custom_dataset_create.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developing Brain Atlas through Deep Learning \n",
    "\n",
    "## A. Iqbal, R. Khan, T. Karayannis\n",
    "# .\n",
    "# .\n",
    "# ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating custom (your own) training and testing dataset for SeBRe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob #for selecting png files in training images folder\n",
    "from natsort import natsorted, ns #for sorting filenames in a directory\n",
    "import skimage\n",
    "from skimage import io\n",
    "import numpy as np\n",
    "\n",
    "# Root directory of the project\n",
    "ROOT_DIR = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save training and validation images\n",
    "1. Save training images in the folder 'myDATASET/mrcnn_train_dataset_images', with the naming convention 'section_img_0.png, section_img_1.png, section_img_2.png,...'.\n",
    "2. Save validation images in the folder 'myDATASET/mrcnn_val_dataset_images' , with the naming convention 'section_img_0.png, section_img_1.png, section_img_2.png,...'.\n",
    "\n",
    "## Load binary masks in SeBRe-readable-format\n",
    "1. Use an SVG editor (eg. BoxySVG, RectLabel, InkScape etc.) to create masks on top of each brain region of interest (eg. cortex, hippocampus, cerebellum etc.), giving the mask for each distinct region a unique RGB color code (eg. cortex[242,25,60], hippocampus[255,72,151],cerebellum[238,93,255]).\n",
    "2. Reduce the opacity of underlying brain tissue image to zero.\n",
    "3. Export masked brain image into PNG file format; save each masked brain image file into a separate subfolder ('masked_section_0','masked_section_1', etc.), inside the folder 'myDATASET/training_images_masked' or 'myDATASET/validation_images_masked'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create regionwise binary masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(ROOT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\argunsah-adm\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\PIL\\Image.py:2575: DecompressionBombWarning: Image size (137023488 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n",
      "  DecompressionBombWarning)\n",
      "c:\\users\\argunsah-adm\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\skimage\\transform\\_warps.py:105: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
      "c:\\users\\argunsah-adm\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n",
      "c:\\users\\argunsah-adm\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\skimage\\util\\dtype.py:141: UserWarning: Possible precision loss when converting from float64 to uint16\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n",
      "c:\\users\\argunsah-adm\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\skimage\\io\\_io.py:140: UserWarning: G:\\Romesa_Nat_Mach_Intel_Code\\Nat_Mach_Intel_Code_Supp\\SeBRe_FINAL_Code\\myDATASET\\mrcnn_train_dataset_masks\\section_masks_0\\section_masks_0_hippocampus_m2.png is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "data": {
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yrUvIGNMxc9PMrMsakrpcWhp9bKylOWM3nZkb9boSXUNA3BEIAQlDGASgUpPdE1QL6+/apmuHRs6urDteV9W/yXFF8UAQhKj5tEuocV2jnTZvmtMaghAtDXuEyh2NW781O/j/bWsJiICYIRACEmLR4UdbSbqnaZ7rUgDERBD0SNKqc+YPe9vqEa/7pqm9K/UdRE1ND6txXaPrMpBwPnQJBTts4rJUfcc5O8yVC7J29YKs61JCVW0gNNHtAHCHQAjwnDFm8e5jW3Ku6wDgTmnwE+cun7Cl+QQzThRDLeVb81lr7VrXdYyl+a7mWD2hSepi6ZFBzmS6oAiFgHiY4roAANXjWHkgfUYb7ypojqNq3Aoeg4aUdQsRBqHWps6dmpUUy0DI9aliozHGdLiuIWrGmO7JBF59bVbGGEsoBLjFKWMAAAAAAAApw8gY4CmWRwPpcnZhm04/c473u3+ikpadQnQHwZXg9Kw4dXQYY7pnbpqZcV3HSEldLH3MLZr0/qCxMEIGuMHIGOAhwiAgXVwcBV+NtT/8mb76458Pu+ziRSfqug+dNuH7BvuAJOnLK05TW8uJFd13YcOKxI+OXd3/jOsSkGJxDCKnzp2acV1DWoW9H6mvzaqlOWMfzD9AMATUEB1CgEeCUzSS/IQHwKCzC9t051fPdV3GIUqDG0myyy6p+DbM5psOueyw1w3+/L/zGx/SN//5Cd181y+18R3v0/wHf1C8zk//+lz97oyGcW/73Ovu1Y8GTqq4Jh809PZkZm6amXNdB9ItLqeOxXF3UCB4jJK2WLq0Qyjq7qfGzsG7isPnGpBkBEKAJ+gKAtIjDh1BYQQ/lRgZEhU2rFBTe9eo9xtc95kvf0CSVHfE1FFvc+rap8Mt0pFgkfjUuVM5Zh6xEIdQqHFdo502b5rLEsaVtJPGWu/NqGdHzgS/nKzlx8U4GRAdAiHAA4RBQHq4CIPOve5eSdKDT+2UFH34U47H+vs0/8EflF2L2XyT/vQP5mjDh4d/r3z/X27SPU3zoiixJoIwSIrnyA7Sa8c5O5w9QY9zd1BgKBDq6GuzWde1hCEIZVwEQiU15Ky1S2p+x0CCEQgBMUcYBKRHLcOg1d/7N92ce1JnHD1TPe+K32jaZAQdRMFj6evC6SAMivsTX6STy0Co+a7m2D+BCR6fpCyWDgIh1x8P3UJAuDh2HgAAAAAAIGU4ZQyIMbqDgHTovnC6JKlwUrTdQaV7geyyS3TTssWR3p8rxTGzO6Qpm2+WNNht4+P3U7qDEFfGGFvrTo1gXImvi/Tqa7PFzwM6hYDJIxACYojTxIB0CE4Si/orvam9S7ct+MNY7AaqtdeWrZQkbXj252r4uT+hUENvD096gRF8+powxnRYa9e6riMscQpfgv1FLkJJIGkYGQNixhjTvfvYFm+etACoTkNvT+THyje1d6nxjumyyy7RBce+JdL7irv22b8nu+wS/flvn9ef//b5Ycua4ybOtQGuBL8s80jWdQFhi9vfQWm3EIDq0CEExAgjYkA6RLk8OhgLs8suSWVH0ERuf/vS4stm802xCt9LgyBrrfFhcS5QKz51B5Xq2ZGTJLU0Z5zWkVRBKESnEFAdAiEgJoLOIADJFlUY1NTepd4lHyIEqkDwWMUlGLIvnFB8mV0pwEE+HDM/lvXbspKkluac0zqSjFAIqB4jYwAAAAAAAClDhxAQE7uPbcm4rgFAdM6qf0qSVOgItzto9ie/pz37XqMzaBLssktkNt8kyd0y/4beHlkd7BCyL5wgHfeUmTZvmm1c1+ikJiAuPO0OykrSg/kHHJeRDn1tVi3NGStJPTtydAoBZSIQAmJg0eFH23ua5rkuA0AEghGxjZoT6u2W7grC5LkcH2vo7Rk2LhYYGhvLSMrVtCAgRnwdF0vSCWPXbM1qvbJaPb/DdSnj2nRmLvi/bb03QygElIGRMcAxYwxhEJBQUewLamrv0vxP/pCl0RGxyy6p6SlfY4VBUnGnUC7fmle+NV+zmoA48TEMGrh1oPiytdYkZbfN6gVZ1yWUpaU5o10XiaX8QBkIhABHjDGWU8WA5Dqr/qlIwiC77BI9v+SDod4uhqtVKHR1/zNjhkHFWl44ofiEcv9j+yOvCYgTX48U33PbnkMua+z0NxO60pMgaKRdF8kaYxa7rgOIMwIhwJHdx7bE4lQbAOFp6O3RgY45OtAxRxs/tXTidyjTa7/5bTEMQm3UIhS6ZuC5sq4X/AJh11W7spEWBMSMj91B0mBXUPB1G/xxXVNa9bXZnDGm23UdQFwRCAEAAAAAAKSMsZbAGqg1RsWA5IliX9CM//1dSdJvfmvpDnKkf+sTkdxuQ29Pzr5wQqaS9zGztkvyt2sCyZJvzSvK3Ti+LpPOt+bV1zb686tgbGyst8fZMbfI+LyXp7HTZCTJWsuxb0AJThkDaowwCEiWIAgqRHCK2PWnvEuSdNkcFs+70tDbE+p4b8kYWqbS9w2Oom9c12inzZsWWk1AnATjPb6FQX1r+gb/P07Y09dm1XpvRo2dxstQaP3WrDeLpUfqa7O5oRf9XeYERIBACKixuxt5YgckRRRdQdJgGNT/3otVN2Vq6LeNythll2jR/f8oSZrsiZANvT0HOypePLGqZ4NDu0iyMzfNzE6qGCCmps6dmnFdQzV+cOy9ZV0vOBrdt1Ao2IPkayAUWL0ga9dvzRIKAUMIhIAaojsI8N/V/c/o3R85RZJUOCmaMOjAmX+hKa9jzV9c9Ox/ZdK3UQyDqgyCAvaFE2Rmbc/mW/PedVAgcbJh36DPY2ItbZmyrx+EQT6PkPnqygVZGWNslOOOgE/YIQTUUP9xi/iCAzwWVUdQoKm9S0+0/KlOqTs6svtA9ardJxRWGDQMo2NwbMc5O0J/Qt18V7N3Pyf1relT/u37K36/0g4hH7qFGjuNrLVe7xEqFXw8rusAXOPXj0CNcOQo4Lcow6Cm9i41tXdp5rTphEExVs0x9EEYFPa/AZnTp9tdV+3KhXmbgEu+/pxU7qjYSME+odZ7M7EPg6TkdTH1tVkZYxa7rgNwjUAIAAAAAAAgZRgZA2qEcTHAP0FHSJRjYrM/+T3t2feaJHG0vAfKHRsLPneC7iD7wgmh1xIcQy/5dyIT/BfmyJjPu4PC6pzxZGwsq6HdUXGvtRw9O3JqvTfD2BhSjQ4hIGLGGOtrGzSQZsXj5CMMgyRpz77XZJddQhjkiXX9z054nYbenpy11kQZBklDx9APPjnL7n+s8h0mQBz4Ggb1rekLPQwKlkzHWDb43ua6kDC0NGf4OR2pxyljQISMMR2cKgb4pRgEaU7k99XU3kUQ5Jl1A89qTf3sUd8W7AuyOrgPJaowKND9/eOykrTkT1/kKHp4o/QJuI9hkKSqFkmPJQiDNi7tDu02o1Banw8dTeUIPgZOHkNaMTIGRIhj5gG/RH2KWKmm9i5JjIn55o780/qjHXsPubxkefRiSbmog6CRgvExX59cwz/VjoxNmzfNNq5rDLucmgpzVKyUD8fQH3OLjCQZY7r72mzGcTmhCj42IE3oEAIiYozpJgwC4u/swjbd+dVzJakmXUElfmGXXXJKLe8Qk3fezDnq3zH6HqEox8MmYl84QWbWdu1/bL84ih5x5et4WCDfmpcUXWDjw9hYsftxaCQ2zuFVpVqaM7ZnRy7efwFAyNghBAAAAAAAkDJ0CAHRybguAMD4ajkiVqqpvUt/8caTvewOevrX/Xp6b7/mTK+XJM35nXrHFdVeQ2+PSjtAiyeKOeoOCgx1CbFLCLHke3eQVJtRrrh33IyoL6cE/by76cwcu4SQOgRCQAQYFwPi76z6p1ToqH0YFLh57nuc3fdIn3vyp5Kkv/nv/5AkHWaMPveWt2vt9p8Vr7PijLfoK23vVoOmaL6OKV7+/918r374yDPF1wsbVuj//+nTkqRL/r6nePlpR71BD737vCg/jJoaCoEyktT9/Vm5zOnTndZTIptvzbNLCLHj++fkxZ9tlxbU5r582CUUBCdJGxvra7OEQkgVlkoDEWCZNBBfrrqCAk3tXVr95gW65uQ/cFZD4PX/fIte+qOLIrv99v/cohue+8Wobzv1zU26eeUivf2zdwy7/APNv6vvve2PIqtpsh555Vd6508Ga3bdETQWM2u7jrl6MLRjnxCiUslSad+7g6JaIu2zxk4T7BHq6GuzWdf1hK2x02SttWtd1wFEjUAICNlQd1DGdR0AhgvGelyGQZLU1N7VaZdd0ua0iJgxm28qvhz8/cTpFLZdB/ZJkhr/+ZbiItW4hkEBM2t7VpIYH0NUygmEhk7d08xNM3ORFxQRl2FQ3I92LzlxLFFdQgFOHUMaMDIGhC/jugAABwUdQTU+QewQz740ELxIGDTCsNDnDinX96KkwXBo52/2SJJmfuzWmodDV/7y37T+f7aqODpgblmcOf13Yh8GDclKg09mfe7MgPdyrgtAdILRKmut2XWREpcIlZ6o5roWICoEQkCIjDGLGRUD4sP1eFipt39uaMwoBh0vcZdpPG7wcbrjYPfQC1+7UOYTN9Xk8Ru6zw3W2o9eo5InBX4EQZIO1spR9HDF9zExScq35nOr53dkXN3/GTMXu7rrspR2BcW9m6kawcfDTiEkGcfOAwAAAAAApAw7hIAQsUwacC8uu4JGitNOHJ8EHULB7p4pxujA+1ZGch/B/RQv92BXUDnMrO2MjSF0E+0Qar6r2dsnGfnWfPBi1vXC5Lh33gTLpSUpiWNjAZZMI6kYGQNCRBgEuBWnETGEY/kbji++bK01f3fx6dZ8Z+zRsSt/+W/6Xu9/SZLmTK/TnOn1enpvvx7YteOQ6wZPYqxuHHa5MebPur8/69tJCIMkqfv7x2lJ64uEQgiVMaZjrCfIPo+L5Vvzxe8NwbgoxjZybGzkZUmxcWl3VhKBEBKHQAgIQfADA4EQ4MaWfS/rtYuPVeEkwqCkuftXzw17/WPf/lfz2m/ebfta9hYva2rvKj6Bu2boT7WS0hVUKnP670gSu4QQtqzGeILsaxgkDe8STGKwEYXS5dJSMjuFWpoz7BJCIhEIASEgCALcoSsofT7Z+ZNhP5DbSzsndXvGmI43HV+XndSNxJx94QRGx2KuZExpULukj1Z4I9+UtOHgqy7+vn3vDpI9+Iu+uARCcR8bWzW/Y9jrca+3Wn1tVsaYbmvtEte1AGEhEAImib1BgDuEQck2tNvnoQmvV/5YR1bS00Mvf1HSG9/y5nrZQslOoldzFVToF0Kh+Bi4dUCStOe2PQeDn60h3PBHNSxEyqskZCoJi8L4HJi5aeYhT459D4OC7o84hRmr5nfo2m3xnlS6ckE2NUHJKoenzgFR4JQxAAAAAACAlOGUMWCS+o9bxBcRUEM+dgU989KA3vG5OyRxyli5Sk8XK15mzFOSTvjS2j/QFR+fH+r9/eSng50U7573RKi3Gzd0CLmVb82H0wkUlqGuoSMvPFJ1H6yr6iZKR918/twKTk0zxtg4dQhJ0jG3yMR9L0/paWNSMvcIBYLl2ewTQhIwMgZMAuNiQG35GAZJ0oNP5ie+EorM5pt05+Xv1bn/5z4TjIPZwsrho10hO33ZJkmDY1VJFoyNSX4/efdFMSxZKOnvFa8wSCqOmO0Z+k8LDr6p3M+PJHweBbuDUL2RIVpS9whJ8RopBCaLQAiYBMIgoDaCIKigOa5Lqcq+137jugRvXLwtp8KGFYMnh33uEhtlCJRWQehFt1B08q156WbFLwCaSEm9eeWLAVHSP09KjplfHNcn+z4ELMUAPyWdM5w6hiQgEAKqRHcQEL2G3h5J8rIrqNSJMxtcl+CN//P50wbDoBoHQUnvDEJtFEfCfAuCxjL0cZSGQ1KyAqIR3UE5d5WMrYLF+U5tXNpdfNlaG/sxt8mKe0AHlINACKgSYRAQnS37XtYDp79OhXP8DoICLSc3H/yN6Vkf4SfIUTyxpE+S9HvtP6h5GJRWnDoWjv2P7Zck7bpqV3KCoNEktHuotMODJ/iT09KccV1CzdElBN8RCAFVoDsIiE4wHvYu14VExGy+icXSI3zr+F/ow6u3SBJhUI0RCk3OsCXRy52WUnujdA9NZjl1rRX3Ow1lQHFcJj1Cdugysqd2AAAgAElEQVRP7AUhiQ9jbkDacew8AAAAAABAyhAIAVVYUzfbdQlAIvl6ihiqd8/cZ/Thm7ZEforYeIIj54Fy9a3pUz4fsyPkXRram7Tnyj3K5/PDjqKPo3xrXtZaU7JMOu7dQbLWrnVdQ7nOmLnYdQk1E/fPG2AiBEJABYwx1hhj19QTCAFhaejtUUNvjw50zEl0GBR8/5DU9cgrv3JdjnO9+36tna17tPxv73U+JnbDt3/h9P5dsy+cEPsn8HGSb83rwHcPSO90XUlMvVPSVik/ezAYyrfmi3uW4mLkzhee1Idr05m54MWssyJqyBjT4boGoFoEQkAF1tTNpjsICFHxOPkEB0EjWWvb3vmTO1yX4VTz/V06/ANGMz92q/MwSJJyD/a6LsG5IBQiGBpb8fGhK6h8Q51Du5bvUr41r741fa4rOuRz3JcTvHxjjOnwqatpkrKuCwCqxVJpoEwskgbClbbxsJEf6/Y9r+iEI49yVI0bZvNNkgYfCxdHy4/lmecGXJeAGNv/2H7tWp7wE8RqYat0QAeUX5DXMVcfo2nzptW8hGBUrPQyuoMik5W0lsXSQLwRCAFlurtxnusSgEQodgVpjutSnAhOX5nyutfZ194Xj0CkFszmm5T/uw9Kkprauy62hZXfdlvRQYtPb3ZdQizYF06QJE4dK0FHUAS2Sru0q+bH1o8Mg4LOIMIKAGnGyBgAAAAAAEDK0CEElGnR4eka7QDC1tDbI+nQ0am0KWxYUfzNtNl8k+yyS1yXFKnefb/WN4/6T0nSzI/deqwk2cLKWC3tOW/5HEkvuS4jNuwLJ6jxOwUnIz1xQndQxIYe27zymvqhqWpc1xjJ3eRb8xlJstY+UHo5nUHRKnl8s2LHDhBbdAgBACKXxuXR4ylsWFEcXbjiFw+5LicyZvNNOvwDRl+44z8kqcsWVvbGLQySpMva57ouIXZ2XbXLdQlOEQbV1oHvHgh9oXm+Na8d5+ww1toHRoZBLJKunRQtlga8RIcQUKZ1/c+K4+aBymzZ97Jeu/hYFU4iCBop2CW08xsfsr+47WVJ0il1RzuuKhwjlkc/L+mNtrCSTwKP2BdOSOUuoWIoQRhUe1ul/IL8pD/n+tb06cDjB3LW2iVjXofuoJpqvTcjadhx9ABigg4hoEzrBp51XQLglYbeHr3r6rep5aR0PaEsVzA6NvNjt751xorDNWPF4cUgxVdm803qO29v8fXGSzuNBsMgd0WhasGS6bQodgURBrmzdfDvobRbqJLOoXxrXvsf22/GC4NQO8aYDkl6MP+AHsw/MNHVfZZzXQBQLWMtCTlqxxjzLUkXj3ed45uOlCSdceJMzX593eD/m47U7NfX1aDC8U1d+7TrEgAvpO1I+cloau+SpLdK0pN/e/4TJ11xu6YYowOenUBmNt9UPE4+sPeFi3XEEZ40I7+ac11BLKWlS6h4tDziY+gUMmutMcbYsT4PS08PM8YslpQr7QBavzU7+P9tazPW2geMMd19bTYTZelha+w0staaXRfJqyduQd1JP9HtmFtkJr4WEE8EQohE8I3/yb89X5LUVHeE03rCEjzR2X1si+NKgHhicXR1mtq7eiXJWnucMcYGwYoPC6dLg6DgB38fO4JM082S0tcVU466K3pV90H3v5SJUn52uPtrEIIFB8Og9b9Yr9WnrB4WTgZBUOk+oIkCh2NukTHGWN+CiQQEQh2S1Ndms45LigSBEHzGyBgAAAAAAEDK0CGEUAW/2U66pvYuuoSAEoyIhaOpvesX1tq3SkNLp2PaIWQ236RvfWSRJOncd7yp2D3pY2dQgA6hsSV9bIwTxWJmxKjY+l+sL75p9Smri2+TBr9PVtLt09g52MjhYYdQ1lq71tcOoeB13+ovFx1C8Jkng/2IO2PMASk9YyKFDSsk9gkBkgiDwlTYsOIUY8yL1trjXNcyGrP5Jv3jJ/5QhfNKdwVt8ToIwsSCE8ckJS4YIgyKkZ9KWqlMcES8McZ+7aWv6ROv/4SkwRBolV2l4G19bbbiYGfV/A5du21tVlI2xMojZ61da4zp8C3ISovg1FDXdQDVoEMIoQjmt9P0pJAuIYAwKCqNl3aauHQIlR4hLykR3UDjYrn0uJp/1u+6hFCxOygmFgzv+pGkIAwqubxj49LurCS1NGeqvqtjbpF3u3h8330UvO7b416JkR8r4As6hBCKND4hLGxYoQZCIaTQln0v611Xv02SVNAct8UkVOmSVBcW/usP9ejul1TYsEJ/U3+qpBQEQShLvjWfiC6h4lHmdAe5NyIM+tpLX5OkkWGQd2EI0qWvzWrXRbLBWCLhEHzBUmlMWlPdEan9F7qwYYXOLjzmugygZhp6e4phEKKzavk8J/drNt+kB9/Zq3uvXSZJamrv2vfZ7z+qz37/UdnCylSEQcFoFEaXmB1LW0UY5NJPJTvfGjvfmuAUqq8Xvm5POP0Efbzp4+bjTR83hEGDgoABfghGGV3/YgcoF4EQAAAAAABAyjAyhklJy6li47nzq60smEYqsC+odr695SlJekTSwijvZ+R+oGeWfUBvuuwfJEnf/9Yf6fxz33x4lPcPP/k+NsYiaccWDB+nKV0ePfLyahZHl6Ox03h30tjq+R2uS0AFgi4hRscQdwRCqBph0EFn1T0pSfrRwEmOKwGicVb9Uyp08PVeKzt3vypr7Tv7bhh8xlLc3zPBkukd+36tY7u/O+rbVp0zX6vPmT/ssuC0sOD23/Lm+lSMhY3n/jvPlvSk6zJiLTh1zNtQKClh0DclbTj04s/MGgwOPvPGrL70fLZ4+ZdeWHvoldslfTSK4sZQ3vJoK/l3NHzUVi/Iui4BFSIUgg84ZQwVM8a87x8/sWTzH/3+LNelxA4njyFJGnp7JKVzabxLn7/9UX39vp8Pe2IkSRsvf6/OOLlZ/3rjTp0z403F65vNN+k9pzTrh5e995DbuviGf9Gmf392tLt5RNLCpsbD9dJTbaF/DN7jpLEJZa8raMOSaa7LqJi33UEjwp+dp4X78/uXns8eDIyienzGXh6dtdaulaRNZ+bsZE4QK4dvJ3Y1dpqstXatjyd0pemUsfEcc4sIhBBbBEIoWxqPlq8GoRCSgPEwd5rau4621r4y2tuMMS2Sttz3xTZ94Xv/Ikn64ccXBR0+T1tr3zzW7ZaGS2nvApqIabo5OcuTI+Rjl5B3gdCC8MOfcs14eOg5bBiPV0kYdGLLifaTd35SH2/6+CHjYbXgWyA0VG9HX5vNuq6lUkEgZIzpkCQfP4YwtN6bUc+OHKEQYolACBMyxtgXvnahjph6mOtSvEEoBJ8RBrnT1N414VG1xhjb971Vqps+2J0x7f1ffNVaO32U631TUvulF5+iDdfx/agSBELlyf3rr3XBEb9xXUZF8rPzrksoj8MgaDQzHjaD42VSZSNmfzH4P/vTwe9rn9z4SSsrfeX9X3ESBkmDHTeSP+GEbwFWqZJAKPVjgBxHj7giEMKYjDF2tJ0TKM9UFk3DQ188dTdf8w41Xlre+cLGmLovZD/SL0mfW/PnMoe/+xFJn5O0me6fkDA2VhYza7skedEpFPvuoJiFQGOppHPIzj/45Pfrha9bScXOIFchQRBS+DC+dM3WrNZvzRIIJQjjY4gbjp0HAAAAAABIGU4ZwzDGGPvz9X8iiV1Bk9V9wRFa8r1XXZcBTOjswjZJ0p1fPVerHdeSVsVTxC7tLOv61toBSaW/ZV9o+zs3m3oWRIfFzNrO2FgZur9/nCTpAvk1OhYrQWfQaa4LKU/QxTRjgZFulvTOMa64QAp6cEbuDfK146XWrt22VuuV1RkzF7suZVJWz+9wXUJstDRnLPuEECeMjEHGmN+X9DgBUPjYJYS4Y19QPJQ7KjYaY8wu2995tCRp/wOh1ZR27BGqjA8LpvN352t7xHo5PBkTG8+Mh82h42PflOw3D44KDR0tn7PWLnEdBvk0MuZTraPx+YS0KI08fQ1wiUAopYJ/oP8ic5KuveAPXJeTaMFv/gmGEDeEQfFQziLpkYwxvQvmzW6WpP/4179R/559Mvu3qO7IqdEUmVbsESpb5vzn9YurjnJdxrhitVB6weD/fA+DRprxGyP9vYofX8nR8sVdMq47g4In45vOzFlJivqY+8nwPRDyeSF21NglhLhgZCxFSo+N50lg7QSP9ZartmrR4fH+YRnpQRgUH5WEQcaY5ZLusv0HR8suW92lL3/heGkqYVDYGBsrX+72N6ruil7VfbDOdSmxd3rLYt1xWs51GZHYeZjVjAWDIcbZq8+2Xzn3K5IOfp+LQzCwamh86dz7lkiKR01jiUuINhm+j7tFxRhj6RJCHNAhlHClIRDcOve6e/WjgZNclwEQBsVIJd1BxhhbGgS98eS/lCQ9/9ifRFMcGBurUJzHxmJzwlgCRsQmEpxCFoyJSZK1dklLc8ZuOjPnrrASQeeKFO9AyOcOG9+7m2qB0THEAaeMAQAAAAAApAwjYwlljLGMhsXLxk+fqQaWTMMxuoPi421X/XDC3wwGv8G2/Z0q7Q6qa16pgWc5USxqtrCSPUIVsC+coOaf9bsuI75S0B0kHRxzGtobtETiVLG04u99fKs4fQ0xQIdQwhhjbN8NbZYnfPHE3wtc6b5wurovnM7nYIw817eneby3ByNipUGQJJ3yjlWEQTVkZm13XYJX8q0xWtwcJykJg4Jxsf4vWUnKBZcTClSmsdOocejwSR675LpyQVbGGFIhOEUglCBBVxDiraG3x3UJSJnuC6er5aSZajkpnrs90mhod9CYz5ynTDnMjgyCJKnnJ0/qFw8tj7Q2DHf/nWe7LsEr7FwaYYFSEwZJB7uDhl5eYoyxwetIF2utIcwqS9Z1AUg3AiHPGWOuf+q6P7VPXfenhEGe4O8JtXSgYw5BUMx8svMn446Kfe87H7evvfydUd/WcmpvZHVhdEtajtNlHb/SZR2/cl2KNwZuHXBdwqEcLZTeeZpNTRgUaPndxar/zMHulhiHAlnF/Mm4r2Fa0N2EiTE2BtcIhDw21BF0WVPdEWqqO8J1OajAuv5nXZeAFDjQMcd1CRhF14P/dfFYb+u57/P2gj9516hvO/aEj0RVEibwlZtf0VdufsV1Gd7Yc9se1yXEwwLXBdTOjIdNcVzsRx/NSVIuzmGGMabDWrvWWrvWdS2jsdYa30+fYhSqPFcuyLouASlHIAQAAAAAAJAyBEKeYl+Q39YN0CGEaNEdFD9N7V3B7qBD5sFaTj/Ztpx+sm1590mjvq+pb1Pvzz8UeY0YnS2sHDxxDGVhj5BStTeoVNAVEpwuFmNZ1wUk2VB3U9Z1HQAmxrHzHjHGTJGkaz7wzgOEQX7j6HlEhSAovsZq/w9OExv1bfWDp4kRRsSDmbWdsKNMfWv61Liu0XUZzqQtDAq+v5WOia1mN0pVGjuN7C2DD2OM9y+NKtgdFNSP8pQsYvd6TBB+IhDyhDHmQGHDCv6+AIyJMCi+mtq7ZC89NPQZLwySCILi5tltF0h6xHUZXjjw+AHXJbizQNJprotwp/4zRvYKq9XsRkm1jUu7XZcAoAyMjHlgaDyMMChBOHoeYSMMiq+Xf71/1N/6TRQGBd1BiI/jZ9XJzNruugwvpLmTKm3dQQifMcbGeSn3WEqXYbc0ZxxXA6AcBEIxx64gABPpvnC66xIwjrd86h8PucwY84WJwiC6g4BwDNw6ULs7S9HJYoHgdDFJ6v+SdxlG7Pg6NlR61LyPYRaQVgRCMTbzqOmEQQnF3yvCclb9U2o5aabrMjCG137z21F/uD/w8nc+N977EQbFF3835cu35pVvzbsuo3a+SXcQwrF6fod3O5h8DbKAtCMQAgAAAAAASBkCoZgxxnzAGPOBvhva7BPXnu+6HESoobeHXUKYlIbeHm381FLXZWAcMz926yGXGWPslMPG/ueX3UHxxx4hv+y5bU9t7mhDbe4GyRWMXa1ekPVqKXfpuJjk3+loQJqxqDhG2BeULsHfdUN7F8fQoyJBkMj3i3h79cBrw1roi8fKjrM7aNkfX8tIkgdsYaX0as51GbEXLJZu/lm/40oiNrQ3iHGxg4wxi+McCsS5Nh8F/9YF/87x+AL+oEMoJgiD0ou/d1SiobdHhQ0r+LzxwKxPfG/Y67a/c9wwSJJ27doRZUkIEV1C5avpUmcHdp5mCYNGsNY+4LoGT2V9W8hc2h20cWk3x80DniEQigHCIDA6hnIEYRDi7fO3P6rP3/7oqN1BYzH1bTL1bXr43vdHXyBCsfov57suwRs1G9lyIYWniiXN+q1Z1yUUWWvXuq6hUiXdQd0tzRmOmwc8w8iYY+86YQZhEFTYsILRMUzoTvYFeeHv/umJRyTp60OvG2N2TdQZ9JMfnxt1WQjZNR1/oCs/f/vgy1e93nE1cIXOoOGuvjfruoSyGWOKx3jFYV9P670Z9Sjn1elijZ1G9pbi10DGYSm+y7ouAOlFhxAAAAAAAEDKGGv5zUatGWMeK2xYMdd1HYifJrqEMI4DHXNcl4AJNLV3DRsVk6TNd6yyy9479niRqW9jkbSnTNPNkg4uT8bYXC6Wzrfmpa0R3PACOoQCb3hIw5YKW2vNrosU6wendPdNHJYgN3YaLx63UsfcouKD6FPdcVP6OAK1RodQjQ3tCyIMwqgKG1Zoy75XXJeBGGLPlB9GhkHGmHHDIIlxMZ/ZwkrCvBQjDBrdmqX+jDxJh37fRnlKAzXfFmEDOIhAqIZYHo1yvOtqNlQCPmpq7zrkKKWJdgeZ+ja9a+GMyGpCbXDi2MQSd9IY/1SP6aozs5KGBwZx1NdmY9EZFLDWGp+CldIgLU6Po2/i/nWC5CMQqpEnrj2fMAhl677gCHVfcITrMhAjq87hRKM4GxoVqy+9rJwf7DMtx0ZXFGqm77/aXJcQe05PGlsY8u39lO6gkYwx1qcwYySelJevsdPQHQQkCIFQDTz7lQvszKOmuy4DHmk5uVktJzfrrLonXZeCmFhNIBR3bxt5wa+e/ua473DFZ29T953LIysItXPM0YfTJRRnfx/y7TElOCYfx6/iEGhcszXruoSq0R00OT5+zSBZCIQiFPy2pO6Iqa5Lgac2fvpMQiEg5j793YdlrS2urA2+97++qW7c97vuq/dEXhsQF4vfnYCu128O/qE7aGJDIUvWdR2+uHbbWkl+hCvWWhOEGHEI03xGZxrigEAIAAAAAAAgZQiEIlTYsELsDcJkbfz0mZwwBcTYt7c8VdX7cTpVsvD3Ob7c7W90XcLkbRj6g0PsPM0WO6fqPzPY9WCtXeuypnL1tVnGdso0sqPFh46mOOPzDnFAIBQRWigRpsKGFYRCQHydUvqK7e8s63QxJA97hJKtNPTA6Iwxw86cX781q/Ue7Mfh5/bylAYYPGaTw7gY4oJAKCJ/+b7fc10CEiYIhQiGgHix1v4yeLncH5D3vnBxZPXAHbqE4iXfmle+NR/OjXHMfLmy1lrT/6XBb4Xrt63V+m1eNAo5F/eAhe6g8DR2GrqDEBsEQhHp+ON3uC4BCRSMIRIKAfHQ1N417PWJOoMk6YKL/05HHDElqpLgGF1CYxu4dcB1CVWjM6g8V8w62CBkjFnssJSKEG5MrHSRdNzDq7hp7DTFP9Lg59uui2RbmjM8lnCOQAjwEKEQED/GmOfKud4//OChqEuBQ3QJjW3PbXtcl1AduoPKtuqNWUnFPUK50hOp4K/S7qC+NkuAVoaRAdBoj9umM3PFcGjXRSIcghMEQgAAAAAAAClDIBQBY8z3XdeA5KNLCIiFi0teLusYJTpIko+xsWRhXKwyo3U5sEDXX6X7buhgGV3QDdSzI1e8rJpOqqBbiMcZtcQSg2ic77oApENhwwo1tHdp97EtrksBUsla+53iy2WeLEYglHy2sFJ6Nee6DGwN4TYWSDothNtJkdPrF+vB3Tkz8LfiSW2ZVs3vUFwjs5IwqJtRsUFRL9jua7MyxljGLVELxlq+sMNmjLGFDStcl4EUef9fbtI9TfNcl4EIHeiY47oElHjz5f8gSXrl1/uLvzUlEEIp03SzJMm+cILjSuLDzNqumZtm1uz+8rNHnDD2zaH/b5C0cOjlhZI+OvZt7PwIPydX4w0PyZR2OVhrza6LCIh8EoQeQSix6cycbWnOuCxpQtdszUqSrlyQDfX2rh06Kc9aa4wxtlbB2Mi/AyAKBEIRIBCCC1PXPu26BEToi6fu1upz5rsuA0OC08WCH9LOa11o77jtsvHfaf8DkdeFGKJTqCjqQKj0iPn6j9Wrv6W/+hu78OCLjIxVbsbDw5+/WmtNS3PGbjoz56QeVOaarVmt35ot/iXWMgSp1ERHuBtjOiRlg9c3Lu1WabDVeu/gyw/mD/4bPfL2gnDT1WPAMfWIEoFQBPpuiOl3TCRa8ASV8bFkaujtEUFzfDS1d+UkyVq7RJI00DXu9/0pR/+ZXtv54egLQ+yYppvpEhqSva6gDUumhXJbQfjTeH3jqG/vu7xPum1y9zF/5am67s03FF9f+vjC4suERBN7w0Mqjo3VXaGadlZgckYGEHHt7ooqKHEdAI3mmFtiO1UIzxEIhcwYM6WwYcUB13UgvegUSiYCoXhpvLRz2G9OyxkXY1QsvQiFDmr+WXVdO6XdP2OFQKUmHQjdLt33y0fGvUoQEBEOjW7Gwyarks4Mxsb8MDJkiWOQN9Eo1VBXUHCdteXcZhxDoFJ0CSEqnDIGAAAAAACQMpwyFj66g+BUQ28PY2NAhH687fnS9SITni4msUg67Th1rHr51rwar28sqysoVD+QNHf8q9w3d7CDaOueRyUNdgzRLTRMduQFjZ0mth0YKMpIg6eKSfHrmBmtU2ZkF1Ppy7suOvTzcDRx+zhHint98BcdQiFjpAOu8TkIROuD38gVXy5tSx/Lfd3/GWU58ISZtd11CV7Y/9h+7X9sv/KteR3YfqD6IGiS+4OCsKfS99m659FDFiqn1c7TrKy1xlpr+r/Ek1kfDIUtD0jSxqXdmY1LuzOOSzrEyHG2XRcpdiNtUSk9uQ8IC4FQiPgiRVw09Pa4LgFIi+xEVzjz3GtqUAbizhZWEgpNIN+al5luZKab2ncEBS7UsBPGqkEwdJAxxhpjbP1nzOC+NXagxFrw92OMsS3NGcXtmPlgd1DweZWWIAiIEoFQCIJvSnRmIC74XASiU/qEppxxseOafyfSeuAPW1ipzPkvKHP+C65LiZVJdwONsHfz3qrf9765j1TVHTTWbc142BAMafD7ZtAl1NjJ4xFH67dmJcVziXSJbFBfjGuMzOr5EzYlAxXjlLEQcMw84qipvYtdQgnCKWPx0XhppzHGXC9Jtr/zsgnfYf8DkdcED6V0p1DpKWPBfqCwVX3C2IXVjYqVy7cdQ6VB1ur5HVq9IFvW+5UGPqfXL5YkPbg7Zxa9JWN7/vsBThsDJoHj5xE2OoQAAAAAAABShlPGJolRMcTVGSfOkAZcVwEkS1N7l+ylnZI0cWeQpDm/f7me/vfzoi0KXqo78WkNPDXHdRlO5FvzkuRuT5AjwQhZ3LqErn0+q799Ya3OmLlYm87MFS/vO6m6Okcb5Rm5Z5PTxoDqGGM6rLVrXdeB5KBDCEiojZ8+U1v2veK6DCCRbH9nWfuDnnn2pRpUAx8NPHdx6pZMm1nbi7uCIg2D/qS6d4tyXKz0Pm7ZeWPk9zOeYK/RjIeNppwkXfWHWfW12WFhUBSstWbP3cUQKBvpnQHJlXVdAJKFQAhIsOV9j7kuAUiazcaYo8u98kc//NYoa4Hn0nTymJm1XY/86Pja3Nn5VbzPJE8Wq8SCI0/V1j2PauueR3Xt89ma3Od5P88UA6BgIW8tO3SC+zpyefG0sbXrt2aLi4wBlGfj0m7XJSBhCISABGOcEQiXtfYsSbvKvf43vnRGhNUgCZIcCmXOf16Z85/Xo9v21SwMOrD9QFXvV4vuoNG875jWyE4hm/Gw0UMNOT3UkNNd5+ViMaJV2iW0fttard/G5AtQiZbmjIwxHDeG0BAITVLp8cNAHDX09qiht8d1GYD3/vnxwaPCT3vnW8q6fvsnvxVlOUiQJIZCZtZ2Xff5N+i6z7+hpvfb/3f9E19ppBp2B40m2C0UltJOoJbmjFqaM6Hd9mQET2KXXZkJuoQMP0cDVcm6LgDJQSAEJNydl79Xd17+XtdlAN77X18fbNN+6P5sWde/4f/eH2E1SBpbWKnN3Xu0uXuP61ImrabjYSFom/ER1yXovrmP6LyfZyZ1G6VBUExlJWnLYw8Mu7D0mHoAE4vx1zg8RCAEAAAAAACQMgRCIXi2MKBnC5zvjXhqOblZLSc36+wCC6Z9xj6oeDDGVDGPApRn2VnLteys5a7LmJRLV+102x30VxVe/6+li2LQISRJa990nR7cndODu3Nlv0/QFRTzzqBhRpw2xvoFoArGGGuM8eOLHrFGIBSC2U11mt1U57oMYFw9+zmCHpiMoSctZX+zt4WVEVaDJNv0wJtdl1CxYHn0R1Yc5baQ36vw+k9EUkXV6g6rV91h9RNez4PxsEOsnn9wD25w2ljwhJbTxoDK1PqkQCQXgdAkGWM2ua4BKAcdJv5iKXh8rFv7v1yXgBRofd+bVHfi067LKIuZtb24QNq1vsv7Kn4fV6eLTWTrnkfHfNuMh42XTwRXL8iWvporfYXTxoDq0CWEySIQmqTcZ88+x3UNQLkIFoDqNLV3SZKu/FTrhNc19W0y9W1Rl4SEG3ju4tifPBYsjy53ROzGrhh1ql4o56eLTeSWnTcWX/ZxNGw0wZNXa+2SPXdbBaNjjI0B1fH5+wHigUBokuYd3+i6BKBsdAn5ib+3eDDGfM11DUiXOB9HX81JYjd+d3dE1VTuvrmPxLY7KLDgyFM142Gj6/ZmE/mk78jlpjg6JnHaGFAtuoQwGQRCAAAAAAAAKUMgBKQMY2P+uCHbOesAACAASURBVLr/GV3d/4zrMjDoaEkfd10E0ifoEopTp1A13UE1cVuZ14v5qFhg6eMLdf9ZjyRqv86ITqespGzpiWMAKle6sB2oFIEQkDJ3Xv5e1yWgTNcMPKdrBp5zXQYkWWvLXn5iCys5YQyhCj6n4hAKxTEM2rt5r/Zu3lv29eM+KiZJV7x4qe4/a7DO+896JFHjVCV7hNZaa9f+y7acjDHd7BECqrN6QVbGmMWu64CfCIQmKVg0Cvii5eRmuoSAMp346e/rxE9/X5Jk+zsdV4O0s4WVGtjzWyf3vXDZc1q47LnYhUGStPfHe7X3x2UGQjHuDlr6+EIddrx02PHS9afdMOxtQSiUhGBo5D6ks9YskaSMxB4hYBJyrguAn6a4LgBA7a1aPk/6mesqMBGWSbvXt2dfRdf/8jd+rMtWHhFRNYD00ydO0ZKFT9b0Phcue043XDujpvcZlbYZH3FdwqiC8bDxBG9v7PTz2PlSxhgbdARZa82v71HwAWWH/gCogO/fE+AOHUKTRHsrfLS6dQFdQkAFjDELy7ne5avpGkW0lrQcV9PRsRu7Xol/GHSbytsfdKF0UQwDoXLCoFJJGCEb+eQ1OG3MWpuchUlAjRljWCaEihEIAQAAAAAApAyBUAia2rvYJQTvsFw63ujgigdrrRnqBP2p61qAQK0WTN/Y9YpOnR/eCOSUt4S/qWD313eXfd35R54a+v1P1mHHq6LuoEASuoSMMTZYMC0pF5w2lpRdSYADWdcFwD8EQuF4degP4I1guTTBAwD4xxZW6va7BiK7/Ue37Qs1DJKkho83hHp7kvTaf71W3hUvlK578w0TX68GSpdHT8b9Zz2inh25UGpyoa/NFkfHrLVLpIO7hVjJAAC1QSAUAmvtdGvt9MIAmRD8UtiwgsXFMbXqnPmuS0i9N1/+DxW/D8fNo5YOr58Xye0+uq2yZepOlbM7SPHpDqp0X9BE6qbUh3ZbrgRdQkcuH54B0SUEANEjEArRSVfc7roEoCp0CcXPagIh53bvPeC6BGBcre97k8ys7aGNjz26bZ9fYVC5YtIdFHYYFPB9xKqkS8jsudvKGLPYGLOYLiEAiB6BUIj4hwu+KmxYobMLj7kuA0Ou7n/GdQkAPGELK2ULK3XxZflJ3c6NXa+EVFHt9F3eV9b14nDUfFRhkDQ4Oub7TqERXUK5oT9ef0wA4AMCoZCxXBq+uvOrra5LwJBrBp5zXQIGtbsuACjXGacvqPp9Fy57LvR9QSNFEji9dYK3XyjnR80vfXxhpGFQqfvPekSt92Yiv58ojOwSCjqF+GUrUD6+XlANAiEAAAAAAICUIRAKGcksfMYuoXg446SZrkuAJGttcenIm2a/3mUpwIQuueiUqnYJmVnbdcO1MyKoaLgbv1v+8fBl+/z4b75v7iO6b270nTljCTqDatEdFPjCO66r2X2FrXRsrGR0jLExoAyNnSbjugb4iUAoAoyNwVeFDSsIhWJg46eWui4BI2QWTTSbArhX6Sl3ZtZ2PfKjSZ597siE+4MurE0dY6nVmNhofA1QSsfGShZMW2ut8fVjAmqhsdNkrLUPuK4DfiIQigBdQvBZYcMKbdn3irbs82/BaBIQyMXDkzuGf/5fvOI9jioBKlNOl1BwMpmvYVA5XHYGffp/LnUWBkmDu4R6duSc3f9kBF1C0mCn0J67D4ZEhELAQcHpgo2dRn1tNrfrItnSrx+gXARCEaFLCD5719UL9K6rq19QCviu5a/vGvZ6puUUR5UAlbGFlfr6t14e8+1BEOR9GHTbOG9z2B20deBRffk97o+4P/e+Ja5LqErQJSQd/AVr8CTXWmta7814uzgbCFNfmy3+Kb3MGEMwhIoQCEWELiEkwVl1T7ouIXUKG1a4LgGSfvNbfpaCv674wujjVC67ghqvbwzttiYaF3PZHfSOt57q7L5L+XwMfekT2iOXm1wwOiZJPTtypmdHjm4hYAwjQyJgIgRCAAAAAAAAKUMgFKGm9i5Gx+C1jZ8+k502NcRj7b+HfrrTdQmAXn3xwzrmrf+tY97638XLErUzaLwd7w7HxR478lF3dz6KPzvhI65LqEpph4O1dknpLqEAO4WA8TE2hnIRCEUoOCXBdR3AZHDyWO0wLhY/xpgpxpgp5V4/e+3PoiwHKNviM96oxWe8UZKUOf/55IRB0rjHzbsYF1v6+EItfXyh3tYYj3GxwJ+d+JHi0lkfle4OOnK50XvmZ4Y9wSUUAsbG2BjKVfYPuaheU3sXT/TgtcKGFWpo79LuY1tcl5JYDb09KmiO6zJwqAOVXPnH9z8fVR1ARe7oOlOSZJpudh4G3dj1irQwnB85+y7vG3uh9IWS5oZyN2Vzebx8ORbELKSqRLAgN/gF66/vkd18Tc5K0rIrM0Y6dPE0T4KBg4KvH9d1IN7oEKoBvhCRBHQKRaOht2cwDCI0BhCBdyxocl2Cbvzu7vBu7K/GflOtu4PiHgZJ0vWn3aDrT7tB67dmXZdSlSAUkgYXTL9nfkbvmZ85ZBwmCI2OuUV0DQFABQiEaoRdQkiCIBQiGApPYcMKwqAYs/2dsv2drssAKvbwz3bq4Z/t1I3XL9Klq/zfbbV3817t3bxX+r1R3nihar47yIcwqNT6bWtdl1C1kfuEjlxu1NdmtesijXq89tAoWa7WdQJxQ8ccykEgVCN0CSEpggCDUGjyeAzjzRhjTX2bTH2b61KAivy4+3lNOex1mnLY4I95jz62z3FFk7f3x3u198d7R33bfXMfqWl3kG9hkCTv6h2pdJ9Q6c/UQQdR6VH1QzK1rhGII5ZLYyIEQgAAAAAAACljrCU0rBVjjGU0BEnTxLLpqrA3KN6a2rv0P5d/oPj6nL8+p/x33v9ABBUB5dny0A79zvRRFjjvc3ck+qIvv6Tpy6ZP6jb6mvtGf8OFtdsdtHVg8DF8x1v9XNT8hz9a6PUISWOnGbZE2uePBaiV0q8bYDR0CNUQX4xIosKGFeq+4Ah2C1WAMMg/jI0h7p5/cUCPbn1p9DBI0sKznqtxRQdNOgy6fIwwSLVdJP2Ot57qbRgk+T82VhoAWWuNr4uygVrauLTbdQmIOQKhGmO5NJKo5eTm4m6hAx1zCIbGQRjkp9JuoYkcP+82HT9vrHOxgXDNPeN2zT3jduV/9eq413vk/vNqVFEExjtmvkauePHS2t0ZxlS6K8jnRdlArbQ0Z1yXgJgjEKoxuoSQBgRDh+J4ef+V2yX0/It79PyLeyKuBpBM0836zjcy+s43MmVdv9ZdQo9ue1WPbhs/qJpIHLqDlj6+UNefdkNN7itqvh/J3tdmS08dMz07cm4LAjzAYmmMZ/S+YkSqqb2LJ4VIhcKGFTog6dzr7pUkPfjUzlTuGyIISobuPz+7rOvdu/HKoZdeiq4YpJ5purnirp9H7j+vpruELl39K0lS4/WN4d/4hZLmhn+zI/l4olgaGGOstdace98Sr/ciAYBrdAgBAAAAAACkDB1Cbrxd0r+7LgKolY2fPrP48gEd3KWV9G6hht4e3fmppSqcRHeQb1Ytn3fIZXOOrtf0N3xYe3/1f8d936VLfn/wBU4bQwRyPS9q3Ze3Vr0T6NJVO3XDtTNCrioaB7YfGH1/0M9rMy6WxO6gpHw8pWNjnDgGANUjEHLAWvsfs19fJ0n6j7/5Y8fVALUXjE8dGHq955c79P7r/0mSvyHRuv5ntW7g2eLrhQ0rVNAcdwVhUla3LtDuXx445PJXXz30srGYpptlC/+PvTsPk6q80z5+P0HcMbEx6qhEUbQ1EkEpJaMwdGNccJc4gyTiFgUmEdFoNDKjtPAmxi1oMPNKu0RtjPa8GQxBRTGGZkANSWMAcQHXBGNEsaPgGtHn/aPqNNXVVdW1nHOeU+d8P9flRXd11TkPLi119285z89jIeG8NrEfX3loxddY9swnPp4oWBt/vlHK97+E6Qq8XSyOYZCnrsXEos3Kaxuz1pq/n6na/w0BAYnDf+8IjrGWf0Fc8IZ7MVcEyK+cjXxhhEjZA7L57zYZ8gVCkrTXtBNKev3ip9Zo+JC/+XkkJFAls4J6FNIcocYH35Uk9R7Qu6LXd+yaZ6D0c9JjDwQX1By1KiUpPpU0+Yycn4rNG8Qd75GRNv+5Oi6/L8Bv3n8rQC4CIcc6ZvF/LsAv2SFSNSHR4k/e1fEdqyQR/iRZ34mz866b7z+jVXZjS0nX+NFPbtJ/fP9gv4+GBAgkCMpIjfy1JKl9fr9Aru85+oXKt+11XNyRv11sbHDtYnGuCso1eKchro/gi7oW02St7dw/b4wZIamtlGDoJyuaJEnXpdfXN3WMs02BHBKIAG/DIBuvkYtAyDFjjOUNJxCc3EqjQkGRVwF02QkH6fITDgr8XIi+QoHQ7JUv6T9/fVFJ1zB9xtE2hpLsMfCX+uvfPpSkwIIgT/PdL0iSxp9eeWBTiqoCodzqoOfSvwRVHZSkMEiSrnz6EknSvKPbnJ6jWnUtpuAbXGPMVElNOQ93CZCyVdp25r3RpjoJUUYghEKYIQQg1nID10ITYJj3g1KdcdAAvf9Beg7L9tttVfS5dmMLw6VRkOl7u6R0ADT33qN7eLZ/xp+1f/qDAFvHJlz2lnTudmW/ruPiTBCUWx00PfNrALODkhYGSdIT6+Lxfek3Ry0s+DVr7dXGmKashwqGQZXyAqkfDmoiDQJQk1g7DwAAAAAAkDC0jEUAc4QAIHr+9No72ueTHfJ+rf+MVkkqaZYQbWOQpN+3v6V/PuY3nZ8H3RZWitTIXwc2Ryg1aq3qZtSV/bqwB0knsTooW9RnCf1kRZM340eS9Juz0xVBw/Zq6Hxsx6ldh+V6A6aHf22EHvlJW7drbntc/uG6lbSMeYN62XKGWsFwaeQiEIoAAiEAiKZDr5grSXr87OO6PP7s23+XJB3/f8f1eI0bfvawLp1YfusMapvXDiZFI/wpKKC2sUoCoTAHSSdhm1gpHv3rPF0+qMn1MSRtnnGSL/Qp+rqmzYOljTF2Y0vXP1b3Gbf5/e8HD1ltd3z+uUPGGFvOHKAlb7bpxAUNptzXAS4RCCEXM4QcM8aczFBpAIimV/6+Me/jB355x5KvcemFx2nmLf+lSeMP9OtYiKDseUDZv0Zd8+z3NP6ML7o+Rlq+MEj+h0GzP25OfBDkuXbl1aEHQrlVPx2Z8TsdlY/haZJUcDZQdkD0eUd6qK4xxn7wUPpxLyDyHi813DnpsUZZCoNQY4wxlsHSyMYMIfd+7foAAIDKmD49VwhJ0oVXPBXwSRA20/f2zr+WrViv9t+dUjMhULbmezcEct2KqoPyGevDYTKOWpVSr37SWfuO9++iNe6sAeH8vahrMekKoH2kH45uUkeT7fzLT9Zak10RlKvPONMmpUOizzvSAVGuHe+R6TxvCagOQi05YpcRro+AiKFCyDFrraFlDACi6blrv5n+4M38Xz95/6+UdB22jcWH6Xt7t/AnNbLrz3bGn9Oo5l903X4U1bCo/XenBLptrGQH5H/Yr+qgpM8KKiSocKyuxeg3Zy/sbPvyO/jJdnnD1C6fe5U++VrHrLWNua/f2GJljFnofS27gsIYYy87aGpnRdP8a9L/XS9+pk3qvtIeiLx5R7e5PgIihgohAAAAAACAhGGodAQYYyxzhAAguvpOnK1XLx6T92t7TTuhpGt47WVsHKs93eYDLX9bE7//hNoXTy/7WqnhV3a5VhQ03/E7X+cILVv5sa7Y8rOSn//pS59q47A887qqHCbN4OjSjJyfqrrtyWuvurxhqi5vaPLhVOXJ3TQmpf98/fCUhRp+QEPnY5kqIWOMscP2T7fOzP+Ptm7X2/6M7pvLvJlDnu2ON02SmmgZQ62pa8k/WB3JRMtYNOwr6UXXhwAAlM/0GVfS+vkDD9g9hNPAb16LmCc18tdqXzxd7YuPVfOdv5MkNf9ioX5x63id/e3hnc978ZV1kqT9Bl2qJ357lbbaqrckdQmRUsOvjEQw1HzvBl8DoQmXv13WDKGNP98oDev+eLVhEEFQ8OpaTCCzgKqVr2VMSreHTflmk5XUsPj5tkWFnu+trvfeNOcOogZq2eUHTe35SUgMKoQigiohAIiuYhVC/We0lhQIed5b/5i+uMOWfh0NAflS/3v0+K+Pk7R5RpAX5qSGX6kRww9Q28NTSr6e6TOuYEVRJIIhH+cIlbtyvmPXPJN9K6wOIgiqzOCdhpT1fC8IiorcCqH3Z/e8/ssbPp09L6jA3KHOa2fCJF0zJ2tLGhVCqEGsn4eHGUIRQdkeAERXscC+UFBUyJf631PtcRAw0/f2zjBowveXZyqCpuvDDz9RaviVshtbygqDpPRgca9dLFf74undBlMnRaHtYoRB0ZO9KSxKYVAur7qnJxtbrDa2WM3/zzbrDZLO3VCWGThtjTEjjDH2mjlX65o5Vytf9RFQS0r97wTxRyAUIX0nznZ9BABAAf1ntPpynXKqiRA+r0UsNfLXUu8hmjXzO0oNv1Kp4Vdq+OH1Vf3zK/ba9sXTpd5DpN5DnIRDqVFrfbtWn+/1qfzFY1XWqnlvlXyvfswKqkaxFevZIVCUg6BKDd+/QRtbrIZ/tcFKmyuHPJngqM0LkHLCoKbwTgr4h8o2eJghFCFef7JU/KfRAIBoKXWOUOfz+97OcOmIyR4c7c0JkqQjT7jG1xCvc6h0kYHU7Yunh95GNv6s/SV94Mu1eg/oXfqT76v8PlQEBSuq84HKMepHDVryQueooKLtYfOntBW8jtc6ltuKZq29+u9nEgqhNnmVca7PAbeoEAIAAAAAAEgYKoQixktp/9+FI+2RB7KRBgDiaM2KGyS94PoYyMjeJOZVB3mzgt59fZaTM4VdJTT+rP19HSxdio6LO7pWCD1X+uwgqoOC47WP1WplUHbFw5Jxi7r8JowxdtgBI/LOAMoeID1ldPo3P2V0U5dreu1kV5y6eUtTXYuh/QZAzWLLWEQZYwa+c+sZz7g+BwBgsw2rP837eLmbxqRMmxltY87dcMtKNQ7frcsmMS8MCoLpM67zPqUIMxTq/Hswv1/F12ie/Z5+lSrt540dSzqk07IeKHGzGGFQMEbOT9VsCFTXZJqkdAuX91ih1fOS9OM5TbrmgauzH2ryXmuMmaqs2UBjh03VLxc3ea1mIyRp3g9tmySdcI2MMcYSCKFWsW0MVAhFlLV2lTGGWUIAECF3/mm1zj24vtvj5W4ak6SZN5zpx5FQpewwSJL27LdTpAZ/ewGVJPer6UvQfO8G1aVKXDl/WtdPCYPcGDk/pT9MWl2zYVBGU+ZXL9TpHBA974fdf1+H1zd5T8236bcp32syz10kSQ9ekf7cm8EybNcGO+/otmrODzjBHCEQCEWYN2SaUAgAomF62/K8gVAlLphwFFVCjnmDpLfaqrc+Xn+n49MU5lUTBV0t1HntgFvHOlfNZ7eLjZU0sPBrjlqVksQmMb95YVAcFQp1JOnEn5h8QVCXr2ez13S91n8vb9K4Y5t0/QULJUlPrFvUIKmt4sMCgCMMlY44a63Z+bv3uj4GAKAHF/6g/KqSKFWiJI0XBtmNLaGGQe2Lp5fcLpbvtWGspPdzBX1eV2b+ynLDXoVnNXlVQYRB/hg5P6XtB/TR9gP6dAmD6ppqs0igrsloZqPVzMbNoY211uSGOlI66DnhGpmeKiIW3GS14CarM46ZmvfrLY+kq4t+cEujV2GxqK7FtNW1mLYqfitA6Gh3BIEQAAAAAABAwtAyVgM2ffa5eeOWb9mte/dyfRQAQAEzb12gn10/ruzXmb630zYWogsue7Lz41qs0Ap7+1i56maUMD/oqzmf/0oatP2QvE9lZpB/lne0a/brd8amRayuyWjywQu7VAZlz0PJVwWU2/qVT/brjprcpJbN86U7nXzwVL3+jHTnd2y3+0oSM4VQS7yZW8wSSiYCoRqx+6T7Dn3n1jP+6PocAJBkA/fY0fdr2o0tOv3MdFvC/beP9P366OrndzwnyU0Y9NEn+bfUlSvoUKj9d6cENkeo26p5Sfof5Z0ftOL9ZYRBPvFmBQ07rNH1UXzhtYnlmtloSxqSm7tJLNfJB6e/J598SP6neI+fe4fR0Pushn7DdnlTveTNNraPAagJBEI1wlrbbkz6/20MmQYANxb95/EFV8+fc/C+FV/3mv8zKfPRsxVfAz0zfW93WhW07U7nVjw/KFf74umacMHNmjVjmC/Xc6nQdrGWDc06RIVnC6G45R3L9Lx9RpJiUxU09KYGScobBmX7xXmy596R/nNzvnDHq+wpl3dNSZ1BUPbnkrTDVxvshufaTGY5zIgjdhnRJklUDCGqCC6TzVjLvwC1hs1jAOBOoUBIkvaadkJV12brWHBch0FS+p+vX4FQp0+DqeRJjfy12uf3K+s1zbPf069SxX/W2LFrR9cHxuYPhGgVq04l28OWvb5UYwaV3/YahkIVQZ5JC9NBTaVBT67s4EfaHPaUY+nYrpvMqBhC1NW1FN++h3iiQqgGsY4eAOLJbmwhFApAFMIgSf6HQZLaV6Vn9qQGdvTwzPIMGbRT2a9pvneD6lKFZwjlbRcrgDCocpeu/G5FFUFD9hgawGmqVywMevjVJm25Y2VBULHQJ7vyp1K7HDSi20wjQiEAUUMgVKMIhQAgekyfcVUHD4RC/opKGLTxg08CuW7q4P7pX4ff6etMoVkzhgU2RyhbbnXQUatSkqTf9SMQKpdXFXTrAPf/vvulWBg0aaEpOwg69w6j3b85VXuc1uRL6JPPhufalDqiQftd1yZJ2nVQg5WkN1e0da67Z+g0oujyg6a6PgIcYO08AAAAAABAwjBDqMZt0esL9q3/+rbrYwBAYkz9xdO65PCv5f1a/xmtvlWjUCVUvahUB0kBzQ/K4ffmsXLnCKVGrS26dr6n+UHMDarMyPnpqio/BkcP6Ltf1dfwix/VQblDoIO0dGz6XuMeKXyflmNNm7W2UWKmEKJpx3vEDKGEoWWsxm367HPzHycPtpJ0yXH536AAAPxzy9LnCgZCj511rG/38VrHJBEMlcn0vV2Sm9Xyhfzvo/8Z+D2CXkffk22O2aaq1w+qG+LTSZLBaxHzc4NYXZNRR5PbkKKuKf1+NF8YtN3e6V/v3Lv4Ga99qEFv7Bh8CORZOtYUDYI84x6xDd5cIW/8g8SWJwDu0DIWAz+au9z8aO5ys+mzz10fBQASbUDdF329nt3Ykg6GMgEHeuZVBUUpDNp/yGXadtutQrlX++LpSo38tT/XKjNY2ubYwoFQx8Xdh17nVgfNGMqK+VJVskWsFnhVQblh0M1/augMg3py7h1GX5rRpq9e1eb/AfMoNQzyjHvEyhhjjTEjvGBox3tk6loozIB7XkiJ5KBCKEZ2+d4ve79z6xmF9yEDAALXWdXjYyDBoOnSRKlFLNvqNX8L9X7ti6dr2R8flSQNGVz+trDAjc38OtDpKWpW3MOgXDf/qUFTvtlW9LVea9jQ+2xgw6LzKTcM8mRe02ZM+txeMCTRSgYgXARCMWKt3WSMOe+dW8/gR8kAEDOEQsVFNQy6697Fgc8OymfIocekP/i0uk1hqVFry5ojVFCRdfMr3l/G7KAeZIdAQYZBroKmQmHQi++2lRQGhdUalq3SMCib9/ozH5VtOdYoq5VsROYpbYRDAIJEyxgAAAAAAEDCEAjFjLX2jr4TZ7/g+hwAkFSvXjxGr148JpBr240t2v4r0auCcS2q1UGSdM7EZqf392ueUE+aZ79X8nMfG9jeZX7Qpa9NCOJIsRHXFjFPsW1igw9pKPraWq4OyqfvmgU2M3R6UeavzvlCS95s8/1+AEDLWAxZaw8wxth3bj3D9VEAILFMn3GBhBTvv3l75/WlZG8gi+I2MY/3z8dFu1i2ajePtf/uFOmTntvOmu/doLpU/pXzHRd3FG0Zo12sMBdh0LVtTZKkyxuaAr1PsW1iknTL8w26fO+2gq+f/GRD6GFQKavlK1W3+tHOX/uuWZDePlZ/jCS1Zc0XmiqpSWIzGQB/UCEUU9Za03fibNfHAIDECqpKyJP0DWRR3CaW7X8f/c9QVs2XotrNYxMue8u/w4zt+ulRq1L+XTtGLl46QdsP6OOkMujatqt1bdvVgd6j0DYxSZq00GjSQqPLj28r+Ppz7zChbRHzvHZNg8Y9YgMJg1qOzb9hrG71o6pb/WjDDi3Xe5VDV+dsJmuoazE6cUFDVfdnwxmQXFQIxVhmKJ2VJKqFAMAf/eq2K/m5QVUJZfOun6SB01FuEZPS/yxcVwblal88XRMuuFmzZgwr+7XLnvmkupsXqQ5CVyPnpwOypLaITVpodOd3Cgcu2dvEwhRUi5gXBHnVQYX0Hjqos3Jow7gfSJI2/WGlJDV51UOe7CqifLIri7wgyHvPQNURcv99QvwRCMVc9gpLQiEAqN5lJxxU8nODrhLK5m0he/bJb0qSvlq/Y2j3DtPAYY8QBlVo1i2TK9o6Nv6s/SV94MsZsmcHSbSLZYvKrKAgz3DiLxoKhkE3/6mhaBg09+mmWM0LknoOgvLZoeX67E+b+q5Z0CRJH83M/33x5NULu3x+jto0t76xW8sZoRCQTLSMAQAAAAAAJIyxlhQ4KagSAgB/bFj9acnP7T+j1UlFSxxbyB753x117DdKr9AK26ebPtfKVX9xfYyiKh4w3cNg6dSotaqb0XWo9IZbNkiSNv2fTZ2PPfajzRVBR61KUSGUEZXqoGyHzaxXR5N/7xOKtYpJ0nZ7F3/9le82aY/Tmnw7TymCrA5qOdZUVCFUjuHasuDX5tY3dlYPrV+6XEvGXWQkadiuDXbe0W2BngvRVNdiaBlLICqEEoRB0wAQvlcvHqPnVr8R+n3txhZpyxE1P3T6rbc/0ltvf6RNvYZHOgySpNvuWtjzkxzzBkyXO2Q6NWqtUqPWFvz6Nsds0+2xTS9v0qaXN4dBCMKaVgAAIABJREFUuQOl4XZ4dJh6CoMmLSz+HvTcO0yoYdDSsSbQMOjNFW2Bh0GZDWUFZbeS7TR0sE5Z02YlacmbbQQCQIIQCCUMoRAAhG/McT9xdu/sYOjjjzfp44839fyiiDB9b9fOux+rnXc/Vlv0ivYfWbbd+TsamtrH9TFK0r54utoXT1fz3S/4ds1tju0eCPUkydVBI+enNHJ+Srd9+5euj1KQHyFVXZMpKQwqNjtICneItBcEBRUGSdLC/5kc2LU99RecVfZrdho62EpsHUsqqoOSiZaxhGL7GABUrpyWMU//Ga2SFImByKbPOG2zdS99+NdzXB+loKhvEssW5UHSPSpxyPSy5eslSUMO+HPerx/9Qveh0x27dnT53GsXW/F++p6HHDCk5GPGSRTbwwqppm2s1KqgnsKgc+8woQVCz01r0Ckz2gK9RxitYh31x3QbJl2qufWNXTYVM2Q6/q5d0SRJ+smKJgKhBCIQSjjmCgFA+SoJhDyuZgrls/avHfrK/pMjNWuo1oIgSbUbBqmCmUIFZgmVEwgdtSq9Wj2JFUK1FAZ5BvTdr+zX9BQGST3PDJLCDYMkab8QljM+1H9B4PeoJhCSNodCEjOFksCrCKNCKJmiXX+NwNFCBgDlW/KXN7XkL29W9NpXLx6jt9Zv9PlElem3e11nS5m2HKE9vvY/oc8cuuTK36fvmTlDrYRBex14cWfrVS3zZgqVqtgcoaKYH6RLV3635sIgKR3ulGPoTYVXy3t++25TadcKuVUsaC3HBn+PT5euqCoMktLzhYwx1hhjl7zZZmghizdrrSEMSi4CIQAAAAAAgIShZQySaB0DgHJ4lZWvXjymotd/8OmnOvCaU/08ku/+tu497Tbggs7P3331TH1xh8IrjMvhVSHVSjVQtrff2ainl7+mnfr2cX0Uf5U4Syg18tdqn9+v2+O5LWMdF3dI92U9MFZ6bGC6RaxX95fH1vKO9N/XYYc1uD1IlXpqGzvxFw2SpNP3buvxWqUMkZbCbRcLcqOYJ4zZQVL17WL5zK1vTF/b53lCP1nRpOtWXt35+WUHTdUPBzX5eg/0bMd7RHVQgm3h+gCIBm94HKEQAARvu969ddrQK/WrpdFtN/qnXb6YN7B5+52N2nmv75Z1re+NP0q33Hhm5+d244iqz+eCNzw6dmGQMkFPCbOExp+1v6Tu84I+euSjopvGxu08XpJ0z1vNOqff+IrPWUtqcV5QIXVNpuBw6VLmBXlKCYPOvSP93jTMdrGgwyBJoYRBknwPg7Kv+c8zz9bqW+7ufLxYQLTkzTad9Fhj7sNN1trOBOhaNelaNRW9tzHGHrHLCDHHKBh1LUb2HgpEkowKIXRBKAQAPau2Qshz5F0P6+W//JcfR0KA4jA8uiQlVgnlGyw9/Kb1XQKh3Aqh7IHSSRgmHacwyPPmhjckScP6N3Q+Vk4Y9PCrTfrXI5uKPifsIdKS9No1DTr6urbAru/NDQpjs5gUTCBUTKZ6qEvQEwRjzFRJTWw98xfVQSAQQjfGmOcl6Z1bz9jf9VkAIIr8CoQkafxvFmvB8murvg6CsdOe/65H517u+hihSA2/UpJ6rhQqJRDq6JC+mvnkV9Jjq9Mh0KVvTNCMobN8OW9UxTEMkqRlry+VJI0ZNE5LXm3Tn//SUNbrS9kqNusb5Z+rUt4Q6SCrg8JqE5Ok4fKnpbcab8+8S5L0ZFYVUQ5fgyMvJCr0dVrQiqtrMWwWA4EQCjPGHPvOrWfMd30OAIgaPwMhSXqpY4MkadiN/6qtt+rtyzVRHa89LIl6WkWfb45QatRa1c2o6/y8y8r5BM0PimsYlO2wmfUlVwV5SmkVm/xkg756VVsVJyvdc9MadMqM4O8Vxop5KZi5QeWaW9+oH/h0hidm3pUbKrVZa7v1n5XDGNP5LyBVRml1Labqv6+ofWwZAwAAAAAASBgqhNCjln9vsMcPjvmP9ACgDO9++A9J0hfW+l9pfeCsB/TBujt8vy5Kl+TqIElqvvN3Gj/uiwW/XmmF0CWvTtBN/xLfdrFLV35Xt46uvc15pTpsZr0k6bJUu65rT5VUJTRpYfp7ZCmDpMOcHbTfjsHfI0ntYn5WBxVzfWbbmR9tTsN2bbBJHlRNuxg8BEIoCcOmAaC7Das/DeS6/We01uRK9lqWmMHRJSrWNtZTIJSkgdJ3v9is7426xPUxAnXYzHpdlur+z62nUCiK6+WlcFbMv7miTctO+Ueg9/BEoV3s7Zl36YhJZ4d+3+vrG6sKNYwxNqntY3UtJvBB4KgNrJ1HSby19JJEMAQAwXr14jEyfcYRCoVgwKBL9G+nDi07CGq+83dq/kXPb8LGfm0fSdIz6zq06q2/F3zekMF7adbM75R1hiDN+tm5kjryfq39d6fkHSzdk7MGxGvdfFLmBeULg6R0pdCkhfm3jN38p4ZIrpcPIwySpIX/M1k7nHJ94PeRpPoLzgrlPoWEVR2Uzw9WL5QxxlYaCmW/v5GSM1soUx1EGARJVAihAlQLAUDabt/7pV648F8Dvw8VQ8HoqTXM27rlGfu1fTT2oH2CPpYk6b6VL+u+Z16W5IUz0pCD+4dyb0/R4dI5gVDeCqGx6a/FcaB0nMOg7NawUuRWCr34bpsGH9JQ9DUu1stL8WsV+3TpCo0cemgo9yrEZSDk+cvS5bp/3EVVtz8lZbU9q+aRjUAIFTPG2D/96BR9pe/2ro8CAE70nTjbt01jPek/o1Ufrb+TLWRVytcallvt85tvHx36uUp10r3prUVhtLZNmHSHZv10cP4vFguEvPlBWYFQnNrF4hoGTZwzTtvYwTpit/Irua5rT0mSBnxphKZ8s63oc1/4W5sWjWuo4ITVCas6KKzNYlI02sX2c3r3zfwKhTxxbSdjdhBy0TKGinnfTIwxdqst0gvr3rjlW07PBABx9erFY/Tmjx6lWqgCL72yTvsOulTti6fr1ky1jVf985tvH60T1FsnRDgEyuaFVW/curgzHJKCCYhmzfyO9Gn+1rDUqLXd5gjl8iqD4mB5xzJts9s2khTLMKhYa1g5Pu31fo/PCTsMWjo2/d436DCo5dj0fcKqDpLkPAyKQnWQ5ytDB3eulvcj8Mi+hnfdWg6I6lrSvx3CIORi7TwAAAAAAEDC0DIGXxljUpL+yIwhAEkQZstYrv4zWiWJaqEitt7pXH3yyeZNcFFuBfPDSfcu8L1SqNAcodxNY17LWPaGMW+7mFTb84Pi2iIm+VMZdF17Sled3PUarUsv0eXHt3V77qxvVHWrsm14rk2pIxoCv0+YW8U8tIsVVu32sWJqsZVsyZttOnFBA5VByItACIFh+DSAuHt05ev65612cX0M2siyeDOCpHCHQEeJ78FQnraxHgOhsV1bxmo1EIpjGORXe9h9q8frnJHNBb8+bW6qy6axsMMgSdphbZt2Pagh8PuEOUjaQyDU3fX1jZKkm1cv1OQAQyGPMWahpIaoB0R1LabBWrvI9TkQTQRCCIUxxp7XsJ+uPf0w10cBAF9tWP1pz08KSf8Zrdp22y0lSR+su8PxacJzyZRf6qcz58e+AqgSfoRDBbeNZQ2W7gyElnRIp6lLIFSLA6W9mUFD9hjq+ii+OWxmvY7YbXxFQ6Nz5asKymfa3PSw6QOuXKgdvtpQ9X3LEdYQaRdhkCQN15ah3zNblOYHSekw6Oac81y4X3hVMVGtHGKINHrCUGmEImsAdYukM6gcAhAXK9d16KBd6lwfQ5K6tK+98B9zdcDPfhXLyiEvAPL85ttHq4EwKK/ffPtopYZfqfHnNGr8uSODv+Fp6V+yq4N+OnRW8Pf1ycj56QAjLlVBzUtnSpJu/8MtvrSGSdJVJ7frqt1Lu9bAH29+3tKx4a2aDysMksIdIi2lK4MkSY7DmH6HDXJ6/1y5YZCUDmnCCkOyl+14j7kOiAiDUAoqhOCM9w2TcAhALXM5R6hUp7U+rmVvrK/pcMhrBXv14jHqP6OVaqAKVFUt1EPbWGrU2vSD90n6lfTY6tpqF1vesUyzX79Dt46u3f9Gsk2cM05P//UPvrSGSaVXBGWbU6C4atWUVODB0H47Bnr5Ti6qg8y4yyRJw1puCvW+uaLULpavOsgzOd1G5qRlyhgzVVKTi2CIMAilYssYAAAAAABAwlAhhEgwxtibzvi6xg0b4PooAFCWWqgQyvX039brm/c/LimaW8q8aqBzDt5PVzUc3Pn4x599ppF3PqRZJw93dbRYOOneBZJUVrVQ8+33SpLGn7V/52MFK4Qy84OOWpVuMYr6/KA4DY4+bGa9+vUZorH1/rTpVVIZ9Nr6dj19fKroc169bYI+eDVddeZ3tdBr1zTo6OvafL1mIQ/1XxDKfbJFYXaQpMjMDypWHZRPpmIo1OqZsOYLnbigQUvebKMqCGUhEEKkGGP+KCkl0UoGoDbUYiBUyL8/+IQeefH1Lo+lDu4vSZo18zs6ZNCeVV3/2hkPSpJ+eFVrl8dnnXSEjt5nj6KvpU0sGKW2kaWGXylJ3YZLp0b+evMnOevmo9wqFqdZQYfNrJckXwdGSyo7CJKku5eMV59LCm8ey+eDV5bp1dsn+BIMhTk7KKmbxaIWCBX/P0dxIQ+dXihJHeNsQxDXp0UMlSIQQqSxuh5A1B1x9TzNP/1Y18eIrf4z0uERYVCwegqGCgVCQxof6HwD4s3L8KqDolgZNHJ+SucfdoHOHzrJ9VGq5tf6eM+SN2bpyTduqygI8raJZQ+RrsSqKZsri8oJiJaOTf9rGFYYtOAHDfr0V1NCuVe2KARCtTI7qBQzx12kF5cuDzVE8btaiCAI1SIQQk0wxtjJxxyoq049uOcnA0DIorR6Pk6oCnKjaDiUM1zaqxDKfkOy8LhlduT8aAVCI+endMjuh8ViaLTfQZBUWWuYZ9rcVNVBUCFeQLT7N6dqj9OaCj4vrCHSnvuuGqQdWq4P96Zy3y4mRSsQqqY6yFOLLWR1LUaSmiTJWnu1P6dCUhEIoaYYY2ZmPryAyiEAUUEg5D/CIPeygyGvQkjqWiWUGwgZY2xUgqC4tIV5LWF+h0BSdUGQFGwYVIwXFA29z2qHtW3a9aCGUO7bcmw6Mwi7VUyKRnXQ3PrGSLSKXZ8JcaqpDspncn1jZIOhTAhENRB8RyCEmkZLGYAoiNMcoSggDIoWbwi1lH4zYoyxhVrHXAdCcQmBpGAqgTzVzAny3Lhygvqf78/w6kqtmpLSvqPG68X56blFQbeMuRgi7SEQ2iyoQEiSXly6XLecebGT4MUY0+VfYMIfhIG18wAAAAAAAAlDhRBiwUvUt9riC3rjlm+5Pg6AhKFCyD9UB0Vf59r6rq1jTdbaqxcetyzUP1h6FUFS7VcFea1hUjDtYVL1LWKSuzaxXKumpDRqZvdzzJ+UCqRSyMVWsWzMD9rMj9lBPQm7fQxwhUAIsWOM2VrSR7SSAQgLgVD1Ln10qSTprIOj8pYDxWRCoabsgaZhtYvFqS1MCrY1zPOXje16av1tOmtY5S1efm0S80OhMMgTRCiU9HaxF2bepZMmne30DFL1m8UqQTiEOCMQQqx5lUMTR+6vH/1bqqenA0BFCISq98xbf3d9BJThxNmPdntzFFQgFKcqICkdAJ1enw5mvtJnSKD38mNWkBSdqiBPvz6lPc+vYMh1dVAUAqGozA8KozooH0IhxBWBEBLDGDNR0v+lcgiA397/+FN9/mfXp6hdtInVHi8Q8n7wYq01frSLZYc/UjwCICkdAvXrM0Rj68MZwuxHa5gkvba+XU8fH50fqJUaBGWbPyl9/mqCIZfVQRLtYh4X1UHZJtc3tllrG50dAAgAgRASyRhjb/vOMEnS6EP3cnsYALHA6vnKEAbVJi8QqjYEWt6xTN9fOkGS9H9PvUdD9hjqw+ncCzsAyuZXGBS1qqCe2sR6Umm1kOvqoA3jfqDjW252dn8PgVDa5PrGBmvtIqeHAHzEljEAAAAAAICEoUIIiWeMuV7SpbSSAahG8/+8oNMH7uP6GDVlWtvTOvWr/V0fAxXwNo2VMzPo7hebdfdLzV0ei0tLmJSuCjpit/E6YrfxTu7vZ2WQFI3h0Z5qq4M8lVQJuW4XY37QZq7mB+WaXN/YJkm0jyEOtnB9AMA1a+0PJP1ASreSXTvmUEnSeY31xV4GAF1c8Vg7gVCZ+u+4g+sjoDpNmb+6yBf8SOnw53u6JPhThcBbEZ+9HSzoTWGF+DU4WpJavrwsUkGQ5F8YJEmjZrZrzcPN2u+40kI71+1iknTEPTOc3l+S+h02yPURItEulqVBSr9vYNA0ah0VQkABxpjzJN1G5RCAUrBprDzMDqptXoVQPnGq+pGkZa8v1b8/cKb6ZTaCuZgLlI9fFUFS9OYFeV65bbxGfL97uOiHTR9ulCTtOqih4HNcVwdJDJT2RCkQOijr40a2j6HGEQgBJTDG2H59t9PyH53q+igAIopAqHR3Pr1ah+6xs+tjoAon3btA1lrzxwvXxOoPkrctnZn+9Q+3SHJX9VPMXzYu01Prm3XWMH+CqaiGQVJlW8XKla+NrOXY9Pt719VBtIttFpV2sTvHXaSbWm7q9jjBEGoVLWNACXK/wRtjLJVDALJtuyX/Sy1V66pXCYQQCbctndkl/NlRZ2c+PtvdoQrIbg3bT/6EQTeunBDZMGjVlJT6+dQqVsyome1qOdZ0CYVcB0HoKirVQZPrG7WwwDkWrl5ICxlqEhVCQBWMMWsl7UE4BEBi9XxP+s9olSRaxWKgFiuEvLk/p9fP0lcy7V+14L7VE9R40HjttZM/Z45yRZCUDoIk+TY3qFRepVAU5gZ5aBdLcx0IzRx3kW7LUxWUT2N9es40wRBqBWvnAQAAAAAAEoYKIcBHxhi78w5b6/nrTnN9FAAOUCFU3DNv/d31EeCTqFYIZbeAeevfXa2B94Ofg6Ol6FcHvfV4s4ac4u6f19KbJ0gP/djZ/XO5rBCam6l0YX6Q9PTMu3T2pLPLfh1zhVALGHgA+Cj7m74xxrZMHKHjBvdzeSQAiAS2isWLy3+WE+eM09N//UPn59mDn3fU2ZGc/1MuLwi6avfkhEGS1P9At618HS8t0w7PrtGG0ZMkuZsl1FF/TPqDCIQxrt0/7iJdWmK7lt8mZ0KxQnODerJw9ULd1HKTvfjMiwmGEFkEQkBAvG/8xpi9Mg+9uu7n39IWvejUBOJq5F0P6XdnH+/6GJFEGISeZFf3ZMvd9PWNf/ovfeOfwjpVuLIHR/vh7iXpaps+lzRr4NBoh0GrpqRCnxuUa4c5M7v82lF/TGTmCYUtCpVBkrT2Dyuc3HdyfaPOuuCsqq8zeOhgLVy9UGdPOttK0t233E04hEihZQwImTHmPEm3MYgaiB9Wz+dHdVA8nXTvggZJbeW+Lg6tXH7zuzVMqo2KIM8rt43XiO83Oz3D/EmpziAo14bRk0INhlwPk47KqnnJXbtYsY1i1coMnm6w1i4K5AZAGagQAkJmrb1d0u3e58aYMyS1/HHaydp75z7uDgYAAdj0+eeEQfHVllUNO/WyVHuT4/PUpKSHQZL0tRMnuD5CwTDI+1qSq4WSxmsVC0omaGozxlAtBOfoXQEAAAAAAEgYWsaAiDHG2H133UGS9PumkxyfBkA5aBnrjnaxeDrp3gWSui5TuPzQZfyhskR+zwrKVmvVQVGYHVSsXSyfIFvIOuqP0cmO27X2c3r3tOszVTo3O/h7Mbm+sWHh6oVtYdyLTWRwjZYxIGJy/6dgjLGS9NeZY7V1715uDgUAFSIMqn0n3btA/XfaTpL06voPZK01drbjQ9WwIFrEPHOGKvLDo3O5DoOk4u1ihZ6/KbONLG5tZFGZH3SsDwOdKzFz3EVSBbPRKrVw9cLOP+sTDMEFKoSAGuT9j+OkQ76iX4z/l6LP7Tsx/ad2a60xxliGWQPBuey+P+g/DznY9TEig+qgaLlv5cu675mXS37+ecP31t47bd/lsSkPrCz4psUYY3M3gmGzoIKgaXPT1Ua1VBUkpSuDJPeBULnVQbk2rXpJH151s2/BEAOl0+I4TLonjfWNDJpG6AiEgJgwxlye+fAnmV/ftdbuWOC5BENAQDas/tT1ESLjmbf+7voIieK1cfXfaTudP3yfQO5RLBCSCIUKCTIMqrUgSIpGm5jnid2f8uU6H1z5M0nSZ8++WFU45DoQikq7mItWMcl5IESVEEJHyxgQE9baazMfXlv0idpckupVGt32nWEafehewR0OQKL0n9EqiXaxoHkBkCT9+NSD9ONTD3J4mjRrrWGWUFp2CHTV7oRB2aIQBs2flK5QqqY6KNt20y/s/Lij/hhJKjsY6qg/RnJYnfPCzLu036Sznd3ftaC3i/Vk0GGDnN4fyUQgBCRY9k8hjDFPSfo6lUMAqkUQFKyT7l2gI/ffxUkAVMo9r2tPKelVQkHOCZJqOwxaNSWlfhEIhLadNjmwa3sh03ujL9Bnz74oqfxwyIXVt9wtRSAQclUdJMlZdZAkDT5ssLN7I7lYOw8AAAAAAJAwzBAC0I3XSvbMNadqtx23c30coKYkfYYQg6SDddK9C5y3hl0xZ0WPMy6S2jYWdGWQVNvVQZLUr4/rE1Q/SLoSn7TO1yetDxetFGJ+UFoSB0pLzBCCG1QIAejGWmustWbgD+eYvhNn3+VtKgMAhO+kexfopHsXaNWHbzoPg0p1XXvK9RFCF0abWK2HQd5msSTaasyoznX13owhdHe9ozk+rucHAa4wQwhAUdbacySdI22uHJIkZg0hrm557DlN/Z+nK3rtmhtO07oNH0uS9tghmdV1VAf5KwoVQbmMMVOttVe7PkcUBD042lPrQZAnCsOkO15sD706KJcXDG0YPUmStPUFZ+jjW2Y7HSgdlXXzLrmsDpJEdRCcIBACULLc/1EZY+zQfb6sh3/AT7pQe0b8n4e06vW/6wtG+uUJX+p8vF7S/Sd+qfALi3h60W+lf/qqJOmvH3Ro3O1P6dPP0znqqxePqfrMUUe7mL+iGAZlNElKfCAURnuYFJ8wKCrDpJf+bKJ2aHAbCHmyg6mPb5mtufWNOjnhoYzLgdKuLF+6XJLUsF+D24MgkZghBMAXXvXQcYP2UMu/Nzg+DdBddutjpYFPKV78Qj9J0r67FB6UcXrzk5Kk//iXQTpvyP6BnSVsv3zmZX1tlzrXx4iFCIdBmvLAyh5/km2MsXHdNBZWECTFJwySojE7SJKe2P0p10fI6+Cs+UGLRk/o/DisgCip84OyW8VcVQg1Zs5AhRBcYIYQAAAAAABAwtAyBsAX+drJ+n95e7VPP8XVkQBJ6cqg+0/8UqBVQdneePcjScUrhO4ff3jnx0s/WNtZMSTVbmvZvNV/pjrIJ1GuDirVEbuNd30E33mDsqkOKl9U2sVcbBerxIg5szo/fvfZNZ0VQ0FVC0VlftD19Y3OWsZczg+iMgguEQgBCET2/9wYRg0Xwg6CqpEbEP1y6Wv6zYo39MKFp0mSturVy9XRSnbhw79nflCVTrp3gSTVfBgkxSsQ8lrEghwanStOYZAUjWHSkiIbBm0YPUnKCoFyeQHRu8+u0Wut8yRJf259UPUXnKX9J50dxhERkMyqedfHQIIRCAEIXJ7qoY2Stl/3829JkrboRfcq/OPNCqqFIKiQbw3dS98aupeWf/yGJOmZ19/Vjx5+LtLVQ4RB1YlDVVAchTkvSEoHQZJiEwZ5a+apDvLPXmNO7PJrdgWRVFkV0Zh7ZvhzOJTlrpl3UR0E5xgqDSASvCoiKohQjR/f/oAO/afeTs9QylDpavz0sRf0h1c7IhUO3b/qZR24M+1ilaq1MKiUodKXH7qspv+AGXYQJMWvKkiKziBpKdqBUPZAaT+81jpPf259UEfcM0M7DR3c4/OTPFDaZatYY31jm7W2sednAsEhEAIQOcaYwZL+5H3+i/HDddIhezo8EaLu9y+9pY2r/+D6GGmZtfNh8eYPuQyIWDdfuVoLg6SeA6Fa3TD2l43L9NT6ZknSWcMKt+8E4e4lE9TnknDvGbRVU1KRaRWTortdTPI/EMpVqIJobma7VRTmB0nJCoQyrWJUB8E5+jQAAAAAAAAShhlCACLHWrtcUrefmhhjLC1lyLXuvY+iUx3kgDeQ2ttW5qJSaOzX9gn9nnFQi9VBceW1iO0nN1U6casOkqShF0bj9zR/UnqOURTbxTaMnpT+oMhAaT8U2loWJS43jLlAdRCigkAIQM3I3VxGOARJ+tP//la9vsCfq6R0OLT0g7WSwm0lG3sQgVC5nlnXUcthUJPrA/jFxQaxXNPmpjRwaHRaq/wQpXaxKAZBrnVuLctsLFvROk8LrvqpDr/gLB2RoK1lZ11wlpP7slkMUUIgBKAmeeEQwVCy7fa92br7uNrdJhaksCqHmB9UGdPnH66PUDFr7dXFvh71+UHXtac0on68JIU+ODpXHAdJR0nHi+3S7q5PUdiIgCuDejIos6lMks7MnGXds2t0T1YFUVTmC/nN1fwgZgchagiEANQ0a60hFEqeOKyWD5NXOeRVDfX+whe0ZvK/Oj5VMk1+9AlJ0vnDqaoK2182LtP9qyc4D4Gk+K2XzxWV6qClP5uoHRqoEMrntdZ52jMrEMp2ZlZQte7ZNZKke0ZPCLSCKO7tYsuXLpdEqxiihy1jAGKD1fXJcNMjq3TgZ6+5PkZhIW8Zq9SGjz7V+JY/Vl059Mxbf/fpRPEXl5lBV8xZUXMr512skS9mzlDXJwhOlNrForxZbMPoSU4rhBaNntAl+CnXPaMn+Fo9FPcNY431jQ2SZK1dFNpNgRKwZQxAbFhrjbXWeNUjiKen/vSc6yPEwg7b9O6sHOo/o1X9Z7Rq7P/7Xcmv/9H/LteP/nd5gCeMl7iEQT3xgvkouK49pevaU9p6d/etYdm86qC4ikoY1PFiNM4RV2fOmaV1z67R9fWNuj6zvr48Mta3AAAd00lEQVQS1b6+UiGHQbLWLiIMQhQRCAEAAAAAACQMLWMAYskYc9+6n3/r9C16kXvHSd+Js6M/N6hGWsYKWf6Xv+snjzzfYytZ/xmtksRA6RLEqTrotsUv65W33y/YMmaMsS6HSt+3eoLWblwWqYqgbHEfIh2ldrH5k1KR3jB2sLZ0ev+tVq3WrgPrfb2mN4y6nFYyrzoozBlCYbaLMUQaUcdQaQCxZK0dK2ksA6fjJfJhUAwM/sqOXYZQ7/Wl7SVJC8853vHJalOcwiBJenX9B66P0M117ekWrD37DtE5I91uberJXudF+3xxstWYUa6PEGl+h0FS901lcd1QVg7CIEQdFUIAYo9h0/FQE9VBUs1XCBXy0T8+0zl3Le2sHGKYdHEn3btAkmIVBknSlAdWFn2DE2aFUNQGRfck7tVBktSvj+sTbMZA6eL2DOk+PQVDH2W2b+07dHBIJwqvQojqINQCeikAxF7WsOklrs+CytVEGBRj22zZq7NyaL+b/1sn3bugM/RAV15VUNzCoIwmlze/rj2lN7daFrlB0aWIcxi0akpKq6ZEZ1j2/EnROUsULcq0doXBGz7tDaDOte/QwaGGQVI4A6UvGncRYRBqAoEQgMSw1g6vm9DCFrIaVCv/zP6y4TPXRwjFp5/bzsBj1YdvEg5lTHnsj1r14ZtxDYIkSdbaq8O833XtKS3d2Kytd1dnCLTXTkPCPIIv4r5ZDOX54oH7uT6CE9mbyeJuxR9WNLg+A1AKAiEAAAAAAICEYag0gMSx1hqGTSMIf97wmb7i+hAOeBUxqz58U1MeWNn5eJI2kMVteHSux59fJ0m6IsB7eBvCpM3tYFftHo82qzi3i0mKzGYxT5S3i0nS4OmXuD6CU16l0D2jJ4S6XUwKfn5QY6b6yVq7KLCbAD4iEAKQSIRCtaVW5ge9+/Hnro8QimLBR/bXVn34ph5/fp0ef2Gdxn5tH409aJ8wjhea7MHRcQ6DJOnxF9aV9Dxrrbn80GUlbyzxNoRddXK7ztk9fhu4vFaxgUOjFZj4adWUlPpFLBCKsk2rXpQGHuj0DIPGnOD0/tnWPrtGknSDg3DIb8wNQi0iEAKQWJlQaAtJumTUwE+nnBzuUEP0zJsdVCuB0Mefsbkz15EH7KIjD9hFUjogktRZRVSrFUQn3btA5w3fO/YhUI4mPy7iBUBSOgSKSwVQIXGvDJKkugHRmuk0f1Iq0hVCH171M8nxhrFBY050ev98Lp0zS0+2zlPrVT+t2WBo+dLlhEGoOQRCABLNWrsp8yEVQxHUZ8va+rPV2x/Gv0LoziWvqKF+56qukd1iJqlm2sxivj2sqHKGST/xRrOO2G185+fXtae0Z98hOmvYrNgHQNmmzU3FujLIM3Ry/Cq7EI5Lc4KxfgPrdemcWZpc3xhYKBRUu1hmxXwg1waCZPgXFwA2IxSKlsceetD1Ecpy9RMbNfW0oa6PEajTm58MJRDJDokkN0HRSfcuUP+dttP5w+PV6lauKQ+sLKsNwhhjpdpbC++3G1dOUP/z4x2WrJqSitz8oCd2f8r1EYo6WFu6PoL2dH2AjFJ+tOB3K1kQ//davnS5Lhp3UW39BAvIoEIIALJk2sj++M6tZ7Aj2LG+E2fXTKuY5+2P4l8hFJbc0MmrJrpt8ct6df0HXb5WbVh038qXdd8zL3e7dxIrgQpoK/cFSQ+DXlu/LPZhEGrTotETdKbjlrVyeK1k7731jkZNOrvi68yfeZck6aAqrlHIxWderIvGXeT7dYEwsHYeAAAAAAAgYagQAoAc1tpDvY+36PUF+9Z/fdvlcVBD1n8U/zZsb0C0K/nat7zqIUmdW816cuT+m4ddf23AdvraAKqBCrHWNpb5fDP1lNI3jcXRPU9M0MDj418lFbV2saU3j5euO8f1MQraMHqS84HSUXHP6AndZggV0m9gvfpJVc0WeuSWuyVJl/tcIZSZHUS7GGoWgRAAFLHps88ZNu3I2P23dn0E5HHk/m4DoZ5kbzVD9aY8sFJXVPC6aXNTiW4bS8J2sSium+946WntoOgGQqjOpXNm6YYygqQwEAah1tEyBgA9sNYab/05wnPyvgRCgGu82SnftLmMoEN0DRpzgusjVGXM9Eu09tk1WvvsGk2uL714ceHqhb5vGGss4/5AVBEIAUAJMqFQn2sfXOH6KInw3F//7voIyOP05iddHwEhyt30Vg6CpPiLWruYJO0wZ6brIxQ1IgKVLYPGnOj6CJK6r5yv9BrlhEJ+4/sc4oBACABKZK19/9p5K03fibNFxVCwhk9/yPURAFRp2txUIqtlktAu9tbjza6PAEgqLRTyNoz5pbG+keogxAaBEAAAAAAAQMIQCAFAmay1hrlCAOLs8efX0Q5RgaRUREWtQujFh5v14sPROlOuDaMnuT5CbF06Z5bWPrtGLy5drheXLu/2dW/DmB+8rWJ8f0RcEAgBQIWstaZuQgvBEBLj8H12cn0EhOTxF9ZVfY0kvmna+cjxro8QiroBQ1wfoYuX5jfrpfnRDoSiYNHoCa6PICm9cj4IW2+/rbbefttA5wol7Xsa4o9ACACq5FUL3fr487r18eddHycWrvrn7V0fAXmccNBuro+AEEx5YKWvb3oWvZCcN+pJCYSGTnY/HLnW7Fnj271qSe5cIb+2izE3CHG0hesDAEAcZL95MsbYd249w+Vxat5Xd+J/T4Arfv8EfNHqZo3YP/5BybS5KQ0cGv+B0qumpNQvYhvGtp022fURerRXRLZ7JYUXCt3sYxhEdRDiiAohAPAZrWSIo9+/st71ERCwKQ+srGrVfCG8iULQthg4QFsMHOD6GHl90vqwPml92PUxJEmDIlKl5MfK+VLv41f7GN/HEFcEQgAQEAZPI05u+u0a10dAgLw2saDe9Ny9JBqzS4KUhHXzkjT0QtrFyvFJ63x90jrf9TEkSYMSWqVUbasXrWKIMwIhAAAAAACAhGFIAwAEyFprjDFbDN3ny59K0sM/OMb1kSLvj6+87foIQKL4PUQ6nz+/syzIyzvlrZpPwvwgSarbN1obxuZPSmmHOTNdHwMRdu2cWZ1VPuUMmPZeQ7sY4owKIQAImLV20+9fesv8/qW3aCErwY0Pr3J9hIr17sWfGVFbwgiDJN5QxcGqKSmtmpJyfYyaM2LOLI0IaWZOIa+1ztNrrfOcnsET1Mr5Qq7N/L2/ds4sXTtnlr5Txv2DbKMFooJACABCxFyhnj226q+uj1Cxc47Y2/URAnP4Pju5PgJ8FlYY5PEqaeJm4I/bEzM/CLXpz60P6s+tD7o+RiRMmH6J/vDsGl007qKiz2usb2wL50SAWwRCABAybwvZbhf80vVR4LOR++/i+giBOeGg3VwfAT7xtomF/ZPvOP6kfdELza6PEJq6AUNUNyBa7WKSIt0utmH0JNdHSLQni1RFfeuH3y04LDqzYp5J0kgEAiEAcOTjTz8zfSfOFhVDAMIS9DaxnsStSmjR6uQEQkMnz9LQyWwYQ3UOH3NCaPd6soeqKG+2UHYwdNfMu2IZXgOFEAgBAAAAAAAkDIEQADjk/aS+78TZ77s+C4B4c9Emlstaa6bNTcWuUiju3no8mpVQS28e7/oINeHMObN0puPB1p7Dx5zo+ghdeMOmvSqhu2+5u8HtiYBwEQgBQARYa/vUTWhh4DQi57uz2/Xd2QzMrXVRCIM8cdrck5Rh0lENhDpeetr1EYpyvV0s6a4t4++/FwpZaxcFeCQgcgiEACBC2EJWu9764DPXRwhEx4f/UMeH/3B9DFQhSmFQtlqvEkrSQGnADyuKDHmOgih+nwSCRiAEABGTCYXOeXTl63p05euuj4MSLf3bp66PAHQT1TBI2tw+VquSNFB61MxoVkKxYaxni0ZPcH2ETit6GPLsp2IbxvK5PEJ/n4AwbeH6AACA7qy1d0m6S5KMMfadW89weh707Ll3NilakxGQZFMeWCkp+j/xttYaY8zCq05ub3B9FgDx8WTrgzq5jHlFUf9eCQSFCiEAiLhMxdDr7yWkbef0r+/t+ggVWbsxni1jX9+7r76+d1/Xx0AZXK+WL5e1tvHqXw8xdy+prZ/QJ2V+0Kop0azievHhaFdoMT+oNlw+egLVQUg0AiEAAAAAAICEoWUMAGqAtbaflIz2sR/9W0p/XLjA9THKtv4j6/oIgThp0O6uj4Ay1UplUK7X1rcbY4yVpKtOTkb1DSr30vxm7XB+dGcIRUVU1s2Hbfy075f0vFr9fgn4hQohAKghmfax8/tOnH3+15t+4/o4gfjStlu6PgJQs6Y8sLLN9Rmq4bW6RX3YdNTPB7eiMlA6ai4NMZzaZ2B9j8+hVQygQggAao619vbMh7cnoWIIQGkys4MaXZ/DD5lh0yPOPGJW2147DXF9nERjwxhirMH1AQDXqBACgBpmrTV1E1pM34mzXR8FMfT7V9a7PgLKELfWB2vtoruXjI98tRCQKyoDpaO0cj5MN5Tw+5515Y2y1i4K4ThApFEhBAAx4L0R9OZv7PalbfXMT0a7PVQV1m74TP126OX6GIl302/X6MenHuT6GCjBlAdW6grXhwhIplooUrOFkrRhrF8EK4TmT0pRIYSqvPLsGtdHACKBCiEAAAAAAICEoUIIAGIkt2XE+6m65zff/4aO2G/XcA9VgR8s2qj7T/yS62MANSNu7WK5vN/fWcOaLTOFEGUbRk+SItIyFhX3ZFq4whoq/Y0xJ/T4nLh/zwRKRYUQAMSYt7HH++vEGx9j5hAQI1MeWKkpD6x0fYzQ3PPEhCbXZ2CmkXtbjRnl+gg1Iakr548ac2LRr7NdDNiMCiEASKDsmUNsKQNqU2arWKJ+ym2tvdoY0xSVWUJxF9UNY1uNOc71EQqKykBpFJa075tAMVQIAUCCWWtN34mzN/WdOHuT67Nk+943DnB9BCDykvqmJqm/b6SHSc+fRIVWKZZfeaPrI4Tu/itv1P09/L6pDgK6IhACgISz1va21vaOUivZtNOYERIFV55woOsjoIAktYnlQ9tW8N5/JZrVQVG1YfSk9PygiHgvgVu0Xn92jV7v4fdNoAx0RSAEAAAAAACQMARCAIBOmRYy18dARGzTu5frIyCPJM4OypX0338YXrt9ousjdNPrwH3V68B9XR8DZTp8zAk6vITNX0G6fPQE2sWAPBgqDQDoInvgtCS5Gjp9+rx3WT0P5CAM2mza3JRcDJce+GNaqVzZbvqFro9QUNSGSUdpw9jhPWz9CgPfN4H8qBACAOTlrarvO3H2u8+s7XB9HCDxCIO6staau5eE9xP/RS80a9ELzaHdD11FeZh0lGYHJdn4ad/X+Gnf7/Y4lUFAYVQIAQCKstbuKKUrhtb9/Fvaolc4P0u4Yexh0vvJG4oZFc++8R4tYxHhDZAmDOruz+8sa5LUFMa9Fq1Oh0EDNT6M2zkX1ZXz6Nm7q1Zrz4H1ro8Run3y/J4vHz2B751AEQRCAICSZLWSPSbpGz09/6Yzvq5xwwZUfL9zRuynxx4iEHLlpsdW64rjvur6GIlHVVBx1tqrjTFNkpy0jwFS9NrFVlz1Uw2K2Jlc4fsnUByBEACgLNbao8p5vjeL6KYzvi5JZYVEp897V5KYJeTAxk82uT5C4hEGlSZ77hmhUPXeejxdCdXvlGhVQu0wZ6brI6ACK1rn6ShHM4QuHz1Bl9lo/XsMRA0zhAAAAAAAABKGQAgAEChvOPXklqfM5JanTN+Js3dktT1Q3JQHVjZRHVQea62ZNje6g4drxVuPN3dWCUUFA6XLM8jxivdsK1ofdHJfZgcBpaFlDAAQKmvtu5I6WzyKrbX3vvbYQ27+QAmEKXt49BWOz1KrrLXGa1OlfQxh+OKB+7k+QjeDIrDm3TXCIKA0BEIAAGey/8BWLBx6+JVPdNzeW4V2LiBszAvyT+7fR2YLIUiDp1/i+gjIeKx1niTpt60PMjsIKBGBEAAgErIHw+YGQ/c8+xGBEGLJC4KoCAoOQ6fLE8V181EdKL1h9CQpYtu8Fo2eoDMjdqaw/DbTnka4DpSOQAgAECn5gqF3bj2DtrGQHXnALq6PEGu3LX5Zr67/oMlae7XrsyRF9veWEfXp6oER+1NFEHXzJ6UiGwghOm5gZhBQEYZKAwAAAAAAJAwVQgCASMqdLyRJ95/4JXcHSpgj96dCyG/e0GiJlgaXcr63TJXURCsZKjEioa1Z5Tg8pI1nfE8FKkMgBACIvMzmoKdOn/fu1yWCIdSWTBDUYK1d5Pos6CrTsne1tDl4lpK7oWzVlJT6RXCGUBR90vqwNOYU18foJmrzgw4PYePZDaMn6HqGSAMVIRACANQEa+0/5z5mjLGEQ4giVsjXnkIVBsYYe+YRs/JuL8v+fOCPCVKCENX5QZ+0zo9kIJQ0zA4CqkMgBACoWZnKoY13HPvF7bfrzZ8H4R5bw+Kn0JvNYgGSREDkhygPlN4zpFaoUi0aPUFS9CqEgkQYBFSPQAgAUNOstX0k99VCQ/vXObs33Hv8+XV6/IV1tIUh7/wzwqH42SuEVigURxgEVI9ACAAQC9krpaXw5wxdfNT+od4P0eFVBbk+B6InNxza67xZ2n7vIS6PVNSoiM0P6nXgvq6PkNeG0ZOkBFXiVGpF6zwdFUBwdkOmGoq5QUD1WDsPAAAAAACQMFQIAQBiJecn8hslbc/g6fL8v/a/aMietMD1JHtwtOOjoAZkVzHudV66uiTK1UJRsN30C10foWZEcXbQitYHfa8QYm4Q4C8CIQBAbHnzhaSuG4EIiIpbtOYtAqEiGByNauS2kTFfKL8oD5QeEcHwJQmebJ1HGAT4jJYxAEAiWGuN99eY3/zdnD7vXf1q9ce+XPuzz23PT6oh69//h+sjRBbzguAna6155oohZtWUlOujKApniLpPWh/WJ60Puz5GN96GsagZM+37vl7vifvn8b0X8BkVQgCARMpq39hJ0ttS5ZVD337oPd3PbMvYym4NoyoIQcgdik/VkLTVmFGuj9DNJ63z0x+MOcXtQWpEv4H1vl3r+lPHEwYBASAQAgAkmrV2vaTsFo4HJJ1CW1myvbL+fd2++BVCIIQqt51s5yPHa+cjk5k2bzXmONdHQBXuGT1Bl/rQWnf/lTdq7arVhEFAQGgZAwAAAAAASBgqhAAAyGKtPdX72BjzdubDnYpVDPXuxQ8v48SbE3Sb64Mg0bLayEZIaktKG9n8SelZRlEcKB3VYdJR3DBWrRsyc5GY2QYEi0AIAIACrLVfzv7cGHOJpBu+seeWkqTzDtpWktTynX8O/WzwF3OCEFXW2kXq2tY6VVLTdv2HqP/5/gUB1lqTvY1x1MxkBFCl2jB6khTD4CUo1bSLsVoeCA+BEAAAJbLW3ijpxuzHvtxnK3vLtxwdCFVjhTxqjbX2aklXe58bYxZmPmyQqhtIne9NuBcShRUQRXGYdJQtGj0hchVClc4P8oKg620y52YBLhAIAQBQhbc3ftJlO9CefdNVQ9d+c7DDU6EnBEGIC2ttY77Hs6t9/AyJgg6IojpM+osH7uf6CN1Edd18ObJbwwiCgPAZa23PzwIAAGUzxmwt6SNJOuQrO0qSLjv2AJdHKsnpzU/qx6ce5PoYvvNCINfnAFzxwpxt+x8iSdr7/GZJ0qopqYr+2zDG2LoBh2jo5GZfzjd/UiqSs4M2jJ4U2flBbaeON4NPP9EOGnNiVde5JydcqqbqaOcSnpMJgtoKBZoAwkGFEAAAAbHWfqys2R/ZjDHzJJ2Q/dgXt+mtCf+yjw7Zsy6M4yVCZjZQk7X2aqqBkHQFWsKmSmry43rGGMvsoXB5IV++n/EPPj0dEi2/f17nYyv/+8HOj7P/+d1doDrHG2p+9LTva9eB9VWf9/4rb9Trz64hnAcigrXzAAAAAAAACUOFEAAADlhry67v934SfP/4w/0/UA3yNoPlyv7JM1VBQHG5Q6qrvFb2NrTOmpUBo87Xvsf1PO8migOlP2l92PURClo0ekJDZgtdcadmVf+0lneP7C132dVk+VrKVrSmK5GOymlfu//K9C4GKoOA6CEQAgCgRuS82RqW+XDxl7ffSjO/NaToa5974z39atlaSdKb732sXl8wOm1IP42oL2XaQzRkr4aXCHuAKCv2xr/QevsoDpT+pHV+pOYHLcoawqyQhzDn2XBns4OhFa3pdjQvEKI9DIg+AiEAAGqQtXZJ5sPcGR6PZT7cIndYZ08lAMaY8yTdJkmPP79OkvT4C+s6v37k/rvoyAN2qfzQZcgNfyQCICAucsLtqZKahl54q9674ALjhUVRGC69adWLro/QxaLMWnbX5/B4Z8kNhrI3h7k5GYBSsWUMAACULfsn/LnyDJrtMrS2/07b6fzh+0jq3vbFGwggebzBxfmGVG/5b6O09eluKoeitF0samFQPpsHXEf7nAA2IxACAAAA4JQxxvbUZrbttAslSVsM3DfQs2wYPUmS9MUD99Pg6ZcEeq9S1EIYBKA2sWUMAAAAAAAgYZghBAAAAMCpnipgempFLWTLf9u8uayU1rMPrry582PX1UGv3T9Pf/7vB0vbJAYAFaBlDAAAAEDiGWOmZjZp5fvaQkkNuY8H0Va2iKHMAEJCIAQAAAAAIfGGaOf5UlvudkgACBKBEAAAAAAAQMIwVBoAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAAAAAAAgYQiEAAAAAAAAEoZACAAAAAAAIGEIhAAAAAAAABKGQAgAAAAAACBhCIQAAAAAAAAShkAIAID/344dCAAAAAAI8rce5MIIAABmhBAAAADAjBACAAAAmBFCAAAAADNCCAAAAGBGCAEAAADMCCEAAACAGSEEAAAAMCOEAAAAAGaEEAAAAMCMEAIAAACYEUIAAAAAM0IIAAAAYEYIAQAAAMwIIQAAAIAZIQQAAAAwI4QAAAAAZoQQAAAAwIwQAgAAAJgRQgAAAAAzQggAAABgRggBAAAAzAghAAAAgBkhBAAAADAjhAAAAABmhBAAAADAjBACAAAAmBFCAAAAADNCCAAAAGBGCAEAAADMCCEAAACAGSEEAAAAMCOEAAAAAGaEEAAAAMCMEAIAAACYEUIAAAAAM0IIAAAAYEYIAQAAAMwIIQAAAIAZIQQAAAAwI4QAAAAAZoQQAAAAwIwQAgAAAJgRQgAAAAAzQggAAABgRggBAAAAzAghAAAAgBkhBAAAADAjhAAAAABmhBAAAADAjBACAAAAmBFCAAAAADNCCAAAAGBGCAEAAADMCCEAAACAGSEEAAAAMCOEAAAAAGaEEAAAAMCMEAIAAACYEUIAAAAAM0IIAAAAYEYIAQAAAMwIIQAAAIAZIQQAAAAwI4QAAAAAZoQQAAAAwIwQAgAAAJgRQgAAAAAzQggAAABgRggBAAAAzAghAAAAgBkhBAAAADAjhAAAAABmhBAAAADAjBACAAAAmBFCAAAAADNCCAAAAGBGCAEAAADMCCEAAACAGSEEAAAAMCOEAAAAAGaEEAAAAMCMEAIAAACYCSyBIAvnmShXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "os.chdir(os.path.join(ROOT_DIR,'myDATASET\\\\training_images_masked')) # FOR CREATING TEST DATASET: os.path.join(ROOT_DIR,'myDATASET\\\\validation_images_masked')\n",
    "all_masked_sections = natsorted(glob.glob('*'))\n",
    "for section_num,section_fold in enumerate(all_masked_sections):   \n",
    "    os.chdir(section_fold)\n",
    "    masked_image_orig = skimage.io.imread(glob.glob('*')[0])\n",
    "    [s1, s2, s3] = np.shape(masked_image_orig)\n",
    "    masked_image = skimage.transform.resize(masked_image_orig,(s1/16,s2/16,s3))\n",
    "    plt.figure(figsize=[20,20])\n",
    "    plt.imshow(masked_image)\n",
    "    plt.axis('off')\n",
    "    plt.title('masked_section_0.png')\n",
    "    [size1, size2, size3] = np.shape(masked_image)\n",
    "    \n",
    "    ### Create empty mask for each region:\n",
    "    cortex_mask = np.zeros([size1,size2])\n",
    "    hippocampus_mask = np.zeros([size1,size2])\n",
    "    cerebellum_mask = np.zeros([size1,size2])\n",
    "    \n",
    "    ### Extract all regionwise masks (eg. cortex, hippocampus, cerebellum, etc.)   \n",
    "    for index,x in np.ndenumerate(masked_image[:,:,0]):\n",
    "        \n",
    "        \n",
    "        ### cortex\n",
    "        if int(masked_image[index[0],index[1],0]*255) == 242 and int(masked_image[index[0],index[1],1]*255) == 25 and int(masked_image[index[0],index[1],2]*255) == 60:\n",
    "            cortex_mask[index[0],index[1]] = 1\n",
    "            \n",
    "        ### hippocampus\n",
    "        elif int(masked_image[index[0],index[1],0]*255) == 255 and int(masked_image[index[0],index[1],1]*255) == 72 and int(masked_image[index[0],index[1],2]*255) == 101:\n",
    "            hippocampus_mask[index[0],index[1]] = 1\n",
    "            \n",
    "        ### cerebellum\n",
    "        elif int(masked_image[index[0],index[1],0]*255) == 238 and int(masked_image[index[0],index[1],1]*255) == 93 and int(masked_image[index[0],index[1],2]*255) == 255:\n",
    "            cerebellum_mask[index[0],index[1]] = 1 \n",
    "    \n",
    "    ### Save binary masks to training or validation folder (set image folder paths accordingly below):\n",
    "    section_masks_dirname = ROOT_DIR+'\\\\myDATASET\\\\mrcnn_train_dataset_masks\\\\section_masks_'+str(section_num)\n",
    "    if not os.path.exists(section_masks_dirname):    \n",
    "        os.makedirs(section_masks_dirname)\n",
    "        \n",
    "    skimage.io.imsave(section_masks_dirname+'\\\\section_masks_'+str(section_num)+'_cortex_m1.png',cortex_mask)    \n",
    "    skimage.io.imsave(section_masks_dirname+'\\\\section_masks_'+str(section_num)+'_hippocampus_m2.png',hippocampus_mask)    \n",
    "    skimage.io.imsave(section_masks_dirname+'\\\\section_masks_'+str(section_num)+'_cerebellum_m3.png',cerebellum_mask)\n",
    "    \n",
    "    \n",
    "    os.chdir('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(ROOT_DIR+'\\\\myDATASET\\\\mrcnn_train_dataset_masks\\\\section_masks_0\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "cortex = skimage.io.imread('section_masks_0_cortex_m1.png')\n",
    "hippocampus = skimage.io.imread('section_masks_0_hippocampus_m2.png')\n",
    "cerebullum = skimage.io.imread('section_masks_0_cerebellum_m3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mask_cortex')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    plt.figure(figsize=[20,20])\n",
    "    plt.imshow(cortex)\n",
    "    plt.axis('off')\n",
    "    plt.title('mask_cortex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mask_hippocampus')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    plt.figure(figsize=[20,20])\n",
    "    plt.imshow(hippocampus)\n",
    "    plt.axis('off')\n",
    "    plt.title('mask_hippocampus')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mask_cerebullum')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    plt.figure(figsize=[20,20])\n",
    "    plt.imshow(cerebullum)\n",
    "    plt.axis('off')\n",
    "    plt.title('mask_cerebullum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}