HM/B9m5udn5jMz8//d9/0vz5fB2FBedizvPv/s4ze9/M/mOYOf+QKP7YsN/C0X9SV3m9G2bf/czPynM/OH9n3/p0dVy7EbOd5t2373zHx23/e/cd1LyrEbObZ5zoi/bWb+w33fv3Vm/pt5njJYlS+ZsZ3nu98/z9MBv2Fmvmrbtj9wdEk5diPHdo2yGsuX3Bi3bfvDM/NkZv5kDpVqrzy2Lzbwf3pm3oPvb8zzZemXTNm27d48B/0/ue/7nzs//EvnS7A5///Z8+NfSuP9bTPze7Zt+7l5noL77du2/Sfz5TG2T8/Mp/d9/2vn3//sPA8EXw5j+5dn5mf3ff9H+74/npk/NzP/vfnyGFvKy47l0/MiZcLjN7Js2/bBmfndM/M/Ok/fzHyBx/bFBv6/PjPv3bbtG7dtuz8zH5iZT36RZbp2Od89/+Mz81P7vv8RnPrkzHzw/PMHZ+YHcfwD27Y92LbtG2fmvfN8Y+bGlX3fP7Lv+xv7vn/DPJ+X/8e+739gvjzG9l/PzC9s2/abzw9958z85HwZjG2ep3h+y7Zt7zi3z++c53tPXw5jS3mpsZyng/7Ztm2/5Vwn/xNcc6PKtm3fNTPfNzO/Z9/3z+HUF3ZsN2Bn+3fN87th/v7M/OEvtjwvKft/f54vq/7OzPyt87/fNTNfOzM/MjM/c/7/a3DNHz4f60/PDbiz4Jrj/I55cVfPl8XYZuZbZubHz+fu/zYzv+7LaGz/9sz8vZn5uzPzf53nd4J8SY5tZv70PN+reDzP2e33vMpYZuZ95/r4+zPzH8z5w6s3cGyfmue5/ODJ/+ntGNvtk7u35bbcltvyFVa+2Kme23JbbsttuS2/yuUW+G/Lbbktt+UrrNwC/225LbfltnyFlVvgvy235bbclq+wcgv8t+W23Jbb8hVWboH/ttyW23JbvsLKLfDflttyW27LV1i5Bf7bcltuy235Civ/f4+85RPB39lwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(kalbWordLoImg[2], cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce613fff70>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(kalbWordLoImg[3], cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(778, 1221)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kalbWordLoImg[3].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce615ddd60>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "newdim9alb = cv2.resize(kalbWordLoImg[3], (int(32*1221/778),32), interpolation = cv2.INTER_AREA)\n",
    "plt.imshow(newdim9alb, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 50)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdim9alb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce5e253790>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(kalbWordLoImg[4], cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce5e0d1ee0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(final_9albWordImg, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce5f3a8cd0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_9albImgBtw = np.zeros(kalbWordLoImg[4].shape, kalbWordLoImg[4].dtype)\n",
    "blackToWhite(final_9albWordImg, final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 50)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_9albImgBtw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce5e04e220>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240\n",
      "240\n",
      "240\n",
      "240\n",
      "248\n",
      "240\n",
      "248\n",
      "143\n",
      "112\n",
      "240\n",
      "240\n",
      "248\n",
      "112\n",
      "240\n",
      "240\n",
      "240\n",
      "192\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce5e09ba00>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61639250>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61688a60>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce616e52b0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce61732ac0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce63211310>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD2CAYAAADPh9xOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMuUlEQVR4nO3dX6icdX7H8c+n0aWlLjRuRglJ2rMsuVgp3VgOQbCUdG1L1i7VvRA20CUXQvZCQcFSrDduC4W96Lq9KQvZGgzUugR0qyzSNqQWu1CsJzbVuKdbRVw36yFnrBTtTUv004t54hmPM5k58+fMfDPvFwwzz2+eOc+X38n55OE3v+f5OYkAAPX83KwLAACMhgAHgKIIcAAoigAHgKIIcAAoigAHgKLGCnDbh23/2Pbrth+cVFEAgME86jxw2zsk/aek35F0QdKLko4k+VG/z+zatStLS0sjHQ8AFtXZs2ffSdLa3H7NGD/zoKTXk7whSba/J+kOSX0DfGlpSSsrK2McEgAWj+2f9GofZwhlj6Sfdm1faNoAANtgnAB3j7ZPjMfYPmZ7xfZKu90e43AAgG7jBPgFSfu6tvdKenvzTkmOJ1lOstxqfWIIBwAwonEC/EVJ+21/1vanJH1V0jOTKQsAMMjIX2ImuWT7Xkl/L2mHpBNJXp1YZQCAKxpnFoqSPCvp2QnVAgDYAq7EBICiCHAAKIoAB4CiCHAAKIoAB4CiCHAAKIoAB4CiCHAAKIoAB4CiCHAAKIoAB4CiCHAAKIoAB4CiCHAAKIoAB4CiCHAAKIoAB4CiCHAAKGqsJdUAYFHZ7vtekm2pgTNwACiKAAeAoghwACiKAAeAoghwACiKAAeAosaaRmj7TUnvS/pA0qUky5MoClhU8zA1rYIr9dN2mYffxyTmgf9Wkncm8HMAAFvAEAoAFDVugEfSP9g+a/tYrx1sH7O9Ynul3W6PeTgAwGXjBvitSX5d0pck3WP7NzfvkOR4kuUky61Wa8zDAQAuGyvAk7zdPK9L+r6kg5MoCgAw2MgBbvsXbX/68mtJvyvp/KQKA4CtSrJtj3kwziyUGyV9v5nOc42kv0nydxOpCgAw0MgBnuQNSV+YYC0AgC1gGiEAFEWAA0BRBDgAFEWAA0BRrIkJoJx+0/gW7WZgnIEDQFEEOAAURYADQFEEOAAURYADQFEEOAAURYADQFEEOAAURYADQFEEOAAURYADQFEEOAAURYADQFEEOAAURYADQFEEOAAURYADQFEEOAAURYADQFEDA9z2Cdvrts93tV1v+7Tt15rnndMtEwCw2TBn4I9JOryp7UFJZ5Lsl3Sm2QYAbKOBAZ7keUnvbmq+Q9LJ5vVJSXdOtiwAwCCjjoHfmGRNkprnGyZXEgBgGFP/EtP2Mdsrtlfa7fa0DwcAC2PUAL9oe7ckNc/r/XZMcjzJcpLlVqs14uEAAJuNGuDPSDravD4q6enJlAMAGNY1g3aw/YSkQ5J22b4g6WFJ35R0yvbdkt6SdNc0i5wHtvu+l2QbKwEWw5X+5ib986r+DQ8M8CRH+rx124RrAQBsAVdiAkBRBDgAFEWAA0BRBDgAFEWAA0BRA2ehALPUb+pX1WlfmIxRfv+TnpY4DzgDB4CiCHAAKIoAB4CiCHAAKIoAB4CimIXS5Wr8lhrA1YszcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKKYRjgkbp4EzI/tWi9z3v/uOQMHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoamCA2z5he932+a62b9j+me1zzeP26ZaJq5ntvo9RPjPKz8P8SbJtj6qGOQN/TNLhHu3fTnKgeTw72bIAAIMMDPAkz0t6dxtqAQBswThj4PfafrkZYtnZbyfbx2yv2F5pt9tjHA4A0G3UAP+OpM9JOiBpTdK3+u2Y5HiS5STLrVZrxMMBADYbKcCTXEzyQZIPJX1X0sHJlgUAGGSkm1nZ3p1krdn8iqTzV9ofGNWkZwiMMhOl8iwFXN0GBrjtJyQdkrTL9gVJD0s6ZPuApEh6U9LXp1ciAKCXgQGe5EiP5kenUAsAYAu4EhMAiiLAAaAoAhwAiiLAAaAo1sTEQuk3JXDQjbPmwTzUwZTK+cIZOAAURYADQFEEOAAURYADQFEEOAAURYADQFFMI+xypSlS8zCF62o1D1PT5qGGeVFhSiU6OAMHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoimmEQ2KaGRYF/9br4AwcAIoiwAGgKAIcAIoiwAGgKAIcAIoaGOC299l+zvaq7Vdt39e0X2/7tO3Xmued0y8XAHDZMGfglyQ9kOTzkm6RdI/tmyQ9KOlMkv2SzjTbAIBtMjDAk6wleal5/b6kVUl7JN0h6WSz20lJd06pRgBAD1saA7e9JOlmSS9IujHJmtQJeUk3TLw6AEBfQwe47eskPSnp/iTvbeFzx2yv2F5pt9uj1AgA6GGoALd9rTrh/XiSp5rmi7Z3N+/vlrTe67NJjidZTrLcarUmUTMAQMPNQrGkRyWtJnmk661nJB1tXh+V9PTkywMA9DPMzaxulfQ1Sa/YPte0PSTpm5JO2b5b0luS7ppKhQCAngYGeJIfSuq3kultky0HADAsrsQEgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIGBrjtfbafs71q+1Xb9zXt37D9M9vnmsft0y8XAHDZNUPsc0nSA0lesv1pSWdtn27e+3aSP59eeQCAfgYGeJI1SWvN6/dtr0raM+3CAABXtqUxcNtLkm6W9ELTdK/tl22fsL2zz2eO2V6xvdJut8erFgDwkaED3PZ1kp6UdH+S9yR9R9LnJB1Q5wz9W70+l+R4kuUky61Wa/yKAQCShgxw29eqE96PJ3lKkpJcTPJBkg8lfVfSwemVCQDYbJhZKJb0qKTVJI90te/u2u0rks5PvjwAQD/DzEK5VdLXJL1i+1zT9pCkI7YPSIqkNyV9fQr1oUvn/9LekmxjJQDmwTCzUH4oqVdyPDv5cgAAw+JKTAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKIIcAAoigAHgKKGWRMTc4J1LwF04wwcAIoiwAGgKAIcAIoiwAGgKAIcAIoaGOC2f972v9r+d9uv2v6Tpv1626dtv9Y875x+uQCAy4Y5A/9fSV9M8gVJByQdtn2LpAclnUmyX9KZZhsAsE0GBng6/qfZvLZ5RNIdkk427Scl3TmNAgEAvQ01Bm57h+1zktYlnU7ygqQbk6xJUvN8w9SqBAB8wlABnuSDJAck7ZV00PavDnsA28dsr9heabfbI5YJANhsS7NQkvy3pH+SdFjSRdu7Jal5Xu/zmeNJlpMst1qt8aoFAHxkmFkoLdu/1Lz+BUm/Lek/JD0j6Wiz21FJT0+pRgBAD8PczGq3pJO2d6gT+KeS/MD2v0g6ZftuSW9JumuKdQIANhkY4ElelnRzj/b/knTbNIoCAAzGlZgAUBQBDgBFEeAAUBQBDgBFEeAAUJS3c51F221JP2k2d0l6Z9sOPt/oiw30xQb6YsOi98WvJPnElZDbGuAfO7C9kmR5JgefM/TFBvpiA32xgb7ojSEUACiKAAeAomYZ4MdneOx5Q19soC820Bcb6IseZjYGDgAYD0MoAFDUTALc9mHbP7b9uu2FWkvT9gnb67bPd7Ut5ALRtvfZfs72arNg9n1N+8L1B4uHf1yzCti/2f5Bs72Q/TDItgd4c1vav5T0JUk3STpi+6btrmOGHlNnQYxui7pA9CVJDyT5vKRbJN3T/FtYxP5g8fCPu0/Satf2ovbDFc3iDPygpNeTvJHk/yR9T50FkhdCkuclvbupeSEXiE6yluSl5vX76vzB7tEC9geLh2+wvVfS70n6q67mheuHYcwiwPdI+mnX9oWmbZEt/ALRtpfUue/8wi6YzeLhH/kLSX8k6cOutkXsh4FmEeDu0cZUmAVm+zpJT0q6P8l7s65nVsZZPPxqYfvLktaTnJ11LRXMIsAvSNrXtb1X0tszqGOeDLVA9NXI9rXqhPfjSZ5qmhe2P6TRFg+/itwq6fdtv6nO8OoXbf+1Fq8fhjKLAH9R0n7bn7X9KUlfVWeB5EW2kAtE27akRyWtJnmk662F6w8WD+9I8sdJ9iZZUicb/jHJH2jB+mFYM7mQx/bt6oxz7ZB0IsmfbXsRM2L7CUmH1Lm72kVJD0v6W0mnJP2ymgWik2z+ovOqY/s3JP2zpFe0Md75kDrj4AvVH7Z/TZ0v57oXD/9T25/RgvXFZbYPSfrDJF9e5H64Eq7EBICiuBITAIoiwAGgKAIcAIoiwAGgKAIcAIoiwAGgKAIcAIoiwAGgqP8HP1kYeIri25AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce6325fb50>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce632be3a0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce6330ac10>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce63368580>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce633ba790>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_9albImgBtw)\n",
    "plt.imshow(final_9albImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "fig, ax = plt.subplots(3,2)\n",
    "\n",
    "sha3alWordImg0 = ax[0][0].imshow(sha3alWordLoImg[0])\n",
    "\n",
    "sha3alWordImg1 = ax[0][1].imshow(sha3alWordLoImg[1])\n",
    "\n",
    "sha3alWordImg2 = ax[1][0].imshow(sha3alWordLoImg[2], cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "sha3alWordImg3 = ax[1][1].imshow(sha3alWordLoImg[3], cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "sha3alWordImg4 = ax[2][0].imshow(sha3alWordLoImg[4], cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "finalSha3alWordDisp = ax[2][1].imshow(final_sha3alWordImg, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "# whiteToBlack(final_sha3alWordImg, sha3alWordLoImg[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce635a0d30>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAC6CAYAAAC3HRZZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAANwElEQVR4nO3df4hl5X3H8fenakhibNU6yuJKNw1iI6GudrEGSzAawyolmj8CCi37h7D5Q0EhUNYW2uS//JEfLbQIprFuaWqwSayLSJJlkxBagnHWqFmjZm1q4sbt7pgQtC2Ear79Y87EcXbGuXt/nmfm/YLDveeZO3M+njv79Znnuec8qSokSe35jVkHkCQNxwIuSY2ygEtSoyzgktQoC7gkNcoCLkmNGqmAJ9mZ5NkkzyXZM65QkqT1ZdjPgSc5BfghcC1wBHgUuLmqfrDW95xzzjm1bdu2oY4nSZvVwYMHX6qquZXtp47wMy8HnquqHwEk+SJwA7BmAd+2bRvz8/MjHFKSNp8kP16tfZQhlPOBF5btH+naVh54d5L5JPMLCwsjHE6StNwoBTyrtJ0wHlNVd1fVjqraMTd3wl8AkqQhjVLAjwAXLNvfCrw4WhxJ0qBGKeCPAhcmeWeStwA3AfvGE0uariQnbFLfDT2JWVWvJrkN+BpwCnBPVT01tmSSpDc1yqdQqKqHgYfHlEWSdBK8ElOSGjVSD1xq1SBj3Ku9xgVQ1Cf2wCWpURZwSWqUBVySGmUBl6RGWcAlqVEWcElqlAVckhplAZekRnkhjzallRfkePOqfhnk/fCiKnvgktQsC7gkNcoCLkmNGmkMPMnzwCvAa8CrVbVjHKEkSesbxyTm+6vqpTH8HEmblJPIw3EIRZIaNWoBL+DrSQ4m2b3aC5LsTjKfZH5hYWHEw0mSloxawK+sqsuA64Bbk7xv5Quq6u6q2lFVO+bm5kY8nCRpyUgFvKpe7B6PAw8Al48jlPpjtdXaN8vq7VV1wqbZ8b040dAFPMnpSc5Yeg58EDg0rmCSpDc3yqdQzgMe6HpgpwL/XFVfHUsqSdK6hi7gVfUj4JIxZpEknQQ/RihJjfJuhJJmzknJ4dgDl6RGWcAlqVEWcElqlGPg+jVXQZHaYg9ckhplAZekRlnAJalRFnBJapQFXJIaZQGXpEZZwCWpUesW8CT3JDme5NCytrOT7E9yuHs8a7IxJUkrDdIDvxfYuaJtD3Cgqi4EDnT7ktQrG31FqXULeFV9G/j5iuYbgL3d873AjeONJUlaz7Bj4OdV1VGA7vHctV7oqvSSNBkTn8R0VXpJmoxhC/ixJFsAusfj44skSRrEsAV8H7Cre74LeHA8cTRNg0zmVNUbto1iI01ktWaSE4vD/JyWJzYH+RjhfcB3gIuSHElyC/BJ4Nokh4Fru31J0hStez/wqrp5jS9dM+YskqST4JWYktQoV+SR1DurjUOPaw5mI83l2AOXpEZZwCWpURZwSWqUBVySGuUk5ibR0sUJ2tgmOYm4kSYoB2EPXJIaZQGXpEZZwCWpUY6Bb1COeUsbnz1wSWqUBVySGmUBl6RGDXI/8HuSHE9yaFnbx5P8NMnj3Xb9ZGNKklYaZBLzXuBvgX9c0f7ZqvrU2BNpIla7wMGJztdtlAtAJvmebpRztJGs2wOvqm8DP59CFknSSRhlDPy2JE92QyxnrfWiJLuTzCeZX1hYGOFwkqTlhi3gdwHvArYDR4FPr/XCqrq7qnZU1Y65ubkhDydJWmmoC3mq6tjS8ySfAx4aWyKdtEmuXqLpaGE+YmVGf8fWN8j7Osp5HKoHnmTLst0PA4fWeq0kaTLW7YEnuQ+4CjgnyRHgr4CrkmwHCnge+OjkIkqSVrNuAa+qm1dp/vwEskiSToJXYkpSo7wboTQD05y0HGaSrIVJVdkDl6RmWcAlqVEWcElqlAVckhrlJGZjvOry5G32CbnN/t8/Syv/ba72Xoxyhas9cElqlAVckhplAZekRjkG3hjHuzeGWb6Pw46J+7vXP/bAJalRFnBJapQFXJIatW4BT3JBkm8meTrJU0lu79rPTrI/yeHucc11MSVJ4zdID/xV4GNV9W7gCuDWJBcDe4ADVXUhcKDbn5kkE9k2sqp6wyZpcH2oFesW8Ko6WlWPdc9fAZ4GzgduAPZ2L9sL3DihjJKkVZzUGHiSbcClwCPAeVV1FBaLPHDuGt+zO8l8kvmFhYUR40qSlgxcwJO8A/gycEdVvTzo91XV3VW1o6p2zM3NDZNRkrSKgS7kSXIai8X7C1X1la75WJItVXW0W6X++KRCjmt8yXHekzfKjXbUDxt9LmcaBjmHszjPg3wKJSwuYvx0VX1m2Zf2Abu657uAB8cfT5K0lkF64FcCfwp8P8njXdufA58E7k9yC/AT4CMTSShJWtW6Bbyq/g1Y62+Da8YbR5I0KK/ElKRG9e5uhINMBDiRps1ompP54145Zlo2W/2wBy5JjbKAS1KjLOCS1KiZj4FvtjErCbw4bRjjOmeDjOWPqy5Nur7ZA5ekRlnAJalRFnBJapQFXJIaNfNJTPXbRpgkG2SSqo937JvluR/nOVv5syZ5roc51iQnGif9HtoDl6RGWcAlqVGjrEr/8SQ/TfJ4t10/+biSpCWDjIEvrUr/WJIzgINJ9ndf+2xVfWpy8TRJfRz3nZZJjstuhHmD1Qx7zoY5t+M6hxv1vVgyyP3AjwJLixe/kmRpVXpJ0gyNsio9wG1JnkxyT5Kz1vgeV6WXpAkYZVX6u4B3AdtZ7KF/erXvc1V6SZqMgQr4aqvSV9Wxqnqtqn4FfA64fHIxJUkrrTsGvtaq9Em2dOPjAB8GDg0TYJCJkRZWAlHb/J06eZ6z2RtlVfqbk2wHCnge+OgE8kmS1jDKqvQPjz+OJGlQXokpSY1q8mZWq42TOx43Os+h1BZ74JLUKAu4JDXKAi5JjbKAS1KjejeJOchKIKvxYp83t5nvPChtVPbAJalRFnBJapQFXJIa1bsx8NUMMp49zIrZG3mcfLP/90ubgT1wSWqUBVySGmUBl6RGrVvAk7w1yXeTPJHkqSSf6NrPTrI/yeHucdU1MSVJkzFID/yXwNVVdQmL61/uTHIFsAc4UFUXAge6/Zmpqjdsg0iy7tY3g2Re626Nw5wjSf21bgGvRf/d7Z7WbQXcAOzt2vcCN04ioCRpdYMuanxKt5zacWB/VT0CnLe0Jmb3eO4a37s7yXyS+YWFhTHFliQNVMC71ee3A1uBy5O8Z9ADVNXdVbWjqnbMzc0NGVOStNJJXchTVb9I8i1gJ3BsaWX6JFtY7J33xjAX/wz7mkGMK8+wP1vSxjPIp1DmkpzZPX8b8AHgGWAfsKt72S7gwQlllCStYpAe+BZgb5JTWCz491fVQ0m+A9yf5BbgJ8BHJphTkrTCugW8qp4ELl2l/WfANZMIJUlan1diSlKjmrgb4aQMO/k3zGSjE5SSxs0euCQ1ygIuSY2ygEtSozb1GPiwHJeW1Af2wCWpURZwSWqUBVySGmUBl6RGZZoTckkWgB8D5wAvTe3A49FiZmgzt5mno8XM0GbuUTP/TlWdcD/uqRbwXx80ma+qHVM/8AhazAxt5jbzdLSYGdrMPanMDqFIUqMs4JLUqFkV8LtndNxRtJgZ2sxt5uloMTO0mXsimWcyBi5JGp1DKJLUKAu4JDVq6gU8yc4kzyZ5LsmeaR9/EEnuSXI8yaFlbWcn2Z/kcPd41iwzrpTkgiTfTPJ0kqeS3N619zZ3krcm+W6SJ7rMn+jae5t5SZJTknwvyUPdfguZn0/y/SSPJ5nv2nqdO8mZSb6U5Jnud/u9fc6c5KLu/C5tLye5Y1KZp1rAu4WR/w64DrgYuDnJxdPMMKB7gZ0r2vYAB6rqQuBAt98nrwIfq6p3A1cAt3bnts+5fwlcXVWXANuBnUmuoN+Zl9wOPL1sv4XMAO+vqu3LPpPc99x/A3y1qn4PuITFc97bzFX1bHd+twN/APwv8ACTylxVU9uA9wJfW7Z/J3DnNDOcRNZtwKFl+88CW7rnW4BnZ51xnfwPAte2kht4O/AY8Id9zwxs7f4RXg081MrvB/A8cM6Ktt7mBn4T+E+6D1u0kHlFzg8C/z7JzNMeQjkfeGHZ/pGurQXnVdVRgO7x3BnnWVOSbcClwCP0PHc3FPE4cBzYX1W9zwz8NfBnwK+WtfU9M0ABX09yMMnurq3PuX8XWAD+oRuu+vskp9PvzMvdBNzXPZ9I5mkX8NVW9vVzjGOU5B3Al4E7qurlWedZT1W9Vot/bm4FLk/ynhlHelNJ/hg4XlUHZ51lCFdW1WUsDmHemuR9sw60jlOBy4C7qupS4H/o0XDJm0nyFuBDwL9M8jjTLuBHgAuW7W8FXpxyhmEdS7IFoHs8PuM8J0hyGovF+wtV9ZWuufe5AarqF8C3WJx76HPmK4EPJXke+CJwdZJ/ot+ZAaiqF7vH4yyOy15Ov3MfAY50f5UBfInFgt7nzEuuAx6rqmPd/kQyT7uAPwpcmOSd3f+hbgL2TTnDsPYBu7rnu1gcY+6NJAE+DzxdVZ9Z9qXe5k4yl+TM7vnbgA8Az9DjzFV1Z1VtraptLP7+fqOq/oQeZwZIcnqSM5aeszg+e4ge566q/wJeSHJR13QN8AN6nHmZm3l9+AQmlXkGA/vXAz8E/gP4i1lPNKyR8T7gKPB/LPYCbgF+m8WJq8Pd49mzzrki8x+xOBz1JPB4t13f59zA7wPf6zIfAv6ya+9t5hX5r+L1ScxeZ2ZxPPmJbntq6d9eA7m3A/Pd78i/Amc1kPntwM+A31rWNpHMXkovSY3ySkxJapQFXJIaZQGXpEZZwCWpURZwSWqUBVySGmUBl6RG/T8dWurfWfJBLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_sha3alWordImgBtw = np.zeros(final_sha3alWordImg.shape, final_sha3alWordImg.dtype)\n",
    "blackToWhite(final_sha3alWordImg, final_sha3alWordImgBtw)\n",
    "plt.imshow(final_sha3alWordImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "fig, ax = plt.subplots(3,2)\n",
    "\n",
    "skeletonize(final_sha3alWordImgBtw)\n",
    "finalSha3alWordImgBtwSkel0 = ax[0][0].imshow(final_sha3alWordImgBtw, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "skeletonize(final_sha3alWordImgBtw)\n",
    "finalSha3alWordImgBtwSkel1 = ax[0][1].imshow(final_sha3alWordImgBtw, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "skeletonize(final_sha3alWordImgBtw)\n",
    "finalSha3alWordImgBtwSkel2 = ax[1][0].imshow(final_sha3alWordImgBtw, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "skeletonize(final_sha3alWordImgBtw)\n",
    "finalSha3alWordImgBtwSkel3 = ax[1][1].imshow(final_sha3alWordImgBtw, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "skeletonize(final_sha3alWordImgBtw)\n",
    "finalSha3alWordImgBtwSkel4 = ax[2][0].imshow(final_sha3alWordImgBtw, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "skeletonize(final_sha3alWordImgBtw)\n",
    "finalSha3alWordImgBtwSkel4 = ax[2][1].imshow(final_sha3alWordImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce63792df0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_sha3alWordImgBtw)\n",
    "plt.imshow(final_sha3alWordImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce637e9fa0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(final_sha3alWordImgBtw)\n",
    "plt.imshow(final_sha3alWordImgBtw, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0 255   0 255   0 255   0 255   0 255   0 255   0 255   0 255]\n",
      " [  0 255   0 255   0 255   0 255   0 255   0 255   0 255   0 255]\n",
      " [  0 255   0 255   0 255   0 255   0 255   0 255   0 255   0 255]\n",
      " [  0 255   0 255   0 255   0 255   0 255   0 255   0 255   0 255]\n",
      " [  0 255   0 255   0 255   0 255   0 255   0 255   0 255   0 255]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[ 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255],\n",
    "              [ 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255],\n",
    "              [ 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255],\n",
    "              [ 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255],\n",
    "              [ 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255, 0, 255]])\n",
    "x.reshape([5,16,1])\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce648290d0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x, cmap='gray', vmin=0, vmax=255) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#results are satisfactory on single word but still need tweaking on single character\n",
    "#still need a way to decide how many iterations of skeletonizations are needed\n",
    "\n",
    "\n",
    "\n",
    "dictionary = [3,6,7,12,24,28,14,224,225,216,143,15,30,48,52,96,112,54,240,227,131,135,99,56,60,120,62,129,192,248,251,195,141]\n",
    "\n",
    "def normalize(img):\n",
    "    for c in range(1, img.shape[1]-1):\n",
    "        for r in range(1, img.shape[0]-1):\n",
    "            if img[r,c]==0:\n",
    "                b = calculateB2(img, r, c)\n",
    "                if int(img[r-1,c+1])==0 and int(img[r,c+1])==255:\n",
    "                    print(\"b: \" + str(b))\n",
    "                    print(\"Go NE start at: \" + str([r,c]))\n",
    "                    trackAndSwitchNE(img, r, c)\n",
    "                \n",
    "                if int(img[r+1,c+1]) == 0 and int(img[r,c+1])==255:\n",
    "                    print(\"b: \" + str(b))\n",
    "                    print(\"Go SE start at: \" + str([r,c]))\n",
    "                    trackAndSwitchSE(img, r, c)\n",
    "                \n",
    "\n",
    "    \n",
    "\n",
    "def calculateB2(img, r, c):\n",
    "    numbers = [int(img[r-1,c]),int(img[r-1,c+1]),int(img[r,c+1]),int(img[r+1,c+1]),int(img[r+1,c]),int(img[r+1,c-1]),int(img[r,c-1]),int(img[r-1,c-1])]\n",
    "    #print(numbers)\n",
    "    b = 0\n",
    "    for i in range(0,8):\n",
    "        if numbers[i]<160:\n",
    "            b+=2**i\n",
    "    #print(b)\n",
    "    return b\n",
    "\n",
    "def trackAndSwitchNE(img,r,c):\n",
    "    row = r\n",
    "    y = 0\n",
    "    column = c\n",
    "    x = 0\n",
    "    addresses = []\n",
    "    while True:\n",
    "        \n",
    "        if int(img[row-1,column+1]) == 0 and int(img[row,column+1]) == 255:\n",
    "            if int(img[row-1,column]) == 0:\n",
    "                print(\"break\")\n",
    "                break\n",
    "            row+=-1\n",
    "            y+=1\n",
    "            column+=1\n",
    "            x+=1\n",
    "            addresses.append([row,column])\n",
    "        elif int(img[row,column+1]) == 0:\n",
    "            if int(img[row-1,column]) == 0:\n",
    "                print(\"break\")\n",
    "                break\n",
    "            column+=1\n",
    "            x+=1\n",
    "            addresses.append([row,column])\n",
    "        else: \n",
    "            break\n",
    "    print(\"addresses: \" + str(addresses))\n",
    "    print(\"x: \" + str(x))\n",
    "    print(\"y: \" + str(y))\n",
    "    for index in addresses:\n",
    "        img[index[0],index[1]]=255\n",
    "    \n",
    "    for increment in range(0,y):\n",
    "        img[r-increment,c]=0\n",
    "        print(\"row build: \" + str([r-increment,c]))\n",
    "    \n",
    "    \n",
    "    for increment2 in range(0,x+1):\n",
    "        #print(\"success\")\n",
    "        img[r-y, c+increment2]=0\n",
    "        print(\"column build: \" + str([r-y, c+increment2]))\n",
    "        \n",
    "def trackAndSwitchSE(img,r,c):\n",
    "    row = r\n",
    "    y = 0\n",
    "    column = c\n",
    "    x = 0\n",
    "    addresses = []\n",
    "    while True:\n",
    "        \n",
    "        if int(img[row+1,column+1]) == 0 and int(img[row,column+1]) == 255:\n",
    "            #if int(img[row-1,column]) == 0:\n",
    "                #break\n",
    "            row+=1\n",
    "            y+=1\n",
    "            column+=1\n",
    "            x+=1\n",
    "            addresses.append([row,column])\n",
    "        elif int(img[row,column+1]) == 0:\n",
    "            if int(img[row-1,column+1]) == 0:\n",
    "                print(\"break\")\n",
    "                break\n",
    "            column+=1\n",
    "            x+=1\n",
    "            addresses.append([row,column])\n",
    "        else: \n",
    "            break\n",
    "    print(\"addresses: \" + str(addresses))\n",
    "    print(\"x: \" + str(x))\n",
    "    print(\"y: \" + str(y))\n",
    "    for index in addresses:\n",
    "        img[index[0],index[1]]=255\n",
    "    \n",
    "    for increment in range(0,y):\n",
    "        img[r+increment,c]=0\n",
    "        print(\"row build: \" + str([r+increment,c]))\n",
    "    \n",
    "    \n",
    "    for increment2 in range(0,x+1):\n",
    "        #print(\"success\")\n",
    "        img[r+y, c+increment2]=0\n",
    "        print(\"column build: \" + str([r+y, c+increment2]))\n",
    "\n",
    "\n",
    "def getBaseline(img):\n",
    "    baselineDensity = 0\n",
    "    baselineRow = 0\n",
    "    for r in range(0, img.shape[0]):\n",
    "        currentSum = 0\n",
    "        for c in range(0, img.shape[1]):\n",
    "            if img[r,c]==0:\n",
    "                currentSum+=1\n",
    "        if currentSum>baselineDensity:\n",
    "            baselineDensity = currentSum\n",
    "            baselineRow = r\n",
    "\n",
    "    return baselineRow\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 5, 7, 10, 12, 15, 17, 20, 22, 25, 27, 30, 32, 35, 37, 40, 42, 45, 47]\n",
      "[0, 2, 5, 7, 10, 12, 15, 17, 20, 22, 25, 27, 30, 32]\n"
     ]
    }
   ],
   "source": [
    "y_value_list = []\n",
    "x_value_list = []\n",
    "\n",
    "for i in range(0,20):\n",
    "    i = int(i*2.5)\n",
    "    x_value_list.append(i)\n",
    "\n",
    "for i in range(0,14):\n",
    "    i = int(i*2.5)\n",
    "    y_value_list.append(i)\n",
    "    \n",
    "print(x_value_list)\n",
    "print(y_value_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseline row: 18\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def makeTestImg(img, finalImg):\n",
    "    for y in range(img.shape[0]):\n",
    "        for x in range(img.shape[1]):     \n",
    "                finalImg[y,x] = img[y,x]\n",
    "\n",
    "test_img2 = np.zeros(final_9albWordImg.shape, final_9albWordImg.dtype)\n",
    "test_img3 = np.zeros(final_sha3alWordImg.shape, final_sha3alWordImg.dtype)\n",
    "\n",
    "\n",
    "makeTestImg(final_9albImgBtw, test_img2)\n",
    "makeTestImg(final_sha3alWordImgBtw, test_img3)\n",
    "#plt.imshow(test_img2, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "img5 = ax.imshow(test_img2, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "\n",
    "\n",
    "#x_label_list = ['A2', 'B2', 'C2', 'D2']\n",
    "\n",
    "ax.set_xticks(x_value_list)\n",
    "ax.set_yticks(y_value_list)\n",
    "\n",
    "#ax.set_xticklabels(x_label_list)\n",
    "\n",
    "#fig.imshow(img5)\n",
    "print(\"baseline row: \" + str(getBaseline(test_img2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce6490fd30>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_img3, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b: 19\n",
      "Go NE start at: [23, 2]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [23, 2]\n",
      "b: 9\n",
      "Go SE start at: [26, 2]\n",
      "addresses: [[27, 3], [27, 4], [28, 5], [29, 6], [29, 7], [30, 8], [30, 9], [31, 10], [31, 11], [31, 12], [31, 13], [32, 14], [32, 15], [32, 16]]\n",
      "x: 14\n",
      "y: 6\n",
      "row build: [26, 2]\n",
      "row build: [27, 2]\n",
      "row build: [28, 2]\n",
      "row build: [29, 2]\n",
      "row build: [30, 2]\n",
      "row build: [31, 2]\n",
      "column build: [32, 2]\n",
      "column build: [32, 3]\n",
      "column build: [32, 4]\n",
      "column build: [32, 5]\n",
      "column build: [32, 6]\n",
      "column build: [32, 7]\n",
      "column build: [32, 8]\n",
      "column build: [32, 9]\n",
      "column build: [32, 10]\n",
      "column build: [32, 11]\n",
      "column build: [32, 12]\n",
      "column build: [32, 13]\n",
      "column build: [32, 14]\n",
      "column build: [32, 15]\n",
      "column build: [32, 16]\n",
      "b: 25\n",
      "Go SE start at: [31, 2]\n",
      "break\n",
      "addresses: [[32, 3], [32, 4], [32, 5], [32, 6], [32, 7], [32, 8]]\n",
      "x: 6\n",
      "y: 1\n",
      "row build: [31, 2]\n",
      "column build: [32, 2]\n",
      "column build: [32, 3]\n",
      "column build: [32, 4]\n",
      "column build: [32, 5]\n",
      "column build: [32, 6]\n",
      "column build: [32, 7]\n",
      "column build: [32, 8]\n",
      "b: 98\n",
      "Go NE start at: [22, 3]\n",
      "addresses: [[21, 4], [21, 5], [21, 6], [20, 7], [20, 8], [20, 9]]\n",
      "x: 6\n",
      "y: 2\n",
      "row build: [22, 3]\n",
      "row build: [21, 3]\n",
      "column build: [20, 3]\n",
      "column build: [20, 4]\n",
      "column build: [20, 5]\n",
      "column build: [20, 6]\n",
      "column build: [20, 7]\n",
      "column build: [20, 8]\n",
      "column build: [20, 9]\n",
      "b: 56\n",
      "Go SE start at: [31, 9]\n",
      "addresses: [[32, 10], [32, 11], [32, 12], [32, 13], [32, 14], [32, 15], [32, 16]]\n",
      "x: 7\n",
      "y: 1\n",
      "row build: [31, 9]\n",
      "column build: [32, 9]\n",
      "column build: [32, 10]\n",
      "column build: [32, 11]\n",
      "column build: [32, 12]\n",
      "column build: [32, 13]\n",
      "column build: [32, 14]\n",
      "column build: [32, 15]\n",
      "column build: [32, 16]\n",
      "b: 66\n",
      "Go NE start at: [32, 16]\n",
      "break\n",
      "addresses: [[31, 17], [31, 18], [30, 19], [30, 20], [29, 21]]\n",
      "x: 5\n",
      "y: 3\n",
      "row build: [32, 16]\n",
      "row build: [31, 16]\n",
      "row build: [30, 16]\n",
      "column build: [29, 16]\n",
      "column build: [29, 17]\n",
      "column build: [29, 18]\n",
      "column build: [29, 19]\n",
      "column build: [29, 20]\n",
      "column build: [29, 21]\n",
      "b: 2\n",
      "Go NE start at: [18, 18]\n",
      "addresses: [[17, 19]]\n",
      "x: 1\n",
      "y: 1\n",
      "row build: [18, 18]\n",
      "column build: [17, 18]\n",
      "column build: [17, 19]\n",
      "b: 50\n",
      "Go NE start at: [16, 19]\n",
      "break\n",
      "addresses: [[15, 20]]\n",
      "x: 1\n",
      "y: 1\n",
      "row build: [16, 19]\n",
      "column build: [15, 19]\n",
      "column build: [15, 20]\n",
      "b: 99\n",
      "Go NE start at: [15, 20]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [15, 20]\n",
      "b: 67\n",
      "Go NE start at: [29, 21]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [29, 21]\n",
      "b: 51\n",
      "Go NE start at: [27, 22]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [27, 22]\n",
      "b: 19\n",
      "Go NE start at: [11, 23]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [11, 23]\n",
      "b: 19\n",
      "Go NE start at: [15, 36]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [15, 36]\n",
      "b: 25\n",
      "Go SE start at: [23, 36]\n",
      "addresses: [[24, 37], [24, 38], [24, 39], [24, 40], [24, 41], [24, 42], [24, 43], [24, 44]]\n",
      "x: 8\n",
      "y: 1\n",
      "row build: [23, 36]\n",
      "column build: [24, 36]\n",
      "column build: [24, 37]\n",
      "column build: [24, 38]\n",
      "column build: [24, 39]\n",
      "column build: [24, 40]\n",
      "column build: [24, 41]\n",
      "column build: [24, 42]\n",
      "column build: [24, 43]\n",
      "column build: [24, 44]\n",
      "b: 19\n",
      "Go NE start at: [6, 37]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [6, 37]\n",
      "b: 66\n",
      "Go NE start at: [24, 44]\n",
      "break\n",
      "addresses: [[23, 45], [23, 46], [22, 47], [22, 48], [22, 49], [21, 50]]\n",
      "x: 6\n",
      "y: 3\n",
      "row build: [24, 44]\n",
      "row build: [23, 44]\n",
      "row build: [22, 44]\n",
      "column build: [21, 44]\n",
      "column build: [21, 45]\n",
      "column build: [21, 46]\n",
      "column build: [21, 47]\n",
      "column build: [21, 48]\n",
      "column build: [21, 49]\n",
      "column build: [21, 50]\n",
      "b: 27\n",
      "Go NE start at: [17, 48]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [17, 48]\n",
      "b: 27\n",
      "Go SE start at: [17, 48]\n",
      "break\n",
      "addresses: [[18, 49], [18, 50], [18, 51]]\n",
      "x: 3\n",
      "y: 1\n",
      "row build: [17, 48]\n",
      "column build: [18, 48]\n",
      "column build: [18, 49]\n",
      "column build: [18, 50]\n",
      "column build: [18, 51]\n",
      "b: 201\n",
      "Go SE start at: [21, 52]\n",
      "addresses: [[22, 53]]\n",
      "x: 1\n",
      "y: 1\n",
      "row build: [21, 52]\n",
      "column build: [22, 52]\n",
      "column build: [22, 53]\n",
      "b: 153\n",
      "Go SE start at: [23, 53]\n",
      "addresses: [[24, 54], [24, 55], [25, 56], [25, 57], [25, 58]]\n",
      "x: 5\n",
      "y: 2\n",
      "row build: [23, 53]\n",
      "row build: [24, 53]\n",
      "column build: [25, 53]\n",
      "column build: [25, 54]\n",
      "column build: [25, 55]\n",
      "column build: [25, 56]\n",
      "column build: [25, 57]\n",
      "column build: [25, 58]\n",
      "b: 25\n",
      "Go SE start at: [24, 53]\n",
      "addresses: [[25, 54], [25, 55], [25, 56], [25, 57], [25, 58]]\n",
      "x: 5\n",
      "y: 1\n",
      "row build: [24, 53]\n",
      "column build: [25, 53]\n",
      "column build: [25, 54]\n",
      "column build: [25, 55]\n",
      "column build: [25, 56]\n",
      "column build: [25, 57]\n",
      "column build: [25, 58]\n",
      "b: 66\n",
      "Go NE start at: [25, 58]\n",
      "addresses: [[24, 59], [24, 60], [24, 61], [23, 62], [23, 63], [23, 64]]\n",
      "x: 6\n",
      "y: 2\n",
      "row build: [25, 58]\n",
      "row build: [24, 58]\n",
      "column build: [23, 58]\n",
      "column build: [23, 59]\n",
      "column build: [23, 60]\n",
      "column build: [23, 61]\n",
      "column build: [23, 62]\n",
      "column build: [23, 63]\n",
      "column build: [23, 64]\n",
      "b: 88\n",
      "Go SE start at: [23, 64]\n",
      "addresses: [[24, 65], [24, 66]]\n",
      "x: 2\n",
      "y: 1\n",
      "row build: [23, 64]\n",
      "column build: [24, 64]\n",
      "column build: [24, 65]\n",
      "column build: [24, 66]\n",
      "b: 66\n",
      "Go NE start at: [24, 66]\n",
      "break\n",
      "addresses: [[23, 67]]\n",
      "x: 1\n",
      "y: 1\n",
      "row build: [24, 66]\n",
      "column build: [23, 66]\n",
      "column build: [23, 67]\n",
      "b: 99\n",
      "Go NE start at: [23, 67]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [23, 67]\n",
      "baseline row: 24\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalize(test_img3)\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "img6 = ax.imshow(test_img3, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "#x_label_list = ['A2', 'B2', 'C2', 'D2']\n",
    "\n",
    "#ax.set_xticks(x_value_list)\n",
    "#ax.set_yticks(y_value_list)\n",
    "#plt.imshow(test_img2, cmap='gray', vmin=0, vmax=255) \n",
    "print(\"baseline row: \" + str(getBaseline(test_img3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseline row: 18\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalize(test_img2)\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "img5 = ax.imshow(test_img2, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "#x_label_list = ['A2', 'B2', 'C2', 'D2']\n",
    "\n",
    "ax.set_xticks(x_value_list)\n",
    "ax.set_yticks(y_value_list)\n",
    "#plt.imshow(test_img2, cmap='gray', vmin=0, vmax=255) \n",
    "print(\"baseline row: \" + str(getBaseline(test_img2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n",
      "3\n",
      "52\n",
      "54\n",
      "141\n",
      "54\n",
      "baseline row: 32\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(test_img3)\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "img5 = ax.imshow(test_img3, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "print(\"baseline row: \" + str(getBaseline(test_img2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseline row: 18\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "skeletonize(test_img2)\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "img5 = ax.imshow(test_img2, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "#x_label_list = ['A2', 'B2', 'C2', 'D2']\n",
    "\n",
    "ax.set_xticks(x_value_list)\n",
    "ax.set_yticks(y_value_list)\n",
    "#plt.imshow(test_img2, cmap='gray', vmin=0, vmax=255)\n",
    "print(\"baseline row: \" + str(getBaseline(test_img2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseline row: 18\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalize(test_img2)\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "img5 = ax.imshow(test_img2, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "#x_label_list = ['A2', 'B2', 'C2', 'D2']\n",
    "\n",
    "ax.set_xticks(x_value_list)\n",
    "ax.set_yticks(y_value_list)\n",
    "#plt.imshow(test_img2, cmap='gray', vmin=0, vmax=255)\n",
    "print(\"baseline row: \" + str(getBaseline(test_img2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b: 19\n",
      "Go NE start at: [23, 2]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [23, 2]\n",
      "b: 25\n",
      "Go SE start at: [31, 2]\n",
      "break\n",
      "addresses: [[32, 3], [32, 4], [32, 5], [32, 6], [32, 7], [32, 8], [32, 9], [32, 10], [32, 11], [32, 12], [32, 13], [32, 14], [32, 15]]\n",
      "x: 13\n",
      "y: 1\n",
      "row build: [31, 2]\n",
      "column build: [32, 2]\n",
      "column build: [32, 3]\n",
      "column build: [32, 4]\n",
      "column build: [32, 5]\n",
      "column build: [32, 6]\n",
      "column build: [32, 7]\n",
      "column build: [32, 8]\n",
      "column build: [32, 9]\n",
      "column build: [32, 10]\n",
      "column build: [32, 11]\n",
      "column build: [32, 12]\n",
      "column build: [32, 13]\n",
      "column build: [32, 14]\n",
      "column build: [32, 15]\n",
      "b: 51\n",
      "Go NE start at: [21, 3]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [21, 3]\n",
      "b: 19\n",
      "Go NE start at: [30, 16]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [30, 16]\n",
      "b: 51\n",
      "Go NE start at: [16, 19]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [16, 19]\n",
      "b: 98\n",
      "Go NE start at: [15, 20]\n",
      "addresses: [[14, 21], [14, 22], [14, 23]]\n",
      "x: 3\n",
      "y: 1\n",
      "row build: [15, 20]\n",
      "column build: [14, 20]\n",
      "column build: [14, 21]\n",
      "column build: [14, 22]\n",
      "column build: [14, 23]\n",
      "b: 66\n",
      "Go NE start at: [29, 21]\n",
      "addresses: [[28, 22]]\n",
      "x: 1\n",
      "y: 1\n",
      "row build: [29, 21]\n",
      "column build: [28, 21]\n",
      "column build: [28, 22]\n",
      "b: 51\n",
      "Go NE start at: [27, 22]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [27, 22]\n",
      "b: 19\n",
      "Go NE start at: [11, 23]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [11, 23]\n",
      "b: 19\n",
      "Go NE start at: [15, 36]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [15, 36]\n",
      "b: 25\n",
      "Go SE start at: [23, 36]\n",
      "break\n",
      "addresses: [[24, 37], [24, 38], [24, 39], [24, 40], [24, 41], [24, 42], [24, 43]]\n",
      "x: 7\n",
      "y: 1\n",
      "row build: [23, 36]\n",
      "column build: [24, 36]\n",
      "column build: [24, 37]\n",
      "column build: [24, 38]\n",
      "column build: [24, 39]\n",
      "column build: [24, 40]\n",
      "column build: [24, 41]\n",
      "column build: [24, 42]\n",
      "column build: [24, 43]\n",
      "b: 19\n",
      "Go NE start at: [6, 37]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [6, 37]\n",
      "b: 19\n",
      "Go NE start at: [22, 44]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [22, 44]\n",
      "b: 27\n",
      "Go NE start at: [17, 48]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [17, 48]\n",
      "b: 27\n",
      "Go SE start at: [17, 48]\n",
      "break\n",
      "addresses: [[18, 49], [18, 50], [18, 51]]\n",
      "x: 3\n",
      "y: 1\n",
      "row build: [17, 48]\n",
      "column build: [18, 48]\n",
      "column build: [18, 49]\n",
      "column build: [18, 50]\n",
      "column build: [18, 51]\n",
      "b: 201\n",
      "Go SE start at: [21, 52]\n",
      "addresses: [[22, 53]]\n",
      "x: 1\n",
      "y: 1\n",
      "row build: [21, 52]\n",
      "column build: [22, 52]\n",
      "column build: [22, 53]\n",
      "b: 25\n",
      "Go SE start at: [24, 53]\n",
      "break\n",
      "addresses: [[25, 54], [25, 55], [25, 56], [25, 57]]\n",
      "x: 4\n",
      "y: 1\n",
      "row build: [24, 53]\n",
      "column build: [25, 53]\n",
      "column build: [25, 54]\n",
      "column build: [25, 55]\n",
      "column build: [25, 56]\n",
      "column build: [25, 57]\n",
      "b: 51\n",
      "Go NE start at: [24, 58]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [24, 58]\n",
      "b: 88\n",
      "Go SE start at: [23, 64]\n",
      "addresses: [[24, 65], [24, 66]]\n",
      "x: 2\n",
      "y: 1\n",
      "row build: [23, 64]\n",
      "column build: [24, 64]\n",
      "column build: [24, 65]\n",
      "column build: [24, 66]\n",
      "b: 66\n",
      "Go NE start at: [24, 66]\n",
      "break\n",
      "addresses: [[23, 67]]\n",
      "x: 1\n",
      "y: 1\n",
      "row build: [24, 66]\n",
      "column build: [23, 66]\n",
      "column build: [23, 67]\n",
      "b: 99\n",
      "Go NE start at: [23, 67]\n",
      "break\n",
      "addresses: []\n",
      "x: 0\n",
      "y: 0\n",
      "column build: [23, 67]\n",
      "baseline row: 32\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAC6CAYAAAC3HRZZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAANJUlEQVR4nO3df6zddX3H8edrgFGRDVgvpKFkdaZhEjMKazoMi0EQU8ki+IcJTbb0D5L6BySQmCxlSzb9zz+mbn8sJHUymsxhmMpoCFGbTmO2GOQWCxYLlrkqla69aAxsS8zA9/643yvX03O5p+fHPedz+3wk35zz/dxz+31x7rlvPvfz+X6/n1QVkqT2/Ma0A0iShmMBl6RGWcAlqVEWcElqlAVckhplAZekRo1UwJPsSPJ8kheS7BlXKEnS6jLseeBJzgN+ANwCnACeBHZW1fdX+p4NGzbU5s2bhzqeJJ2rDh069HJVzfW2nz/Cv7kdeKGqfgiQ5IvAbcCKBXzz5s3Mz8+PcEhJOvck+VG/9lGGUK4AXly2f6Jr6z3w7iTzSeYXFhZGOJwkablRCnj6tJ0xHlNVe6tqW1Vtm5s74y8ASdKQRingJ4Arl+1vAl4aLY4kaVCjFPAngS1J3pnkLcAdwP7xxJIkrWboScyqei3J3cDXgPOAB6rq2bElkyS9qVHOQqGqHgceH1MWSdJZ8EpMSWrUSD1waT1LzjzRygVQNEvsgUtSoyzgktQoC7gkNcoCLkmNsoBLUqMs4JLUKAu4JDXKAi5JjbKAS1KjLOCS1CgLuCQ1ygIuSY0a6WZWSY4DrwKvA69V1bZxhJIkrW4cdyN8f1W9PIZ/R5qafnce1PQM8vPwzpAOoUhSs0Yt4AV8PcmhJLv7vSDJ7iTzSeYXFhZGPJwkacmoBfyGqroO+BBwV5L39b6gqvZW1baq2jY3Nzfi4SRJS0Yq4FX1Uvd4GngE2D6OUNIsqKozNq2Nfu+9P4szDV3Ak1yY5KKl58AHgSPjCiZJenOjnIVyOfBIN1t8PvBPVfXVsaSSJK1q6AJeVT8ErhljFknSWfA0Qklq1Dgu5NE61ntBhZNH0uywBy5JjbKAS1KjLOCS1CgLuCQ1ygIuSY2ygEtSoyzgktQoC7gkNcoCLkmNsoBLUqMs4JLUqFULeJIHkpxOcmRZ26VJDiQ51j1eMtmYkqReg/TAHwR29LTtAQ5W1RbgYLevxiU5Y5M0u1Yt4FX1LeBnPc23Afu65/uA28cbS5K0mmHHwC+vqpMA3eNlK73QVeklaTImPonpqvSSNBnDFvBTSTYCdI+nxxdJkjSIYQv4fmBX93wX8Oh44mjWVNWvbVJL+k3MD7K1YpDTCB8Cvg1cleREkjuBTwG3JDkG3NLtS5LW0KprYlbVzhW+dPOYs0iSzoJXYkpSo1yVXtLUDTLuPMwczHqft7EHLkmNsoBLUqMs4JLUKAu4JDXKSUxJU7feJxsnxR64JDXKAi5JjbKAS1KjHANfp1q6Ic80+P5oPbAHLkmNsoBLUqMs4JLUqEHuB/5AktNJjixr+0SSnyQ53G23TjamJKnXID3wB4Edfdo/W1Vbu+3x8cbS2RhkRZHelXX6becy3w+1aNUCXlXfAn62BlkkSWdhlDHwu5M80w2xXLLSi5LsTjKfZH5hYWGEw0mSlhu2gN8PvAvYCpwEPr3SC6tqb1Vtq6ptc3NzQx5OktRrqAt5qurU0vMknwMeG1sijYVjuOemSV6g5GdqdCvNTw1rqB54ko3Ldj8CHFnptZKkyVi1B57kIeBGYEOSE8BfATcm2QoUcBz42OQiSpL6WbWAV9XOPs2fn0AWSdJZ8EpMSWqUdyNsjHfR09kaZpLMz1kb7IFLUqMs4JLUKAu4JDXKAi5JjXISszFeDaez5YTk+mUPXJIaZQGXpEZZwCWpUY6BS+vIIHMkjomvH/bAJalRFnBJapQFXJIatWoBT3Jlkm8kOZrk2ST3dO2XJjmQ5Fj3uOK6mJKk8RukB/4a8PGqejdwPXBXkquBPcDBqtoCHOz2JyLJ1DZJmlWrFvCqOllVT3XPXwWOAlcAtwH7upftA26fUEZJUh9nNQaeZDNwLfAEcHlVnYTFIg9ctsL37E4yn2R+YWFhxLiSpCUDF/Ak7wC+DNxbVa8M+n1VtbeqtlXVtrm5uWEySpL6GOhCniQXsFi8v1BVX+maTyXZWFUnu1XqT08qZC9v6KT1xvkWDWOQs1DC4iLGR6vqM8u+tB/Y1T3fBTw6/niSpJUM0gO/AfhT4HtJDndtfw58Cng4yZ3Aj4GPTiShJKmvVQt4Vf0bsNLfdzePN44kaVBeiSlJjZq5uxE6mSMtmuZkfb/fw962c+lkglmtS/bAJalRFnBJapQFXJIaNXNj4P2cS2Nt0izo9zs3yXHgaY4xD/vfOgt1yR64JDXKAi5JjbKAS1KjLOCS1KgmJjGlSZvVCzXOVZOcIOz9WbcyYdmPPXBJapQFXJIaNcqq9J9I8pMkh7vt1snHlSQtGWQMfGlV+qeSXAQcSnKg+9pnq+qvJxdPmoxZHdNsSavzBuvpZz/I/cBPAkuLF7+aZGlVeknSFI2yKj3A3UmeSfJAkktW+B5XpZekCRhlVfr7gXcBW1nsoX+63/e5Kr0kTcZABbzfqvRVdaqqXq+qXwKfA7ZPLqYkqdeqY+ArrUqfZGM3Pg7wEeDIZCJqUlqdhNJ0rKfJv/VilFXpdybZChRwHPjYBPJJklYwyqr0j48/jiRpUF6JKUmN8mZW+hXHOKW22AOXpEZZwCWpURZwSWqUBVySGuUk5jnCi3ak9cceuCQ1ygIuSY2ygEtSo9bNGHjvGG+LF6Ws9Th1i++RpDfYA5ekRlnAJalRFnBJatSqBTzJW5N8J8nTSZ5N8smu/dIkB5Ic6x77rokpSZqMQXrgvwBuqqprWFz/ckeS64E9wMGq2gIc7Panpqp+bVsvev+7xrlJatuqBbwW/Xe3e0G3FXAbsK9r3wfcPomAkqT+Bl3U+LxuObXTwIGqegK4fGlNzO7xshW+d3eS+STzCwsLY4otSRqogHerz28FNgHbk7xn0ANU1d6q2lZV2+bm5oaMKUnqdVZnoVTVz4FvAjuAU0k2wuIK9Sz2ziVJa2SQs1DmklzcPX8b8AHgOWA/sKt72S7g0QlllCT1Mcil9BuBfUnOY7HgP1xVjyX5NvBwkjuBHwMfnWBOSVKPVQt4VT0DXNun/afAzZMIJUlanVdiSlKjmrgboavJSNKZ7IFLUqMs4JLUKAu4JDVq5sbAvcmSJA3GHrgkNcoCLkmNsoBLUqMs4JLUqKzlpGGSBeBHwAbg5TU78Hi0mBnazG3mtdFiZmgz96iZf6eqzrgf95oW8F8dNJmvqm1rfuARtJgZ2sxt5rXRYmZoM/ekMjuEIkmNsoBLUqOmVcD3Tum4o2gxM7SZ28xro8XM0GbuiWSeyhi4JGl0DqFIUqMs4JLUqDUv4El2JHk+yQtJ9qz18QeR5IEkp5McWdZ2aZIDSY51j5dMM2OvJFcm+UaSo0meTXJP1z6zuZO8Ncl3kjzdZf5k1z6zmZckOS/Jd5M81u23kPl4ku8lOZxkvmub6dxJLk7ypSTPdZ/t985y5iRXde/v0vZKknsnlXlNC3i3MPLfAR8CrgZ2Jrl6LTMM6EFgR0/bHuBgVW0BDnb7s+Q14ONV9W7geuCu7r2d5dy/AG6qqmuArcCOJNcz25mX3AMcXbbfQmaA91fV1mXnJM967r8FvlpVvwdcw+J7PrOZq+r57v3dCvwB8L/AI0wqc1Wt2Qa8F/jasv37gPvWMsNZZN0MHFm2/zywsXu+EXh+2hlXyf8ocEsruYG3A08BfzjrmYFN3S/hTcBjrXw+gOPAhp62mc0N/Cbwn3QnW7SQuSfnB4F/n2TmtR5CuQJ4cdn+ia6tBZdX1UmA7vGyKedZUZLNwLXAE8x47m4o4jBwGjhQVTOfGfgb4M+AXy5rm/XMAAV8PcmhJLu7tlnO/bvAAvAP3XDV3ye5kNnOvNwdwEPd84lkXusC3m91Ys9jHKMk7wC+DNxbVa9MO89qqur1WvxzcxOwPcl7phzpTSX5Y+B0VR2adpYh3FBV17E4hHlXkvdNO9AqzgeuA+6vqmuB/2GGhkveTJK3AB8G/nmSx1nrAn4CuHLZ/ibgpTXOMKxTSTYCdI+np5znDEkuYLF4f6GqvtI1z3xugKr6OfBNFuceZjnzDcCHkxwHvgjclOQfme3MAFTVS93jaRbHZbcz27lPACe6v8oAvsRiQZ/lzEs+BDxVVae6/YlkXusC/iSwJck7u/9D3QHsX+MMw9oP7Oqe72JxjHlmJAnweeBoVX1m2ZdmNneSuSQXd8/fBnwAeI4ZzlxV91XVpqrazOLn91+r6k+Y4cwASS5MctHScxbHZ48ww7mr6r+AF5Nc1TXdDHyfGc68zE7eGD6BSWWewsD+rcAPgP8A/mLaEw0rZHwIOAn8H4u9gDuB32Zx4upY93jptHP2ZP4jFoejngEOd9uts5wb+H3gu13mI8Bfdu0zm7kn/428MYk505lZHE9+utueXfrdayD3VmC++4z8C3BJA5nfDvwU+K1lbRPJ7KX0ktQor8SUpEZZwCWpURZwSWqUBVySGmUBl6RGWcAlqVEWcElq1P8DEPjlsfkma7gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalize(test_img3)\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "img5 = ax.imshow(test_img3, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "print(\"baseline row: \" + str(getBaseline(test_img2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseline row: 32\n"
     ]
    }
   ],
   "source": [
    "print(\"baseline row: \" + str(getBaseline(test_img2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets redo the whole thing using the new pre-processing techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pre-processing functions skelotanize and normalize\n",
    "\n",
    "def blackToWhite(img, finalImg):\n",
    "    for y in range(img.shape[0]):\n",
    "        for x in range(img.shape[1]):\n",
    "            if img[y,x] == 0:     \n",
    "                finalImg[y,x] = 255\n",
    "            else :\n",
    "                finalImg[y,x] = 0\n",
    "def whiteToBlack(final_img, img ):\n",
    "    for y in range(img.shape[0]):\n",
    "        for x in range(img.shape[1]):\n",
    "            if img[y,x] < 125:     \n",
    "                final_img[y,x] = 255\n",
    "            else :\n",
    "                final_img[y,x] = 0\n",
    "\n",
    "\n",
    "dictionary = [3,6,7,12,24,28,14,224,225,216,143,15,30,48,52,96,112,54,240,227,131,135,99,56,60,120,62,129,192,248,251,195,141]\n",
    "\n",
    "def skeletonize(img):\n",
    "    for c in range(1, img.shape[1]-1):\n",
    "        for r in range(1, img.shape[0]-1):\n",
    "            if img[r,c]==0:\n",
    "                b = calculateB(img, r, c)\n",
    "                #print(str([r,c]) + \": b = \" + str(b) )\n",
    "                if classifyB(b):\n",
    "                    img[r,c] = 150\n",
    "    for c in range(1, img.shape[1]-1):\n",
    "        for r in range(1, img.shape[0]-1):\n",
    "            if img[r,c]==150:\n",
    "                #print(True)\n",
    "                img[r,c] = 255\n",
    "    \n",
    "\n",
    "def calculateB(img, r, c):\n",
    "    numbers = [int(img[r-1,c]),int(img[r-1,c+1]),int(img[r,c+1]),int(img[r+1,c+1]),int(img[r+1,c]),int(img[r+1,c-1]),int(img[r,c-1]),int(img[r-1,c-1])]\n",
    "    #numbers = [int(img[c,r+1]),int(img[c+1,r+1]),int(img[c+1,r]),int(img[c+1,r-1]),int(img[c,r-1]),int(img[c-1,r-1]),int(img[c-1,r]),int(img[c-1,r+1])]\n",
    "    #print(numbers)\n",
    "    b = 0\n",
    "    for i in range(0,8):\n",
    "        if numbers[i]<140:\n",
    "            b+=2**i\n",
    "    #print(b)\n",
    "    return b\n",
    "\n",
    "def classifyB(b):\n",
    "    for i in dictionary:\n",
    "        if int(b) == int(i):\n",
    "            #print(b)\n",
    "            return True\n",
    "    return False \n",
    "\n",
    "#results are satisfactory on single word but still need tweaking on single character\n",
    "#still need a way to decide how many iterations of skeletonizations are needed\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def normalize(img):\n",
    "    for c in range(1, img.shape[1]-1):\n",
    "        for r in range(1, img.shape[0]-1):\n",
    "            if img[r,c]==0:\n",
    "                b = calculateB2(img, r, c)\n",
    "                if int(img[r-1,c+1])==0 and int(img[r,c+1])==255:\n",
    "                    #print(\"b: \" + str(b))\n",
    "                    #print(\"Go NE start at: \" + str([r,c]))\n",
    "                    trackAndSwitchNE(img, r, c)\n",
    "                \n",
    "                if int(img[r+1,c+1]) == 0 and int(img[r,c+1])==255:\n",
    "                    #print(\"b: \" + str(b))\n",
    "                    #print(\"Go SE start at: \" + str([r,c]))\n",
    "                    trackAndSwitchSE(img, r, c)\n",
    "                \n",
    "\n",
    "    \n",
    "\n",
    "def calculateB2(img, r, c):\n",
    "    numbers = [int(img[r-1,c]),int(img[r-1,c+1]),int(img[r,c+1]),int(img[r+1,c+1]),int(img[r+1,c]),int(img[r+1,c-1]),int(img[r,c-1]),int(img[r-1,c-1])]\n",
    "    #print(numbers)\n",
    "    b = 0\n",
    "    for i in range(0,8):\n",
    "        if numbers[i]<160:\n",
    "            b+=2**i\n",
    "    #print(b)\n",
    "    return b\n",
    "\n",
    "def trackAndSwitchNE(img,r,c):\n",
    "    row = r\n",
    "    y = 0\n",
    "    column = c\n",
    "    x = 0\n",
    "    addresses = []\n",
    "    while row < img.shape[0]-1 and column < img.shape[1]-1:\n",
    "        \n",
    "        if int(img[row-1,column+1]) == 0 and int(img[row,column+1]) == 255:\n",
    "            if int(img[row-1,column]) == 0:\n",
    "                #print(\"break\")\n",
    "                break\n",
    "            row+=-1\n",
    "            y+=1\n",
    "            column+=1\n",
    "            x+=1\n",
    "            addresses.append([row,column])\n",
    "        elif int(img[row,column+1]) == 0:\n",
    "            if int(img[row-1,column]) == 0:\n",
    "                #print(\"break\")\n",
    "                break\n",
    "            column+=1\n",
    "            x+=1\n",
    "            addresses.append([row,column])\n",
    "        else: \n",
    "            break\n",
    "    #print(\"addresses: \" + str(addresses))\n",
    "    #print(\"x: \" + str(x))\n",
    "    #print(\"y: \" + str(y))\n",
    "    for index in addresses:\n",
    "        img[index[0],index[1]]=255\n",
    "    \n",
    "    for increment in range(0,y):\n",
    "        img[r-increment,c]=0\n",
    "        #print(\"row build: \" + str([r-increment,c]))\n",
    "    \n",
    "    \n",
    "    for increment2 in range(0,x+1):\n",
    "        #print(\"success\")\n",
    "        img[r-y, c+increment2]=0\n",
    "        #print(\"column build: \" + str([r-y, c+increment2]))\n",
    "        \n",
    "def trackAndSwitchSE(img,r,c):\n",
    "    row = r\n",
    "    y = 0\n",
    "    column = c\n",
    "    x = 0\n",
    "    addresses = []\n",
    "    while row < img.shape[0]-1 and column < img.shape[1]-1:\n",
    "        \n",
    "        if int(img[row+1,column+1]) == 0 and int(img[row,column+1]) == 255:\n",
    "            #if int(img[row-1,column]) == 0:\n",
    "                #break\n",
    "            row+=1\n",
    "            y+=1\n",
    "            column+=1\n",
    "            x+=1\n",
    "            addresses.append([row,column])\n",
    "        elif int(img[row,column+1]) == 0:\n",
    "            if int(img[row-1,column+1]) == 0:\n",
    "                #print(\"break\")\n",
    "                break\n",
    "            column+=1\n",
    "            x+=1\n",
    "            addresses.append([row,column])\n",
    "        else: \n",
    "            break\n",
    "    #print(\"addresses: \" + str(addresses))\n",
    "    #print(\"x: \" + str(x))\n",
    "    #print(\"y: \" + str(y))\n",
    "    for index in addresses:\n",
    "        img[index[0],index[1]]=255\n",
    "    \n",
    "    for increment in range(0,y):\n",
    "        img[r+increment,c]=0\n",
    "        #print(\"row build: \" + str([r+increment,c]))\n",
    "    \n",
    "    \n",
    "    for increment2 in range(0,x+1):\n",
    "        #print(\"success\")\n",
    "        img[r+y, c+increment2]=0\n",
    "        #print(\"column build: \" + str([r+y, c+increment2]))\n",
    "\n",
    "\n",
    "def getBaseline(img):\n",
    "    baselineDensity = 0\n",
    "    baselineRow = 0\n",
    "    for r in range(0, img.shape[0]):\n",
    "        currentSum = 0\n",
    "        for c in range(0, img.shape[1]):\n",
    "            if img[r,c]==0:\n",
    "                currentSum+=1\n",
    "        if currentSum>baselineDensity:\n",
    "            baselineDensity = currentSum\n",
    "            baselineRow = r\n",
    "\n",
    "    return baselineRow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 13440/13440 [00:04<00:00, 2982.70it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 3360/3360 [00:00<00:00, 5264.45it/s]\n"
     ]
    }
   ],
   "source": [
    "#we need to convert the images to a matrix so we can build our test and train sets\n",
    "\n",
    "DIRECTORY = r\"C:\\Users\\rassa\\Documents\\Machine Learning A-Z  Udemy\\Arabic Handwritten Characters Dataset\"\n",
    "train_data = []\n",
    "test_data = []\n",
    "trainPath = DIRECTORY + r\"\\Train Images 13440x32x32\\train\"\n",
    "testPath  = DIRECTORY + r\"\\Test Images 3360x32x32\\test\"\n",
    "\n",
    "def getImgId(imgName):\n",
    "    return int(imgName.split('_')[1])\n",
    "\n",
    "def makeData(data, directory, sortingCriteria):\n",
    "    imgFileNameList = os.listdir(directory)     #get list of directories in path (ie. list of image names)\n",
    "    imgFileNameList.sort(key = sortingCriteria) #sort list based on key\n",
    "    \n",
    "    for img in tqdm(imgFileNameList):  # iterate over the sorted list\n",
    "                try:\n",
    "                    img_array = cv2.imread(os.path.join(directory, img) ,cv2.IMREAD_GRAYSCALE)  # convert image to array\n",
    "                    data.append([img_array])  # add this to our data\n",
    "                except Exception as e:  # just in case there is a mistake.\n",
    "                    pass\n",
    "\n",
    "makeData(train_data, trainPath, getImgId)\n",
    "makeData(test_data, testPath, getImgId)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to ndarray and reshape\n",
    "train_data = np.asarray(train_data).reshape([-1, 32, 32, 1])\n",
    "test_data = np.asarray(test_data).reshape([-1, 32, 32, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13440"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preProcessData(data):\n",
    "    for i in range(len(data)):  # iterate over the sorted list\n",
    "        try:\n",
    "            test_img1 = data[i].reshape([32,32])\n",
    "            test_img1_BTW = np.zeros(test_img1.shape, test_img1.dtype)\n",
    "            blackToWhite(test_img1, test_img1_BTW) \n",
    "            skeletonize(test_img1_BTW)\n",
    "            skeletonize(test_img1_BTW)\n",
    "            skeletonize(test_img1_BTW)\n",
    "            normalize(test_img1_BTW)\n",
    "            normalize(test_img1_BTW)\n",
    "            normalize(test_img1_BTW)\n",
    "            whiteToBlack(test_img1_BTW,test_img1_BTW)\n",
    "            test_img1_BTW = test_img1_BTW.reshape([32,32,1])\n",
    "            data[i]=test_img1_BTW\n",
    "        except Exception as e:  # just in case there is a mistake.\n",
    "            print(i)\n",
    "preProcessData(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 1)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_data[11], cmap='gray', vmin=0, vmax=255)\n",
    "train_data[11].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce603126a0>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMIElEQVR4nO3dX8hk9X3H8fenRmmJQrU72sU/3USkREqzyrAIlpDWGrbeqBeBeBH2Qrq5iBAhvRALrb2zpRpyUYS1SrbFGgQVpUibZUmRQLA+2nVds2k1sk02LruPlaC9aap+ezFn4XH7zPPMzpyZeXZ/7xcMc85vzuz58tv5POfMOXN+J1WFpPPfryy7AEmLYdilRhh2qRGGXWqEYZcaYdilRnxqljcn2Q18G7gA+NuqenCj5bdt21Y7duyYZZWSNnDs2DHefffdrPfa1GFPcgHwN8CtwHHg5STPV9WPxr1nx44drKysTLtKSZsYDodjX5tlN34X8FZVvV1VvwS+C9w+w78naY5mCfuVwM/WzB/v2iRtQbOEfb3vBf/vt7dJ9iZZSbKyuro6w+okzWKWsB8Hrl4zfxXwzpkLVdW+qhpW1XAwGMywOkmzmCXsLwPXJflMkouArwDP91OWpL5NfTS+qj5Mcg/wz4xOvT1eVW/0VpmkXs10nr2qXgBe6KkWSXPkL+ikRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRsx0R5gkx4APgI+AD6tq/J3gJS3VTGHv/H5VvdvDvyNpjtyNlxoxa9gL+F6SV5Ls7aMgSfMx6278zVX1TpLLgQNJflxVL65doPsjsBfgmmuumXF1kqY105a9qt7pnk8BzwK71llmX1UNq2o4GAxmWZ2kGUwd9iSfTnLJ6WngS8CRvgqT1K9ZduOvAJ5Ncvrf+Yeq+qdeqpLUu6nDXlVvA5/vsRZJc+SpN6kRhl1qhGGXGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRm4Y9yeNJTiU5sqbtsiQHkrzZPV863zIlzWqSLft3gN1ntN0HHKyq64CD3bykLWzTsHf3W3/vjObbgf3d9H7gjn7LktS3ab+zX1FVJwC658v7K0nSPMz9AF2SvUlWkqysrq7Oe3WSxpg27CeTbAfonk+NW7Cq9lXVsKqGg8FgytVJmtW0YX8e2NNN7wGe66ccSfMyyam3J4EfAr+d5HiSu4EHgVuTvAnc2s1L2sI+tdkCVXXXmJdu6bkWSXPkL+ikRhh2qRGGXWqEYZcaYdilRmx6NF7SZJJM9b6q6rmS9blllxph2KVGGHapEYZdaoRhlxph2KVGeOptzrb66Rgt37jPSN+fAbfsUiMMu9QIwy41wrBLjTDsUiM8Gr9FbXQU3yP1moZbdqkRhl1qhGGXGmHYpUYYdqkRhl1qxCS3f3o8yakkR9a0PZDk50kOdY/b5lumpFlNsmX/DrB7nfZvVdXO7vFCv2VJ6tumYa+qF4H3FlCLpDma5Tv7PUkOd7v5l/ZWkaS5mDbsjwDXAjuBE8BD4xZMsjfJSpKV1dXVKVcnaVZThb2qTlbVR1X1MfAosGuDZfdV1bCqhoPBYNo6Jc1oqrAn2b5m9k7gyLhlJW0Nm171luRJ4IvAtiTHgT8HvphkJ1DAMeBr8ytRUh82DXtV3bVO82NzqEXSHPkLOqkRhl1qhGGXGmHYpUYYdqkRDjgp9WSjgUCnuQ1Y34OOumWXGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qhBfCSOegaS6sccsuNcKwS40w7FIjDLvUCMMuNcKwS43YNOxJrk7y/SRHk7yR5Btd+2VJDiR5s3v2ts0LkmTsQxpnki37h8A3q+pzwE3A15NcD9wHHKyq64CD3bykLWrTsFfViap6tZv+ADgKXAncDuzvFtsP3DGnGiX14Ky+syfZAdwAvARcUVUnYPQHAbi89+ok9WbisCe5GHgauLeq3j+L9+1NspJkZXV1dZoaJfVgorAnuZBR0J+oqme65pNJtnevbwdOrffeqtpXVcOqGg4Ggz5qljSFSY7Gh9H92I9W1cNrXnoe2NNN7wGe6788SX2Z5Kq3m4GvAq8nOdS13Q88CDyV5G7gp8CX51LhOa7vWwKpHy32/aZhr6ofAON65pZ+y5E0L/6CTmqEYZcaYdilRhh2qRGGXWqEA06eZ1o8paTJuGWXGmHYpUYYdqkRhl1qhGGXGmHYpUZ46k06B427mnI4HI59j1t2qRGGXWqEYZcaYdilRhh2qREejV8ix6c792z0f7bVuWWXGmHYpUYYdqkRhl1qhGGXGmHYpUZseuotydXA3wG/CXwM7Kuqbyd5APhj4PStWe+vqhfmVWhrzuVTPNqaJjnP/iHwzap6NcklwCtJDnSvfauq/np+5UnqyyT3ejsBnOimP0hyFLhy3oVJ6tdZfWdPsgO4AXipa7onyeEkjye5tO/iJPVn4rAnuRh4Gri3qt4HHgGuBXYy2vI/NOZ9e5OsJFlZXV1dbxFJCzBR2JNcyCjoT1TVMwBVdbKqPqqqj4FHgV3rvbeq9lXVsKqGg8Ggr7olnaVNw57RFRmPAUer6uE17dvXLHYncKT/8iT1ZZKj8TcDXwVeT3Koa7sfuCvJTqCAY8DX5lCfpJ5McjT+B8B611t6Tl06h/gLOqkRhl1qhGGXGmHYpUYYdqkRDjipc0LfA3C2eFWhW3apEYZdaoRhlxph2KVGGHapEYZdaoSn3tSkjU7lna+n5dyyS40w7FIjDLvUCMMuNcKwS40w7FIjPPWmc8JGp8P6viLufOWWXWqEYZcaYdilRhh2qRGGXWrEJPd6+9Uk/5rktSRvJPmLrv2yJAeSvNk9e8tmLUVV9fo4X02yZf8f4A+q6vOMbs+8O8lNwH3Awaq6DjjYzUvaojYNe438dzd7Yfco4HZgf9e+H7hjHgVK6sek92e/oLuD6yngQFW9BFxRVScAuufL51alpJlNFPaq+qiqdgJXAbuS/M6kK0iyN8lKkpXV1dUpy5Q0q7M6Gl9VvwD+BdgNnEyyHaB7PjXmPfuqalhVw8FgMFu1kqY2ydH4QZJf76Z/DfhD4MfA88CebrE9wHNzqlFSDya5EGY7sD/JBYz+ODxVVf+Y5IfAU0nuBn4KfHmOdUqa0aZhr6rDwA3rtP8XcMs8ipLUP39BJzXCsEuNMOxSIwy71AjDLjUii7zKJ8kq8J/d7Dbg3YWtfDzr+CTr+KRzrY7fqqp1f7220LB/YsXJSlUNl7Jy67COButwN15qhGGXGrHMsO9b4rrXso5Pso5POm/qWNp3dkmL5W681IilhD3J7iT/nuStJEsbuy7JsSSvJzmUZGWB6308yakkR9a0LXwAzzF1PJDk512fHEpy2wLquDrJ95Mc7QY1/UbXvtA+2aCOhfbJ3AZ57XtkzglG7rwA+AnwWeAi4DXg+kXX0dVyDNi2hPV+AbgROLKm7a+A+7rp+4C/XFIdDwB/suD+2A7c2E1fAvwHcP2i+2SDOhbaJ0CAi7vpC4GXgJtm7Y9lbNl3AW9V1dtV9Uvgu4wGr2xGVb0IvHdG88IH8BxTx8JV1YmqerWb/gA4ClzJgvtkgzoWqkZ6H+R1GWG/EvjZmvnjLKFDOwV8L8krSfYuqYbTttIAnvckOdzt5i/0fgBJdjAaP2Gpg5qeUQcsuE/mMcjrMsK+3v11l3VK4OaquhH4I+DrSb6wpDq2kkeAaxndI+AE8NCiVpzkYuBp4N6qen9R652gjoX3Sc0wyOs4ywj7ceDqNfNXAe8soQ6q6p3u+RTwLKOvGMsy0QCe81ZVJ7sP2sfAoyyoT5JcyChgT1TVM13zwvtkvTqW1Sfdun/BWQ7yOs4ywv4ycF2SzyS5CPgKo8ErFyrJp5Nccnoa+BJwZON3zdWWGMDz9IepcycL6JMkAR4DjlbVw2teWmifjKtj0X0yt0FeF3WE8YyjjbcxOtL5E+BPl1TDZxmdCXgNeGORdQBPMtod/F9Gezp3A7/B6DZab3bPly2pjr8HXgcOdx+u7Quo4/cYfZU7DBzqHrctuk82qGOhfQL8LvBv3fqOAH/Wtc/UH/6CTmqEv6CTGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qxP8B35FpOoQm8ioAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img1 = train_data[11].reshape([32,32])\n",
    "\n",
    "test_img1_BTW = np.zeros(test_img1.shape, test_img1.dtype)\n",
    "blackToWhite(test_img1, test_img1_BTW)\n",
    "plt.imshow(test_img1_BTW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce5f53b670>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(test_img1_BTW)\n",
    "skeletonize(test_img1_BTW)\n",
    "skeletonize(test_img1_BTW)\n",
    "normalize(test_img1_BTW)\n",
    "normalize(test_img1_BTW)\n",
    "normalize(test_img1_BTW)\n",
    "plt.imshow(test_img1_BTW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce5f70cac0>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skeletonize(test_img1_BTW)\n",
    "plt.imshow(test_img1_BTW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce5f7e83d0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAL2ElEQVR4nO3dX6icdX7H8fenbmTLKlSbUYLGZlekVJZulCEIlmVb65LmRr1YWC+WXAjZixUUthdhC629s6W69KIIsYYNxboIKoYi7YZgkYXFerQxJs22cSXdjYbkWFm0N92q317MI5yk5+RMZp6ZOfp7v+AwM8/MnOfLQ96ZP2fmeVJVSPrs+7VFDyBpPoxdaoSxS40wdqkRxi41wtilRnxumjsn2Qn8NXAZ8LdV9fDFbr958+batm3bNKuUdBGnTp3i3XffzWrXTRx7ksuAvwHuBE4DryQ5WFX/ttZ9tm3bxtLS0qSrlLSO4XC45nXTPI3fAbxZVW9V1a+AHwJ3TfH7JM3QNLFfB/xixeXT3TJJG9A0sa/2uuD/ffY2yZ4kS0mWlpeXp1idpGlME/tpYOuKy9cD71x4o6raV1XDqhoOBoMpVidpGtPE/gpwU5IvJrkc+CZwsJ+xJPVt4nfjq+rDJPcD/8ToT2/7q+p4b5NJ6tVUf2evqheAF3qaRdIM+Qk6qRHGLjXC2KVGGLvUCGOXGmHsUiOMXWqEsUuNMHapEcYuNcLYpUYYu9QIY5caYexSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIjjF1qhLFLjTB2qRFTHREmySngA+Aj4MOqWvtI8JIWaqrYO79fVe/28HskzZBP46VGTBt7AT9K8mqSPX0MJGk2pn0af3tVvZPkGuBQkp9W1Usrb9D9J7AH4IYbbphydZImNdUje1W9052eA54Ddqxym31VNayq4WAwmGZ1kqYwcexJvpDkyk/OA18HjvU1mKR+TfM0/lrguSSf/J6/r6p/7GUqSb2bOPaqegv4So+zSJoh//QmNcLYpUYYu9QIY5caYexSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIjjF1qhLFLjTB2qRHGLjXC2KVGGLvUCGOXGmHsUiOMXWqEsUuNMHapEcYuNWLd2JPsT3IuybEVy65OcijJye70qtmOKWla4zyy/wDYecGyvcDhqroJONxdlrSBrRt7d7z19y5YfBdwoDt/ALi737Ek9W3S1+zXVtUZgO70mv5GkjQLM3+DLsmeJEtJlpaXl2e9OklrmDT2s0m2AHSn59a6YVXtq6phVQ0Hg8GEq5M0rUljPwjs7s7vBp7vZxxJszLOn96eAn4C/HaS00nuAx4G7kxyErizuyxpA/vcejeoqnvXuOqOnmeRNEN+gk5qhLFLjTB2qRHGLjXC2KVGGLvUCGOXGmHsUiOMXWqEsUuNMHapEcYuNWLdL8JImq0kqy6vql7X4yO71Ahjlxph7FIjjF1qhLFLjfDd+A1qrXdoof93adUGH9mlRhi71Ahjlxph7FIjjF1qhLFLjRjn8E/7k5xLcmzFsoeSvJ3kSPeza7ZjSprWOI/sPwB2rrL8+1W1vft5od+xJPVt3dir6iXgvTnMImmGpnnNfn+So93T/Kt6m0jSTEwa+2PAjcB24AzwyFo3TLInyVKSpeXl5QlXJ2laE8VeVWer6qOq+hh4HNhxkdvuq6phVQ0Hg8Gkc0qa0kSxJ9my4uI9wLG1bitpY1j3W29JngK+BmxOchr4M+BrSbYDBZwCvj27ESX1Yd3Yq+reVRY/MYNZJM2Qn6CTGmHsUiOMXWqEsUuNMHapEcYuNcLYpUYYu9QIY5caYexSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIj1t0tlaTFSNLr7/ORXWqEsUuNMHapEcYuNcLYpUYYu9SIdWNPsjXJi0lOJDme5IFu+dVJDiU52Z162GZpAxvnkf1D4LtV9TvAbcB3ktwM7AUOV9VNwOHusqQNat3Yq+pMVb3Wnf8AOAFcB9wFHOhudgC4e0YzSurBJb1mT7INuAV4Gbi2qs7A6D8E4Jrep5PUm7FjT3IF8AzwYFW9fwn325NkKcnS8vLyJDNK6sFYsSfZxCj0J6vq2W7x2SRbuuu3AOdWu29V7auqYVUNB4NBHzNLmsA478aH0fHYT1TVoyuuOgjs7s7vBp7vfzxJfRnnW2+3A98C3khypFv2PeBh4Okk9wE/B74xkwkl9WLd2Kvqx8Ba37W7o99xJM2Kn6CTGmHsUiOMXWqEsUuNMHapEe5w8lOo7x0Rqg0+skuNMHapEcYuNcLYpUYYu9QIY5ca4Z/eNqiqWvQI+hQaDodrXucju9QIY5caYexSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIjjF1qxDjHetua5MUkJ5IcT/JAt/yhJG8nOdL97Jr9uJImNc633j4EvltVryW5Eng1yaHuuu9X1V/NbjxJfRnnWG9ngDPd+Q+SnACum/Vgkvp1Sa/Zk2wDbgFe7hbdn+Rokv1Jrup7OEn9GTv2JFcAzwAPVtX7wGPAjcB2Ro/8j6xxvz1JlpIsLS8vTz+xpImMFXuSTYxCf7KqngWoqrNV9VFVfQw8DuxY7b5Vta+qhlU1HAwGfc0t6RKN8258gCeAE1X16IrlW1bc7B7gWP/jSerLOO/G3w58C3gjyZFu2feAe5NsBwo4BXx7BvNJ6sk478b/GFjt4GIv9D+OpFnxE3RSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIjjF1qhLFLjTB2qRHjfOtN+swZfXN7dVU1x0nmx0d2qRHGLjXC2KVGGLvUCGOXGmHsUiOMXWqEsUuNMHapEcYuNcLYpUYYu9SIcY719vkk/5Lk9STHk/x5t/zqJIeSnOxOPWSzPjWqas2fz6pxHtn/B/iDqvoKo8Mz70xyG7AXOFxVNwGHu8uSNqh1Y6+R/+4ubup+CrgLONAtPwDcPYsBJfVj3OOzX9YdwfUccKiqXgauraozAN3pNTObUtLUxoq9qj6qqu3A9cCOJF8edwVJ9iRZSrK0vLw84ZiSpnVJ78ZX1S+BfwZ2AmeTbAHoTs+tcZ99VTWsquFgMJhuWkkTG+fd+EGS3+jO/zrwh8BPgYPA7u5mu4HnZzSjpB6Msw+6LcCBJJcx+s/h6ar6hyQ/AZ5Och/wc+AbM5xT0pTWjb2qjgK3rLL8v4A7ZjGUpP75CTqpEcYuNcLYpUYYu9QIY5cakXl+yyfJMvCf3cXNwLtzW/nanON8znG+T9scv1VVq356ba6xn7fiZKmqhgtZuXM4R4Nz+DReaoSxS41YZOz7FrjulZzjfM5xvs/MHAt7zS5pvnwaLzViIbEn2Znk35O8mWRh+65LcirJG0mOJFma43r3JzmX5NiKZXPfgecaczyU5O1umxxJsmsOc2xN8mKSE91OTR/ols91m1xkjrluk5nt5PVie9mcxQ9wGfAz4EvA5cDrwM3znqOb5RSweQHr/SpwK3BsxbK/BPZ25/cCf7GgOR4C/njO22MLcGt3/krgP4Cb571NLjLHXLcJEOCK7vwm4GXgtmm3xyIe2XcAb1bVW1X1K+CHjHZe2Yyqegl474LFc9+B5xpzzF1Vnamq17rzHwAngOuY8za5yBxzVSO97+R1EbFfB/xixeXTLGCDdgr4UZJXk+xZ0Ayf2Eg78Lw/ydHuaf5cjweQZBuj/ScsdKemF8wBc94ms9jJ6yJizyrLFvUngdur6lbgj4DvJPnqgubYSB4DbmR0jIAzwCPzWnGSK4BngAer6v15rXeMOea+TWqKnbyuZRGxnwa2rrh8PfDOAuagqt7pTs8BzzF6ibEoY+3Ac9aq6mz3D+1j4HHmtE2SbGIU2JNV9Wy3eO7bZLU5FrVNunX/kkvcyetaFhH7K8BNSb6Y5HLgm4x2XjlXSb6Q5MpPzgNfB45d/F4ztSF24PnJP6bOPcxhmyQJ8ARwoqoeXXHVXLfJWnPMe5vMbCev83qH8YJ3G3cxeqfzZ8CfLGiGLzH6S8DrwPF5zgE8xejp4P8yeqZzH/CbjA6jdbI7vXpBc/wd8AZwtPvHtWUOc/weo5dyR4Ej3c+ueW+Ti8wx120C/C7wr936jgF/2i2fanv4CTqpEX6CTmqEsUuNMHapEcYuNcLYpUYYu9QIY5caYexSI/4P/d0ep9IHrMoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img1_BTW = test_img1_BTW.reshape([32,32,1])\n",
    "plt.imshow(test_img1_BTW, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 1)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_img1_BTW.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 1)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[10].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_img1_BTW.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[10].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's look at character segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from character_segmentation import segment\n",
    "from segmentation import line_horizontal_projection, word_vertical_projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "kalbWordImg = test_img2#cv2.imread(r\"D:\\School\\MedTech\\Sophomore Pre-Eng\\ISS296\\CNN\\images\\9alb.png\")\n",
    "sha3alWordImg  = test_img3#cv2.imread(r\"C:\\Users\\rassa\\Documents\\Machine Learning A-Z  Udemy\\ifnenit-database-demo\\ifnenit-database-demo\\data\\set_a\\bmp\\ae07_050.bmp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce689d0490>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(kalbWordImg, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "whiteToBlack(sha3alWordImg,sha3alWordImg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce68b2eeb0>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAC6CAYAAAC3HRZZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAANkElEQVR4nO3df6zddX3H8edrgFGRjTKg6SgbbiFMYkZxhGEwC6KYShbBJSaQbGkykvoHLJCYbMUlm2ZZ4h+bbn8sJnUymsxhmOJoyKI2nc65GOQWCxQLlrkqla4dIwbckm3Ae3/c79XL7bk9p+fX/X5un4/km3O+n557vq977jnvfs7n++OTqkKS1J6fWusAkqTxWMAlqVEWcElqlAVckhplAZekRlnAJalRExXwJFuTPJ3kmSQ7phVKkjRcxj0OPMkZwHeAG4AjwCPArVX17ZP8jAedS9Kpe76qLljZOEkP/Grgmar6blX9L/BZ4KYJnk+SNNj3BjVOUsAvAp5dtn6ka3uNJNuTLCRZmGBbkqQVzpzgZzOg7YQhkqraCewEh1AkaZom6YEfAS5etr4ZeG6yOJKkUU1SwB8BLk3y5iSvA24Bdk8nliRpmLGHUKrq5SR3AF8CzgDuqaonp5ZMknRSYx9GONbGHAOXpHHsq6qrVjZ6JqYkNWqSo1CkdW3Qt9Nk0MFX0tqwBy5JjbKAS1KjLOCS1CgLuCQ1ygIuSY2ygEtSoyzgktQoC7gkNcoCLkmNsoBLUqMs4JLUKAu4JDVqootZJTkMvAS8Arw86HKHkqTZmMbVCN9ZVc9P4XmkNTPP6+JruFH+Hl4Z0iEUSWrWpAW8gC8n2Zdk+6AHJNmeZCHJwoTbkiQtM9GUakl+rqqeS3IhsAf43ar62kke7/dU9dKonwO/ts+HQygnmP6UalX1XHd7HPgCcPUkzyf1SZITFs3HoNfev8WJxi7gSc5Ocs7SfeA9wIFpBZMkndwkR6FsBL7Q/U94JvC3VfXFqaSSJA01dgGvqu8CV0wxiyTpFHgYoSQ1ahon8mgdW3k0gDuPpP6wBy5JjbKAS1KjLOCS1CgLuCQ1ygIuSY2ygEtSoyzgktQoC7gkNcoCLkmNsoBLUqMs4JLUqKEFPMk9SY4nObCs7bwke5Ic6m43zDamJGmlUXrg9wJbV7TtAPZW1aXA3m5djauqExZJ/TW0gHdzXL6wovkmYFd3fxdw83RjSZKGGfdyshur6ihAVR3tJjUeqJutfuCM9ZKk8c38euBVtRPYCc5KL0nTNO5RKMeSbALobo9PL5IkaRTjFvDdwLbu/jbgwenEUd8kec0itWTQjvlRllaMchjhfcA3gMuSHElyG/Ax4IYkh4AbunVJ0hxlnv/bOAbeb4PeC6dLr/t0/t3Xs3HrWw//9vuq6qqVjZ6JKUmNclZ6SWtulJ7yOL3iHvakp8oeuCQ1ygIuSY2ygEtSoyzgktQod2JKWnPrfWfjrNgDl6RGWcAlqVEWcElqlGPg61RLF+RZC74+Wg/sgUtSoyzgktQoC7gkNWqU64Hfk+R4kgPL2j6S5AdJ9nfLjbONKUlaaZQe+L3A1gHtn6iqLd3yD9ONpWlbObPOoOV05uuhFg0t4FX1NeCFOWSRJJ2CScbA70jyeDfEsmG1ByXZnmQhycIE25IkrTBuAf8k8EvAFuAo8GerPbCqdlbVVYOmA5IkjW+sE3mq6tjS/SSfAh6aWiKdMudz1JJZnqDke2py0/6sjtUDT7Jp2er7gQOrPVaSNBtDe+BJ7gOuA85PcgT4I+C6JFuAAg4DH5xdREnSIJnnNSGSeAGKGZjW17LTaShm5e+6Xn5Ph1D6bYLP2L5B+xE9E1OSGuXVCKV1blrfxtQ/9sAlqVEWcElqlAVckhplAZekRrkTszGn06F+mg53SK5f9sAlqVEWcElqlAVckhrlGHhjHO/WyYzy/nBMfP2wBy5JjbKAS1KjLOCS1KihBTzJxUm+kuRgkieT3Nm1n5dkT5JD3e2q82JKkqZvlB74y8CHquotwDXA7UkuB3YAe6vqUmBvt95rVXXKiyT11dACXlVHq+rR7v5LwEHgIuAmYFf3sF3AzTPKKEka4JQOI0xyCXAl8DCwsaqOwmKRT3LhKj+zHdg+YU5J0gojF/AkbwI+D9xVVS+OejxyVe0EdnbP4ZiEJE3JSAU8yVksFu/PVNUDXfOxJJu63vcm4PisQq407ti0J8FIWk9GOQolwKeBg1X18WX/tBvY1t3fBjw4/XiSpNUMnZU+yTuAfwaeAF7tmj/M4jj4/cDPA98HPlBVLwx5rqkModgDn43T6VK163VW+lH4+Vk7056VfugQSlV9HVhtC+8aZcuSpOnzTExJalTvrkY4ytc7v8pJi8YZDhn38+OwU//YA5ekRlnAJalRFnBJalTvxsAHOZ3G2qQl7g86dfPcJ9CH194euCQ1ygIuSY2ygEtSoyzgktSoJnZiSrPWxxM1+rCT7GRGfc3G+T1meb2Wlc/d8s5ie+CS1CgLuCQ1apJZ6T+S5AdJ9nfLjbOPK0laMsoY+NKs9I8mOQfYl2RP92+fqKo/nV08aTb6OqbZZ+OML6/WNo1tjWs9/e1HuR74UWBp8uKXkizNSi9JWkOnNAa+YlZ6gDuSPJ7kniQbVvmZ7UkWkixMFlWStNzQKdV+/MDFWen/CfiTqnogyUbgeaCAPwY2VdXvDHmOoRs7nab16htfe01qWodj+r47wcAp1UbqgQ+alb6qjlXVK1X1KvAp4OppppUkndzQMfDVZqVPsqkbHwd4P3BgNhE1K308eUVts+c8X6MchXIt8NvAE0n2d20fBm5NsoXFIZTDwAdnkE+StIqRx8CnsjHHwHul5VOIpdPM+GPgkqT+6d3FrAb1+OwpzoevodQWe+CS1CgLuCQ1ygIuSY2ygEtSo3q3E3OQca6C5g651/KkHWn9sQcuSY2ygEtSoyzgktSoJsbAx+HJP8Od7r+/1Dp74JLUKAu4JDXKAi5JjRpawJO8Psk3kzyW5MkkH+3az0uyJ8mh7nbgnJiSpNkYej3wbkaes6vqR93Ual8H7gR+E3ihqj6WZAewoap+f8hz9epskvWyo3O9/B6SVjXe9cBr0Y+61bO6pYCbgF1d+y7g5unklCSNYtRJjc/oplM7DuypqoeBjUtzYna3F67ys9uTLCRZmFJmSRIjFvBu9vktwGbg6iRvHXUDVbWzqq4a1P2XJI3vlE7kqaofJvkqsBU4tjQzfZJNLPbOmzLK7D/TugjUuGPQjm9LWs0oR6FckOTc7v4bgHcDTwG7gW3dw7YBD84ooyRpgFF64JuAXUnOYLHg319VDyX5BnB/ktuA7wMfmGFOSdIKQw8jnOrGenYY4SCzej0cQpE0gfEOI5Qk9dO6vRrhuKbVm53WzlB715JWYw9ckhplAZekRlnAJalRjoHPiGPXkmbNHrgkNcoCLkmNsoBLUqMs4JLUqHnvxHwe+B5wfne/JS1mhjZzm3k+WswMbeaeNPMvDGqc67VQfrzRZKG164O3mBnazG3m+WgxM7SZe1aZHUKRpEZZwCWpUWtVwHeu0XYn0WJmaDO3meejxczQZu6ZZF6TMXBJ0uQcQpGkRlnAJalRcy/gSbYmeTrJM0l2zHv7o0hyT5LjSQ4sazsvyZ4kh7rbDWuZcaUkFyf5SpKDSZ5McmfX3tvcSV6f5JtJHusyf7Rr723mJUnOSPKtJA916y1kPpzkiST7kyx0bb3OneTcJJ9L8lT33n57nzMnuax7fZeWF5PcNavMcy3g3cTIfwm8F7gcuDXJ5fPMMKJ7ga0r2nYAe6vqUmBvt94nLwMfqqq3ANcAt3evbZ9z/w9wfVVdAWwBtia5hn5nXnIncHDZeguZAd5ZVVuWHZPc99x/AXyxqn4ZuILF17y3mavq6e713QL8KvDfwBeYVeaqmtsCvB340rL1u4G755nhFLJeAhxYtv40sKm7vwl4eq0zDsn/IHBDK7mBNwKPAr/W98zA5u5DeD3wUCvvD+AwcP6Ktt7mBn4a+De6gy1ayLwi53uAf5ll5nkPoVwEPLts/UjX1oKNVXUUoLu9cI3zrCrJJcCVwMP0PHc3FLEfOA7sqareZwb+HPg94NVlbX3PDFDAl5PsS7K9a+tz7l8E/gP462646q+SnE2/My93C3Bfd38mmeddwAfNcuBxjFOU5E3A54G7qurFtc4zTFW9UotfNzcDVyd56xpHOqkkvwEcr6p9a51lDNdW1dtYHMK8Pcmvr3WgIc4E3gZ8sqquBP6LHg2XnEyS1wHvA/5ultuZdwE/Aly8bH0z8NycM4zrWJJNAN3t8TXOc4IkZ7FYvD9TVQ90zb3PDVBVPwS+yuK+hz5nvhZ4X5LDwGeB65P8Df3ODEBVPdfdHmdxXPZq+p37CHCk+1YG8DkWC3qfMy95L/BoVR3r1meSed4F/BHg0iRv7v6HugXYPecM49oNbOvub2NxjLk3sjiH26eBg1X18WX/1NvcSS5Icm53/w3Au4Gn6HHmqrq7qjZX1SUsvn//sap+ix5nBkhydpJzlu6zOD57gB7nrqp/B55NclnX9C7g2/Q48zK38pPhE5hV5jUY2L8R+A7wr8AfrPWOhlUy3gccBf6PxV7AbcDPsrjj6lB3e95a51yR+R0sDkc9Duzvlhv7nBv4FeBbXeYDwB927b3NvCL/dfxkJ2avM7M4nvxYtzy59NlrIPcWYKF7j/w9sKGBzG8E/hP4mWVtM8nsqfSS1CjPxJSkRlnAJalRFnBJapQFXJIaZQGXpEZZwCWpURZwSWrU/wMhDfuGNo991AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(sha3alWordImg, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def extendImg(img):\n",
    "    paddedimg  = np.zeros((img.shape[0], img.shape[1]*3), img.dtype)\n",
    "    for y in range(0, paddedimg.shape[0]):\n",
    "        for x in range(paddedimg.shape[1]//3, paddedimg.shape[1]*2//3):\n",
    "                paddedimg[y,x] = img[y,x-img.shape[1]]\n",
    "    return paddedimg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce68d48eb0>"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# code from segmentor goes as follows and requires a line and also white text- black background\n",
    "\n",
    "# lines = line_horizontal_projection(img)\n",
    "\n",
    "# line = lines[0]\n",
    "# words = word_vertical_projection(line)\n",
    "\n",
    "# word = words[0]\n",
    "\n",
    "# cr = segment(line, word)\n",
    "\n",
    "extendedSha3alWordImg = extendImg(sha3alWordImg) #we will use this as our line\n",
    "plt.imshow(extendedSha3alWordImg, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_Sha3alWord = word_vertical_projection(extendedSha3alWordImg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       [0, 0, 0, ..., 0, 0, 0],\n",
      "       [0, 0, 0, ..., 0, 0, 0],\n",
      "       ...,\n",
      "       [0, 0, 0, ..., 0, 0, 0],\n",
      "       [0, 0, 0, ..., 0, 0, 0],\n",
      "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
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      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
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      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
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      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
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      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0],\n",
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      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0],\n",
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      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0],\n",
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      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255, 255,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
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      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0, 255, 255, 255, 255,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0, 255,   0,   0, 255,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0, 255,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0, 255,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0, 255,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0, 255, 255, 255,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0,   0,   0, 255, 255, 255,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0, 255, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0],\n",
      "       [  0, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0, 255, 255,   0,   0,   0],\n",
      "       [  0,   0, 255, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0,   0],\n",
      "       [  0,   0,   0,   0, 255,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0, 255, 255,   0,   0,   0,   0],\n",
      "       [  0,   0,   0,   0, 255, 255, 255,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0, 255,   0,   0,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0, 255, 255,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0, 255, 255,   0,   0,   0,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0, 255, 255, 255, 255, 255,\n",
      "          0,   0,   0, 255, 255,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255,\n",
      "        255, 255, 255,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
      "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
      "      dtype=uint8)]\n"
     ]
    }
   ],
   "source": [
    "print(words_Sha3alWord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_Sha3alWord = words_Sha3alWord[0]\n",
    "len(words_Sha3alWord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_Sha3alWord1 = segment(extendedSha3alWordImg, word_Sha3alWord)\n",
    "len(cr_Sha3alWord1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce68e051c0>"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAD0AAAD5CAYAAACUGz1LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAHDUlEQVR4nO2dW6gVZRTHf/+8UFRgZdrBJAt8yIIsRLo9WFSIPVhBUA/RQ2BBQlEvUdDlobeoCHoxigy6EHQlukkUvXRTUVPMMulyUrLoor5U2uphvpPT9uzZs+eyz96t9YNhz3XP+p359uw5s2Z9W2aGN46a6gCmgpD2Qkh7waU0ZlZ5AJYDO4CdwF0l1rcBDT8VxlFDeBrwNXAGMBPYDCwaEun1RXHUad5LgZ1mtsvM/gReAFbWeL+BUUd6HvB9bno8zRt6ptfYVpPMO+KaVtIqYFWN/TROHelxYH5u+lRgd+dKZrYGWAMgaSgu9Os078+AhZJOlzQTuA54vZmw2qXykTazg5JWA++QncmfMrNtjUXWIhrkv5YDbN4bzGxJt4Uur8hC2gsupet8T08pRSdgabLrpsO4PNIh7YWQ9kJIeyGkvRDSXghpL4S0F0LaCy6la90ukvQNsB84BBwsutdclTbuyzdxj+wSM/u5gfcZGC6bd11pA96VtCGlZEeCus37IjPbLWkOsE7SF2b2YX6FYcxPN5bAk3Q/cMDMHipYp++dVYlPUjsJPEnHSjp+Yhy4Atha9f0q7L/r0Is6zXsu8ErayXTgOTN7u8b7DYw6SfldwDkNxjIw4ivLCyHthZD2Qkh7IaS9ENJeCGkvhLQXQtoLIe2FkPZCSE+GpKck7ZW0NTfvREnrJH2VXk9oN8xmKXOknyYrDs9zF/CemS0E3kvTo0PJAvEFwNbc9A5gLI2PATtKvk/fBeDd6LFdK0Xjc81sT/qj7QHmVHyfKaH1EqVhzE9XPdI/ShoDSK97u61oZmvMbEkbD+FUpar068CNafxG4LVmwhkQJU4+zwN7gL/IquNvAk4iO2t/lV5PHKUT2dDXT3eLr8cTB1E/3UlIeyGkvRDSXghpL4S0F0LaCyHthZD2wlB02DTIuzfg9EiHtBdC2gtV89P3S/pB0qY0rGgrwKpVdkVUzU8DPGJmi9PwZq0oBkxP6VQl+8sAYhkYdT7TqyVtSc2/6+MXklZJWi9pfY19NUvFJxHmknV6fBTwIFnnx5UTeEV026bH0PyTCGb2o5kdMrO/gSfIurIfGSpJTyTkE1czwLrpJuj5D4ek54FlwGxJ48B9wDJJi8ma0jfAze2F2DxDkZ8uiqHid3LkpzsJaS+EtBdC2gsh7YWQ9kJIeyGkvRDSXghpL4S0F0LaC2Xy0/MlvS9pu6Rtkm5L80e2hrrMkT4I3GlmZwLnA7dKWsQo11CXyTZ2ZB5fAy6nQg01Q5K17OvhOUkLgHOBT+iooU7dXU+2zdCVEvdzhI8DNgDXpOnfOpb/OipHutTZW9IM4CXgWTN7Oc0uXUM9bJQ5ewt4EthuZg/nFo1uDXWJJnkxWZPZAmxKwwoq1FAzJM078tNeCGkvhLQXQtoLIe2FkPZCSHshpL0Q0l4YivrpIqrc2el1t8XlkQ5pL4S0F+rkpxuroS76NdIqQy/KfGVN5Kc3pp9p3SBpXVr2iBX8Nu2w0lM6pWMnUrL7JW0H5rUdWJv09ZnuyE9DiRrqka2f7pKf7ruGmmrJuMYTeGWFZwDvAHd0Wb6AXFH5sEtXzk+Pcg11mbP3RcANwOeSNqV5dwPXj2oN9VDkp1sg8tOdhLQXQtoLIe2FkPZCSHshpL0Q0l4IaS+EtBdC2gsh7YUyGY6jJX0qaXPKTz+Q5v+v66f/AC41s3OAxcBySeczwvXTZfr3NjM7kCZnpMGAlcDaNH8tcFUbAbZB2araaSmPtRdYZ2ZH1E/T5TeoRzo/nXJes4D3gbNpsH66haG5/r3N7DfgA7I+/P/X9dMnS5qVxo8BLgO+YITrp8vkp8eAtZImHrV40czekPQR8KKkm4DvgGtbjLNRIj/thZD2Qkh7IaS9ENJeCGkvhLQXQtoLIe2FQddP/wx8m8Znp+mqFG1/WtGGA71d9J8dS+uLbum0ub3L5h3SA2bNVG0/ZZ/pqSSadxtIWi5ph6Sdko7IYSvjsbR8i6TzcssmLVjv2H6ZpN9zxev39gyqn6xlvwNZ1e3XwBnATGAzsKhjnRXAW4DIOk3/JLdsDDgvjR8PfDnJ9suAN/qJq+0jvRTYaWa7zOxP4AWyZH6elcAzKfn/MTBrIhtqZnvMbGMa3w80UrDetvQ84Pvc9DhHBl1mnckK1vNckJ6JeUvSWb2CavsydLJeGTq/LnquI+k4sv7FbzezfR3rbgROM7MDqTOKV4GFRUG1faTHgfm56VOB3f2s06VD9X8xs30Tz8SY2ZvADEmzC6Nq+UQ2HdgFnM7hE9lZHetcyX9PZJ/mlgl4Bni0YB+ncPh6YylZrlyFcbUpnTs7f0l2Fr8nzbsFuCUn9nha/jmwJLdttw7V89uvBralP+jHwIW9YoorMi+EtBdC2gsh7QWX0v8ApJu0zgN66ecAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cr_Sha3alWord1[4], cmap='gray', vmin=0, vmax=255)\n",
    "######"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EURIKA!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what happens if we use words_Sha3alWord[1]\n",
    "cr_Sha3alWord2 = segment(extendedSha3alWordImg, words_Sha3alWord[1])\n",
    "len(cr_Sha3alWord)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce68e592e0>"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cr_Sha3alWord2[0], cmap='gray', vmin=0, vmax=255)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " makes sense, it saw the lam as another word because it is meant for printed text where spaces between words are standardized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34, 26)"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_Sha3alWord2[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce5ff4efd0>"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resized2 = cv2.resize(cr_Sha3alWord2[0], (int(32*cr_Sha3alWord2[0].shape[1]/cr_Sha3alWord2[0].shape[0]),32), interpolation = cv2.INTER_AREA)\n",
    "plt.imshow(resized2, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "LamImage = cr_Sha3alWord2[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ce69f5ad00>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def resizeImg(img):\n",
    "    widthDifference = (32 - img.shape[1])\n",
    "    resizedImg  = np.zeros((32, 32), img.dtype)\n",
    "    if (widthDifference % 2) == 0:\n",
    "        for y in range(0, 32):\n",
    "            for x in range(widthDifference//2, 32 - widthDifference//2):\n",
    "                resizedImg[y,x] = img[y,x-widthDifference//2]\n",
    "    else:\n",
    "        for y in range(0, 32):\n",
    "            for x in range(widthDifference//2, 31 - widthDifference//2):\n",
    "                resizedImg[y,x] = img[y,x-widthDifference//2]\n",
    "    return resizedImg\n",
    "\n",
    "resizedLam = resizeImg(LamImage)\n",
    "plt.imshow(resizedLam, cmap='gray', vmin=0, vmax=255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-3%2"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}