UNET_segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow.keras\n",
    "from PIL import Image\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ny9OrKPHHbLQUJ8A4FMpIa22ZY83sUOD9PDkj3XWROTc7OZ5N1e9x4Kkfuv2A55HSdi7LwpjlHG200a6DtcXp44R2YfP1j0ZZUBrEZNILPofjE6fAUXS+17GB1bgrozxQV5kguTOTEqmd1wzEx4CGkhipDWmw+S5DKWoLsclYitS62mSRAJt2YxqiGxjRtix7gf/2O1ripdfMI6gBae1UgDOwUT16ZWsdukc/3tiLkwKxb+ZAnawnx/ZoHlU1SMAF2t8/pWt4D4HSeGNoH38AXUSakr0DAxqXsvKvqPVGye1BFHuH+lK2Xt9Rd38NCmQ+gTEI1XUtfSKilwH8fQDPAwgAXmHmnyWimwB+FcD7AHwRwPcw86PzxmLITeLqH1WV07sK5kr4jGI+UL93hqa8f1g7wdOUe+Sylr2JZMhV8XZGRYMdr3vdGT9wdDmGgr5pwmCouNodn7ocneuXFjqXpT3gNi4RwEk7OlsEMsfFci6iTnj6+/hJJrmOXNfxXKwOkuzBTQvzh85tsBB9yAYKy/19EpIeCIPCm07SqsONAzqij3ad9bpMsn5tKxX9ydwlCtIeDpRnsSJjqA+FW4x62U1/3kN8uCcwZlx5Yu3THF0N4G8y858E8GcB/DUi+iYAPwbgt5j5AwB+S16PNtpo73gjhAv+e7vsiREaM78G4DX5/xER/QGAlwB8F4Bvlc1+BcBvA/jR88YiTcMPsLc996eXch9SIxiQVzF00ITuqo02ITCUejdX4xz3crATlKsjtYqGxA0moJ8UmE2jLFLilng3w1CTlyByHCXrI5q6GRlFVyxNHAAdaosJNCYilH5frPtervo1pwOs9c61cqg0foXj5277DmIaoleoDSV8HCrrIZgQ+tJQSKg7/rj8uejv4++NoXphHUtdyib0kZkTZ6QUjTWhHxIYekaS80q379WbPqExrj5CuxTnmojeB+BPA/gEgOdksgMzv0ZEd8/4zkcAfAQAZrR1GYcx2mijPWO79kkBItoG8D8A+PeZ+fBMVnZizPwKgFcAYC+7xefqPsXv6D7b1x61eSSnxzaLNY6ArOzJ8YXlqk9k9ZpguroNxMY6KCHt/lPGJibaPMTXAlocxqEO+276matT9OogKSIJvhpAzGuq9VQovAy5Jxeb/LiIMyq6ms2i4sZMrs/OFjJFEUIC9fWYvSoMoKv3hS7q6KmQuO1NcrooYrfxoaTNAF1iEPGliI7IUFWv6sD9BuQ6WVmjEkX51imLjTbika6Nq9uv133E6jvVp8jSCXV2khSpqKWTdh8UQX0CY9D1FngkohLtZPYPmPkfy9uvE9ELgs5eAPDGm42jSQGf5RwSLexlML2r44Oz+nCkcL0o4k2skjvOnYMPyqYlV1ok7JvnDj0cXsQxZZ0PsPzNnPsVmkRkEACjW2XQmeS8Eu557kWa4OiUf7kMop6DPpA727IRgU9O221Ol/F7c2nTJhOkTpTEbJOcTWhNE3sE6KTkZHt6vSTqupusARCaEFv3eU7ggHQOgLNFDpNSpmw67XfLsrHIAv6xB0UVXXxdAG0Sy+w6Bn+tyq54Jsj9ZgOJAi3ut3thqKtTCDGJMsh5vDyXs7ritZxPjB+p/cV/EcAfMPN/5T76GIAfkP//AIDfePLDG2200a6OtY2GL/Lv7bKnmW7/HIC/AuD/JaJ/Ju/9RwB+GsCvEdEPAfgSgL/8ZgNpUiAsl1Z/N9j1aajY+xwX1VwDXRV9BxznClGCDtiL8yUrHk2nfRqGPzZHAenVZvpOUuek7ykr+uMndJPOuXsO0ln9FBzai+fp6giNupBH5Lvp8/PM7dpa6Buxk9PWACJVfqDKA52c9ukjIURkPoAeew14y4igB2ttU/ffN0MeCBMMVT/0RDkpM3c0my5kmxAD+fr7aW9SN56h36IwN76D/LXhcuZQG9CiuOS3aEUJ5F5wrviZQpND3sATGuMaVwow8/8JnDkVf/uTjjvaaKNdXRsVay9qRMjm81jDlwRnyQWCOwgjURkgoN9TUwPe81mvAsF3HDIy6mx6NlFxIB7R6XruqBS9OIzG7RzCsAWvaazxCGm8bkjwDy6ultI7PH1Et3GxqxSJ+K5PUa2iiokFE5jUGtfQJ4vmeayZvP+w/avnsbWIAo8yBjkyb68zFdDp6AUMxLKALkXDo8GzFFLyvEvraQfuj+u/k44PxBiavyf0OuhPlMW4plUPOHSY7e60X/Px35T4Wm16n3V6e6ZyTUC3osLbJSptMNOlIjQi+jCAn0V7U/8CM/908vl70VK/9mWbH2Pmj5835tWZ0EYbbbQrbW1S4HIyptRKK/8cgO8A8CqATxLRx5j5M26z/xjArzHzzwtp/+NoK5DOtCsxoTGzI14mq6CuUK4hisU+gF42kus6ijOqPLfLcGkTDv2LaSxtsdVsvYnH42MXQFftwFEH4qrpVrAUYQ2VKrltic4g8Q7Ev4ABeodv/KHZNtmWvQhlKjyIZNXX72o8kONvoAiNNLPpaSLWt7JFGOHwqK8158qLDAW7fXR+W6CjTNIhC/v4m9oQ5cO/Dwwisw4KTJRAOrE3n5UFQJPSSMgpHYO9UoahVBfDzRRZNn26ho+h6e9pn7ms/CbGg9OMunkgToXk6e1Sewp8EMDnmPnzAEBEH0VLyvcTGgPYlf/vAfjqmw16JSY0NRoIbnaKrIdcsMTN6HBuknQ/LzdgSbPb+C7I39mnTqTeNQW6TWut5i4fvmlSLpPdZFn/5gXQk/IJKr2TRS6dkd+daqt7+Kw+MU/OaVJ2eobqOfUKy5njg2u1s26iSF12n+DQicpRGCxorkkBl0Dx6rCDhd/6mYo4Ohc7rWLw454b5fEup35XP/NhAvtdorvdc+ua2EzYJn9JlhDzsPKvjjvUNavS6ywfNQHaIqLjlppUUFR3tqbGadLL9yt9SmuTAhce6zYRfcq9fkW4p2ovAfiye/0qgA8lY/wkgP+FiH4EwBaAP/9mO71SE9poo412te0tVArcZ+ZvOefzoZkxRQXfB+CXmfm/JKJ/GcB/R0TfzDwk4tfa1ZnQAne6MkVBu/jak0ptmwSlUFF0tdaBCNt3diL7XYPPy+WgSKStagkZlWbT+EtU0QVJJZ491cJIoFMvJJkkM4hU5KJH6eC6RqfLOABGFHM0pEYZqOx+t0OYVVTlJaTVfcpcIiCtKeyg3kSmBgBv1JVt96VEUq7qfnLHS+64ygwMXT89hgHxRE6OkfKs43p761BcbIA+4gFiIoYsTOCQolPeMEtQr3oANJ3GSgx1OdebKKmt90Q5jUTmpP62JYLr79mXUTJX3G3XQ5EDiaIntUuuFHgVwMvu9XvQdyl/CMCHAYCZ/28imgG4jXPI+lebVDLaaKNdKQvILvTvAvZJAB8govcT0QTA96Il5Xv7EoQCRkR/EsAMwL3zBr0SCI3gVtghAiQAZNRPFDRN7BLlG6HoSiereDg6avdTFHFcRSlV1SfAnifS6JGJkw0flM/OuuP5wG1I4lMMjjG04ILCwJlxNq6T2kwfMLZjTAiocOjDq1AohWJTxfiUNktR21QxmG1/CVka/7IduTrCIUR0UeTgVEf8fvxnQ6VgZr4saqA3agcxpkiHYxwz3WcHWQp6o702hs2HRxG1Hcr9t73VTaJAkF1K2/AeVZI/6vQfdYkqSxCkdolS3MxAFS4HAzFzTUQ/DOA30UYNf4mZP01EPwXgU8z8MQB/E8B/S0R/Ha07+oM8VBPp7EpMaAwNgGYR8g9UCtgPqfdk5rhpRQwc8+awfU9cvGwhrHbftk3dr8BO2154WnUdkwx6A/o6voEsZad9GVrXtMcJc4z+qAsfM1xpvaFNWBkNTpjmYlVJIFi+461zPTv8LHXVuxlNALFSwLtfaaH9dBL3mwSkOzJJg9csSWp485k/eYisntW5l77SIu4ryYr6c/fik2mooVUw7LznOY9oIp8RaO85dSG5bt1siDCpPzba3rKxrFJAssRtMD/Jcg65+pm69YVLfsTQSq+W0xern1U98hatdTkvz6kTTtnHk/d+3P3/M2grki5sV2JCG2200d4ZNlYKvAXjJtgqxdRdCTzk92iFLQEQV3utteutwE6iJ7+x37633kTXYEDuOSQ0j2w6jdQFRxmI7o78da5sqiQx6FqHSM2IJxoTAdzIMbpqgrSyABzid1JZHd/DVIfP84gGNl2xSBvPW57HQgWT3d5ENy1BdFQUcf+e7pFKWbsESs8o6ydJmgG3vmkQO0wN1PcOVQpcoJ+DudHTSaxfVa6ZKI8AiDWrjp/HRyfte3ru85hQsioW329Wfzu9xq4vp51TVQ+KOfbec1y/y1TbuNbyQaONNtq7yS7X5XwWduUmtF4swK/KQ/STdFV28tlI4msAIqlTV1ffEEUTBa7u0dYjHyweQjppFYMTieypSwB9Ub+M+sobjpIQNskqO0DR6HxX4yvGKo/B59gUhKIEtKKOOlIt4jHWdl168R133PY9H7RWJJcmXvyxDZn/rYeSJGmM0P3G6bhn1bj2kgjnxZrqGvzocWd8IopBfkV0GkM7PrbYJiuQO0BMWKkCjP/d7eDES1nHQP+ZOm1qA93lAfRI409rb2e/gIvY1ZnQ5IftuV0dXf1utguIP7T94HneK3r2DP20qbB/6OLklfWrB6ZOvDANwnu3Qc1LEHm+lexn8EZLJYX0+Ou607KsfTMMPuiDsktQ/peO7wrStbJARRknZbx+aVekPAeKhBk/n8WHyJX9AGirBJIJrSNFpMfoF4iLVFD46+CynGdaCL0KhM51cqVpZ1UscBOQqcvpBEHtOmjoQyY0bgJIklG0NY/7siSC7jMHGldB4v5SkXXvU9nPULY3VaVN2wdehrVZzsup5XxWdnUmtNFGG+1K27WX4L5UOyso7CoGBlnkYhagX0ZuWk+4z7tAXsBPGeDK2PbyNIpmfN1hKt/CdH7vzVQSpyj6PQsciuiZl2n2q7iX8JHr0aNCDF0/RbPuu4aqZjNk2wkjXo/L9yDQZMn9hx3JnM455TmQt79F5o4nDWB7aSgvj9QZC/43QxfBIQl+n4fehmSBHJ0mpAF0DT3kGcLRsRyIc7vNBRdUpRSNJkRKx3FMDtjv4yWC0uum99emiveMXvuq7gf5m6bHbzMJrYE+Gk9jo8s52mijXQsbs5xPYynZMjDOS7BonKfTw1JRhEMHGrjOZCVFYCcD5GoS05S+Z+MnSYdOlyhZXbPFPNboaSzK0Rs0bX8udWEopjYg+WPdrVbrfmC7iLSGHgLM8258DABCAz4RNJrG7VarvrTQYh4TIpO+sCbXSR/UPGvJoXA0D99Ry8Q2HSXFEj86SP9G8B3AvCIJ0E0AGAm5qnsVCMjzfg9VI1YTqIxoTd9LrzefruTvqRFwLWGwWsXr3ElUJfda0/+NO2olA7Ljg2KZ6MfWntbGLOdoo412LYyZUI8T2gUtTV8Pid4Nrd62vSuPSTNlvjxG6zslBZ9NSke61PhT7KmZySqrqCP41VMRTNNYdsvidpMyohkRAfQlR1TIcaSaY94G3uucu6ouyLG15TlJ2Y8jlOrVoql8Np12NLU618C916Fx+JpZoM1yKupRmW1FQ0WBTAQ0fewo1aRDlpkoZyx9c7//QJazU1sp4zO72F1inb6WQHttLf7lyqHSeJOP4SlK8vHVgSY6gMTSdHwdMnC/V6fbhylx6PmWRS+jTkCnOYrtN5WMd7Hiy0Rpo8t5IeMO0xuAu/EG1DuHRO9sKPdAJp17iMgemExdsSyLN6HcZGG9jjeEh/po3TtTu9XxyzIWcmsg+OTEelcGSaF7VV07F58csD6RiW4/IgPdbif3oAWdeMqi54ZqETmfnsZ9Kc/p9BS8UlmkWJ9oD11CN+GTU/ewxe5W5pqmdYRAz+3uBLTdg3amJv6Q+ZpcNx6lLnruHr60MiMj6zDmeYLmvuvxeKFP6xIl5+46ZEXRzETAEXAcRTpbPFM/9+8FlwDQa1tV/d4JXpQgbSbtCuif1sYY2mijjXatbJzQ3op5tzOhKdBkEhHMkHyMSU/nhiI6NY5oYwAp6kCW2eqqn2XzWS/Iat+rKvBxV5s/nC4BlYhxIn201SYesru32/2r+N+miiu6pzEBQJwAACAASURBVHSkbrY33c4hE3WH8zu32nFdx3cjeGqA3ktCO8UJcnr37a7db6BIbSHobWsRtxP0ibq2zuqsY+n2q03stO6OI+1Y7hVP+tUSA3LlHpW530yrHlTRokMuHVBxScmnNJk42kvW+8y+pwmdWRRxNGSnhNl6E6sB1GonFWRSUvFc7f7wtKA0ZlWWPfkgAF11Df/aU5Ce0kYe2mijjXatbOShXcgkNd9ZqbuBet5s4go9kCjoJAhUuyyJHWQFdRuVQOJOQ5pnSWciryhh6K2Mq62VxWgSYVJat3B4JCKvUx03nJF2VxuqT7TjeNzqv8GjCN+pyX2nPRe3Yqcxwo7ss8QRdXwX4zTliTwHDHkqJUHidtMJcKtVNcmU6Hl4FGObeozoUy7eLD6aUjfCcgUWIc9I84i/XUoq1vc7wy9Xkf6TJkuAeE9ofJII4dGBbC/XXmOplEXybOkoPUnMLdvZjseUdG6SM5NdRVSWSst3EhND8bLLkuBmoL4kgcdnZU89oUl/vU8B+Aoz/0Uiej+AjwK4CeCfAvgrzPwmhWUuM6kXzDhCKtVzxs080AnqLLkUZu6LKFZ1dFe1u5DnNA3o1Kurpy4O7Wy5oK2MVdUWEFf3zG7i+Qz5zRuyf3lgnOtrKqV+Ak9dMX8O6oYuV3ZsNkm75AZLkbmNEIJNgtnN/Tieuomny85YvnDd3PTZzCoI+ETcUGHUM3MrAAnEbkhbi7ZTlBtDzx9wD67Lulpmd6D9Xvw+mUilVY14IURNEAyJSboxkO5fqxm2FvFaqstZFlYZYFwzqQpA00QpIXH1mSMPUus82XXZUvPXJd6Trvg8zVp6tzKpcOm8dwl21V3OyzjTfw/AH7jX/xmAn2HmDwB4hLbRwWijjfYON42hXeTf22VPhdCI6D0A/k0A/ymAv0Ht8vptAL5fNvkVtL31fv5iA7oAsPLK/JSbast71OaRGneD9h2hRCV5O8Z7RAURQZlEsq50buXtCUceHiHlI4Fiir4nq1PVQJmk40OIUuGKgsSVG1LmoMnE9ZV0FA5BA9o3wCgp02lc+b2uviIWVcrY2QJEHYKU06aoc72xxEIH6Zq7qi6noLFJaTSWcO9Bu6lDv4bKfC3ngKuZJnc6liJ7957nX52l4gKgk4Dq92iNta6m1KEIfVNFAUZFoibdPnHJKKfcoeenqK2uI5oesB5SnE2jCysu7VANcEeJ41L7ClxthPa0Lud/DeA/BLAjr28BOGBmvSteRdtQtGdE9BEAHwGAGRZPeRijjTba18OubVKAiP4igDeY+XeJ6Fv17YFNByUkpIvyKwCwm93iVAvKbKB6wBjhALjqVg2w276jzqBjazzG1dlZTERRWV2DJdibBl65Cba6kiKBmeuOpPGksmiD4gBQdOMadLoG6bkIqsnqxoLrJhLou0VZh+4YU+kRh4siJiWU1HsaZaKVSKoBfd5sIhNdL9GmAqQe0QLYu0LLWOzG1V7jZUQxdqYIWonHIQByDZTaQctVT/Cyg57O7iHrNmFQNkAg1XMo553PjDLij9FXBAyIQ/bY9SHECoF1JEob0lcULlQd1LXrvelioQn5mOazeP9pYsH6eK7jsZcuSbFKOrJnWYeK48+Pm3BuDfRbsZZZdE0nNLTdWP4SEX0n2n55u2gR2z4RFYLShpqHjjbaaO9IIzTXNcvJzH8bwN8GAEFof4uZ/y0i+kcAvhttpvMHAPzGBQZrYysD1IuOJZlP5HmP7kA5AO6iNcuSla4PpasnJKfz1b6e9fWlBpAfVhFhaBzEVuyqttgTS0wqzARd7S2QLbslQVhvEJSc6xFXe8JREcTVbfaagXRq+5L+k1mUrWbfak0Rn9ZhZpGyYuM9eCRDUUSqep7zWcyCKqFV0a2zTju7tP9pE1yzk95Xe+bvk1iaFhvE2OeOmsMu29vuOzaZyfxvnWqSDVEinOZdrHONfV6BFv3SQF/TiAAlTnpy2keFS1eH6bwGO3/1Aqw+tjozk9lp+XcJdt1jaEP2owA+SkR/B8DvAfjFC3/TJwXUBuSl7QdarwcLkVUksFPgDLSTEnWZ4Nw0wCap0QM6NZCAm2R2ts1dtKBsE+LNrsdWFgjbEhyX97JjcS2q2prPWjA+z5Ep415vXv3MTwID3CNzVfIsuokp4z3PLZhsvSGJAL1G5IQs9btJx6bOA6qF1ETgPXEn1cW2ZEYTFyH9XtP03a7pNAbQ9ZzdxGPnqYuREzmMVI6zHzTebHrUD26aHiWi5SSKi5zIQYFD9/eQ4ydf04ruQmnF5l4eKZVj396KfDzfCQroUnnchGWJgoFeq0P9Si/L3jW1nMz82wB+W/7/eQAfvIxxRxtttCtkjF4y/6rZFakUOMec65nKS9N0GqkL/kr3VicXOB6o0Ut7RyIwSNHVThvktR6MmyrSKlww2xCFkEppMY+sc11Rxa3j+RS0K4lhDd47JYuOK6EmSKATzDYypbprwdBdttXtTconJ4bM4pixCzefOjqGjpcG+TdV3+06OAQOZfu93fa9/fbcaBV7cBrhdOPUIjRJkWUI+rmYoafNJrqL+lmexyTQOZUiXqJc1VUims07CRNAfsNU1twhJAsnGGk5xBDAUA9WPUaIDPnWIqI2Q3kR3Zv0lKPGeDQYzy9BSV7WyZCre24ucRa6tlnO0UYb7d1lfJ2TAs/EeCB9P5Qc0E2G6hRdAN1MkwNgF3NxK5pSC1yNnsW4dMUTakbYbHp1mFzH8qlcSmG4CSBBZGFf1SgUsdWggxbJaX9QmrjO5knpFjODNNis5zufG3Ky861rQ3CdvqNyrBrjMqUMlCBBVYYimcGKTpdOIQMA724hbAmqqtvfJzs4Bmupk4hmav0mT0pLKJhNpzH2qE1HptOemgjeLEFkH8UEECXxIkM8Vd0rffKJEUvolEVMMmhpl1dlUeQk1AyaRXFQi+/qdZ+UUYlDkXkdEaASoJVK0zGl9+zumIrLUG9XH/uz99297s/3smx0Od+CdW7KtBEvXGZoqOuTpsdc34BUpZQK52K5myE8bm+aTCeq2cySAla8vY6uR35DHlh5CMOjWNSsblT1J96DZtoeR75u95VLZpNOYzIjkwkl3H8QA9IafLd9r3uZMF6v40Pps5syoWbpGKfuAV7Me99TC0fHsR5OJ0y9fss18kdSqK6ZtukERDvdfcjERpMJeH/XrhsA0HoD1DKBbGvlRGWcPuO0iXleYSe7mVQUUEZxgUnFHzPXZ8J1YPJVAPrXJrfk2gzVuobDU9fPtCsVxKdLhIPHnc9oUlpixq5xOYlVA+uED7nexHvX/Z5WDeKkqjoZYzlnIAlRXIK9G7Oco4022jW0tj/LOKFdzByPCEC/DyUc7KYkGA4H+bNzgqBDnY/gOkDpCl9VPS1323dVGV8se/5u+7W9XTQ3W7fy9I6srjmhPBY3dCXIT//Wjrqgx3Drpq3QqTpHp25SXYuyjPwvPVbmiAoSpQxkFCkFA9wqrfnM7t626gWr5VSXaTEz95y/+nocX9GJoDHWngWnCaMdgug2LUrS69jppp6g69adGqrv7IckekkjT/NR2oa6eE6Gp6NM4igT/jN+9Di6qx1XT2WalLYT5a6M6jPEOdNrtjqK91jVdSt9typvJh3uqCuR3tEVyuz02LgEe1fQNkYbbbR3h40xtIsYySpzVvd0tYGYT49sO2Q6bplHvTLXySgSWVXjaxpXOlmhMyexbIz4afvZ8o/tg1Wbb9muhtPXj8Ff+HL73efuQA5SDzpSOCQOR1kWP9f4mmpmOUFIQxqowE2CIpqAzDdiAcDLSLOweI/WBa43VmNptqmAY9FD01pVpSus1q0aBwD+xve22zcMrCWhoAhJExM3tpHfeyzHIbErT861uE9sEGO0B69vpyDMdf3KUha+317jZf6eUOSktJbpNCLidUSSdq4aZ7TfjO1eIKfVZqRjuS5Yy/1CTtpdddFu3wRefa173GVp557tCZVHr2Nd96W12fWRVUQ3cXE6RZEpneQSjEEIY5bzAsbDWar2swF1Whf81WyOwu98e6ujLtsOEW8AE9hTyD+dRpkfZbq7Uqhey7X1OroQ8vAVxxWoaY+j/PL99rPjY9DLLwIAwkxu8iMJeA+UXXFZ9Bk+ejNvmmHmdyKPRMT9ILImN0IcIyY4KLLfd2Wi2tsCVXKddWLT6xECIDJA+VIyt7OJfZ7JxEaHp/EzDfKr+u1y1eNKBS9omEzqVBbxAdfJP8/jtdHJua57PQI6CrtJETszR7kmvaZOURYJr4zX617VCIiQScaT0yB8nsVqFM3gvnYU7zs/8aQBfZ0k3YRpCYNJ2et3wet15FDqsWkFA/OldX0CMKw0cYXsak+3o4022tUxSQpc5N9FjIg+TESfJaLPEdGPnbHN9xDRZ4jo00T037/ZmFcDoZ1npg+fQVWDfHJAU9eZFoAvV8N69MCZnJwen+vEUxwE6fiuT6qh/9obAIDy9Tyy8LUCoJzY/jLhnNlqv78TOWkrQR1u1U1XYOR5dHm13s9TNRy/zNyQpN4v299zEtJy/Nu7CDeEclEqkgugTXL9Dg7jdRI+nrnKHF2x+nY7Vi41q3j9fqx1VFRb1z22fDafDReD6/WQ3yNWBdSxNtPRWfLnn2u/IIinuf9ALgH1kzubTeR7qWx5lsWie+Wwheh2a+iAXRKBj6TC4X77V1E7sty4ix1kN4nuPtDSbCwJlAiCdmgk3gWmJGwCV4C+7iZizvR8ntQuCaKJdP/PAfgOtLqJnySijzHzZ9w2H0ArgPHnmPkREd19s3FHhDbaaKNd2C4RoX0QwOeY+fPSc+SjAL4r2ebfAfBzzPyo3Te/8WaDXg2ERkAq0JiuKuwkZrx8kAV2PSNe+2Vq3CGP6COOJ3Dv6CguOpqqn88islCkk0tQ+fk7yEQAUVdnmk5AghBV1JGnBWjZVUoIO7LKEoEVOSnx9TjWcqa1pbxyqFPjLU6YMrguQKlkjRJJw4OHnTpG/YyUaHpLmrbMJzGuJug33I2kUgRJIihDYlPbuagKSZBkSXbrholFGgm4cpLThVyP5cpQb6YIV6/Z4VGk1biu5J6q0u6c0Nx/KO/JdXSVApzcC506YK2nrY4s2aDnbgmUjGJlgxpzvE+0jlUrBeo6InQlYq9Whsz0PNn1NbVhlXLjExf6O00mCKsusZYmk27jG291fbbH8haNAYRwYbR3m4g+5V6/IqKuai8B+LJ7/SqADyVjfCMAENH/hbYg+yeZ+Z+ct9OrMaGNNtpoV98YOE+mKbH7zPwt53x+EXXrAsAHAHwrWrHY/4OIvpmZ+4J77gtvv3EfmcXXCWoBOrWLata+bT63uIe1eFz3CZ6UxZiO1dPZGLO4QiuCcsoZ9n+peUQIURZZkUZRmOpEs9ftmUBVg1ziajwXKgUvYk2kZuv0uEPohy6yDJlIY/u+nyYHrseoJMyjY9d6TmtWi5gJ1F6aZY4gJGGVSstO5DiquqfwwXkOngsiO5Y44tfuy5gFWOs2BfXR1las5dT3ipjhbR4+sveAFn3YcSvCDBy/q+eZ56B51DrTawSglQ3PktKkTdVDxNnW3GpbO9QJMUNrVYxPWjxwnRBbi9jvNcg5ZXduxZpgFcF0ZG+7D62utYmIVdH68THyhFDNq3Usgwpdesp5dbBPYpfIQ3sVwMvu9ZC69asAfoeZKwBfIKLPop3gPnnWoFdjQlMLDZDJDZq6nHUdfxyn6GrupAsqG61DvmuToZcO8hQQHc+5c/bL6USliYL1OqrZ6rjTCcLzt9oxxM2sbm7ZrrSGU2+2MCsR7u61h7Foxyoer5GpS6UPh+/0E7p0Bn8Ose61jo1vy6LzGe1sR8qCJgW8Mq+cX366sqA3iUutxeo8n0QKykau36YGqmQC0frNTdUr6EZoYqJnEjt1WcctcS/Dobh37glSuZ9ssYhuq3we1uvoTiZqw9xkUQbI9l123FVAaD36nR0p0FcqClFMEHhKxFQmUQk5QOqCKYsdpHy1QW+yXSwiVFGKkhNOsGoKpYP4rmM6xsx19Ervk6a53Ent8ia0TwL4gPTx/QqA70XsFqf2PwL4PgC/TES30bqgnz9v0Ks1oY022mhX2C5OyXgzY+aaiH4YwG+ijY/9EjN/moh+CsCnmPlj8tm/TkSfQSvO/h8w84Pzxr1SExoVxZnVApRRp4N3+x92K1G7ujWPH53Z59Cn77UmEZsqBqzVBZlOWnFCoC/Il+fxPbXVyugdYUf+TnOjFJzeal24ZtbeDMUy2EpXnLbj1ntTZILWMkEHmdZCNg3wxoPOuVOexcB42nMS6NEgwuFRP9lweBTZ7xKYDjtzI9aajJD1A2BQJS6N0DyaeYlspSRUSQ54hY+023lRRCSsv+fxiamEmFqIV0URF0zRG2+qWPWgLrarpjAahJfK9lJCch2NXa+UiL2dWElyJORgRbNbC0A7yqu7u78b0awy87VioAlGdzHLCDSV8IP1iFiZ1FOuUk7+d1JPxdUW+7piAB2ScNp7YlDW/mnsEpm1zPxxAB9P3vtx938G8Dfk34XsSk1oo4022hU2BvjiWc63xa7UhNZBZ0nq3QeCTSTPdVbSpEB+93YMcEscxuuGxYSC7CeESGNQHa2vvWG9K3HnZnscrg8llEQrGlknf/yW1RsWJy0qmBys0UiwfHIkxy36h/UiR72QeI+KZzzeoHgoqfnDbnKAN1VctfU9oNPBGwAwnUbJcKFOaAwIRye9Tu783O0YQxMxx+xoafSS5oZcA00OVFElJBQxOcDSdzJTVCtlVOH5GyYESa+KOkdRRBKyUgyqOvYT1essumE0JSDIeerxE0UE4rtPqZiIxps0sTSZRAFOoadgNgU/bOONRpMATL9N+48qcuVJ2Wq5AcBOpMaQxMyM2Ko0FXadrApJzByfIFNEKe9hGVBora9SfmRMFEVEVypJvlq7+zXGgVPU1lHduNTEwDihXcxUPiiBx1Y47GVUlB1eFL3gN6/XoCAPs2YBfW1nUg+KsogTpbLfd3ZihlEVVzXDtbcDSAB49XIb2C9OGmTq8k7VBWaUb7Q3ZtjuqpKGcobpo3ZfxWPhks0L1LfaiSCfaRG0PMD3D6wW0jhNJ6fRbdHrkVFMKCgfyibmG6CH7YxqKrlNMP6cJT/mM3M5y6/J9tvtQ9hsTS3BweJe5ssKrDWZsh3JNtlp7ClgrmFdA3clgSIPfzg6Bq27E5QFxrOilxX1XCuVS8KDh1EaSMzLAmVJA2Y+OrYuW9Ci8PsPo+uo2VN1tx88ivdAEWs5aaO8tontCwDC/YeRa6b31WIRJy11X5tgv1Xz+r32lNWN3Vr0BEmBbsbf9ll1q0sMCFRVt5Hz09oVL+a8OhPaaKONdvVtnNDegnV6CiRXjhmkbohTf0ilkmk2s34AfKz9M6v4PVkhdWU3xQU3Li1mUTpnJ6pKAGhrMOXYVFq7vH8cg7YvtQhq9eICzVT4XI1uL4ochxts9trxNnsSDA/A5ECQTaLK0bov2r1JkFeeWVNemkbJG0UW5nYrQnr42JCCyX7f3DERRqMrnC4B4cZpbaZace/QqgFKpWrAIa1dCUwLSg15aegtU/S2qZE9lvpHrZa4sR9dKmHEW+DdH4BTuTAE4vo7GHUh7cnQhFh/q7W/d25FSou4+OGFuxY6UJQKlT2aTq3/qNE8iMCJkoq6wjSbxubNmoTZ3W5d/9RUtUXUWSxUsqkM+RG032rTkTuyc0yfl6UKfE4ujzz21oi1b4tdrQlttNFGu9I2Cjy+VTvniinr3BvtCqpSET0vrazM/9ttYD/cfwhsuil9ZkZ284Z9FwCw3sQ4halbxMA3i05Y2bTjrl7aRb2lwoRyrDmQr1oEVyy7iKHeLq2MYeuP2tU4u3eAcKNFTtVzbWyufF3OkTnKPmsMxpF/O8eqAe5U6DFw1Eaz+BCbvLZ18iaKhGShaGiwnwIb4rLkwLRE9fzt9vLN5BpV7WfFSY1M1ESyQ0Gd9x/Fyg3d52QSr73+Znqe601UldA4kUvuGJVnubLf1OJl+ttxQHbrJrzxo8eRrqH7DAG4lyQKtMa1yI0eo4iOZtNY3SFxQUXGeHgA0v9rLO/RoZGDrcJhOkGQMXJFY9NIJ+GTEztnAMju7PZQrI+f2e+u2ndVrCm9FLvOWU4i2gfwCwC+GS0g/bcBfBbArwJ4H4AvAvgerZZ/U7vg9G837tYCuNE+/FoUTidL0EvPtxtqGYjK37xw11wElkxVVWYoTuShe3RsY1ggVwujVZJmZ4FMOGfr51sXZHKwxkQqWTSrV29P7IHV7k8aSK8XOZqJfPYNrVu3jZgEYMkgrt/bPoTTL963DFvHrUq6HGFvO3K79D3lQoWmJ0tEy5U9zM172kxbKLLIK5PjCOJuhxsLEzIsHwpr/3SN6autCAIfCUv+PS+02+9vtYkBADDmfzCXV403GyCXh16zdVm3KxaAbpWEZsR18draitUl9pl027qxb/eWTVRbi9i5SieX5RpBs9pyvcNc7quqidxETaQAdv+RdsPSkjqijlConpv+BpqQYObYRUyL5LV8bmseM5SuzMkmalf5Yn0GtEzMJcuspeElGF1xhPa0+dyfBfBPmPlPAPhTAP4AwI8B+C1m/gCA35LXo4022jvd+C38e5vsiREaEe0C+FcB/CAAiKbRhoi+C211PAD8CoDfBvCjbzrgRdCZuiiultO6D2kKfjYFi/Cire7qktUNgjC1V7fblbeeZViIylK5lhV1UpoEjrL9FTU1WyV42f6/PJRVvMzt8/y4fa+8d4ywK6KTykivJTmwCWBZXde7sqL/izuY32u/WzxqV2ot9ub51NxEHDTxnBKqAx2fxgoIpblo3eFi3usg1OxtIcwkSC7oMNsE42BZaaHwzKgOhtq0UoC3pqhfEOkcQUiTh0u7LrZiCoWGqlnkf2lR9tYiHq/vM4BIeQDQQSa9bkjVJkqup67nZmPfNarGdBLDCVovOZ/HagcN6IucOLIsVhHoIdV1RGF6n0hCBeu1jc8uKZX2PMXJaexnKve1cuV81yd1S3F03OlcBQCc54bqeg26NxujgaDb8vQJjK58UuBpENo3ALgH4O8R0e8R0S8Q0RaA55j5NQCQv4Mqk0T0ESL6FBF9qkI/azPaaKNdQbuuCE2++2cA/Agzf4KIfhZvwb0UsbdXAGCXbl4weCarYC0dy09Pkd9pA9JGMj0+ccKHItuiBMfZBMvn2pVx+w+kxvVr94CX25gPCVs+bM9Q70myQWJiGjMKZW5xhGYRL1+QwHVzS1DB3TnW++3n5akE1zeCfIgwf6NFY9NHEkjfNEbvaLak+/rNFk0WJxXyV+/JuQtK2FrE4L2KIR6fRCKoJlDu3IrXT/ttynlS1SATRGKdmqYFuIi0BG+cETJJFNS7kS2vMUgl1Gr8qTw4tnhT2BaU2jTIFBn5piAaO0sC6cRszPlOZUSqslLXpuhBKa2hquM9IJJOtIyqKTou51m8DntbnSE4p1jjKug0W1bRs5C4qpKX+c5NQJMprrbYAvpCoqXZNFKDBKk1D/S+3TbJc4vlevUMRWNN/H+srNHu8bhcYu0lDvUs7GkQ2qsAXmXmT8jrX0c7wb1ORC8AgPx9U9nc0UYb7R1gykO7yL+3yZ4YoTHz14joy0T0x5n5swC+HcBn5N8PAPhp+fsbl3KkQIyhFbLiBUb91a8BAHJFIjf3kUmNpTYeYSF+4quvY/Hl1zpj4aXnUN1ukVC13caCipMmZvEetNmrcKv9bHNjYnSM8pEQShclmq1MxpDVu2JMH3fpGuWhZLg2NRpBLKYr1jiQqtJXM4lTZQR6rs2+ZfcFAXg6g/YOvX3T0UwEdSiqnU1t+aKVXMfVOpb6bEnW9/Yc9ULigdJjtDxt0VM9zbHZk5ibxAMnhxXy++01Msnu7bmdZ9BmMIpupjlY6CkmBLk1N+IyHkumWWkbswlIS5P0WH1toiKT3e34m2qmV+NOZWFaZqpagtksko/nkaityDw/6WaEs9ONEYhNYWO9cXW0cvyKIomsRIqncj1OlpEqcrPNjuL+gWnYZc+30ZlczpePjk0PzWKAe5HsrHEzIurF1UxdZL2+NAlu4OpnOZ+Wh/YjAP4BEU3QCq/9VbSPza8R0Q8B+BKAv/yU+4imD6e4U1QUbUAZiGqijx7HXpoqqSJ0AmRZpHSoGxCCBetVygcEbO5IgfWL7Q1UyYS184ePLVjfiFuyuj2xIvOdP2z3Ve9NjZumE6DSNrytb7U3XrEMKA/kpj3ViU+khXYmqG60D0qh7PqTVRQcdA1+TfLHyc0A7aSbrcUtlwc4zEtzIfRBrheZLbCZTLJ6fcqjCvkmqcyoGc3tXTmXbj+D6f0lgrhnhdaliusMALgtwe+yiK6afKSCAuTcQJssjk+N48Ub7V1axc9Ty3KbeKzmsiyiW6mLSZmjOJBJ4kT+inve3L2BIMX62tWKF1PbzpI2mkxYxa5SluggijwxnViJzFXmx0qx4XismuAQLiWYY8XHNCaxkCREgquM4KbP33xiu84TGjP/MwBDuuHf/jTjjjbaaKM9iV29SoELmDHjKYsJgCBUgDp2+IFKNm8LZSDPrPM3SXVA9fwe8hMhTJLUV+5PkEsAX92vyeMWYazvbuHBt7WrJWtpYZSdx6NvbF2J6SNGLv0tN9vipol3Ui5nmBwpumrfW94qjICbVcpcF7fu0Rqou6x9FDnQRF1/QFxITeUn6g/54yVYaz5ljKwJ5nYVAmLL+6eo7rRIZPKGdIxaaN3pBEGlgqooF90I9aN83I5bb7Wv650JiuOqMwZevINcajnVJaPDk0g9URKyupcnp5GhL+4WbS+iPLkSWTcVIF2fSHX4lWx6ehorEYTR3+zNDQFDfsd6dxbl0oWOEURss9mNPQmsl+rhcZQnVzlslpvBi1tqX1FX6WKok+4U0gAAIABJREFU7HQJNIKulBQrXgevNyC5d7UvAfLcqBwmynl4ZGomVjmhskZV7XrK4qnturuco4022rvFGNe79OnSbUDzbOjzSJycxMYjGjcLLhCv9YYqcewoDCyreTEtY7mKoI9QElY325Vuebvd5/G/IIewt0HQdPyJIkUGTwRN3WtXw2qLUGjwXat5VJ+yaAPsAFCsFcUR6ln3vWIpxNOqtASEBqbRhEisFCpC2NsCrR1cRIwBIYSoUqLxJGYEQW2r5wWJTGPA/fjFFsVOD6WEaNlYYmF1q70+zQsTFBIjnN9vx1UVkjDJsLkx7YybrwNmm25PTd5ZGGq0pIYGzYEY1NbP6ibGrFRyJDRRlFOTJbfb5FB2Oov3jnwrW9dGhq7220A9hVgfq4ohzSIis2ypMUh5r9iz7bMDQUQS+2OieL2NklJE+oXq1k1KkPQnpYRMTIs5mntt4sRKpY5PWgIwgOZ1IZDv7ERdOC3/cso1XF2mbvblDfUs7GpNaG9mJvonAeGqth9OWeFt0DRKrQCINX7rNSBJAQ3QZ288ivItcvNyRshqeW8lLuehMPtvzrG6JcehTYOYUL4m/QJkvsnWQN3tXmeZQc5gMjWaCaWQx2y3Cp1KkqJ8uESz1R5bs2jd3eLekWVxLYi8rqOCqkwIzQtSm7iK6r5Wi5hFTpUmLvJVQC3t4Mrk5q23cgTlqGkDrg3bxFtt6QQvfwJj8qh9cCcPZRE6WZsLqf0XsqNVr0EuHYpbupi1FRDuuCl3RfKnkf5uE4EW7asC8Sz2iKBKisM3JYKMl8tEVS8KVPtyTKq0a78Z9d2tEGyirF9sr7OFL3KKpH2dfInAUvOpGWqaTaMklGTnjUP26LEF/oNy8SZl7KTl6je130JzoEXFEhqYlFGC6akrBUaXc7TRRrtONk5oFzSiYTdzyNStLAtkO6J2oCns6cTgvMktr7Wmro6ugUD00DQmPZS/IXygwykmosqxfk7Y502LWm58doNqRwLYsgLP/+jAFCkOv1EC0gRMvqDoqz2v5e32e5tdQtDyulvC3cqB2eN2QE1AaAD+9L075sblwglDWRjvS10nWm6sRlCRlyl4lLnVKZprH4LxxJRfVs0J5VLlf7o8ulBmRlLXlXr2qLYazuVt7fDU/pkeBlQSTNckQgkgfyhcM0FByDNDX3xDAv8qPOnoGEZFybLoeisNYn83uouaMFDkunK0BUmWNHtbqG520ZjW5gKRgqKSSMUyoLwvSEr2U+8vzMVTKgdMjqkAz2JzaqDlr5GEB7SeFczx3lUELYgUrktZpqg6cKwWKePjq7JE+X6L8oJWJDAPCkI+sY0T2mijjXYdjHh0OS9uF0FnWULqzPO4Kmugu2li9yZZyYIEarPFwoKnumplOzsWTA5aXzeft6l+AJNPSxcn607OyK1/p6ysLz1vCYXtL7X7Wt+eYn1D4lMa5Je/sy82tvJvdiRYvmFUC6kjlEqI6YEGzwn5uj0OJX5ylhkCUGIoZRmC6sLVsW4UAHiWoZGUfilqHuvntw3F1NIztNomrPc1gN/+nR1ofI0RNBwk4CoUZP9XCooGbbJNsLrQWiga5UM2RKdxQSwmLpit6FDIyzd2IlWlcn0/y+6tS1Udk0BKmJUu9mFvC2EuCHRHWP4NW0xTr9/qubldj5QMPXm4MuqJVj1QE8BI1EckwZQdL5FpZUEWKzOMZPu8dHoKwcQYNf5pdbgOjWktavPgUezapXSWPEMuNCSriJB7lDdNvE9XeHobs5yjjTbadbERoT2paZxHgjaU5zFr6bNGSsDUOJlTI9DsptZ+huXKtKGsUcjxCUjLbCTN3jw+jHE6PQ7XjENNW8qF+cR+6PxAYhnLCgvNlCmKlLFWL21bG7vZPSHwbhU4eUGoEMYUEMLqSWMZxPxUtpkVKI4EZWp952xiJUEaGzO9szrY9vWe9DBdNaiE9Lv7uTbmsr41w+qmjCHoai0oMltEtFnPBdEtCsvOKtE4l5DN+kbRdokHUB6pjluGShrJ5KonNytiTav0ETVisH9PSraYqCcdjuXK4mO6vcausjwzusn8cQtTVi/toJ4raVqyyUc1SkHAYSLoWrTv6p2JoTW9LtQwipVkbx+391C9r3WsZfQa9FhDiHQNRfxlEWO+2q9UmgHx6akhM42NZnWk5Vi532aD+nVXUgY4zcBsjKFdCUubo3KIgn36ozZN1Ke37RgkN541Lg5yEzmeWxBJ6Gx3O6bNQxQcNAFB7fepzYrL0upH1axLk+xf96Xuh0rtqIhisWqw2W1vRnU5mwnZzaJVBOqqVIsMxUqO7bEcaz2LVAeVkF5tosujDZiFdlLvL9AI611dwnxZW0WEBu+RxQe2EU9FJyhwpGss7rXjr/ZzbHaEAqM9bsWFC7nnn4k00jQ3Ppe6gZsbE0weyuQmzHsr/J8X4HJHzkUmCCKjbbAsQlkIliSpb7YuZ3Yq0uGzwhIn+r3iqLLrsL4p1IiSYsJH3NHZPZH0mReRD2eLLdl2SkUp/r9W/CC85w5ydTlFWihMCgtNaBKBHh9HGSOd5JSSsl5H6R+pFKD53AL+Pkyj96Tx4OoqbqOhmm6O563bGEMbbbTRrpWNE9pbMF8pELrLCSOPvRW1E3r7ovueC6Ra30epcwvrNYLCelm1mkeP7btBXZStRQcFAjA5FtpaOHHBPG6z6dYArp/fQb0Qcu5SVTPalTg/ra0HJ9XtTzB9sMLpe9pVdrOlCQMh6y4Z5bGgmj1R01hXCNrwpdSEBaxOMggqaHZiow425CXXLDCWtyVRcCyUkUcb1JKwYAGD+hkoilQevyQS5gtfo6rIzJFvSV1UGTMrUZ5ocL8da/bGyhCiBuXXd6We9NE61m0uHbteqQ2KoIvcfu/ijUN4a3b3sN4XFCR8mWors/MqT4QukZFRZart9u8qE1dvHRAmmR1ve/wNmkU73sk3tsTayYGgyXWNSiotyi+KJODdGxaSMCrH/g5Iaz21m7rraGVdnxLicbuB/J73H/S6xmv9MprGNdbpD/FWjQYO4yrZ0zZJGW200Ua7Mna1EBpzF6UBjgTauM1isJ8yWb2VYLleI9+X1naKmnyCQf+rcaeiiK3FRPqaisISEFbnqZSAh49sX9b3c3srlhNpZ+5Ng4nGVSTQvBYVi+XzUyy+0r43f61dgU9f3rbzmz0SiobQJpY3CccvKZm3PcbZVyoUr4vCiHaD31sgaIdyoRbkJ22sRuW8gVaUEZByHl1xtVva7sToItZ+r4jB8M2OED0lTra4F32QeioUDbkU08dsyQMrJWoYjdbMioR5eVQZYVhjXRPt40nUSl2jbYunRkomVgJuE4yY2twUMc47Iv+dEzJRPpmIIkhWRSUQq+EtyHTvipNuT9VmmpkIp0eTmdBp8qT3KucZikct4mru3pBjbCw+pugtP16bYogJdWp7vRCcKocgu9ks9gIV4ni+u2vEWtO/E7RHeR5jyZdho8v5Fi2p10xdTwDwmuqpUmc2naLRRrBZkhxwBbs2KeU5WHRVwvFx/FxdzDzhvvkCY23menQM0qypuH+Trx5YUH/zcnvzqgJtcRJij05Trg1WnK6wWTOD+Zqsea9xuHanaKTJsmUIN3WcgCfq1vUfyNO7sWZVC8tVnXazX6CQZ0Mfbu2JkK8YpSQstDg9FNTjpjUysZ08TyiPuy5n2cRFS7l4FGIfAz0O1gbSIRbQqzEBRTLJhSLDZl/OS+t0tQ4zJ4RSfous3aZaZDh+UYvC23EnJ8Ee2NO77T53vhzvGQ0haNXG5GATOWbqlWtiqWpQiUioZnMREHuFHspEvN7EetQdmby0j+x0atfKuGdV7GNgiy5gtZyW9ddr1TSd+/6pbEwKjDbaaNfKxgntrZm5jklgn4oyssl1ZaIsrlba9SmwobrewkQUOWmnsZu0orB8Z8fGMs6bMu3f+2L7erW2IC4W4hrMJqbwoSiIZxNzgSZfaXlihfTpzA5OsPqGVspIKwCmbyxRHsuxiQu0FCTVxKZBqLba41nMsljfqX9XjclsN1uSMMiiC6/jqsLH6kZhyE/dy2pOUIyoSGe9G1EqCdlsta8oKFI5FAXpmOUpTORS+WickfHVpgcqNR6MxqI1n+TcJEWZitSoCYZO9QHjnMydrMWVVTQRytxVREiw/0ZmyKxcRfWU8lhRcfvewQfa32Dny7WFKxS5ZpsCeaW/QWPnp8daPBb5bqmICJOp9SpQJZDs1HkNcr8E5ao1oXOfAkJZ0eoB7Ttb1eaVWI+Fwt00mTzml1EpME5oo4022nUwwtXPcl6tCS3LIzJLjGvXA9ESB00kEGpczcfcki5RXG2iDLLE6MghGGswsVpHfTWNkXxFOrTPpjENromAqgZLp6NmLkz+RWGrmYkzriL1YvYVqdvTc6obQ1XH74kseaBFC4o2tB40X8d4j+qXhYIQbiidopvAzirG5FDQm6CUrGFsBH3pjVrPqSX5IqIrG6MBmlKpJO17s4PGYlWKfvRYV3sZSO6w9V772eINx3SvHdlWkZkkD4pDqeXcmmAjqhgaxM9PIoFYr1m+bmJnqTxJZpQxKaC3yeJ+MGSrCLSeE06ek6Y1gtpu/X6LkNa3p4beLDa6X1jyQBMt+ns20xzZqSBWbXpz6o5Xvle9sI/8WJC7dGmnF59r/1Z1W+XgjNdR+8xqV5cru1/V2zDB04wur+vTJcfQiOjDAH4WrQj6LzDzT5+x3XcD+EcA/iVm/tR5Y16tCW0oATBUtD743tnftUnMSRRZaUhZmLto6qOuEiA2aRVRwoen9t1s4iaeozZbqRe0uH3D+GHqchRabF6HWJJ01N54Dz94B5vtrkhkeSrHypG9r+5cKMncRA3KT47i55NDdZ3av6fPTay0auurUVz+VCSN1JUtT9geZpU40kmM1oz5fXHtxR3d7GZW2K4ZTRV8LJZsCYXZG1J2tVWg2tUyrnasfFljdVeTAHJcL4rabM2Y3W9/P80aVne2LNCuruTqziwWzJd6PFIlMSWwVBDlcn0222RduUyos4mTuFZJ3P9T7b0wfcwotORJ9jm/tzFXWYvep/fbmT5Mc+ukpY2Y6+0S5QPhmklpWnGwbKsFEEuftD8A8ixyHnWCahpLLDTSJSqbzyxrr/dytiv9Bh4fold18zR2SRMaEeUAfg7Ad6Dt8ftJIvoYM38m2W4HwL8L4BP9Ufo28tBGG220ixtf8N+b2wcBfI6ZP8/MGwAfBfBdA9v9JwD+c1wwAni1ENpbEXl8EhsYO6zWEaZLypuy2D/RaBCaiAhsMEL13uGoHdluG6ilk6Xxi7KDdgUO++2q2SzKtjYQwPID0vdzQZgeCrfrdaEkSBD/5LnCENRURCC3vlZj+qBFKacvSHXCbmZsfa02UAnx9R4Zgjp5QXtwApmA1+kBx/fkVM29FDc3FISj96ogZaRG6HFPhKKhxfX1jMwd3uxEioSOp5yzMCVzh9WtVBQUJpmN0cwkaUMAtqIMkFquheIiM6TVFc00x3q/HeP4RUFtcyDIcSqyK06BIO725DF3rvfsYY31DakVVeQ3m6I80eJ7RWFCw1k3dg7KW6vnOTjbss+BNuFhkuFSbB4EeRGRda5SFzLb3UYjdZ2ZCJOG5Qok/VKt34ZLnFlY5hLsLbict4nIu4evMPMr7vVLAL7sXr8K4EOdfRH9aQAvM/P/RER/6yI7vVoT2mijjXa17eIT2n1mHurZqzYkrGajE1EG4GcA/OCF94irNqE9S3QGtIkAFb7TFYwDtDGjTxCoBevgLSvgGQFWIzZql3ZEMqRKv2g9nvWDBLD4Wjve9pejGKJ2M1dEQAHY+0K7ymqN4eP3T4zAOhUBxslxMEqBxtV0m/KEjeS6vCMk16NIuZjfd12CBH1p/CtoyeqEsHijS6vQRADg4nv6/SpWCmiioZkALAyD8lgTOWyxPlW+UFkeqtmSGFbl0QCNiGGqdNHkYIMw7ZKgG3m9uplbbOzWZ9rruN7Psd4lGw8Aql2yc9XrZxSNukAh12P6SEjIe0Wk6ci9o+KZYVqg2u/WV5bHUa5c5Yk4y3pilZnFfiOyyiVRwIfHKJ67CwCotevT9nasOdZYmnt9aQgtOieXYa8CeNm9fg+Ar7rXOwC+GcBvC3XqeQAfI6K/dF5i4KliaET014no00T0z4noHxLRjIjeT0SfIKI/JKJfJaLJm4802mijvSPs8mJonwTwAZkvJgC+F8DHbDfMj5n5NjO/j5nfB+B3AJw7mQFPgdCI6CW02YdvYuYlEf2aHNR3AvgZZv4oEf1dAD8E4OefdD+XYT2yLgCDQUC/3V2eW1xMS6BMK21WxDZjPrGqmVKJgyAjU96w8hitO1xuQFsisngaV09VmNC6QEUE5XEwGoYirskRQxdeRR+Hf6xAJRl9jYPlq4hkGgGKWtq094Xa4kGzh7Vtp5pgGrfTv5OjeKcqDSIr2FCMojAzighQkVS+cVlTlQ4PjM2e0iWEOpPr+WaYPJb41DzWka5vtNdZG8pwRlaapKixWmifVWB1U9U+cjseRRvliVBhVqxN1F35VFQaOXlOsrNy7ttf2Rhq1HhnLW3wQpGhOOk2u8nWjW2vmnDZaoPmdht3zR8IulfKUF0bbcg0+4rC6jQ1hkZ5ZmiOEyFSbhrkSsCV5utPY5dF22Dmmoh+GMBvonWRfomZP01EPwXgU8z8sfNHGLandTkLAHMiqgAsALwG4NsAfL98/isAfhJv14SmLP8zuG0AOkq4IOUpND3X0tzHECxRYEXEm8qCt0OBAdORVwmYw+OoN69dgHJqpXIQH/7ZaawBnEoAvRG6x+rOBKub7YbHL7eP4eJrjFKeiUbblKqUTxldR2Xqb7YzayKsXZ/A0d0y+oXQOKpFZgF/DbzXsyhMmVu9aff7gHPhFpnx1uxB3wQTmtR6Vn0NAtbCrdOg/2a3cPQVqaq4U9oEpZQVo7q4n1/PhULso7Dej66n1Z5qua6Mcfx8FDZQOs3qVhknNJ0Am0ibUUFKLvSikUkQaS0nmtjbU015dLS9Zc2VVdSR9nZBz91uN3zjQTvEwWNkUsSuyspWTbBYRJGGy7BLjAox88cBfDx578fP2PZbLzLmE7uczPwVAP8FgC+hncgeA/hdAAfMrLfQq2izGT0joo8Q0aeI6FMVLlEieLTRRns2dlF3820sj3oal/MGWt7I+wEcoGXy/oWBTQdPT1K4rwDALt283EugK16aZHBdoyzI73oQWMp7Nu2PodSMqoodq7WDdVEgHB52xkWeR+qH9oK82dI2+O6u1VxmisIotx6a6uZol6Hs6NSOZyN9DIrTgJt/IAx0Ydmvb05M0rt27pbaVFpBFqfKJI0S4NH9CoZo1L1VF3FyFLDebbdXNYqsYhN43OypWyeXYB0/0/cmxxyVQwbgrMr16DnVi9zuIEN0NZs7uVApJHJoVN067ea0Heks6hZX21ElJBfUNn/IOH5B6S6C5AS8Tx8H+65eU185MX/QXrTp6y2SqvdnJhel557VbNUR5OTTs0dCrJXerrQUpP74yPpc0HtbXMCvvWE9PZU8m81mUQFGzFDZfGZikjjBUxnhcisFnoU9jcv55wF8gZnvAQAR/WMA/wqAfSIqBKWlmYvRRhvtHWzXeUL7EoA/S0QLAEsA3w7gUwD+NwDfjZb5+wMAfuNpD/It21n0Dye/wbXLP7vOUgA6cscqy63p8GyxiA1TFOUxuy47QrVYzGMXHxmjkHgIsswaephM+MEBWBqEqOpCptLTm8ricLPPt3ET3pr1znPr0SmK59uYXMgl7iRoaL0XY117X5QypHkeSaVCaN3sTyx+NTkQ6XDpOMUElIJIZvLZZiezfqKFBtdln/kmxqk82XYqxNryNBJfTVtO6y+1NOgk9jBV1La6Xdq4yzuSTDgJsQzqZpe+kVeRUqIB/ZBnCLtaTtZut7xJmD+QGsuFJkR0lAgnNZHSTNiunwpfriUAP3tQo5DAv+afyoenqG60gfz6RvtbFw9P7PfWrmDaBYq2FvEeeiBNUmaz2BDFpLonaO6194XGdbW2Mxw8RpbIcz+VXdcJjZk/QUS/DuCfAqgB/B5aF/J/BvBRIvo78t4vXsaBXooNTXREXfdT/mrG0wL/+tly2c+AFgWg3adEdZTrOn6euK1hZ26FyOpmVO97Drm0mUuPk7fmVheqBdhoGDzttqzL6oDplx4CAMrD9kmsd9rxpwc5lreEl6XijDlhIiKSyouaHFXYSF3i8pZUBTiXU905yyAWZBPC+oYE12WjkEdXdu8LMgFuZyhOI8esPVi2QvHNnvORARR1Y+5zKWPN36hw+L72vE6elyTCKnOTi9bExuuox1tqw6QCKA+VyS/F+Es2F9L2L/MJNXFcrR4gjgkHzfRaXSiz9UzQjCYTmTpu8VDa3t3cQqFJI5PCkuszn1kVgSW4VitY6FsrDHa2kSu/TfsTaPhkZ9uUbS/FruuEBgDM/BMAfiJ5+/No67RGG22062R8vV3O62GURR6al9tW/k8q9MjcasMD5sLS1jzKJ0snKDo6sWQAC2rD45ZTQWVh77GivSYAhTLilWoRURmJq7S52waCy0dL205dwqxmTI7F5ZUEA1XiutWM/ddFreLGTMZYYflSe9ynz4vAYh0pFopINDBez8jcyeI4Su8cv9RNQExUGSmL9ZInz4vL/Iixuhl7CQBt8F5dTaWUKF0ClGN5S3srxIoBSzIcqgsZ96W8MkUTWe3qNdeRtrG6pTWZgA6sKhvxwVX3mFEKFczz7WoTuuwmER6/b4r5Q2lgrL9JE3sQ6G+bn2xi31aRl8r1vpmWMaCv92FRRDdU7p3m9TeQ3xbBUHF5WepBQVlUk7kMGye00UYb7brYKPB41c0nCkyggKyHocbBVNSRisKqAVQrjZgjGVL7LVYVIOxt07eSeBxWa9TPtfQLjbPkJxujabD03rRuR7OJxV40MF7dmJsY4vReCx02txaoBMGZ1LOsqM00Q/2cJB2UNFxkmD5qz6s8URWK3ALcStvwwX5FJ6v99tZZ75NVJSgp1dBNIOu6rjWd1TrGoLRukxrGZk9Y/lp7KlUSzSQG6hW1nTyXxdjVQuJOOWH2sPu05a6xjMqYn96O4pYqs11qbekJmyZdLsBI43CrGzFWqH/rBTB/qIRg+d4mIrXNdpfmWSyDadgpATojih2xVKBSO9zfPzDiNUlcjU+X8X4Sy/b3EA5amXcj1ErnM3hR00uw0eV8J5kWrod4IxpfSN3RLIvwXzJJBu8BQG+2JoCmSRmrC/4WX2mzUuGWlKX4zKoE6Cvt6tSEWD4lExrnBJKHev1c6zYWx5XJElV3pSxGh+VYAK7WzIqoEHvU3vRMUyyEC7b1te7dG/IMmUzi1Xa7n8lRbu7f6d1uETngiuTlsoSSEMS1zuU5W92Mk4VlIWUCnx0ErPallGknDqyTso4RSsbRe2VcFciUCbZYR9FKehQnIHWRNdGx2aaOSwoAa7dPdbc2u7EUTKsvdJKOZWLBkh82YdWM/ET6HmzHhJEuXLFhtPzGL96OzZU1ATCfWYZcJzlarix0oa6nShBpmdSl2NtMmr2IjRPaaKONdnEbJ7R3oHGI1QO6MipHKM9Bk0SIrygi+lJO23QSg/vKANfgLBF4V4sFZZeTApk0BeZtkdA5blfnZqtEtoldk4B21Ve5HHVDsyoYW793SjlF6RoNuE+ph9ryVY3iniQvRFa8ec+d9u/+FLRUnWvZfsMWtFdkpoF1UKwfNRerAuot5Zq17+28Gswl1N4DWiVezwhNwmVbvB6rDU6E2T89YAvIa2JBv5dXQC3HpHWYnMXKgniNIvNfx1AEuJkSCldMD7RITVGyutT6wO8cVIZiNyI5XpzGvgfmujUc+Wf6lvz++aMTu6+aG+J6Vg2yo4Ty7+kdZeRBAgCcnNXT2nWvFBhttNHeZUaX2YX9Gdg4oZ1l1t9QVjxl9heFobBM+3huNrFSQIm4J6egnTZArz0YoYHdvW2EqUASVWl4fIogfTs1lqKIip1Kg9U1TjJTeFDZ6HqWu/93JX3mD2qU0lik1s5Dp1XcV66kWCBIBytIV3JV+CiWDeqdsrP9/I01pgeCSgUpnr7YBsxO72axjlHVMYpIjdC/R5MMrIrnEkvT+Fa1ExHn9FFk+58+J8F1jZOtGLMvyv7vdOtTQwGUGq+TeNzkONasWjXFblQTUeqHJjwW9xknsk+TYTp2+5DjnYp0d7VTWEetifRBLY8q6y1aiBAkisx6jBaPRWZbzjfsLZDfa4P95EUga81YOWmhu23sDNJwpRHxx0u1MYY22mijXScbXc6rbsxRWcPVdJqU8f/f3rcHSXaV9/2+++rbPT3vmX3qsStrJR4GWwpBAqcCBTgIiqAiBcXDSaiYmEoKEidOVYCQmAoVV0GcwvEDE6sMwVAYAsQJCjbGBHAcG4NYgZBWSIpW0u5q3zvv6Xffe7/8cb7vnNuzs7sjaXemZzi/qqnuvn3n3nNP3z79PX7f79OSJiXTBn23f1CqAbUlT7JfNXXZqpqk+KpikvT6CLRGVFuSTdTtr7daZqHE0KI8L43RkW5JsmM9yXJSwShEpyzsDMbconZueoXCxeYAgERgsjct5M6MwWLB6X6Z0CuCPltRRlXFKOLAUU9kPOlcKI+uB6jWV+aJI942rteSI2eZWa00ifPFDaeaYWkbe8lmMlV0kgpX3mS7qWtoimHlthVFTOhMyucnx4+brtGLo4WIZto42XPp+IsIjpYiTAuN6XXHCbULmhFWqgbZrHI+KlbZSgdh01FPAEffCJabrr5X7qWiEqOYNJnxgViaaKMVV7PMaT34BW3IMdBpShaBrLhIxVY5Z0G16rhrtrdn7GgdWhXQ7rj3lwYDs1yt2OBtPq7ifwGiOekOJWq2hVAwwpUclOs3TB5HUuubVI4v2Gsm/n6DAAAgAElEQVTJZqU4XRIGquIa9HLrOgZ6TWEA1sC51FJyRHZh0mB1Zd6sHkUcIpAvmy6+RRxYmkk8b75glRXzpertn7BfUqU1VOa7dpGdOmKupb2nZhe8TNy5dF7lfmC/RKlQLmgeli/WW7NQAW4RV6oIFWz5Yroozr+IwGJupPMqKQSEPfNceye09ggNY4mduKV+FHVy51BhxyVXWaBJilgSKb2JBNUzItQoC1t3d902og5U0Vi5jNWKLVRnLTrv9ECrg1QMGq2DV+XeaV1FmsY68Baah4fHzoFf0IYcZZezBBuElaxO2QW13amVjV1JbDdr7XDNzKCoRMaFo28gcy6k1u8BANfkV7jRlveUf1C4elBR1qCmswCL8VKnd3UxhSibnJdqg2psz2lpB1OJlbVWCyOrhaBskBBs3eI0snWjuUhl9+uBVezo7DeuUF51Hck1KaGE2fbu1PYBUAQZo37SjLcnFQhKnwi7QFdcw5UDTu4nWmOIhF22AXy1uHWfPAX61UE6y+iTjqir7i4VbOW4lSRcf1qsq1FyXbnELY5XGbnQNbS/qdI8spRs9UCiaiXLmbVsw9ycNJlvOZWNajLwCGZAkzZa55sH4MTMcyZua/LEWXDjOao3bgTsS588PDx2CDwPbbvAdq6WX8g8H1A3AOCsspGqJdZqQwrkBVhF+bSmc6TmypXUehMLSekZ5n9VK41Aaq1pgkF+lTl28txWgSOJjdUFF0QGka3hDLrSx3N6xI4hXJVYWGr+Lz25aoPTLLSQeLlvY2KKouZeq5WiRNIiAjoTGvBX0Uq5jNXCxqzSeYkLhYTOjDl/v+YsQa2rVGhyIG4ZIUXAdXenwgX8+yWSroY2+1qPqcyIDtsx2dKqiKxShxWfrBFGzvDAMVSRI08cRURrXKlgS0vR/dFQ/TJg5Kx07yolUFQbrXWdofRUFrqWGK2fY3/C1WraxIyUSsVzLZsgsLHOkSqKM2exKbjWvXOfI/yCBrjMofaFo8BVCmgXHXHTimbb6bzXSpyzCanJ1A+813edojQbKsmBgOtuP1VqLfGMLDdMXcSsQD6imUZRkZ10C6YmB/ojEeJVUYGtD4pLhi2netubMl+YqBrZdnGW5V8PEEiCINS60UgD9oENdGtHp37Ncc2cdr7s02FwIByvKamuiMi6t7ow9MYIybIMo9BjOJHGpKk8Ma2ScBw2XUiYXMbRukUynt5ouVZUxhqWuGaaGe65CgStAOhNapbYLeYKJkK6pnC+zEuzCx+rsABb3mFlXlSDR2MkF0SUURs0L0nYIg5RxCri6drk9YULWDsh9ZrHT2Gz4C00Dw+PnQFPrN0mWGtGU8la002RCCCO1R0nTRIAVKvatDmptFCna0UirdxLSX3DWmHqQuaMvKbyD+Yh6KlLW3ouCLqZ/fVWxEVmg/Y20Bw690iVHrR/ADFszaSqWwCu0XAubp/WkeYJIW6K9SZWXGWFbA8CldRWqkhzV2i7Lek1dSYD9CSX4jpNAe1dyp8z22wN5RjZ/bQ7U3uGLFtf3cogd7WhZF1P/T/HZSsHtbXSQus2s5TQ3Kd8P/NQkea863WoCjInp6RqHuqKU+4SIqqEUVnooyt8P22onB5bHEjqACVLd7EBTJqLSH943Bz/5n2onjKWvg1JXEV5oCvBJwU8PDx2DPyCtl0hMbRwyggxqsVTNJrrdtFxyQOx7JLYynKjcTHZkW0AXX7RG20ENBgTszpZUYBswvyKZ9U1ncXh1BziZu4sCTmWanLlaYSsLkmETOsNQ8RCubDdzlu565upMTpyMSbteKSPIKA/InEeZbpoLKjFF9E2upOu8Yiy6zupO393RjtOyfy3TfwKcBZPusCuE3tdr5MtTaK1XwP/Qt9oOu2zyqKLdWmsT5ulRB1GuCa23hvXJIgbh36pOSD0JHSqMT+1IivL7tozG2OMUT8uzVEkDpbdNGVJtqEV+5R7qJLYWs7+LaYvp00AASh+9Ag2FQyfFNgWWKcxsa0QEH5PISVQQbXqJIXCUuJAkwKaAGi2wco/k/IVdTmz8dRmEjXD1bt+3HLC9D0avXjh1NZvgBNZVPevXw9twD2d00J0YfSHhL4o0SoPrbKQWfcwbprjFnHgJIq02a8sokHGdgHMZUFjIsvd0sC73lVFRC7ZIF/u+knH9VLuFjHQ3K+ZO8kuXidKunMxotYg8z9uAqza/9rvoErWdUzPy/Fzl4lV97N5o7nOZCFEIFUBugAHuVtYbRNiSTSEHVeArpJIQVbKvOaD+xeRG48mMMI+sHSruRcS6ckQNwoky4Mijpr55iiwGe9owdyHHIeg0xewVfBJAQ8Pj50Dv6ANOUq1nJaHlvUBGuwPqd10AACyn+3FCQDzS+YY4yIpVK+BtLGwygZpdUA7cj0IxIWoNLrWotNfwUAKx/tTqZVxtsgZUVMtOuFKwfXQ7E2Ie6kUNQZGnxI2uRyrSCNbPK5WG+WMfl1cyHBwPGHH7ac0hd5oUGL1m+NW57X6oETNEBmgXp0s5UOLvHtjhPoJ6VcpAozJQ9KzYJKsa2pd1FlY60ppIZUlstaVWkSx1IMGAVnpoalHtHjcWVqFUkBqcC0v11hX6XxJRly+NckKl3omyByp1VmwdUPVteXAjVF5d3mF0NonslFiKdZOSoiiIHAymDAI51aQzc1jK+CJtR4eHjsHzF7gcehRipvZ9HcQOtp5LCTaugRhsgyFUjT27rL/Sx1RRZD3UJtx59CaTnkMGm0UqnzRdbLcti+njKkrjU6iZuastp4LCgfnjYnTvW7CbqudlmoAledWykUaWmFHtQQMGVXjZDKefoHKotA6tLO5jKc3mViWv720DqM6NyjjrWByjH+nmMG2o5PK/VTnc5tk6Eh8belWke2ZyWwn9soFlSUiGxPT96hwTH5NFOhjZZGdKoeGqSpkrVe1uCpLJmkBAN0pDYrJeYoQnVlJWCw7aoYeI10Q2oYkJorYKXHofNdPl+ZJrd4uW3khTSj0J8QsLBiJKJ0obSc/vUkVAZfCcK9nCK60AxF9iojOE9GR0rYpIvoGET0uj5OynYjot4joKBE9SES3X8vBe3h4bC6IN/a3VdiIhfZpAL8D4DOlbe8H8E1m/ggRvV9evw/A6wAckr87AHxCHrcHgvCiTTaT2RDLK4pch/VFkUeujzhax4G95lAnzgOS3cwnRIq70Jq9zP7iatwMAdCbFPUEKYVJW462YS1JzcjmjGLUmADJeYmN0Ygj5UrGVGs0By5TRQYroVWOiCTLyRHZWF8udAzNvlbPtNAfN2PUzCexszAUGlPrjru+nFYJI2N0JrW0C3YONNamAosTf2nGEzX6Vpq6dYOJYzb3RAg7cg6xqPr1EslWe2pKPK49S6hekOe7lOYB5Jp8FusqTwmxTOXIA4H9X8AITcYN6RBfas2ncT2N6WlsrDbnrE5tANOrB/a5xtqCjFFZGYyxJgsdO5dqpSeLMpHluO1mg2FL9YYVV1zQmPkviOjAms13A3ilPP8DAH8Os6DdDeAzbHgN3yWiCSLay8xnrtaAryWUqgHASvPYZsK6kPT6wFoeWhgaXXcAwTFzqZSmtpIgEMVadTN5tIagpXe0oz+kJ01tXm+3LIB9V9SuTH5dvDiNXDJDFsrkXBNFTd1KcWOE0xTmbN9Tt5WJLlLHLdLIMtC1Ka5WDrT2JIgbKqcj4y6tZUrNKNMrNPAetV3NY0U09tUVy1JC7YIU/3ediwwAxURi3ed41VzL9PkWSLtVyfmz8aodpy6YuohmtYupJVnN8eF0kUtWXN+CcuAfkN6hliJiHotOib8n11mVxbG5J0Qqx7IqvLGretCCeBDZDlqFuKvZqNB70hDJktwn9z2EocBwr2dXdjkvgd26SMmjBpP2A3i6tN9J2XYRiOjdRHSYiA73sYW/Oh4eHhvG1XQ5ieguInpMQlTvX+f9XyGiH0v46ptEdOOVjnm1kwLrVLytv6Yz8z0A7gGAMZoainXfym4niavXVDdxTOgYna5NGLDot9PoCKAVBctSZzdeB7Wk1rMt0spSexnMLTuxR6VqENmf+XhBjituaTFSQdAdrCIAYEm0tuogK+zNpOxzK0kzVTUuJoDKnCPn9saFHjFlrEIOCJUlOW+ixFSxvGJCYHtdSgC7GrgAva25lMceg8Ry6Y65TkydKRGpLFTtAojaSusYJP8WcYDmnsHbNG6l1lJUegXlbryjJ7syXum+vkA2waGd37M0tO67dmcKMrY9BLR/gLUiq2Q7U5WVQXhNlEKTGv26o2jYxMFi4UjLKhvFjixNYs6qNR63MkQrZjLXT7tsPq5WlpOIQgAfB/DzMIbP94noXmb+cWm3HwJ4CTO3iOifAviPAN56ueM+WwvtHBHtlYHtBaA9s04CuL6033UATj/Lc3h4eAwT+Bn8XRkvBXCUmZ9k5h6AL8CErNzpmL/NzFo3+F2Y9eSyeLYW2r0A3gngI/L4ldL29xLRF2CSAcvbIn62RoKbiEDTU+aFNqyQGk1utxFMGmuMdplaTV5esQkF0g5Pyw3XMEUQLIr1Vq1Y602bpXC1Ygm4+bg5hv3xZ7aKDdYqI7JCgJXj8/YYucS/ColBRavu7oqXB8UCg37haBVWXIRt7CxbQ7oFlRQkNKAfuriUrauUOzorCJEEyVUJI2oB6YLSNiTg3nbd1/UY1QtO4cPGqTruWjS21Npr4plFTJbA2pRtqgySrPQRihXUmZG57RXIhDai8T0q2FI/1KJT4vHMkcyWSPXGRR+u4nqMtneb9xo3CDF4iRB1nNIJALR2hRg9mQ3MLeAsSdVbC6X+Nmz2kT/8GIYFhli7YQtthogOl17fI16ZYr3w1OUSiO8C8LUrnfSKCxoRfR4mATBDRCcBfAhmIfsiEb0LwAkAb5Hd/wTA6wEcBdAC8I+udPyhgDYOlgWo6PURLJsAvYo5qs3PzChUv10eg/ExcEdE+eT/ECfW/9a2Y4pgfumixY5WmuAJ49aqqqm6ixwENitaiMprsNxCIl2QihETpe7P1BBpEuCCuF2yOEZLXZuU0IB7NhKistAb2Naeie1CUDtv3mvuMeeknO1Cpq4tB26hKVclABhQlrULYWALFazmf5aSFVvUY7BtouxcWFWnZQKae016sSrdmeI2u85Va36guhMxOgfMPKRLmkwIbBG+ZliLOLDqwn1R4Y2EMxe2MlREeLF2QtztmRp6Y+ZznP2BuZjVg+ZHrrEPaP9t8wOWP2l88ukjBVZuNJ+BLthUuEoPdUfjeUkwrbYxKBo1BNi42sYcM7/kMu9vODxFRH8fwEsAvOJKJ91IlvPtl3jr1evsywDec6Vjenh4bE88AwvtSthQeIqIXgPggwBewcxXzB76SgEAgVhhhdReUhha6W30xO0qW1TF4M8Udzquv4C6hlFo3cmgqbozYmFMjFoXUgUeEToKhYI0ORAEyKaNFab0inzC1fgpfy3sFdaqU8usOyla9HFgb0ZV0ehXQmthaJA67LOtB+1Mq/a/cw31N1SFDKOOs9qUja+1mlnNXZNaXt1JWPWMvqgrpfOM9oy6nDIdwlaIG2zdUHXvohYjUllu6QjVH2EEfbG05aNQd5oKl7Cw8kcVQiKWaGZ7BAAdSZKoq5zL59OdCFET11Dd0eTEAoJZ7cBk5nn0qOErjpyKkD1stnUl55VVyHLjnNS4a95sqzbGzWcdPPgohgobj49tBN8HcIiIDgI4BeBtAN5R3oGIbgPwewDuYubzFx/iYvgFzcPDY4O4erWczJwR0XsBfB0mXPwpZn6YiD4M4DAz3wvg1wHUAXxJeKAnmPmNlzuuX9AAFG2xhJLEPVoKh8TVxMoKqqmjd9RKSghaPVASeqSq694DALlQI4Jm11pQ1oLJCsvqJ6m/7O83iYk8jZxOl/wfMdv0fiaM/s50hOzAIOlX41lKNgUGKQNRaJ7H81KLSrC6aUrXqEhiIU9cB3TtMN7cHdqu5GoZ2Q5LnbIahVOeUEsrG5FY1BjAodYxSmxJaCqjxwLXeEQSC72EbBMTe87CqXgoKVbnNqu551bfrOesxlisJo3RmffNtsqCEH7zwtW7BtJV/ZZZpOdMvMvW2jallrY2agUbbccrcpZZe5e5r+JmYWN5sfRSpYceN8fAEOIqCjwy85/AxN3L23619Pw1z/SYfkHz8PDYGBhegns7wEpqx2qNtYBCNb2kfZzonKHn2sGV6+qsiq1umJ5EIdZdsGgyn+GceeRqBWFjML6Z1xJrRfQl7qW6ZGBGsioZTdnUq4cu62c1ttwAbLs5iQW1ZgOruKpS0mGpdKdzk7H88hSoSeawJ8cvWzCUD1IXk4aLf1krkjVr6FRetcaxfpLRHZeyrJ6LXWkcrno2HDhWd8JZcvGKzM84I73g1GgBY71p7G/tHIRd155Ox0OFqa0043BxLbX4VPmiM63y5oU7PrSHac91NBf09o/b54GUZ1kKTSdHIfsrMbiICVHTdaYHgKDjajmHDl6Ce8gRhCj0BirdSMHo6OB+6lImpUyPuJcUxyDlk0kFQDFSsVLKxYy5yW2Hp36OvG4WyvYu6e0ZAokw9G1/xlXHD+8KjUAXhn6tJBYoAfQicvI1NiDOrmi6dl63mf3bU4TmfpUs0gt3PQLUNVQp6zxxbpnWM1YWgaC3/k2ep05CJ6tposEVgCt/jTLnTmpSQPcZf8ItJCrmyGeBSALoWoEQZIzexCBHrjfhFkItTrfyQUlJeFG7PlXILm556hZbAOiNhvY9nYPueBUjZ80Pk12oVqQmtiRlrn0dAFfor7y4oJ+jqMh99FcPrDuPQ4XhXs/8gubh4bFxUDHcPqdf0IqLq+TCsTHXU1OqAoq5BfNmnttKgTJ9Q7uLc0Wkr/s5ciHBqsuhYotIQktk1e5NcSNDZ9rsr9LaGuwP+mzZ7I192sXc1RaWLR3FqjB8tMtRVgMWbtcouDauDBCKJE4+bQ4SLMXWVVMJ6axu9o9WA2vx5YmoStxQmreWnkssoyZQPcd2vIBT4gBKwfvEWZlqSSkRtzfqFCrUIo2bpWRKoZQHYOSUyiKZ92pWC9EJPHZEd5NyoCdef/2Uo6zoedcWWHPgXFolEieNHH1pQtPaVboYAPVTPZt8yVLtqgLr+qoMT3qigUDuo2Gp17wkGEOaqXDwC5qHh8eGQOCrSay9JvALGmCbowRSQoQoQrFkJBVC6Xoe1KVuc6QKlniZym6jKFxNphJlswJ5VXtvmgeN7XR2pTauoiTNlRtTa21kWqJUsgga14klJbGmdIHRnRTqxD5XP6hxo6Iq1I9Zc55gNbLWmG3QkgO825gpe2ZMwmLshi66uQTCQ/O/I5G5zqVuFRMVk1FoZmYg51frCMScabbNtrwr42/EYKlxrZ1luRYXg7Ld0UddokItKY1rRS3CxJNmx8Wbhag6MRjwB4wFGgrVY/wpc01tkQsff3gR7esNATZ6UIjHcWCpE66NHVtLUlHu/K5jUjoLEyEbCeScJv7a3CMCmCOh3U/jmVka2HKyaEXMQyLkR5/CtoFf0IYcJZValkJ0QikpoHWBWtMZBG6bVBbke6adeKKozHb21C4iIWa1xD7XwL8WPwf9i/tVaqC8PRtY/pSyzhf3MCrzwhNbcNk/XST0+EFHFpfpPmhVFt2aE0cMw8ExhkGBQlzN6dRE5juZubaJShsdWexGY/MFvmHPIo41DF9uomZWJZaVu7y/LoTznRGbBT23Yua4dW4ErUj5cMLUb7gKgzN3molItdkRlTowJZpAYZt4mH+BKPkKf+78y6Ztpray5DKh1fPSVUuEIYuYUJuTelBxbzWDSxlbNV2XHAiQSOJGFzIVrwy7ue2bqpUARR1WwinSTPmZDRHghwd+QfPw8NgR8DG0bYAid/WXo8IZyN02FkmfYkplsXNbL9m7xfQPCFd6lrXP0lkp7BY2qK9BanU5ow7bILJlwYeOxW7rHqed+6PuTryq/ivQ3S1uq7haHDM4Vsq61G1WtSY1BIsbSmINpfWutaZCcRtXuilGYmPm9XLpshT17etcTJeeRPQfXdqFWJjzup+6oMeXJpEVZv+pXaIgAcZC27j2s6Om7nFqpIVG11hhkweNJdfqm3ncO7KC48umccDC0yYZUz0dWZFFtdQ6s4SxY5o4kQ5SKsfTY7TG1GV3ApWp9u3M1PIqULlgLM/eVDrwXp6QdSEzpaKkZF1qtei0v0Jn0tE81KJPGgXSsyKhrjLnK6LOsk3gs5weHh47BOxdzm0B7cGZlxLnKuy4fzcAWDHFsJ2BVXVBaRiBk7xWOkZvIrIqDtoARKWn81K5ZV/KQdMlp1rR2qNdxM3rrOYstGTZCTxmElyvLDpyrGXrV8TqaGnTFKA3ob+uYlUcq6Cz11znqXlJiMSlX2AZT1w1+1TSPkYqIhKpcwBnTZ04PQ0AGBk3VlZjbgSz+0xH+TMtE5SfrTZs0mF3zeiFjcdtFDLwcx0TV7PxuzxGGpv523fTnLm2g4Q0MtvOrxjLubWSonmjua7Ro6p9bR46M4RYurQnwtBvzwRozbqYJmASLf0Rk/xR5QulywR9cgRfDau22RKZFZrIocJpqXWljjZuFMirYsH/0Ag3DvfysAYMv6ANPYisVBBrl6bROnrPM+5ktGr8QBtk7xeACDD2ps0i1z5QRU0CzFqQHDcKVESAsblXuGnaxCl3fCSSRa89FdiAtC5amg2MGihJ/5hjTD2WIzsm3CptjjvpAucddVe1K1KNEa+Kmq1wyDp7MlAmi+eKLgLhOt8yM/7mWIHu8poypwojlv/lXWZeWgsiaFkrcOGsqZK4IC7wiXgKldRcxPGnZs0p0xzVupnniixe6gpnRYBIXFp1XwFgoWcWnlxKsYKoQDJjFtv2+KDQf/BEFZC5nHuxeW/i8cJ2dFJ0x8lKFaXyI5FIi7lkObMuZ2/CfG3CToHe6JrAf+wym1rkb7lnAOITF8y1DHN50+Uw3B6nX9A8PDw2Ds9DG3aUPqBgxtAPislRIBx0JbT5a+PGEctVqoormawWyGrarcjJKavcjHKl9Nc7qzr3RakFRQj06jSwvyYHyux6rT9c+qnQvt8XgyidA3qq9i3GTC5JgmSZbJG3/l/1ZGStNRKqBkdsmf66LR8VyaDVwElvl+YmG5H3m2IBVkvv9rTgVFzllRgdEpkm9ZRzQqtl5qqdDVqWnBQYmzXJA7XGuu0YYSz8OrHewpjRE4pK0dEaW0mCHGqic74qxxMqxyQhEl5e7azSZRix9JNWa1oFAjpTISpLrreo2QmIxa3UulvtIBW3CtubYeRp7fMBZKe2ec8gv6B5eHjsCDAD+XD7nH5BC0KE48as4UXDBeDZccQXTFC6eZOhCvSFEZ40chsb6wgTvbLQd8J9s2ZbspqXegQq49+RNrW+zypDjBIyYY0oc107e1PuZG9aJkeBsOviaZFYFZ1p2OC3dvkmUcoIO7BdiLSGsj/KqJ6TuJBYdvEiWZqJXkBwRgQfk5KUtVqNq4Q8FetLqSVaH1ph+17YVgoF2TpQlQ9igt1PA/nZpJiiGaGxKqKWYuUV7QiBPM9WRGopKhDXRelCLLTKiHndbSYYvcHQI1bmpOIDAfqTZpJW5LOtHw+stRsLm0JjaskKW+vb0momQisqqZaZWt7LB2N7nyjNJ7iwNHxNT54pvIXm4eGxY+AXtCFHkdu2dKppRlmBhdtNPC1dzOXR/La2pyNEEhvRDuPNvYlt+KExsX49tGn7zqRad+a91esDqyfW2SuZzXm2AoaBkm21FDQoxcYkiJVFTmNM42rpvKsNTRe1dlLkomdCqPmj1lURAZ1ZnQfz0J1ycthaN8qRBs7I1lzamFsI5FXNwA4qcUQNciVMyoypMqLmxdtqZ5woJAD0xcrqjzEqp2Sy4PbvaXwsFXJxWKC/IJZcPhj/5IKsZaaxvCDto2gNloKt3lIgWhLajcx3RURWKAca+zXm5uantUviZGdcH1EAqM4VGDkzmCHf/vEzWJWQYYVf0AArFRTMGh5VwYz6SeOurIhGf3XO3PSV5dy6F41bRCrmdI5EBAlVBbU9BvRuMtOrixdbNoH7AlcWtavQxYKD6tqgcK5kZVGGnDsxxK7QNho3sHXVNCDOGpTPcqAinKqKOfBrbnkUFTnpXNccLGPnKCvjX+kSBRPSUPqDyuJYMNn3Mx6kS2RFgEbPzF8uvvVio4beMXMuq2bbITSvk7H1Bhe7ZNGNJ6+4ZEVyOrHPAaA/WgDjMoEdM45+X8aTk62ccB2SXRUFF3LOkEETxs/PMlnYZlQOilA9LdtGzf6jpzK0RUK4PetqcgGgssy24fHkn5uFbNu7m2DH2RxS+AXNw8NjY2D4pMB2AEtvAJYeAHktsQH/+mlRz5g0U9WrE1JJ39dPS1D5+tDSGdRNy1PH9FfyvZOpcS6NBu+jjnMxVSq7tUusidBZbcu3yvGrhXOttBP5/hbefouRcX5h7RQA4N/+3zcBACaOxNj1u98ZuO5jA68WLzE7g2huaC+H6prX9XX2mX/Xy9B+vTFB79x/HACwmhnr5vATN4IWzaRWz0od6RgjlARH7Zy6+AHyC9KDVK3lMVOG0d6fIejKBE5L79UAyNsislkVmaSRDjpdkRSSHp9IxNKNgKyuct/m3CdfFaB+YlAhRcm6eUJONujsucvM0DbDkMfQgivtQESfIqLzRHSktO3XiehRInqQiP4HEU2U3vsAER0loseI6LXXauAeHh5bAOaN/W0RNmKhfRrA7wD4TGnbNwB8QJqFfhTABwC8j4heANMB+YUA9gH430R0CzMPvbpwGdFCE7RHypqmB1Ux0qUC7WktIXLB5/asBMJFhjqdZ4RCu7CdlWaEutAGNM6ucaSgC+RizqwckCYcu2TaCoAmjQlw24GnAQB70lV8/Vu3AwBu/qyxroojj+J++Y26H0aD+xYcfnvqhm8AAAo0SURBVLbTsGmY/uRfA580z0/arYaLcgjzaN/9UgDAuXcYk/d5e87jqQWTtGn/wPyWclASiRQpO7WQp+8P0Z4V+sXjZpK7E0B3WhIL8+ZD6DWryCalhlMSHNFBM47OUop+fZBwXFkIEMo5GzeKZSb6dpVFxq4vP2K2PZtJGUrsgOJ0Zv4LIjqwZtuflV5+F8Cb5fndAL7AzF0ATxHRUQAvBfDXV2W01xj5408CALqv+5t2EVKXsDumWbhSkbKy2UNgzPyrdSWb+4HerAkDh6ta02feo4Ksa6KZyix1XYr233YGAPDyWXPQz3/vTtz6UfPNWT1iCrRXAdwk0zrcUY3njupX7gMAHPiKed0FsA+mYUDjLXcAAFbesWoL5xd+ZFK3qmCb1QgjZ1Q11hwjWSFU55SXp4kFIFmSagT5MYkeMqsjzeYuw1xTHp3rqTDxqHnsia+SJ4RcVI93DBgDfTSGEVd0OTeAXwTwNXm+H8DTpfdOyraLQETvJqLDRHS4j+56u3h4eAwbdoDLeUkQ0QdhstGf003r7Lbu1THzPQDuAYAxmhoqO3bkgadx4e8cBDBYRwmYX3OrlKE8qgph4UXCYh91yfnqpLGqskXzK68WQ2ea0TEMEaS3Gnmdl+87jvs//WIAQOV9xwDAuo+34L4db4U9W9S/9D15dNvGcRQAcOJXXw4AmHntKZw6vA+AkyvvjTGSVVdFAQD1k4XlkY09aea+tce8xwuhrfO01Qypc3Ob+2Sb1Mse/Dfbwil5htjBpU9E9E4AbwDwama7JJ8EJHhjcB2Abc4m9PDwACAhtB24oBHRXQDeB+AVzNwqvXUvgD8koo/BJAUOAbjvOY9yk5GdOQvAWGiqSaa0iXSObexs9Sbz2JvtWwZ6KGTN6g9q6Iqgo9YM9o00GF552yP4P/e9AACw700/BgCcADC7PUKN2wY3fNjRVG5+vqF0PPVhqSZgIJf4mMbVmALULki3rJoG/qXyYt7dC2qhRSUOi5Kn9/3V9qfPXhbbvVKAiD4P4JUAZojoJIAPwWQ1KwC+QUYv/bvM/E+Y+WEi+iKAH8O4ou/ZbhlOxcxXzELz9C+9EIBRjQWAxvWETLJd2ai5tNp0C+F3zGrVvF4az/5MGy+6wRinB+pGdfGxXzwEADj9o1Ucwvc24So8FPkjjwMAbniL29b5uyZ7uvpLJnjffHAKhZS1pXPmM9ZuUVSUKjnkBy1qs814jz0lFQJ//P1rdxHDgB2Q5Xz7Ops/eZn9fw3Arz2XQXl4eAwhmIc+y+krBS4BTblrwLhxs+FcRIsRWOoCkzmp4+uNIrnDMN3ffOghAMDx1hQWf85UNj9ij+qeeWw90v91nzya13sO3YQTHzW+4/zTxh0df1yluBljx83NwFL729yTWNn0ift2Sr3mFbDdLTQPDw8PAwbnwx1B8gvaFbD7t01gufG52wAA4bkIEEWI3g2GyPnBO/4Y/+E7bwAAPHCb/ufCpo7T47kjf/xJ7P97g9uOfuxOAFIBwiZ7MP6EyYMljQL9miiNHDuxeQPdKnj5IA8Pjx2FnUjb+EnE2F+aX+elO3p444t+BAD4s6eeBwD44vP3bIuaSY9njpt/5bv2+eO/LWVWB41Y5NQjOSa+ZrLhw+2IXR0wAL6KFprQv34TplHs7zPzR9a8X4GpIf8bAOYBvJWZj13umH5B2yBmP2E4Yj/4dw/gtft+FgBwPY5c7l88dhgO/TNDtQlfaDScnvxQgvqXVrZySJsLvnoCj0QUAvg4gJ+HIeR/n4juZeYfl3Z7F4BFZr6ZiN4G4KMA3nq5416NWk4PD4+fEHCeb+hvA3gpgKPM/CQz9wB8AUbcooy7AfyBPP8ygFeTEF8vBeIhSMMS0QUY7cC5rR4LgBn4cZThxzGI7TyOG5l59sq7rQ8i+lM570aQAii3h79H6rf1WG8GcBcz/2N5/Q8A3MHM7y3tc0T2OSmvn5B9LnndQ+FyMvMsER1m5pds9Vj8OPw4/DjWBzPfdRUPtxEhiw2LXSi8y+nh4bEV2IiQhd2HiCIA47gCH8ovaB4eHluB7wM4REQHiSiBUbq+d80+9wJ4pzx/M4Bv8RViZEPhcgruufIumwI/jkH4cQzCj+MqQOT73wvg6zC0jU+JuMWHARxm5nthasY/K8rXCzCL3mUxFEkBDw8Pj6sB73J6eHjsGPgFzcPDY8dgKBY0IrpL+ngeJaL3b9I5ryeibxPRI0T0MBH9smyfIqJvENHj8ji5SeMJieiHRPRVeX2QiL4n4/hvEji91mOYIKIvS8/VR4joZVsxH0T0L+UzOUJEnyeidLPm4xJ9aNedAzL4LblvHySi26/xOHw/3Ctgyxe0UgnE6wC8AMDbpb/ntUYG4F8x8/MB3AngPXLe9wP4JjMfAvBNeb0Z+GUMCqZ9FMBvyDgWYcpArjV+E8CfMvPzAPyMjGdT54OI9gP45wBewsw/DRMw1rKXzZiPTwNYy7e61By8DkZm/hCAdwP4xDUexzcA/DQzvxjA/4NRjsaafrh3Afhd+V795IGZt/QPwMsAfL30+gMwTYw3exxfgakrewzAXtm2F8Bjm3Du62C+KK8C8FUYQuEcgGi9ObpGYxgD8BQkUVTavqnzAdcKcQomC/9VAK/dzPkAcADAkSvNAYDfA/D29fa7FuNY896bAHxOng98Z2Ayhy+7lp/TsP5tuYWGZ9DL81pBGinfBuB7AHYz8xkAkMddmzCE/wzgX8P1DJ4GsMTMKoC6GXNyE4ALAP6ruL6/T0Qj2OT5YOZTAP4TTN+YMwCWAdyPzZ+PMi41B1t57z6rfrg7HcOwoD3j8oarenKiOoD/DuBfMPOmSycQ0RsAnGfm+8ub19n1Ws9JBOB2AJ9g5ttgams3y922kPjU3TBtt/YBGIFx7dZiGPhGW3LvPpd+uDsdw7CgbVkvTyKKYRazzzHzH8nmc0S0V97fC+D8NR7GzwF4IxEdg1EceBWMxTYh5R7A5szJSQAnmVnbUX0ZZoHb7Pl4DYCnmPkCM/cB/BGAl2Pz56OMS83Bpt+75Prh/gKLf7kV4xhWDMOCtpESiKsOkSH5JIBHmPljpbfK5RbvhImtXTMw8weY+TpmPgBz7d9i5l8A8G2Yco/NGsdZAE8T0a2y6dUw7Qg3dT5gXM07iagmn5GOY1PnYw0uNQf3AviHku28E8CyuqbXAuT64b6RL+6H+zYiqhDRQWzTfrhXBVsdxJMfmdfDZG2eAPDBTTrn34Ixyx8E8ID8vR4mfvVNAI/L49QmzsMrAXxVnt8Ec1MeBfAlAJVNOP/PAjgsc/I/AUxuxXwA+PcAHgVwBMBnYXrAbsp8APg8TOyuD2P5vOtScwDj6n1c7tuHYDKz13IcR2FiZXq//pfS/h+UcTwG4HWbdc8O258vffLw8NgxGAaX08PDw+OqwC9oHh4eOwZ+QfPw8Ngx8Auah4fHjoFf0Dw8PHYM/ILm4eGxY+AXNA8Pjx2D/w9+YTchtGiFKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "def array_from_folder(path):\n",
    "    pics = os.listdir(path)\n",
    "    pics.sort()\n",
    "    pics = [np.array(Image.open(os.path.join(path, p))) for p in pics]\n",
    "    pics = np.array(pics)\n",
    "    return pics\n",
    "\n",
    "# Download this dataset using the notebook DownloadSpineSegmentationData\n",
    "train_inputs = array_from_folder(\"SpineSegmentationData/training/ultrasounds/\") / 255.\n",
    "train_outputs = array_from_folder(\"SpineSegmentationData/training/segmentations/\") / 255.\n",
    "\n",
    "test_inputs = array_from_folder(\"SpineSegmentationData/testing/ultrasounds/\") / 255.\n",
    "test_outputs = np.expand_dims(array_from_folder(\"SpineSegmentationData/testing/segmentations/\"), -1) / 255.\n",
    "\n",
    "#poor man's one hot encoding\n",
    "test_outputs = np.concatenate([test_outputs, 1-test_outputs], -1)\n",
    "\n",
    "plt.imshow(train_inputs[0])\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(train_outputs[0])\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.ndimage\n",
    "augmented_inputs  = []\n",
    "augmented_outputs = []\n",
    "\n",
    "for i in range(6):\n",
    "    dx = np.random.randint(-15, 15)\n",
    "    dy = np.random.randint(-15, 15)\n",
    "    augmented_inputs.append(scipy.ndimage.shift(train_inputs, (0, dx, dy), order=0))\n",
    "    augmented_outputs.append(scipy.ndimage.shift(train_outputs, (0, dx, dy), order=0))\n",
    "\n",
    "augmented_inputs = np.concatenate(augmented_inputs)\n",
    "augmented_inputs = np.expand_dims(augmented_inputs, axis=-1)\n",
    "augmented_outputs = np.expand_dims(np.concatenate(augmented_outputs), axis=-1)\n",
    "#poor man's one hot encoding\n",
    "augmented_outputs = np.concatenate([augmented_outputs, 1-augmented_outputs], -1)\n",
    "\n",
    "\n",
    "test_inputs_augmented = np.expand_dims(test_inputs, axis=-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "\n",
    "def nvidia_unet(patch_size=128, num_classes=2, num_input_channels=1):\n",
    "    input_ = Input((patch_size, patch_size, num_input_channels))\n",
    "    skips = []\n",
    "    output = input_\n",
    "    c = num_classes\n",
    "    \n",
    "    for shape, filters in zip([5, 3, 3, 3, 3, 3, 3], [c*8, c*16, c*32, c*32, c*32, c*32, c*32]):\n",
    "        skips.append(output)\n",
    "        output= Conv2D(filters, (shape, shape), strides=2, padding=\"same\", activation=\"relu\")(output)\n",
    "    \n",
    "    for shape, filters in zip([4, 4, 4, 4, 4, 4, 4, 4], [c*32, c*32, c*32, c*32, c*16, c*8, c]):\n",
    "        output = UpSampling2D()(output)\n",
    "        skip_output = skips.pop()\n",
    "        output = concatenate([output, skip_output], axis=3)\n",
    "        if filters != c:\n",
    "            activation = \"relu\"\n",
    "        else:\n",
    "            activation = \"softmax\"\n",
    "        output = Conv2D(filters, (shape, shape), activation=activation, padding=\"same\")(output)\n",
    "        if filters != c:\n",
    "            output = BatchNormalization(momentum=.9)(output)\n",
    "    \n",
    "    assert len(skips) == 0\n",
    "    return Model([input_], [output])\n",
    "\n",
    "model = nvidia_unet()\n",
    "\n",
    "model.compile(optimizer=\"adam\",\n",
    "               loss= \"binary_crossentropy\",\n",
    "               metrics=[\"accuracy\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 19740 samples, validate on 1892 samples\n",
      "Epoch 1/30\n",
      "19740/19740 [==============================] - 25s 1ms/step - loss: 0.4741 - acc: 0.9972 - val_loss: 0.5495 - val_acc: 0.9973\n",
      "Epoch 2/30\n",
      "19740/19740 [==============================] - 21s 1ms/step - loss: 0.4502 - acc: 0.9973 - val_loss: 0.6443 - val_acc: 0.9973\n",
      "Epoch 3/30\n",
      "19740/19740 [==============================] - 23s 1ms/step - loss: 0.4352 - acc: 0.9973 - val_loss: 0.5921 - val_acc: 0.9974\n",
      "Epoch 4/30\n",
      "19740/19740 [==============================] - 23s 1ms/step - loss: 0.4233 - acc: 0.9974 - val_loss: 0.6053 - val_acc: 0.9974\n",
      "Epoch 5/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.4110 - acc: 0.9974 - val_loss: 0.5427 - val_acc: 0.9974\n",
      "Epoch 6/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.4013 - acc: 0.9975 - val_loss: 0.6425 - val_acc: 0.9973\n",
      "Epoch 7/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3925 - acc: 0.9975 - val_loss: 0.6948 - val_acc: 0.9973\n",
      "Epoch 8/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3838 - acc: 0.9975 - val_loss: 0.5578 - val_acc: 0.9973\n",
      "Epoch 9/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3749 - acc: 0.9976 - val_loss: 0.5729 - val_acc: 0.9974\n",
      "Epoch 10/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3666 - acc: 0.9976 - val_loss: 0.6143 - val_acc: 0.9974\n",
      "Epoch 11/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3593 - acc: 0.9977 - val_loss: 0.6480 - val_acc: 0.9973\n",
      "Epoch 12/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3528 - acc: 0.9977 - val_loss: 0.6355 - val_acc: 0.9974\n",
      "Epoch 13/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3459 - acc: 0.9977 - val_loss: 0.6242 - val_acc: 0.9974\n",
      "Epoch 14/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3381 - acc: 0.9978 - val_loss: 0.6993 - val_acc: 0.9973\n",
      "Epoch 15/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3322 - acc: 0.9978 - val_loss: 0.6804 - val_acc: 0.9973\n",
      "Epoch 16/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3262 - acc: 0.9978 - val_loss: 0.6961 - val_acc: 0.9973\n",
      "Epoch 17/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3199 - acc: 0.9979 - val_loss: 0.7121 - val_acc: 0.9972\n",
      "Epoch 18/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3137 - acc: 0.9979 - val_loss: 0.7169 - val_acc: 0.9974\n",
      "Epoch 19/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3071 - acc: 0.9979 - val_loss: 0.7239 - val_acc: 0.9973\n",
      "Epoch 20/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.3022 - acc: 0.9980 - val_loss: 0.7645 - val_acc: 0.9971\n",
      "Epoch 21/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.2969 - acc: 0.9980 - val_loss: 0.7289 - val_acc: 0.9973\n",
      "Epoch 22/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.2918 - acc: 0.9980 - val_loss: 0.7995 - val_acc: 0.9973\n",
      "Epoch 23/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.2862 - acc: 0.9981 - val_loss: 0.7181 - val_acc: 0.9971\n",
      "Epoch 24/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.2808 - acc: 0.9981 - val_loss: 0.7740 - val_acc: 0.9971\n",
      "Epoch 25/30\n",
      "19740/19740 [==============================] - 24s 1ms/step - loss: 0.2766 - acc: 0.9981 - val_loss: 0.7349 - val_acc: 0.9972\n",
      "Epoch 26/30\n",
      "19520/19740 [============================>.] - ETA: 0s - loss: 0.2741 - acc: 0.9982"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-50-26b79b516f65>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m               \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m               \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m               validation_data=(test_inputs_augmented, test_outputs))\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/stylegan/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1637\u001b[0m           \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1638\u001b[0m           \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1639\u001b[0;31m           validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1641\u001b[0m   def evaluate(self,\n",
      "\u001b[0;32m~/anaconda3/envs/stylegan/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    213\u001b[0m           \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m           \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/stylegan/lib/python3.6/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2969\u001b[0m         \u001b[0mtensor_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2970\u001b[0m         array_vals.append(np.asarray(value,\n\u001b[0;32m-> 2971\u001b[0;31m                                      dtype=tensor_type.as_numpy_dtype))\n\u001b[0m\u001b[1;32m   2972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2973\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/stylegan/lib/python3.6/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "loss_curve = model.fit(augmented_inputs, \n",
    "              augmented_outputs,\n",
    "              batch_size=64,\n",
    "              epochs=30,\n",
    "              verbose=1,\n",
    "              validation_data=(test_inputs_augmented, test_outputs))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique name of training run for logging:\n",
      "zero_mean\n"
     ]
    }
   ],
   "source": [
    "print(\"unique name of training run for logging:\")\n",
    "save_name = input()\n",
    "save_dir = \"runs/\" + save_name\n",
    "import os\n",
    "import shutil\n",
    "os.mkdir(save_dir)\n",
    "\n",
    "\n",
    "model.save(save_dir + \"/final_weights\")\n",
    "\n",
    "pickle.dump(loss_curve.history, open(\"runs/\" + save_name + \"/loss_\"+ str(loss_curve.history[\"val_loss\"][-1]) + \".pickle\", \"wb\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = model.predict(test_inputs_augmented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vAB+Y0jZ/jdGQ6QHg/u7nakb747uAvd3lmin2w2t57tOBC7s/5D7g74FVU9j+K4HZrk/+EThrMfoD+FPgW8CDwN8yOuo9lf4AbmV0LOIwo3fY647VB4yG4X/VPW+/CcwMXMc+Rvv+R56vnxhb/wNdHY8Ab3wh2/K0YalxS2F3QNIiMgSkxhkCUuMMAalxhoDUOENAapwhIDXufwGOwrCqXdmviAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "j = np.random.randint(500)\n",
    "\n",
    "plt.imshow(test_inputs_augmented[j, :, :, 0])\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(out[j, :, :, 0] * 3, vmax=1) # *3 == haxx\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(test_outputs[j, :, :, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}